Instruction: What field is the article from?

Prompt:
Title: Smart Traffic Management of Vehicles using Faster R-CNN based Deep Learning Method
Abstract: With constant growth of civilization and modernization of cities all across
the world since past few centuries smart traffic management of vehicles is one
of the most sorted after problem by research community. It is a challenging
problem in computer vision and artificial intelligence domain. Smart traffic
management basically involves segmentation of vehicles, estimation of traffic
density and tracking of vehicles. The vehicle segmentation from traffic videos
helps realization of niche applications such as monitoring of speed and
estimation of traffic. When occlusions, background with clutters and traffic
with density variations are present, this problem becomes more intractable in
nature. Keeping this motivation in this research work, we investigate Faster
R-CNN based deep learning method towards segmentation of vehicles. This problem
is addressed in four steps viz minimization with adaptive background model,
Faster R-CNN based subnet operation, Faster R-CNN initial refinement and result
optimization with extended topological active nets. The computational framework
uses ideas of adaptive background modeling. It also addresses shadow and
illumination related issues. Higher segmentation accuracy is achieved through
topological active net deformable models. The topological and extended
topological active nets help to achieve stated deformations. Mesh deformation
is achieved with minimization of energy. The segmentation accuracy is improved
with modified version of extended topological active net. The experimental
results demonstrate superiority of this computational framework

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: ViP-Mixer: A Convolutional Mixer for Video Prediction
Abstract: Video prediction aims to predict future frames from a video's previous
content. Existing methods mainly process video data where the time dimension
mingles with the space and channel dimensions from three distinct angles: as a
sequence of individual frames, as a 3D volume in spatiotemporal coordinates, or
as a stacked image where frames are treated as separate channels. Most of them
generally focus on one of these perspectives and may fail to fully exploit the
relationships across different dimensions. To address this issue, this paper
introduces a convolutional mixer for video prediction, termed ViP-Mixer, to
model the spatiotemporal evolution in the latent space of an autoencoder. The
ViP-Mixers are stacked sequentially and interleave feature mixing at three
levels: frames, channels, and locations. Extensive experiments demonstrate that
our proposed method achieves new state-of-the-art prediction performance on
three benchmark video datasets covering both synthetic and real-world
scenarios.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection
Abstract: In the realm of aerial image analysis, object detection plays a pivotal role,
with significant implications for areas such as remote sensing, urban planning,
and disaster management. This study addresses the inherent challenges in this
domain, notably the detection of small objects, managing densely packed
elements, and accounting for diverse orientations. We present an in-depth
evaluation of an object detection model that integrates the Large Selective
Kernel Network (LSKNet)as its backbone with the DiffusionDet head, utilizing
the iSAID dataset for empirical analysis. Our approach encompasses the
introduction of novel methodologies and extensive ablation studies. These
studies critically assess various aspects such as loss functions, box
regression techniques, and classification strategies to refine the model's
precision in object detection. The paper details the experimental application
of the LSKNet backbone in synergy with the DiffusionDet heads, a combination
tailored to meet the specific challenges in aerial image object detection. The
findings of this research indicate a substantial enhancement in the model's
performance, especially in the accuracy-time tradeoff. The proposed model
achieves a mean average precision (MAP) of approximately 45.7%, which is a
significant improvement, outperforming the RCNN model by 4.7% on the same
dataset. This advancement underscores the effectiveness of the proposed
modifications and sets a new benchmark in aerial image analysis, paving the way
for more accurate and efficient object detection methodologies. The code is
publicly available at https://github.com/SashaMatsun/LSKDiffDet

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Navigating Complex Search Tasks with AI Copilots
Abstract: As many of us in the information retrieval (IR) research community know and
appreciate, search is far from being a solved problem. Millions of people
struggle with tasks on search engines every day. Often, their struggles relate
to the intrinsic complexity of their task and the failure of search systems to
fully understand the task and serve relevant results. The task motivates the
search, creating the gap/problematic situation that searchers attempt to
bridge/resolve and drives search behavior as they work through different task
facets. Complex search tasks require more than support for rudimentary fact
finding or re-finding. Research on methods to support complex tasks includes
work on generating query and website suggestions, personalizing and
contextualizing search, and developing new search experiences, including those
that span time and space. The recent emergence of generative artificial
intelligence (AI) and the arrival of assistive agents, or copilots, based on
this technology, has the potential to offer further assistance to searchers,
especially those engaged in complex tasks. There are profound implications from
these advances for the design of intelligent systems and for the future of
search itself. This article, based on a keynote by the author at the 2023 ACM
SIGIR Conference, explores these issues and charts a course toward new horizons
in information access guided by AI copilots.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: Constrained Meta-Reinforcement Learning for Adaptable Safety Guarantee with Differentiable Convex Programming
Abstract: Despite remarkable achievements in artificial intelligence, the deployability
of learning-enabled systems in high-stakes real-world environments still faces
persistent challenges. For example, in safety-critical domains like autonomous
driving, robotic manipulation, and healthcare, it is crucial not only to
achieve high performance but also to comply with given constraints.
Furthermore, adaptability becomes paramount in non-stationary domains, where
environmental parameters are subject to change. While safety and adaptability
are recognized as key qualities for the new generation of AI, current
approaches have not demonstrated effective adaptable performance in constrained
settings. Hence, this paper breaks new ground by studying the unique challenges
of ensuring safety in non-stationary environments by solving constrained
problems through the lens of the meta-learning approach (learning-to-learn).
While unconstrained meta-learning al-ready encounters complexities in
end-to-end differentiation of the loss due to the bi-level nature, its
constrained counterpart introduces an additional layer of difficulty, since the
constraints imposed on task-level updates complicate the differentiation
process. To address the issue, we first employ successive convex-constrained
policy updates across multiple tasks with differentiable convexprogramming,
which allows meta-learning in constrained scenarios by enabling end-to-end
differentiation. This approach empowers the agent to rapidly adapt to new tasks
under non-stationarity while ensuring compliance with safety constraints.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Scene-Driven Multimodal Knowledge Graph Construction for Embodied AI
Abstract: Embodied AI is one of the most popular studies in artificial intelligence and
robotics, which can effectively improve the intelligence of real-world agents
(i.e. robots) serving human beings. Scene knowledge is important for an agent
to understand the surroundings and make correct decisions in the varied open
world. Currently, knowledge base for embodied tasks is missing and most
existing work use general knowledge base or pre-trained models to enhance the
intelligence of an agent. For conventional knowledge base, it is sparse,
insufficient in capacity and cost in data collection. For pre-trained models,
they face the uncertainty of knowledge and hard maintenance. To overcome the
challenges of scene knowledge, we propose a scene-driven multimodal knowledge
graph (Scene-MMKG) construction method combining conventional knowledge
engineering and large language models. A unified scene knowledge injection
framework is introduced for knowledge representation. To evaluate the
advantages of our proposed method, we instantiate Scene-MMKG considering
typical indoor robotic functionalities (Manipulation and Mobility), named
ManipMob-MMKG. Comparisons in characteristics indicate our instantiated
ManipMob-MMKG has broad superiority in data-collection efficiency and knowledge
quality. Experimental results on typical embodied tasks show that
knowledge-enhanced methods using our instantiated ManipMob-MMKG can improve the
performance obviously without re-designing model structures complexly. Our
project can be found at https://sites.google.com/view/manipmob-mmkg

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval
Abstract: Dense retrieval models have predominantly been studied for English, where
models have shown great success, due to the availability of human-labeled
training pairs. However, there has been limited success for multilingual
retrieval so far, as training data is uneven or scarcely available across
multiple languages. Synthetic training data generation is promising (e.g.,
InPars or Promptagator), but has been investigated only for English. Therefore,
to study model capabilities across both cross-lingual and monolingual retrieval
tasks, we develop SWIM-IR, a synthetic retrieval training dataset containing 33
(high to very-low resource) languages for training multilingual dense retrieval
models without requiring any human supervision. To construct SWIM-IR, we
propose SAP (summarize-then-ask prompting), where the large language model
(LLM) generates a textual summary prior to the query generation step. SAP
assists the LLM in generating informative queries in the target language. Using
SWIM-IR, we explore synthetic fine-tuning of multilingual dense retrieval
models and evaluate them robustly on three retrieval benchmarks: XOR-Retrieve
(cross-lingual), XTREME-UP (cross-lingual) and MIRACL (monolingual). Our
models, called SWIM-X, are competitive with human-supervised dense retrieval
models, e.g., mContriever, finding that SWIM-IR can cheaply substitute for
expensive human-labeled retrieval training data.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: Temporal Knowledge Question Answering via Abstract Reasoning Induction
Abstract: In this paper, we tackle the significant challenge of temporal knowledge
reasoning in Large Language Models (LLMs), an area where such models frequently
encounter difficulties. These difficulties often result in the generation of
misleading or incorrect information, primarily due to their limited capacity to
process evolving factual knowledge and complex temporal logic. In response, we
propose a novel, constructivism-based approach that advocates for a paradigm
shift in LLM learning towards an active, ongoing process of knowledge synthesis
and customization. At the heart of our proposal is the Abstract Reasoning
Induction ARI framework, which divides temporal reasoning into two distinct
phases: Knowledge-agnostic and Knowledge-based. This division aims to reduce
instances of hallucinations and improve LLMs' capacity for integrating abstract
methodologies derived from historical data. Our approach achieves remarkable
improvements, with relative gains of 29.7\% and 9.27\% on two temporal QA
datasets, underscoring its efficacy in advancing temporal reasoning in LLMs.
The code will be released at https://github.com/czy1999/ARI.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine
Abstract: We propose the Multimodal Clinical Benchmark for Emergency Care (MC-BEC), a
comprehensive benchmark for evaluating foundation models in Emergency Medicine
using a dataset of 100K+ continuously monitored Emergency Department visits
from 2020-2022. MC-BEC focuses on clinically relevant prediction tasks at
timescales from minutes to days, including predicting patient decompensation,
disposition, and emergency department (ED) revisit, and includes a standardized
evaluation framework with train-test splits and evaluation metrics. The
multimodal dataset includes a wide range of detailed clinical data, including
triage information, prior diagnoses and medications, continuously measured
vital signs, electrocardiogram and photoplethysmograph waveforms, orders placed
and medications administered throughout the visit, free-text reports of imaging
studies, and information on ED diagnosis, disposition, and subsequent revisits.
We provide performance baselines for each prediction task to enable the
evaluation of multimodal, multitask models. We believe that MC-BEC will
encourage researchers to develop more effective, generalizable, and accessible
foundation models for multimodal clinical data.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Cross-modal Contrastive Learning with Asymmetric Co-attention Network for Video Moment Retrieval
Abstract: Video moment retrieval is a challenging task requiring fine-grained
interactions between video and text modalities. Recent work in image-text
pretraining has demonstrated that most existing pretrained models suffer from
information asymmetry due to the difference in length between visual and
textual sequences. We question whether the same problem also exists in the
video-text domain with an auxiliary need to preserve both spatial and temporal
information. Thus, we evaluate a recently proposed solution involving the
addition of an asymmetric co-attention network for video grounding tasks.
Additionally, we incorporate momentum contrastive loss for robust,
discriminative representation learning in both modalities. We note that the
integration of these supplementary modules yields better performance compared
to state-of-the-art models on the TACoS dataset and comparable results on
ActivityNet Captions, all while utilizing significantly fewer parameters with
respect to baseline.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Training Multi-layer Neural Networks on Ising Machine
Abstract: As a dedicated quantum device, Ising machines could solve large-scale binary
optimization problems in milliseconds. There is emerging interest in utilizing
Ising machines to train feedforward neural networks due to the prosperity of
generative artificial intelligence. However, existing methods can only train
single-layer feedforward networks because of the complex nonlinear network
topology. This paper proposes an Ising learning algorithm to train quantized
neural network (QNN), by incorporating two essential techinques, namely binary
representation of topological network and order reduction of loss function. As
far as we know, this is the first algorithm to train multi-layer feedforward
networks on Ising machines, providing an alternative to gradient-based
backpropagation. Firstly, training QNN is formulated as a quadratic constrained
binary optimization (QCBO) problem by representing neuron connection and
activation function as equality constraints. All quantized variables are
encoded by binary bits based on binary encoding protocol. Secondly, QCBO is
converted to a quadratic unconstrained binary optimization (QUBO) problem, that
can be efficiently solved on Ising machines. The conversion leverages both
penalty function and Rosenberg order reduction, who together eliminate equality
constraints and reduce high-order loss function into a quadratic one. With some
assumptions, theoretical analysis shows the space complexity of our algorithm
is $\mathcal{O}(H^2L + HLN\log H)$, quantifying the required number of Ising
spins. Finally, the algorithm effectiveness is validated with a simulated Ising
machine on MNIST dataset. After annealing 700 ms, the classification accuracy
achieves 98.3%. Among 100 runs, the success probability of finding the optimal
solution is 72%. Along with the increasing number of spins on Ising machine,
our algorithm has the potential to train deeper neural networks.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective
Abstract: This review paper takes a comprehensive look at malicious attacks against FL,
categorizing them from new perspectives on attack origins and targets, and
providing insights into their methodology and impact. In this survey, we focus
on threat models targeting the learning process of FL systems. Based on the
source and target of the attack, we categorize existing threat models into four
types, Data to Model (D2M), Model to Data (M2D), Model to Model (M2M) and
composite attacks. For each attack type, we discuss the defense strategies
proposed, highlighting their effectiveness, assumptions and potential areas for
improvement. Defense strategies have evolved from using a singular metric to
excluding malicious clients, to employing a multifaceted approach examining
client models at various phases. In this survey paper, our research indicates
that the to-learn data, the learning gradients, and the learned model at
different stages all can be manipulated to initiate malicious attacks that
range from undermining model performance, reconstructing private local data,
and to inserting backdoors. We have also seen these threat are becoming more
insidious. While earlier studies typically amplified malicious gradients,
recent endeavors subtly alter the least significant weights in local models to
bypass defense measures. This literature review provides a holistic
understanding of the current FL threat landscape and highlights the importance
of developing robust, efficient, and privacy-preserving defenses to ensure the
safe and trusted adoption of FL in real-world applications.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Developing Linguistic Patterns to Mitigate Inherent Human Bias in Offensive Language Detection
Abstract: With the proliferation of social media, there has been a sharp increase in
offensive content, particularly targeting vulnerable groups, exacerbating
social problems such as hatred, racism, and sexism. Detecting offensive
language use is crucial to prevent offensive language from being widely shared
on social media. However, the accurate detection of irony, implication, and
various forms of hate speech on social media remains a challenge. Natural
language-based deep learning models require extensive training with large,
comprehensive, and labeled datasets. Unfortunately, manually creating such
datasets is both costly and error-prone. Additionally, the presence of
human-bias in offensive language datasets is a major concern for deep learning
models. In this paper, we propose a linguistic data augmentation approach to
reduce bias in labeling processes, which aims to mitigate the influence of
human bias by leveraging the power of machines to improve the accuracy and
fairness of labeling processes. This approach has the potential to improve
offensive language classification tasks across multiple languages and reduce
the prevalence of offensive content on social media.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification
Abstract: Recently, graph neural networks (GNNs) have shown prominent performance in
semi-supervised node classification by leveraging knowledge from the graph
database. However, most existing GNNs follow the homophily assumption, where
connected nodes are more likely to exhibit similar feature distributions and
the same labels, and such an assumption has proven to be vulnerable in a
growing number of practical applications. As a supplement, heterophily reflects
dissimilarity in connected nodes, which has gained significant attention in
graph learning. To this end, data engineers aim to develop a powerful GNN model
that can ensure performance under both homophily and heterophily. Despite
numerous attempts, most existing GNNs struggle to achieve optimal node
representations due to the constraints of undirected graphs. The neglect of
directed edges results in sub-optimal graph representations, thereby hindering
the capacity of GNNs. To address this issue, we introduce AMUD, which
quantifies the relationship between node profiles and topology from a
statistical perspective, offering valuable insights for \underline{A}daptively
\underline{M}odeling the natural directed graphs as the \underline{U}ndirected
or \underline{D}irected graph to maximize the benefits from subsequent graph
learning. Furthermore, we propose \underline{A}daptive \underline{D}irected
\underline{P}attern \underline{A}ggregation (ADPA) as a new directed graph
learning paradigm for AMUD. Empirical studies have demonstrated that AMUD
guides efficient graph learning. Meanwhile, extensive experiments on 14
benchmark datasets substantiate the impressive performance of ADPA,
outperforming baselines by significant margins of 3.96\%.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications
Abstract: Machine learning algorithms minimizing average risk are susceptible to
distributional shifts. Distributionally Robust Optimization (DRO) addresses
this issue by optimizing the worst-case risk within an uncertainty set.
However, DRO suffers from over-pessimism, leading to low-confidence
predictions, poor parameter estimations as well as poor generalization. In this
work, we conduct a theoretical analysis of a probable root cause of
over-pessimism: excessive focus on noisy samples. To alleviate the impact of
noise, we incorporate data geometry into calibration terms in DRO, resulting in
our novel Geometry-Calibrated DRO (GCDRO) for regression. We establish the
connection between our risk objective and the Helmholtz free energy in
statistical physics, and this free-energy-based risk can extend to standard DRO
methods. Leveraging gradient flow in Wasserstein space, we develop an
approximate minimax optimization algorithm with a bounded error ratio and
elucidate how our approach mitigates noisy sample effects. Comprehensive
experiments confirm GCDRO's superiority over conventional DRO methods.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks
Abstract: High-dimensional and complex spectral structures make the clustering of
hyperspectral images (HSI) a challenging task. Subspace clustering is an
effective approach for addressing this problem. However, current subspace
clustering algorithms are primarily designed for a single view and do not fully
exploit the spatial or textural feature information in HSI. In this study,
contrastive multi-view subspace clustering of HSI was proposed based on graph
convolutional networks. Pixel neighbor textural and spatial-spectral
information were sent to construct two graph convolutional subspaces to learn
their affinity matrices. To maximize the interaction between different views, a
contrastive learning algorithm was introduced to promote the consistency of
positive samples and assist the model in extracting robust features. An
attention-based fusion module was used to adaptively integrate these affinity
matrices, constructing a more discriminative affinity matrix. The model was
evaluated using four popular HSI datasets: Indian Pines, Pavia University,
Houston, and Xu Zhou. It achieved overall accuracies of 97.61%, 96.69%, 87.21%,
and 97.65%, respectively, and significantly outperformed state-of-the-art
clustering methods. In conclusion, the proposed model effectively improves the
clustering accuracy of HSI.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Virtual Fusion with Contrastive Learning for Single Sensor-based Activity Recognition
Abstract: Various types of sensors can be used for Human Activity Recognition (HAR),
and each of them has different strengths and weaknesses. Sometimes a single
sensor cannot fully observe the user's motions from its perspective, which
causes wrong predictions. While sensor fusion provides more information for
HAR, it comes with many inherent drawbacks like user privacy and acceptance,
costly set-up, operation, and maintenance. To deal with this problem, we
propose Virtual Fusion - a new method that takes advantage of unlabeled data
from multiple time-synchronized sensors during training, but only needs one
sensor for inference. Contrastive learning is adopted to exploit the
correlation among sensors. Virtual Fusion gives significantly better accuracy
than training with the same single sensor, and in some cases, it even surpasses
actual fusion using multiple sensors at test time. We also extend this method
to a more general version called Actual Fusion within Virtual Fusion (AFVF),
which uses a subset of training sensors during inference. Our method achieves
state-of-the-art accuracy and F1-score on UCI-HAR and PAMAP2 benchmark
datasets. Implementation is available upon request.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Adaptive Proximal Policy Optimization with Upper Confidence Bound
Abstract: Trust Region Policy Optimization (TRPO) attractively optimizes the policy
while constraining the update of the new policy within a trust region, ensuring
the stability and monotonic optimization. Building on the theoretical
guarantees of trust region optimization, Proximal Policy Optimization (PPO)
successfully enhances the algorithm's sample efficiency and reduces deployment
complexity by confining the update of the new and old policies within a
surrogate trust region. However, this approach is limited by the fixed setting
of surrogate trust region and is not sufficiently adaptive, because there is no
theoretical proof that the optimal clipping bound remains consistent throughout
the entire training process, truncating the ratio of the new and old policies
within surrogate trust region can ensure that the algorithm achieves its best
performance, therefore, exploring and researching a dynamic clip bound for
improving PPO's performance can be quite beneficial. To design an adaptive
clipped trust region and explore the dynamic clip bound's impact on the
performance of PPO, we introduce an adaptive PPO-CLIP (Adaptive-PPO) method
that dynamically explores and exploits the clip bound using a bandit during the
online training process. Furthermore, ample experiments will initially
demonstrate that our Adaptive-PPO exhibits sample efficiency and performance
compared to PPO-CLIP.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Chatbots as social companions: How people perceive consciousness, human likeness, and social health benefits in machines
Abstract: As artificial intelligence (AI) becomes more widespread, one question that
arises is how human-AI interaction might impact human-human interaction.
Chatbots, for example, are increasingly used as social companions, but little
is known about how their use impacts human relationships. A common hypothesis
is that these companion bots are detrimental to social health by harming or
replacing human interaction. To understand how companion bots impact social
health, we studied people who used companion bots and people who did not.
Contrary to expectations, companion bot users indicated that these
relationships were beneficial to their social health, whereas nonusers viewed
them as harmful. Another common assumption is that people perceive conscious,
humanlike AI as disturbing and threatening. Among both users and nonusers,
however, we found the opposite: perceiving companion bots as more conscious and
humanlike correlated with more positive opinions and better social health
benefits. Humanlike bots may aid social health by supplying reliable and safe
interactions, without necessarily harming human relationships.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Efficient Data Fusion using the Tsetlin Machine
Abstract: We propose a novel way of assessing and fusing noisy dynamic data using a
Tsetlin Machine. Our approach consists in monitoring how explanations in form
of logical clauses that a TM learns changes with possible noise in dynamic
data. This way TM can recognize the noise by lowering weights of previously
learned clauses, or reflect it in the form of new clauses. We also perform a
comprehensive experimental study using notably different datasets that
demonstrated high performance of the proposed approach.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Italian Crossword Generator: Enhancing Education through Interactive Word Puzzles
Abstract: Educational crosswords offer numerous benefits for students, including
increased engagement, improved understanding, critical thinking, and memory
retention. Creating high-quality educational crosswords can be challenging, but
recent advances in natural language processing and machine learning have made
it possible to use language models to generate nice wordplays. The exploitation
of cutting-edge language models like GPT3-DaVinci, GPT3-Curie, GPT3-Babbage,
GPT3-Ada, and BERT-uncased has led to the development of a comprehensive system
for generating and verifying crossword clues. A large dataset of clue-answer
pairs was compiled to fine-tune the models in a supervised manner to generate
original and challenging clues from a given keyword. On the other hand, for
generating crossword clues from a given text, Zero/Few-shot learning techniques
were used to extract clues from the input text, adding variety and creativity
to the puzzles. We employed the fine-tuned model to generate data and labeled
the acceptability of clue-answer parts with human supervision. To ensure
quality, we developed a classifier by fine-tuning existing language models on
the labeled dataset. Conversely, to assess the quality of clues generated from
the given text using zero/few-shot learning, we employed a zero-shot learning
approach to check the quality of generated clues. The results of the evaluation
have been very promising, demonstrating the effectiveness of the approach in
creating high-standard educational crosswords that offer students engaging and
rewarding learning experiences.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Improving Adaptability and Generalizability of Efficient Transfer Learning for Vision-Language Models
Abstract: Vision-Language Models (VLMs) like CLIP have demonstrated remarkable
applicability across a variety of downstream tasks, including zero-shot image
classification. Recently, the use of prompts or adapters for efficient transfer
learning has gained significant attention for effectively adapting to
downstream tasks. However, the roles of vision and text prompts, as well as
adapters in terms of generalization and transfer difficulty, have been
overlooked, limiting performance on unseen tasks. In this paper, we empirically
analyze how VLMs behave when using vision and text prompts, adapters, and a
combination of these components, marking a novel exploration by our study. Our
observations find that utilizing vision prompts for class separability and text
adapters for task adaptation is crucial for adaptability and generalizability.
Moreover, to improve generalization across every domain, we propose an adaptive
ensemble method that effectively combines the general knowledge of VLMs with
task-specific knowledge according to transfer difficulty. Upon experimenting
with extensive benchmarks, our method consistently outperforms all baselines,
particularly on unseen tasks, demonstrating the effectiveness of our proposed
approach.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: GOPlan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models
Abstract: Offline goal-conditioned RL (GCRL) offers a feasible paradigm to learn
general-purpose policies from diverse and multi-task offline datasets. Despite
notable recent progress, the predominant offline GCRL methods have been
restricted to model-free approaches, constraining their capacity to tackle
limited data budgets and unseen goal generalization. In this work, we propose a
novel two-stage model-based framework, Goal-conditioned Offline Planning
(GOPlan), including (1) pretraining a prior policy capable of capturing
multi-modal action distribution within the multi-goal dataset; (2) employing
the reanalysis method with planning to generate imagined trajectories for
funetuning policies. Specifically, the prior policy is based on an
advantage-weighted Conditioned Generative Adversarial Networks that exhibits
distinct mode separation to overcome the pitfalls of out-of-distribution (OOD)
actions. For further policy optimization, the reanalysis method generates
high-quality imaginary data by planning with learned models for both
intra-trajectory and inter-trajectory goals. Through experimental evaluations,
we demonstrate that GOPlan achieves state-of-the-art performance on various
offline multi-goal manipulation tasks. Moreover, our results highlight the
superior ability of GOPlan to handle small data budgets and generalize to OOD
goals.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Instant3D: Instant Text-to-3D Generation
Abstract: Text-to-3D generation, which aims to synthesize vivid 3D objects from text
prompts, has attracted much attention from the computer vision community. While
several existing works have achieved impressive results for this task, they
mainly rely on a time-consuming optimization paradigm. Specifically, these
methods optimize a neural field from scratch for each text prompt, taking
approximately one hour or more to generate one object. This heavy and
repetitive training cost impedes their practical deployment. In this paper, we
propose a novel framework for fast text-to-3D generation, dubbed Instant3D.
Once trained, Instant3D is able to create a 3D object for an unseen text prompt
in less than one second with a single run of a feedforward network. We achieve
this remarkable speed by devising a new network that directly constructs a 3D
triplane from a text prompt. The core innovation of our Instant3D lies in our
exploration of strategies to effectively inject text conditions into the
network. Furthermore, we propose a simple yet effective activation function,
the scaled-sigmoid, to replace the original sigmoid function, which speeds up
the training convergence by more than ten times. Finally, to address the Janus
(multi-head) problem in 3D generation, we propose an adaptive Perp-Neg
algorithm that can dynamically adjust its concept negation scales according to
the severity of the Janus problem during training, effectively reducing the
multi-head effect. Extensive experiments on a wide variety of benchmark
datasets demonstrate that the proposed algorithm performs favorably against the
state-of-the-art methods both qualitatively and quantitatively, while achieving
significantly better efficiency. The project page is at
https://ming1993li.github.io/Instant3DProj.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Two-Stage Classifier for Campaign Negativity Detection using Axis Embeddings: A Case Study on Tweets of Political Users during 2021 Presidential Election in Iran
Abstract: In elections around the world, the candidates may turn their campaigns toward
negativity due to the prospect of failure and time pressure. In the digital
age, social media platforms such as Twitter are rich sources of political
discourse. Therefore, despite the large amount of data that is published on
Twitter, the automatic system for campaign negativity detection can play an
essential role in understanding the strategy of candidates and parties in their
campaigns. In this paper, we propose a hybrid model for detecting campaign
negativity consisting of a two-stage classifier that combines the strengths of
two machine learning models. Here, we have collected Persian tweets from 50
political users, including candidates and government officials. Then we
annotated 5,100 of them that were published during the year before the 2021
presidential election in Iran. In the proposed model, first, the required
datasets of two classifiers based on the cosine similarity of tweet embeddings
with axis embeddings (which are the average of embedding in positive and
negative classes of tweets) from the training set (85\%) are made, and then
these datasets are considered the training set of the two classifiers in the
hybrid model. Finally, our best model (RF-RF) was able to achieve 79\% for the
macro F1 score and 82\% for the weighted F1 score. By running the best model on
the rest of the tweets of 50 political users that were published one year
before the election and with the help of statistical models, we find that the
publication of a tweet by a candidate has nothing to do with the negativity of
that tweet, and the presence of the names of political persons and political
organizations in the tweet is directly related to its negativity.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: SteloCoder: a Decoder-Only LLM for Multi-Language to Python Code Translation
Abstract: With the recent focus on Large Language Models (LLMs), both StarCoder (Li et
al., 2023) and Code Llama (Rozi\`ere et al., 2023) have demonstrated remarkable
performance in code generation. However, there is still a need for improvement
in code translation functionality with efficient training techniques. In
response to this, we introduce SteloCoder, a decoder-only StarCoder-based LLM
designed specifically for multi-programming language-to-Python code
translation. In particular, SteloCoder achieves C++, C#, JavaScript, Java, or
PHP-to-Python code translation without specifying the input programming
language. We modified StarCoder model architecture by incorporating a
Mixture-of-Experts (MoE) technique featuring five experts and a gating network
for multi-task handling. Experts are obtained by StarCoder fine-tuning.
Specifically, we use a Low-Rank Adaptive Method (LoRA) technique, limiting each
expert size as only 0.06% of number of StarCoder's parameters. At the same
time, to enhance training efficiency in terms of time, we adopt curriculum
learning strategy and use self-instruct data for efficient fine-tuning. As a
result, each expert takes only 6 hours to train on one single 80Gb A100 HBM.
With experiments on XLCoST datasets, SteloCoder achieves an average of 73.76
CodeBLEU score in multi-programming language-to-Python translation, surpassing
the top performance from the leaderboard by at least 3.5. This accomplishment
is attributed to only 45M extra parameters with StarCoder as the backbone and
32 hours of valid training on one 80GB A100 HBM. The source code is release
here: https://github.com/sade-adrien/SteloCoder.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Towards Transferable Multi-modal Perception Representation Learning for Autonomy: NeRF-Supervised Masked AutoEncoder
Abstract: This work proposes a unified self-supervised pre-training framework for
transferable multi-modal perception representation learning via masked
multi-modal reconstruction in Neural Radiance Field (NeRF), namely
NeRF-Supervised Masked AutoEncoder (NS-MAE). Specifically, conditioned on
certain view directions and locations, multi-modal embeddings extracted from
corrupted multi-modal input signals, i.e., Lidar point clouds and images, are
rendered into projected multi-modal feature maps via neural rendering. Then,
original multi-modal signals serve as reconstruction targets for the rendered
multi-modal feature maps to enable self-supervised representation learning.
Extensive experiments show that the representation learned via NS-MAE shows
promising transferability for diverse multi-modal and single-modal (camera-only
and Lidar-only) perception models on diverse 3D perception downstream tasks (3D
object detection and BEV map segmentation) with diverse amounts of fine-tuning
labeled data. Moreover, we empirically find that NS-MAE enjoys the synergy of
both the mechanism of masked autoencoder and neural radiance field. We hope
this study can inspire exploration of more general multi-modal representation
learning for autonomous agents.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment
Abstract: Image Quality Assessment (IQA) with reference images have achieved great
success by imitating the human vision system, in which the image quality is
effectively assessed by comparing the query image with its pristine reference
image. However, for the images in the wild, it is quite difficult to access
accurate reference images. We argue that it is possible to learn reference
knowledge under the No-Reference Image Quality Assessment (NR-IQA) setting,
which is effective and efficient empirically. Concretely, by innovatively
introducing a novel feature distillation method in IQA, we propose a new
framework to learn comparative knowledge from non-aligned reference images. And
then, to achieve fast convergence and avoid overfitting, we further propose an
inductive bias regularization. Such a framework not only solves the congenital
defects of NR-IQA but also improves the feature extraction framework, enabling
it to express more abundant quality information. Surprisingly, our method
utilizes less input while obtaining a more significant improvement compared to
the teacher models. Extensive experiments on eight standard NR-IQA datasets
demonstrate the superior performance to the state-of-the-art NR-IQA methods,
i.e., achieving the PLCC values of 0.917 (vs. 0.884 in LIVEC) and 0.686 (vs.
0.661 in LIVEFB).

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Data-Efficient Multimodal Fusion on a Single GPU
Abstract: The goal of multimodal alignment is to learn a single latent space that is
shared between multimodal inputs. The most powerful models in this space have
been trained using massive datasets of paired inputs and large-scale
computational resources, making them prohibitively expensive to train in many
practical scenarios. We surmise that existing unimodal encoders pre-trained on
large amounts of unimodal data should provide an effective bootstrap to create
multimodal models from unimodal ones at much lower costs. We therefore propose
FuseMix, a multimodal augmentation scheme that operates on the latent spaces of
arbitrary pre-trained unimodal encoders. Using FuseMix for multimodal
alignment, we achieve competitive performance -- and in certain cases
outperform state-of-the art methods -- in both image-text and audio-text
retrieval, with orders of magnitude less compute and data: for example, we
outperform CLIP on the Flickr30K text-to-image retrieval task with $\sim \!
600\times$ fewer GPU days and $\sim \! 80\times$ fewer image-text pairs.
Additionally, we show how our method can be applied to convert pre-trained
text-to-image generative models into audio-to-image ones. Code is available at:
https://github.com/layer6ai-labs/fusemix.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: SoloPose: One-Shot Kinematic 3D Human Pose Estimation with Video Data Augmentation
Abstract: While recent two-stage many-to-one deep learning models have demonstrated
great success in 3D human pose estimation, such models are inefficient ways to
detect 3D key points in a sequential video relative to one-shot and
many-to-many models. Another key drawback of two-stage and many-to-one models
is that errors in the first stage will be passed onto the second stage. In this
paper, we introduce SoloPose, a novel one-shot, many-to-many spatio-temporal
transformer model for kinematic 3D human pose estimation of video. SoloPose is
further fortified by HeatPose, a 3D heatmap based on Gaussian Mixture Model
distributions that factors target key points as well as kinematically adjacent
key points. Finally, we address data diversity constraints with the 3D
AugMotion Toolkit, a methodology to augment existing 3D human pose datasets,
specifically by projecting four top public 3D human pose datasets (Humans3.6M,
MADS, AIST Dance++, MPI INF 3DHP) into a novel dataset (Humans7.1M) with a
universal coordinate system. Extensive experiments are conducted on Human3.6M
as well as the augmented Humans7.1M dataset, and SoloPose demonstrates superior
results relative to the state-of-the-art approaches.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making
Abstract: With recent advances in multi-modal foundation models, the previously
text-only large language models (LLM) have evolved to incorporate visual input,
opening up unprecedented opportunities for various applications in
visualization. Our work explores the utilization of the visual perception
ability of multi-modal LLMs to develop Autonomous Visualization Agents (AVAs)
that can interpret and accomplish user-defined visualization objectives through
natural language. We propose the first framework for the design of AVAs and
present several usage scenarios intended to demonstrate the general
applicability of the proposed paradigm. The addition of visual perception
allows AVAs to act as the virtual visualization assistant for domain experts
who may lack the knowledge or expertise in fine-tuning visualization outputs.
Our preliminary exploration and proof-of-concept agents suggest that this
approach can be widely applicable whenever the choices of appropriate
visualization parameters require the interpretation of previous visual output.
Feedback from unstructured interviews with experts in AI research, medical
visualization, and radiology has been incorporated, highlighting the
practicality and potential of AVAs. Our study indicates that AVAs represent a
general paradigm for designing intelligent visualization systems that can
achieve high-level visualization goals, which pave the way for developing
expert-level visualization agents in the future.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: GeoChat: Grounded Large Vision-Language Model for Remote Sensing
Abstract: Recent advancements in Large Vision-Language Models (VLMs) have shown great
promise in natural image domains, allowing users to hold a dialogue about given
visual content. However, such general-domain VLMs perform poorly for Remote
Sensing (RS) scenarios, leading to inaccurate or fabricated information when
presented with RS domain-specific queries. Such a behavior emerges due to the
unique challenges introduced by RS imagery. For example, to handle
high-resolution RS imagery with diverse scale changes across categories and
many small objects, region-level reasoning is necessary alongside holistic
scene interpretation. Furthermore, the lack of domain-specific multimodal
instruction following data as well as strong backbone models for RS make it
hard for the models to align their behavior with user queries. To address these
limitations, we propose GeoChat - the first versatile remote sensing VLM that
offers multitask conversational capabilities with high-resolution RS images.
Specifically, GeoChat can not only answer image-level queries but also accepts
region inputs to hold region-specific dialogue. Furthermore, it can visually
ground objects in its responses by referring to their spatial coordinates. To
address the lack of domain-specific datasets, we generate a novel RS multimodal
instruction-following dataset by extending image-text pairs from existing
diverse RS datasets. We establish a comprehensive benchmark for RS multitask
conversations and compare with a number of baseline methods. GeoChat
demonstrates robust zero-shot performance on various RS tasks, e.g., image and
region captioning, visual question answering, scene classification, visually
grounded conversations and referring detection. Our code is available at
https://github.com/mbzuai-oryx/geochat.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Preserving Patient Privacy in MRI Scans: A Comprehensive Approach with 3D Masked Autoencoders
Abstract: MRI scans provide valuable medical information, however they also contain
sensitive and personally identifiable information (PII) that needs to be
protected. Whereas MRI metadata is easily sanitized, MRI image data is a
privacy risk because it contains information to render highly-realistic 3D
visualizations of a patient's head, enabling malicious actors to possibly
identify the subject by cross-referencing a database. Data anonymization and
de-identification is concerned with ensuring the privacy and confidentiality of
individuals' personal information. Traditional MRI de-identification methods
remove privacy-sensitive parts (e.g. eyes, nose etc.) from a given scan. This
comes at the expense of introducing a domain shift that can throw off
downstream analyses. Recently, a GAN-based approach was proposed to de-identify
a patient's scan by remodeling it (\eg changing the face) rather than by
removing parts. In this work, we propose CP-MAE, a model that de-identifies the
face using masked autoencoders and that outperforms all previous approaches in
terms of downstream task performance as well as de-identification. With our
method we are able to synthesize scans of resolution up to $256^3$ (previously
$128^3$) which constitutes an eight-fold increase in the number of voxels.
Using our construction we were able to design a system that exhibits a highly
robust training stage, making it easy to fit the network on novel data.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Intelligent Virtual Assistants with LLM-based Process Automation
Abstract: While intelligent virtual assistants like Siri, Alexa, and Google Assistant
have become ubiquitous in modern life, they still face limitations in their
ability to follow multi-step instructions and accomplish complex goals
articulated in natural language. However, recent breakthroughs in large
language models (LLMs) show promise for overcoming existing barriers by
enhancing natural language processing and reasoning capabilities. Though
promising, applying LLMs to create more advanced virtual assistants still faces
challenges like ensuring robust performance and handling variability in
real-world user commands. This paper proposes a novel LLM-based virtual
assistant that can automatically perform multi-step operations within mobile
apps based on high-level user requests. The system represents an advance in
assistants by providing an end-to-end solution for parsing instructions,
reasoning about goals, and executing actions. LLM-based Process Automation
(LLMPA) has modules for decomposing instructions, generating descriptions,
detecting interface elements, predicting next actions, and error checking.
Experiments demonstrate the system completing complex mobile operation tasks in
Alipay based on natural language instructions. This showcases how large
language models can enable automated assistants to accomplish real-world tasks.
The main contributions are the novel LLMPA architecture optimized for app
process automation, the methodology for applying LLMs to mobile apps, and
demonstrations of multi-step task completion in a real-world environment.
Notably, this work represents the first real-world deployment and extensive
evaluation of a large language model-based virtual assistant in a widely used
mobile application with an enormous user base numbering in the hundreds of
millions.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Charting New Territories: Exploring the Geographic and Geospatial Capabilities of Multimodal LLMs
Abstract: Multimodal large language models (MLLMs) have shown remarkable capabilities
across a broad range of tasks but their knowledge and abilities in the
geographic and geospatial domains are yet to be explored, despite potential
wide-ranging benefits to navigation, environmental research, urban development,
and disaster response. We conduct a series of experiments exploring various
vision capabilities of MLLMs within these domains, particularly focusing on the
frontier model GPT-4V, and benchmark its performance against open-source
counterparts. Our methodology involves challenging these models with a
small-scale geographic benchmark consisting of a suite of visual tasks, testing
their abilities across a spectrum of complexity. The analysis uncovers not only
where such models excel, including instances where they outperform humans, but
also where they falter, providing a balanced view of their capabilities in the
geographic domain. To enable the comparison and evaluation of future models,
our benchmark will be publicly released.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Interpretable Neural PDE Solvers using Symbolic Frameworks
Abstract: Partial differential equations (PDEs) are ubiquitous in the world around us,
modelling phenomena from heat and sound to quantum systems. Recent advances in
deep learning have resulted in the development of powerful neural solvers;
however, while these methods have demonstrated state-of-the-art performance in
both accuracy and computational efficiency, a significant challenge remains in
their interpretability. Most existing methodologies prioritize predictive
accuracy over clarity in the underlying mechanisms driving the model's
decisions. Interpretability is crucial for trustworthiness and broader
applicability, especially in scientific and engineering domains where neural
PDE solvers might see the most impact. In this context, a notable gap in
current research is the integration of symbolic frameworks (such as symbolic
regression) into these solvers. Symbolic frameworks have the potential to
distill complex neural operations into human-readable mathematical expressions,
bridging the divide between black-box predictions and solutions.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Score Models for Offline Goal-Conditioned Reinforcement Learning
Abstract: Offline Goal-Conditioned Reinforcement Learning (GCRL) is tasked with
learning to achieve multiple goals in an environment purely from offline
datasets using sparse reward functions. Offline GCRL is pivotal for developing
generalist agents capable of leveraging pre-existing datasets to learn diverse
and reusable skills without hand-engineering reward functions. However,
contemporary approaches to GCRL based on supervised learning and contrastive
learning are often suboptimal in the offline setting. An alternative
perspective on GCRL optimizes for occupancy matching, but necessitates learning
a discriminator, which subsequently serves as a pseudo-reward for downstream
RL. Inaccuracies in the learned discriminator can cascade, negatively
influencing the resulting policy. We present a novel approach to GCRL under a
new lens of mixture-distribution matching, leading to our discriminator-free
method: SMORe. The key insight is combining the occupancy matching perspective
of GCRL with a convex dual formulation to derive a learning objective that can
better leverage suboptimal offline data. SMORe learns scores or unnormalized
densities representing the importance of taking an action at a state for
reaching a particular goal. SMORe is principled and our extensive experiments
on the fully offline GCRL benchmark composed of robot manipulation and
locomotion tasks, including high-dimensional observations, show that SMORe can
outperform state-of-the-art baselines by a significant margin.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policies
Abstract: While reinforcement learning has achieved remarkable successes in several
domains, its real-world application is limited due to many methods failing to
generalise to unfamiliar conditions. In this work, we consider the problem of
generalising to new transition dynamics, corresponding to cases in which the
environment's response to the agent's actions differs. For example, the
gravitational force exerted on a robot depends on its mass and changes the
robot's mobility. Consequently, in such cases, it is necessary to condition an
agent's actions on extrinsic state information and pertinent contextual
information reflecting how the environment responds. While the need for
context-sensitive policies has been established, the manner in which context is
incorporated architecturally has received less attention. Thus, in this work,
we present an investigation into how context information should be incorporated
into behaviour learning to improve generalisation. To this end, we introduce a
neural network architecture, the Decision Adapter, which generates the weights
of an adapter module and conditions the behaviour of an agent on the context
information. We show that the Decision Adapter is a useful generalisation of a
previously proposed architecture and empirically demonstrate that it results in
superior generalisation performance compared to previous approaches in several
environments. Beyond this, the Decision Adapter is more robust to irrelevant
distractor variables than several alternative methods.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: An Intelligent Social Learning-based Optimization Strategy for Black-box Robotic Control with Reinforcement Learning
Abstract: Implementing intelligent control of robots is a difficult task, especially
when dealing with complex black-box systems, because of the lack of visibility
and understanding of how these robots work internally. This paper proposes an
Intelligent Social Learning (ISL) algorithm to enable intelligent control of
black-box robotic systems. Inspired by mutual learning among individuals in
human social groups, ISL includes learning, imitation, and self-study styles.
Individuals in the learning style use the Levy flight search strategy to learn
from the best performer and form the closest relationships. In the imitation
style, individuals mimic the best performer with a second-level rapport by
employing a random perturbation strategy. In the self-study style, individuals
learn independently using a normal distribution sampling method while
maintaining a distant relationship with the best performer. Individuals in the
population are regarded as autonomous intelligent agents in each style. Neural
networks perform strategic actions in three styles to interact with the
environment and the robot and iteratively optimize the network policy. Overall,
ISL builds on the principles of intelligent optimization, incorporating ideas
from reinforcement learning, and possesses strong search capabilities, fast
computation speed, fewer hyperparameters, and insensitivity to sparse rewards.
The proposed ISL algorithm is compared with four state-of-the-art methods on
six continuous control benchmark cases in MuJoCo to verify its effectiveness
and advantages. Furthermore, ISL is adopted in the simulation and experimental
grasping tasks of the UR3 robot for validations, and satisfactory solutions are
yielded.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Legal-HNet: Mixing Legal Long-Context Tokens with Hartley Transform
Abstract: Since its introduction, the transformers architecture has seen great adoption
in NLP applications, but it also has limitations. Although the self-attention
mechanism allows for generating very rich representations of the input text,
its effectiveness may be limited in specialized domains such as legal, where,
for example, language models often have to process very long texts. In this
paper, we explore alternatives to replace the attention-based layers with
simpler token-mixing mechanisms: Hartley and Fourier transforms. Using these
non-parametric techniques, we train models with long input documents from
scratch in the legal domain setting. We also introduce a new hybrid Seq2Seq
architecture, a no-attention-based encoder connected with an attention-based
decoder, which performs quite well on existing summarization tasks with much
less compute and memory requirements. We believe that similar, if not better
performance, as in the case of long correlations of abstractive text
summarization tasks, can be achieved by adopting these simpler infrastructures.
This not only makes training models from scratch accessible to more people, but
also contributes to the reduction of the carbon footprint during training.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging
Abstract: Artificial Intelligence (AI) models are vulnerable to information leakage of
their training data, which can be highly sensitive, for example in medical
imaging. Privacy Enhancing Technologies (PETs), such as Differential Privacy
(DP), aim to circumvent these susceptibilities. DP is the strongest possible
protection for training models while bounding the risks of inferring the
inclusion of training samples or reconstructing the original data. DP achieves
this by setting a quantifiable privacy budget. Although a lower budget
decreases the risk of information leakage, it typically also reduces the
performance of such models. This imposes a trade-off between robust performance
and stringent privacy. Additionally, the interpretation of a privacy budget
remains abstract and challenging to contextualize. In this study, we contrast
the performance of AI models at various privacy budgets against both,
theoretical risk bounds and empirical success of reconstruction attacks. We
show that using very large privacy budgets can render reconstruction attacks
impossible, while drops in performance are negligible. We thus conclude that
not using DP -- at all -- is negligent when applying AI models to sensitive
data. We deem those results to lie a foundation for further debates on striking
a balance between privacy risks and model performance.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach
Abstract: The significant advancements in large language models (LLMs) have presented
novel opportunities for tackling planning and decision-making within
multi-agent systems. However, as the number of agents increases, the issues of
hallucination in LLMs and coordination in multi-agent systems (MAS) have become
increasingly pronounced. Additionally, the efficient utilization of tokens
becomes a critical consideration when employing LLMs to facilitate the
interactions of large numbers of agents. In this paper, we present a novel
framework aimed at enhancing coordination and decision-making capabilities of
LLMs within large-scale multi-agent environments. Our approach draws
inspiration from the actor-critic framework employed in multi-agent
reinforcement learning, and we develop a modular and token-efficient solution
that effectively addresses challenges presented by LLMs and MAS. Through
evaluations conducted in experiments involving system resource allocation and
robot grid transportation, we demonstrate the considerable advantages afforded
by our proposed approach.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Adversarial Learning for Feature Shift Detection and Correction
Abstract: Data shift is a phenomenon present in many real-world applications, and while
there are multiple methods attempting to detect shifts, the task of localizing
and correcting the features originating such shifts has not been studied in
depth. Feature shifts can occur in many datasets, including in multi-sensor
data, where some sensors are malfunctioning, or in tabular and structured data,
including biomedical, financial, and survey data, where faulty standardization
and data processing pipelines can lead to erroneous features. In this work, we
explore using the principles of adversarial learning, where the information
from several discriminators trained to distinguish between two distributions is
used to both detect the corrupted features and fix them in order to remove the
distribution shift between datasets. We show that mainstream supervised
classifiers, such as random forest or gradient boosting trees, combined with
simple iterative heuristics, can localize and correct feature shifts,
outperforming current statistical and neural network-based techniques. The code
is available at https://github.com/AI-sandbox/DataFix.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Redefining the Laparoscopic Spatial Sense: AI-based Intra- and Postoperative Measurement from Stereoimages
Abstract: A significant challenge in image-guided surgery is the accurate measurement
task of relevant structures such as vessel segments, resection margins, or
bowel lengths. While this task is an essential component of many surgeries, it
involves substantial human effort and is prone to inaccuracies. In this paper,
we develop a novel human-AI-based method for laparoscopic measurements
utilizing stereo vision that has been guided by practicing surgeons. Based on a
holistic qualitative requirements analysis, this work proposes a comprehensive
measurement method, which comprises state-of-the-art machine learning
architectures, such as RAFT-Stereo and YOLOv8. The developed method is assessed
in various realistic experimental evaluation environments. Our results outline
the potential of our method achieving high accuracies in distance measurements
with errors below 1 mm. Furthermore, on-surface measurements demonstrate
robustness when applied in challenging environments with textureless regions.
Overall, by addressing the inherent challenges of image-guided surgery, we lay
the foundation for a more robust and accurate solution for intra- and
postoperative measurements, enabling more precise, safe, and efficient surgical
procedures.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Calibrated Language Models Must Hallucinate
Abstract: Recent language models generate false but plausible-sounding text with
surprising frequency. Such "hallucinations" are an obstacle to the usability of
language-based AI systems and can harm people who rely upon their outputs. This
work shows shows that there is an inherent statistical lower-bound on the rate
that pretrained language models hallucinate certain types of facts, having
nothing to do with the transformer LM architecture or data quality. For
"arbitrary" facts whose veracity cannot be determined from the training data,
we show that hallucinations must occur at a certain rate for language models
that satisfy a statistical calibration condition appropriate for generative
language models. Specifically, if the maximum probability of any fact is
bounded, we show that the probability of generating a hallucination is close to
the fraction of facts that occur exactly once in the training data (a
"Good-Turing" estimate), even assuming ideal training data without errors.
  One conclusion is that models pretrained to be sufficiently good predictors
(i.e., calibrated) may require post-training to mitigate hallucinations on the
type of arbitrary facts that tend to appear once in the training set. However,
our analysis also suggests that there is no statistical reason that pretraining
will lead to hallucination on facts that tend to appear more than once in the
training data (like references to publications such as articles and books,
whose hallucinations have been particularly notable and problematic) or on
systematic facts (like arithmetic calculations). Therefore, different
architectures and learning algorithms may mitigate these latter types of
hallucinations.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: HI-TOM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Models
Abstract: Theory of Mind (ToM) is the ability to reason about one's own and others'
mental states. ToM plays a critical role in the development of intelligence,
language understanding, and cognitive processes. While previous work has
primarily focused on first and second-order ToM, we explore higher-order ToM,
which involves recursive reasoning on others' beliefs. We introduce HI-TOM, a
Higher Order Theory of Mind benchmark. Our experimental evaluation using
various Large Language Models (LLMs) indicates a decline in performance on
higher-order ToM tasks, demonstrating the limitations of current LLMs. We
conduct a thorough analysis of different failure cases of LLMs, and share our
thoughts on the implications of our findings on the future of NLP.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: A Contrastive Compositional Benchmark for Text-to-Image Synthesis: A Study with Unified Text-to-Image Fidelity Metrics
Abstract: Text-to-image (T2I) synthesis has recently achieved significant advancements.
However, challenges remain in the model's compositionality, which is the
ability to create new combinations from known components. We introduce
Winoground-T2I, a benchmark designed to evaluate the compositionality of T2I
models. This benchmark includes 11K complex, high-quality contrastive sentence
pairs spanning 20 categories. These contrastive sentence pairs with subtle
differences enable fine-grained evaluations of T2I synthesis models.
Additionally, to address the inconsistency across different metrics, we propose
a strategy that evaluates the reliability of various metrics by using
comparative sentence pairs. We use Winoground-T2I with a dual objective: to
evaluate the performance of T2I models and the metrics used for their
evaluation. Finally, we provide insights into the strengths and weaknesses of
these metrics and the capabilities of current T2I models in tackling challenges
across a range of complex compositional categories. Our benchmark is publicly
available at https://github.com/zhuxiangru/Winoground-T2I .

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen Classification from Microscopic Images
Abstract: Distribution shifts are characterized by differences between the training and
test data distributions. They can significantly reduce the accuracy of machine
learning models deployed in real-world scenarios. This paper explores the
distribution shift problem when classifying pollen grains from microscopic
images collected in the wild with a low-cost camera sensor. We leverage the
domain knowledge that geometric features are highly important for accurate
pollen identification and introduce two novel geometric image augmentation
techniques to significantly narrow the accuracy gap between the model
performance on the train and test datasets. In particular, we show that
Tenengrad and ImageToSketch filters are highly effective to balance the shape
and texture information while leaving out unimportant details that may confuse
the model. Extensive evaluations on various model architectures demonstrate a
consistent improvement of the model generalization to field data of up to 14%
achieved by the geometric augmentation techniques when compared to a wide range
of standard image augmentations. The approach is validated through an ablation
study using pollen hydration tests to recover the shape of dry pollen grains.
The proposed geometric augmentations also receive the highest scores according
to the affinity and diversity measures from the literature.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: An Efficient Self-Supervised Cross-View Training For Sentence Embedding
Abstract: Self-supervised sentence representation learning is the task of constructing
an embedding space for sentences without relying on human annotation efforts.
One straightforward approach is to finetune a pretrained language model (PLM)
with a representation learning method such as contrastive learning. While this
approach achieves impressive performance on larger PLMs, the performance
rapidly degrades as the number of parameters decreases. In this paper, we
propose a framework called Self-supervised Cross-View Training (SCT) to narrow
the performance gap between large and small PLMs. To evaluate the effectiveness
of SCT, we compare it to 5 baseline and state-of-the-art competitors on seven
Semantic Textual Similarity (STS) benchmarks using 5 PLMs with the number of
parameters ranging from 4M to 340M. The experimental results show that STC
outperforms the competitors for PLMs with less than 100M parameters in 18 of 21
cases.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Will Code Remain a Relevant User Interface for End-User Programming with Generative AI Models?
Abstract: The research field of end-user programming has largely been concerned with
helping non-experts learn to code sufficiently well in order to achieve their
tasks. Generative AI stands to obviate this entirely by allowing users to
generate code from naturalistic language prompts. In this essay, we explore the
extent to which "traditional" programming languages remain relevant for
non-expert end-user programmers in a world with generative AI. We posit the
"generative shift hypothesis": that generative AI will create qualitative and
quantitative expansions in the traditional scope of end-user programming. We
outline some reasons that traditional programming languages may still be
relevant and useful for end-user programmers. We speculate whether each of
these reasons might be fundamental and enduring, or whether they may disappear
with further improvements and innovations in generative AI. Finally, we
articulate a set of implications for end-user programming research, including
the possibility of needing to revisit many well-established core concepts, such
as Ko's learning barriers and Blackwell's attention investment model.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: ALYMPICS: Language Agents Meet Game Theory
Abstract: This paper introduces Alympics, a platform that leverages Large Language
Model (LLM) agents to facilitate investigations in game theory. By employing
LLMs and autonomous agents to simulate human behavior and enable multi-agent
collaborations, we can construct realistic and dynamic models of human
interactions for game theory hypothesis formulating and testing. To demonstrate
this, we present and implement a survival game involving unequal competition
for limited resources. Through manipulation of resource availability and agent
personalities, we observe how different agents engage in the competition and
adapt their strategies. The use of LLM agents in game theory research offers
significant advantages, including simulating realistic behavior, providing a
controlled, scalable, and reproducible environment. Our work highlights the
potential of LLM agents in enhancing the understanding of strategic
decision-making within complex socioeconomic contexts. All codes are available
at https://github.com/microsoft/Alympics

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: MUFFIN: Curating Multi-Faceted Instructions for Improving Instruction-Following
Abstract: In the realm of large language models (LLMs), enhancing instruction-following
capability often involves curating expansive training data. This is achieved
through two primary schemes: i) Scaling-Inputs: Amplifying (input, output)
pairs per task instruction, aiming for better instruction adherence. ii)
Scaling Input-Free Tasks: Enlarging tasks, each composed of an (instruction,
output) pair (without requiring a separate input anymore). However, LLMs under
Scaling-Inputs tend to be overly sensitive to inputs, leading to
misinterpretation or non-compliance with instructions. Conversely, Scaling
Input-Free Tasks demands a substantial number of tasks but is less effective in
instruction following when dealing with instances in Scaling-Inputs. This work
introduces MUFFIN, a new scheme of instruction-following dataset curation.
Specifically, we automatically Scale Tasks per Input by diversifying these
tasks with various input facets. Experimental results across four zero-shot
benchmarks, spanning both Scaling-Inputs and Scaling Input-Free Tasks schemes,
reveal that LLMs, at various scales, trained on MUFFIN generally demonstrate
superior instruction-following capabilities compared to those trained on the
two aforementioned schemes.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Retrieving Conditions from Reference Images for Diffusion Models
Abstract: Recent diffusion-based subject driven generative methods have enabled image
generations with good fidelity for specific objects or human portraits.
However, to achieve better versatility for applications, we argue that not only
improved datasets and evaluations are desired, but also more careful methods to
retrieve only relevant information from conditional images are anticipated. To
this end, we propose an anime figures dataset RetriBooru-V1, with enhanced
identity and clothing labels. We state new tasks enabled by this dataset, and
introduce a new diversity metric to measure success in completing these tasks,
quantifying the flexibility of image generations. We establish an RAG-inspired
baseline method, designed to retrieve precise conditional information from
reference images. Then, we compare with current methods on existing task to
demonstrate the capability of the proposed method. Finally, we provide baseline
experiment results on new tasks, and conduct ablation studies on the possible
structural choices.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: SelfOcc: Self-Supervised Vision-Based 3D Occupancy Prediction
Abstract: 3D occupancy prediction is an important task for the robustness of
vision-centric autonomous driving, which aims to predict whether each point is
occupied in the surrounding 3D space. Existing methods usually require 3D
occupancy labels to produce meaningful results. However, it is very laborious
to annotate the occupancy status of each voxel. In this paper, we propose
SelfOcc to explore a self-supervised way to learn 3D occupancy using only video
sequences. We first transform the images into the 3D space (e.g., bird's eye
view) to obtain 3D representation of the scene. We directly impose constraints
on the 3D representations by treating them as signed distance fields. We can
then render 2D images of previous and future frames as self-supervision signals
to learn the 3D representations. We propose an MVS-embedded strategy to
directly optimize the SDF-induced weights with multiple depth proposals. Our
SelfOcc outperforms the previous best method SceneRF by 58.7% using a single
frame as input on SemanticKITTI and is the first self-supervised work that
produces reasonable 3D occupancy for surround cameras on nuScenes. SelfOcc
produces high-quality depth and achieves state-of-the-art results on novel
depth synthesis, monocular depth estimation, and surround-view depth estimation
on the SemanticKITTI, KITTI-2015, and nuScenes, respectively. Code:
https://github.com/huang-yh/SelfOcc.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: A Local Appearance Model for Volumetric Capture of Diverse Hairstyle
Abstract: Hair plays a significant role in personal identity and appearance, making it
an essential component of high-quality, photorealistic avatars. Existing
approaches either focus on modeling the facial region only or rely on
personalized models, limiting their generalizability and scalability. In this
paper, we present a novel method for creating high-fidelity avatars with
diverse hairstyles. Our method leverages the local similarity across different
hairstyles and learns a universal hair appearance prior from multi-view
captures of hundreds of people. This prior model takes 3D-aligned features as
input and generates dense radiance fields conditioned on a sparse point cloud
with color. As our model splits different hairstyles into local primitives and
builds prior at that level, it is capable of handling various hair topologies.
Through experiments, we demonstrate that our model captures a diverse range of
hairstyles and generalizes well to challenging new hairstyles. Empirical
results show that our method improves the state-of-the-art approaches in
capturing and generating photorealistic, personalized avatars with complete
hair.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion
Abstract: Deep reinforcement learning (RL) can enable robots to autonomously acquire
complex behaviors, such as legged locomotion. However, RL in the real world is
complicated by constraints on efficiency, safety, and overall training
stability, which limits its practical applicability. We present APRL, a policy
regularization framework that modulates the robot's exploration over the course
of training, striking a balance between flexible improvement potential and
focused, efficient exploration. APRL enables a quadrupedal robot to efficiently
learn to walk entirely in the real world within minutes and continue to improve
with more training where prior work saturates in performance. We demonstrate
that continued training with APRL results in a policy that is substantially
more capable of navigating challenging situations and is able to adapt to
changes in dynamics with continued training.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: NeuroFlow: Development of lightweight and efficient model integration scheduling strategy for autonomous driving system
Abstract: This paper proposes a specialized autonomous driving system that takes into
account the unique constraints and characteristics of automotive systems,
aiming for innovative advancements in autonomous driving technology. The
proposed system systematically analyzes the intricate data flow in autonomous
driving and provides functionality to dynamically adjust various factors that
influence deep learning models. Additionally, for algorithms that do not rely
on deep learning models, the system analyzes the flow to determine resource
allocation priorities. In essence, the system optimizes data flow and schedules
efficiently to ensure real-time performance and safety. The proposed system was
implemented in actual autonomous vehicles and experimentally validated across
various driving scenarios. The experimental results provide evidence of the
system's stable inference and effective control of autonomous vehicles, marking
a significant turning point in the development of autonomous driving systems.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Modyn: A Platform for Model Training on Dynamic Datasets With Sample-Level Data Selection
Abstract: Machine learning training data is often dynamic in real-world use cases,
i.e., data is added or removed and may experience distribution shifts over
time. Models must incorporate this evolving training data to improve
generalization, adapt to potential distribution shifts, and adhere to privacy
regulations. However, the cost of model (re)training is proportional to how
often the model trains and on how much data it trains on. While ML research
explores these topics in isolation, there is no end-to-end open-source platform
to facilitate the exploration of model retraining and data selection policies
and the deployment these algorithms efficiently at scale.
  We present Modyn, a platform for model training on dynamic datasets that
enables sample-level data selection and triggering policies. Modyn orchestrates
continuous training pipelines while optimizing the underlying system
infrastructure to support fast access to arbitrary data samples for efficient
data selection. Modyn's extensible architecture allows users to run training
pipelines without modifying the platform code, and enables researchers to
effortlessly extend the system. We evaluate Modyn's training throughput,
showing that even in memory-bound recommendation systems workloads, Modyn is
able to reach 80 to 100 % of the throughput compared to loading big chunks of
data locally without sample-level data selection. Additionally, we showcase
Modyn's functionality with three different data selection policies.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents
Abstract: Text embedding models have emerged as powerful tools for transforming
sentences into fixed-sized feature vectors that encapsulate semantic
information. While these models are essential for tasks like information
retrieval, semantic clustering, and text re-ranking, most existing open-source
models, especially those built on architectures like BERT, struggle to
represent lengthy documents and often resort to truncation. One common approach
to mitigate this challenge involves splitting documents into smaller paragraphs
for embedding. However, this strategy results in a much larger set of vectors,
consequently leading to increased memory consumption and computationally
intensive vector searches with elevated latency.
  To address these challenges, we introduce Jina Embeddings 2, an open-source
text embedding model capable of accommodating up to 8192 tokens. This model is
designed to transcend the conventional 512-token limit and adeptly process long
documents. Jina Embeddings 2 not only achieves state-of-the-art performance on
a range of embedding-related tasks in the MTEB benchmark but also matches the
performance of OpenAI's proprietary ada-002 model. Additionally, our
experiments indicate that an extended context can enhance performance in tasks
such as NarrativeQA.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Explained anomaly detection in text reviews: Can subjective scenarios be correctly evaluated?
Abstract: This paper presents a pipeline to detect and explain anomalous reviews in
online platforms. The pipeline is made up of three modules and allows the
detection of reviews that do not generate value for users due to either
worthless or malicious composition. The classifications are accompanied by a
normality score and an explanation that justifies the decision made. The
pipeline's ability to solve the anomaly detection task was evaluated using
different datasets created from a large Amazon database. Additionally, a study
comparing three explainability techniques involving 241 participants was
conducted to assess the explainability module. The study aimed to measure the
impact of explanations on the respondents' ability to reproduce the
classification model and their perceived usefulness. This work can be useful to
automate tasks in review online platforms, such as those for electronic
commerce, and offers inspiration for addressing similar problems in the field
of anomaly detection in textual data. We also consider it interesting to have
carried out a human evaluation of the capacity of different explainability
techniques in a real and infrequent scenario such as the detection of anomalous
reviews, as well as to reflect on whether it is possible to explain tasks as
humanly subjective as this one.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: InteRACT: Transformer Models for Human Intent Prediction Conditioned on Robot Actions
Abstract: In collaborative human-robot manipulation, a robot must predict human intents
and adapt its actions accordingly to smoothly execute tasks. However, the
human's intent in turn depends on actions the robot takes, creating a
chicken-or-egg problem. Prior methods ignore such inter-dependency and instead
train marginal intent prediction models independent of robot actions. This is
because training conditional models is hard given a lack of paired human-robot
interaction datasets.
  Can we instead leverage large-scale human-human interaction data that is more
easily accessible? Our key insight is to exploit a correspondence between human
and robot actions that enables transfer learning from human-human to
human-robot data. We propose a novel architecture, InteRACT, that pre-trains a
conditional intent prediction model on large human-human datasets and
fine-tunes on a small human-robot dataset. We evaluate on a set of real-world
collaborative human-robot manipulation tasks and show that our conditional
model improves over various marginal baselines. We also introduce new
techniques to tele-operate a 7-DoF robot arm and collect a diverse range of
human-robot collaborative manipulation data, which we open-source.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Class-Discriminative Attention Maps for Vision Transformers
Abstract: Interpretability methods are critical components for examining and exploring
deep neural networks (DNN), as well as increasing our understanding of and
trust in them. Vision transformers (ViT), which can be trained to
state-of-the-art performance with a self-supervised learning (SSL) training
method, provide built-in attention maps (AM). While AMs can provide
high-quality semantic segmentation of input images, they do not account for any
signal coming from a downstream classifier. We introduce class-discriminative
attention maps (CDAM), a novel post-hoc explanation method that is highly
sensitive to the target class. Our method essentially scales attention scores
by how relevant the corresponding tokens are for the predictions of a
classifier head. Alternative to classifier outputs, CDAM can also explain a
user-defined concept by targeting similarity measures in the latent space of
the ViT. This allows for explanations of arbitrary concepts, defined by the
user through a few sample images. We investigate the operating characteristics
of CDAM in comparison with relevance propagation (RP) and token ablation maps
(TAM), an alternative to pixel occlusion methods. CDAM is highly
class-discriminative and semantically relevant, while providing implicit
regularization of relevance scores.
  PyTorch implementation: \url{https://github.com/lenbrocki/CDAM}
  Web live demo: \url{https://cdam.informatism.com/}

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: KnowGPT: Black-Box Knowledge Injection for Large Language Models
Abstract: Generative Large Language Models (LLMs), such as ChatGPT, offer interactive
APIs that can answer common questions at a human-expert level. However, these
models often give inaccurate or incorrect responses when faced with questions
requiring domain-specific or professional-specific knowledge not covered in
their training corpus. Furthermore, many state-of-the-art LLMs are not
open-source, making it challenging to inject knowledge with model APIs only. In
this work, we introduce KnowGPT, a black-box knowledge injection framework for
LLMs in question answering. KnowGPT leverages deep reinforcement learning (RL)
to extract relevant knowledge from Knowledge Graphs (KGs) and use Multi-Armed
Bandit (MAB) to construct the most suitable prompt for each question. Our
extensive experiments on three benchmark datasets showcase that KnowGPT
significantly enhances the existing methods. Notably, KnowGPT achieves an
average improvement of 23.7% over ChatGPT and an average improvement of 2.9%
over GPT-4. Additionally, KnowGPT attains a 91.6% accuracy on the OpenbookQA
official leaderboard, which is comparable to human-level performance.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies
Abstract: Thematic analysis and other variants of inductive coding are widely used
qualitative analytic methods within empirical legal studies (ELS). We propose a
novel framework facilitating effective collaboration of a legal expert with a
large language model (LLM) for generating initial codes (phase 2 of thematic
analysis), searching for themes (phase 3), and classifying the data in terms of
the themes (to kick-start phase 4). We employed the framework for an analysis
of a dataset (n=785) of facts descriptions from criminal court opinions
regarding thefts. The goal of the analysis was to discover classes of typical
thefts. Our results show that the LLM, namely OpenAI's GPT-4, generated
reasonable initial codes, and it was capable of improving the quality of the
codes based on expert feedback. They also suggest that the model performed well
in zero-shot classification of facts descriptions in terms of the themes.
Finally, the themes autonomously discovered by the LLM appear to map fairly
well to the themes arrived at by legal experts. These findings can be leveraged
by legal researchers to guide their decisions in integrating LLMs into their
thematic analyses, as well as other inductive coding projects.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Optimal Cost Constrained Adversarial Attacks For Multiple Agent Systems
Abstract: Finding optimal adversarial attack strategies is an important topic in
reinforcement learning and the Markov decision process. Previous studies
usually assume one all-knowing coordinator (attacker) for whom attacking
different recipient (victim) agents incurs uniform costs. However, in reality,
instead of using one limitless central attacker, the attacks often need to be
performed by distributed attack agents. We formulate the problem of performing
optimal adversarial agent-to-agent attacks using distributed attack agents, in
which we impose distinct cost constraints on each different attacker-victim
pair. We propose an optimal method integrating within-step static constrained
attack-resource allocation optimization and between-step dynamic programming to
achieve the optimal adversarial attack in a multi-agent system. Our numerical
results show that the proposed attacks can significantly reduce the rewards
received by the attacked agents.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Function Space Bayesian Pseudocoreset for Bayesian Neural Networks
Abstract: A Bayesian pseudocoreset is a compact synthetic dataset summarizing essential
information of a large-scale dataset and thus can be used as a proxy dataset
for scalable Bayesian inference. Typically, a Bayesian pseudocoreset is
constructed by minimizing a divergence measure between the posterior
conditioning on the pseudocoreset and the posterior conditioning on the full
dataset. However, evaluating the divergence can be challenging, particularly
for the models like deep neural networks having high-dimensional parameters. In
this paper, we propose a novel Bayesian pseudocoreset construction method that
operates on a function space. Unlike previous methods, which construct and
match the coreset and full data posteriors in the space of model parameters
(weights), our method constructs variational approximations to the coreset
posterior on a function space and matches it to the full data posterior in the
function space. By working directly on the function space, our method could
bypass several challenges that may arise when working on a weight space,
including limited scalability and multi-modality issue. Through various
experiments, we demonstrate that the Bayesian pseudocoresets constructed from
our method enjoys enhanced uncertainty quantification and better robustness
across various model architectures.

Output: Machine Learning