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2307.02733 | Ruiyang Xia | Ruiyang Xia, Decheng Liu, Jie Li, Lin Yuan, Nannan Wang, Xinbo Gao | MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential
Deepfake Detection | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Advanced manipulation techniques have provided criminals with opportunities
to make social panic or gain illicit profits through the generation of
deceptive media, such as forged face images. In response, various deepfake
detection methods have been proposed to assess image authenticity. Sequential
deepfake detection, which is an extension of deepfake detection, aims to
identify forged facial regions with the correct sequence for recovery.
Nonetheless, due to the different combinations of spatial and sequential
manipulations, forged face images exhibit substantial discrepancies that
severely impact detection performance. Additionally, the recovery of forged
images requires knowledge of the manipulation model to implement inverse
transformations, which is difficult to ascertain as relevant techniques are
often concealed by attackers. To address these issues, we propose
Multi-Collaboration and Multi-Supervision Network (MMNet) that handles various
spatial scales and sequential permutations in forged face images and achieve
recovery without requiring knowledge of the corresponding manipulation method.
Furthermore, existing evaluation metrics only consider detection accuracy at a
single inferring step, without accounting for the matching degree with
ground-truth under continuous multiple steps. To overcome this limitation, we
propose a novel evaluation metric called Complete Sequence Matching (CSM),
which considers the detection accuracy at multiple inferring steps, reflecting
the ability to detect integrally forged sequences. Extensive experiments on
several typical datasets demonstrate that MMNet achieves state-of-the-art
detection performance and independent recovery performance.
| [
{
"created": "Thu, 6 Jul 2023 02:32:08 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Xia",
"Ruiyang",
""
],
[
"Liu",
"Decheng",
""
],
[
"Li",
"Jie",
""
],
[
"Yuan",
"Lin",
""
],
[
"Wang",
"Nannan",
""
],
[
"Gao",
"Xinbo",
""
]
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] |
2307.02736 | Chaoxing Huang | Chaoxing Huang, Vincent Wai Sun Wong, Queen Chan, Winnie Chiu Wing
Chu, Weitian Chen | An Uncertainty Aided Framework for Learning based Liver $T_1\rho$
Mapping and Analysis | null | null | null | null | physics.med-ph cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Objective: Quantitative $T_1\rho$ imaging has potential for assessment of
biochemical alterations of liver pathologies. Deep learning methods have been
employed to accelerate quantitative $T_1\rho$ imaging. To employ artificial
intelligence-based quantitative imaging methods in complicated clinical
environment, it is valuable to estimate the uncertainty of the predicated
$T_1\rho$ values to provide the confidence level of the quantification results.
The uncertainty should also be utilized to aid the post-hoc quantitative
analysis and model learning tasks. Approach: To address this need, we propose a
parametric map refinement approach for learning-based $T_1\rho$ mapping and
train the model in a probabilistic way to model the uncertainty. We also
propose to utilize the uncertainty map to spatially weight the training of an
improved $T_1\rho$ mapping network to further improve the mapping performance
and to remove pixels with unreliable $T_1\rho$ values in the region of
interest. The framework was tested on a dataset of 51 patients with different
liver fibrosis stages. Main results: Our results indicate that the
learning-based map refinement method leads to a relative mapping error of less
than 3% and provides uncertainty estimation simultaneously. The estimated
uncertainty reflects the actual error level, and it can be used to further
reduce relative $T_1\rho$ mapping error to 2.60% as well as removing unreliable
pixels in the region of interest effectively. Significance: Our studies
demonstrate the proposed approach has potential to provide a learning-based
quantitative MRI system for trustworthy $T_1\rho$ mapping of the liver.
| [
{
"created": "Thu, 6 Jul 2023 02:44:32 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Huang",
"Chaoxing",
""
],
[
"Wong",
"Vincent Wai Sun",
""
],
[
"Chan",
"Queen",
""
],
[
"Chu",
"Winnie Chiu Wing",
""
],
[
"Chen",
"Weitian",
""
]
] | [
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] |
2307.02737 | Haoran Xiong | Haoran Xiong, Zicheng Ye, Huazi Zhang, Jun Wang, Ke Liu, Dawei Yin,
Guanghui Wang, Guiying Yan and Zhiming Ma | Theoretical Bounds for the Size of Elementary Trapping Sets by Graphic
Methods | 5 pages, 3 figures | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Elementary trapping sets (ETSs) are the main culprits for the performance of
LDPC codes in the error floor region. Due to the large quantity, complex
structures, and computational difficulties of ETSs, how to eliminate dominant
ETSs in designing LDPC codes becomes a pivotal issue to improve the error floor
behavior. In practice, researchers commonly address this problem by avoiding
some special graph structures to free specific ETSs in Tanner graph. In this
paper, we deduce the accurate Tur\'an number of $\theta(1,2,2)$ and prove that
all $(a,b)$-ETSs in Tanner graph with variable-regular degree $d_L(v)=\gamma$
must satisfy the bound $b\geq a\gamma-\frac{1}{2}a^2$, which improves the lower
bound obtained by Amirzade when the girth is 6. For the case of girth 8, by
limiting the relation between any two 8-cycles in the Tanner graph, we prove a
similar inequality $b\geq a\gamma-\frac{a(\sqrt{8a-7}-1)}{2}$. The simulation
results show that the designed codes have good performance with lower error
floor over additive white Gaussian noise channels.
| [
{
"created": "Thu, 6 Jul 2023 02:51:17 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Xiong",
"Haoran",
""
],
[
"Ye",
"Zicheng",
""
],
[
"Zhang",
"Huazi",
""
],
[
"Wang",
"Jun",
""
],
[
"Liu",
"Ke",
""
],
[
"Yin",
"Dawei",
""
],
[
"Wang",
"Guanghui",
""
],
[
"Yan",
"Guiying",
""
],
[
"Ma",
"Zhiming",
""
]
] | [
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2307.02738 | Brandon Kynoch | Brandon Kynoch, Hugo Latapie | RecallM: An Architecture for Temporal Context Understanding and Question
Answering | 10 pages, 4 figures Our code is publicly available online at:
https://github.com/cisco-open/DeepVision/tree/main/recallm | null | null | null | cs.AI cs.CL cs.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ideal long-term memory mechanism for Large Language Model (LLM) based
chatbots, would lay the foundation for continual learning, complex reasoning
and allow sequential and temporal dependencies to be learnt. Creating this type
of memory mechanism is an extremely challenging problem. In this paper we
explore different methods of achieving the effect of long-term memory. We
propose a new architecture focused on creating adaptable and updatable
long-term memory for AGI systems. We demonstrate through various experiments
the benefits of the RecallM architecture, particularly the improved temporal
understanding it provides.
| [
{
"created": "Thu, 6 Jul 2023 02:51:54 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Kynoch",
"Brandon",
""
],
[
"Latapie",
"Hugo",
""
]
] | [
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] | RecallM: An Architecture for Temporal Context Understanding and Question Answering | https://arxiv.org/abs/2307.02738v2 | https://arxiv.org/pdf/2307.02738v2.pdf | https://github.com/cisco-open/deepvision | true | true | https://paperswithcode.com/paper/recallm-an-architecture-for-temporal-context | [
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2307.02740 | Helia Hashemi | Helia Hashemi, Yong Zhuang, Sachith Sri Ram Kothur, Srivas Prasad,
Edgar Meij, W. Bruce Croft | Dense Retrieval Adaptation using Target Domain Description | null | null | null | null | cs.IR cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In information retrieval (IR), domain adaptation is the process of adapting a
retrieval model to a new domain whose data distribution is different from the
source domain. Existing methods in this area focus on unsupervised domain
adaptation where they have access to the target document collection or
supervised (often few-shot) domain adaptation where they additionally have
access to (limited) labeled data in the target domain. There also exists
research on improving zero-shot performance of retrieval models with no
adaptation. This paper introduces a new category of domain adaptation in IR
that is as-yet unexplored. Here, similar to the zero-shot setting, we assume
the retrieval model does not have access to the target document collection. In
contrast, it does have access to a brief textual description that explains the
target domain. We define a taxonomy of domain attributes in retrieval tasks to
understand different properties of a source domain that can be adapted to a
target domain. We introduce a novel automatic data construction pipeline that
produces a synthetic document collection, query set, and pseudo relevance
labels, given a textual domain description. Extensive experiments on five
diverse target domains show that adapting dense retrieval models using the
constructed synthetic data leads to effective retrieval performance on the
target domain.
| [
{
"created": "Thu, 6 Jul 2023 02:59:47 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Hashemi",
"Helia",
""
],
[
"Zhuang",
"Yong",
""
],
[
"Kothur",
"Sachith Sri Ram",
""
],
[
"Prasad",
"Srivas",
""
],
[
"Meij",
"Edgar",
""
],
[
"Croft",
"W. Bruce",
""
]
] | [
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] |
2307.02744 | Shuvendu Roy | Shuvendu Roy, Ali Etemad | Active Learning with Contrastive Pre-training for Facial Expression
Recognition | Accepted at 11th International Conference on Affective Computing and
Intelligent Interaction (ACII'2023) | null | null | null | cs.CV | http://creativecommons.org/licenses/by-sa/4.0/ | Deep learning has played a significant role in the success of facial
expression recognition (FER), thanks to large models and vast amounts of
labelled data. However, obtaining labelled data requires a tremendous amount of
human effort, time, and financial resources. Even though some prior works have
focused on reducing the need for large amounts of labelled data using different
unsupervised methods, another promising approach called active learning is
barely explored in the context of FER. This approach involves selecting and
labelling the most representative samples from an unlabelled set to make the
best use of a limited 'labelling budget'. In this paper, we implement and study
8 recent active learning methods on three public FER datasets, FER13, RAF-DB,
and KDEF. Our findings show that existing active learning methods do not
perform well in the context of FER, likely suffering from a phenomenon called
'Cold Start', which occurs when the initial set of labelled samples is not well
representative of the entire dataset. To address this issue, we propose
contrastive self-supervised pre-training, which first learns the underlying
representations based on the entire unlabelled dataset. We then follow this
with the active learning methods and observe that our 2-step approach shows up
to 9.2% improvement over random sampling and up to 6.7% improvement over the
best existing active learning baseline without the pre-training. We will make
the code for this study public upon publication at:
github.com/ShuvenduRoy/ActiveFER.
| [
{
"created": "Thu, 6 Jul 2023 03:08:03 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Roy",
"Shuvendu",
""
],
[
"Etemad",
"Ali",
""
]
] | [
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2307.02747 | Tiankui Zhang | Yuanpeng Zheng, Tiankui Zhang, Rong Huang and Yapeng Wang | Computing Offloading and Semantic Compression for Intelligent Computing
Tasks in MEC Systems | null | null | 10.1109/WCNC55385.2023.10118995 | null | cs.NI eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper investigates the intelligent computing task-oriented computing
offloading and semantic compression in mobile edge computing (MEC) systems.
With the popularity of intelligent applications in various industries,
terminals increasingly need to offload intelligent computing tasks with complex
demands to MEC servers for computing, which is a great challenge for bandwidth
and computing capacity allocation in MEC systems. Considering the accuracy
requirement of intelligent computing tasks, we formulate an optimization
problem of computing offloading and semantic compression. We jointly optimize
the system utility which are represented as computing accuracy and task delay
respectively to acquire the optimized system utility. To solve the proposed
optimization problem, we decompose it into computing capacity allocation
subproblem and compression offloading subproblem and obtain solutions through
convex optimization and successive convex approximation. After that, the
offloading decisions, computing capacity and compressed ratio are obtained in
closed forms. We design the computing offloading and semantic compression
algorithm for intelligent computing tasks in MEC systems then. Simulation
results represent that our algorithm converges quickly and acquires better
performance and resource utilization efficiency through the trend with total
number of users and computing capacity compared with benchmarks.
| [
{
"created": "Thu, 6 Jul 2023 03:15:58 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Zheng",
"Yuanpeng",
""
],
[
"Zhang",
"Tiankui",
""
],
[
"Huang",
"Rong",
""
],
[
"Wang",
"Yapeng",
""
]
] | [
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2307.02748 | Tiankui Zhang | Yuanpeng Zheng, Tiankui Zhang and Jonathan Loo | Dynamic Multi-time Scale User Admission and Resource Allocation for
Semantic Extraction in MEC Systems | null | null | 10.1109/TVT.2023.3290546 | null | cs.NI eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper investigates the semantic extraction task-oriented dynamic
multi-time scale user admission and resourceallocation in mobile edge computing
(MEC) systems. Amid prevalence artifi cial intelligence applications in various
industries,the offloading of semantic extraction tasks which are mainlycomposed
of convolutional neural networks of computer vision isa great challenge for
communication bandwidth and computing capacity allocation in MEC systems.
Considering the stochasticnature of the semantic extraction tasks, we formulate
a stochastic optimization problem by modeling it as the dynamic arrival of
tasks in the temporal domain. We jointly optimize the system revenue and cost
which are represented as user admission in the long term and resource
allocation in the short term respectively. To handle the proposed stochastic
optimization problem, we decompose it into short-time-scale subproblems and a
long-time-scale subproblem by using the Lyapunov optimization technique. After
that, the short-time-scale optimization variables of resource allocation,
including user association, bandwidth allocation, and computing capacity
allocation are obtained in closed form. The user admission optimization on
long-time scales is solved by a heuristic iteration method. Then, the
multi-time scale user admission and resource allocation algorithm is proposed
for dynamic semantic extraction task computing in MEC systems. Simulation
results demonstrate that, compared with the benchmarks, the proposed algorithm
improves the performance of user admission and resource allocation efficiently
and achieves a flexible trade-off between system revenue and cost at multi-time
scales and considering semantic extraction tasks.
| [
{
"created": "Thu, 6 Jul 2023 03:16:36 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Zheng",
"Yuanpeng",
""
],
[
"Zhang",
"Tiankui",
""
],
[
"Loo",
"Jonathan",
""
]
] | [
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] |
2307.02751 | Zhifeng Wang | Zhifeng Wang, Chunyan Zeng, Surong Duan, Hongjie Ouyang, Hongmin Xu | DSARSR: Deep Stacked Auto-encoders Enhanced Robust Speaker Recognition | 12 pages, 3 figures | null | null | null | cs.SD cs.CR eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Speaker recognition is a biometric modality that utilizes the speaker's
speech segments to recognize the identity, determining whether the test speaker
belongs to one of the enrolled speakers. In order to improve the robustness of
the i-vector framework on cross-channel conditions and explore the nova method
for applying deep learning to speaker recognition, the Stacked Auto-encoders
are used to get the abstract extraction of the i-vector instead of applying
PLDA. After pre-processing and feature extraction, the speaker and
channel-independent speeches are employed for UBM training. The UBM is then
used to extract the i-vector of the enrollment and test speech. Unlike the
traditional i-vector framework, which uses linear discriminant analysis (LDA)
to reduce dimension and increase the discrimination between speaker subspaces,
this research use stacked auto-encoders to reconstruct the i-vector with lower
dimension and different classifiers can be chosen to achieve final
classification. The experimental results show that the proposed method achieves
better performance than the state-of-the-art method.
| [
{
"created": "Thu, 6 Jul 2023 03:19:40 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Wang",
"Zhifeng",
""
],
[
"Zeng",
"Chunyan",
""
],
[
"Duan",
"Surong",
""
],
[
"Ouyang",
"Hongjie",
""
],
[
"Xu",
"Hongmin",
""
]
] | [
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] |
2307.02752 | Li Jiang | Li Jiang, Sijie Chen, Jielin Qiu, Haoran Xu, Wai Kin Chan, Zhao Ding | Offline Reinforcement Learning with Imbalanced Datasets | null | ICML 2023, workshop on Data-centric Machine Learning Research | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | The prevalent use of benchmarks in current offline reinforcement learning
(RL) research has led to a neglect of the imbalance of real-world dataset
distributions in the development of models. The real-world offline RL dataset
is often imbalanced over the state space due to the challenge of exploration or
safety considerations. In this paper, we specify properties of imbalanced
datasets in offline RL, where the state coverage follows a power law
distribution characterized by skewed policies. Theoretically and empirically,
we show that typically offline RL methods based on distributional constraints,
such as conservative Q-learning (CQL), are ineffective in extracting policies
under the imbalanced dataset. Inspired by natural intelligence, we propose a
novel offline RL method that utilizes the augmentation of CQL with a retrieval
process to recall past related experiences, effectively alleviating the
challenges posed by imbalanced datasets. We evaluate our method on several
tasks in the context of imbalanced datasets with varying levels of imbalance,
utilizing the variant of D4RL. Empirical results demonstrate the superiority of
our method over other baselines.
| [
{
"created": "Thu, 6 Jul 2023 03:22:19 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Jiang",
"Li",
""
],
[
"Chen",
"Sijie",
""
],
[
"Qiu",
"Jielin",
""
],
[
"Xu",
"Haoran",
""
],
[
"Chan",
"Wai Kin",
""
],
[
"Ding",
"Zhao",
""
]
] | [
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] |
2307.02753 | Jincheng Lu | Jincheng Lu, Xipeng Yang, Jin Ye, Yifu Zhang, Zhikang Zou, Wei Zhang,
Xiao Tan | CityTrack: Improving City-Scale Multi-Camera Multi-Target Tracking by
Location-Aware Tracking and Box-Grained Matching | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-Camera Multi-Target Tracking (MCMT) is a computer vision technique that
involves tracking multiple targets simultaneously across multiple cameras. MCMT
in urban traffic visual analysis faces great challenges due to the complex and
dynamic nature of urban traffic scenes, where multiple cameras with different
views and perspectives are often used to cover a large city-scale area. Targets
in urban traffic scenes often undergo occlusion, illumination changes, and
perspective changes, making it difficult to associate targets across different
cameras accurately. To overcome these challenges, we propose a novel systematic
MCMT framework, called CityTrack. Specifically, we present a Location-Aware
SCMT tracker which integrates various advanced techniques to improve its
effectiveness in the MCMT task and propose a novel Box-Grained Matching (BGM)
method for the ICA module to solve the aforementioned problems. We evaluated
our approach on the public test set of the CityFlowV2 dataset and achieved an
IDF1 of 84.91%, ranking 1st in the 2022 AI CITY CHALLENGE. Our experimental
results demonstrate the effectiveness of our approach in overcoming the
challenges posed by urban traffic scenes.
| [
{
"created": "Thu, 6 Jul 2023 03:25:37 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Lu",
"Jincheng",
""
],
[
"Yang",
"Xipeng",
""
],
[
"Ye",
"Jin",
""
],
[
"Zhang",
"Yifu",
""
],
[
"Zou",
"Zhikang",
""
],
[
"Zhang",
"Wei",
""
],
[
"Tan",
"Xiao",
""
]
] | [
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2307.02754 | Md Arafat Habib | Md Arafat Habib, Hao Zhou, Pedro Enrique Iturria-Rivera, Medhat
Elsayed, Majid Bavand, Raimundas Gaigalas, Yigit Ozcan, Melike Erol-Kantarci | Intent-driven Intelligent Control and Orchestration in O-RAN Via
Hierarchical Reinforcement Learning | Accepted by IEEE MASS 2023 | null | null | null | cs.NI | http://creativecommons.org/licenses/by/4.0/ | rApps and xApps need to be controlled and orchestrated well in the open radio
access network (O-RAN) so that they can deliver a guaranteed network
performance in a complex multi-vendor environment. This paper proposes a novel
intent-driven intelligent control and orchestration scheme based on
hierarchical reinforcement learning (HRL). The proposed scheme can orchestrate
multiple rApps or xApps according to the operator's intent of optimizing
certain key performance indicators (KPIs), such as throughput, energy
efficiency, and latency. Specifically, we propose a bi-level architecture with
a meta-controller and a controller. The meta-controller provides the target
performance in terms of KPIs, while the controller performs xApp orchestration
at the lower level. Our simulation results show that the proposed HRL-based
intent-driven xApp orchestration mechanism achieves 7.5% and 21.4% increase in
average system throughput with respect to two baselines, i.e., a single xApp
baseline and a non-machine learning-based algorithm, respectively. Similarly,
17.3% and 37.9% increase in energy efficiency are observed in comparison to the
same baselines.
| [
{
"created": "Thu, 6 Jul 2023 03:26:11 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Habib",
"Md Arafat",
""
],
[
"Zhou",
"Hao",
""
],
[
"Iturria-Rivera",
"Pedro Enrique",
""
],
[
"Elsayed",
"Medhat",
""
],
[
"Bavand",
"Majid",
""
],
[
"Gaigalas",
"Raimundas",
""
],
[
"Ozcan",
"Yigit",
""
],
[
"Erol-Kantarci",
"Melike",
""
]
] | [
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] |
2307.02757 | Lina Bariah | Hang Zou, Qiyang Zhao, Lina Bariah, Mehdi Bennis, Merouane Debbah | Wireless Multi-Agent Generative AI: From Connected Intelligence to
Collective Intelligence | null | null | null | null | cs.MA | http://creativecommons.org/licenses/by/4.0/ | The convergence of generative large language models (LLMs), edge networks,
and multi-agent systems represents a groundbreaking synergy that holds immense
promise for future wireless generations, harnessing the power of collective
intelligence and paving the way for self-governed networks where intelligent
decision-making happens right at the edge. This article puts the stepping-stone
for incorporating multi-agent generative artificial intelligence (AI) in
wireless networks, and sets the scene for realizing on-device LLMs, where
multi-agent LLMs are collaboratively planning and solving tasks to achieve a
number of network goals. We further investigate the profound limitations of
cloud-based LLMs, and explore multi-agent LLMs from a game theoretic
perspective, where agents collaboratively solve tasks in competitive
environments. Moreover, we establish the underpinnings for the architecture
design of wireless multi-agent generative AI systems at the network level and
the agent level, and we identify the wireless technologies that are envisioned
to play a key role in enabling on-device LLM. To demonstrate the promising
potentials of wireless multi-agent generative AI networks, we highlight the
benefits that can be achieved when implementing wireless generative agents in
intent-based networking, and we provide a case study to showcase how on-device
LLMs can contribute to solving network intents in a collaborative fashion. We
finally shed lights on potential challenges and sketch a research roadmap
towards realizing the vision of wireless collective intelligence.
| [
{
"created": "Thu, 6 Jul 2023 03:41:15 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Zou",
"Hang",
""
],
[
"Zhao",
"Qiyang",
""
],
[
"Bariah",
"Lina",
""
],
[
"Bennis",
"Mehdi",
""
],
[
"Debbah",
"Merouane",
""
]
] | [
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] |
2307.02758 | Aparna Ananthasubramaniam | Aparna Ananthasubramaniam, Hong Chen, Jason Yan, Kenan Alkiek, Jiaxin
Pei, Agrima Seth, Lavinia Dunagan, Minje Choi, Benjamin Litterer, David
Jurgens | Exploring Linguistic Style Matching in Online Communities: The Role of
Social Context and Conversation Dynamics | Equal contributions from authors 1-9 (AA, HC, JY, KA, JP, AS, LD, MC,
BL) | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Linguistic style matching (LSM) in conversations can be reflective of several
aspects of social influence such as power or persuasion. However, how LSM
relates to the outcomes of online communication on platforms such as Reddit is
an unknown question. In this study, we analyze a large corpus of two-party
conversation threads in Reddit where we identify all occurrences of LSM using
two types of style: the use of function words and formality. Using this
framework, we examine how levels of LSM differ in conversations depending on
several social factors within Reddit: post and subreddit features, conversation
depth, user tenure, and the controversiality of a comment. Finally, we measure
the change of LSM following loss of status after community banning. Our
findings reveal the interplay of LSM in Reddit conversations with several
community metrics, suggesting the importance of understanding conversation
engagement when understanding community dynamics.
| [
{
"created": "Thu, 6 Jul 2023 03:43:45 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Ananthasubramaniam",
"Aparna",
""
],
[
"Chen",
"Hong",
""
],
[
"Yan",
"Jason",
""
],
[
"Alkiek",
"Kenan",
""
],
[
"Pei",
"Jiaxin",
""
],
[
"Seth",
"Agrima",
""
],
[
"Dunagan",
"Lavinia",
""
],
[
"Choi",
"Minje",
""
],
[
"Litterer",
"Benjamin",
""
],
[
"Jurgens",
"David",
""
]
] | [
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] |
2307.02759 | Yuhao Yang | Yuhao Yang, Chao Huang, Lianghao Xia, Chunzhen Huang | Knowledge Graph Self-Supervised Rationalization for Recommendation | Accepted by KDD'23 | null | 10.1145/3580305.3599400 | null | cs.IR cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we introduce a new self-supervised rationalization method,
called KGRec, for knowledge-aware recommender systems. To effectively identify
informative knowledge connections, we propose an attentive knowledge
rationalization mechanism that generates rational scores for knowledge
triplets. With these scores, KGRec integrates generative and contrastive
self-supervised tasks for recommendation through rational masking. To highlight
rationales in the knowledge graph, we design a novel generative task in the
form of masking-reconstructing. By masking important knowledge with high
rational scores, KGRec is trained to rebuild and highlight useful knowledge
connections that serve as rationales. To further rationalize the effect of
collaborative interactions on knowledge graph learning, we introduce a
contrastive learning task that aligns signals from knowledge and user-item
interaction views. To ensure noise-resistant contrasting, potential noisy edges
in both graphs judged by the rational scores are masked. Extensive experiments
on three real-world datasets demonstrate that KGRec outperforms
state-of-the-art methods. We also provide the implementation codes for our
approach at https://github.com/HKUDS/KGRec.
| [
{
"created": "Thu, 6 Jul 2023 03:44:40 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Yang",
"Yuhao",
""
],
[
"Huang",
"Chao",
""
],
[
"Xia",
"Lianghao",
""
],
[
"Huang",
"Chunzhen",
""
]
] | [
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] |
2307.02761 | Haokai Ma | Haokai Ma, Zhuang Qi, Xinxin Dong, Xiangxian Li, Yuze Zheng, Xiangxu
Mengand Lei Meng | Cross-Modal Content Inference and Feature Enrichment for Cold-Start
Recommendation | Accepted by International Joint Conference on Neural Networks 2023
(IJCNN) | null | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multimedia recommendation aims to fuse the multi-modal information of items
for feature enrichment to improve the recommendation performance. However,
existing methods typically introduce multi-modal information based on
collaborative information to improve the overall recommendation precision,
while failing to explore its cold-start recommendation performance. Meanwhile,
these above methods are only applicable when such multi-modal data is
available. To address this problem, this paper proposes a recommendation
framework, named Cross-modal Content Inference and Feature Enrichment
Recommendation (CIERec), which exploits the multi-modal information to improve
its cold-start recommendation performance. Specifically, CIERec first
introduces image annotation as the privileged information to help guide the
mapping of unified features from the visual space to the semantic space in the
training phase. And then CIERec enriches the content representation with the
fusion of collaborative, visual, and cross-modal inferred representations, so
as to improve its cold-start recommendation performance. Experimental results
on two real-world datasets show that the content representations learned by
CIERec are able to achieve superior cold-start recommendation performance over
existing visually-aware recommendation algorithms. More importantly, CIERec can
consistently achieve significant improvements with different conventional
visually-aware backbones, which verifies its universality and effectiveness.
| [
{
"created": "Thu, 6 Jul 2023 03:54:19 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Ma",
"Haokai",
""
],
[
"Qi",
"Zhuang",
""
],
[
"Dong",
"Xinxin",
""
],
[
"Li",
"Xiangxian",
""
],
[
"Zheng",
"Yuze",
""
],
[
"Meng",
"Xiangxu Mengand Lei",
""
]
] | [
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] |
2307.02762 | Ruosen Li | Ruosen Li, Teerth Patel, Xinya Du | PRD: Peer Rank and Discussion Improve Large Language Model based
Evaluations | null | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nowadays, the quality of responses generated by different modern large
language models (LLMs) are hard to evaluate and compare automatically. Recent
studies suggest and predominantly use LLMs as a reference-free metric for
open-ended question answering. More specifically, they use the recognized
"strongest" LLM as the evaluator, which conducts pairwise comparisons of
candidate models' answers and provides a ranking score. However, this intuitive
method has multiple problems, such as bringing in self-enhancement (favoring
its own answers) and positional bias. We draw insights and lessons from the
educational domain (Cho and MacArthur, 2011; Walsh, 2014) to improve LLM-based
evaluations. Specifically, we propose the (1) peer rank (PR) algorithm that
takes into account each peer LLM's pairwise preferences of all answer pairs,
and outputs a final ranking of models; and (2) peer discussion (PD), where we
prompt two LLMs to discuss and try to reach a mutual agreement on preferences
of two answers. We conduct experiments on two benchmark datasets. We find that
our approaches achieve higher accuracy and align better with human judgments,
respectively. Interestingly, PR can induce a relatively accurate self-ranking
of models under the anonymous setting, where each model's name is unrevealed.
Our work provides space to explore evaluating models that are hard to compare
for humans.
| [
{
"created": "Thu, 6 Jul 2023 04:05:44 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Li",
"Ruosen",
""
],
[
"Patel",
"Teerth",
""
],
[
"Du",
"Xinya",
""
]
] | [
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] |
2307.02763 | David Jurgens | David Jurgens, Agrima Seth, Jackson Sargent, Athena Aghighi, Michael
Geraci | Your spouse needs professional help: Determining the Contextual
Appropriateness of Messages through Modeling Social Relationships | ACL 2023, 18 pages, 8 figures, 11 tables | null | null | null | cs.CL cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding interpersonal communication requires, in part, understanding
the social context and norms in which a message is said. However, current
methods for identifying offensive content in such communication largely operate
independent of context, with only a few approaches considering community norms
or prior conversation as context. Here, we introduce a new approach to
identifying inappropriate communication by explicitly modeling the social
relationship between the individuals. We introduce a new dataset of
contextually-situated judgments of appropriateness and show that large language
models can readily incorporate relationship information to accurately identify
appropriateness in a given context. Using data from online conversations and
movie dialogues, we provide insight into how the relationships themselves
function as implicit norms and quantify the degree to which context-sensitivity
is needed in different conversation settings. Further, we also demonstrate that
contextual-appropriateness judgments are predictive of other social factors
expressed in language such as condescension and politeness.
| [
{
"created": "Thu, 6 Jul 2023 04:06:05 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Jurgens",
"David",
""
],
[
"Seth",
"Agrima",
""
],
[
"Sargent",
"Jackson",
""
],
[
"Aghighi",
"Athena",
""
],
[
"Geraci",
"Michael",
""
]
] | [
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] | Your spouse needs professional help: Determining the Contextual Appropriateness of Messages through Modeling Social Relationships | https://arxiv.org/abs/2307.02763v1 | https://arxiv.org/pdf/2307.02763v1.pdf | https://github.com/davidjurgens/contextual-appropriateness | true | true | https://paperswithcode.com/paper/your-spouse-needs-professional-help | [
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2307.02764 | Wittawat Jitkrittum | Wittawat Jitkrittum, Neha Gupta, Aditya Krishna Menon, Harikrishna
Narasimhan, Ankit Singh Rawat, Sanjiv Kumar | When Does Confidence-Based Cascade Deferral Suffice? | null | null | null | null | cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | Cascades are a classical strategy to enable inference cost to vary adaptively
across samples, wherein a sequence of classifiers are invoked in turn. A
deferral rule determines whether to invoke the next classifier in the sequence,
or to terminate prediction. One simple deferral rule employs the confidence of
the current classifier, e.g., based on the maximum predicted softmax
probability. Despite being oblivious to the structure of the cascade -- e.g.,
not modelling the errors of downstream models -- such confidence-based deferral
often works remarkably well in practice. In this paper, we seek to better
understand the conditions under which confidence-based deferral may fail, and
when alternate deferral strategies can perform better. We first present a
theoretical characterisation of the optimal deferral rule, which precisely
characterises settings under which confidence-based deferral may suffer. We
then study post-hoc deferral mechanisms, and demonstrate they can significantly
improve upon confidence-based deferral in settings where (i) downstream models
are specialists that only work well on a subset of inputs, (ii) samples are
subject to label noise, and (iii) there is distribution shift between the train
and test set.
| [
{
"created": "Thu, 6 Jul 2023 04:13:57 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Jitkrittum",
"Wittawat",
""
],
[
"Gupta",
"Neha",
""
],
[
"Menon",
"Aditya Krishna",
""
],
[
"Narasimhan",
"Harikrishna",
""
],
[
"Rawat",
"Ankit Singh",
""
],
[
"Kumar",
"Sanjiv",
""
]
] | [
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] |
2307.02766 | Liwei Lu | Liwei Lu, Hailong Guo, Xu Yang, Yi Zhu | Temporal Difference Learning for High-Dimensional PIDEs with Jumps | null | null | null | null | math.NA cs.LG cs.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a deep learning framework for solving
high-dimensional partial integro-differential equations (PIDEs) based on the
temporal difference learning. We introduce a set of Levy processes and
construct a corresponding reinforcement learning model. To simulate the entire
process, we use deep neural networks to represent the solutions and non-local
terms of the equations. Subsequently, we train the networks using the temporal
difference error, termination condition, and properties of the non-local terms
as the loss function. The relative error of the method reaches O(10^{-3}) in
100-dimensional experiments and O(10^{-4}) in one-dimensional pure jump
problems. Additionally, our method demonstrates the advantages of low
computational cost and robustness, making it well-suited for addressing
problems with different forms and intensities of jumps.
| [
{
"created": "Thu, 6 Jul 2023 04:27:16 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Lu",
"Liwei",
""
],
[
"Guo",
"Hailong",
""
],
[
"Yang",
"Xu",
""
],
[
"Zhu",
"Yi",
""
]
] | [
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] |
2307.02768 | Y-Lan Boureau | Mounica Maddela, Megan Ung, Jing Xu, Andrea Madotto, Heather Foran,
Y-Lan Boureau | Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts | ACL 2023 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many cognitive approaches to well-being, such as recognizing and reframing
unhelpful thoughts, have received considerable empirical support over the past
decades, yet still lack truly widespread adoption in self-help format. A
barrier to that adoption is a lack of adequately specific and diverse dedicated
practice material. This work examines whether current language models can be
leveraged to both produce a virtually unlimited quantity of practice material
illustrating standard unhelpful thought patterns matching specific given
contexts, and generate suitable positive reframing proposals. We propose
PATTERNREFRAME, a novel dataset of about 10k examples of thoughts containing
unhelpful thought patterns conditioned on a given persona, accompanied by about
27k positive reframes. By using this dataset to train and/or evaluate current
models, we show that existing models can already be powerful tools to help
generate an abundance of tailored practice material and hypotheses, with no or
minimal additional model training required.
| [
{
"created": "Thu, 6 Jul 2023 04:40:52 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Maddela",
"Mounica",
""
],
[
"Ung",
"Megan",
""
],
[
"Xu",
"Jing",
""
],
[
"Madotto",
"Andrea",
""
],
[
"Foran",
"Heather",
""
],
[
"Boureau",
"Y-Lan",
""
]
] | [
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] |
2307.02770 | TaeHo Yoon | TaeHo Yoon, Kibeom Myoung, Keon Lee, Jaewoong Cho, Albert No, Ernest
K. Ryu | Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Diffusion models have recently shown remarkable success in high-quality image
generation. Sometimes, however, a pre-trained diffusion model exhibits partial
misalignment in the sense that the model can generate good images, but it
sometimes outputs undesirable images. If so, we simply need to prevent the
generation of the bad images, and we call this task censoring. In this work, we
present censored generation with a pre-trained diffusion model using a reward
model trained on minimal human feedback. We show that censoring can be
accomplished with extreme human feedback efficiency and that labels generated
with a mere few minutes of human feedback are sufficient. Code available at:
https://github.com/tetrzim/diffusion-human-feedback.
| [
{
"created": "Thu, 6 Jul 2023 04:45:14 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Yoon",
"TaeHo",
""
],
[
"Myoung",
"Kibeom",
""
],
[
"Lee",
"Keon",
""
],
[
"Cho",
"Jaewoong",
""
],
[
"No",
"Albert",
""
],
[
"Ryu",
"Ernest K.",
""
]
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] | Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback | https://arxiv.org/abs/2307.02770v1 | https://arxiv.org/pdf/2307.02770v1.pdf | https://github.com/tetrzim/diffusion-human-feedback | true | true | https://paperswithcode.com/paper/censored-sampling-of-diffusion-models-using-3 | [
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2307.02772 | Corwin Sinnamon | Corwin Sinnamon and Robert E. Tarjan | Efficiency of Self-Adjusting Heaps | null | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Since the invention of the pairing heap by Fredman et al., it has been an
open question whether this or any other simple "self-adjusting" heap supports
decrease-key operations on $n$-item heaps in $O(\log\log n)$ time. Using
powerful new techniques, we answer this question in the affirmative. We prove
that both slim and smooth heaps, recently introduced self-adjusting heaps,
support heap operations on an $n$-item heap in the following amortized time
bounds: $O(\log n)$ for delete-min and delete, $O(\log\log n)$ for
decrease-key, and $O(1)$ for all other heap operations, including insert and
meld. We also analyze the multipass pairing heap, a variant of pairing heaps.
For this heap implementation, we obtain the same bounds except for
decrease-key, for which our bound is $O(\log\log n \log\log\log n)$. Our bounds
significantly improve the best previously known bounds for all three data
structures. For slim and smooth heaps our bounds are tight, since they match
lower bounds of Iacono and Ozkan; for multipass pairing heaps our bounds are
tight except for decrease-key, which by the lower bounds of Fredman and Iacono
and \"Ozkan must take $O(\log\log n)$ amortized time if delete-min takes
$O(\log n)$ time.
| [
{
"created": "Thu, 6 Jul 2023 04:48:24 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Sinnamon",
"Corwin",
""
],
[
"Tarjan",
"Robert E.",
""
]
] | [
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2307.02773 | Shwetank Choudhary | Tuneer Khargonkar, Shwetank Choudhary, Sumit Kumar, Barath Raj KR | SeLiNet: Sentiment enriched Lightweight Network for Emotion Recognition
in Images | Paper submitted in ISCAS 2023 | null | null | null | cs.CV cs.HC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this paper, we propose a sentiment-enriched lightweight network SeLiNet
and an end-to-end on-device pipeline for contextual emotion recognition in
images. SeLiNet model consists of body feature extractor, image aesthetics
feature extractor, and learning-based fusion network which jointly estimates
discrete emotion and human sentiments tasks. On the EMOTIC dataset, the
proposed approach achieves an Average Precision (AP) score of 27.17 in
comparison to the baseline AP score of 27.38 while reducing the model size by
>85%. In addition, we report an on-device AP score of 26.42 with reduction in
model size by >93% when compared to the baseline.
| [
{
"created": "Thu, 6 Jul 2023 04:49:47 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Khargonkar",
"Tuneer",
""
],
[
"Choudhary",
"Shwetank",
""
],
[
"Kumar",
"Sumit",
""
],
[
"KR",
"Barath Raj",
""
]
] | [
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] |
2307.02774 | Young-San Lin | Elena Grigorescu, Nithish Kumar, Young-San Lin | Approximation Algorithms for Directed Weighted Spanners | null | APPROX 2023 | null | null | cs.DS cs.DM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the pairwise weighted spanner problem, the input consists of an
$n$-vertex-directed graph, where each edge is assigned a cost and a length.
Given $k$ vertex pairs and a distance constraint for each pair, the goal is to
find a minimum-cost subgraph in which the distance constraints are satisfied.
This formulation captures many well-studied connectivity problems, including
spanners, distance preservers, and Steiner forests.
In the offline setting, we show:
1. An $\tilde{O}(n^{4/5 + \epsilon})$-approximation algorithm for pairwise
weighted spanners. When the edges have unit costs and lengths, the best
previous algorithm gives an $\tilde{O}(n^{3/5 + \epsilon})$-approximation, due
to Chlamt{\'a}{\v{c}}, Dinitz, Kortsarz, and Laekhanukit (TALG, 2020).
2. An $\tilde{O}(n^{1/2+\epsilon})$-approximation algorithm for all-pair
weighted distance preservers. When the edges have unit costs and arbitrary
lengths, the best previous algorithm gives an
$\tilde{O}(n^{1/2})$-approximation for all-pair spanners, due to Berman,
Bhattacharyya, Makarychev, Raskhodnikova, and Yaroslavtsev (Information and
Computation, 2013).
In the online setting, we show:
1. An $\tilde{O}(k^{1/2 + \epsilon})$-competitive algorithm for pairwise
weighted spanners. The state-of-the-art results are
$\tilde{O}(n^{4/5})$-competitive when edges have unit costs and arbitrary
lengths, and $\min\{\tilde{O}(k^{1/2 + \epsilon}), \tilde{O}(n^{2/3 +
\epsilon})\}$-competitive when edges have unit costs and lengths, due to
Grigorescu, Lin, and Quanrud (APPROX, 2021).
2. An $\tilde{O}(k^{\epsilon})$-competitive algorithm for single-source
weighted spanners. Without distance constraints, this problem is equivalent to
the directed Steiner tree problem. The best previous algorithm for online
directed Steiner trees is $\tilde{O}(k^{\epsilon})$-competitive, due to
Chakrabarty, Ene, Krishnaswamy, and Panigrahi (SICOMP, 2018).
| [
{
"created": "Thu, 6 Jul 2023 05:04:16 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Grigorescu",
"Elena",
""
],
[
"Kumar",
"Nithish",
""
],
[
"Lin",
"Young-San",
""
]
] | [
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2307.02776 | Stanislav Baturin | A. Halavanau, P. Piot and S. S. Baturin | Single-shot Transverse Wakefield Mapping with a Hollow Electron Beam | 13 pages 15 figures | null | null | null | physics.acc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Beam-driven wakefield accelerators are foreseen to enable compact
accelerator-based light sources and play a critical role in future
linear-collider concepts. This class of wakefield acceleration has been
extensively studied over the last four decades with a focus on demonstrating
its ability to support high-accelerating gradient and, most recently, enhanced
transformer ratios. Yet, the associated detrimental transverse wakefields have
not been examined in as many details due to the limited diagnostics available.
In this paper, we introduce a beam-based single-shot transverse-wakefield
measurement technique. The approach employs a witness ''hollow" electron beam
to probe the wakefields generated by a drive bunch. We show how the transverse
distortions of the hollow probe provide a direct measurement of the wakefield
distribution within the area circumscribed by the probe. The ability to
directly measure a full structure of the transverse wakefield could help to
develop mitigation schemes and ultimately suppress the adverse beam-break-up
instabilities. We discuss a practical implementation of the method and
demonstrate its performance with the help of start-to-end simulations.
| [
{
"created": "Thu, 6 Jul 2023 05:11:18 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Halavanau",
"A.",
""
],
[
"Piot",
"P.",
""
],
[
"Baturin",
"S. S.",
""
]
] | [
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] |
2307.02779 | Yifei Shen | Yifei Shen, Jiawei Shao, Xinjie Zhang, Zehong Lin, Hao Pan, Dongsheng
Li, Jun Zhang, Khaled B. Letaief | Large Language Models Empowered Autonomous Edge AI for Connected
Intelligence | Magazine paper | null | null | null | cs.IT cs.LG cs.NI eess.SP math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The evolution of wireless networks gravitates towards connected intelligence,
a concept that envisions seamless interconnectivity among humans, objects, and
intelligence in a hyper-connected cyber-physical world. Edge AI emerges as a
promising solution to achieve connected intelligence by delivering
high-quality, low-latency, and privacy-preserving AI services at the network
edge. In this article, we introduce an autonomous edge AI system that
automatically organizes, adapts, and optimizes itself to meet users' diverse
requirements. The system employs a cloud-edge-client hierarchical architecture,
where the large language model, i.e., Generative Pretrained Transformer (GPT),
resides in the cloud, and other AI models are co-deployed on devices and edge
servers. By leveraging the powerful abilities of GPT in language understanding,
planning, and code generation, we present a versatile framework that
efficiently coordinates edge AI models to cater to users' personal demands
while automatically generating code to train new models via edge federated
learning. Experimental results demonstrate the system's remarkable ability to
accurately comprehend user demands, efficiently execute AI models with minimal
cost, and effectively create high-performance AI models through federated
learning.
| [
{
"created": "Thu, 6 Jul 2023 05:16:55 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Shen",
"Yifei",
""
],
[
"Shao",
"Jiawei",
""
],
[
"Zhang",
"Xinjie",
""
],
[
"Lin",
"Zehong",
""
],
[
"Pan",
"Hao",
""
],
[
"Li",
"Dongsheng",
""
],
[
"Zhang",
"Jun",
""
],
[
"Letaief",
"Khaled B.",
""
]
] | [
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2307.02780 | Zahmeeth Sayed Sakkaff | Zahmeeth Sayed Sakkaff | Brain Computer Interface (BCI) based on Electroencephalographic (EEG)
patterns due to new cognitive tasks | arXiv admin note: text overlap with arXiv:1404.1100 by other authors | null | null | null | cs.HC q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | New mental tasks were investigated for suitability in Brain-Computer
Interface (BCI). Electroencephalography (EEG) signals were collected and
analyzed to identify these mental tasks. MS Windows-based software was
developed for investigating and classifying recorded EEG data with unnecessary
frequencies filtered out with Bandpass filtering. To identify the best feature
vector construction method for a given mental task, feature vectors were
constructed using Bandpower, Principal Component Analysis, and Downsampling
separately. These feature vectors were then classified with Linear Discriminant
Analysis, Linear Support Vector Machines, Critical Distance Classifiers,
Nearest Neighbor Classifiers, and their Non-Linear counterparts to find the
best-performing classifier. For comparison purposes, performances of already
well-known mental tasks in the BCI community were computed along with that of
new mental tasks introduced in this thesis. In the preliminary studies, it was
found that the most promising new mental task which a BCI system could identify
is the imagination of hitting a given square with an imaginary arrow from above
(or below) and right, (or left) to the screen. The group of these mental tasks
was named as 'Hit Series' (HS). A detailed investigation of HS was carried out
and compared with the performance of Motor Imagery (MI) events which are the
most heavily used mental tasks in EEG-based BCI systems. One subject achieved
the maximum average performance for HS, 100 pct in the binary classifications
while 99 pct in overall combined performance. The best average performances of
the other two subjects for the same mental tasks were 93 pct and 87pct with the
overall performance of 89 pct and 78 pct. Performances of the same three
subjects for mental tasks in MI were relatively poor. The average performances
were 92, 78, and 92 pct while overall performances were 87, 69, and 88 pct.
| [
{
"created": "Thu, 6 Jul 2023 05:17:45 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Sakkaff",
"Zahmeeth Sayed",
""
]
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] |
2307.02783 | Triet Minh Thai | Triet M. Thai, Anh T. Vo, Hao K. Tieu, Linh N.P. Bui, Thien T.B.
Nguyen | UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image
Enhancement for Gastrointestinal Visual Question Answering | ImageCLEF2023 | null | null | null | cs.CV cs.HC | http://creativecommons.org/licenses/by/4.0/ | In recent years, artificial intelligence has played an important role in
medicine and disease diagnosis, with many applications to be mentioned, one of
which is Medical Visual Question Answering (MedVQA). By combining computer
vision and natural language processing, MedVQA systems can assist experts in
extracting relevant information from medical image based on a given question
and providing precise diagnostic answers. The ImageCLEFmed-MEDVQA-GI-2023
challenge carried out visual question answering task in the gastrointestinal
domain, which includes gastroscopy and colonoscopy images. Our team approached
Task 1 of the challenge by proposing a multimodal learning method with image
enhancement to improve the VQA performance on gastrointestinal images. The
multimodal architecture is set up with BERT encoder and different pre-trained
vision models based on convolutional neural network (CNN) and Transformer
architecture for features extraction from question and endoscopy image. The
result of this study highlights the dominance of Transformer-based vision
models over the CNNs and demonstrates the effectiveness of the image
enhancement process, with six out of the eight vision models achieving better
F1-Score. Our best method, which takes advantages of BERT+BEiT fusion and image
enhancement, achieves up to 87.25% accuracy and 91.85% F1-Score on the
development test set, while also producing good result on the private test set
with accuracy of 82.01%.
| [
{
"created": "Thu, 6 Jul 2023 05:22:20 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Thai",
"Triet M.",
""
],
[
"Vo",
"Anh T.",
""
],
[
"Tieu",
"Hao K.",
""
],
[
"Bui",
"Linh N. P.",
""
],
[
"Nguyen",
"Thien T. B.",
""
]
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] |
2307.02784 | Seyoung Ahn | Seyoung Ahn, Soohyeong Kim, Yongseok Kwon, Joohan Park, Jiseung Youn,
Sunghyun Cho | On the Spatial-Wideband Effects in Millimeter-Wave Cell-Free Massive
MIMO | null | null | null | null | cs.IT cs.NI eess.SP math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we investigate the spatial-wideband effects in cell-free
massive MIMO (CF-mMIMO) systems in mmWave bands. The utilization of mmWave
frequencies brings challenges such as signal attenuation and the need for
denser networks like ultra-dense networks (UDN) to maintain communication
performance. CF-mMIMO is introduced as a solution, where distributed access
points (APs) transmit signals to a central processing unit (CPU) for joint
processing. CF-mMIMO offers advantages in reducing non-line-of-sight (NLOS)
conditions and overcoming signal blockage. We investigate the synchronization
problem in CF-mMIMO due to time delays between APs. It proposes a minimum
cyclic prefix length to mitigate inter-symbol interference (ISI) in OFDM
systems. Furthermore, the spatial correlations of channel responses are
analyzed in the frequency-phase domain. The impact of these correlations on
system performance is examined. The findings contribute to improving the
performance of CF-mMIMO systems and enhancing the effective utilization of
mmWave communication.
| [
{
"created": "Thu, 6 Jul 2023 05:22:37 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Ahn",
"Seyoung",
""
],
[
"Kim",
"Soohyeong",
""
],
[
"Kwon",
"Yongseok",
""
],
[
"Park",
"Joohan",
""
],
[
"Youn",
"Jiseung",
""
],
[
"Cho",
"Sunghyun",
""
]
] | [
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] |
2307.02786 | Vinod Kumar | Vinod Kumar, Rituraj Sharma, Abhishek Bhatt, I. Csarnovics, Petr
Nemec, H. Jain, and K. V. Adarsh | Ultrafast Third-Order Nonlinear Optical Response of Charge Coupled Gold
Nanoparticle-Ge24Se76 Heterostructure | null | null | null | null | cond-mat.mtrl-sci physics.optics | http://creativecommons.org/licenses/by/4.0/ | The donor-acceptor interaction of a charge-coupled heterostructure
encompassing a metal and an amorphous semiconductor subjected to a laser field
has many potential applications in the realm of nonlinear optics. In this work,
we fabricate an electron donor gold nanoparticle (AuNP) and acceptor amorphous
Ge24Se76 heterostructure on a quartz substrate using a sequential thermal
evaporation technique. In this charge-coupled heterostructure, we demonstrate
the ultrafast third-order nonlinear absorptive and refractive response and
their sign reversal compared to pristine Ge24Se76. Enhanced optical
nonlinearity in these heterostructures of varying plasmonic wavelengths is due
to charge transfer, verified by the Raman spectroscopy. Further, the ultrafast
transient absorption measurements support the thesis of charge transfer in the
AuNP/Ge24Se76 heterostructure. These findings open up exciting opportunities
for developing novel device technologies with far-reaching applications in
nonlinear optics.
| [
{
"created": "Thu, 6 Jul 2023 05:43:11 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Kumar",
"Vinod",
""
],
[
"Sharma",
"Rituraj",
""
],
[
"Bhatt",
"Abhishek",
""
],
[
"Csarnovics",
"I.",
""
],
[
"Nemec",
"Petr",
""
],
[
"Jain",
"H.",
""
],
[
"Adarsh",
"K. V.",
""
]
] | [
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2307.02787 | Hirotaka Ono | Tesshu Hanaka, Hirotaka Ono, Kunihiko Sadakane, Kosuke Sadakane | Shortest Beer Path Queries based on Graph Decomposition | 25 pages, 9 figures | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Given a directed edge-weighted graph $G=(V, E)$ with beer vertices
$B\subseteq V$, a beer path between two vertices $u$ and $v$ is a path between
$u$ and $v$ that visits at least one beer vertex in $B$, and the beer distance
between two vertices is the shortest length of beer paths. We consider
\emph{indexing problems} on beer paths, that is, a graph is given a priori, and
we construct some data structures (called indexes) for the graph. Then later,
we are given two vertices, and we find the beer distance or beer path between
them using the data structure. For such a scheme, efficient algorithms using
indexes for the beer distance and beer path queries have been proposed for
outerplanar graphs and interval graphs. For example, Bacic et al. (2021)
present indexes with size $O(n)$ for outerplanar graphs and an algorithm using
them that answers the beer distance between given two vertices in
$O(\alpha(n))$ time, where $\alpha(\cdot)$ is the inverse Ackermann function;
the performance is shown to be optimal. This paper proposes indexing data
structures and algorithms for beer path queries on general graphs based on two
types of graph decomposition: the tree decomposition and the triconnected
component decomposition. We propose indexes with size $O(m+nr^2)$ based on the
triconnected component decomposition, where $r$ is the size of the largest
triconnected component. For a given query $u,v\in V$, our algorithm using the
indexes can output the beer distance in query time $O(\alpha(m))$. In
particular, our indexing data structures and algorithms achieve the optimal
performance (the space and the query time) for series-parallel graphs, which is
a wider class of outerplanar graphs.
| [
{
"created": "Thu, 6 Jul 2023 05:43:53 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Hanaka",
"Tesshu",
""
],
[
"Ono",
"Hirotaka",
""
],
[
"Sadakane",
"Kunihiko",
""
],
[
"Sadakane",
"Kosuke",
""
]
] | [
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2307.02790 | Bach Long Nguyen Dr. | Bach Long Nguyen, Anh-Dzung Doan, Tat-Jun Chin, Christophe Guettier,
Estelle Parra, Ian Reid, and Markus Wagner | Sensor Allocation and Online-Learning-based Path Planning for Maritime
Situational Awareness Enhancement: A Multi-Agent Approach | null | null | null | null | cs.MA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Countries with access to large bodies of water often aim to protect their
maritime transport by employing maritime surveillance systems. However, the
number of available sensors (e.g., cameras) is typically small compared to the
to-be-monitored targets, and their Field of View (FOV) and range are often
limited. This makes improving the situational awareness of maritime transports
challenging. To this end, we propose a method that not only distributes
multiple sensors but also plans paths for them to observe multiple targets,
while minimizing the time needed to achieve situational awareness. In
particular, we provide a formulation of this sensor allocation and path
planning problem which considers the partial awareness of the targets' state,
as well as the unawareness of the targets' trajectories. To solve the problem
we present two algorithms: \emph{1)} a greedy algorithm for assigning sensors
to targets, and \emph{2)} a distributed multi-agent path planning algorithm
based on regret-matching learning. Because a quick convergence is a requirement
for algorithms developed for high mobility environments, we employ a forgetting
factor to quickly converge to correlated equilibrium solutions. Experimental
results show that our combined approach achieves situational awareness more
quickly than related work.
| [
{
"created": "Thu, 6 Jul 2023 06:05:35 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Nguyen",
"Bach Long",
""
],
[
"Doan",
"Anh-Dzung",
""
],
[
"Chin",
"Tat-Jun",
""
],
[
"Guettier",
"Christophe",
""
],
[
"Parra",
"Estelle",
""
],
[
"Reid",
"Ian",
""
],
[
"Wagner",
"Markus",
""
]
] | [
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] |
2307.02791 | Ben Glocker | Charles Jones, M\'elanie Roschewitz, Ben Glocker | The Role of Subgroup Separability in Group-Fair Medical Image
Classification | Accepted at MICCAI 2023. Code available under
https://github.com/biomedia-mira/subgroup-separability | null | null | null | cs.CV cs.AI cs.CY cs.LG | http://creativecommons.org/licenses/by/4.0/ | We investigate performance disparities in deep classifiers. We find that the
ability of classifiers to separate individuals into subgroups varies
substantially across medical imaging modalities and protected characteristics;
crucially, we show that this property is predictive of algorithmic bias.
Through theoretical analysis and extensive empirical evaluation, we find a
relationship between subgroup separability, subgroup disparities, and
performance degradation when models are trained on data with systematic bias
such as underdiagnosis. Our findings shed new light on the question of how
models become biased, providing important insights for the development of fair
medical imaging AI.
| [
{
"created": "Thu, 6 Jul 2023 06:06:47 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Jones",
"Charles",
""
],
[
"Roschewitz",
"Mélanie",
""
],
[
"Glocker",
"Ben",
""
]
] | [
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2307.02792 | Xinming Tu | Xinming Tu, James Zou, Weijie J. Su, Linjun Zhang | What Should Data Science Education Do with Large Language Models? | null | null | null | null | cs.CY cs.AI cs.CL | http://creativecommons.org/licenses/by/4.0/ | The rapid advances of large language models (LLMs), such as ChatGPT, are
revolutionizing data science and statistics. These state-of-the-art tools can
streamline complex processes. As a result, it reshapes the role of data
scientists. We argue that LLMs are transforming the responsibilities of data
scientists, shifting their focus from hands-on coding, data-wrangling and
conducting standard analyses to assessing and managing analyses performed by
these automated AIs. This evolution of roles is reminiscent of the transition
from a software engineer to a product manager. We illustrate this transition
with concrete data science case studies using LLMs in this paper. These
developments necessitate a meaningful evolution in data science education.
Pedagogy must now place greater emphasis on cultivating diverse skillsets among
students, such as LLM-informed creativity, critical thinking, AI-guided
programming. LLMs can also play a significant role in the classroom as
interactive teaching and learning tools, contributing to personalized
education. This paper discusses the opportunities, resources and open
challenges for each of these directions. As with any transformative technology,
integrating LLMs into education calls for careful consideration. While LLMs can
perform repetitive tasks efficiently, it's crucial to remember that their role
is to supplement human intelligence and creativity, not to replace it.
Therefore, the new era of data science education should balance the benefits of
LLMs while fostering complementary human expertise and innovations. In
conclusion, the rise of LLMs heralds a transformative period for data science
and its education. This paper seeks to shed light on the emerging trends,
potential opportunities, and challenges accompanying this paradigm shift,
hoping to spark further discourse and investigation into this exciting,
uncharted territory.
| [
{
"created": "Thu, 6 Jul 2023 06:07:29 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Tu",
"Xinming",
""
],
[
"Zou",
"James",
""
],
[
"Su",
"Weijie J.",
""
],
[
"Zhang",
"Linjun",
""
]
] | [
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] |
2307.02794 | Ayush Kumar | Ayush Kumar, David K. Yau | A Testbed To Study Adversarial Cyber-Attack Strategies in Enterprise
Networks | null | null | null | null | cs.CR | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this work, we propose a testbed environment to capture the attack
strategies of an adversary carrying out a cyber-attack on an enterprise
network. The testbed contains nodes with known security vulnerabilities which
can be exploited by hackers. Participants can be invited to play the role of a
hacker (e.g., black-hat, hacktivist) and attack the testbed. The testbed is
designed such that there are multiple attack pathways available to hackers. We
describe the working of the testbed components and discuss its implementation
on a VMware ESXi server. Finally, we subject our testbed implementation to a
few well-known cyber-attack strategies, collect data during the process and
present our analysis of the data.
| [
{
"created": "Thu, 6 Jul 2023 06:10:52 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Kumar",
"Ayush",
""
],
[
"Yau",
"David K.",
""
]
] | [
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] |
2307.02796 | Nan Tang | Nan Tang and Chenyu Yang and Ju Fan and Lei Cao | VerifAI: Verified Generative AI | 8 pages, 4 figures | null | null | null | cs.DB cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | Generative AI has made significant strides, yet concerns about the accuracy
and reliability of its outputs continue to grow. Such inaccuracies can have
serious consequences such as inaccurate decision-making, the spread of false
information, privacy violations, legal liabilities, and more. Although efforts
to address these risks are underway, including explainable AI and responsible
AI practices such as transparency, privacy protection, bias mitigation, and
social and environmental responsibility, misinformation caused by generative AI
will remain a significant challenge. We propose that verifying the outputs of
generative AI from a data management perspective is an emerging issue for
generative AI. This involves analyzing the underlying data from multi-modal
data lakes, including text files, tables, and knowledge graphs, and assessing
its quality and consistency. By doing so, we can establish a stronger
foundation for evaluating the outputs of generative AI models. Such an approach
can ensure the correctness of generative AI, promote transparency, and enable
decision-making with greater confidence. Our vision is to promote the
development of verifiable generative AI and contribute to a more trustworthy
and responsible use of AI.
| [
{
"created": "Thu, 6 Jul 2023 06:11:51 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Tang",
"Nan",
""
],
[
"Yang",
"Chenyu",
""
],
[
"Fan",
"Ju",
""
],
[
"Cao",
"Lei",
""
]
] | [
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] |
2307.02797 | Weihua Li | Mengyan Wang, Yuxuan Hu, Zihan Yuan, Chenting Jiang, Weihua Li,
Shiqing Wu and Quan Bai | BHEISR: Nudging from Bias to Balance -- Promoting Belief Harmony by
Eliminating Ideological Segregation in Knowledge-based Recommendations | 26 pages | null | null | null | cs.IR cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In the realm of personalized recommendation systems, the increasing concern
is the amplification of belief imbalance and user biases, a phenomenon
primarily attributed to the filter bubble. Addressing this critical issue, we
introduce an innovative intermediate agency (BHEISR) between users and existing
recommendation systems to attenuate the negative repercussions of the filter
bubble effect in extant recommendation systems. The main objective is to strike
a belief balance for users while minimizing the detrimental influence caused by
filter bubbles. The BHEISR model amalgamates principles from nudge theory while
upholding democratic and transparent principles. It harnesses user-specific
category information to stimulate curiosity, even in areas users might
initially deem uninteresting. By progressively stimulating interest in novel
categories, the model encourages users to broaden their belief horizons and
explore the information they typically overlook. Our model is time-sensitive
and operates on a user feedback loop. It utilizes the existing recommendation
algorithm of the model and incorporates user feedback from the prior time
frame. This approach endeavors to transcend the constraints of the filter
bubble, enrich recommendation diversity, and strike a belief balance among
users while also catering to user preferences and system-specific business
requirements. To validate the effectiveness and reliability of the BHEISR
model, we conducted a series of comprehensive experiments with real-world
datasets. These experiments compared the performance of the BHEISR model
against several baseline models using nearly 200 filter bubble-impacted users
as test subjects. Our experimental results conclusively illustrate the superior
performance of the BHEISR model in mitigating filter bubbles and balancing user
perspectives.
| [
{
"created": "Thu, 6 Jul 2023 06:12:37 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Wang",
"Mengyan",
""
],
[
"Hu",
"Yuxuan",
""
],
[
"Yuan",
"Zihan",
""
],
[
"Jiang",
"Chenting",
""
],
[
"Li",
"Weihua",
""
],
[
"Wu",
"Shiqing",
""
],
[
"Bai",
"Quan",
""
]
] | [
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] |
2307.02798 | Hritam Basak | Hritam Basak, Zhaozheng Yin | Semi-supervised Domain Adaptive Medical Image Segmentation through
Consistency Regularized Disentangled Contrastive Learning | Paper accepted at MICCAI 2023 | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Although unsupervised domain adaptation (UDA) is a promising direction to
alleviate domain shift, they fall short of their supervised counterparts. In
this work, we investigate relatively less explored semi-supervised domain
adaptation (SSDA) for medical image segmentation, where access to a few labeled
target samples can improve the adaptation performance substantially.
Specifically, we propose a two-stage training process. First, an encoder is
pre-trained in a self-learning paradigm using a novel domain-content
disentangled contrastive learning (CL) along with a pixel-level feature
consistency constraint. The proposed CL enforces the encoder to learn
discriminative content-specific but domain-invariant semantics on a global
scale from the source and target images, whereas consistency regularization
enforces the mining of local pixel-level information by maintaining spatial
sensitivity. This pre-trained encoder, along with a decoder, is further
fine-tuned for the downstream task, (i.e. pixel-level segmentation) using a
semi-supervised setting. Furthermore, we experimentally validate that our
proposed method can easily be extended for UDA settings, adding to the
superiority of the proposed strategy. Upon evaluation on two domain adaptive
image segmentation tasks, our proposed method outperforms the SoTA methods,
both in SSDA and UDA settings. Code is available at
https://github.com/hritam-98/GFDA-disentangled
| [
{
"created": "Thu, 6 Jul 2023 06:13:22 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Basak",
"Hritam",
""
],
[
"Yin",
"Zhaozheng",
""
]
] | [
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] | Semi-supervised Domain Adaptive Medical Image Segmentation through Consistency Regularized Disentangled Contrastive Learning | https://arxiv.org/abs/2307.02798v1 | https://arxiv.org/pdf/2307.02798v1.pdf | https://github.com/hritam-98/gfda-disentangled | true | true | https://paperswithcode.com/paper/semi-supervised-domain-adaptive-medical-image | [
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2307.02799 | Yuya Moroto | Yuya Moroto, Keisuke Maeda, Takahiro Ogawa and Miki Haseyama | Few-Shot Personalized Saliency Prediction Using Tensor Regression for
Preserving Structural Global Information | 5pages, 3 figures | null | null | null | eess.IV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a few-shot personalized saliency prediction using
tensor-to-matrix regression for preserving the structural global information of
personalized saliency maps (PSMs). In contrast to a general saliency map, a PSM
has been great potential since its map indicates the person-specific visual
attention that is useful for obtaining individual visual preferences from
heterogeneity of gazed areas. The PSM prediction is needed for acquiring the
PSM for the unseen image, but its prediction is still a challenging task due to
the complexity of individual gaze patterns. For recognizing individual gaze
patterns from the limited amount of eye-tracking data, the previous methods
adopt the similarity of gaze tendency between persons. However, in the previous
methods, the PSMs are vectorized for the prediction model. In this way, the
structural global information of the PSMs corresponding to the image is
ignored. For automatically revealing the relationship between PSMs, we focus on
the tensor-based regression model that can preserve the structural information
of PSMs, and realize the improvement of the prediction accuracy. In the
experimental results, we confirm the proposed method including the tensor-based
regression outperforms the comparative methods.
| [
{
"created": "Thu, 6 Jul 2023 06:17:57 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Moroto",
"Yuya",
""
],
[
"Maeda",
"Keisuke",
""
],
[
"Ogawa",
"Takahiro",
""
],
[
"Haseyama",
"Miki",
""
]
] | [
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2307.02800 | Mikhail Rybakov | M. Rybakov, D. Shkatov | Algorithmic properties of QK4.3 and QS4.3 | Smirnov Readings, 2021, Moscow | null | null | null | math.LO cs.LO | http://creativecommons.org/publicdomain/zero/1.0/ | We prove that predicate modal logics QK4.3 and QS4.3 are undecidable in
languages with two individual variables, one modandic predicate letter, and one
proposition letter.
| [
{
"created": "Thu, 6 Jul 2023 06:24:59 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Rybakov",
"M.",
""
],
[
"Shkatov",
"D.",
""
]
] | [
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2307.02801 | Yyuqing Zhu | Yuqing Zhu, Yiwen Zhu, Aoyu Gong, Yan Lin, and Yijin Zhang | Age-of-Information Dependent Random Access for Periodic Updating | null | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper considers an uplink Internet of Things system with synchronous
periodic traffic, where multiple devices generate their status updates at the
beginning of each global frame and attempt to send them to a common access
point. To achieve a low network-wide age of information (AoI) in an easily
implementable manner, we require each device to adopt an age-dependent random
access protocol, i.e., to transmit with a certain probability only when its
corresponding AoI reaches a certain threshold. We analyze the time-average
expected AoI by a multi-layer Markov model where an external infinite-horizon
Markov chain manages the jumps between the beginnings of frames, while two
internal finite-horizon Markov chains manage the evolution during an arbitrary
frame for different cases. Simulation results verify the accuracy of the
modeling and the AoI advantage over age-independent schemes.
| [
{
"created": "Thu, 6 Jul 2023 06:31:07 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Zhu",
"Yuqing",
""
],
[
"Zhu",
"Yiwen",
""
],
[
"Gong",
"Aoyu",
""
],
[
"Lin",
"Yan",
""
],
[
"Zhang",
"Yijin",
""
]
] | [
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2307.02804 | Mohammad Abu-Shaira | Mohammad Abu-Shaira and Greg Speegle | OLR-WA Online Regression with Weighted Average | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Machine Learning requires a large amount of training data in order to build
accurate models. Sometimes the data arrives over time, requiring significant
storage space and recalculating the model to account for the new data. On-line
learning addresses these issues by incrementally modifying the model as data is
encountered, and then discarding the data. In this study we introduce a new
online linear regression approach. Our approach combines newly arriving data
with a previously existing model to create a new model. The introduced model,
named OLR-WA (OnLine Regression with Weighted Average) uses user-defined
weights to provide flexibility in the face of changing data to bias the results
in favor of old or new data. We have conducted 2-D and 3-D experiments
comparing OLR-WA to a static model using the entire data set. The results show
that for consistent data, OLR-WA and the static batch model perform similarly
and for varying data, the user can set the OLR-WA to adapt more quickly or to
resist change.
| [
{
"created": "Thu, 6 Jul 2023 06:39:27 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Abu-Shaira",
"Mohammad",
""
],
[
"Speegle",
"Greg",
""
]
] | [
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] |
2307.02807 | Kuiliang Wang | Kuiliang Wang, Hong Liang, Chong Zhao, Xin Bian | Dynamics of a droplet in shear flow by smoothed particle hydrodynamics | 33 pages, 20 figures | null | null | null | physics.flu-dyn | http://creativecommons.org/licenses/by/4.0/ | We employ a multi-phase smoothed particle hydrodynamics (SPH) method to study
droplet dynamics in shear flow. With an extensive range of Reynolds number,
capillary number, wall confinement, and density/viscosity ratio between the
droplet and the matrix fluid, we are able to investigate systematically the
droplet dynamics such as deformation and breakup. We conduct the majority of
the simulations in two dimensions due to economical computations, while perform
a few representative simulations in three dimensions to corroborate the former.
Comparison between current results and those in literature indicates that the
SPH method adopted has an excellent accuracy and is capable of simulating
scenarios with large density or/and viscosity ratios. We generate slices of
phase diagram in five dimensions, scopes of which are unprecedented. Based on
the phase diagram, critical capillary numbers can be identified on the boundary
of different states. As a realistic application, we perform simulations with
actual parameters of water droplet in air flow to predict the critical
conditions of breakup, which is crucial in the context of atomization.
| [
{
"created": "Thu, 6 Jul 2023 06:53:20 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Wang",
"Kuiliang",
""
],
[
"Liang",
"Hong",
""
],
[
"Zhao",
"Chong",
""
],
[
"Bian",
"Xin",
""
]
] | [
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] |
2307.02808 | Zicheng Zhang | Zicheng Zhang, Wei Sun, Yingjie Zhou, Haoning Wu, Chunyi Li, Xiongkuo
Min, Xiaohong Liu, Guangtao Zhai, Weisi Lin | Advancing Zero-Shot Digital Human Quality Assessment through
Text-Prompted Evaluation | null | null | null | null | eess.IV cs.CV cs.DB | http://creativecommons.org/licenses/by/4.0/ | Digital humans have witnessed extensive applications in various domains,
necessitating related quality assessment studies. However, there is a lack of
comprehensive digital human quality assessment (DHQA) databases. To address
this gap, we propose SJTU-H3D, a subjective quality assessment database
specifically designed for full-body digital humans. It comprises 40
high-quality reference digital humans and 1,120 labeled distorted counterparts
generated with seven types of distortions. The SJTU-H3D database can serve as a
benchmark for DHQA research, allowing evaluation and refinement of processing
algorithms. Further, we propose a zero-shot DHQA approach that focuses on
no-reference (NR) scenarios to ensure generalization capabilities while
mitigating database bias. Our method leverages semantic and distortion features
extracted from projections, as well as geometry features derived from the mesh
structure of digital humans. Specifically, we employ the Contrastive
Language-Image Pre-training (CLIP) model to measure semantic affinity and
incorporate the Naturalness Image Quality Evaluator (NIQE) model to capture
low-level distortion information. Additionally, we utilize dihedral angles as
geometry descriptors to extract mesh features. By aggregating these measures,
we introduce the Digital Human Quality Index (DHQI), which demonstrates
significant improvements in zero-shot performance. The DHQI can also serve as a
robust baseline for DHQA tasks, facilitating advancements in the field. The
database and the code are available at https://github.com/zzc-1998/SJTU-H3D.
| [
{
"created": "Thu, 6 Jul 2023 06:55:30 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Zhang",
"Zicheng",
""
],
[
"Sun",
"Wei",
""
],
[
"Zhou",
"Yingjie",
""
],
[
"Wu",
"Haoning",
""
],
[
"Li",
"Chunyi",
""
],
[
"Min",
"Xiongkuo",
""
],
[
"Liu",
"Xiaohong",
""
],
[
"Zhai",
"Guangtao",
""
],
[
"Lin",
"Weisi",
""
]
] | [
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] |
2307.02813 | Yuanchen Bei | Yuanchen Bei, Hao Xu, Sheng Zhou, Huixuan Chi, Mengdi Zhang, Zhao Li,
Jiajun Bu | CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural
Networks | 12 pages, 6 figures | null | null | null | cs.LG cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dynamic graph data mining has gained popularity in recent years due to the
rich information contained in dynamic graphs and their widespread use in the
real world. Despite the advances in dynamic graph neural networks (DGNNs), the
rich information and diverse downstream tasks have posed significant
difficulties for the practical application of DGNNs in industrial scenarios. To
this end, in this paper, we propose to address them by pre-training and present
the Contrastive Pre-Training Method for Dynamic Graph Neural Networks (CPDG).
CPDG tackles the challenges of pre-training for DGNNs, including generalization
and long-short term modeling capability, through a flexible structural-temporal
subgraph sampler along with structural-temporal contrastive pre-training
schemes. Extensive experiments conducted on both large-scale research and
industrial dynamic graph datasets show that CPDG outperforms existing methods
in dynamic graph pre-training for various downstream tasks under three transfer
settings.
| [
{
"created": "Thu, 6 Jul 2023 07:18:22 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Bei",
"Yuanchen",
""
],
[
"Xu",
"Hao",
""
],
[
"Zhou",
"Sheng",
""
],
[
"Chi",
"Huixuan",
""
],
[
"Zhang",
"Mengdi",
""
],
[
"Li",
"Zhao",
""
],
[
"Bu",
"Jiajun",
""
]
] | [
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2307.02814 | Dwip Dalal | Dwip Dalal, Gautam Vashishtha, Prajwal Singh, Shanmuganathan Raman | Single Image LDR to HDR Conversion using Conditional Diffusion | null | IEEE International Conference on Image Processing 2023 | null | null | cs.CV eess.IV | http://creativecommons.org/licenses/by/4.0/ | Digital imaging aims to replicate realistic scenes, but Low Dynamic Range
(LDR) cameras cannot represent the wide dynamic range of real scenes, resulting
in under-/overexposed images. This paper presents a deep learning-based
approach for recovering intricate details from shadows and highlights while
reconstructing High Dynamic Range (HDR) images. We formulate the problem as an
image-to-image (I2I) translation task and propose a conditional Denoising
Diffusion Probabilistic Model (DDPM) based framework using classifier-free
guidance. We incorporate a deep CNN-based autoencoder in our proposed framework
to enhance the quality of the latent representation of the input LDR image used
for conditioning. Moreover, we introduce a new loss function for LDR-HDR
translation tasks, termed Exposure Loss. This loss helps direct gradients in
the opposite direction of the saturation, further improving the results'
quality. By conducting comprehensive quantitative and qualitative experiments,
we have effectively demonstrated the proficiency of our proposed method. The
results indicate that a simple conditional diffusion-based method can replace
the complex camera pipeline-based architectures.
| [
{
"created": "Thu, 6 Jul 2023 07:19:47 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Dalal",
"Dwip",
""
],
[
"Vashishtha",
"Gautam",
""
],
[
"Singh",
"Prajwal",
""
],
[
"Raman",
"Shanmuganathan",
""
]
] | [
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] |
2307.02816 | David Wood | Vida Dujmovi\'c and Robert Hickingbotham and J\k{e}drzej Hodor and
Gwean\"el Joret and Hoang La and Piotr Micek and Pat Morin and Cl\'ement
Rambaud and David R. Wood | The grid-minor theorem revisited | null | null | null | null | math.CO cs.DM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We prove that for every planar graph $X$ of treedepth $h$, there exists a
positive integer $c$ such that for every $X$-minor-free graph $G$, there exists
a graph $H$ of treewidth at most $f(h)$ such that $G$ is isomorphic to a
subgraph of $H\boxtimes K_c$. This is a qualitative strengthening of the
Grid-Minor Theorem of Robertson and Seymour (JCTB 1986), and treedepth is the
optimal parameter in such a result. As an example application, we use this
result to improve the upper bound for weak coloring numbers of graphs excluding
a fixed graph as a minor.
| [
{
"created": "Thu, 6 Jul 2023 07:20:44 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Dujmović",
"Vida",
""
],
[
"Hickingbotham",
"Robert",
""
],
[
"Hodor",
"Jędrzej",
""
],
[
"Joret",
"Gweanël",
""
],
[
"La",
"Hoang",
""
],
[
"Micek",
"Piotr",
""
],
[
"Morin",
"Pat",
""
],
[
"Rambaud",
"Clément",
""
],
[
"Wood",
"David R.",
""
]
] | [
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2307.02817 | Mayuko Kori | Mayuko Kori, Flavio Ascari, Filippo Bonchi, Roberto Bruni, Roberta
Gori, Ichiro Hasuo | Exploiting Adjoints in Property Directed Reachability Analysis | 44 pages, 11 figures, the full version of the paper accepted by CAV
2023 | null | null | null | cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We formulate, in lattice-theoretic terms, two novel algorithms inspired by
Bradley's property directed reachability algorithm. For finding safe invariants
or counterexamples, the first algorithm exploits over-approximations of both
forward and backward transition relations, expressed abstractly by the notion
of adjoints. In the absence of adjoints, one can use the second algorithm,
which exploits lower sets and their principals. As a notable example of
application, we consider quantitative reachability problems for Markov Decision
Processes.
| [
{
"created": "Thu, 6 Jul 2023 07:22:09 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Kori",
"Mayuko",
""
],
[
"Ascari",
"Flavio",
""
],
[
"Bonchi",
"Filippo",
""
],
[
"Bruni",
"Roberto",
""
],
[
"Gori",
"Roberta",
""
],
[
"Hasuo",
"Ichiro",
""
]
] | [
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] |
2307.02818 | Sagnik Nandy | Sagnik Nandy and Bhaswar B. Bhattacharya | Degree Heterogeneity in Higher-Order Networks: Inference in the
Hypergraph $\boldsymbol{\beta}$-Model | null | null | null | null | math.ST cs.IT cs.SI math.IT stat.ML stat.TH | http://creativecommons.org/licenses/by/4.0/ | The $\boldsymbol{\beta}$-model for random graphs is commonly used for
representing pairwise interactions in a network with degree heterogeneity.
Going beyond pairwise interactions, Stasi et al. (2014) introduced the
hypergraph $\boldsymbol{\beta}$-model for capturing degree heterogeneity in
networks with higher-order (multi-way) interactions. In this paper we initiate
the rigorous study of the hypergraph $\boldsymbol{\beta}$-model with multiple
layers, which allows for hyperedges of different sizes across the layers. To
begin with, we derive the rates of convergence of the maximum likelihood (ML)
estimate and establish their minimax rate optimality. We also derive the
limiting distribution of the ML estimate and construct asymptotically valid
confidence intervals for the model parameters. Next, we consider the
goodness-of-fit problem in the hypergraph $\boldsymbol{\beta}$-model.
Specifically, we establish the asymptotic normality of the likelihood ratio
(LR) test under the null hypothesis, derive its detection threshold, and also
its limiting power at the threshold. Interestingly, the detection threshold of
the LR test turns out to be minimax optimal, that is, all tests are
asymptotically powerless below this threshold. The theoretical results are
further validated in numerical experiments. In addition to developing the
theoretical framework for estimation and inference for hypergraph
$\boldsymbol{\beta}$-models, the above results fill a number of gaps in the
graph $\boldsymbol{\beta}$-model literature, such as the minimax optimality of
the ML estimates and the non-null properties of the LR test, which, to the best
of our knowledge, have not been studied before.
| [
{
"created": "Thu, 6 Jul 2023 07:23:06 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Nandy",
"Sagnik",
""
],
[
"Bhattacharya",
"Bhaswar B.",
""
]
] | [
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] |
2307.02819 | Xufeng Caesar Dai | Nathan Koome Murungi, Michael Vinh Pham, Xufeng Dai, Xiaodong Qu | Trends in Machine Learning and Electroencephalogram (EEG): A Review for
Undergraduate Researchers | 14 pages, 1 figure, HCI International 2023 Conference | null | null | null | cs.HC cs.LG | http://creativecommons.org/licenses/by/4.0/ | This paper presents a systematic literature review on Brain-Computer
Interfaces (BCIs) in the context of Machine Learning. Our focus is on
Electroencephalography (EEG) research, highlighting the latest trends as of
2023. The objective is to provide undergraduate researchers with an accessible
overview of the BCI field, covering tasks, algorithms, and datasets. By
synthesizing recent findings, our aim is to offer a fundamental understanding
of BCI research, identifying promising avenues for future investigations.
| [
{
"created": "Thu, 6 Jul 2023 07:24:38 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Murungi",
"Nathan Koome",
""
],
[
"Pham",
"Michael Vinh",
""
],
[
"Dai",
"Xufeng",
""
],
[
"Qu",
"Xiaodong",
""
]
] | [
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2307.02820 | Ayhan Kucukmanisa Dr. | Zeynep Hilal Kilimci, Ulku Bayraktar, Ayhan Kucukmanisa | Evaluating raw waveforms with deep learning frameworks for speech
emotion recognition | 14 pages, 6 Figures, 8 Tables | null | null | null | cs.SD cs.AI eess.AS | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Speech emotion recognition is a challenging task in speech processing field.
For this reason, feature extraction process has a crucial importance to
demonstrate and process the speech signals. In this work, we represent a model,
which feeds raw audio files directly into the deep neural networks without any
feature extraction stage for the recognition of emotions utilizing six
different data sets, EMO-DB, RAVDESS, TESS, CREMA, SAVEE, and TESS+RAVDESS. To
demonstrate the contribution of proposed model, the performance of traditional
feature extraction techniques namely, mel-scale spectogram, mel-frequency
cepstral coefficients, are blended with machine learning algorithms, ensemble
learning methods, deep and hybrid deep learning techniques. Support vector
machine, decision tree, naive Bayes, random forests models are evaluated as
machine learning algorithms while majority voting and stacking methods are
assessed as ensemble learning techniques. Moreover, convolutional neural
networks, long short-term memory networks, and hybrid CNN- LSTM model are
evaluated as deep learning techniques and compared with machine learning and
ensemble learning methods. To demonstrate the effectiveness of proposed model,
the comparison with state-of-the-art studies are carried out. Based on the
experiment results, CNN model excels existent approaches with 95.86% of
accuracy for TESS+RAVDESS data set using raw audio files, thence determining
the new state-of-the-art. The proposed model performs 90.34% of accuracy for
EMO-DB with CNN model, 90.42% of accuracy for RAVDESS with CNN model, 99.48% of
accuracy for TESS with LSTM model, 69.72% of accuracy for CREMA with CNN model,
85.76% of accuracy for SAVEE with CNN model in speaker-independent audio
categorization problems.
| [
{
"created": "Thu, 6 Jul 2023 07:27:59 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Kilimci",
"Zeynep Hilal",
""
],
[
"Bayraktar",
"Ulku",
""
],
[
"Kucukmanisa",
"Ayhan",
""
]
] | [
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] |
2307.02825 | Lei Xie | Yuanjing Feng, Lei Xie, Jingqiang Wang, Jianzhong He, Fei Gao | Bundle-specific Tractogram Distribution Estimation Using Higher-order
Streamline Differential Equation | null | null | null | null | cs.CV eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Tractography traces the peak directions extracted from fiber orientation
distribution (FOD) suffering from ambiguous spatial correspondences between
diffusion directions and fiber geometry, which is prone to producing erroneous
tracks while missing true positive connections. The peaks-based tractography
methods 'locally' reconstructed streamlines in 'single to single' manner, thus
lacking of global information about the trend of the whole fiber bundle. In
this work, we propose a novel tractography method based on a bundle-specific
tractogram distribution function by using a higher-order streamline
differential equation, which reconstructs the streamline bundles in 'cluster to
cluster' manner. A unified framework for any higher-order streamline
differential equation is presented to describe the fiber bundles with disjoint
streamlines defined based on the diffusion tensor vector field. At the global
level, the tractography process is simplified as the estimation of
bundle-specific tractogram distribution (BTD) coefficients by minimizing the
energy optimization model, and is used to characterize the relations between
BTD and diffusion tensor vector under the prior guidance by introducing the
tractogram bundle information to provide anatomic priors. Experiments are
performed on simulated Hough, Sine, Circle data, ISMRM 2015 Tractography
Challenge data, FiberCup data, and in vivo data from the Human Connectome
Project (HCP) data for qualitative and quantitative evaluation. The results
demonstrate that our approach can reconstruct the complex global fiber bundles
directly. BTD reduces the error deviation and accumulation at the local level
and shows better results in reconstructing long-range, twisting, and large
fanning tracts.
| [
{
"created": "Thu, 6 Jul 2023 07:45:31 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Feng",
"Yuanjing",
""
],
[
"Xie",
"Lei",
""
],
[
"Wang",
"Jingqiang",
""
],
[
"He",
"Jianzhong",
""
],
[
"Gao",
"Fei",
""
]
] | [
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] |
2307.02826 | Jie Qian | Zi-Yuan Wang, Jie Qian, Yi-Pu Wang, Jie Li, J. Q. You | Realization of the unidirectional amplification in a cavity magnonic
system | 7 pages, 4 figures | null | null | null | physics.app-ph | http://creativecommons.org/licenses/by/4.0/ | We experimentally demonstrate the nonreciprocal microwave amplification using
a cavity magnonic system, consisting of a passive cavity (i.e., the split-ring
resonator), an active feedback circuit integrated with an amplifier, and a
ferromagnetic spin ensemble (i.e., a yttrium-iron-garnet sphere). Combining the
amplification provided by the active circuit and the nonreciprocity supported
by the cavity magnonics, we implement a nonreciprocal amplifier with the
functions of both unidirectional amplification and reverse isolation. The
microwave signal is amplified by 11.5 dB in the forward propagating direction
and attenuated in the reverse direction by -34.7 dB, giving an isolation ratio
of 46.2 dB. Such a unidirectional amplifier can be readily employed in quantum
technologies, where the device can simultaneously amplify the weak signal
output by the quantum system and isolate the sensitive quantum system from the
backscattered external noise. Also, it is promising to explore more functions
and applications using a cavity magnonic system with real gain.
| [
{
"created": "Thu, 6 Jul 2023 07:45:35 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Wang",
"Zi-Yuan",
""
],
[
"Qian",
"Jie",
""
],
[
"Wang",
"Yi-Pu",
""
],
[
"Li",
"Jie",
""
],
[
"You",
"J. Q.",
""
]
] | [
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] |
2307.02827 | Zhilong Liu | Zhilong Liu, Jiayi Zhang, Ziheng Liu, Hongyang Du, Zhe Wang, Dusit
Niyato, Mohsen Guizani, Bo Ai | Cell-Free XL-MIMO Meets Multi-Agent Reinforcement Learning:
Architectures, Challenges, and Future Directions | null | null | null | null | cs.IT eess.SP math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cell-free massive multiple-input multiple-output (mMIMO) and extremely
large-scale MIMO (XL-MIMO) are regarded as promising innovations for the
forthcoming generation of wireless communication systems. Their significant
advantages in augmenting the number of degrees of freedom have garnered
considerable interest. In this article, we first review the essential
opportunities and challenges induced by XL-MIMO systems. We then propose the
enhanced paradigm of cell-free XL-MIMO, which incorporates multi-agent
reinforcement learning (MARL) to provide a distributed strategy for tackling
the problem of high-dimension signal processing and costly energy consumption.
Based on the unique near-field characteristics, we propose two categories of
the low-complexity design, i.e., antenna selection and power control, to adapt
to different cell-free XL-MIMO scenarios and achieve the maximum data rate. For
inspiration, several critical future research directions pertaining to green
cell-free XL-MIMO systems are presented.
| [
{
"created": "Thu, 6 Jul 2023 07:50:47 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Liu",
"Zhilong",
""
],
[
"Zhang",
"Jiayi",
""
],
[
"Liu",
"Ziheng",
""
],
[
"Du",
"Hongyang",
""
],
[
"Wang",
"Zhe",
""
],
[
"Niyato",
"Dusit",
""
],
[
"Guizani",
"Mohsen",
""
],
[
"Ai",
"Bo",
""
]
] | [
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] |
2307.02828 | Kun He Prof. | Xu Han, Anmin Liu, Chenxuan Yao, Yanbo Fan, Kun He | Sampling-based Fast Gradient Rescaling Method for Highly Transferable
Adversarial Attacks | 10 pages, 6 figures, 7 tables. arXiv admin note: substantial text
overlap with arXiv:2204.02887 | null | null | null | cs.CV cs.CR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep neural networks are known to be vulnerable to adversarial examples
crafted by adding human-imperceptible perturbations to the benign input. After
achieving nearly 100% attack success rates in white-box setting, more focus is
shifted to black-box attacks, of which the transferability of adversarial
examples has gained significant attention. In either case, the common
gradient-based methods generally use the sign function to generate
perturbations on the gradient update, that offers a roughly correct direction
and has gained great success. But little work pays attention to its possible
limitation. In this work, we observe that the deviation between the original
gradient and the generated noise may lead to inaccurate gradient update
estimation and suboptimal solutions for adversarial transferability. To this
end, we propose a Sampling-based Fast Gradient Rescaling Method (S-FGRM).
Specifically, we use data rescaling to substitute the sign function without
extra computational cost. We further propose a Depth First Sampling method to
eliminate the fluctuation of rescaling and stabilize the gradient update. Our
method could be used in any gradient-based attacks and is extensible to be
integrated with various input transformation or ensemble methods to further
improve the adversarial transferability. Extensive experiments on the standard
ImageNet dataset show that our method could significantly boost the
transferability of gradient-based attacks and outperform the state-of-the-art
baselines.
| [
{
"created": "Thu, 6 Jul 2023 07:52:42 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Han",
"Xu",
""
],
[
"Liu",
"Anmin",
""
],
[
"Yao",
"Chenxuan",
""
],
[
"Fan",
"Yanbo",
""
],
[
"He",
"Kun",
""
]
] | [
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] |
2307.02829 | Jialei Huang | Jialei Huang, Zhaoheng Yin, Yingdong Hu, Yang Gao | Policy Contrastive Imitation Learning | null | null | null | null | cs.LG cs.RO | http://creativecommons.org/licenses/by/4.0/ | Adversarial imitation learning (AIL) is a popular method that has recently
achieved much success. However, the performance of AIL is still unsatisfactory
on the more challenging tasks. We find that one of the major reasons is due to
the low quality of AIL discriminator representation. Since the AIL
discriminator is trained via binary classification that does not necessarily
discriminate the policy from the expert in a meaningful way, the resulting
reward might not be meaningful either. We propose a new method called Policy
Contrastive Imitation Learning (PCIL) to resolve this issue. PCIL learns a
contrastive representation space by anchoring on different policies and
generates a smooth cosine-similarity-based reward. Our proposed representation
learning objective can be viewed as a stronger version of the AIL objective and
provide a more meaningful comparison between the agent and the policy. From a
theoretical perspective, we show the validity of our method using the
apprenticeship learning framework. Furthermore, our empirical evaluation on the
DeepMind Control suite demonstrates that PCIL can achieve state-of-the-art
performance. Finally, qualitative results suggest that PCIL builds a smoother
and more meaningful representation space for imitation learning.
| [
{
"created": "Thu, 6 Jul 2023 07:52:50 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Huang",
"Jialei",
""
],
[
"Yin",
"Zhaoheng",
""
],
[
"Hu",
"Yingdong",
""
],
[
"Gao",
"Yang",
""
]
] | [
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] |
2307.02830 | Xuefeng Li | Xuefeng Li, Liwen Wang, Guanting Dong, Keqing He, Jinzheng Zhao, Hao
Lei, Jiachi Liu, Weiran Xu | Generative Zero-Shot Prompt Learning for Cross-Domain Slot Filling with
Inverse Prompting | Accepted by the Findings of ACL2023 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Zero-shot cross-domain slot filling aims to transfer knowledge from the
labeled source domain to the unlabeled target domain. Existing models either
encode slot descriptions and examples or design handcrafted question templates
using heuristic rules, suffering from poor generalization capability or
robustness. In this paper, we propose a generative zero-shot prompt learning
framework for cross-domain slot filling, both improving generalization and
robustness than previous work. Besides, we introduce a novel inverse prompting
strategy to distinguish different slot types to avoid the multiple prediction
problem, and an efficient prompt-tuning strategy to boost higher performance by
only training fewer prompt parameters. Experiments and analysis demonstrate the
effectiveness of our proposed framework, especially huge improvements (+13.44%
F1) on the unseen slots.
| [
{
"created": "Thu, 6 Jul 2023 07:53:46 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Li",
"Xuefeng",
""
],
[
"Wang",
"Liwen",
""
],
[
"Dong",
"Guanting",
""
],
[
"He",
"Keqing",
""
],
[
"Zhao",
"Jinzheng",
""
],
[
"Lei",
"Hao",
""
],
[
"Liu",
"Jiachi",
""
],
[
"Xu",
"Weiran",
""
]
] | [
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] |
2307.02832 | Hsuan-Wei Lee | Hsuan-Wei Lee, Colin Cleveland, Attila Szolnoki | Restoring spatial cooperation with myopic agents in a three-strategy
social dilemma | 9 pages, 4 figures, accepted by Applied Mathematics and Computation | null | null | null | physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | Introducing strategy complexity into the basic conflict of cooperation and
defection is a natural response to avoid the tragedy of the common state. As an
intermediate approach, quasi-cooperators were recently suggested to address the
original problem. In this study, we test its vitality in structured populations
where players have fixed partners. Naively, the latter condition should support
cooperation unambiguously via enhanced network reciprocity. However, the
opposite is true because the spatial structure may provide a humbler
cooperation level than a well-mixed population. This unexpected behavior can be
understood if we consider that at a certain parameter interval the original
prisoner's dilemma game is transformed into a snow-drift game. If we replace
the original imitating strategy protocol by assuming myopic players, the
spatial population becomes a friendly environment for cooperation. This
observation is valid in a huge region of parameter space. This study highlights
that spatial structure can reveal a new aspect of social dilemmas when strategy
complexity is introduced.
| [
{
"created": "Thu, 6 Jul 2023 07:54:49 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Lee",
"Hsuan-Wei",
""
],
[
"Cleveland",
"Colin",
""
],
[
"Szolnoki",
"Attila",
""
]
] | [
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] |
2307.02833 | Alessandro Berti Mr | Zahra Sadeghibogar, Alessandro Berti, Marco Pegoraro, Wil M.P. van der
Aalst | Applying Process Mining on Scientific Workflows: a Case Study | null | null | null | null | cs.DB | http://creativecommons.org/licenses/by/4.0/ | Computer-based scientific experiments are becoming increasingly
data-intensive. High-Performance Computing (HPC) clusters are ideal for
executing large scientific experiment workflows. Executing large scientific
workflows in an HPC cluster leads to complex flows of data and control within
the system, which are difficult to analyze. This paper presents a case study
where process mining is applied to logs extracted from SLURM-based HPC
clusters, in order to document the running workflows and find the performance
bottlenecks. The challenge lies in correlating the jobs recorded in the system
to enable the application of mainstream process mining techniques. Users may
submit jobs with explicit or implicit interdependencies, leading to the
consideration of different event correlation techniques. We present a log
extraction technique from SLURM clusters, completed with an experimental.
| [
{
"created": "Thu, 6 Jul 2023 08:02:56 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Sadeghibogar",
"Zahra",
""
],
[
"Berti",
"Alessandro",
""
],
[
"Pegoraro",
"Marco",
""
],
[
"van der Aalst",
"Wil M. P.",
""
]
] | [
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] |
2307.02836 | Shiqi Deng | Shiqi Deng and Zhiyu Sun and Ruiyan Zhuang and Jun Gong | Noise-to-Norm Reconstruction for Industrial Anomaly Detection and
Localization | null | null | null | null | cs.CV eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Anomaly detection has a wide range of applications and is especially
important in industrial quality inspection. Currently, many top-performing
anomaly-detection models rely on feature-embedding methods. However, these
methods do not perform well on datasets with large variations in object
locations. Reconstruction-based methods use reconstruction errors to detect
anomalies without considering positional differences between samples. In this
study, a reconstruction-based method using the noise-to-norm paradigm is
proposed, which avoids the invariant reconstruction of anomalous regions. Our
reconstruction network is based on M-net and incorporates multiscale fusion and
residual attention modules to enable end-to-end anomaly detection and
localization. Experiments demonstrate that the method is effective in
reconstructing anomalous regions into normal patterns and achieving accurate
anomaly detection and localization. On the MPDD and VisA datasets, our proposed
method achieved more competitive results than the latest methods, and it set a
new state-of-the-art standard on the MPDD dataset.
| [
{
"created": "Thu, 6 Jul 2023 08:06:48 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Deng",
"Shiqi",
""
],
[
"Sun",
"Zhiyu",
""
],
[
"Zhuang",
"Ruiyan",
""
],
[
"Gong",
"Jun",
""
]
] | [
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2307.02839 | Xiaolin Chen | Le Xiao and Xiaolin Chen | Enhancing LLM with Evolutionary Fine Tuning for News Summary Generation | 12 pages, 2 figures | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | News summary generation is an important task in the field of intelligence
analysis, which can provide accurate and comprehensive information to help
people better understand and respond to complex real-world events. However,
traditional news summary generation methods face some challenges, which are
limited by the model itself and the amount of training data, as well as the
influence of text noise, making it difficult to generate reliable information
accurately. In this paper, we propose a new paradigm for news summary
generation using LLM with powerful natural language understanding and
generative capabilities. We use LLM to extract multiple structured event
patterns from the events contained in news paragraphs, evolve the event pattern
population with genetic algorithm, and select the most adaptive event pattern
to input into the LLM to generate news summaries. A News Summary Generator
(NSG) is designed to select and evolve the event pattern populations and
generate news summaries. The experimental results show that the news summary
generator is able to generate accurate and reliable news summaries with some
generalization ability.
| [
{
"created": "Thu, 6 Jul 2023 08:13:53 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Xiao",
"Le",
""
],
[
"Chen",
"Xiaolin",
""
]
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2307.02841 | Alexey Verbitskiy | A. Verbitskiy and A. Yulin | Excitation of Wannier-Stark states in a chain of coupled optical
resonators with linear gain and nonlinear losses | null | null | null | null | physics.optics nlin.PS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we theoretically study the nonlinear dynamics of Wannier-Stark
states in the dissipative system consisting of interacting optical resonators,
whose resonant frequencies depend linearly on their number. It is shown that
the negative losses in some resonators can switch the system into a lasing
regime with Wannier-Stark states serving as working modes. It is shown by
extensive numerical simulations that there may be single-frequency stationary
regimes as well as multi-frequency regimes. In the latter case Bloch
oscillations can appear in the system. The possibility of selective excitation
of Wannier-Stark states by the appropriate choice of the dissipation profile is
investigated. A simple perturbation theory describing the quasi-linear regimes
is developed and compared against the numerical results.
| [
{
"created": "Thu, 6 Jul 2023 08:14:53 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Verbitskiy",
"A.",
""
],
[
"Yulin",
"A.",
""
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] |
2307.02842 | Yu Chen | Yu Chen, Yihan Du, Pihe Hu, Siwei Wang, Desheng Wu, Longbo Huang | Provably Efficient Iterated CVaR Reinforcement Learning with Function
Approximation | 27 pages, 3 figures | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Risk-sensitive reinforcement learning (RL) aims to optimize policies that
balance the expected reward and risk. In this paper, we investigate a novel
risk-sensitive RL formulation with an Iterated Conditional Value-at-Risk (CVaR)
objective under linear and general function approximations. This new
formulation, named ICVaR-RL with function approximation, provides a principled
way to guarantee safety at each decision step. For ICVaR-RL with linear
function approximation, we propose a computationally efficient algorithm
ICVaR-L, which achieves an
$\widetilde{O}(\sqrt{\alpha^{-(H+1)}(d^2H^4+dH^6)K})$ regret, where $\alpha$ is
the risk level, $d$ is the dimension of state-action features, $H$ is the
length of each episode, and $K$ is the number of episodes. We also establish a
matching lower bound $\Omega(\sqrt{\alpha^{-(H-1)}d^2K})$ to validate the
optimality of ICVaR-L with respect to $d$ and $K$. For ICVaR-RL with general
function approximation, we propose algorithm ICVaR-G, which achieves an
$\widetilde{O}(\sqrt{\alpha^{-(H+1)}DH^4K})$ regret, where $D$ is a dimensional
parameter that depends on the eluder dimension and covering number.
Furthermore, our analysis provides several novel techniques for risk-sensitive
RL, including an efficient approximation of the CVaR operator, a new ridge
regression with CVaR-adapted features, and a refined elliptical potential
lemma.
| [
{
"created": "Thu, 6 Jul 2023 08:14:54 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Chen",
"Yu",
""
],
[
"Du",
"Yihan",
""
],
[
"Hu",
"Pihe",
""
],
[
"Wang",
"Siwei",
""
],
[
"Wu",
"Desheng",
""
],
[
"Huang",
"Longbo",
""
]
] | [
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2307.02845 | Pablo Rosillo-Rodes | Pablo Rosillo-Rodes, Maxi San Miguel, David Sanchez | Modelling language ideologies for the dynamics of languages in contact | 18 pages, 15 figures | null | null | null | physics.soc-ph cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In multilingual societies, it is common to encounter different language
varieties. Various approaches have been proposed to discuss different
mechanisms of language shift. However, current models exploring language shift
in languages in contact often overlook the influence of language ideologies.
Language ideologies play a crucial role in understanding language usage within
a cultural community, encompassing shared beliefs, assumptions, and feelings
towards specific language forms. These ideologies shed light on the social
perceptions of different language varieties expressed as language attitudes. In
this study, we introduce an approach that incorporates language ideologies into
a model for contact varieties by considering speaker preferences as a
parameter. Our findings highlight the significance of preference in language
shift, which can even outweigh the influence of language prestige associated,
for example, with a standard variety. Furthermore, we investigate the impact of
the degree of interaction between individuals holding opposing preferences on
the language shift process. Quite expectedly, our results indicate that when
communities with different preferences mix, the coexistence of language
varieties becomes less likely. However, variations in the degree of interaction
between individuals with contrary preferences notably lead to non-trivial
transitions from states of coexistence of varieties to the extinction of a
given variety, followed by a return to coexistence, ultimately culminating in
the dominance of the previously extinct variety. By studying finite-size
effects, we observe that the duration of coexistence states increases
exponentially with network size. Ultimately, our work constitutes a
quantitative approach to the study of language ideologies in sociolinguistics.
| [
{
"created": "Thu, 6 Jul 2023 08:22:50 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Rosillo-Rodes",
"Pablo",
""
],
[
"Miguel",
"Maxi San",
""
],
[
"Sanchez",
"David",
""
]
] | [
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] |
2307.02847 | Jinyi Deng | Jinyi Deng, Xinru Tang, Jiahao Zhang, Yuxuan Li, Linyun Zhang, Fengbin
Tu, Leibo Liu, Shaojun Wei, Yang Hu and Shouyi Yin | Towards Efficient Control Flow Handling in Spatial Architecture via
Architecting the Control Flow Plane | null | null | null | null | cs.AR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spatial architecture is a high-performance architecture that uses control
flow graphs and data flow graphs as the computational model and
producer/consumer models as the execution models. However, existing spatial
architectures suffer from control flow handling challenges. Upon categorizing
their PE execution models, we find that they lack autonomous, peer-to-peer, and
temporally loosely-coupled control flow handling capability. This leads to
limited performance in intensive control programs.
A spatial architecture, Marionette, is proposed, with an explicit-designed
control flow plane. The Control Flow Plane enables autonomous, peer-to-peer and
temporally loosely-coupled control flow handling. The Proactive PE
Configuration ensures timely and computation-overlapped configuration to
improve handling Branch Divergence. The Agile PE Assignment enhance the
pipeline performance of Imperfect Loops. We develop full stack of Marionette
(ISA, compiler, simulator, RTL) and demonstrate that in a variety of
challenging intensive control programs, compared to state-of-the-art spatial
architectures, Marionette outperforms Softbrain, TIA, REVEL, and RipTide by
geomean 2.88x, 3.38x, 1.55x, and 2.66x.
| [
{
"created": "Thu, 6 Jul 2023 08:26:31 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Deng",
"Jinyi",
""
],
[
"Tang",
"Xinru",
""
],
[
"Zhang",
"Jiahao",
""
],
[
"Li",
"Yuxuan",
""
],
[
"Zhang",
"Linyun",
""
],
[
"Tu",
"Fengbin",
""
],
[
"Liu",
"Leibo",
""
],
[
"Wei",
"Shaojun",
""
],
[
"Hu",
"Yang",
""
],
[
"Yin",
"Shouyi",
""
]
] | [
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] |
2307.02848 | Yu-Huan Wu | Yun Liu, Yu-Huan Wu, Shi-Chen Zhang, Li Liu, Min Wu, and Ming-Ming
Cheng | Revisiting Computer-Aided Tuberculosis Diagnosis | 14 pages | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Tuberculosis (TB) is a major global health threat, causing millions of deaths
annually. Although early diagnosis and treatment can greatly improve the
chances of survival, it remains a major challenge, especially in developing
countries. Recently, computer-aided tuberculosis diagnosis (CTD) using deep
learning has shown promise, but progress is hindered by limited training data.
To address this, we establish a large-scale dataset, namely the Tuberculosis
X-ray (TBX11K) dataset, which contains 11,200 chest X-ray (CXR) images with
corresponding bounding box annotations for TB areas. This dataset enables the
training of sophisticated detectors for high-quality CTD. Furthermore, we
propose a strong baseline, SymFormer, for simultaneous CXR image classification
and TB infection area detection. SymFormer incorporates Symmetric Search
Attention (SymAttention) to tackle the bilateral symmetry property of CXR
images for learning discriminative features. Since CXR images may not strictly
adhere to the bilateral symmetry property, we also propose Symmetric Positional
Encoding (SPE) to facilitate SymAttention through feature recalibration. To
promote future research on CTD, we build a benchmark by introducing evaluation
metrics, evaluating baseline models reformed from existing detectors, and
running an online challenge. Experiments show that SymFormer achieves
state-of-the-art performance on the TBX11K dataset. The data, code, and models
will be released.
| [
{
"created": "Thu, 6 Jul 2023 08:27:48 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Liu",
"Yun",
""
],
[
"Wu",
"Yu-Huan",
""
],
[
"Zhang",
"Shi-Chen",
""
],
[
"Liu",
"Li",
""
],
[
"Wu",
"Min",
""
],
[
"Cheng",
"Ming-Ming",
""
]
] | [
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2307.02849 | Zi'ou Zheng | Zi'ou Zheng and Xiaodan Zhu | NatLogAttack: A Framework for Attacking Natural Language Inference
Models with Natural Logic | Published as a conference paper at ACL 2023 | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Reasoning has been a central topic in artificial intelligence from the
beginning. The recent progress made on distributed representation and neural
networks continues to improve the state-of-the-art performance of natural
language inference. However, it remains an open question whether the models
perform real reasoning to reach their conclusions or rely on spurious
correlations. Adversarial attacks have proven to be an important tool to help
evaluate the Achilles' heel of the victim models. In this study, we explore the
fundamental problem of developing attack models based on logic formalism. We
propose NatLogAttack to perform systematic attacks centring around natural
logic, a classical logic formalism that is traceable back to Aristotle's
syllogism and has been closely developed for natural language inference. The
proposed framework renders both label-preserving and label-flipping attacks. We
show that compared to the existing attack models, NatLogAttack generates better
adversarial examples with fewer visits to the victim models. The victim models
are found to be more vulnerable under the label-flipping setting. NatLogAttack
provides a tool to probe the existing and future NLI models' capacity from a
key viewpoint and we hope more logic-based attacks will be further explored for
understanding the desired property of reasoning.
| [
{
"created": "Thu, 6 Jul 2023 08:32:14 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Zheng",
"Zi'ou",
""
],
[
"Zhu",
"Xiaodan",
""
]
] | [
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] |
2307.02850 | Jonas Fritzsch | Jonas Fritzsch, Marvin Wyrich, Justus Bogner, Stefan Wagner | Resist the Hype! Practical Recommendations to Cope With
R\'esum\'e-Driven Development | 8 pages, 4 figures | null | 10.1109/MS.2023.3277034 | null | cs.SE cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Technology trends play an important role in the hiring process for software
and IT professionals. In a recent study of 591 software professionals in both
hiring (130) and technical (558) roles, we found empirical support for a
tendency to overemphasize technology trends in r\'esum\'es and the application
process. 60% of the hiring professionals agreed that such trends would
influence their job advertisements. Among the software professionals, 82%
believed that using trending technologies in their daily work would make them
more attractive for potential future employers. This phenomenon has previously
been reported anecdotally and somewhat humorously under the label
R\'esum\'e-Driven Development (RDD). Our article seeks to initiate a more
serious debate about the consequences of RDD on software development practice.
We explain how the phenomenon may constitute a harmful self-sustaining dynamic,
and provide practical recommendations for both the hiring and applicant
perspectives to change the current situation for the better.
| [
{
"created": "Thu, 6 Jul 2023 08:32:38 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Fritzsch",
"Jonas",
""
],
[
"Wyrich",
"Marvin",
""
],
[
"Bogner",
"Justus",
""
],
[
"Wagner",
"Stefan",
""
]
] | [
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2307.02851 | Samir Suweis Dr. | Samir Suweis, Francesco Ferraro, Sandro Azaele, Amos Maritan | Generalized Lotka-Volterra Systems with Time Correlated Stochastic
Interactions | 4 Figures | null | null | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The dynamics of species communities are typically modelled considering fixed
parameters for species interactions. The problem of over-parameterization that
ensues when considering large communities has been overcome by sampling species
interactions from a probability distribution. However, species interactions are
not fixed in time, but they can change on a time scale comparable to population
dynamics. Here we investigate the impact of time-dependent species interactions
using the generalized Lotka-Volterra model, which serves as a paradigmatic
theoretical framework in several scientific fields. In this work we model
species interactions as stochastic colored noise. Assuming a large number of
species and a steady state, we obtain analytical predictions for the species
abundance distribution, which matches well empirical observations. In
particular, our results suggest the absence of extinctions, unlike scenarios
with fixed species interactions.
| [
{
"created": "Thu, 6 Jul 2023 08:32:53 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Suweis",
"Samir",
""
],
[
"Ferraro",
"Francesco",
""
],
[
"Azaele",
"Sandro",
""
],
[
"Maritan",
"Amos",
""
]
] | [
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] |
2307.02852 | Xuyang Zhao | Xuyang Zhao, Chengpu Yu, Erpei Xu and Yixuan Liu | TDLE: 2-D LiDAR Exploration With Hierarchical Planning Using Regional
Division | Accepted in IEEE International Conference on Automation Science and
Engineering (CASE) 2023 | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Exploration systems are critical for enhancing the autonomy of robots. Due to
the unpredictability of the future planning space, existing methods either
adopt an inefficient greedy strategy or require a lot of resources to obtain a
global solution. In this work, we address the challenge of obtaining global
exploration routes with minimal computing resources. A hierarchical planning
framework dynamically divides the planning space into subregions and arranges
their orders to provide global guidance for exploration. Indicators that are
compatible with the subregion order are used to choose specific exploration
targets, thereby considering estimates of spatial structure and extending the
planning space to unknown regions. Extensive simulations and field tests
demonstrate the efficacy of our method in comparison to existing 2D LiDAR-based
approaches. Our code has been made public for further investigation.
| [
{
"created": "Thu, 6 Jul 2023 08:36:08 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Zhao",
"Xuyang",
""
],
[
"Yu",
"Chengpu",
""
],
[
"Xu",
"Erpei",
""
],
[
"Liu",
"Yixuan",
""
]
] | [
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] | TDLE: 2-D LiDAR Exploration With Hierarchical Planning Using Regional Division | https://arxiv.org/abs/2307.02852v1 | https://arxiv.org/pdf/2307.02852v1.pdf | https://github.com/seanzsya/tdle | true | true | https://paperswithcode.com/paper/tdle-2-d-lidar-exploration-with-hierarchical | [
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] |
2307.02855 | Jason R.C. Nurse Dr | Nandita Pattnaik, Jason R. C. Nurse, Sarah Turner, Gareth Mott, Jamie
MacColl, Pia Huesch, James Sullivan | It's more than just money: The real-world harms from ransomware attacks | 17th International Symposium on Human Aspects of Information Security
& Assurance (HAISA 2023) | null | null | null | cs.CR cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As cyber-attacks continue to increase in frequency and sophistication,
organisations must be better prepared to face the reality of an incident. Any
organisational plan that intends to be successful at managing security risks
must clearly understand the harm (i.e., negative impact) and the various
parties affected in the aftermath of an attack. To this end, this article
conducts a novel exploration into the multitude of real-world harms that can
arise from cyber-attacks, with a particular focus on ransomware incidents given
their current prominence. This exploration also leads to the proposal of a new,
robust methodology for modelling harms from such incidents. We draw on
publicly-available case data on high-profile ransomware incidents to examine
the types of harm that emerge at various stages after a ransomware attack and
how harms (e.g., an offline enterprise server) may trigger other negative,
potentially more substantial impacts for stakeholders (e.g., the inability for
a customer to access their social welfare benefits or bank account). Prominent
findings from our analysis include the identification of a notable set of
social/human harms beyond the business itself (and beyond the financial payment
of a ransom) and a complex web of harms that emerge after attacks regardless of
the industry sector. We also observed that deciphering the full extent and
sequence of harms can be a challenging undertaking because of the lack of
complete data available. This paper consequently argues for more transparency
on ransomware harms, as it would lead to a better understanding of the
realities of these incidents to the benefit of organisations and society more
generally.
| [
{
"created": "Thu, 6 Jul 2023 08:46:16 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Pattnaik",
"Nandita",
""
],
[
"Nurse",
"Jason R. C.",
""
],
[
"Turner",
"Sarah",
""
],
[
"Mott",
"Gareth",
""
],
[
"MacColl",
"Jamie",
""
],
[
"Huesch",
"Pia",
""
],
[
"Sullivan",
"James",
""
]
] | [
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2307.02858 | Usman Muhammad Mr | Usman Muhammad, Md Ziaul Hoque, Mourad Oussalah and Jorma Laaksonen | Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Face presentation attacks, also known as spoofing attacks, pose a significant
threat to biometric systems that rely on facial recognition systems, such as
access control systems, mobile payments, and identity verification systems. To
prevent spoofing, several video-based methods have been presented in the
literature that analyze facial motion in successive video frames. However,
estimating the motion between adjacent frames is a challenging task and
requires high computational cost. In this paper, we reformulate the face
anti-spoofing task as a motion prediction problem and introduce a deep ensemble
learning model with a frame skipping mechanism. The proposed frame skipping is
based on a uniform sampling approach where the original video is divided into
fixed size video clips. In this way, every nth frame of the clip is selected to
ensure that the temporal patterns can easily be perceived during the training
of three different recurrent neural networks (RNNs). Motivated by the
performance of each RNNs, a meta-model is developed to improve the overall
recognition performance by combining the predictions of the individual RNNs.
Extensive experiments were conducted on four datasets, and state-of-the-art
performance is reported for MSU-MFSD (3.12\%), Replay-Attack (11.19\%), and
OULU-NPU (12.23\%) using half total error rate (HTER) in the most challenging
cross-dataset test scenario.
| [
{
"created": "Thu, 6 Jul 2023 08:50:29 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Muhammad",
"Usman",
""
],
[
"Hoque",
"Md Ziaul",
""
],
[
"Oussalah",
"Mourad",
""
],
[
"Laaksonen",
"Jorma",
""
]
] | [
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] |
2307.02860 | Anh Mai | Anh Mai, Matteo Brucateo, Azza Abouzied, Peter J.Haas, and Alexandra
Meliou | Scaling Package Queries to a Billion Tuples via Hierarchical
Partitioning and Customized Optimization | null | null | null | null | cs.DB | http://creativecommons.org/licenses/by/4.0/ | A package query returns a package -- a multiset of tuples -- that maximizes
or minimizes a linear objective function subject to linear constraints, thereby
enabling in-database decision support. Prior work has established the
equivalence of package queries to Integer Linear Programs (ILPs) and developed
the SketchRefine algorithm for package query processing. While this algorithm
was an important first step toward supporting prescriptive analytics scalably
inside a relational database, it struggles when the data size grows beyond a
few hundred million tuples or when the constraints become very tight. In this
paper, we present Progressive Shading, a novel algorithm for processing package
queries that can scale efficiently to billions of tuples and gracefully handle
tight constraints. Progressive Shading solves a sequence of optimization
problems over a hierarchy of relations, each resulting from an ever-finer
partitioning of the original tuples into homogeneous groups until the original
relation is obtained. This strategy avoids the premature discarding of
high-quality tuples that can occur with SketchRefine. Our novel partitioning
scheme, Dynamic Low Variance, can handle very large relations with multiple
attributes and can dynamically adapt to both concentrated and spread-out sets
of attribute values, provably outperforming traditional partitioning schemes
such as KD-Tree. We further optimize our system by replacing our off-the-shelf
optimization software with customized ILP and LP solvers, called Dual Reducer
and Parallel Dual Simplex respectively, that are highly accurate and orders of
magnitude faster.
| [
{
"created": "Thu, 6 Jul 2023 08:52:28 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Mai",
"Anh",
""
],
[
"Brucateo",
"Matteo",
""
],
[
"Abouzied",
"Azza",
""
],
[
"Haas",
"Peter J.",
""
],
[
"Meliou",
"Alexandra",
""
]
] | [
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] |
2307.02861 | Jiawei Wang | Jiawei Wang, Jia Xu Brian Sia, Xiang Li, Xin Guo, Wanjun Wang,
Zhongliang Qiao, Callum G. Littlejohns. Chongyang Liu, Graham T. Reed, Rusli,
Hong Wang | High-speed 4 ${\times}$ 4 silicon photonic electro-optic switch,
operating at the 2 {\mu}m waveband | null | null | null | null | physics.optics physics.app-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The escalating need for expansive data bandwidth, and the resulting capacity
constraints of the single mode fiber (SMF) have positioned the 2-${\mu}$m
waveband as a prospective window for emerging applications in optical
communication. This has initiated an ecosystem of silicon photonic components
in the region driven by CMOS compatibility, low cost, high efficiency and
potential for large-scale integration. In this study, we demonstrate a plasma
dispersive, 4 ${\times}$ 4 electro-optic switch operating at the 2-${\mu}$m
waveband with the shortest switching times. The demonstrated switch operates
across a 45-nm bandwidth, with 10-90% rise and 90-10% fall time of 1.78 ns and
3.02 ns respectively. In a 4 ${\times}$ 4 implementation, crosstalk below -15
dB and power consumption below 19.15 mW across all 16 ports are indicated. The
result brings high-speed optical switching to the portfolio of devices at the
promising waveband.
| [
{
"created": "Thu, 6 Jul 2023 08:55:59 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Wang",
"Jiawei",
""
],
[
"Sia",
"Jia Xu Brian",
""
],
[
"Li",
"Xiang",
""
],
[
"Guo",
"Xin",
""
],
[
"Wang",
"Wanjun",
""
],
[
"Qiao",
"Zhongliang",
""
],
[
"Liu",
"Callum G. Littlejohns. Chongyang",
""
],
[
"Reed",
"Graham T.",
""
],
[
"Rusli",
"",
""
],
[
"Wang",
"Hong",
""
]
] | [
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] |
2307.02862 | Shiqi Yang | Shiqi Yang, Atsushi Hashimoto, Yoshitaka Ushiku | A Critical Look at the Current Usage of Foundation Model for Dense
Recognition Task | This is a short report on the current usage of foundation model
(mainly pretrained diffusion model) for downstream dense recognition task
(e.g., open vocabulary segmentation). We hope this short report could give an
insight to the future research | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | In recent years large model trained on huge amount of cross-modality data,
which is usually be termed as foundation model, achieves conspicuous
accomplishment in many fields, such as image recognition and generation. Though
achieving great success in their original application case, it is still unclear
whether those foundation models can be applied to other different downstream
tasks. In this paper, we conduct a short survey on the current methods for
discriminative dense recognition tasks, which are built on the pretrained
foundation model. And we also provide some preliminary experimental analysis of
an existing open-vocabulary segmentation method based on Stable Diffusion,
which indicates the current way of deploying diffusion model for segmentation
is not optimal. This aims to provide insights for future research on adopting
foundation model for downstream task.
| [
{
"created": "Thu, 6 Jul 2023 08:57:53 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Yang",
"Shiqi",
""
],
[
"Hashimoto",
"Atsushi",
""
],
[
"Ushiku",
"Yoshitaka",
""
]
] | [
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2307.02863 | Lukas Birkenmaier | Lukas Birkenmaier and Clemens Lechner and Claudia Wagner | ValiTex -- a uniform validation framework for computational text-based
measures of social science constructs | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Guidance on how to validate computational text-based measures of social
science constructs is fragmented. Whereas scholars are generally acknowledging
the importance of validating their text-based measures, they often lack common
terminology and a unified framework to do so. This paper introduces a new
validation framework called ValiTex, designed to assist scholars to measure
social science constructs based on textual data. The framework draws on a
long-established tradition within psychometrics while extending the framework
for the purpose of computational text analysis. ValiTex consists of two
components, a conceptual model, and a dynamic checklist. Whereas the conceptual
model provides a general structure along distinct phases on how to approach
validation, the dynamic checklist defines specific validation steps and
provides guidance on which steps might be considered recommendable (i.e.,
providing relevant and necessary validation evidence) or optional (i.e., useful
for providing additional supporting validation evidence. The utility of the
framework is demonstrated by applying it to a use case of detecting sexism from
social media data.
| [
{
"created": "Thu, 6 Jul 2023 09:03:10 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Birkenmaier",
"Lukas",
""
],
[
"Lechner",
"Clemens",
""
],
[
"Wagner",
"Claudia",
""
]
] | [
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2307.02865 | Mattia Giovanni Campana | Valerio Arnaboldi, Mattia Giovanni Campana, Franca Delmastro, Elena
Pagani | PLIERS: a Popularity-Based Recommender System for Content Dissemination
in Online Social Networks | Published in SAC '16: Proceedings of the 31st Annual ACM Symposium on
Applied Computing | null | 10.1145/2851613.2851940 | null | cs.IR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a novel tag-based recommender system called PLIERS,
which relies on the assumption that users are mainly interested in items and
tags with similar popularity to those they already own. PLIERS is aimed at
reaching a good tradeoff between algorithmic complexity and the level of
personalization of recommended items. To evaluate PLIERS, we performed a set of
experiments on real OSN datasets, demonstrating that it outperforms
state-of-the-art solutions in terms of personalization, relevance, and novelty
of recommendations.
| [
{
"created": "Thu, 6 Jul 2023 09:04:58 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Arnaboldi",
"Valerio",
""
],
[
"Campana",
"Mattia Giovanni",
""
],
[
"Delmastro",
"Franca",
""
],
[
"Pagani",
"Elena",
""
]
] | [
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] |
2307.02867 | Marc Zeller | Marc Zeller, Thomas Waschulzik, Reiner Schmid, Claus Bahlmann | Towards a safe MLOps Process for the Continuous Development and Safety
Assurance of ML-based Systems in the Railway Domain | null | null | null | null | cs.SE cs.AI cs.LG cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traditional automation technologies alone are not sufficient to enable
driverless operation of trains (called Grade of Automation (GoA) 4) on
non-restricted infrastructure. The required perception tasks are nowadays
realized using Machine Learning (ML) and thus need to be developed and deployed
reliably and efficiently. One important aspect to achieve this is to use an
MLOps process for tackling improved reproducibility, traceability,
collaboration, and continuous adaptation of a driverless operation to changing
conditions. MLOps mixes ML application development and operation (Ops) and
enables high frequency software releases and continuous innovation based on the
feedback from operations. In this paper, we outline a safe MLOps process for
the continuous development and safety assurance of ML-based systems in the
railway domain. It integrates system engineering, safety assurance, and the ML
life-cycle in a comprehensive workflow. We present the individual stages of the
process and their interactions. Moreover, we describe relevant challenges to
automate the different stages of the safe MLOps process.
| [
{
"created": "Thu, 6 Jul 2023 09:08:20 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Zeller",
"Marc",
""
],
[
"Waschulzik",
"Thomas",
""
],
[
"Schmid",
"Reiner",
""
],
[
"Bahlmann",
"Claus",
""
]
] | [
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] |
2307.02868 | Guo Yanqiang | Yanqiang Guo, Zinan Hu, Jianchao Zhang, Chenyu Zhu, Xiaomin Guo | High-speed photon correlation monitoring of amplified quantum noise by
chaos using deep-learning balanced homodyne detection | 6 pages, 6 figures | null | null | null | quant-ph nlin.CD physics.optics | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Precision experimental determination of photon correlation requires the
massive amounts of data and extensive measurement time. We present a technique
to monitor second-order photon correlation $g^{(2)}(0)$ of amplified quantum
noise based on wideband balanced homodyne detection and deep-learning
acceleration. The quantum noise is effectively amplified by an injection of
weak chaotic laser and the $g^{(2)}(0)$ of the amplified quantum noise is
measured with a real-time sample rate of 1.4 GHz. We also exploit a photon
correlation convolutional neural network accelerating correlation data using a
few quadrature fluctuations to perform a parallel processing of the
$g^{(2)}(0)$ for various chaos injection intensities and effective bandwidths.
The deep-learning method accelerates the $g^{(2)}(0)$ experimental acquisition
with a high accuracy, estimating 6107 sets of photon correlation data with a
mean square error of 0.002 in 22 seconds and achieving a three orders of
magnitude acceleration in data acquisition time. This technique contributes to
a high-speed and precision coherence evaluation of entropy source in secure
communication and quantum imaging.
| [
{
"created": "Thu, 6 Jul 2023 09:11:25 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Guo",
"Yanqiang",
""
],
[
"Hu",
"Zinan",
""
],
[
"Zhang",
"Jianchao",
""
],
[
"Zhu",
"Chenyu",
""
],
[
"Guo",
"Xiaomin",
""
]
] | [
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] |
2307.02869 | Pandeng Li | Pandeng Li, Chen-Wei Xie, Hongtao Xie, Liming Zhao, Lei Zhang, Yun
Zheng, Deli Zhao, Yongdong Zhang | MomentDiff: Generative Video Moment Retrieval from Random to Real | 12 pages, 5 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Video moment retrieval pursues an efficient and generalized solution to
identify the specific temporal segments within an untrimmed video that
correspond to a given language description. To achieve this goal, we provide a
generative diffusion-based framework called MomentDiff, which simulates a
typical human retrieval process from random browsing to gradual localization.
Specifically, we first diffuse the real span to random noise, and learn to
denoise the random noise to the original span with the guidance of similarity
between text and video. This allows the model to learn a mapping from arbitrary
random locations to real moments, enabling the ability to locate segments from
random initialization. Once trained, MomentDiff could sample random temporal
segments as initial guesses and iteratively refine them to generate an accurate
temporal boundary. Different from discriminative works (e.g., based on
learnable proposals or queries), MomentDiff with random initialized spans could
resist the temporal location biases from datasets. To evaluate the influence of
the temporal location biases, we propose two anti-bias datasets with location
distribution shifts, named Charades-STA-Len and Charades-STA-Mom. The
experimental results demonstrate that our efficient framework consistently
outperforms state-of-the-art methods on three public benchmarks, and exhibits
better generalization and robustness on the proposed anti-bias datasets. The
code, model, and anti-bias evaluation datasets are available at
https://github.com/IMCCretrieval/MomentDiff.
| [
{
"created": "Thu, 6 Jul 2023 09:12:13 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Li",
"Pandeng",
""
],
[
"Xie",
"Chen-Wei",
""
],
[
"Xie",
"Hongtao",
""
],
[
"Zhao",
"Liming",
""
],
[
"Zhang",
"Lei",
""
],
[
"Zheng",
"Yun",
""
],
[
"Zhao",
"Deli",
""
],
[
"Zhang",
"Yongdong",
""
]
] | [
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] |
2307.02871 | Hanzhang Xue | Hanzhang Xue, Xiaochang Hu, Rui Xie, Hao Fu, Liang Xiao, Yiming Nie,
Bin Dai | Contrastive Label Disambiguation for Self-Supervised Terrain
Traversability Learning in Off-Road Environments | 9 pages, 11 figures | null | null | null | cs.RO | http://creativecommons.org/licenses/by/4.0/ | Discriminating the traversability of terrains is a crucial task for
autonomous driving in off-road environments. However, it is challenging due to
the diverse, ambiguous, and platform-specific nature of off-road
traversability. In this paper, we propose a novel self-supervised terrain
traversability learning framework, utilizing a contrastive label disambiguation
mechanism. Firstly, weakly labeled training samples with pseudo labels are
automatically generated by projecting actual driving experiences onto the
terrain models constructed in real time. Subsequently, a prototype-based
contrastive representation learning method is designed to learn distinguishable
embeddings, facilitating the self-supervised updating of those pseudo labels.
As the iterative interaction between representation learning and pseudo label
updating, the ambiguities in those pseudo labels are gradually eliminated,
enabling the learning of platform-specific and task-specific traversability
without any human-provided annotations. Experimental results on the RELLIS-3D
dataset and our Gobi Desert driving dataset demonstrate the effectiveness of
the proposed method.
| [
{
"created": "Thu, 6 Jul 2023 09:15:38 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Xue",
"Hanzhang",
""
],
[
"Hu",
"Xiaochang",
""
],
[
"Xie",
"Rui",
""
],
[
"Fu",
"Hao",
""
],
[
"Xiao",
"Liang",
""
],
[
"Nie",
"Yiming",
""
],
[
"Dai",
"Bin",
""
]
] | [
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2307.02873 | Arne Schmidt | Patrick Blumenberg and Arne Schmidt and Aaron T. Becker | Computing Motion Plans for Assembling Particles with Global Control | 20 pages, 12 figures | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate motion planning algorithms for the assembly of shapes in the
\emph{tilt model} in which unit-square tiles move in a grid world under the
influence of uniform external forces and self-assemble according to certain
rules. We provide several heuristics and experimental evaluation of their
success rate, solution length, runtime, and memory consumption.
| [
{
"created": "Thu, 6 Jul 2023 09:19:03 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Blumenberg",
"Patrick",
""
],
[
"Schmidt",
"Arne",
""
],
[
"Becker",
"Aaron T.",
""
]
] | [
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] | Computing Motion Plans for Assembling Particles with Global Control | https://arxiv.org/abs/2307.02873v1 | https://arxiv.org/pdf/2307.02873v1.pdf | https://github.com/roboticswarmcontrol/2023tiltmotionplan | true | true | https://paperswithcode.com/paper/computing-motion-plans-for-assembling | [
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2307.02875 | Han Zou | Han Zou, Masanori Suganuma, Takayuki Okatani | Reference-based Motion Blur Removal: Learning to Utilize Sharpness in
the Reference Image | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the recent advancement in the study of removing motion blur in an
image, it is still hard to deal with strong blurs. While there are limits in
removing blurs from a single image, it has more potential to use multiple
images, e.g., using an additional image as a reference to deblur a blurry
image. A typical setting is deburring an image using a nearby sharp image(s) in
a video sequence, as in the studies of video deblurring. This paper proposes a
better method to use the information present in a reference image. The method
does not need a strong assumption on the reference image. We can utilize an
alternative shot of the identical scene, just like in video deblurring, or we
can even employ a distinct image from another scene. Our method first matches
local patches of the target and reference images and then fuses their features
to estimate a sharp image. We employ a patch-based feature matching strategy to
solve the difficult problem of matching the blurry image with the sharp
reference. Our method can be integrated into pre-existing networks designed for
single image deblurring. The experimental results show the effectiveness of the
proposed method.
| [
{
"created": "Thu, 6 Jul 2023 09:24:55 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Zou",
"Han",
""
],
[
"Suganuma",
"Masanori",
""
],
[
"Okatani",
"Takayuki",
""
]
] | [
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] |
2307.02877 | Binbin Xiang | Binbin Xiang, Torben Peters, Theodora Kontogianni, Frawa Vetterli,
Stefano Puliti, Rasmus Astrup, Konrad Schindler | Towards accurate instance segmentation in large-scale LiDAR point clouds | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Panoptic segmentation is the combination of semantic and instance
segmentation: assign the points in a 3D point cloud to semantic categories and
partition them into distinct object instances. It has many obvious applications
for outdoor scene understanding, from city mapping to forest management.
Existing methods struggle to segment nearby instances of the same semantic
category, like adjacent pieces of street furniture or neighbouring trees, which
limits their usability for inventory- or management-type applications that rely
on object instances. This study explores the steps of the panoptic segmentation
pipeline concerned with clustering points into object instances, with the goal
to alleviate that bottleneck. We find that a carefully designed clustering
strategy, which leverages multiple types of learned point embeddings,
significantly improves instance segmentation. Experiments on the NPM3D urban
mobile mapping dataset and the FOR-instance forest dataset demonstrate the
effectiveness and versatility of the proposed strategy.
| [
{
"created": "Thu, 6 Jul 2023 09:29:03 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Xiang",
"Binbin",
""
],
[
"Peters",
"Torben",
""
],
[
"Kontogianni",
"Theodora",
""
],
[
"Vetterli",
"Frawa",
""
],
[
"Puliti",
"Stefano",
""
],
[
"Astrup",
"Rasmus",
""
],
[
"Schindler",
"Konrad",
""
]
] | [
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] | Towards accurate instance segmentation in large-scale LiDAR point clouds | https://arxiv.org/abs/2307.02877v1 | https://arxiv.org/pdf/2307.02877v1.pdf | https://github.com/bxiang233/panopticsegforlargescalepointcloud | true | true | https://paperswithcode.com/paper/towards-accurate-instance-segmentation-in | [
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] |
2307.02879 | Xavier Caruso | Xavier Caruso (IMB, LFANT), Antoine Leudi\`ere (CARAMBA, UL) | Algorithms for computing norms and characteristic polynomials on general
Drinfeld modules | null | null | null | null | cs.SC math.NT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We provide two families of algorithms to compute characteristic polynomials
of endomorphisms and norms of isogenies of Drinfeld modules. Our algorithms
work for Drinfeld modules of any rank, defined over any base curve. When the
base curve is $\mathbb P^1_{\mathbb F_q}$, we do a thorough study of the
complexity, demonstrating that our algorithms are, in many cases, the most
asymptotically performant. The first family of algorithms relies on the
correspondence between Drinfeld modules and Anderson motives, reducing the
computation to linear algebra over a polynomial ring. The second family,
available only for the Frobenius endomorphism, is based on a new formula
expressing the characteristic polynomial of the Frobenius as a reduced norm in
a central simple algebra.
| [
{
"created": "Thu, 6 Jul 2023 09:33:36 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Caruso",
"Xavier",
"",
"IMB, LFANT"
],
[
"Leudière",
"Antoine",
"",
"CARAMBA, UL"
]
] | [
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] |
2307.02881 | Zhiwei Xu | Peter Tu, Zhaoyuan Yang, Richard Hartley, Zhiwei Xu, Jing Zhang, Dylan
Campbell, Jaskirat Singh, Tianyu Wang | Probabilistic and Semantic Descriptions of Image Manifolds and Their
Applications | 23 pages, 17 figures, 1 table | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | This paper begins with a description of methods for estimating probability
density functions for images that reflects the observation that such data is
usually constrained to lie in restricted regions of the high-dimensional image
space - not every pattern of pixels is an image. It is common to say that
images lie on a lower-dimensional manifold in the high-dimensional space.
However, although images may lie on such lower-dimensional manifolds, it is not
the case that all points on the manifold have an equal probability of being
images. Images are unevenly distributed on the manifold, and our task is to
devise ways to model this distribution as a probability distribution. In
pursuing this goal, we consider generative models that are popular in AI and
computer vision community. For our purposes, generative/probabilistic models
should have the properties of 1) sample generation: it should be possible to
sample from this distribution according to the modelled density function, and
2) probability computation: given a previously unseen sample from the dataset
of interest, one should be able to compute the probability of the sample, at
least up to a normalising constant. To this end, we investigate the use of
methods such as normalising flow and diffusion models. We then show that such
probabilistic descriptions can be used to construct defences against
adversarial attacks. In addition to describing the manifold in terms of
density, we also consider how semantic interpretations can be used to describe
points on the manifold. To this end, we consider an emergent language framework
which makes use of variational encoders to produce a disentangled
representation of points that reside on a given manifold. Trajectories between
points on a manifold can then be described in terms of evolving semantic
descriptions.
| [
{
"created": "Thu, 6 Jul 2023 09:36:45 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Tu",
"Peter",
""
],
[
"Yang",
"Zhaoyuan",
""
],
[
"Hartley",
"Richard",
""
],
[
"Xu",
"Zhiwei",
""
],
[
"Zhang",
"Jing",
""
],
[
"Campbell",
"Dylan",
""
],
[
"Singh",
"Jaskirat",
""
],
[
"Wang",
"Tianyu",
""
]
] | [
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2307.02882 | Burak Kilic | Burak Kilic, Florix Bex, Albert Gatt | Contrast Is All You Need | 10 pages + bib, 12 figures, ACAIL2023/ASAIL2023 Workshop | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this study, we analyze data-scarce classification scenarios, where
available labeled legal data is small and imbalanced, potentially hurting the
quality of the results. We focused on two finetuning objectives; SetFit
(Sentence Transformer Finetuning), a contrastive learning setup, and a vanilla
finetuning setup on a legal provision classification task. Additionally, we
compare the features that are extracted with LIME (Local Interpretable
Model-agnostic Explanations) to see which particular features contributed to
the model's classification decisions. The results show that a contrastive setup
with SetFit performed better than vanilla finetuning while using a fraction of
the training samples. LIME results show that the contrastive learning approach
helps boost both positive and negative features which are legally informative
and contribute to the classification results. Thus a model finetuned with a
contrastive objective seems to base its decisions more confidently on legally
informative features.
| [
{
"created": "Thu, 6 Jul 2023 09:36:54 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Kilic",
"Burak",
""
],
[
"Bex",
"Florix",
""
],
[
"Gatt",
"Albert",
""
]
] | [
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2307.02883 | Nadine Halberstadt | Ernesto Garc\'ia-Alfonso (LCAR), Francesco Ancilotto, Manuel Barranco,
Mart\'i Pi, Nadine Halberstadt (LCAR) | Quantized vortex nucleation in collisions of superfluid nanoscopic
helium droplets at zero temperature | null | null | null | null | physics.atm-clus | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We address the collision of two superfluid 4 He droplets at non-zero initial
relative velocities and impact parameters within the framework of liquid 4 He
time-dependent density functional theory at zero temperature. In spite of the
small size of these droplets (1000 He atoms in the merged droplet) imposed by
computational limitations, we have found that quantized vortices may be readily
nucleated for reasonable collision parameters. At variance with head-on
collisions, where only vortex rings are produced, collisions with non-zero
impact parameter produce linear vortices which are nucleated at indentations
appearing on the surface of the deformed merged droplet. Whereas for equal-size
droplets vortices are produced in pairs, an odd number of vortices can appear
when the colliding droplet sizes are different. In all cases vortices coexist
with surface capillary waves. The possibility for collisions to be at the
origin of vortex nucleation in experiments involving very large droplets is
discussed. An additional surprising result is the observation of the drops
coalescence even for grazing and distal collisions at relative velocities as
high as 80 m/s and 40 m/s, respectively, induced by the long-range Van der
Waals attraction between the droplets.
| [
{
"created": "Thu, 6 Jul 2023 09:37:46 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"García-Alfonso",
"Ernesto",
"",
"LCAR"
],
[
"Ancilotto",
"Francesco",
"",
"LCAR"
],
[
"Barranco",
"Manuel",
"",
"LCAR"
],
[
"Pi",
"Martí",
"",
"LCAR"
],
[
"Halberstadt",
"Nadine",
"",
"LCAR"
]
] | [
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] |
2307.02884 | Jiacheng Guo | Jiacheng Guo, Minshuo Chen, Huan Wang, Caiming Xiong, Mengdi Wang, Yu
Bai | Sample-Efficient Learning of POMDPs with Multiple Observations In
Hindsight | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper studies the sample-efficiency of learning in Partially Observable
Markov Decision Processes (POMDPs), a challenging problem in reinforcement
learning that is known to be exponentially hard in the worst-case. Motivated by
real-world settings such as loading in game playing, we propose an enhanced
feedback model called ``multiple observations in hindsight'', where after each
episode of interaction with the POMDP, the learner may collect multiple
additional observations emitted from the encountered latent states, but may not
observe the latent states themselves. We show that sample-efficient learning
under this feedback model is possible for two new subclasses of POMDPs:
\emph{multi-observation revealing POMDPs} and \emph{distinguishable POMDPs}.
Both subclasses generalize and substantially relax \emph{revealing POMDPs} -- a
widely studied subclass for which sample-efficient learning is possible under
standard trajectory feedback. Notably, distinguishable POMDPs only require the
emission distributions from different latent states to be \emph{different}
instead of \emph{linearly independent} as required in revealing POMDPs.
| [
{
"created": "Thu, 6 Jul 2023 09:39:01 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Guo",
"Jiacheng",
""
],
[
"Chen",
"Minshuo",
""
],
[
"Wang",
"Huan",
""
],
[
"Xiong",
"Caiming",
""
],
[
"Wang",
"Mengdi",
""
],
[
"Bai",
"Yu",
""
]
] | [
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] |
2307.02885 | Ana Dobrota | Milica S. Ritope\v{c}ki, Natalia V. Skorodumova, Ana S. Dobrota, Igor
A. Pa\v{s}ti | Boron-doped graphene -- DFT study of the role of dopant concentration
and oxidation on sodium and aluminium storage applications | 22 pages, 2 of which are supplementary information. 10 figures, 2
tables | null | null | null | cond-mat.mtrl-sci physics.chem-ph physics.comp-ph | http://creativecommons.org/licenses/by/4.0/ | Graphene is thought to be a promising materials for many applications.
However, pristine graphene is not suitable for most electrochemical devices,
where defect engineering is crucial for its performance. We demonstrate how
boron doping of graphene can alter its reactivity, electrical conductivity and
potential application for sodium and aluminium storage, with the emphasis on
novel metal-ion batteries. Using DFT calculations, we investigate both the
influence of boron concentration and the oxidation of the material, on the
mentioned properties. It is demonstrated that the presence of boron in graphene
increases its reactivity towards atomic hydrogen and oxygen-containing species,
in other words, it makes B-doped graphene more prone to oxidation.
Additionally, the presence of these surface functional groups significantly
alters the type and strength of the interaction of Na and Al with the given
materials. Boron-doping and oxidation of graphene is found to increase Na
storage capacity of graphene by the factor of up to 4.
| [
{
"created": "Thu, 6 Jul 2023 09:41:21 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Ritopečki",
"Milica S.",
""
],
[
"Skorodumova",
"Natalia V.",
""
],
[
"Dobrota",
"Ana S.",
""
],
[
"Pašti",
"Igor A.",
""
]
] | [
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] |
2307.02889 | Ruiqi Zhu | Ruiqi Zhu, Siyuan Li, Tianhong Dai, Chongjie Zhang, Oya Celiktutan | Learning to Solve Tasks with Exploring Prior Behaviours | null | null | null | null | cs.RO cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Demonstrations are widely used in Deep Reinforcement Learning (DRL) for
facilitating solving tasks with sparse rewards. However, the tasks in
real-world scenarios can often have varied initial conditions from the
demonstration, which would require additional prior behaviours. For example,
consider we are given the demonstration for the task of \emph{picking up an
object from an open drawer}, but the drawer is closed in the training. Without
acquiring the prior behaviours of opening the drawer, the robot is unlikely to
solve the task. To address this, in this paper we propose an Intrinsic Rewards
Driven Example-based Control \textbf{(IRDEC)}. Our method can endow agents with
the ability to explore and acquire the required prior behaviours and then
connect to the task-specific behaviours in the demonstration to solve
sparse-reward tasks without requiring additional demonstration of the prior
behaviours. The performance of our method outperforms other baselines on three
navigation tasks and one robotic manipulation task with sparse rewards. Codes
are available at https://github.com/Ricky-Zhu/IRDEC.
| [
{
"created": "Thu, 6 Jul 2023 09:48:51 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Zhu",
"Ruiqi",
""
],
[
"Li",
"Siyuan",
""
],
[
"Dai",
"Tianhong",
""
],
[
"Zhang",
"Chongjie",
""
],
[
"Celiktutan",
"Oya",
""
]
] | [
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] | Learning to Solve Tasks with Exploring Prior Behaviours | https://arxiv.org/abs/2307.02889v1 | https://arxiv.org/pdf/2307.02889v1.pdf | https://github.com/ricky-zhu/irdec | true | true | https://paperswithcode.com/paper/learning-to-solve-tasks-with-exploring-prior | [
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2307.02891 | Ruta Binkyte | Ruta Binkyte, Daniele Gorla, Catuscia Palamidessi | BaBE: Enhancing Fairness via Estimation of Latent Explaining Variables | null | null | null | null | cs.LG cs.AI cs.CY | http://creativecommons.org/licenses/by/4.0/ | We consider the problem of unfair discrimination between two groups and
propose a pre-processing method to achieve fairness. Corrective methods like
statistical parity usually lead to bad accuracy and do not really achieve
fairness in situations where there is a correlation between the sensitive
attribute S and the legitimate attribute E (explanatory variable) that should
determine the decision. To overcome these drawbacks, other notions of fairness
have been proposed, in particular, conditional statistical parity and equal
opportunity. However, E is often not directly observable in the data, i.e., it
is a latent variable. We may observe some other variable Z representing E, but
the problem is that Z may also be affected by S, hence Z itself can be biased.
To deal with this problem, we propose BaBE (Bayesian Bias Elimination), an
approach based on a combination of Bayes inference and the
Expectation-Maximization method, to estimate the most likely value of E for a
given Z for each group. The decision can then be based directly on the
estimated E. We show, by experiments on synthetic and real data sets, that our
approach provides a good level of fairness as well as high accuracy.
| [
{
"created": "Thu, 6 Jul 2023 09:53:56 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Binkyte",
"Ruta",
""
],
[
"Gorla",
"Daniele",
""
],
[
"Palamidessi",
"Catuscia",
""
]
] | [
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] | BaBE: Enhancing Fairness via Estimation of Latent Explaining Variables | https://arxiv.org/abs/2307.02891v1 | https://arxiv.org/pdf/2307.02891v1.pdf | https://github.com/babe-algorithm/babe | true | true | https://paperswithcode.com/paper/babe-enhancing-fairness-via-estimation-of | [
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] |
2307.02892 | Fuxiang Tao | Fuxiang Tao, Wei Ma, Xuri Ge, Anna Esposito, Alessandro Vinciarelli | The Relationship Between Speech Features Changes When You Get Depressed:
Feature Correlations for Improving Speed and Performance of Depression
Detection | null | null | null | null | cs.CL cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work shows that depression changes the correlation between features
extracted from speech. Furthermore, it shows that using such an insight can
improve the training speed and performance of depression detectors based on
SVMs and LSTMs. The experiments were performed over the Androids Corpus, a
publicly available dataset involving 112 speakers, including 58 people
diagnosed with depression by professional psychiatrists. The results show that
the models used in the experiments improve in terms of training speed and
performance when fed with feature correlation matrices rather than with feature
vectors. The relative reduction of the error rate ranges between 23.1% and
26.6% depending on the model. The probable explanation is that feature
correlation matrices appear to be more variable in the case of depressed
speakers. Correspondingly, such a phenomenon can be thought of as a depression
marker.
| [
{
"created": "Thu, 6 Jul 2023 09:54:35 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Tao",
"Fuxiang",
""
],
[
"Ma",
"Wei",
""
],
[
"Ge",
"Xuri",
""
],
[
"Esposito",
"Anna",
""
],
[
"Vinciarelli",
"Alessandro",
""
]
] | [
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] |
2307.02893 | Alberto Villois | A. Villois, D.N. Puzyrev, D.V. Skryabin and M. Onorato | Walk-off induced dissipative breathers and dissipative breather gas in
microresonators | 10 pages, 10 figures | null | null | null | physics.optics nlin.PS | http://creativecommons.org/licenses/by/4.0/ | Dissipative solitons in optical microcavities have attracted significant
attention in recent years due to their direct association with the generation
of optical frequency combs. Here, we address the problem of dissipative soliton
breathers in a microresonator with second-order nonlinearity, operating at the
exact phase-matching for efficient second-harmonic generation. We elucidate the
vital role played by the group velocity difference between the first and second
harmonic pulses for the breather existence. We report the dissipative breather
gas phenomenon, when multiple breathers propagate randomly in the resonator and
collide nearly elastically. Finally, when the breather gas reaches an
out-of-equilibrium statistical stationarity, we show how the velocity locking
between first and second harmonic is still preserved, naming such phenomena
turbulence locking.
| [
{
"created": "Thu, 6 Jul 2023 09:57:15 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Villois",
"A.",
""
],
[
"Puzyrev",
"D. N.",
""
],
[
"Skryabin",
"D. V.",
""
],
[
"Onorato",
"M.",
""
]
] | [
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] |
2307.02894 | Georg Rutishauser | Georg Rutishauser, Francesco Conti, Luca Benini | Free Bits: Latency Optimization of Mixed-Precision Quantized Neural
Networks on the Edge | Accepted for publication at the 2023 IEEE International Conference of
Artificial Intelligence Circuits and Systems (AICAS '23) | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mixed-precision quantization, where a deep neural network's layers are
quantized to different precisions, offers the opportunity to optimize the
trade-offs between model size, latency, and statistical accuracy beyond what
can be achieved with homogeneous-bit-width quantization. To navigate the
intractable search space of mixed-precision configurations for a given network,
this paper proposes a hybrid search methodology. It consists of a
hardware-agnostic differentiable search algorithm followed by a hardware-aware
heuristic optimization to find mixed-precision configurations latency-optimized
for a specific hardware target. We evaluate our algorithm on MobileNetV1 and
MobileNetV2 and deploy the resulting networks on a family of multi-core RISC-V
microcontroller platforms with different hardware characteristics. We achieve
up to 28.6% reduction of end-to-end latency compared to an 8-bit model at a
negligible accuracy drop from a full-precision baseline on the 1000-class
ImageNet dataset. We demonstrate speedups relative to an 8-bit baseline, even
on systems with no hardware support for sub-byte arithmetic at negligible
accuracy drop. Furthermore, we show the superiority of our approach with
respect to differentiable search targeting reduced binary operation counts as a
proxy for latency.
| [
{
"created": "Thu, 6 Jul 2023 09:57:48 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Rutishauser",
"Georg",
""
],
[
"Conti",
"Francesco",
""
],
[
"Benini",
"Luca",
""
]
] | [
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] |
2307.02896 | Yuan Liao | Yuan Liao, Vasilis Friderikos, Halim Yanikomeroglu | Robust Deployment and Resource Allocation for Robotic Aerial Base
Station Enabled OFDM Integrated Sensing and Communication | null | null | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The envisioned robotic aerial base station (RABS) concept is expected to
bring further flexibility to integrated sensing and communication (ISAC)
systems. In this letter, characterizing the spatial traffic distribution on a
grid-based model, the RABS-assisted ISAC system is formulated as a robust
optimization problem to maximize the minimum satisfaction rate (SR) under a
cardinality constrained uncertainty set. The problem is reformulated as a
mixed-integer linear programming (MILP) and solved approximately by the
iterative linear programming rounding algorithm. Numerical investigations show
that the minimum SR can be improved by 28.61% on average compared to fixed
small cells.
| [
{
"created": "Thu, 6 Jul 2023 10:03:08 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Liao",
"Yuan",
""
],
[
"Friderikos",
"Vasilis",
""
],
[
"Yanikomeroglu",
"Halim",
""
]
] | [
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] |
2307.02897 | Han Zou | Han Zou, Masanori Suganuma, Takayuki Okatani | RefVSR++: Exploiting Reference Inputs for Reference-based Video
Super-resolution | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Smartphones equipped with a multi-camera system comprising multiple cameras
with different field-of-view (FoVs) are becoming more prevalent. These camera
configurations are compatible with reference-based SR and video SR, which can
be executed simultaneously while recording video on the device. Thus, combining
these two SR methods can improve image quality. Recently, Lee et al. have
presented such a method, RefVSR. In this paper, we consider how to optimally
utilize the observations obtained, including input low-resolution (LR) video
and reference (Ref) video. RefVSR extends conventional video SR quite simply,
aggregating the LR and Ref inputs over time in a single bidirectional stream.
However, considering the content difference between LR and Ref images due to
their FoVs, we can derive the maximum information from the two image sequences
by aggregating them independently in the temporal direction. Then, we propose
an improved method, RefVSR++, which can aggregate two features in parallel in
the temporal direction, one for aggregating the fused LR and Ref inputs and the
other for Ref inputs over time. Furthermore, we equip RefVSR++ with enhanced
mechanisms to align image features over time, which is the key to the success
of video SR. We experimentally show that RefVSR++ outperforms RefVSR by over
1dB in PSNR, achieving the new state-of-the-art.
| [
{
"created": "Thu, 6 Jul 2023 10:09:52 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Zou",
"Han",
""
],
[
"Suganuma",
"Masanori",
""
],
[
"Okatani",
"Takayuki",
""
]
] | [
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2307.02900 | Zelin Ji Mr | Zelin Ji, Zhijin Qin, and Xiaoming Tao | Meta Federated Reinforcement Learning for Distributed Resource
Allocation | Submitted to TWC | null | null | null | eess.SP cs.SY eess.SY | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In cellular networks, resource allocation is usually performed in a
centralized way, which brings huge computation complexity to the base station
(BS) and high transmission overhead. This paper explores a distributed resource
allocation method that aims to maximize energy efficiency (EE) while ensuring
the quality of service (QoS) for users. Specifically, in order to address
wireless channel conditions, we propose a robust meta federated reinforcement
learning (\textit{MFRL}) framework that allows local users to optimize transmit
power and assign channels using locally trained neural network models, so as to
offload computational burden from the cloud server to the local users, reducing
transmission overhead associated with local channel state information. The BS
performs the meta learning procedure to initialize a general global model,
enabling rapid adaptation to different environments with improved EE
performance. The federated learning technique, based on decentralized
reinforcement learning, promotes collaboration and mutual benefits among users.
Analysis and numerical results demonstrate that the proposed \textit{MFRL}
framework accelerates the reinforcement learning process, decreases
transmission overhead, and offloads computation, while outperforming the
conventional decentralized reinforcement learning algorithm in terms of
convergence speed and EE performance across various scenarios.
| [
{
"created": "Thu, 6 Jul 2023 10:21:14 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Ji",
"Zelin",
""
],
[
"Qin",
"Zhijin",
""
],
[
"Tao",
"Xiaoming",
""
]
] | [
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] |
2307.02903 | Carine Rebello | Vinicius Viena Santana, Carine Menezes Rebello, Luana P. Queiroz, Ana
Mafalda Ribeiro, Nadia Shardta, and Idelfonso B. R. Nogueira | PUFFIN: A Path-Unifying Feed-Forward Interfaced Network for Vapor
Pressure Prediction | null | null | null | null | physics.chem-ph cs.LG | http://creativecommons.org/licenses/by/4.0/ | Accurately predicting vapor pressure is vital for various industrial and
environmental applications. However, obtaining accurate measurements for all
compounds of interest is not possible due to the resource and labor intensity
of experiments. The demand for resources and labor further multiplies when a
temperature-dependent relationship for predicting vapor pressure is desired. In
this paper, we propose PUFFIN (Path-Unifying Feed-Forward Interfaced Network),
a machine learning framework that combines transfer learning with a new
inductive bias node inspired by domain knowledge (the Antoine equation) to
improve vapor pressure prediction. By leveraging inductive bias and transfer
learning using graph embeddings, PUFFIN outperforms alternative strategies that
do not use inductive bias or that use generic descriptors of compounds. The
framework's incorporation of domain-specific knowledge to overcome the
limitation of poor data availability shows its potential for broader
applications in chemical compound analysis, including the prediction of other
physicochemical properties. Importantly, our proposed machine learning
framework is partially interpretable, because the inductive Antoine node yields
network-derived Antoine equation coefficients. It would then be possible to
directly incorporate the obtained analytical expression in process design
software for better prediction and control of processes occurring in industry
and the environment.
| [
{
"created": "Thu, 6 Jul 2023 10:29:03 GMT",
"version": "v1"
}
] | 2023-07-07 | [
[
"Santana",
"Vinicius Viena",
""
],
[
"Rebello",
"Carine Menezes",
""
],
[
"Queiroz",
"Luana P.",
""
],
[
"Ribeiro",
"Ana Mafalda",
""
],
[
"Shardta",
"Nadia",
""
],
[
"Nogueira",
"Idelfonso B. R.",
""
]
] | [
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