Instruction: What field is the article from?

Prompt:
Title: Diverse Conventions for Human-AI Collaboration
Abstract: Conventions are crucial for strong performance in cooperative multi-agent
games, because they allow players to coordinate on a shared strategy without
explicit communication. Unfortunately, standard multi-agent reinforcement
learning techniques, such as self-play, converge to conventions that are
arbitrary and non-diverse, leading to poor generalization when interacting with
new partners. In this work, we present a technique for generating diverse
conventions by (1) maximizing their rewards during self-play, while (2)
minimizing their rewards when playing with previously discovered conventions
(cross-play), stimulating conventions to be semantically different. To ensure
that learned policies act in good faith despite the adversarial optimization of
cross-play, we introduce \emph{mixed-play}, where an initial state is randomly
generated by sampling self-play and cross-play transitions and the player
learns to maximize the self-play reward from this initial state. We analyze the
benefits of our technique on various multi-agent collaborative games, including
Overcooked, and find that our technique can adapt to the conventions of humans,
surpassing human-level performance when paired with real users.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: SequenceMatch: Revisiting the design of weak-strong augmentations for Semi-supervised learning
Abstract: Semi-supervised learning (SSL) has become popular in recent years because it
allows the training of a model using a large amount of unlabeled data. However,
one issue that many SSL methods face is the confirmation bias, which occurs
when the model is overfitted to the small labeled training dataset and produces
overconfident, incorrect predictions. To address this issue, we propose
SequenceMatch, an efficient SSL method that utilizes multiple data
augmentations. The key element of SequenceMatch is the inclusion of a medium
augmentation for unlabeled data. By taking advantage of different augmentations
and the consistency constraints between each pair of augmented examples,
SequenceMatch helps reduce the divergence between the prediction distribution
of the model for weakly and strongly augmented examples. In addition,
SequenceMatch defines two different consistency constraints for high and
low-confidence predictions. As a result, SequenceMatch is more data-efficient
than ReMixMatch, and more time-efficient than both ReMixMatch ($\times4$) and
CoMatch ($\times2$) while having higher accuracy. Despite its simplicity,
SequenceMatch consistently outperforms prior methods on standard benchmarks,
such as CIFAR-10/100, SVHN, and STL-10. It also surpasses prior
state-of-the-art methods by a large margin on large-scale datasets such as
ImageNet, with a 38.46\% error rate. Code is available at
https://github.com/beandkay/SequenceMatch.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Using Early Readouts to Mediate Featural Bias in Distillation
Abstract: Deep networks tend to learn spurious feature-label correlations in real-world
supervised learning tasks. This vulnerability is aggravated in distillation,
where a student model may have lesser representational capacity than the
corresponding teacher model. Often, knowledge of specific spurious correlations
is used to reweight instances & rebalance the learning process. We propose a
novel early readout mechanism whereby we attempt to predict the label using
representations from earlier network layers. We show that these early readouts
automatically identify problem instances or groups in the form of confident,
incorrect predictions. Leveraging these signals to modulate the distillation
loss on an instance level allows us to substantially improve not only group
fairness measures across benchmark datasets, but also overall accuracy of the
student model. We also provide secondary analyses that bring insight into the
role of feature learning in supervision and distillation.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination
Abstract: Knowledge Graphs (KGs) have emerged as fundamental platforms for powering
intelligent decision-making and a wide range of Artificial Intelligence (AI)
services across major corporations such as Google, Walmart, and AirBnb. KGs
complement Machine Learning (ML) algorithms by providing data context and
semantics, thereby enabling further inference and question-answering
capabilities. The integration of KGs with neuronal learning (e.g., Large
Language Models (LLMs)) is currently a topic of active research, commonly named
neuro-symbolic AI. Despite the numerous benefits that can be accomplished with
KG-based AI, its growing ubiquity within online services may result in the loss
of self-determination for citizens as a fundamental societal issue. The more we
rely on these technologies, which are often centralised, the less citizens will
be able to determine their own destinies. To counter this threat, AI
regulation, such as the European Union (EU) AI Act, is being proposed in
certain regions. The regulation sets what technologists need to do, leading to
questions concerning: How can the output of AI systems be trusted? What is
needed to ensure that the data fuelling and the inner workings of these
artefacts are transparent? How can AI be made accountable for its
decision-making? This paper conceptualises the foundational topics and research
pillars to support KG-based AI for self-determination. Drawing upon this
conceptual framework, challenges and opportunities for citizen
self-determination are illustrated and analysed in a real-world scenario. As a
result, we propose a research agenda aimed at accomplishing the recommended
objectives.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Exploring the Robustness of Decentralized Training for Large Language Models
Abstract: Decentralized training of large language models has emerged as an effective
way to democratize this technology. However, the potential threats associated
with this approach have not been carefully discussed, which would hinder the
development of decentralized training infrastructures. This paper aims to
initiate discussion towards this end by exploring the robustness of
decentralized training from three main perspectives. First, we demonstrate the
vulnerabilities inherent in decentralized training frameworks in terms of
hardware, data, and models. Second, we highlight the fundamental difference
between decentralized foundation model training and vanilla federated learning,
where the security techniques employed in federated learning cannot be applied
directly. Third, we discuss the essential components required for a robust and
efficient decentralized training framework and present a case study by modeling
a concrete threat model. Our objective in this vision paper is to emphasize the
importance of addressing security concerns in the context of decentralized
training for large language models.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecasting
Abstract: Spatio-temporal forecasting in various domains, like traffic prediction and
weather forecasting, is a challenging endeavor, primarily due to the
difficulties in modeling propagation dynamics and capturing high-dimensional
interactions among nodes. Despite the significant strides made by graph-based
networks in spatio-temporal forecasting, there remain two pivotal factors
closely related to forecasting performance that need further consideration:
time delays in propagation dynamics and multi-scale high-dimensional
interactions. In this work, we present a Series-Aligned Multi-Scale Graph
Learning (SAMSGL) framework, aiming to enhance forecasting performance. In
order to handle time delays in spatial interactions, we propose a
series-aligned graph convolution layer to facilitate the aggregation of
non-delayed graph signals, thereby mitigating the influence of time delays for
the improvement in accuracy. To understand global and local spatio-temporal
interactions, we develop a spatio-temporal architecture via multi-scale graph
learning, which encompasses two essential components: multi-scale graph
structure learning and graph-fully connected (Graph-FC) blocks. The multi-scale
graph structure learning includes a global graph structure to learn both
delayed and non-delayed node embeddings, as well as a local one to learn node
variations influenced by neighboring factors. The Graph-FC blocks
synergistically fuse spatial and temporal information to boost prediction
accuracy. To evaluate the performance of SAMSGL, we conduct experiments on
meteorological and traffic forecasting datasets, which demonstrate its
effectiveness and superiority.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: SemanticBoost: Elevating Motion Generation with Augmented Textual Cues
Abstract: Current techniques face difficulties in generating motions from intricate
semantic descriptions, primarily due to insufficient semantic annotations in
datasets and weak contextual understanding. To address these issues, we present
SemanticBoost, a novel framework that tackles both challenges simultaneously.
Our framework comprises a Semantic Enhancement module and a Context-Attuned
Motion Denoiser (CAMD). The Semantic Enhancement module extracts supplementary
semantics from motion data, enriching the dataset's textual description and
ensuring precise alignment between text and motion data without depending on
large language models. On the other hand, the CAMD approach provides an
all-encompassing solution for generating high-quality, semantically consistent
motion sequences by effectively capturing context information and aligning the
generated motion with the given textual descriptions. Distinct from existing
methods, our approach can synthesize accurate orientational movements, combined
motions based on specific body part descriptions, and motions generated from
complex, extended sentences. Our experimental results demonstrate that
SemanticBoost, as a diffusion-based method, outperforms auto-regressive-based
techniques, achieving cutting-edge performance on the Humanml3D dataset while
maintaining realistic and smooth motion generation quality.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: GPT4All: An Ecosystem of Open Source Compressed Language Models
Abstract: Large language models (LLMs) have recently achieved human-level performance
on a range of professional and academic benchmarks. The accessibility of these
models has lagged behind their performance. State-of-the-art LLMs require
costly infrastructure; are only accessible via rate-limited, geo-locked, and
censored web interfaces; and lack publicly available code and technical
reports. In this paper, we tell the story of GPT4All, a popular open source
repository that aims to democratize access to LLMs. We outline the technical
details of the original GPT4All model family, as well as the evolution of the
GPT4All project from a single model into a fully fledged open source ecosystem.
It is our hope that this paper acts as both a technical overview of the
original GPT4All models as well as a case study on the subsequent growth of the
GPT4All open source ecosystem.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems
Abstract: High-level reasoning can be defined as the capability to generalize over
knowledge acquired via experience, and to exhibit robust behavior in novel
situations. Such form of reasoning is a basic skill in humans, who seamlessly
use it in a broad spectrum of tasks, from language communication to decision
making in complex situations. When it manifests itself in understanding and
manipulating the everyday world of objects and their interactions, we talk
about common sense or commonsense reasoning. State-of-the-art AI systems don't
possess such capability: for instance, Large Language Models have recently
become popular by demonstrating remarkable fluency in conversing with humans,
but they still make trivial mistakes when probed for commonsense competence; on
a different level, performance degradation outside training data prevents
self-driving vehicles to safely adapt to unseen scenarios, a serious and
unsolved problem that limits the adoption of such technology. In this paper we
propose to enable high-level reasoning in AI systems by integrating cognitive
architectures with external neuro-symbolic components. We illustrate a hybrid
framework centered on ACT-R and we discuss the role of generative models in
recent and future applications.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Survey on Memory-Augmented Neural Networks: Cognitive Insights to AI Applications
Abstract: This paper explores Memory-Augmented Neural Networks (MANNs), delving into
how they blend human-like memory processes into AI. It covers different memory
types, like sensory, short-term, and long-term memory, linking psychological
theories with AI applications. The study investigates advanced architectures
such as Hopfield Networks, Neural Turing Machines, Correlation Matrix Memories,
Memformer, and Neural Attention Memory, explaining how they work and where they
excel. It dives into real-world uses of MANNs across Natural Language
Processing, Computer Vision, Multimodal Learning, and Retrieval Models, showing
how memory boosters enhance accuracy, efficiency, and reliability in AI tasks.
Overall, this survey provides a comprehensive view of MANNs, offering insights
for future research in memory-based AI systems.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Resolving Crash Bugs via Large Language Models: An Empirical Study
Abstract: Crash bugs cause unexpected program behaviors or even termination, requiring
high-priority resolution. However, manually resolving crash bugs is challenging
and labor-intensive, and researchers have proposed various techniques for their
automated localization and repair. ChatGPT, a recent large language model
(LLM), has garnered significant attention due to its exceptional performance
across various domains. This work performs the first investigation into
ChatGPT's capability in resolve real-world crash bugs, focusing on its
effectiveness in both localizing and repairing code-related and
environment-related crash bugs. Specifically, we initially assess ChatGPT's
fundamental ability to resolve crash bugs with basic prompts in a single
iteration. We observe that ChatGPT performs better at resolving code-related
crash bugs compared to environment-related ones, and its primary challenge in
resolution lies in inaccurate localization. Additionally, we explore ChatGPT's
potential with various advanced prompts. Furthermore, by stimulating ChatGPT's
self-planning, it methodically investigates each potential crash-causing
environmental factor through proactive inquiry, ultimately identifying the root
cause of the crash. Based on our findings, we propose IntDiagSolver, an
interaction methodology designed to facilitate precise crash bug resolution
through continuous interaction with LLMs. Evaluating IntDiagSolver on multiple
LLMs reveals consistent enhancement in the accuracy of crash bug resolution,
including ChatGPT, Claude, and CodeLlama.

Output: Software Engineering


Instruction: What field is the article from?

Prompt:
Title: Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering
Abstract: Graph collaborative filtering, which learns user and item representations
through message propagation over the user-item interaction graph, has been
shown to effectively enhance recommendation performance. However, most current
graph collaborative filtering models mainly construct the interaction graph on
a single behavior domain (e.g. click), even though users exhibit various types
of behaviors on real-world platforms, including actions like click, cart, and
purchase. Furthermore, due to variations in user engagement, there exists an
imbalance in the scale of different types of behaviors. For instance, users may
click and view multiple items but only make selective purchases from a small
subset of them. How to alleviate the behavior imbalance problem and utilize
information from the multiple behavior graphs concurrently to improve the
target behavior conversion (e.g. purchase) remains underexplored. To this end,
we propose IMGCF, a simple but effective model to alleviate behavior data
imbalance for multi-behavior graph collaborative filtering. Specifically, IMGCF
utilizes a multi-task learning framework for collaborative filtering on
multi-behavior graphs. Then, to mitigate the data imbalance issue, IMGCF
improves representation learning on the sparse behavior by leveraging
representations learned from the behavior domain with abundant data volumes.
Experiments on two widely-used multi-behavior datasets demonstrate the
effectiveness of IMGCF.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: Benchmarks for Physical Reasoning AI
Abstract: Physical reasoning is a crucial aspect in the development of general AI
systems, given that human learning starts with interacting with the physical
world before progressing to more complex concepts. Although researchers have
studied and assessed the physical reasoning of AI approaches through various
specific benchmarks, there is no comprehensive approach to evaluating and
measuring progress. Therefore, we aim to offer an overview of existing
benchmarks and their solution approaches and propose a unified perspective for
measuring the physical reasoning capacity of AI systems. We select benchmarks
that are designed to test algorithmic performance in physical reasoning tasks.
While each of the selected benchmarks poses a unique challenge, their ensemble
provides a comprehensive proving ground for an AI generalist agent with a
measurable skill level for various physical reasoning concepts. This gives an
advantage to such an ensemble of benchmarks over other holistic benchmarks that
aim to simulate the real world by intertwining its complexity and many
concepts. We group the presented set of physical reasoning benchmarks into
subcategories so that more narrow generalist AI agents can be tested first on
these groups.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Leveraging Diffusion Perturbations for Measuring Fairness in Computer Vision
Abstract: Computer vision models have been known to encode harmful biases, leading to
the potentially unfair treatment of historically marginalized groups, such as
people of color. However, there remains a lack of datasets balanced along
demographic traits that can be used to evaluate the downstream fairness of
these models. In this work, we demonstrate that diffusion models can be
leveraged to create such a dataset. We first use a diffusion model to generate
a large set of images depicting various occupations. Subsequently, each image
is edited using inpainting to generate multiple variants, where each variant
refers to a different perceived race. Using this dataset, we benchmark several
vision-language models on a multi-class occupation classification task. We find
that images generated with non-Caucasian labels have a significantly higher
occupation misclassification rate than images generated with Caucasian labels,
and that several misclassifications are suggestive of racial biases. We measure
a model's downstream fairness by computing the standard deviation in the
probability of predicting the true occupation label across the different
perceived identity groups. Using this fairness metric, we find significant
disparities between the evaluated vision-and-language models. We hope that our
work demonstrates the potential value of diffusion methods for fairness
evaluations.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Distance-Based Propagation for Efficient Knowledge Graph Reasoning
Abstract: Knowledge graph completion (KGC) aims to predict unseen edges in knowledge
graphs (KGs), resulting in the discovery of new facts. A new class of methods
have been proposed to tackle this problem by aggregating path information.
These methods have shown tremendous ability in the task of KGC. However they
are plagued by efficiency issues. Though there are a few recent attempts to
address this through learnable path pruning, they often sacrifice the
performance to gain efficiency. In this work, we identify two intrinsic
limitations of these methods that affect the efficiency and representation
quality. To address the limitations, we introduce a new method, TAGNet, which
is able to efficiently propagate information. This is achieved by only
aggregating paths in a fixed window for each source-target pair. We demonstrate
that the complexity of TAGNet is independent of the number of layers. Extensive
experiments demonstrate that TAGNet can cut down on the number of propagated
messages by as much as 90% while achieving competitive performance on multiple
KG datasets. The code is available at https://github.com/HarryShomer/TAGNet.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting
Abstract: Automatically generated reports from medical images promise to improve the
workflow of radiologists. Existing methods consider an image-to-report modeling
task by directly generating a fully-fledged report from an image. However, this
conflates the content of the report (e.g., findings and their attributes) with
its style (e.g., format and choice of words), which can lead to clinically
inaccurate reports. To address this, we propose a two-step approach for
radiology report generation. First, we extract the content from an image; then,
we verbalize the extracted content into a report that matches the style of a
specific radiologist. For this, we leverage RadGraph -- a graph representation
of reports -- together with large language models (LLMs). In our quantitative
evaluations, we find that our approach leads to beneficial performance. Our
human evaluation with clinical raters highlights that the AI-generated reports
are indistinguishably tailored to the style of individual radiologist despite
leveraging only a few examples as context.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Learning Safety Constraints From Demonstration Using One-Class Decision Trees
Abstract: The alignment of autonomous agents with human values is a pivotal challenge
when deploying these agents within physical environments, where safety is an
important concern. However, defining the agent's objective as a reward and/or
cost function is inherently complex and prone to human errors. In response to
this challenge, we present a novel approach that leverages one-class decision
trees to facilitate learning from expert demonstrations. These decision trees
provide a foundation for representing a set of constraints pertinent to the
given environment as a logical formula in disjunctive normal form. The learned
constraints are subsequently employed within an oracle constrained
reinforcement learning framework, enabling the acquisition of a safe policy. In
contrast to other methods, our approach offers an interpretable representation
of the constraints, a vital feature in safety-critical environments. To
validate the effectiveness of our proposed method, we conduct experiments in
synthetic benchmark domains and a realistic driving environment.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery
Abstract: Geographic location is essential for modeling tasks in fields ranging from
ecology to epidemiology to the Earth system sciences. However, extracting
relevant and meaningful characteristics of a location can be challenging, often
entailing expensive data fusion or data distillation from global imagery
datasets. To address this challenge, we introduce Satellite Contrastive
Location-Image Pretraining (SatCLIP), a global, general-purpose geographic
location encoder that learns an implicit representation of locations from
openly available satellite imagery. Trained location encoders provide vector
embeddings summarizing the characteristics of any given location for convenient
usage in diverse downstream tasks. We show that SatCLIP embeddings, pretrained
on globally sampled multi-spectral Sentinel-2 satellite data, can be used in
various predictive tasks that depend on location information but not
necessarily satellite imagery, including temperature prediction, animal
recognition in imagery, and population density estimation. Across tasks,
SatCLIP embeddings consistently outperform embeddings from existing pretrained
location encoders, ranging from models trained on natural images to models
trained on semantic context. SatCLIP embeddings also help to improve geographic
generalization. This demonstrates the potential of general-purpose location
encoders and opens the door to learning meaningful representations of our
planet from the vast, varied, and largely untapped modalities of geospatial
data.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: FedSN: A General Federated Learning Framework over LEO Satellite Networks
Abstract: Recently, a large number of Low Earth Orbit (LEO) satellites have been
launched and deployed successfully in space by commercial companies, such as
SpaceX. Due to multimodal sensors equipped by the LEO satellites, they serve
not only for communication but also for various machine learning applications,
such as space modulation recognition, remote sensing image classification, etc.
However, the ground station (GS) may be incapable of downloading such a large
volume of raw sensing data for centralized model training due to the limited
contact time with LEO satellites (e.g. 5 minutes). Therefore, federated
learning (FL) has emerged as the promising solution to address this problem via
on-device training. Unfortunately, to enable FL on LEO satellites, we still
face three critical challenges that are i) heterogeneous computing and memory
capabilities, ii) limited uplink rate, and iii) model staleness. To this end,
we propose FedSN as a general FL framework to tackle the above challenges, and
fully explore data diversity on LEO satellites. Specifically, we first present
a novel sub-structure scheme to enable heterogeneous local model training
considering different computing, memory, and communication constraints on LEO
satellites. Additionally, we propose a pseudo-synchronous model aggregation
strategy to dynamically schedule model aggregation for compensating model
staleness. To further demonstrate the effectiveness of the FedSN, we evaluate
it using space modulation recognition and remote sensing image classification
tasks by leveraging the data from real-world satellite networks. Extensive
experimental results demonstrate that FedSN framework achieves higher accuracy,
lower computing, and communication overhead than the state-of-the-art
benchmarks and the effectiveness of each components in FedSN.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters
Abstract: Recent work has demonstrated a remarkable ability to customize text-to-image
diffusion models to multiple, fine-grained concepts in a sequential (i.e.,
continual) manner while only providing a few example images for each concept.
This setting is known as continual diffusion. Here, we ask the question: Can we
scale these methods to longer concept sequences without forgetting? Although
prior work mitigates the forgetting of previously learned concepts, we show
that its capacity to learn new tasks reaches saturation over longer sequences.
We address this challenge by introducing a novel method, STack-And-Mask
INcremental Adapters (STAMINA), which is composed of low-ranked
attention-masked adapters and customized MLP tokens. STAMINA is designed to
enhance the robust fine-tuning properties of LoRA for sequential concept
learning via learnable hard-attention masks parameterized with low rank MLPs,
enabling precise, scalable learning via sparse adaptation. Notably, all
introduced trainable parameters can be folded back into the model after
training, inducing no additional inference parameter costs. We show that
STAMINA outperforms the prior SOTA for the setting of text-to-image continual
customization on a 50-concept benchmark composed of landmarks and human faces,
with no stored replay data. Additionally, we extended our method to the setting
of continual learning for image classification, demonstrating that our gains
also translate to state-of-the-art performance in this standard benchmark.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Personas as a Way to Model Truthfulness in Language Models
Abstract: Large Language Models (LLMs) are trained on vast amounts of text from the
internet, which contains both factual and misleading information about the
world. Can language models discern truth from falsehood in this contradicting
data? Expanding on the view that LLMs can model different communicative agents,
we present the persona hypothesis: LLMs can cluster agents into personas using
common features of their generations. For instance, a truthful persona is a
group of agents that are likely to produce truthful text and that share similar
features like formal writing styles and scientific references. By modeling this
persona, LLMs can generalize truthfulness beyond the specific contexts in which
each agent generated the training text. For example, the model can infer that
the agent "Wikipedia" will behave truthfully on topics that were only generated
by "Science" because they both belong to the truthful persona. We show evidence
for the persona hypothesis via two observations: (1) we can probe whether a
model's answer will be truthful before it is generated; (2) finetuning a model
on a set of facts improves its truthfulness on unseen topics. Next, using
arithmetics as a synthetic environment, we show that language models can
separate true and false statements, and generalize truthfulness across agents;
but only if agents in the training data share a truthful generative process
that enables the creation of a truthful persona. Overall, our findings suggest
that models can exploit hierarchical structures in the data to learn abstract
concepts like truthfulness.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Panoptic Video Scene Graph Generation
Abstract: Towards building comprehensive real-world visual perception systems, we
propose and study a new problem called panoptic scene graph generation (PVSG).
PVSG relates to the existing video scene graph generation (VidSGG) problem,
which focuses on temporal interactions between humans and objects grounded with
bounding boxes in videos. However, the limitation of bounding boxes in
detecting non-rigid objects and backgrounds often causes VidSGG to miss key
details crucial for comprehensive video understanding. In contrast, PVSG
requires nodes in scene graphs to be grounded by more precise, pixel-level
segmentation masks, which facilitate holistic scene understanding. To advance
research in this new area, we contribute the PVSG dataset, which consists of
400 videos (289 third-person + 111 egocentric videos) with a total of 150K
frames labeled with panoptic segmentation masks as well as fine, temporal scene
graphs. We also provide a variety of baseline methods and share useful design
practices for future work.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: A Unified View on Forgetting and Strong Equivalence Notions in Answer Set Programming
Abstract: Answer Set Programming (ASP) is a prominent rule-based language for knowledge
representation and reasoning with roots in logic programming and non-monotonic
reasoning. The aim to capture the essence of removing (ir)relevant details in
ASP programs led to the investigation of different notions, from strong
persistence (SP) forgetting, to faithful abstractions, and, recently, strong
simplifications, where the latter two can be seen as relaxed and strengthened
notions of forgetting, respectively. Although it was observed that these
notions are related, especially given that they have characterizations through
the semantics for strong equivalence, it remained unclear whether they can be
brought together. In this work, we bridge this gap by introducing a novel
relativized equivalence notion, which is a relaxation of the recent
simplification notion, that is able to capture all related notions from the
literature. We provide necessary and sufficient conditions for relativized
simplifiability, which shows that the challenging part is for when the context
programs do not contain all the atoms to remove. We then introduce an operator
that combines projection and a relaxation of (SP)-forgetting to obtain the
relativized simplifications. We furthermore present complexity results that
complete the overall picture.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Probable Object Location (POLo) Score Estimation for Efficient Object Goal Navigation
Abstract: To advance the field of autonomous robotics, particularly in object search
tasks within unexplored environments, we introduce a novel framework centered
around the Probable Object Location (POLo) score. Utilizing a 3D object
probability map, the POLo score allows the agent to make data-driven decisions
for efficient object search. We further enhance the framework's practicality by
introducing POLoNet, a neural network trained to approximate the
computationally intensive POLo score. Our approach addresses critical
limitations of both end-to-end reinforcement learning methods, which suffer
from memory decay over long-horizon tasks, and traditional map-based methods
that neglect visibility constraints. Our experiments, involving the first phase
of the OVMM 2023 challenge, demonstrate that an agent equipped with POLoNet
significantly outperforms a range of baseline methods, including end-to-end RL
techniques and prior map-based strategies. To provide a comprehensive
evaluation, we introduce new performance metrics that offer insights into the
efficiency and effectiveness of various agents in object goal navigation.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Efficiently Adapting Pretrained Language Models To New Languages
Abstract: Recent large language models (LLM) exhibit sub-optimal performance on
low-resource languages, as the training data of these models is usually
dominated by English and other high-resource languages. Furthermore, it is
challenging to train models for low-resource languages, especially from
scratch, due to a lack of high quality training data. Adapting pretrained LLMs
reduces the need for data in the new language while also providing cross
lingual transfer capabilities. However, naively adapting to new languages leads
to catastrophic forgetting and poor tokenizer efficiency. In this work, we
study how to efficiently adapt any existing pretrained LLM to a new language
without running into these issues. In particular, we improve the encoding
efficiency of the tokenizer by adding new tokens from the target language and
study the data mixing recipe to mitigate forgetting. Our experiments on
adapting an English LLM to Hungarian and Thai show that our recipe can reach
better performance than open source models on the target language, with minimal
regressions on English.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning
Abstract: Split Federated Learning (SFL) has recently emerged as a promising
distributed learning technology, leveraging the strengths of both federated
learning and split learning. It emphasizes the advantages of rapid convergence
while addressing privacy concerns. As a result, this innovation has received
significant attention from both industry and academia. However, since the model
is split at a specific layer, known as a cut layer, into both client-side and
server-side models for the SFL, the choice of the cut layer in SFL can have a
substantial impact on the energy consumption of clients and their privacy, as
it influences the training burden and the output of the client-side models.
Moreover, the design challenge of determining the cut layer is highly
intricate, primarily due to the inherent heterogeneity in the computing and
networking capabilities of clients. In this article, we provide a comprehensive
overview of the SFL process and conduct a thorough analysis of energy
consumption and privacy. This analysis takes into account the influence of
various system parameters on the cut layer selection strategy. Additionally, we
provide an illustrative example of the cut layer selection, aiming to minimize
the risk of clients from reconstructing the raw data at the server while
sustaining energy consumption within the required energy budget, which involve
trade-offs. Finally, we address open challenges in this field including their
applications to 6G technology. These directions represent promising avenues for
future research and development.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Causal Models Applied to the Patterns of Human Migration due to Climate Change
Abstract: The impacts of mass migration, such as crisis induced by climate change,
extend beyond environmental concerns and can greatly affect social
infrastructure and public services, such as education, healthcare, and
security. These crises exacerbate certain elements like cultural barriers, and
discrimination by amplifying the challenges faced by these affected
communities. This paper proposes an innovative approach to address migration
crises in the context of crisis management through a combination of modeling
and imbalance assessment tools. By employing deep learning for forecasting and
integrating causal reasoning via Bayesian networks, this methodology enables
the evaluation of imbalances and risks in the socio-technological landscape,
providing crucial insights for informed decision-making. Through this
framework, critical systems can be analyzed to understand how fluctuations in
migration levels may impact them, facilitating effective crisis governance
strategies.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Greedy PIG: Adaptive Integrated Gradients
Abstract: Deep learning has become the standard approach for most machine learning
tasks. While its impact is undeniable, interpreting the predictions of deep
learning models from a human perspective remains a challenge. In contrast to
model training, model interpretability is harder to quantify and pose as an
explicit optimization problem. Inspired by the AUC softmax information curve
(AUC SIC) metric for evaluating feature attribution methods, we propose a
unified discrete optimization framework for feature attribution and feature
selection based on subset selection. This leads to a natural adaptive
generalization of the path integrated gradients (PIG) method for feature
attribution, which we call Greedy PIG. We demonstrate the success of Greedy PIG
on a wide variety of tasks, including image feature attribution, graph
compression/explanation, and post-hoc feature selection on tabular data. Our
results show that introducing adaptivity is a powerful and versatile method for
making attribution methods more powerful.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Cooperative Network Learning for Large-Scale and Decentralized Graphs
Abstract: Graph research, the systematic study of interconnected data points
represented as graphs, plays a vital role in capturing intricate relationships
within networked systems. However, in the real world, as graphs scale up,
concerns about data security among different data-owning agencies arise,
hindering information sharing and, ultimately, the utilization of graph data.
Therefore, establishing a mutual trust mechanism among graph agencies is
crucial for unlocking the full potential of graphs. Here, we introduce a
Cooperative Network Learning (CNL) framework to ensure secure graph computing
for various graph tasks. Essentially, this CNL framework unifies the local and
global perspectives of GNN computing with distributed data for an agency by
virtually connecting all participating agencies as a global graph without a
fixed central coordinator. Inter-agency computing is protected by various
technologies inherent in our framework, including homomorphic encryption and
secure transmission. Moreover, each agency has a fair right to design or employ
various graph learning models from its local or global perspective. Thus, CNL
can collaboratively train GNN models based on decentralized graphs inferred
from local and global graphs. Experiments on contagion dynamics prediction and
traditional graph tasks (i.e., node classification and link prediction)
demonstrate that our CNL architecture outperforms state-of-the-art GNNs
developed at individual sites, revealing that CNL can provide a reliable, fair,
secure, privacy-preserving, and global perspective to build effective and
personalized models for network applications. We hope this framework will
address privacy concerns in graph-related research and integrate decentralized
graph data structures to benefit the network research community in cooperation
and innovation.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales
Abstract: Machine reasoning has made great progress in recent years owing to large
language models (LLMs). In the clinical domain, however, most NLP-driven
projects mainly focus on clinical classification or reading comprehension, and
under-explore clinical reasoning for disease diagnosis due to the expensive
rationale annotation with clinicians. In this work, we present a
``reasoning-aware'' diagnosis framework that rationalizes the diagnostic
process via prompt-based learning in a time- and labor-efficient manner, and
learns to reason over the prompt-generated rationales. Specifically, we address
the clinical reasoning for disease diagnosis, where the LLM generates
diagnostic rationales providing its insight on presented patient data and the
reasoning path towards the diagnosis, namely Clinical Chain-of-Thought
(Clinical CoT). We empirically demonstrate LLMs/LMs' ability of clinical
reasoning via extensive experiments and analyses on both rationale generation
and disease diagnosis in various settings. We further propose a novel set of
criteria for evaluating machine-generated rationales' potential for real-world
clinical settings, facilitating and benefiting future research in this area.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding
Abstract: We propose a method for accelerating large-scale pre-training with online
data selection policies. For the first time, we demonstrate that model-based
data selection can reduce the total computation needed to reach the performance
of models trained with uniform sampling. The key insight which enables this
"compute-positive" regime is that small models provide good proxies for the
loss of much larger models, such that computation spent on scoring data can be
drastically scaled down but still significantly accelerate training of the
learner.. These data selection policies also strongly generalize across
datasets and tasks, opening an avenue for further amortizing the overhead of
data scoring by re-using off-the-shelf models and training sequences. Our
methods, ClassAct and ActiveCLIP, require 46% and 51% fewer training updates
and up to 25% less total computation when training visual classifiers on JFT
and multimodal models on ALIGN, respectively. Finally, our paradigm seamlessly
applies to the curation of large-scale image-text datasets, yielding a new
state-of-the-art in several multimodal transfer tasks and pre-training regimes.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Apollo: Zero-shot MultiModal Reasoning with Multiple Experts
Abstract: We propose a modular framework that leverages the expertise of different
foundation models over different modalities and domains in order to perform a
single, complex, multi-modal task, without relying on prompt engineering or
otherwise tailor-made multi-modal training. Our approach enables decentralized
command execution and allows each model to both contribute and benefit from the
expertise of the other models. Our method can be extended to a variety of
foundation models (including audio and vision), above and beyond only language
models, as it does not depend on prompts. We demonstrate our approach on two
tasks. On the well-known task of stylized image captioning, our experiments
show that our approach outperforms semi-supervised state-of-the-art models,
while being zero-shot and avoiding costly training, data collection, and prompt
engineering. We further demonstrate this method on a novel task, audio-aware
image captioning, in which an image and audio are given and the task is to
generate text that describes the image within the context of the provided
audio. Our code is available on GitHub.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: A multi-modal table tennis robot system
Abstract: In recent years, robotic table tennis has become a popular research challenge
for perception and robot control. Here, we present an improved table tennis
robot system with high accuracy vision detection and fast robot reaction. Based
on previous work, our system contains a KUKA robot arm with 6 DOF, with four
frame-based cameras and two additional event-based cameras. We developed a
novel calibration approach to calibrate this multimodal perception system. For
table tennis, spin estimation is crucial. Therefore, we introduced a novel, and
more accurate spin estimation approach. Finally, we show how combining the
output of an event-based camera and a Spiking Neural Network (SNN) can be used
for accurate ball detection.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Sample Dominance Aware Framework via Non-Parametric Estimation for Spontaneous Brain-Computer Interface
Abstract: Deep learning has shown promise in decoding brain signals, such as
electroencephalogram (EEG), in the field of brain-computer interfaces (BCIs).
However, the non-stationary characteristics of EEG signals pose challenges for
training neural networks to acquire appropriate knowledge. Inconsistent EEG
signals resulting from these non-stationary characteristics can lead to poor
performance. Therefore, it is crucial to investigate and address sample
inconsistency to ensure robust performance in spontaneous BCIs. In this study,
we introduce the concept of sample dominance as a measure of EEG signal
inconsistency and propose a method to modulate its effect on network training.
We present a two-stage dominance score estimation technique that compensates
for performance degradation caused by sample inconsistencies. Our proposed
method utilizes non-parametric estimation to infer sample inconsistency and
assigns each sample a dominance score. This score is then aggregated with the
loss function during training to modulate the impact of sample inconsistency.
Furthermore, we design a curriculum learning approach that gradually increases
the influence of inconsistent signals during training to improve overall
performance. We evaluate our proposed method using public spontaneous BCI
dataset. The experimental results confirm that our findings highlight the
importance of addressing sample dominance for achieving robust performance in
spontaneous BCIs.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Enhancing Person Re-Identification through Tensor Feature Fusion
Abstract: In this paper, we present a novel person reidentification (PRe-ID) system
that based on tensor feature representation and multilinear subspace learning.
Our approach utilizes pretrained CNNs for high-level feature extraction, along
with Local Maximal Occurrence (LOMO) and Gaussian Of Gaussian (GOG )
descriptors. Additionally, Cross-View Quadratic Discriminant Analysis (TXQDA)
algorithm is used for multilinear subspace learning, which models the data in a
tensor framework to enhance discriminative capabilities. Similarity measure
based on Mahalanobis distance is used for matching between training and test
pedestrian images. Experimental evaluations on VIPeR and PRID450s datasets
demonstrate the effectiveness of our method.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Improved DDIM Sampling with Moment Matching Gaussian Mixtures
Abstract: We propose using a Gaussian Mixture Model (GMM) as reverse transition
operator (kernel) within the Denoising Diffusion Implicit Models (DDIM)
framework, which is one of the most widely used approaches for accelerated
sampling from pre-trained Denoising Diffusion Probabilistic Models (DDPM).
Specifically we match the first and second order central moments of the DDPM
forward marginals by constraining the parameters of the GMM. We see that moment
matching is sufficient to obtain samples with equal or better quality than the
original DDIM with Gaussian kernels. We provide experimental results with
unconditional models trained on CelebAHQ and FFHQ and class-conditional models
trained on ImageNet datasets respectively. Our results suggest that using the
GMM kernel leads to significant improvements in the quality of the generated
samples when the number of sampling steps is small, as measured by FID and IS
metrics. For example on ImageNet 256x256, using 10 sampling steps, we achieve a
FID of 6.94 and IS of 207.85 with a GMM kernel compared to 10.15 and 196.73
respectively with a Gaussian kernel.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion Models
Abstract: Diffusion models have made tremendous progress in text-driven image and video
generation. Now text-to-image foundation models are widely applied to various
downstream image synthesis tasks, such as controllable image generation and
image editing, while downstream video synthesis tasks are less explored for
several reasons. First, it requires huge memory and compute overhead to train a
video generation foundation model. Even with video foundation models,
additional costly training is still required for downstream video synthesis
tasks. Second, although some works extend image diffusion models into videos in
a training-free manner, temporal consistency cannot be well kept. Finally,
these adaption methods are specifically designed for one task and fail to
generalize to different downstream video synthesis tasks. To mitigate these
issues, we propose a training-free general-purpose video synthesis framework,
coined as BIVDiff, via bridging specific image diffusion models and general
text-to-video foundation diffusion models. Specifically, we first use an image
diffusion model (like ControlNet, Instruct Pix2Pix) for frame-wise video
generation, then perform Mixed Inversion on the generated video, and finally
input the inverted latents into the video diffusion model for temporal
smoothing. Decoupling image and video models enables flexible image model
selection for different purposes, which endows the framework with strong task
generalization and high efficiency. To validate the effectiveness and general
use of BIVDiff, we perform a wide range of video generation tasks, including
controllable video generation video editing, video inpainting and outpainting.
Our project page is available at https://bivdiff.github.io.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Proceedings Fifth International Workshop on Formal Methods for Autonomous Systems
Abstract: This EPTCS volume contains the proceedings for the Fifth International
Workshop on Formal Methods for Autonomous Systems (FMAS 2023), which was held
on the 15th and 16th of November 2023. FMAS 2023 was co-located with 18th
International Conference on integrated Formal Methods (iFM) (iFM'22), organised
by Leiden Institute of Advanced Computer Science of Leiden University. The
workshop itself was held at Scheltema Leiden, a renovated 19th Century blanket
factory alongside the canal.
  FMAS 2023 received 25 submissions. We received 11 regular papers, 3
experience reports, 6 research previews, and 5 vision papers. The researchers
who submitted papers to FMAS 2023 were from institutions in: Australia, Canada,
Colombia, France, Germany, Ireland, Italy, the Netherlands, Sweden, the United
Kingdom, and the United States of America. Increasing our number of submissions
for the third year in a row is an encouraging sign that FMAS has established
itself as a reputable publication venue for research on the formal modelling
and verification of autonomous systems. After each paper was reviewed by three
members of our Programme Committee we accepted a total of 15 papers: 8 long
papers and 7 short papers.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Learning adaptive planning representations with natural language guidance
Abstract: Effective planning in the real world requires not only world knowledge, but
the ability to leverage that knowledge to build the right representation of the
task at hand. Decades of hierarchical planning techniques have used
domain-specific temporal action abstractions to support efficient and accurate
planning, almost always relying on human priors and domain knowledge to
decompose hard tasks into smaller subproblems appropriate for a goal or set of
goals. This paper describes Ada (Action Domain Acquisition), a framework for
automatically constructing task-specific planning representations using
task-general background knowledge from language models (LMs). Starting with a
general-purpose hierarchical planner and a low-level goal-conditioned policy,
Ada interactively learns a library of planner-compatible high-level action
abstractions and low-level controllers adapted to a particular domain of
planning tasks. On two language-guided interactive planning benchmarks (Mini
Minecraft and ALFRED Household Tasks), Ada strongly outperforms other
approaches that use LMs for sequential decision-making, offering more accurate
plans and better generalization to complex tasks.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: HALO: An Ontology for Representing Hallucinations in Generative Models
Abstract: Recent progress in generative AI, including large language models (LLMs) like
ChatGPT, has opened up significant opportunities in fields ranging from natural
language processing to knowledge discovery and data mining. However, there is
also a growing awareness that the models can be prone to problems such as
making information up or `hallucinations', and faulty reasoning on seemingly
simple problems. Because of the popularity of models like ChatGPT, both
academic scholars and citizen scientists have documented hallucinations of
several different types and severity. Despite this body of work, a formal model
for describing and representing these hallucinations (with relevant meta-data)
at a fine-grained level, is still lacking. In this paper, we address this gap
by presenting the Hallucination Ontology or HALO, a formal, extensible ontology
written in OWL that currently offers support for six different types of
hallucinations known to arise in LLMs, along with support for provenance and
experimental metadata. We also collect and publish a dataset containing
hallucinations that we inductively gathered across multiple independent Web
sources, and show that HALO can be successfully used to model this dataset and
answer competency questions.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Revolutionizing Healthcare Image Analysis in Pandemic-Based Fog-Cloud Computing Architectures
Abstract: The emergence of pandemics has significantly emphasized the need for
effective solutions in healthcare data analysis. One particular challenge in
this domain is the manual examination of medical images, such as X-rays and CT
scans. This process is time-consuming and involves the logistical complexities
of transferring these images to centralized cloud computing servers.
Additionally, the speed and accuracy of image analysis are vital for efficient
healthcare image management. This research paper introduces an innovative
healthcare architecture that tackles the challenges of analysis efficiency and
accuracy by harnessing the capabilities of Artificial Intelligence (AI).
Specifically, the proposed architecture utilizes fog computing and presents a
modified Convolutional Neural Network (CNN) designed specifically for image
analysis. Different architectures of CNN layers are thoroughly explored and
evaluated to optimize overall performance. To demonstrate the effectiveness of
the proposed approach, a dataset of X-ray images is utilized for analysis and
evaluation. Comparative assessments are conducted against recent models such as
VGG16, VGG19, MobileNet, and related research papers. Notably, the proposed
approach achieves an exceptional accuracy rate of 99.88% in classifying normal
cases, accompanied by a validation rate of 96.5%, precision and recall rates of
100%, and an F1 score of 100%. These results highlight the immense potential of
fog computing and modified CNNs in revolutionizing healthcare image analysis
and diagnosis, not only during pandemics but also in the future. By leveraging
these technologies, healthcare professionals can enhance the efficiency and
accuracy of medical image analysis, leading to improved patient care and
outcomes.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Three Conjectures on Unexpectedeness
Abstract: Unexpectedness is a central concept in Simplicity Theory, a theory of
cognition relating various inferential processes to the computation of
Kolmogorov complexities, rather than probabilities. Its predictive power has
been confirmed by several experiments with human subjects, yet its theoretical
basis remains largely unexplored: why does it work? This paper lays the
groundwork for three theoretical conjectures. First, unexpectedness can be seen
as a generalization of Bayes' rule. Second, the frequentist core of
unexpectedness can be connected to the function of tracking ergodic properties
of the world. Third, unexpectedness can be seen as constituent of various
measures of divergence between the entropy of the world (environment) and the
variety of the observer (system). The resulting framework hints to research
directions that go beyond the division between probabilistic and logical
approaches, potentially bringing new insights into the extraction of causal
relations, and into the role of descriptive mechanisms in learning.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3D
Abstract: Lifting 2D diffusion for 3D generation is a challenging problem due to the
lack of geometric prior and the complex entanglement of materials and lighting
in natural images. Existing methods have shown promise by first creating the
geometry through score-distillation sampling (SDS) applied to rendered surface
normals, followed by appearance modeling. However, relying on a 2D RGB
diffusion model to optimize surface normals is suboptimal due to the
distribution discrepancy between natural images and normals maps, leading to
instability in optimization. In this paper, recognizing that the normal and
depth information effectively describe scene geometry and be automatically
estimated from images, we propose to learn a generalizable Normal-Depth
diffusion model for 3D generation. We achieve this by training on the
large-scale LAION dataset together with the generalizable image-to-depth and
normal prior models. In an attempt to alleviate the mixed illumination effects
in the generated materials, we introduce an albedo diffusion model to impose
data-driven constraints on the albedo component. Our experiments show that when
integrated into existing text-to-3D pipelines, our models significantly enhance
the detail richness, achieving state-of-the-art results. Our project page is
https://lingtengqiu.github.io/RichDreamer/.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities
Abstract: Recent advances in artificial general intelligence (AGI), particularly large
language models and creative image generation systems have demonstrated
impressive capabilities on diverse tasks spanning the arts and humanities.
However, the swift evolution of AGI has also raised critical questions about
its responsible deployment in these culturally significant domains
traditionally seen as profoundly human. This paper provides a comprehensive
analysis of the applications and implications of AGI for text, graphics, audio,
and video pertaining to arts and the humanities. We survey cutting-edge systems
and their usage in areas ranging from poetry to history, marketing to film, and
communication to classical art. We outline substantial concerns pertaining to
factuality, toxicity, biases, and public safety in AGI systems, and propose
mitigation strategies. The paper argues for multi-stakeholder collaboration to
ensure AGI promotes creativity, knowledge, and cultural values without
undermining truth or human dignity. Our timely contribution summarizes a
rapidly developing field, highlighting promising directions while advocating
for responsible progress centering on human flourishing. The analysis lays the
groundwork for further research on aligning AGI's technological capacities with
enduring social goods.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptation
Abstract: The study addresses the foundational and challenging task of peg-in-hole
assembly in robotics, where misalignments caused by sensor inaccuracies and
mechanical errors often result in insertion failures or jamming. This research
introduces PolyFit, representing a paradigm shift by transitioning from a
reinforcement learning approach to a supervised learning methodology. PolyFit
is a Force/Torque (F/T)-based supervised learning framework designed for 5-DoF
peg-in-hole assembly. It utilizes F/T data for accurate extrinsic pose
estimation and adjusts the peg pose to rectify misalignments. Extensive
training in a simulated environment involves a dataset encompassing a diverse
range of peg-hole shapes, extrinsic poses, and their corresponding contact F/T
readings. To enhance extrinsic pose estimation, a multi-point contact strategy
is integrated into the model input, recognizing that identical F/T readings can
indicate different poses. The study proposes a sim-to-real adaptation method
for real-world application, using a sim-real paired dataset to enable effective
generalization to complex and unseen polygon shapes. PolyFit achieves
impressive peg-in-hole success rates of 97.3% and 96.3% for seen and unseen
shapes in simulations, respectively. Real-world evaluations further demonstrate
substantial success rates of 86.7% and 85.0%, highlighting the robustness and
adaptability of the proposed method.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: LongQLoRA: Efficient and Effective Method to Extend Context Length of Large Language Models
Abstract: We present LongQLoRA, an efficient and effective method to extend context
length of large language models with less training resources. LongQLoRA
combines the advantages of Position Interpolation, QLoRA and Shift Short
Attention of LongLoRA. With a single 32GB V100 GPU, LongQLoRA can extend the
context length of LLaMA2 7B and 13B from 4096 to 8192 and even to 12k within
1000 finetuning steps. LongQLoRA achieves competitive perplexity performance on
PG19 and Proof-pile datasets, our model outperforms LongLoRA and is very close
to MPT-7B-8K within the evaluation context length of 8192. We collect and build
39k long instruction data to extend context length of Vicuna-13B from 4096 to
8192 and achieve good performance both in long and short context generation
task. We also do some ablation experiments to study the effect of LoRA rank,
finetuning steps and attention patterns in inference.The model weights,
training data and code are avaliable at
https://github.com/yangjianxin1/LongQLoRA.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Representing visual classification as a linear combination of words
Abstract: Explainability is a longstanding challenge in deep learning, especially in
high-stakes domains like healthcare. Common explainability methods highlight
image regions that drive an AI model's decision. Humans, however, heavily rely
on language to convey explanations of not only "where" but "what".
Additionally, most explainability approaches focus on explaining individual AI
predictions, rather than describing the features used by an AI model in
general. The latter would be especially useful for model and dataset auditing,
and potentially even knowledge generation as AI is increasingly being used in
novel tasks. Here, we present an explainability strategy that uses a
vision-language model to identify language-based descriptors of a visual
classification task. By leveraging a pre-trained joint embedding space between
images and text, our approach estimates a new classification task as a linear
combination of words, resulting in a weight for each word that indicates its
alignment with the vision-based classifier. We assess our approach using two
medical imaging classification tasks, where we find that the resulting
descriptors largely align with clinical knowledge despite a lack of
domain-specific language training. However, our approach also identifies the
potential for 'shortcut connections' in the public datasets used. Towards a
functional measure of explainability, we perform a pilot reader study where we
find that the AI-identified words can enable non-expert humans to perform a
specialized medical task at a non-trivial level. Altogether, our results
emphasize the potential of using multimodal foundational models to deliver
intuitive, language-based explanations of visual tasks.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Constraint Model for the Satellite Image Mosaic Selection Problem
Abstract: Satellite imagery solutions are widely used to study and monitor different
regions of the Earth. However, a single satellite image can cover only a
limited area. In cases where a larger area of interest is studied, several
images must be stitched together to create a single larger image, called a
mosaic, that can cover the area. Today, with the increasing number of satellite
images available for commercial use, selecting the images to build the mosaic
is challenging, especially when the user wants to optimize one or more
parameters, such as the total cost and the cloud coverage percentage in the
mosaic. More precisely, for this problem the input is an area of interest,
several satellite images intersecting the area, a list of requirements relative
to the image and the mosaic, such as cloud coverage percentage, image
resolution, and a list of objectives to optimize. We contribute to the
constraint and mixed integer lineal programming formulation of this new
problem, which we call the \textit{satellite image mosaic selection problem},
which is a multi-objective extension of the polygon cover problem. We propose a
dataset of realistic and challenging instances, where the images were captured
by the satellite constellations SPOT, Pl\'eiades and Pl\'eiades Neo. We
evaluate and compare the two proposed models and show their efficiency for
large instances, up to 200 images.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Nova$^+$: Generative Language Models for Binaries
Abstract: Generative large language models (LLMs) pre-trained on code have shown
impressive effectiveness in code generation, program repair, and document
analysis. However, existing generative LLMs focus on source code and are not
specialized for binaries. There are three main challenges for LLMs to model and
learn binary code: hex-decimal values, complex global dependencies, and
compiler optimization levels. To bring the benefit of LLMs to the binary
domain, we develop Nova and Nova$^+$, which are LLMs pre-trained on binary
corpora. Nova is pre-trained with the standard language modeling task, showing
significantly better capability on five benchmarks for three downstream tasks:
binary code similarity detection (BCSD), binary code translation (BCT), and
binary code recovery (BCR), over GPT-3.5 and other existing techniques. We
build Nova$^+$ to further boost Nova using two new pre-training tasks, i.e.,
optimization generation and optimization level prediction, which are designed
to learn binary optimization and align equivalent binaries. Nova$^+$ shows
overall the best performance for all three downstream tasks on five benchmarks,
demonstrating the contributions of the new pre-training tasks.

Output: Software Engineering


Instruction: What field is the article from?

Prompt:
Title: tsMorph: generation of semi-synthetic time series to understand algorithm performance
Abstract: Time series forecasting is a subject of significant scientific and industrial
importance. Despite the widespread utilization of forecasting methods, there is
a dearth of research aimed at comprehending the conditions under which these
methods yield favorable or unfavorable performances. Empirical studies,
although common, encounter challenges due to the limited availability of
datasets, impeding the extraction of reliable insights. To address this, we
present tsMorph, a straightforward approach for generating semi-synthetic time
series through dataset morphing. tsMorph operates by creating a sequence of
datasets derived from two original datasets. These newly generated datasets
exhibit a progressive departure from the characteristics of one dataset and a
convergence toward the attributes of the other. This method provides a valuable
alternative for obtaining substantial datasets. In this paper, we demonstrate
the utility of tsMorph by assessing the performance of the Long Short-Term
Memory Network forecasting algorithm. The time series under examination are
sourced from the NN5 Competition. The findings reveal compelling insights.
Notably, the performance of the Long Short-Term Memory Network improves
proportionally with the frequency of the time series. These experiments affirm
that tsMorph serves as an effective tool for gaining an understanding of
forecasting algorithm behaviors, offering a pathway to overcome the limitations
posed by empirical studies and enabling more extensive and reliable
experimentation.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: PCRDiffusion: Diffusion Probabilistic Models for Point Cloud Registration
Abstract: We propose a new framework that formulates point cloud registration as a
denoising diffusion process from noisy transformation to object transformation.
During training stage, object transformation diffuses from ground-truth
transformation to random distribution, and the model learns to reverse this
noising process. In sampling stage, the model refines randomly generated
transformation to the output result in a progressive way. We derive the
variational bound in closed form for training and provide implementations of
the model. Our work provides the following crucial findings: (i) In contrast to
most existing methods, our framework, Diffusion Probabilistic Models for Point
Cloud Registration (PCRDiffusion) does not require repeatedly update source
point cloud to refine the predicted transformation. (ii) Point cloud
registration, one of the representative discriminative tasks, can be solved by
a generative way and the unified probabilistic formulation. Finally, we discuss
and provide an outlook on the application of diffusion model in different
scenarios for point cloud registration. Experimental results demonstrate that
our model achieves competitive performance in point cloud registration. In
correspondence-free and correspondence-based scenarios, PCRDifussion can both
achieve exceeding 50\% performance improvements.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Forecasting Auxiliary Energy Consumption for Electric Heavy-Duty Vehicles
Abstract: Accurate energy consumption prediction is crucial for optimizing the
operation of electric commercial heavy-duty vehicles, e.g., route planning for
charging. Moreover, understanding why certain predictions are cast is paramount
for such a predictive model to gain user trust and be deployed in practice.
Since commercial vehicles operate differently as transportation tasks, ambient,
and drivers vary, a heterogeneous population is expected when building an AI
system for forecasting energy consumption. The dependencies between the input
features and the target values are expected to also differ across
sub-populations. One well-known example of such a statistical phenomenon is the
Simpson paradox. In this paper, we illustrate that such a setting poses a
challenge for existing XAI methods that produce global feature statistics, e.g.
LIME or SHAP, causing them to yield misleading results. We demonstrate a
potential solution by training multiple regression models on subsets of data.
It not only leads to superior regression performance but also more relevant and
consistent LIME explanations. Given that the employed groupings correspond to
relevant sub-populations, the associations between the input features and the
target values are consistent within each cluster but different across clusters.
Experiments on both synthetic and real-world datasets show that such splitting
of a complex problem into simpler ones yields better regression performance and
interpretability.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: CG3D: Compositional Generation for Text-to-3D via Gaussian Splatting
Abstract: With the onset of diffusion-based generative models and their ability to
generate text-conditioned images, content generation has received a massive
invigoration. Recently, these models have been shown to provide useful guidance
for the generation of 3D graphics assets. However, existing work in
text-conditioned 3D generation faces fundamental constraints: (i) inability to
generate detailed, multi-object scenes, (ii) inability to textually control
multi-object configurations, and (iii) physically realistic scene composition.
In this work, we propose CG3D, a method for compositionally generating scalable
3D assets that resolves these constraints. We find that explicit Gaussian
radiance fields, parameterized to allow for compositions of objects, possess
the capability to enable semantically and physically consistent scenes. By
utilizing a guidance framework built around this explicit representation, we
show state of the art results, capable of even exceeding the guiding diffusion
model in terms of object combinations and physics accuracy.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Prototypical Self-Explainable Models Without Re-training
Abstract: Explainable AI (XAI) has unfolded in two distinct research directions with,
on the one hand, post-hoc methods that explain the predictions of a pre-trained
black-box model and, on the other hand, self-explainable models (SEMs) which
are trained directly to provide explanations alongside their predictions. While
the latter is preferred in most safety-critical scenarios, post-hoc approaches
have received the majority of attention until now, owing to their simplicity
and ability to explain base models without retraining. Current SEMs instead,
require complex architectures and heavily regularized loss functions, thus
necessitating specific and costly training. To address this shortcoming and
facilitate wider use of SEMs, we propose a simple yet efficient universal
method called KMEx (K-Means Explainer), which can convert any existing
pre-trained model into a prototypical SEM. The motivation behind KMEx is to
push towards more transparent deep learning-based decision-making via
class-prototype-based explanations that are guaranteed to be diverse and
trustworthy without retraining the base model. We compare models obtained from
KMEx to state-of-the-art SEMs using an extensive qualitative evaluation to
highlight the strengths and weaknesses of each model, further paving the way
toward a more reliable and objective evaluation of SEMs.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: UniIR: Training and Benchmarking Universal Multimodal Information Retrievers
Abstract: Existing information retrieval (IR) models often assume a homogeneous format,
limiting their applicability to diverse user needs, such as searching for
images with text descriptions, searching for a news article with a headline
image, or finding a similar photo with a query image. To approach such
different information-seeking demands, we introduce UniIR, a unified
instruction-guided multimodal retriever capable of handling eight distinct
retrieval tasks across modalities. UniIR, a single retrieval system jointly
trained on ten diverse multimodal-IR datasets, interprets user instructions to
execute various retrieval tasks, demonstrating robust performance across
existing datasets and zero-shot generalization to new tasks. Our experiments
highlight that multi-task training and instruction tuning are keys to UniIR's
generalization ability. Additionally, we construct the M-BEIR, a multimodal
retrieval benchmark with comprehensive results, to standardize the evaluation
of universal multimodal information retrieval.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Model-Based Minimum Bayes Risk Decoding
Abstract: Minimum Bayes Risk (MBR) decoding has been shown to be a powerful alternative
to beam search decoding in a variety of text generation tasks. MBR decoding
selects a hypothesis from a pool of hypotheses that has the least expected risk
under a probability model according to a given utility function. Since it is
impractical to compute the expected risk exactly over all possible hypotheses,
two approximations are commonly used in MBR. First, it integrates over a
sampled set of hypotheses rather than over all possible hypotheses. Second, it
estimates the probability of each hypothesis using a Monte Carlo estimator.
While the first approximation is necessary to make it computationally feasible,
the second is not essential since we typically have access to the model
probability at inference time. We propose Model-Based MBR (MBMBR), a variant of
MBR that uses the model probability itself as the estimate of the probability
distribution instead of the Monte Carlo estimate. We show analytically and
empirically that the model-based estimate is more promising than the Monte
Carlo estimate in text generation tasks. Our experiments show that MBMBR
outperforms MBR in several text generation tasks, both with encoder-decoder
models and with large language models.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Large Human Language Models: A Need and the Challenges
Abstract: As research in human-centered NLP advances, there is a growing recognition of
the importance of incorporating human and social factors into NLP models. At
the same time, our NLP systems have become heavily reliant on LLMs, most of
which do not model authors. To build NLP systems that can truly understand
human language, we must better integrate human contexts into LLMs. This brings
to the fore a range of design considerations and challenges in terms of what
human aspects to capture, how to represent them, and what modeling strategies
to pursue. To address these, we advocate for three positions toward creating
large human language models (LHLMs) using concepts from psychological and
behavioral sciences: First, LM training should include the human context.
Second, LHLMs should recognize that people are more than their group(s). Third,
LHLMs should be able to account for the dynamic and temporally-dependent nature
of the human context. We refer to relevant advances and present open challenges
that need to be addressed and their possible solutions in realizing these
goals.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: THOS: A Benchmark Dataset for Targeted Hate and Offensive Speech
Abstract: Detecting harmful content on social media, such as Twitter, is made difficult
by the fact that the seemingly simple yes/no classification conceals a
significant amount of complexity. Unfortunately, while several datasets have
been collected for training classifiers in hate and offensive speech, there is
a scarcity of datasets labeled with a finer granularity of target classes and
specific targets. In this paper, we introduce THOS, a dataset of 8.3k tweets
manually labeled with fine-grained annotations about the target of the message.
We demonstrate that this dataset makes it feasible to train classifiers, based
on Large Language Models, to perform classification at this level of
granularity.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Computational Hypergraph Discovery, a Gaussian Process framework for connecting the dots
Abstract: Most scientific challenges can be framed into one of the following three
levels of complexity of function approximation. Type 1: Approximate an unknown
function given input/output data. Type 2: Consider a collection of variables
and functions, some of which are unknown, indexed by the nodes and hyperedges
of a hypergraph (a generalized graph where edges can connect more than two
vertices). Given partial observations of the variables of the hypergraph
(satisfying the functional dependencies imposed by its structure), approximate
all the unobserved variables and unknown functions. Type 3: Expanding on Type
2, if the hypergraph structure itself is unknown, use partial observations of
the variables of the hypergraph to discover its structure and approximate its
unknown functions. While most Computational Science and Engineering and
Scientific Machine Learning challenges can be framed as Type 1 and Type 2
problems, many scientific problems can only be categorized as Type 3. Despite
their prevalence, these Type 3 challenges have been largely overlooked due to
their inherent complexity. Although Gaussian Process (GP) methods are sometimes
perceived as well-founded but old technology limited to Type 1 curve fitting,
their scope has recently been expanded to Type 2 problems. In this paper, we
introduce an interpretable GP framework for Type 3 problems, targeting the
data-driven discovery and completion of computational hypergraphs. Our approach
is based on a kernel generalization of Row Echelon Form reduction from linear
systems to nonlinear ones and variance-based analysis. Here, variables are
linked via GPs and those contributing to the highest data variance unveil the
hypergraph's structure. We illustrate the scope and efficiency of the proposed
approach with applications to (algebraic) equation discovery, network discovery
(gene pathways, chemical, and mechanical) and raw data analysis.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: A Unifying Tensor View for Lightweight CNNs
Abstract: Despite the decomposition of convolutional kernels for lightweight CNNs being
well studied, existing works that rely on tensor network diagrams or
hyperdimensional abstraction lack geometry intuition. This work devises a new
perspective by linking a 3D-reshaped kernel tensor to its various slice-wise
and rank-1 decompositions, permitting a straightforward connection between
various tensor approximations and efficient CNN modules. Specifically, it is
discovered that a pointwise-depthwise-pointwise (PDP) configuration constitutes
a viable construct for lightweight CNNs. Moreover, a novel link to the latest
ShiftNet is established, inspiring a first-ever shift layer pruning that
achieves nearly 50% compression with < 1% drop in accuracy for ShiftResNet.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Towards Unsupervised Representation Learning: Learning, Evaluating and Transferring Visual Representations
Abstract: Unsupervised representation learning aims at finding methods that learn
representations from data without annotation-based signals. Abstaining from
annotations not only leads to economic benefits but may - and to some extent
already does - result in advantages regarding the representation's structure,
robustness, and generalizability to different tasks. In the long run,
unsupervised methods are expected to surpass their supervised counterparts due
to the reduction of human intervention and the inherently more general setup
that does not bias the optimization towards an objective originating from
specific annotation-based signals. While major advantages of unsupervised
representation learning have been recently observed in natural language
processing, supervised methods still dominate in vision domains for most tasks.
In this dissertation, we contribute to the field of unsupervised (visual)
representation learning from three perspectives: (i) Learning representations:
We design unsupervised, backpropagation-free Convolutional Self-Organizing
Neural Networks (CSNNs) that utilize self-organization- and Hebbian-based
learning rules to learn convolutional kernels and masks to achieve deeper
backpropagation-free models. (ii) Evaluating representations: We build upon the
widely used (non-)linear evaluation protocol to define pretext- and
target-objective-independent metrics for measuring and investigating the
objective function mismatch between various unsupervised pretext tasks and
target tasks. (iii) Transferring representations: We contribute CARLANE, the
first 3-way sim-to-real domain adaptation benchmark for 2D lane detection, and
a method based on prototypical self-supervised learning. Finally, we contribute
a content-consistent unpaired image-to-image translation method that utilizes
masks, global and local discriminators, and similarity sampling to mitigate
content inconsistencies.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Augmenting Unsupervised Reinforcement Learning with Self-Reference
Abstract: Humans possess the ability to draw on past experiences explicitly when
learning new tasks and applying them accordingly. We believe this capacity for
self-referencing is especially advantageous for reinforcement learning agents
in the unsupervised pretrain-then-finetune setting. During pretraining, an
agent's past experiences can be explicitly utilized to mitigate the
nonstationarity of intrinsic rewards. In the finetuning phase, referencing
historical trajectories prevents the unlearning of valuable exploratory
behaviors. Motivated by these benefits, we propose the Self-Reference (SR)
approach, an add-on module explicitly designed to leverage historical
information and enhance agent performance within the pretrain-finetune
paradigm. Our approach achieves state-of-the-art results in terms of
Interquartile Mean (IQM) performance and Optimality Gap reduction on the
Unsupervised Reinforcement Learning Benchmark for model-free methods, recording
an 86% IQM and a 16% Optimality Gap. Additionally, it improves current
algorithms by up to 17% IQM and reduces the Optimality Gap by 31%. Beyond
performance enhancement, the Self-Reference add-on also increases sample
efficiency, a crucial attribute for real-world applications.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Text-to-3D with Classifier Score Distillation
Abstract: Text-to-3D generation has made remarkable progress recently, particularly
with methods based on Score Distillation Sampling (SDS) that leverages
pre-trained 2D diffusion models. While the usage of classifier-free guidance is
well acknowledged to be crucial for successful optimization, it is considered
an auxiliary trick rather than the most essential component. In this paper, we
re-evaluate the role of classifier-free guidance in score distillation and
discover a surprising finding: the guidance alone is enough for effective
text-to-3D generation tasks. We name this method Classifier Score Distillation
(CSD), which can be interpreted as using an implicit classification model for
generation. This new perspective reveals new insights for understanding
existing techniques. We validate the effectiveness of CSD across a variety of
text-to-3D tasks including shape generation, texture synthesis, and shape
editing, achieving results superior to those of state-of-the-art methods. Our
project page is https://xinyu-andy.github.io/Classifier-Score-Distillation

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: ROME: Evaluating Pre-trained Vision-Language Models on Reasoning beyond Visual Common Sense
Abstract: Humans possess a strong capability for reasoning beyond common sense. For
example, given an unconventional image of a goldfish laying on the table next
to an empty fishbowl, a human would effortlessly determine that the fish is not
inside the fishbowl. The case, however, may be different for a vision-language
model, whose reasoning could gravitate towards the common scenario that the
fish is inside the bowl, despite the visual input. In this paper, we introduce
a novel probing dataset named ROME (reasoning beyond commonsense knowledge) to
evaluate whether the state-of-the-art pre-trained vision-language models have
the reasoning capability to correctly interpret counter-intuitive content. ROME
contains images that defy commonsense knowledge with regards to color, shape,
material, size and positional relation. Experiments on the state-of-the-art
pre-trained vision-language models reveal that most of these models are still
largely incapable of interpreting counter-intuitive scenarios. We hope that
ROME will spur further investigations on reasoning beyond commonsense knowledge
in vision-language research.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Experimental Insights Towards Explainable and Interpretable Pedestrian Crossing Prediction
Abstract: In the context of autonomous driving, pedestrian crossing prediction is a key
component for improving road safety. Presently, the focus of these predictions
extends beyond achieving trustworthy results; it is shifting towards the
explainability and interpretability of these predictions. This research
introduces a novel neuro-symbolic approach that combines deep learning and
fuzzy logic for an explainable and interpretable pedestrian crossing
prediction. We have developed an explainable predictor (ExPedCross), which
utilizes a set of explainable features and employs a fuzzy inference system to
predict whether the pedestrian will cross or not. Our approach was evaluated on
both the PIE and JAAD datasets. The results offer experimental insights into
achieving explainability and interpretability in the pedestrian crossing
prediction task. Furthermore, the testing results yield a set of guidelines and
recommendations regarding the process of dataset selection, feature selection,
and explainability.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: A Multi-solution Study on GDPR AI-enabled Completeness Checking of DPAs
Abstract: Specifying legal requirements for software systems to ensure their compliance
with the applicable regulations is a major concern to requirements engineering
(RE). Personal data which is collected by an organization is often shared with
other organizations to perform certain processing activities. In such cases,
the General Data Protection Regulation (GDPR) requires issuing a data
processing agreement (DPA) which regulates the processing and further ensures
that personal data remains protected. Violating GDPR can lead to huge fines
reaching to billions of Euros. Software systems involving personal data
processing must adhere to the legal obligations stipulated in GDPR and outlined
in DPAs. Requirements engineers can elicit from DPAs legal requirements for
regulating the data processing activities in software systems. Checking the
completeness of a DPA according to the GDPR provisions is therefore an
essential prerequisite to ensure that the elicited requirements are complete.
Analyzing DPAs entirely manually is time consuming and requires adequate legal
expertise. In this paper, we propose an automation strategy to address the
completeness checking of DPAs against GDPR. Specifically, we pursue ten
alternative solutions which are enabled by different technologies, namely
traditional machine learning, deep learning, language modeling, and few-shot
learning. The goal of our work is to empirically examine how these different
technologies fare in the legal domain. We computed F2 score on a set of 30 real
DPAs. Our evaluation shows that best-performing solutions yield F2 score of
86.7% and 89.7% are based on pre-trained BERT and RoBERTa language models. Our
analysis further shows that other alternative solutions based on deep learning
(e.g., BiLSTM) and few-shot learning (e.g., SetFit) can achieve comparable
accuracy, yet are more efficient to develop.

Output: Software Engineering


Instruction: What field is the article from?

Prompt:
Title: The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI
Abstract: The race to train language models on vast, diverse, and inconsistently
documented datasets has raised pressing concerns about the legal and ethical
risks for practitioners. To remedy these practices threatening data
transparency and understanding, we convene a multi-disciplinary effort between
legal and machine learning experts to systematically audit and trace 1800+ text
datasets. We develop tools and standards to trace the lineage of these
datasets, from their source, creators, series of license conditions,
properties, and subsequent use. Our landscape analysis highlights the sharp
divides in composition and focus of commercially open vs closed datasets, with
closed datasets monopolizing important categories: lower resource languages,
more creative tasks, richer topic variety, newer and more synthetic training
data. This points to a deepening divide in the types of data that are made
available under different license conditions, and heightened implications for
jurisdictional legal interpretations of copyright and fair use. We also observe
frequent miscategorization of licenses on widely used dataset hosting sites,
with license omission of 70%+ and error rates of 50%+. This points to a crisis
in misattribution and informed use of the most popular datasets driving many
recent breakthroughs. As a contribution to ongoing improvements in dataset
transparency and responsible use, we release our entire audit, with an
interactive UI, the Data Provenance Explorer, which allows practitioners to
trace and filter on data provenance for the most popular open source finetuning
data collections: www.dataprovenance.org.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements
Abstract: In this paper, we present a novel robust framework for low-level vision
tasks, including denoising, object removal, frame interpolation, and
super-resolution, that does not require any external training data corpus. Our
proposed approach directly learns the weights of neural modules by optimizing
over the corrupted test sequence, leveraging the spatio-temporal coherence and
internal statistics of videos. Furthermore, we introduce a novel spatial
pyramid loss that leverages the property of spatio-temporal patch recurrence in
a video across the different scales of the video. This loss enhances robustness
to unstructured noise in both the spatial and temporal domains. This further
results in our framework being highly robust to degradation in input frames and
yields state-of-the-art results on downstream tasks such as denoising, object
removal, and frame interpolation. To validate the effectiveness of our
approach, we conduct qualitative and quantitative evaluations on standard video
datasets such as DAVIS, UCF-101, and VIMEO90K-T.

Output: Computer Vision