Instruction: What field is the article from?

Prompt:
Title: Unknown Sample Discovery for Source Free Open Set Domain Adaptation
Abstract: Open Set Domain Adaptation (OSDA) aims to adapt a model trained on a source
domain to a target domain that undergoes distribution shift and contains
samples from novel classes outside the source domain. Source-free OSDA
(SF-OSDA) techniques eliminate the need to access source domain samples, but
current SF-OSDA methods utilize only the known classes in the target domain for
adaptation, and require access to the entire target domain even during
inference after adaptation, to make the distinction between known and unknown
samples. In this paper, we introduce Unknown Sample Discovery (USD) as an
SF-OSDA method that utilizes a temporally ensembled teacher model to conduct
known-unknown target sample separation and adapts the student model to the
target domain over all classes using co-training and temporal consistency
between the teacher and the student. USD promotes Jensen-Shannon distance (JSD)
as an effective measure for known-unknown sample separation. Our
teacher-student framework significantly reduces error accumulation resulting
from imperfect known-unknown sample separation, while curriculum guidance helps
to reliably learn the distinction between target known and target unknown
subspaces. USD appends the target model with an unknown class node, thus
readily classifying a target sample into any of the known or unknown classes in
subsequent post-adaptation inference stages. Empirical results show that USD is
superior to existing SF-OSDA methods and is competitive with current OSDA
models that utilize both source and target domains during adaptation.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism
Abstract: We present EE-LLM, a framework for large-scale training and inference of
early-exit large language models (LLMs). While recent works have shown
preliminary evidence for the efficacy of early exiting in accelerating LLM
inference, EE-LLM makes a foundational step towards scaling up early-exit LLMs
by supporting their training and inference with massive 3D parallelism. Built
upon Megatron-LM, EE-LLM implements a variety of algorithmic innovations and
performance optimizations tailored to early exiting, including a lightweight
method that facilitates backpropagation for the early-exit training objective
with pipeline parallelism, techniques of leveraging idle resources in the
original pipeline schedule for computation related to early-exit layers, and
two approaches of early-exit inference that are compatible with KV caching for
autoregressive generation. Our analytical and empirical study shows that EE-LLM
achieves great training efficiency with negligible computational overhead
compared to standard LLM training, as well as outstanding inference speedup
without compromising output quality. To facilitate further research and
adoption, we release EE-LLM at https://github.com/pan-x-c/EE-LLM.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Casual Social Media Use among the Youth: Effects on Online and Offline Political Participation
Abstract: Background: Previous studies suggest that social media use among the youth is
correlated with online and offline political participation. There is also a
mixed and inconclusive debate on whether more online political participation in
the youth increases their offline political participation. Methods: This study
uses three models of OLS, two-way fixed effects, and an instrumental variable
approach to make causal inferences about social media use, online, and offline
political participation of the youth. Findings: The analyses provide evidence
of a large effect of casual social media use on online political participation,
and no effect or negligible effect on offline political participation and
voting behavior. The results from fixed effects and instrumental variable
models provide strong evidence of elasticity between online and offline
political participation in young individuals. On average, a one percent
increase in online political participation increases the offline political
activity index by 0.12 percent.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Two-Step Reinforcement Learning for Multistage Strategy Card Game
Abstract: In the realm of artificial intelligence and card games, this study introduces
a two-step reinforcement learning (RL) strategy tailored for "The Lord of the
Rings: The Card Game (LOTRCG)," a complex multistage strategy card game. This
research diverges from conventional RL methods by adopting a phased learning
approach, beginning with a foundational learning stage in a simplified version
of the game and subsequently progressing to the complete, intricate game
environment. This methodology notably enhances the AI agent's adaptability and
performance in the face of LOTRCG's unpredictable and challenging nature. The
paper also explores a multi-agent system, where distinct RL agents are employed
for various decision-making aspects of the game. This approach has demonstrated
a remarkable improvement in game outcomes, with the RL agents achieving a
winrate of 78.5% across a set of 10,000 random games.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Online Advertisements with LLMs: Opportunities and Challenges
Abstract: This paper explores the potential for leveraging Large Language Models (LLM)
in the realm of online advertising systems. We delve into essential
requirements including privacy, latency, reliability, users and advertisers'
satisfaction, which such a system must fulfill. We further introduce a general
framework for LLM advertisement, consisting of modification, bidding,
prediction, and auction modules. Different design considerations for each
module is presented, with an in-depth examination of their practicality and the
technical challenges inherent to their implementation.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning
Abstract: Personal sensing, leveraging data passively and near-continuously collected
with wearables from patients in their ecological environment, is a promising
paradigm to monitor mood disorders (MDs), a major determinant of worldwide
disease burden. However, collecting and annotating wearable data is very
resource-intensive. Studies of this kind can thus typically afford to recruit
only a couple dozens of patients. This constitutes one of the major obstacles
to applying modern supervised machine learning techniques to MDs detection. In
this paper, we overcome this data bottleneck and advance the detection of MDs
acute episode vs stable state from wearables data on the back of recent
advances in self-supervised learning (SSL). This leverages unlabelled data to
learn representations during pre-training, subsequently exploited for a
supervised task. First, we collected open-access datasets recording with an
Empatica E4 spanning different, unrelated to MD monitoring, personal sensing
tasks -- from emotion recognition in Super Mario players to stress detection in
undergraduates -- and devised a pre-processing pipeline performing on-/off-body
detection, sleep-wake detection, segmentation, and (optionally) feature
extraction. With 161 E4-recorded subjects, we introduce E4SelfLearning, the
largest to date open access collection, and its pre-processing pipeline.
Second, we show that SSL confidently outperforms fully-supervised pipelines
using either our novel E4-tailored Transformer architecture (E4mer) or
classical baseline XGBoost: 81.23% against 75.35% (E4mer) and 72.02% (XGBoost)
correctly classified recording segments from 64 (half acute, half stable)
patients. Lastly, we illustrate that SSL performance is strongly associated
with the specific surrogate task employed for pre-training as well as with
unlabelled data availability.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Sample-Efficient Learning to Solve a Real-World Labyrinth Game Using Data-Augmented Model-Based Reinforcement Learning
Abstract: Motivated by the challenge of achieving rapid learning in physical
environments, this paper presents the development and training of a robotic
system designed to navigate and solve a labyrinth game using model-based
reinforcement learning techniques. The method involves extracting
low-dimensional observations from camera images, along with a cropped and
rectified image patch centered on the current position within the labyrinth,
providing valuable information about the labyrinth layout. The learning of a
control policy is performed purely on the physical system using model-based
reinforcement learning, where the progress along the labyrinth's path serves as
a reward signal. Additionally, we exploit the system's inherent symmetries to
augment the training data. Consequently, our approach learns to successfully
solve a popular real-world labyrinth game in record time, with only 5 hours of
real-world training data.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Agent-Aware Training for Agent-Agnostic Action Advising in Deep Reinforcement Learning
Abstract: Action advising endeavors to leverage supplementary guidance from expert
teachers to alleviate the issue of sampling inefficiency in Deep Reinforcement
Learning (DRL). Previous agent-specific action advising methods are hindered by
imperfections in the agent itself, while agent-agnostic approaches exhibit
limited adaptability to the learning agent. In this study, we propose a novel
framework called Agent-Aware trAining yet Agent-Agnostic Action Advising (A7)
to strike a balance between the two. The underlying concept of A7 revolves
around utilizing the similarity of state features as an indicator for
soliciting advice. However, unlike prior methodologies, the measurement of
state feature similarity is performed by neither the error-prone learning agent
nor the agent-agnostic advisor. Instead, we employ a proxy model to extract
state features that are both discriminative (adaptive to the agent) and
generally applicable (robust to agent noise). Furthermore, we utilize behavior
cloning to train a model for reusing advice and introduce an intrinsic reward
for the advised samples to incentivize the utilization of expert guidance.
Experiments are conducted on the GridWorld, LunarLander, and six prominent
scenarios from Atari games. The results demonstrate that A7 significantly
accelerates the learning process and surpasses existing methods (both
agent-specific and agent-agnostic) by a substantial margin. Our code will be
made publicly available.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Defense Against Adversarial Attacks using Convolutional Auto-Encoders
Abstract: Deep learning models, while achieving state-of-the-art performance on many
tasks, are susceptible to adversarial attacks that exploit inherent
vulnerabilities in their architectures. Adversarial attacks manipulate the
input data with imperceptible perturbations, causing the model to misclassify
the data or produce erroneous outputs. This work is based on enhancing the
robustness of targeted classifier models against adversarial attacks. To
achieve this, an convolutional autoencoder-based approach is employed that
effectively counters adversarial perturbations introduced to the input images.
By generating images closely resembling the input images, the proposed
methodology aims to restore the model's accuracy.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Fast Training of Diffusion Transformer with Extreme Masking for 3D Point Clouds Generation
Abstract: Diffusion Transformers have recently shown remarkable effectiveness in
generating high-quality 3D point clouds. However, training voxel-based
diffusion models for high-resolution 3D voxels remains prohibitively expensive
due to the cubic complexity of attention operators, which arises from the
additional dimension of voxels. Motivated by the inherent redundancy of 3D
compared to 2D, we propose FastDiT-3D, a novel masked diffusion transformer
tailored for efficient 3D point cloud generation, which greatly reduces
training costs. Specifically, we draw inspiration from masked autoencoders to
dynamically operate the denoising process on masked voxelized point clouds. We
also propose a novel voxel-aware masking strategy to adaptively aggregate
background/foreground information from voxelized point clouds. Our method
achieves state-of-the-art performance with an extreme masking ratio of nearly
99%. Moreover, to improve multi-category 3D generation, we introduce
Mixture-of-Expert (MoE) in 3D diffusion model. Each category can learn a
distinct diffusion path with different experts, relieving gradient conflict.
Experimental results on the ShapeNet dataset demonstrate that our method
achieves state-of-the-art high-fidelity and diverse 3D point cloud generation
performance. Our FastDiT-3D improves 1-Nearest Neighbor Accuracy and Coverage
metrics when generating 128-resolution voxel point clouds, using only 6.5% of
the original training cost.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: ChatGPT-Powered Hierarchical Comparisons for Image Classification
Abstract: The zero-shot open-vocabulary challenge in image classification is tackled by
pretrained vision-language models like CLIP, which benefit from incorporating
class-specific knowledge from large language models (LLMs) like ChatGPT.
However, biases in CLIP lead to similar descriptions for distinct but related
classes, prompting our novel image classification framework via hierarchical
comparisons: using LLMs to recursively group classes into hierarchies and
classifying images by comparing image-text embeddings at each hierarchy level,
resulting in an intuitive, effective, and explainable approach.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Neural Implicit Field Editing Considering Object-environment Interaction
Abstract: The 3D scene editing method based on neural implicit field has gained wide
attention. It has achieved excellent results in 3D editing tasks. However,
existing methods often blend the interaction between objects and scene
environment. The change of scene appearance like shadows is failed to be
displayed in the rendering view. In this paper, we propose an Object and Scene
environment Interaction aware (OSI-aware) system, which is a novel two-stream
neural rendering system considering object and scene environment interaction.
To obtain illuminating conditions from the mixture soup, the system
successfully separates the interaction between objects and scene environment by
intrinsic decomposition method. To study the corresponding changes to the scene
appearance from object-level editing tasks, we introduce a depth map guided
scene inpainting method and shadow rendering method by point matching strategy.
Extensive experiments demonstrate that our novel pipeline produce reasonable
appearance changes in scene editing tasks. It also achieve competitive
performance for the rendering quality in novel-view synthesis tasks.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents
Abstract: Large language models (LLMs) have dramatically enhanced the field of language
intelligence, as demonstrably evidenced by their formidable empirical
performance across a spectrum of complex reasoning tasks. Additionally,
theoretical proofs have illuminated their emergent reasoning capabilities,
providing a compelling showcase of their advanced cognitive abilities in
linguistic contexts. Critical to their remarkable efficacy in handling complex
reasoning tasks, LLMs leverage the intriguing chain-of-thought (CoT) reasoning
techniques, obliging them to formulate intermediate steps en route to deriving
an answer. The CoT reasoning approach has not only exhibited proficiency in
amplifying reasoning performance but also in enhancing interpretability,
controllability, and flexibility. In light of these merits, recent research
endeavors have extended CoT reasoning methodologies to nurture the development
of autonomous language agents, which adeptly adhere to language instructions
and execute actions within varied environments. This survey paper orchestrates
a thorough discourse, penetrating vital research dimensions, encompassing: (i)
the foundational mechanics of CoT techniques, with a focus on elucidating the
circumstances and justification behind its efficacy; (ii) the paradigm shift in
CoT; and (iii) the burgeoning of language agents fortified by CoT approaches.
Prospective research avenues envelop explorations into generalization,
efficiency, customization, scaling, and safety. This paper caters to a wide
audience, including beginners seeking comprehensive knowledge of CoT reasoning
and language agents, as well as experienced researchers interested in
foundational mechanics and engaging in cutting-edge discussions on these
topics. A repository for the related papers is available at
https://github.com/Zoeyyao27/CoT-Igniting-Agent.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards
Abstract: Exploration poses a fundamental challenge in Reinforcement Learning (RL) with
sparse rewards, limiting an agent's ability to learn optimal decision-making
due to a lack of informative feedback signals. Self-Imitation Learning
(self-IL) has emerged as a promising approach for exploration, leveraging a
replay buffer to store and reproduce successful behaviors. However, traditional
self-IL methods, which rely on high-return transitions and assume singleton
environments, face challenges in generalization, especially in
procedurally-generated (PCG) environments. Therefore, new self-IL methods have
been proposed to rank which experiences to persist, but they replay transitions
uniformly regardless of their significance, and do not address the diversity of
the stored demonstrations. In this work, we propose tailored self-IL sampling
strategies by prioritizing transitions in different ways and extending
prioritization techniques to PCG environments. We also address diversity loss
through modifications to counteract the impact of generalization requirements
and bias introduced by prioritization techniques. Our experimental analysis,
conducted over three PCG sparse reward environments, including MiniGrid and
ProcGen, highlights the benefits of our proposed modifications, achieving a new
state-of-the-art performance in the MiniGrid-MultiRoom-N12-S10 environment.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Grasp Force Optimization as a Bilinear Matrix Inequality Problem: A Deep Learning Approach
Abstract: Grasp force synthesis is a non-convex optimization problem involving
constraints that are bilinear. Traditional approaches to this problem involve
general-purpose gradient-based nonlinear optimization and semi-definite
programming. With a view towards dealing with postural synergies and non-smooth
but convex positive semidefinite constraints, we look beyond gradient-based
optimization. The focus of this paper is to undertake a grasp analysis of
biomimetic grasping in multi-fingered robotic hands as a bilinear matrix
inequality (BMI) problem. Our analysis is to solve it using a deep learning
approach to make the algorithm efficiently generate force closure grasps with
optimal grasp quality on untrained/unseen objects.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Utilizing Explainability Techniques for Reinforcement Learning Model Assurance
Abstract: Explainable Reinforcement Learning (XRL) can provide transparency into the
decision-making process of a Deep Reinforcement Learning (DRL) model and
increase user trust and adoption in real-world use cases. By utilizing XRL
techniques, researchers can identify potential vulnerabilities within a trained
DRL model prior to deployment, therefore limiting the potential for mission
failure or mistakes by the system. This paper introduces the ARLIN (Assured RL
Model Interrogation) Toolkit, an open-source Python library that identifies
potential vulnerabilities and critical points within trained DRL models through
detailed, human-interpretable explainability outputs. To illustrate ARLIN's
effectiveness, we provide explainability visualizations and vulnerability
analysis for a publicly available DRL model. The open-source code repository is
available for download at https://github.com/mitre/arlin.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Assessing the Interpretability of Programmatic Policies with Large Language Models
Abstract: Although the synthesis of programs encoding policies often carries the
promise of interpretability, systematic evaluations to assess the
interpretability of these policies were never performed, likely because of the
complexity of such an evaluation. In this paper, we introduce a novel metric
that uses large-language models (LLM) to assess the interpretability of
programmatic policies. For our metric, an LLM is given both a program and a
description of its associated programming language. The LLM then formulates a
natural language explanation of the program. This explanation is subsequently
fed into a second LLM, which tries to reconstruct the program from the natural
language explanation. Our metric measures the behavioral similarity between the
reconstructed program and the original. We validate our approach using
obfuscated programs that are used to solve classic programming problems. We
also assess our metric with programmatic policies synthesized for playing a
real-time strategy game, comparing the interpretability scores of programmatic
policies synthesized by an existing system to lightly obfuscated versions of
the same programs. Our LLM-based interpretability score consistently ranks less
interpretable programs lower and more interpretable ones higher. These findings
suggest that our metric could serve as a reliable and inexpensive tool for
evaluating the interpretability of programmatic policies.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Dissecting In-Context Learning of Translations in GPTs
Abstract: Most of the recent work in leveraging Large Language Models (LLMs) such as
GPT-3 for Machine Translation (MT) has focused on selecting the few-shot
samples for prompting. In this work, we try to better understand the role of
demonstration attributes for the in-context learning of translations through
perturbations of high-quality, in-domain demonstrations. We find that
asymmetric perturbation of the source-target mappings yield vastly different
results. We show that the perturbation of the source side has surprisingly
little impact, while target perturbation can drastically reduce translation
quality, suggesting that it is the output text distribution that provides the
most important learning signal during in-context learning of translations. We
propose a method named Zero-Shot-Context to add this signal automatically in
Zero-Shot prompting. We demonstrate that it improves upon the zero-shot
translation performance of GPT-3, even making it competitive with few-shot
prompted translations.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Generation of Explanations for Logic Reasoning
Abstract: This thesis delves into a fortiori arguments in deductive reasoning,
underscoring their relevance in various domains such as law, philosophy, and
artificial intelligence. The research is centred on employing GPT-3.5-turbo to
automate the analysis of these arguments, with a focus on understanding
intricate reasoning processes, generating clear and coherent explanations, and
creating novel arguments. The methodology encompasses a series of tasks
including detailed reasoning, interpretation, and the augmentation of a
fortiori arguments. It involves meticulously identifying these arguments in
diverse contexts, differentiating comparative elements, and categorizing them
based on their logical structure.
  Extensive experiments reveals the challenges encountered by GPT-3.5-turbo in
accurately detecting and classifying a fortiori arguments. Nevertheless, the
model demonstrates a performance that rivals specialized models, particularly
in extracting key components and interpreting underlying properties. The
integration of external information into the model's processing significantly
elevates the quality of the generated explanations. Additionally, the model
exhibits a noteworthy capability in augmenting arguments, thus contributing to
the enrichment of the data set.
  Despite facing certain limitations, this thesis makes significant
contributions to the fields of artificial intelligence and logical reasoning.
It introduces novel methodologies, establishes a rigorous evaluation framework,
and provides deep insights that set the stage for future advancements in
automated logical reasoning. The findings and methodologies presented herein
not only underscore the potential of AI in complex reasoning tasks but also
highlight areas for future research and development.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Enhancing the Spatial Awareness Capability of Multi-Modal Large Language Model
Abstract: The Multi-Modal Large Language Model (MLLM) refers to an extension of the
Large Language Model (LLM) equipped with the capability to receive and infer
multi-modal data. Spatial awareness stands as one of the crucial abilities of
MLLM, encompassing diverse skills related to understanding spatial
relationships among objects and between objects and the scene area. Industries
such as autonomous driving, smart healthcare, robotics, virtual, and augmented
reality heavily demand MLLM's spatial awareness capabilities. However, there
exists a noticeable gap between the current spatial awareness capabilities of
MLLM and the requirements set by human needs. To address this issue, this paper
proposes using more precise spatial position information between objects to
guide MLLM in providing more accurate responses to user-related inquiries.
Specifically, for a particular multi-modal task, we utilize algorithms for
acquiring geometric spatial information and scene graphs to obtain relevant
geometric spatial information and scene details of objects involved in the
query. Subsequently, based on this information, we direct MLLM to address
spatial awareness-related queries posed by the user. Extensive experiments were
conducted in benchmarks such as MME, MM-Vet, and other multi-modal large
language models. The experimental results thoroughly confirm the efficacy of
the proposed method in enhancing the spatial awareness tasks and associated
tasks of MLLM.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Soulstyler: Using Large Language Model to Guide Image Style Transfer for Target Object
Abstract: Image style transfer occupies an important place in both computer graphics
and computer vision. However, most current methods require reference to
stylized images and cannot individually stylize specific objects. To overcome
this limitation, we propose the "Soulstyler" framework, which allows users to
guide the stylization of specific objects in an image through simple textual
descriptions. We introduce a large language model to parse the text and
identify stylization goals and specific styles. Combined with a CLIP-based
semantic visual embedding encoder, the model understands and matches text and
image content. We also introduce a novel localized text-image block matching
loss that ensures that style transfer is performed only on specified target
objects, while non-target regions remain in their original style. Experimental
results demonstrate that our model is able to accurately perform style transfer
on target objects according to textual descriptions without affecting the style
of background regions. Our code will be available at
https://github.com/yisuanwang/Soulstyler.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Guiding LLM to Fool Itself: Automatically Manipulating Machine Reading Comprehension Shortcut Triggers
Abstract: Recent applications of LLMs in Machine Reading Comprehension (MRC) systems
have shown impressive results, but the use of shortcuts, mechanisms triggered
by features spuriously correlated to the true label, has emerged as a potential
threat to their reliability. We analyze the problem from two angles: LLMs as
editors, guided to edit text to mislead LLMs; and LLMs as readers, who answer
questions based on the edited text. We introduce a framework that guides an
editor to add potential shortcuts-triggers to samples. Using GPT4 as the
editor, we find it can successfully edit trigger shortcut in samples that fool
LLMs. Analysing LLMs as readers, we observe that even capable LLMs can be
deceived using shortcut knowledge. Strikingly, we discover that GPT4 can be
deceived by its own edits (15% drop in F1). Our findings highlight inherent
vulnerabilities of LLMs to shortcut manipulations. We publish ShortcutQA, a
curated dataset generated by our framework for future research.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Scalable Motion Style Transfer with Constrained Diffusion Generation
Abstract: Current training of motion style transfer systems relies on consistency
losses across style domains to preserve contents, hindering its scalable
application to a large number of domains and private data. Recent image
transfer works show the potential of independent training on each domain by
leveraging implicit bridging between diffusion models, with the content
preservation, however, limited to simple data patterns. We address this by
imposing biased sampling in backward diffusion while maintaining the domain
independence in the training stage. We construct the bias from the source
domain keyframes and apply them as the gradient of content constraints,
yielding a framework with keyframe manifold constraint gradients (KMCGs). Our
validation demonstrates the success of training separate models to transfer
between as many as ten dance motion styles. Comprehensive experiments find a
significant improvement in preserving motion contents in comparison to baseline
and ablative diffusion-based style transfer models. In addition, we perform a
human study for a subjective assessment of the quality of generated dance
motions. The results validate the competitiveness of KMCGs.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: MacGyver: Are Large Language Models Creative Problem Solvers?
Abstract: We explore the creative problem-solving capabilities of modern large language
models (LLMs) in a constrained setting. The setting requires circumventing a
cognitive bias known in psychology as ''functional fixedness'' to use familiar
objects in innovative or unconventional ways. To this end, we create MacGyver,
an automatically generated dataset consisting of 1,600 real-world problems that
deliberately trigger functional fixedness and require thinking
'out-of-the-box'. We then present our collection of problems to both LLMs and
humans to compare and contrast their problem-solving abilities. We show that
MacGyver is challenging for both groups, but in unique and complementary ways.
For example, humans typically excel in solving problems that they are familiar
with but may struggle with tasks requiring domain-specific knowledge, leading
to a higher variance. On the other hand, LLMs, being exposed to a variety of
highly specialized knowledge, attempt broader problems but are prone to
overconfidence and propose actions that are physically infeasible or
inefficient. We also provide a detailed error analysis of LLMs, and demonstrate
the potential of enhancing their problem-solving ability with novel prompting
techniques such as iterative step-wise reflection and divergent-convergent
thinking. This work provides insight into the creative problem-solving
capabilities of humans and AI and illustrates how psychological paradigms can
be extended into large-scale tasks for comparing humans and machines.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Simul-LLM: A Framework for Exploring High-Quality Simultaneous Translation with Large Language Models
Abstract: Large language models (LLMs) with billions of parameters and pretrained on
massive amounts of data are now capable of near or better than state-of-the-art
performance in a variety of downstream natural language processing tasks.
Neural machine translation (NMT) is one such task that LLMs have been applied
to with great success. However, little research has focused on applying LLMs to
the more difficult subset of NMT called simultaneous translation (SimulMT),
where translation begins before the entire source context is available to the
model. In this paper, we address key challenges facing LLMs fine-tuned for
SimulMT, validate classical SimulMT concepts and practices in the context of
LLMs, explore adapting LLMs that are fine-tuned for NMT to the task of SimulMT,
and introduce Simul-LLM, the first open-source fine-tuning and evaluation
pipeline development framework for LLMs focused on SimulMT.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: AuthentiGPT: Detecting Machine-Generated Text via Black-Box Language Models Denoising
Abstract: Large language models (LLMs) have opened up enormous opportunities while
simultaneously posing ethical dilemmas. One of the major concerns is their
ability to create text that closely mimics human writing, which can lead to
potential misuse, such as academic misconduct, disinformation, and fraud. To
address this problem, we present AuthentiGPT, an efficient classifier that
distinguishes between machine-generated and human-written texts. Under the
assumption that human-written text resides outside the distribution of
machine-generated text, AuthentiGPT leverages a black-box LLM to denoise input
text with artificially added noise, and then semantically compares the denoised
text with the original to determine if the content is machine-generated. With
only one trainable parameter, AuthentiGPT eliminates the need for a large
training dataset, watermarking the LLM's output, or computing the
log-likelihood. Importantly, the detection capability of AuthentiGPT can be
easily adapted to any generative language model. With a 0.918 AUROC score on a
domain-specific dataset, AuthentiGPT demonstrates its effectiveness over other
commercial algorithms, highlighting its potential for detecting
machine-generated text in academic settings.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Auto-ICL: In-Context Learning without Human Supervision
Abstract: In the era of Large Language Models (LLMs), human-computer interaction has
evolved towards natural language, offering unprecedented flexibility. Despite
this, LLMs are heavily reliant on well-structured prompts to function
efficiently within the realm of In-Context Learning. Vanilla In-Context
Learning relies on human-provided contexts, such as labeled examples, explicit
instructions, or other guiding mechanisms that shape the model's outputs. To
address this challenge, our study presents a universal framework named
Automatic In-Context Learning. Upon receiving a user's request, we ask the
model to independently generate examples, including labels, instructions, or
reasoning pathways. The model then leverages this self-produced context to
tackle the given problem. Our approach is universally adaptable and can be
implemented in any setting where vanilla In-Context Learning is applicable. We
demonstrate that our method yields strong performance across a range of tasks,
standing up well when compared to existing methods.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Rethinking Variational Inference for Probabilistic Programs with Stochastic Support
Abstract: We introduce Support Decomposition Variational Inference (SDVI), a new
variational inference (VI) approach for probabilistic programs with stochastic
support. Existing approaches to this problem rely on designing a single global
variational guide on a variable-by-variable basis, while maintaining the
stochastic control flow of the original program. SDVI instead breaks the
program down into sub-programs with static support, before automatically
building separate sub-guides for each. This decomposition significantly aids in
the construction of suitable variational families, enabling, in turn,
substantial improvements in inference performance.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs
Abstract: The latest advancements in large language models (LLMs) have revolutionized
the field of natural language processing (NLP). Inspired by the success of LLMs
in NLP tasks, some recent work has begun investigating the potential of
applying LLMs in graph learning tasks. However, most of the existing work
focuses on utilizing LLMs as powerful node feature augmenters, leaving
employing LLMs to enhance graph topological structures an understudied problem.
In this work, we explore how to leverage the information retrieval and text
generation capabilities of LLMs to refine/enhance the topological structure of
text-attributed graphs (TAGs) under the node classification setting. First, we
propose using LLMs to help remove unreliable edges and add reliable ones in the
TAG. Specifically, we first let the LLM output the semantic similarity between
node attributes through delicate prompt designs, and then perform edge deletion
and edge addition based on the similarity. Second, we propose using
pseudo-labels generated by the LLM to improve graph topology, that is, we
introduce the pseudo-label propagation as a regularization to guide the graph
neural network (GNN) in learning proper edge weights. Finally, we incorporate
the two aforementioned LLM-based methods for graph topological refinement into
the process of GNN training, and perform extensive experiments on four
real-world datasets. The experimental results demonstrate the effectiveness of
LLM-based graph topology refinement (achieving a 0.15%--2.47% performance gain
on public benchmarks).

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Distilling and Retrieving Generalizable Knowledge for Robot Manipulation via Language Corrections
Abstract: Today's robot policies exhibit subpar performance when faced with the
challenge of generalizing to novel environments. Human corrective feedback is a
crucial form of guidance to enable such generalization. However, adapting to
and learning from online human corrections is a non-trivial endeavor: not only
do robots need to remember human feedback over time to retrieve the right
information in new settings and reduce the intervention rate, but also they
would need to be able to respond to feedback that can be arbitrary corrections
about high-level human preferences to low-level adjustments to skill
parameters. In this work, we present Distillation and Retrieval of Online
Corrections (DROC), a large language model (LLM)-based system that can respond
to arbitrary forms of language feedback, distill generalizable knowledge from
corrections, and retrieve relevant past experiences based on textual and visual
similarity for improving performance in novel settings. DROC is able to respond
to a sequence of online language corrections that address failures in both
high-level task plans and low-level skill primitives. We demonstrate that DROC
effectively distills the relevant information from the sequence of online
corrections in a knowledge base and retrieves that knowledge in settings with
new task or object instances. DROC outperforms other techniques that directly
generate robot code via LLMs by using only half of the total number of
corrections needed in the first round and requires little to no corrections
after two iterations. We show further results, videos, prompts and code on
https://sites.google.com/stanford.edu/droc .

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Is Probing All You Need? Indicator Tasks as an Alternative to Probing Embedding Spaces
Abstract: The ability to identify and control different kinds of linguistic information
encoded in vector representations of words has many use cases, especially for
explainability and bias removal. This is usually done via a set of simple
classification tasks, termed probes, to evaluate the information encoded in the
embedding space. However, the involvement of a trainable classifier leads to
entanglement between the probe's results and the classifier's nature. As a
result, contemporary works on probing include tasks that do not involve
training of auxiliary models. In this work we introduce the term indicator
tasks for non-trainable tasks which are used to query embedding spaces for the
existence of certain properties, and claim that this kind of tasks may point to
a direction opposite to probes, and that this contradiction complicates the
decision on whether a property exists in an embedding space. We demonstrate our
claims with two test cases, one dealing with gender debiasing and another with
the erasure of morphological information from embedding spaces. We show that
the application of a suitable indicator provides a more accurate picture of the
information captured and removed compared to probes. We thus conclude that
indicator tasks should be implemented and taken into consideration when
eliciting information from embedded representations.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data
Abstract: We study the problem of identifying the unknown intervention targets in
structural causal models where we have access to heterogeneous data collected
from multiple environments. The unknown intervention targets are the set of
endogenous variables whose corresponding exogenous noises change across the
environments. We propose a two-phase approach which in the first phase recovers
the exogenous noises corresponding to unknown intervention targets whose
distributions have changed across environments. In the second phase, the
recovered noises are matched with the corresponding endogenous variables. For
the recovery phase, we provide sufficient conditions for learning these
exogenous noises up to some component-wise invertible transformation. For the
matching phase, under the causal sufficiency assumption, we show that the
proposed method uniquely identifies the intervention targets. In the presence
of latent confounders, the intervention targets among the observed variables
cannot be determined uniquely. We provide a candidate intervention target set
which is a superset of the true intervention targets. Our approach improves
upon the state of the art as the returned candidate set is always a subset of
the target set returned by previous work. Moreover, we do not require
restrictive assumptions such as linearity of the causal model or performing
invariance tests to learn whether a distribution is changing across
environments which could be highly sample inefficient. Our experimental results
show the effectiveness of our proposed algorithm in practice.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?
Abstract: Programming language understanding and representation (a.k.a code
representation learning) has always been a hot and challenging task in software
engineering. It aims to apply deep learning techniques to produce numerical
representations of the source code features while preserving its semantics.
These representations can be used for facilitating subsequent code-related
tasks. The abstract syntax tree (AST), a fundamental code feature, illustrates
the syntactic information of the source code and has been widely used in code
representation learning. However, there is still a lack of systematic and
quantitative evaluation of how well AST-based code representation facilitates
subsequent code-related tasks. In this paper, we first conduct a comprehensive
empirical study to explore the effectiveness of the AST-based code
representation in facilitating follow-up code-related tasks. To do so, we
compare the performance of models trained with code token sequence (Token for
short) based code representation and AST-based code representation on three
popular types of code-related tasks. Surprisingly, the overall quantitative
statistical results demonstrate that models trained with AST-based code
representation consistently perform worse across all three tasks compared to
models trained with Token-based code representation. Our further quantitative
analysis reveals that models trained with AST-based code representation
outperform models trained with Token-based code representation in certain
subsets of samples across all three tasks. We also conduct comprehensive
experiments to evaluate and reveal the impact of the choice of AST
parsing/preprocessing/encoding methods on AST-based code representation and
subsequent code-related tasks. Our study provides future researchers with
detailed guidance on how to select solutions at each stage to fully exploit
AST.

Output: Software Engineering


Instruction: What field is the article from?

Prompt:
Title: Unlearning via Sparse Representations
Abstract: Machine \emph{unlearning}, which involves erasing knowledge about a
\emph{forget set} from a trained model, can prove to be costly and infeasible
by existing techniques. We propose a nearly compute-free zero-shot unlearning
technique based on a discrete representational bottleneck. We show that the
proposed technique efficiently unlearns the forget set and incurs negligible
damage to the model's performance on the rest of the data set. We evaluate the
proposed technique on the problem of \textit{class unlearning} using three
datasets: CIFAR-10, CIFAR-100, and LACUNA-100. We compare the proposed
technique to SCRUB, a state-of-the-art approach which uses knowledge
distillation for unlearning. Across all three datasets, the proposed technique
performs as well as, if not better than SCRUB while incurring almost no
computational cost.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Curriculum Learning and Imitation Learning for Model-free Control on Financial Time-series
Abstract: Curriculum learning and imitation learning have been leveraged extensively in
the robotics domain. However, minimal research has been done on leveraging
these ideas on control tasks over highly stochastic time-series data. Here, we
theoretically and empirically explore these approaches in a representative
control task over complex time-series data. We implement the fundamental ideas
of curriculum learning via data augmentation, while imitation learning is
implemented via policy distillation from an oracle. Our findings reveal that
curriculum learning should be considered a novel direction in improving
control-task performance over complex time-series. Our ample random-seed
out-sample empirics and ablation studies are highly encouraging for curriculum
learning for time-series control. These findings are especially encouraging as
we tune all overlapping hyperparameters on the baseline -- giving an advantage
to the baseline. On the other hand, we find that imitation learning should be
used with caution.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: LOKE: Linked Open Knowledge Extraction for Automated Knowledge Graph Construction
Abstract: While the potential of Open Information Extraction (Open IE) for Knowledge
Graph Construction (KGC) may seem promising, we find that the alignment of Open
IE extraction results with existing knowledge graphs to be inadequate. The
advent of Large Language Models (LLMs), especially the commercially available
OpenAI models, have reset expectations for what is possible with deep learning
models and have created a new field called prompt engineering. We investigate
the use of GPT models and prompt engineering for knowledge graph construction
with the Wikidata knowledge graph to address a similar problem to Open IE,
which we call Open Knowledge Extraction (OKE) using an approach we call the
Linked Open Knowledge Extractor (LOKE, pronounced like "Loki"). We consider the
entity linking task essential to construction of real world knowledge graphs.
We merge the CaRB benchmark scoring approach with data from the TekGen dataset
for the LOKE task. We then show that a well engineered prompt, paired with a
naive entity linking approach (which we call LOKE-GPT), outperforms AllenAI's
OpenIE 4 implementation on the OKE task, although it over-generates triples
compared to the reference set due to overall triple scarcity in the TekGen set.
Through an analysis of entity linkability in the CaRB dataset, as well as
outputs from OpenIE 4 and LOKE-GPT, we see that LOKE-GPT and the "silver"
TekGen triples show that the task is significantly different in content from
OIE, if not structure. Through this analysis and a qualitative analysis of
sentence extractions via all methods, we found that LOKE-GPT extractions are of
high utility for the KGC task and suitable for use in semi-automated extraction
settings.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: FloodBrain: Flood Disaster Reporting by Web-based Retrieval Augmented Generation with an LLM
Abstract: Fast disaster impact reporting is crucial in planning humanitarian
assistance. Large Language Models (LLMs) are well known for their ability to
write coherent text and fulfill a variety of tasks relevant to impact
reporting, such as question answering or text summarization. However, LLMs are
constrained by the knowledge within their training data and are prone to
generating inaccurate, or "hallucinated", information. To address this, we
introduce a sophisticated pipeline embodied in our tool FloodBrain
(floodbrain.com), specialized in generating flood disaster impact reports by
extracting and curating information from the web. Our pipeline assimilates
information from web search results to produce detailed and accurate reports on
flood events. We test different LLMs as backbones in our tool and compare their
generated reports to human-written reports on different metrics. Similar to
other studies, we find a notable correlation between the scores assigned by
GPT-4 and the scores given by human evaluators when comparing our generated
reports to human-authored ones. Additionally, we conduct an ablation study to
test our single pipeline components and their relevancy for the final reports.
With our tool, we aim to advance the use of LLMs for disaster impact reporting
and reduce the time for coordination of humanitarian efforts in the wake of
flood disasters.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing
Abstract: In this paper, we address the issue of increasing the performance of
reinforcement learning (RL) solutions for autonomous racing cars when
navigating under conditions where practical vehicle modelling errors (commonly
known as \emph{model mismatches}) are present. To address this challenge, we
propose a partial end-to-end algorithm that decouples the planning and control
tasks. Within this framework, an RL agent generates a trajectory comprising a
path and velocity, which is subsequently tracked using a pure pursuit steering
controller and a proportional velocity controller, respectively. In contrast,
many current learning-based (i.e., reinforcement and imitation learning)
algorithms utilise an end-to-end approach whereby a deep neural network
directly maps from sensor data to control commands. By leveraging the
robustness of a classical controller, our partial end-to-end driving algorithm
exhibits better robustness towards model mismatches than standard end-to-end
algorithms.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm
Abstract: Contemporary machine learning requires training large neural networks on
massive datasets and thus faces the challenges of high computational demands.
Dataset distillation, as a recent emerging strategy, aims to compress
real-world datasets for efficient training. However, this line of research
currently struggle with large-scale and high-resolution datasets, hindering its
practicality and feasibility. To this end, we re-examine the existing dataset
distillation methods and identify three properties required for large-scale
real-world applications, namely, realism, diversity, and efficiency. As a
remedy, we propose RDED, a novel computationally-efficient yet effective data
distillation paradigm, to enable both diversity and realism of the distilled
data. Extensive empirical results over various neural architectures and
datasets demonstrate the advancement of RDED: we can distill the full
ImageNet-1K to a small dataset comprising 10 images per class within 7 minutes,
achieving a notable 42% top-1 accuracy with ResNet-18 on a single RTX-4090 GPU
(while the SOTA only achieves 21% but requires 6 hours).

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: A Language and Its Dimensions: Intrinsic Dimensions of Language Fractal Structures
Abstract: The present paper introduces a novel object of study - a language fractal
structure. We hypothesize that a set of embeddings of all $n$-grams of a
natural language constitutes a representative sample of this fractal set. (We
use the term Hailonakea to refer to the sum total of all language fractal
structures, over all $n$). The paper estimates intrinsic (genuine) dimensions
of language fractal structures for the Russian and English languages. To this
end, we employ methods based on (1) topological data analysis and (2) a minimum
spanning tree of a data graph for a cloud of points considered (Steele
theorem). For both languages, for all $n$, the intrinsic dimensions appear to
be non-integer values (typical for fractal sets), close to 9 for both of the
Russian and English language.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Token Recycling for Efficient Sequential Inference with Vision Transformers
Abstract: Vision Transformers (ViTs) overpass Convolutional Neural Networks in
processing incomplete inputs because they do not require the imputation of
missing values. Therefore, ViTs are well suited for sequential decision-making,
e.g. in the Active Visual Exploration problem. However, they are
computationally inefficient because they perform a full forward pass each time
a piece of new sequential information arrives.
  To reduce this computational inefficiency, we introduce the TOken REcycling
(TORE) modification for the ViT inference, which can be used with any
architecture. TORE divides ViT into two parts, iterator and aggregator. An
iterator processes sequential information separately into midway tokens, which
are cached. The aggregator processes midway tokens jointly to obtain the
prediction. This way, we can reuse the results of computations made by
iterator.
  Except for efficient sequential inference, we propose a complementary
training policy, which significantly reduces the computational burden
associated with sequential decision-making while achieving state-of-the-art
accuracy.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: A Reusable AI-Enabled Defect Detection System for Railway Using Ensembled CNN
Abstract: Accurate Defect detection is crucial for ensuring the trustworthiness of
intelligent railway systems. Current approaches rely on single deep-learning
models, like CNNs, which employ a large amount of data to capture underlying
patterns. Training a new defect classifier with limited samples often leads to
overfitting and poor performance on unseen images. To address this, researchers
have advocated transfer learning and fine-tuning the pre-trained models.
However, using a single backbone network in transfer learning still may cause
bottleneck issues and inconsistent performance if it is not suitable for a
specific problem domain. To overcome these challenges, we propose a reusable
AI-enabled defect detection approach. By combining ensemble learning with
transfer learning models (VGG-19, MobileNetV3, and ResNet-50), we improved the
classification accuracy and achieved consistent performance at a certain phase
of training. Our empirical analysis demonstrates better and more consistent
performance compared to other state-of-the-art approaches. The consistency
substantiates the reusability of the defect detection system for newly evolved
defected rail parts. Therefore we anticipate these findings to benefit further
research and development of reusable AI-enabled solutions for railway systems.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Class-Aware Pruning for Efficient Neural Networks
Abstract: Deep neural networks (DNNs) have demonstrated remarkable success in various
fields. However, the large number of floating-point operations (FLOPs) in DNNs
poses challenges for their deployment in resource-constrained applications,
e.g., edge devices. To address the problem, pruning has been introduced to
reduce the computational cost in executing DNNs. Previous pruning strategies
are based on weight values, gradient values and activation outputs. Different
from previous pruning solutions, in this paper, we propose a class-aware
pruning technique to compress DNNs, which provides a novel perspective to
reduce the computational cost of DNNs. In each iteration, the neural network
training is modified to facilitate the class-aware pruning. Afterwards, the
importance of filters with respect to the number of classes is evaluated. The
filters that are only important for a few number of classes are removed. The
neural network is then retrained to compensate for the incurred accuracy loss.
The pruning iterations end until no filter can be removed anymore, indicating
that the remaining filters are very important for many classes. This pruning
technique outperforms previous pruning solutions in terms of accuracy, pruning
ratio and the reduction of FLOPs. Experimental results confirm that this
class-aware pruning technique can significantly reduce the number of weights
and FLOPs, while maintaining a high inference accuracy.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Implicit Chain of Thought Reasoning via Knowledge Distillation
Abstract: To augment language models with the ability to reason, researchers usually
prompt or finetune them to produce chain of thought reasoning steps before
producing the final answer. However, although people use natural language to
reason effectively, it may be that LMs could reason more effectively with some
intermediate computation that is not in natural language. In this work, we
explore an alternative reasoning approach: instead of explicitly producing the
chain of thought reasoning steps, we use the language model's internal hidden
states to perform implicit reasoning. The implicit reasoning steps are
distilled from a teacher model trained on explicit chain-of-thought reasoning,
and instead of doing reasoning "horizontally" by producing intermediate words
one-by-one, we distill it such that the reasoning happens "vertically" among
the hidden states in different layers. We conduct experiments on a multi-digit
multiplication task and a grade school math problem dataset and find that this
approach enables solving tasks previously not solvable without explicit
chain-of-thought, at a speed comparable to no chain-of-thought.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Using Analytics on Student Created Data to Content Validate Pedagogical Tools
Abstract: Conceptual and simulation models can function as useful pedagogical tools,
however it is important to categorize different outcomes when evaluating them
in order to more meaningfully interpret results. VERA is a ecology-based
conceptual modeling software that enables users to simulate interactions
between biotics and abiotics in an ecosystem, allowing users to form and then
verify hypothesis through observing a time series of the species populations.
In this paper, we classify this time series into common patterns found in the
domain of ecological modeling through two methods, hierarchical clustering and
curve fitting, illustrating a general methodology for showing content validity
when combining different pedagogical tools. When applied to a diverse sample of
263 models containing 971 time series collected from three different VERA user
categories: a Georgia Tech (GATECH), North Georgia Technical College (NGTC),
and ``Self Directed Learners'', results showed agreement between both
classification methods on 89.38\% of the sample curves in the test set. This
serves as a good indication that our methodology for determining content
validity was successful.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Axiomatic Preference Modeling for Longform Question Answering
Abstract: The remarkable abilities of large language models (LLMs) like GPT-4 partially
stem from post-training processes like Reinforcement Learning from Human
Feedback (RLHF) involving human preferences encoded in a reward model. However,
these reward models (RMs) often lack direct knowledge of why, or under what
principles, the preferences annotations were made. In this study, we identify
principles that guide RMs to better align with human preferences, and then
develop an axiomatic framework to generate a rich variety of preference signals
to uphold them. We use these axiomatic signals to train a model for scoring
answers to longform questions. Our approach yields a Preference Model with only
about 220M parameters that agrees with gold human-annotated preference labels
more often than GPT-4. The contributions of this work include: training a
standalone preference model that can score human- and LLM-generated answers on
the same scale; developing an axiomatic framework for generating training data
pairs tailored to certain principles; and showing that a small amount of
axiomatic signals can help small models outperform GPT-4 in preference scoring.
We release our model on huggingface:
https://huggingface.co/corbyrosset/axiomatic_preference_model

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Survey on AI Ethics: A Socio-technical Perspective
Abstract: The past decade has observed a great advancement in AI with deep
learning-based models being deployed in diverse scenarios including
safety-critical applications. As these AI systems become deeply embedded in our
societal infrastructure, the repercussions of their decisions and actions have
significant consequences, making the ethical implications of AI deployment
highly relevant and important. The ethical concerns associated with AI are
multifaceted, including challenging issues of fairness, privacy and data
protection, responsibility and accountability, safety and robustness,
transparency and explainability, and environmental impact. These principles
together form the foundations of ethical AI considerations that concern every
stakeholder in the AI system lifecycle. In light of the present ethical and
future x-risk concerns, governments have shown increasing interest in
establishing guidelines for the ethical deployment of AI. This work unifies the
current and future ethical concerns of deploying AI into society. While we
acknowledge and appreciate the technical surveys for each of the ethical
principles concerned, in this paper, we aim to provide a comprehensive overview
that not only addresses each principle from a technical point of view but also
discusses them from a social perspective.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: QWID: Quantized Weed Identification Deep neural network
Abstract: In this paper, we present an efficient solution for weed classification in
agriculture. We focus on optimizing model performance at inference while
respecting the constraints of the agricultural domain. We propose a Quantized
Deep Neural Network model that classifies a dataset of 9 weed classes using
8-bit integer (int8) quantization, a departure from standard 32-bit floating
point (fp32) models. Recognizing the hardware resource limitations in
agriculture, our model balances model size, inference time, and accuracy,
aligning with practical requirements. We evaluate the approach on ResNet-50 and
InceptionV3 architectures, comparing their performance against their int8
quantized versions. Transfer learning and fine-tuning are applied using the
DeepWeeds dataset. The results show staggering model size and inference time
reductions while maintaining accuracy in real-world production scenarios like
Desktop, Mobile and Raspberry Pi. Our work sheds light on a promising direction
for efficient AI in agriculture, holding potential for broader applications.
  Code: https://github.com/parikshit14/QNN-for-weed

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Can Reinforcement Learning support policy makers? A preliminary study with Integrated Assessment Models
Abstract: Governments around the world aspire to ground decision-making on evidence.
Many of the foundations of policy making - e.g. sensing patterns that relate to
societal needs, developing evidence-based programs, forecasting potential
outcomes of policy changes, and monitoring effectiveness of policy programs -
have the potential to benefit from the use of large-scale datasets or
simulations together with intelligent algorithms. These could, if designed and
deployed in a way that is well grounded on scientific evidence, enable a more
comprehensive, faster, and rigorous approach to policy making. Integrated
Assessment Models (IAM) is a broad umbrella covering scientific models that
attempt to link main features of society and economy with the biosphere into
one modelling framework. At present, these systems are probed by policy makers
and advisory groups in a hypothesis-driven manner. In this paper, we
empirically demonstrate that modern Reinforcement Learning can be used to probe
IAMs and explore the space of solutions in a more principled manner. While the
implication of our results are modest since the environment is simplistic, we
believe that this is a stepping stone towards more ambitious use cases, which
could allow for effective exploration of policies and understanding of their
consequences and limitations.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter
Abstract: The compact muon solenoid (CMS) experiment is a general-purpose detector for
high-energy collision at the large hadron collider (LHC) at CERN. It employs an
online data quality monitoring (DQM) system to promptly spot and diagnose
particle data acquisition problems to avoid data quality loss. In this study,
we present semi-supervised spatio-temporal anomaly detection (AD) monitoring
for the physics particle reading channels of the hadronic calorimeter (HCAL) of
the CMS using three-dimensional digi-occupancy map data of the DQM. We propose
the GraphSTAD system, which employs convolutional and graph neural networks to
learn local spatial characteristics induced by particles traversing the
detector, and global behavior owing to shared backend circuit connections and
housing boxes of the channels, respectively. Recurrent neural networks capture
the temporal evolution of the extracted spatial features. We have validated the
accuracy of the proposed AD system in capturing diverse channel fault types
using the LHC Run-2 collision data sets. The GraphSTAD system has achieved
production-level accuracy and is being integrated into the CMS core production
system--for real-time monitoring of the HCAL. We have also provided a
quantitative performance comparison with alternative benchmark models to
demonstrate the promising leverage of the presented system.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Word for Person: Zero-shot Composed Person Retrieval
Abstract: Searching for specific person has great security value and social benefits,
and it often involves a combination of visual and textual information.
Conventional person retrieval methods, whether image-based or text-based,
usually fall short in effectively harnessing both types of information, leading
to the loss of accuracy. In this paper, a whole new task called Composed Person
Retrieval (CPR) is proposed to jointly utilize both image and text information
for target person retrieval. However, the supervised CPR must depend on very
costly manual annotation dataset, while there are currently no available
resources. To mitigate this issue, we firstly introduce the Zero-shot Composed
Person Retrieval (ZS-CPR), which leverages existing domain-related data to
resolve the CPR problem without reliance on expensive annotations. Secondly, to
learn ZS-CPR model, we propose a two-stage learning framework, Word4Per, where
a lightweight Textual Inversion Network (TINet) and a text-based person
retrieval model based on fine-tuned Contrastive Language-Image Pre-training
(CLIP) network are learned without utilizing any CPR data. Thirdly, a finely
annotated Image-Text Composed Person Retrieval dataset (ITCPR) is built as the
benchmark to assess the performance of the proposed Word4Per framework.
Extensive experiments under both Rank-1 and mAP demonstrate the effectiveness
of Word4Per for the ZS-CPR task, surpassing the comparative methods by over
10%. The code and ITCPR dataset will be publicly available at
https://github.com/Delong-liu-bupt/Word4Per.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations
Abstract: Peer learning is a novel high-level reinforcement learning framework for
agents learning in groups. While standard reinforcement learning trains an
individual agent in trial-and-error fashion, all on its own, peer learning
addresses a related setting in which a group of agents, i.e., peers, learns to
master a task simultaneously together from scratch. Peers are allowed to
communicate only about their own states and actions recommended by others:
"What would you do in my situation?". Our motivation is to study the learning
behavior of these agents. We formalize the teacher selection process in the
action advice setting as a multi-armed bandit problem and therefore highlight
the need for exploration. Eventually, we analyze the learning behavior of the
peers and observe their ability to rank the agents' performance within the
study group and understand which agents give reliable advice. Further, we
compare peer learning with single agent learning and a state-of-the-art action
advice baseline. We show that peer learning is able to outperform single-agent
learning and the baseline in several challenging discrete and continuous OpenAI
Gym domains. Doing so, we also show that within such a framework complex
policies from action recommendations beyond discrete action spaces can evolve.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Implementation of AI Deep Learning Algorithm For Multi-Modal Sentiment Analysis
Abstract: A multi-modal emotion recognition method was established by combining
two-channel convolutional neural network with ring network. This method can
extract emotional information effectively and improve learning efficiency. The
words were vectorized with GloVe, and the word vector was input into the
convolutional neural network. Combining attention mechanism and maximum pool
converter BiSRU channel, the local deep emotion and pre-post sequential emotion
semantics are obtained. Finally, multiple features are fused and input as the
polarity of emotion, so as to achieve the emotion analysis of the target.
Experiments show that the emotion analysis method based on feature fusion can
effectively improve the recognition accuracy of emotion data set and reduce the
learning time. The model has a certain generalization.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Understanding Practices around Computational News Discovery Tools in the Domain of Science Journalism
Abstract: Science and technology journalists today face challenges in finding
newsworthy leads due to increased workloads, reduced resources, and expanding
scientific publishing ecosystems. Given this context, we explore computational
methods to aid these journalists' news discovery in terms of time-efficiency
and agency. In particular, we prototyped three computational information
subsidies into an interactive tool that we used as a probe to better understand
how such a tool may offer utility or more broadly shape the practices of
professional science journalists. Our findings highlight central considerations
around science journalists' agency, context, and responsibilities that such
tools can influence and could account for in design. Based on this, we suggest
design opportunities for greater and longer-term user agency; incorporating
contextual, personal and collaborative notions of newsworthiness; and
leveraging flexible interfaces and generative models. Overall, our findings
contribute a richer view of the sociotechnical system around computational news
discovery tools, and suggest ways to improve such tools to better support the
practices of science journalists.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Successor Features for Efficient Multisubject Controlled Text Generation
Abstract: While large language models (LLMs) have achieved impressive performance in
generating fluent and realistic text, controlling the generated text so that it
exhibits properties such as safety, factuality, and non-toxicity remains
challenging. % such as DExperts, GeDi, and rectification Existing
decoding-based methods are static in terms of the dimension of control; if the
target subject is changed, they require new training. Moreover, it can quickly
become prohibitive to concurrently control multiple subjects. In this work, we
introduce SF-GEN, which is grounded in two primary concepts: successor features
(SFs) to decouple the LLM's dynamics from task-specific rewards, and language
model rectification to proportionally adjust the probability of selecting a
token based on the likelihood that the finished text becomes undesired. SF-GEN
seamlessly integrates the two to enable dynamic steering of text generation
with no need to alter the LLM's parameters. Thanks to the decoupling effect
induced by successor features, our method proves to be memory-wise and
computationally efficient for training as well as decoding, especially when
dealing with multiple target subjects. To the best of our knowledge, our
research represents the first application of successor features in text
generation. In addition to its computational efficiency, the resultant language
produced by our method is comparable to the SOTA (and outperforms baselines) in
both control measures as well as language quality, which we demonstrate through
a series of experiments in various controllable text generation tasks.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: SEA++: Multi-Graph-based High-Order Sensor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation
Abstract: Unsupervised Domain Adaptation (UDA) methods have been successful in reducing
label dependency by minimizing the domain discrepancy between a labeled source
domain and an unlabeled target domain. However, these methods face challenges
when dealing with Multivariate Time-Series (MTS) data. MTS data typically
consist of multiple sensors, each with its own unique distribution. This
characteristic makes it hard to adapt existing UDA methods, which mainly focus
on aligning global features while overlooking the distribution discrepancies at
the sensor level, to reduce domain discrepancies for MTS data. To address this
issue, a practical domain adaptation scenario is formulated as Multivariate
Time-Series Unsupervised Domain Adaptation (MTS-UDA). In this paper, we propose
SEnsor Alignment (SEA) for MTS-UDA, aiming to reduce domain discrepancy at both
the local and global sensor levels. At the local sensor level, we design
endo-feature alignment, which aligns sensor features and their correlations
across domains. To reduce domain discrepancy at the global sensor level, we
design exo-feature alignment that enforces restrictions on global sensor
features. We further extend SEA to SEA++ by enhancing the endo-feature
alignment. Particularly, we incorporate multi-graph-based high-order alignment
for both sensor features and their correlations. Extensive empirical results
have demonstrated the state-of-the-art performance of our SEA and SEA++ on
public MTS datasets for MTS-UDA.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Construction
Abstract: Knowledge graph construction (KGC) is a multifaceted undertaking involving
the extraction of entities, relations, and events. Traditionally, large
language models (LLMs) have been viewed as solitary task-solving agents in this
complex landscape. However, this paper challenges this paradigm by introducing
a novel framework, CooperKGC. Departing from the conventional approach,
CooperKGC establishes a collaborative processing network, assembling a KGC
collaboration team capable of concurrently addressing entity, relation, and
event extraction tasks. Our experiments unequivocally demonstrate that
fostering collaboration and information interaction among diverse agents within
CooperKGC yields superior results compared to individual cognitive processes
operating in isolation. Importantly, our findings reveal that the collaboration
facilitated by CooperKGC enhances knowledge selection, correction, and
aggregation capabilities across multiple rounds of interactions.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Navigating Open Set Scenarios for Skeleton-based Action Recognition
Abstract: In real-world scenarios, human actions often fall outside the distribution of
training data, making it crucial for models to recognize known actions and
reject unknown ones. However, using pure skeleton data in such open-set
conditions poses challenges due to the lack of visual background cues and the
distinct sparse structure of body pose sequences. In this paper, we tackle the
unexplored Open-Set Skeleton-based Action Recognition (OS-SAR) task and
formalize the benchmark on three skeleton-based datasets. We assess the
performance of seven established open-set approaches on our task and identify
their limits and critical generalization issues when dealing with skeleton
information. To address these challenges, we propose a distance-based
cross-modality ensemble method that leverages the cross-modal alignment of
skeleton joints, bones, and velocities to achieve superior open-set recognition
performance. We refer to the key idea as CrossMax - an approach that utilizes a
novel cross-modality mean max discrepancy suppression mechanism to align latent
spaces during training and a cross-modality distance-based logits refinement
method during testing. CrossMax outperforms existing approaches and
consistently yields state-of-the-art results across all datasets and backbones.
The benchmark, code, and models will be released at
https://github.com/KPeng9510/OS-SAR.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: A Novel Neural Network-Based Federated Learning System for Imbalanced and Non-IID Data
Abstract: With the growth of machine learning techniques, privacy of data of users has
become a major concern. Most of the machine learning algorithms rely heavily on
large amount of data which may be collected from various sources. Collecting
these data yet maintaining privacy policies has become one of the most
challenging tasks for the researchers. To combat this issue, researchers have
introduced federated learning, where a prediction model is learnt by ensuring
the privacy of data of clients data. However, the prevalent federated learning
algorithms possess an accuracy and efficiency trade-off, especially for non-IID
data. In this research, we propose a centralized, neural network-based
federated learning system. The centralized algorithm incorporates micro-level
parallel processing inspired by the traditional mini-batch algorithm where the
client devices and the server handle the forward and backward propagation
respectively. We also devise a semi-centralized version of our proposed
algorithm. This algorithm takes advantage of edge computing for minimizing the
load from the central server, where clients handle both the forward and
backward propagation while sacrificing the overall train time to some extent.
We evaluate our proposed systems on five well-known benchmark datasets and
achieve satisfactory performance in a reasonable time across various data
distribution settings as compared to some existing benchmark algorithms.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Large Trajectory Models are Scalable Motion Predictors and Planners
Abstract: Motion prediction and planning are vital tasks in autonomous driving, and
recent efforts have shifted to machine learning-based approaches. The
challenges include understanding diverse road topologies, reasoning traffic
dynamics over a long time horizon, interpreting heterogeneous behaviors, and
generating policies in a large continuous state space. Inspired by the success
of large language models in addressing similar complexities through model
scaling, we introduce a scalable trajectory model called State Transformer
(STR). STR reformulates the motion prediction and motion planning problems by
arranging observations, states, and actions into one unified sequence modeling
task. With a simple model design, STR consistently outperforms baseline
approaches in both problems. Remarkably, experimental results reveal that large
trajectory models (LTMs), such as STR, adhere to the scaling laws by presenting
outstanding adaptability and learning efficiency. Qualitative results further
demonstrate that LTMs are capable of making plausible predictions in scenarios
that diverge significantly from the training data distribution. LTMs also learn
to make complex reasonings for long-term planning, without explicit loss
designs or costly high-level annotations.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Exploring the Potential of Generative AI for the World Wide Web
Abstract: Generative Artificial Intelligence (AI) is a cutting-edge technology capable
of producing text, images, and various media content leveraging generative
models and user prompts. Between 2022 and 2023, generative AI surged in
popularity with a plethora of applications spanning from AI-powered movies to
chatbots. In this paper, we delve into the potential of generative AI within
the realm of the World Wide Web, specifically focusing on image generation. Web
developers already harness generative AI to help crafting text and images,
while Web browsers might use it in the future to locally generate images for
tasks like repairing broken webpages, conserving bandwidth, and enhancing
privacy. To explore this research area, we have developed WebDiffusion, a tool
that allows to simulate a Web powered by stable diffusion, a popular
text-to-image model, from both a client and server perspective. WebDiffusion
further supports crowdsourcing of user opinions, which we use to evaluate the
quality and accuracy of 409 AI-generated images sourced from 60 webpages. Our
findings suggest that generative AI is already capable of producing pertinent
and high-quality Web images, even without requiring Web designers to manually
input prompts, just by leveraging contextual information available within the
webpages. However, we acknowledge that direct in-browser image generation
remains a challenge, as only highly powerful GPUs, such as the A40 and A100,
can (partially) compete with classic image downloads. Nevertheless, this
approach could be valuable for a subset of the images, for example when fixing
broken webpages or handling highly private content.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Data Science for Social Good
Abstract: Data science has been described as the fourth paradigm for scientific
discovery. The latest wave of data science research, pertaining to machine
learning and artificial intelligence (AI), is growing exponentially and
garnering millions of annual citations. However, this growth has been
accompanied by a diminishing emphasis on social good challenges - our analysis
reveals that the proportion of data science research focusing on social good is
less than it has ever been. At the same time, the proliferation of machine
learning and generative AI have sparked debates about the socio-technical
prospects and challenges associated with data science for human flourishing,
organizations, and society. Against this backdrop, we present a framework for
"data science for social good" (DSSG) research that considers the interplay
between relevant data science research genres, social good challenges, and
different levels of socio-technical abstraction. We perform an analysis of the
literature to empirically demonstrate the paucity of work on DSSG in
information systems (and other related disciplines) and highlight current
impediments. We then use our proposed framework to introduce the articles
appearing in the special issue. We hope that this article and the special issue
will spur future DSSG research and help reverse the alarming trend across data
science research over the past 30-plus years in which social good challenges
are garnering proportionately less attention with each passing day.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Large Language Model Enhanced Multi-Agent Systems for 6G Communications
Abstract: The rapid development of the Large Language Model (LLM) presents huge
opportunities for 6G communications, e.g., network optimization and management
by allowing users to input task requirements to LLMs by nature language.
However, directly applying native LLMs in 6G encounters various challenges,
such as a lack of private communication data and knowledge, limited logical
reasoning, evaluation, and refinement abilities. Integrating LLMs with the
capabilities of retrieval, planning, memory, evaluation and reflection in
agents can greatly enhance the potential of LLMs for 6G communications. To this
end, we propose a multi-agent system with customized communication knowledge
and tools for solving communication related tasks using natural language,
comprising three components: (1) Multi-agent Data Retrieval (MDR), which
employs the condensate and inference agents to refine and summarize
communication knowledge from the knowledge base, expanding the knowledge
boundaries of LLMs in 6G communications; (2) Multi-agent Collaborative Planning
(MCP), which utilizes multiple planning agents to generate feasible solutions
for the communication related task from different perspectives based on the
retrieved knowledge; (3) Multi-agent Evaluation and Reflecxion (MER), which
utilizes the evaluation agent to assess the solutions, and applies the
reflexion agent and refinement agent to provide improvement suggestions for
current solutions. Finally, we validate the effectiveness of the proposed
multi-agent system by designing a semantic communication system, as a case
study of 6G communications.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: JADE: A Linguistics-based Safety Evaluation Platform for Large Language Models
Abstract: In this paper, we present JADE, a targeted linguistic fuzzing platform which
strengthens the linguistic complexity of seed questions to simultaneously and
consistently break a wide range of widely-used LLMs categorized in three
groups: eight open-sourced Chinese, six commercial Chinese and four commercial
English LLMs. JADE generates three safety benchmarks for the three groups of
LLMs, which contain unsafe questions that are highly threatening: the questions
simultaneously trigger harmful generation of multiple LLMs, with an average
unsafe generation ratio of $70\%$ (please see the table below), while are still
natural questions, fluent and preserving the core unsafe semantics. We release
the benchmark demos generated for commercial English LLMs and open-sourced
English LLMs in the following link: https://github.com/whitzard-ai/jade-db. For
readers who are interested in evaluating on more questions generated by JADE,
please contact us.
  JADE is based on Noam Chomsky's seminal theory of transformational-generative
grammar. Given a seed question with unsafe intention, JADE invokes a sequence
of generative and transformational rules to increment the complexity of the
syntactic structure of the original question, until the safety guardrail is
broken. Our key insight is: Due to the complexity of human language, most of
the current best LLMs can hardly recognize the invariant evil from the infinite
number of different syntactic structures which form an unbound example space
that can never be fully covered. Technically, the generative/transformative
rules are constructed by native speakers of the languages, and, once developed,
can be used to automatically grow and transform the parse tree of a given
question, until the guardrail is broken. For more evaluation results and demo,
please check our website: https://whitzard-ai.github.io/jade.html.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Cost Aware Untargeted Poisoning Attack against Graph Neural Networks,
Abstract: Graph Neural Networks (GNNs) have become widely used in the field of graph
mining. However, these networks are vulnerable to structural perturbations.
While many research efforts have focused on analyzing vulnerability through
poisoning attacks, we have identified an inefficiency in current attack losses.
These losses steer the attack strategy towards modifying edges targeting
misclassified nodes or resilient nodes, resulting in a waste of structural
adversarial perturbation. To address this issue, we propose a novel attack loss
framework called the Cost Aware Poisoning Attack (CA-attack) to improve the
allocation of the attack budget by dynamically considering the classification
margins of nodes. Specifically, it prioritizes nodes with smaller positive
margins while postponing nodes with negative margins. Our experiments
demonstrate that the proposed CA-attack significantly enhances existing attack
strategies

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: A New Fine-grained Alignment Method for Image-text Matching
Abstract: Image-text retrieval is a widely studied topic in the field of computer
vision due to the exponential growth of multimedia data, whose core concept is
to measure the similarity between images and text. However, most existing
retrieval methods heavily rely on cross-attention mechanisms for cross-modal
fine-grained alignment, which takes into account excessive irrelevant regions
and treats prominent and non-significant words equally, thereby limiting
retrieval accuracy. This paper aims to investigate an alignment approach that
reduces the involvement of non-significant fragments in images and text while
enhancing the alignment of prominent segments. For this purpose, we introduce
the Cross-Modal Prominent Fragments Enhancement Aligning Network(CPFEAN), which
achieves improved retrieval accuracy by diminishing the participation of
irrelevant regions during alignment and relatively increasing the alignment
similarity of prominent words. Additionally, we incorporate prior textual
information into image regions to reduce misalignment occurrences. In practice,
we first design a novel intra-modal fragments relationship reasoning method,
and subsequently employ our proposed alignment mechanism to compute the
similarity between images and text. Extensive quantitative comparative
experiments on MS-COCO and Flickr30K datasets demonstrate that our approach
outperforms state-of-the-art methods by about 5% to 10% in the rSum metric.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Multi-view Relation Learning for Cross-domain Few-shot Hyperspectral Image Classification
Abstract: Cross-domain few-shot hyperspectral image classification focuses on learning
prior knowledge from a large number of labeled samples from source domain and
then transferring the knowledge to the tasks which contain only few labeled
samples in target domains. Following the metric-based manner, many current
methods first extract the features of the query and support samples, and then
directly predict the classes of query samples according to their distance to
the support samples or prototypes. The relations between samples have not been
fully explored and utilized. Different from current works, this paper proposes
to learn sample relations from different views and take them into the model
learning process, to improve the cross-domain few-shot hyperspectral image
classification. Building on current DCFSL method which adopts a domain
discriminator to deal with domain-level distribution difference, the proposed
method applys contrastive learning to learn the class-level sample relations to
obtain more discriminable sample features. In addition, it adopts a transformer
based cross-attention learning module to learn the set-level sample relations
and acquire the attentions from query samples to support samples. Our
experimental results have demonstrated the contribution of the multi-view
relation learning mechanism for few-shot hyperspectral image classification
when compared with the state of the art methods.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: FinA: Fairness of Adverse Effects in Decision-Making of Human-Cyber-Physical-System
Abstract: Ensuring fairness in decision-making systems within
Human-Cyber-Physical-Systems (HCPS) is a pressing concern, particularly when
diverse individuals, each with varying behaviors and expectations, coexist
within the same application space, influenced by a shared set of control
actions in the system. The long-term adverse effects of these actions further
pose the challenge, as historical experiences and interactions shape individual
perceptions of fairness. This paper addresses the challenge of fairness from an
equity perspective of adverse effects, taking into account the dynamic nature
of human behavior and evolving preferences while recognizing the lasting impact
of adverse effects. We formally introduce the concept of
Fairness-in-Adverse-Effects (FinA) within the HCPS context. We put forth a
comprehensive set of five formulations for FinA, encompassing both the
instantaneous and long-term aspects of adverse effects. To empirically validate
the effectiveness of our FinA approach, we conducted an evaluation within the
domain of smart homes, a pertinent HCPS application. The outcomes of our
evaluation demonstrate that the adoption of FinA significantly enhances the
overall perception of fairness among individuals, yielding an average
improvement of 66.7% when compared to the state-of-the-art method.

Output: Artificial Intelligence