Instruction: What field is the article from?

Input:
Title: Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation
Abstract: We introduce EV3, a novel meta-optimization framework designed to efficiently
train scalable machine learning models through an intuitive
explore-assess-adapt protocol. In each iteration of EV3, we explore various
model parameter updates, assess them using pertinent evaluation methods, and
then adapt the model based on the optimal updates and previous progress
history. EV3 offers substantial flexibility without imposing stringent
constraints like differentiability on the key objectives relevant to the tasks
of interest, allowing for exploratory updates with intentionally-biased
gradients and through a diversity of losses and optimizers. Additionally, the
assessment phase provides reliable safety controls to ensure robust
generalization, and can dynamically prioritize tasks in scenarios with multiple
objectives. With inspiration drawn from evolutionary algorithms, meta-learning,
and neural architecture search, we investigate an application of EV3 to
knowledge distillation. Our experimental results illustrate EV3's capability to
safely explore the modeling landscape, while hinting at its potential
applicability across numerous domains due to its inherent flexibility and
adaptability. Finally, we provide a JAX implementation of EV3, along with
source code for experiments, available at:
https://github.com/google-research/google-research/tree/master/ev3.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: VIGraph: Self-supervised Learning for Class-Imbalanced Node Classification
Abstract: Class imbalance in graph data poses significant challenges for node
classification. Existing methods, represented by SMOTE-based approaches,
partially alleviate this issue but still exhibit limitations during imbalanced
scenario construction. Self-supervised learning (SSL) offers a promising
solution by synthesizing minority nodes from the data itself, yet its potential
remains unexplored. In this paper, we analyze the limitations of SMOTE-based
approaches and introduce VIGraph, a novel SSL model based on the
self-supervised Variational Graph Auto-Encoder (VGAE) that leverages
Variational Inference (VI) to generate minority nodes. Specifically, VIGraph
strictly adheres to the concept of imbalance when constructing imbalanced
graphs and utilizes the generative VGAE to generate minority nodes. Moreover,
VIGraph introduces a novel Siamese contrastive strategy at the decoding phase
to improve the overall quality of generated nodes. VIGraph can generate
high-quality nodes without reintegrating them into the original graph,
eliminating the "Generating, Reintegrating, and Retraining" process found in
SMOTE-based methods. Experiments on multiple real-world datasets demonstrate
that VIGraph achieves promising results for class-imbalanced node
classification tasks.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos
Abstract: In this paper, we aim to model 3D scene dynamics from multi-view videos.
Unlike the majority of existing works which usually focus on the common task of
novel view synthesis within the training time period, we propose to
simultaneously learn the geometry, appearance, and physical velocity of 3D
scenes only from video frames, such that multiple desirable applications can be
supported, including future frame extrapolation, unsupervised 3D semantic scene
decomposition, and dynamic motion transfer. Our method consists of three major
components, 1) the keyframe dynamic radiance field, 2) the interframe velocity
field, and 3) a joint keyframe and interframe optimization module which is the
core of our framework to effectively train both networks. To validate our
method, we further introduce two dynamic 3D datasets: 1) Dynamic Object
dataset, and 2) Dynamic Indoor Scene dataset. We conduct extensive experiments
on multiple datasets, demonstrating the superior performance of our method over
all baselines, particularly in the critical tasks of future frame extrapolation
and unsupervised 3D semantic scene decomposition.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Large-Scale Multi-Robot Assembly Planning for Autonomous Manufacturing
Abstract: Mobile autonomous robots have the potential to revolutionize manufacturing
processes. However, employing large robot fleets in manufacturing requires
addressing challenges including collision-free movement in a shared workspace,
effective multi-robot collaboration to manipulate and transport large payloads,
complex task allocation due to coupled manufacturing processes, and spatial
planning for parallel assembly and transportation of nested subassemblies. We
propose a full algorithmic stack for large-scale multi-robot assembly planning
that addresses these challenges and can synthesize construction plans for
complex assemblies with thousands of parts in a matter of minutes. Our approach
takes in a CAD-like product specification and automatically plans a full-stack
assembly procedure for a group of robots to manufacture the product. We propose
an algorithmic stack that comprises: (i) an iterative radial layout
optimization procedure to define a global staging layout for the manufacturing
facility, (ii) a graph-repair mixed-integer program formulation and a modified
greedy task allocation algorithm to optimally allocate robots and robot
sub-teams to assembly and transport tasks, (iii) a geometric heuristic and a
hill-climbing algorithm to plan collaborative carrying configurations of robot
sub-teams, and (iv) a distributed control policy that enables robots to execute
the assembly motion plan collision-free. We also present an open-source
multi-robot manufacturing simulator implemented in Julia as a resource to the
research community, to test our algorithms and to facilitate multi-robot
manufacturing research more broadly. Our empirical results demonstrate the
scalability and effectiveness of our approach by generating plans to
manufacture a LEGO model of a Saturn V launch vehicle with 1845 parts, 306
subassemblies, and 250 robots in under three minutes on a standard laptop
computer.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Surprisal Driven $k$-NN for Robust and Interpretable Nonparametric Learning
Abstract: Nonparametric learning is a fundamental concept in machine learning that aims
to capture complex patterns and relationships in data without making strong
assumptions about the underlying data distribution. Owing to simplicity and
familiarity, one of the most well-known algorithms under this paradigm is the
$k$-nearest neighbors ($k$-NN) algorithm. Driven by the usage of machine
learning in safety-critical applications, in this work, we shed new light on
the traditional nearest neighbors algorithm from the perspective of information
theory and propose a robust and interpretable framework for tasks such as
classification, regression, and anomaly detection using a single model. Instead
of using a traditional distance measure which needs to be scaled and
contextualized, we use a novel formulation of \textit{surprisal} (amount of
information required to explain the difference between the observed and
expected result). Finally, we demonstrate this architecture's capability to
perform at-par or above the state-of-the-art on classification, regression, and
anomaly detection tasks using a single model with enhanced interpretability by
providing novel concepts for characterizing data and predictions.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Rule Learning as Machine Translation using the Atomic Knowledge Bank
Abstract: Machine learning models, and in particular language models, are being applied
to various tasks that require reasoning. While such models are good at
capturing patterns their ability to reason in a trustable and controlled manner
is frequently questioned. On the other hand, logic-based rule systems allow for
controlled inspection and already established verification methods. However it
is well-known that creating such systems manually is time-consuming and prone
to errors. We explore the capability of transformers to translate sentences
expressing rules in natural language into logical rules. We see reasoners as
the most reliable tools for performing logical reasoning and focus on
translating language into the format expected by such tools. We perform
experiments using the DKET dataset from the literature and create a dataset for
language to logic translation based on the Atomic knowledge bank.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Towards Possibilities & Impossibilities of AI-generated Text Detection: A Survey
Abstract: Large Language Models (LLMs) have revolutionized the domain of natural
language processing (NLP) with remarkable capabilities of generating human-like
text responses. However, despite these advancements, several works in the
existing literature have raised serious concerns about the potential misuse of
LLMs such as spreading misinformation, generating fake news, plagiarism in
academia, and contaminating the web. To address these concerns, a consensus
among the research community is to develop algorithmic solutions to detect
AI-generated text. The basic idea is that whenever we can tell if the given
text is either written by a human or an AI, we can utilize this information to
address the above-mentioned concerns. To that end, a plethora of detection
frameworks have been proposed, highlighting the possibilities of AI-generated
text detection. But in parallel to the development of detection frameworks,
researchers have also concentrated on designing strategies to elude detection,
i.e., focusing on the impossibilities of AI-generated text detection. This is a
crucial step in order to make sure the detection frameworks are robust enough
and it is not too easy to fool a detector. Despite the huge interest and the
flurry of research in this domain, the community currently lacks a
comprehensive analysis of recent developments. In this survey, we aim to
provide a concise categorization and overview of current work encompassing both
the prospects and the limitations of AI-generated text detection. To enrich the
collective knowledge, we engage in an exhaustive discussion on critical and
challenging open questions related to ongoing research on AI-generated text
detection.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: A Central Motor System Inspired Pre-training Reinforcement Learning for Robotic Control
Abstract: Designing controllers to achieve natural motor capabilities for multi-joint
robots is a significant challenge. However, animals in nature are naturally
with basic motor abilities and can master various complex motor skills through
acquired learning. On the basis of analyzing the mechanism of the central motor
system in mammals, we propose a novel pre-training reinforcement learning
algorithm that enables robots to learn rich motor skills and apply them to
complex task environments without relying on external data. We first design a
skill based network similar to the cerebellum by utilizing the selection
mechanism of voluntary movements in the basal ganglia and the basic motor
regulation ability of the cerebellum. Subsequently, by imitating the structure
of advanced centers in the central motor system, we propose a high-level policy
to generate different skill combinations, thereby enabling the robot to acquire
natural motor abilities. We conduct experiments on 4 types of robots and 22
task environments, and the results show that the proposed method can enable
different types of robots to achieve flexible motor skills. Overall, our
research provides a promising framework for the design of neural network motor
controllers.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Are Vision Transformers More Data Hungry Than Newborn Visual Systems?
Abstract: Vision transformers (ViTs) are top performing models on many computer vision
benchmarks and can accurately predict human behavior on object recognition
tasks. However, researchers question the value of using ViTs as models of
biological learning because ViTs are thought to be more data hungry than
brains, with ViTs requiring more training data to reach similar levels of
performance. To test this assumption, we directly compared the learning
abilities of ViTs and animals, by performing parallel controlled rearing
experiments on ViTs and newborn chicks. We first raised chicks in impoverished
visual environments containing a single object, then simulated the training
data available in those environments by building virtual animal chambers in a
video game engine. We recorded the first-person images acquired by agents
moving through the virtual chambers and used those images to train self
supervised ViTs that leverage time as a teaching signal, akin to biological
visual systems. When ViTs were trained through the eyes of newborn chicks, the
ViTs solved the same view invariant object recognition tasks as the chicks.
Thus, ViTs were not more data hungry than newborn visual systems: both learned
view invariant object representations in impoverished visual environments. The
flexible and generic attention based learning mechanism in ViTs combined with
the embodied data streams available to newborn animals appears sufficient to
drive the development of animal-like object recognition.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Physics-Enhanced Multi-fidelity Learning for Optical Surface Imprint
Abstract: Human fingerprints serve as one unique and powerful characteristic for each
person, from which policemen can recognize the identity. Similar to humans,
many natural bodies and intrinsic mechanical qualities can also be uniquely
identified from surface characteristics. To measure the elasto-plastic
properties of one material, one formally sharp indenter is pushed into the
measured body under constant force and retracted, leaving a unique residual
imprint of the minute size from several micrometers to nanometers. However, one
great challenge is how to map the optical image of this residual imprint into
the real wanted mechanical properties, i.e., the tensile force curve. In this
paper, we propose a novel method to use multi-fidelity neural networks (MFNN)
to solve this inverse problem. We first actively train the NN model via pure
simulation data, and then bridge the sim-to-real gap via transfer learning. The
most innovative part is that we use NN to dig out the unknown physics and also
implant the known physics into the transfer learning framework, thus highly
improving the model stability and decreasing the data requirement. This work
serves as one great example of applying machine learning into the real
experimental research, especially under the constraints of data limitation and
fidelity variance.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Assessing Knowledge Editing in Language Models via Relation Perspective
Abstract: Knowledge Editing (KE) for modifying factual knowledge in Large Language
Models (LLMs) has been receiving increasing attention. However, existing
knowledge editing methods are entity-centric, and it is unclear whether this
approach is suitable for a relation-centric perspective. To address this gap,
this paper constructs a new benchmark named RaKE, which focuses on Relation
based Knowledge Editing. In this paper, we establish a suite of innovative
metrics for evaluation and conduct comprehensive experiments involving various
knowledge editing baselines. We notice that existing knowledge editing methods
exhibit the potential difficulty in their ability to edit relations. Therefore,
we further explore the role of relations in factual triplets within the
transformer. Our research results confirm that knowledge related to relations
is not only stored in the FFN network but also in the attention layers. This
provides experimental support for future relation-based knowledge editing
methods.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Nexus at ArAIEval Shared Task: Fine-Tuning Arabic Language Models for Propaganda and Disinformation Detection
Abstract: The spread of disinformation and propagandistic content poses a threat to
societal harmony, undermining informed decision-making and trust in reliable
sources. Online platforms often serve as breeding grounds for such content, and
malicious actors exploit the vulnerabilities of audiences to shape public
opinion. Although there have been research efforts aimed at the automatic
identification of disinformation and propaganda in social media content, there
remain challenges in terms of performance. The ArAIEval shared task aims to
further research on these particular issues within the context of the Arabic
language. In this paper, we discuss our participation in these shared tasks. We
competed in subtasks 1A and 2A, where our submitted system secured positions
9th and 10th, respectively. Our experiments consist of fine-tuning transformer
models and using zero- and few-shot learning with GPT-4.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Fine-Tuning InstructPix2Pix for Advanced Image Colorization
Abstract: This paper presents a novel approach to human image colorization by
fine-tuning the InstructPix2Pix model, which integrates a language model
(GPT-3) with a text-to-image model (Stable Diffusion). Despite the original
InstructPix2Pix model's proficiency in editing images based on textual
instructions, it exhibits limitations in the focused domain of colorization. To
address this, we fine-tuned the model using the IMDB-WIKI dataset, pairing
black-and-white images with a diverse set of colorization prompts generated by
ChatGPT. This paper contributes by (1) applying fine-tuning techniques to
stable diffusion models specifically for colorization tasks, and (2) employing
generative models to create varied conditioning prompts. After finetuning, our
model outperforms the original InstructPix2Pix model on multiple metrics
quantitatively, and we produce more realistically colored images qualitatively.
The code for this project is provided on the GitHub Repository
https://github.com/AllenAnZifeng/DeepLearning282.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Investigating YOLO Models Towards Outdoor Obstacle Detection For Visually Impaired People
Abstract: The utilization of deep learning-based object detection is an effective
approach to assist visually impaired individuals in avoiding obstacles. In this
paper, we implemented seven different YOLO object detection models
\textit{viz}., YOLO-NAS (small, medium, large), YOLOv8, YOLOv7, YOLOv6, and
YOLOv5 and performed comprehensive evaluation with carefully tuned
hyperparameters, to analyze how these models performed on images containing
common daily-life objects presented on roads and sidewalks. After a systematic
investigation, YOLOv8 was found to be the best model, which reached a precision
of $80\%$ and a recall of $68.2\%$ on a well-known Obstacle Dataset which
includes images from VOC dataset, COCO dataset, and TT100K dataset along with
images collected by the researchers in the field. Despite being the latest
model and demonstrating better performance in many other applications, YOLO-NAS
was found to be suboptimal for the obstacle detection task.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Modeling subjectivity (by Mimicking Annotator Annotation) in toxic comment identification across diverse communities
Abstract: The prevalence and impact of toxic discussions online have made content
moderation crucial.Automated systems can play a vital role in identifying
toxicity, and reducing the reliance on human moderation.Nevertheless,
identifying toxic comments for diverse communities continues to present
challenges that are addressed in this paper.The two-part goal of this study is
to(1)identify intuitive variances from annotator disagreement using
quantitative analysis and (2)model the subjectivity of these viewpoints.To
achieve our goal, we published a new
dataset\footnote{\url{https://github.com/XXX}} with expert annotators'
annotations and used two other public datasets to identify the subjectivity of
toxicity.Then leveraging the Large Language Model(LLM),we evaluate the model's
ability to mimic diverse viewpoints on toxicity by varying size of the training
data and utilizing same set of annotators as the test set used during model
training and a separate set of annotators as the test set.We conclude that
subjectivity is evident across all annotator groups, demonstrating the
shortcomings of majority-rule voting. Moving forward, subjective annotations
should serve as ground truth labels for training models for domains like
toxicity in diverse communities.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Content Augmented Graph Neural Networks
Abstract: In recent years, graph neural networks (GNNs) have become a popular tool for
solving various problems over graphs. In these models, the link structure of
the graph is typically exploited and nodes' embeddings are iteratively updated
based on adjacent nodes. Nodes' contents are used solely in the form of feature
vectors, served as nodes' first-layer embeddings. However, the filters or
convolutions, applied during iterations/layers to these initial embeddings lead
to their impact diminish and contribute insignificantly to the final
embeddings. In order to address this issue, in this paper we propose augmenting
nodes' embeddings by embeddings generating from their content, at higher GNN
layers. More precisely, we propose models wherein a structural embedding using
a GNN and a content embedding are computed for each node. These two are
combined using a combination layer to form the embedding of a node at a given
layer. We suggest methods such as using an auto-encoder or building a content
graph, to generate content embeddings. In the end, by conducting experiments
over several real-world datasets, we demonstrate the high accuracy and
performance of our models.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Best uses of ChatGPT and Generative AI for computer science research
Abstract: Generative Artificial Intelligence (AI), particularly tools like OpenAI's
popular ChatGPT, is reshaping the landscape of computer science research. Used
wisely, these tools can boost the productivity of a computer research
scientist. This paper provides an exploration of the diverse applications of
ChatGPT and other generative AI technologies in computer science academic
research, making recommendations about the use of Generative AI to make more
productive the role of the computer research scientist, with the focus of
writing new research papers. We highlight innovative uses such as brainstorming
research ideas, aiding in the drafting and styling of academic papers and
assisting in the synthesis of state-of-the-art section. Further, we delve into
using these technologies in understanding interdisciplinary approaches, making
complex texts simpler, and recommending suitable academic journals for
publication. Significant focus is placed on generative AI's contributions to
synthetic data creation, research methodology, and mentorship, as well as in
task organization and article quality assessment. The paper also addresses the
utility of AI in article review, adapting texts to length constraints,
constructing counterarguments, and survey development. Moreover, we explore the
capabilities of these tools in disseminating ideas, generating images and
audio, text transcription, and engaging with editors. We also describe some
non-recommended uses of generative AI for computer science research, mainly
because of the limitations of this technology.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Towards the Law of Capacity Gap in Distilling Language Models
Abstract: Language model (LM) distillation is a trending area that aims to distil the
knowledge resided in a large teacher LM to a small student one. While various
methods have been proposed to push the distillation to its limits, it is still
a pain distilling LMs when a large capacity gap is exhibited between the
teacher and the student LMs. The pain is mainly resulted by the curse of
capacity gap, which describes that a larger teacher LM cannot always lead to a
better student LM than one distilled from a smaller teacher LM due to the
affect of capacity gap increment. That is, there is likely an optimal point
yielding the best student LM along the scaling course of the teacher LM. Even
worse, the curse of capacity gap can be only partly yet not fully lifted as
indicated in previous studies.
  However, the tale is not ever one-sided. Although a larger teacher LM has
better performance than a smaller teacher LM, it is much more
resource-demanding especially in the context of recent large LMs (LLMs).
Consequently, instead of sticking to lifting the curse, leaving the curse as is
should be arguably fine. Even better, in this paper, we reveal that the optimal
capacity gap is almost consistent across different student scales and
architectures, fortunately turning the curse into the law of capacity gap. The
law later guides us to distil a 3B student LM (termed MiniMA) from a 7B teacher
LM (adapted LLaMA2-7B). MiniMA is demonstrated to yield a new
compute-performance pareto frontier among existing 3B LMs on commonly used
benchmarks, and its instruction-tuned version (termed MiniChat) outperforms a
wide range of 3B competitors in GPT4 evaluation and could even compete with
several 7B chat models.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Learning interactions to boost human creativity with bandits and GPT-4
Abstract: This paper considers how interactions with AI algorithms can boost human
creative thought. We employ a psychological task that demonstrates limits on
human creativity, namely semantic feature generation: given a concept name,
respondents must list as many of its features as possible. Human participants
typically produce only a fraction of the features they know before getting
"stuck." In experiments with humans and with a language AI (GPT-4) we contrast
behavior in the standard task versus a variant in which participants can ask
for algorithmically-generated hints. Algorithm choice is administered by a
multi-armed bandit whose reward indicates whether the hint helped generating
more features. Humans and the AI show similar benefits from hints, and
remarkably, bandits learning from AI responses prefer the same prompting
strategy as those learning from human behavior. The results suggest that
strategies for boosting human creativity via computer interactions can be
learned by bandits run on groups of simulated participants.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Classification for everyone : Building geography agnostic models for fairer recognition
Abstract: In this paper, we analyze different methods to mitigate inherent geographical
biases present in state of the art image classification models. We first
quantitatively present this bias in two datasets - The Dollar Street Dataset
and ImageNet, using images with location information. We then present different
methods which can be employed to reduce this bias. Finally, we analyze the
effectiveness of the different techniques on making these models more robust to
geographical locations of the images.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Learning Reusable Manipulation Strategies
Abstract: Humans demonstrate an impressive ability to acquire and generalize
manipulation "tricks." Even from a single demonstration, such as using soup
ladles to reach for distant objects, we can apply this skill to new scenarios
involving different object positions, sizes, and categories (e.g., forks and
hammers). Additionally, we can flexibly combine various skills to devise
long-term plans. In this paper, we present a framework that enables machines to
acquire such manipulation skills, referred to as "mechanisms," through a single
demonstration and self-play. Our key insight lies in interpreting each
demonstration as a sequence of changes in robot-object and object-object
contact modes, which provides a scaffold for learning detailed samplers for
continuous parameters. These learned mechanisms and samplers can be seamlessly
integrated into standard task and motion planners, enabling their compositional
use.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Modeling the Telemarketing Process using Genetic Algorithms and Extreme Boosting: Feature Selection and Cost-Sensitive Analytical Approach
Abstract: Currently, almost all direct marketing activities take place virtually rather
than in person, weakening interpersonal skills at an alarming pace.
Furthermore, businesses have been striving to sense and foster the tendency of
their clients to accept a marketing offer. The digital transformation and the
increased virtual presence forced firms to seek novel marketing research
approaches. This research aims at leveraging the power of telemarketing data in
modeling the willingness of clients to make a term deposit and finding the most
significant characteristics of the clients. Real-world data from a Portuguese
bank and national socio-economic metrics are used to model the telemarketing
decision-making process. This research makes two key contributions. First,
propose a novel genetic algorithm-based classifier to select the best
discriminating features and tune classifier parameters simultaneously. Second,
build an explainable prediction model. The best-generated classification models
were intensively validated using 50 times repeated 10-fold stratified
cross-validation and the selected features have been analyzed. The models
significantly outperform the related works in terms of class of interest
accuracy, they attained an average of 89.07\% and 0.059 in terms of geometric
mean and type I error respectively. The model is expected to maximize the
potential profit margin at the least possible cost and provide more insights to
support marketing decision-making.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Leveraging Speculative Sampling and KV-Cache Optimizations Together for Generative AI using OpenVINO
Abstract: Inference optimizations are critical for improving user experience and
reducing infrastructure costs and power consumption. In this article, we
illustrate a form of dynamic execution known as speculative sampling to reduce
the overall latency of text generation and compare it with standard
autoregressive sampling. This can be used together with model-based
optimizations (e.g. quantization) to provide an optimized solution. Both
sampling methods make use of KV caching. A Jupyter notebook and some sample
executions are provided.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Explainable Spatio-Temporal Graph Neural Networks
Abstract: Spatio-temporal graph neural networks (STGNNs) have gained popularity as a
powerful tool for effectively modeling spatio-temporal dependencies in diverse
real-world urban applications, including intelligent transportation and public
safety. However, the black-box nature of STGNNs limits their interpretability,
hindering their application in scenarios related to urban resource allocation
and policy formulation. To bridge this gap, we propose an Explainable
Spatio-Temporal Graph Neural Networks (STExplainer) framework that enhances
STGNNs with inherent explainability, enabling them to provide accurate
predictions and faithful explanations simultaneously. Our framework integrates
a unified spatio-temporal graph attention network with a positional information
fusion layer as the STG encoder and decoder, respectively. Furthermore, we
propose a structure distillation approach based on the Graph Information
Bottleneck (GIB) principle with an explainable objective, which is instantiated
by the STG encoder and decoder. Through extensive experiments, we demonstrate
that our STExplainer outperforms state-of-the-art baselines in terms of
predictive accuracy and explainability metrics (i.e., sparsity and fidelity) on
traffic and crime prediction tasks. Furthermore, our model exhibits superior
representation ability in alleviating data missing and sparsity issues. The
implementation code is available at: https://github.com/HKUDS/STExplainer.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
Abstract: Large foundation models are becoming ubiquitous, but training them from
scratch is prohibitively expensive. Thus, efficiently adapting these powerful
models to downstream tasks is increasingly important. In this paper, we study a
principled finetuning paradigm -- Orthogonal Finetuning (OFT) -- for downstream
task adaptation. Despite demonstrating good generalizability, OFT still uses a
fairly large number of trainable parameters due to the high dimensionality of
orthogonal matrices. To address this, we start by examining OFT from an
information transmission perspective, and then identify a few key desiderata
that enable better parameter-efficiency. Inspired by how the Cooley-Tukey fast
Fourier transform algorithm enables efficient information transmission, we
propose an efficient orthogonal parameterization using butterfly structures. We
apply this parameterization to OFT, creating a novel parameter-efficient
finetuning method, called Orthogonal Butterfly (BOFT). By subsuming OFT as a
special case, BOFT introduces a generalized orthogonal finetuning framework.
Finally, we conduct an extensive empirical study of adapting large vision
transformers, large language models, and text-to-image diffusion models to
various downstream tasks in vision and language.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Learning to Design and Use Tools for Robotic Manipulation
Abstract: When limited by their own morphologies, humans and some species of animals
have the remarkable ability to use objects from the environment toward
accomplishing otherwise impossible tasks. Robots might similarly unlock a range
of additional capabilities through tool use. Recent techniques for jointly
optimizing morphology and control via deep learning are effective at designing
locomotion agents. But while outputting a single morphology makes sense for
locomotion, manipulation involves a variety of strategies depending on the task
goals at hand. A manipulation agent must be capable of rapidly prototyping
specialized tools for different goals. Therefore, we propose learning a
designer policy, rather than a single design. A designer policy is conditioned
on task information and outputs a tool design that helps solve the task. A
design-conditioned controller policy can then perform manipulation using these
tools. In this work, we take a step towards this goal by introducing a
reinforcement learning framework for jointly learning these policies. Through
simulated manipulation tasks, we show that this framework is more sample
efficient than prior methods in multi-goal or multi-variant settings, can
perform zero-shot interpolation or fine-tuning to tackle previously unseen
goals, and allows tradeoffs between the complexity of design and control
policies under practical constraints. Finally, we deploy our learned policies
onto a real robot. Please see our supplementary video and website at
https://robotic-tool-design.github.io/ for visualizations.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks
Abstract: Machine learning (ML) has gained significant adoption in Android malware
detection to address the escalating threats posed by the rapid proliferation of
malware attacks. However, recent studies have revealed the inherent
vulnerabilities of ML-based detection systems to evasion attacks. While efforts
have been made to address this critical issue, many of the existing defensive
methods encounter challenges such as lower effectiveness or reduced
generalization capabilities. In this paper, we introduce a novel Android
malware detection method, MalPurifier, which exploits adversarial purification
to eliminate perturbations independently, resulting in attack mitigation in a
light and flexible way. Specifically, MalPurifier employs a Denoising
AutoEncoder (DAE)-based purification model to preprocess input samples,
removing potential perturbations from them and then leading to correct
classification. To enhance defense effectiveness, we propose a diversified
adversarial perturbation mechanism that strengthens the purification model
against different manipulations from various evasion attacks. We also
incorporate randomized "protective noises" onto benign samples to prevent
excessive purification. Furthermore, we customize a loss function for improving
the DAE model, combining reconstruction loss and prediction loss, to enhance
feature representation learning, resulting in accurate reconstruction and
classification. Experimental results on two Android malware datasets
demonstrate that MalPurifier outperforms the state-of-the-art defenses, and it
significantly strengthens the vulnerable malware detector against 37 evasion
attacks, achieving accuracies over 90.91%. Notably, MalPurifier demonstrates
easy scalability to other detectors, offering flexibility and robustness in its
implementation.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: Active Wildfires Detection and Dynamic Escape Routes Planning for Humans through Information Fusion between Drones and Satellites
Abstract: UAVs are playing an increasingly important role in the field of wilderness
rescue by virtue of their flexibility. This paper proposes a fusion of UAV
vision technology and satellite image analysis technology for active wildfires
detection and road networks extraction of wildfire areas and real-time dynamic
escape route planning for people in distress. Firstly, the fire source location
and the segmentation of smoke and flames are targeted based on Sentinel 2
satellite imagery. Secondly, the road segmentation and the road condition
assessment are performed by D-linkNet and NDVI values in the central area of
the fire source by UAV. Finally, the dynamic optimal route planning for humans
in real time is performed by the weighted A* algorithm in the road network with
the dynamic fire spread model. Taking the Chongqing wildfire on August 24,
2022, as a case study, the results demonstrate that the dynamic escape route
planning algorithm can provide an optimal real-time navigation path for humans
in the presence of fire through the information fusion of UAVs and satellites.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion
Abstract: Recent advancements in open-world 3D object generation have been remarkable,
with image-to-3D methods offering superior fine-grained control over their
text-to-3D counterparts. However, most existing models fall short in
simultaneously providing rapid generation speeds and high fidelity to input
images - two features essential for practical applications. In this paper, we
present One-2-3-45++, an innovative method that transforms a single image into
a detailed 3D textured mesh in approximately one minute. Our approach aims to
fully harness the extensive knowledge embedded in 2D diffusion models and
priors from valuable yet limited 3D data. This is achieved by initially
finetuning a 2D diffusion model for consistent multi-view image generation,
followed by elevating these images to 3D with the aid of multi-view conditioned
3D native diffusion models. Extensive experimental evaluations demonstrate that
our method can produce high-quality, diverse 3D assets that closely mirror the
original input image. Our project webpage:
https://sudo-ai-3d.github.io/One2345plus_page.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: (Ir)rationality in AI: State of the Art, Research Challenges and Open Questions
Abstract: The concept of rationality is central to the field of artificial
intelligence. Whether we are seeking to simulate human reasoning, or the goal
is to achieve bounded optimality, we generally seek to make artificial agents
as rational as possible. Despite the centrality of the concept within AI, there
is no unified definition of what constitutes a rational agent. This article
provides a survey of rationality and irrationality in artificial intelligence,
and sets out the open questions in this area. The understanding of rationality
in other fields has influenced its conception within artificial intelligence,
in particular work in economics, philosophy and psychology. Focusing on the
behaviour of artificial agents, we consider irrational behaviours that can
prove to be optimal in certain scenarios. Some methods have been developed to
deal with irrational agents, both in terms of identification and interaction,
however work in this area remains limited. Methods that have up to now been
developed for other purposes, namely adversarial scenarios, may be adapted to
suit interactions with artificial agents. We further discuss the interplay
between human and artificial agents, and the role that rationality plays within
this interaction; many questions remain in this area, relating to potentially
irrational behaviour of both humans and artificial agents.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Hypergraph-Guided Disentangled Spectrum Transformer Networks for Near-Infrared Facial Expression Recognition
Abstract: With the strong robusticity on illumination variations, near-infrared (NIR)
can be an effective and essential complement to visible (VIS) facial expression
recognition in low lighting or complete darkness conditions. However, facial
expression recognition (FER) from NIR images presents more challenging problem
than traditional FER due to the limitations imposed by the data scale and the
difficulty of extracting discriminative features from incomplete visible
lighting contents. In this paper, we give the first attempt to deep NIR facial
expression recognition and proposed a novel method called near-infrared facial
expression transformer (NFER-Former). Specifically, to make full use of the
abundant label information in the field of VIS, we introduce a Self-Attention
Orthogonal Decomposition mechanism that disentangles the expression information
and spectrum information from the input image, so that the expression features
can be extracted without the interference of spectrum variation. We also
propose a Hypergraph-Guided Feature Embedding method that models some key
facial behaviors and learns the structure of the complex correlations between
them, thereby alleviating the interference of inter-class similarity.
Additionally, we have constructed a large NIR-VIS Facial Expression dataset
that includes 360 subjects to better validate the efficiency of NFER-Former.
Extensive experiments and ablation studies show that NFER-Former significantly
improves the performance of NIR FER and achieves state-of-the-art results on
the only two available NIR FER datasets, Oulu-CASIA and Large-HFE.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Towards Formal Fault Injection for Safety Assessment of Automated Systems
Abstract: Reasoning about safety, security, and other dependability attributes of
autonomous systems is a challenge that needs to be addressed before the
adoption of such systems in day-to-day life. Formal methods is a class of
methods that mathematically reason about a system's behavior. Thus, a
correctness proof is sufficient to conclude the system's dependability.
However, these methods are usually applied to abstract models of the system,
which might not fully represent the actual system. Fault injection, on the
other hand, is a testing method to evaluate the dependability of systems.
However, the amount of testing required to evaluate the system is rather large
and often a problem. This vision paper introduces formal fault injection, a
fusion of these two techniques throughout the development lifecycle to enhance
the dependability of autonomous systems. We advocate for a more cohesive
approach by identifying five areas of mutual support between formal methods and
fault injection. By forging stronger ties between the two fields, we pave the
way for developing safe and dependable autonomous systems. This paper delves
into the integration's potential and outlines future research avenues,
addressing open challenges along the way.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Improving embedding of graphs with missing data by soft manifolds
Abstract: Embedding graphs in continous spaces is a key factor in designing and
developing algorithms for automatic information extraction to be applied in
diverse tasks (e.g., learning, inferring, predicting). The reliability of graph
embeddings directly depends on how much the geometry of the continuous space
matches the graph structure. Manifolds are mathematical structure that can
enable to incorporate in their topological spaces the graph characteristics,
and in particular nodes distances. State-of-the-art of manifold-based graph
embedding algorithms take advantage of the assumption that the projection on a
tangential space of each point in the manifold (corresponding to a node in the
graph) would locally resemble a Euclidean space. Although this condition helps
in achieving efficient analytical solutions to the embedding problem, it does
not represent an adequate set-up to work with modern real life graphs, that are
characterized by weighted connections across nodes often computed over sparse
datasets with missing records. In this work, we introduce a new class of
manifold, named soft manifold, that can solve this situation. In particular,
soft manifolds are mathematical structures with spherical symmetry where the
tangent spaces to each point are hypocycloids whose shape is defined according
to the velocity of information propagation across the data points. Using soft
manifolds for graph embedding, we can provide continuous spaces to pursue any
task in data analysis over complex datasets. Experimental results on
reconstruction tasks on synthetic and real datasets show how the proposed
approach enable more accurate and reliable characterization of graphs in
continuous spaces with respect to the state-of-the-art.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: CausalCite: A Causal Formulation of Paper Citations
Abstract: Evaluating the significance of a paper is pivotal yet challenging for the
scientific community. While the citation count is the most commonly used proxy
for this purpose, they are widely criticized for failing to accurately reflect
a paper's true impact. In this work, we propose a causal inference method,
TextMatch, which adapts the traditional matching framework to high-dimensional
text embeddings. Specifically, we encode each paper using the text embeddings
by large language models (LLMs), extract similar samples by cosine similarity,
and synthesize a counterfactual sample by the weighted average of similar
papers according to their similarity values. We apply the resulting metric,
called CausalCite, as a causal formulation of paper citations. We show its
effectiveness on various criteria, such as high correlation with paper impact
as reported by scientific experts on a previous dataset of 1K papers,
(test-of-time) awards for past papers, and its stability across various
sub-fields of AI. We also provide a set of findings that can serve as suggested
ways for future researchers to use our metric for a better understanding of a
paper's quality. Our code and data are at
https://github.com/causalNLP/causal-cite.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: SceneDM: Scene-level Multi-agent Trajectory Generation with Consistent Diffusion Models
Abstract: Realistic scene-level multi-agent motion simulations are crucial for
developing and evaluating self-driving algorithms. However, most existing works
focus on generating trajectories for a certain single agent type, and typically
ignore the consistency of generated trajectories. In this paper, we propose a
novel framework based on diffusion models, called SceneDM, to generate joint
and consistent future motions of all the agents, including vehicles, bicycles,
pedestrians, etc., in a scene. To enhance the consistency of the generated
trajectories, we resort to a new Transformer-based network to effectively
handle agent-agent interactions in the inverse process of motion diffusion. In
consideration of the smoothness of agent trajectories, we further design a
simple yet effective consistent diffusion approach, to improve the model in
exploiting short-term temporal dependencies. Furthermore, a scene-level scoring
function is attached to evaluate the safety and road-adherence of the generated
agent's motions and help filter out unrealistic simulations. Finally, SceneDM
achieves state-of-the-art results on the Waymo Sim Agents Benchmark. Project
webpage is available at https://alperen-hub.github.io/SceneDM.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Structured World Representations in Maze-Solving Transformers
Abstract: Transformer models underpin many recent advances in practical machine
learning applications, yet understanding their internal behavior continues to
elude researchers. Given the size and complexity of these models, forming a
comprehensive picture of their inner workings remains a significant challenge.
To this end, we set out to understand small transformer models in a more
tractable setting: that of solving mazes. In this work, we focus on the
abstractions formed by these models and find evidence for the consistent
emergence of structured internal representations of maze topology and valid
paths. We demonstrate this by showing that the residual stream of only a single
token can be linearly decoded to faithfully reconstruct the entire maze. We
also find that the learned embeddings of individual tokens have spatial
structure. Furthermore, we take steps towards deciphering the circuity of
path-following by identifying attention heads (dubbed $\textit{adjacency
heads}$), which are implicated in finding valid subsequent tokens.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Vanishing Gradients in Reinforcement Finetuning of Language Models
Abstract: Pretrained language models are commonly aligned with human preferences and
downstream tasks via reinforcement finetuning (RFT), which entails maximizing a
(possibly learned) reward function using policy gradient algorithms. This work
highlights a fundamental optimization obstacle in RFT: we prove that the
expected gradient for an input vanishes when its reward standard deviation
under the model is small, even if the expected reward is far from optimal.
Through experiments on an RFT benchmark and controlled environments, as well as
a theoretical analysis, we then demonstrate that vanishing gradients due to
small reward standard deviation are prevalent and detrimental, leading to
extremely slow reward maximization. Lastly, we explore ways to overcome
vanishing gradients in RFT. We find the common practice of an initial
supervised finetuning (SFT) phase to be the most promising candidate, which
sheds light on its importance in an RFT pipeline. Moreover, we show that a
relatively small number of SFT optimization steps on as few as 1% of the input
samples can suffice, indicating that the initial SFT phase need not be
expensive in terms of compute and data labeling efforts. Overall, our results
emphasize that being mindful for inputs whose expected gradient vanishes, as
measured by the reward standard deviation, is crucial for successful execution
of RFT.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Revamping AI Models in Dermatology: Overcoming Critical Challenges for Enhanced Skin Lesion Diagnosis
Abstract: The surge in developing deep learning models for diagnosing skin lesions
through image analysis is notable, yet their clinical black faces challenges.
Current dermatology AI models have limitations: limited number of possible
diagnostic outputs, lack of real-world testing on uncommon skin lesions,
inability to detect out-of-distribution images, and over-reliance on
dermoscopic images. To address these, we present an All-In-One
\textbf{H}ierarchical-\textbf{O}ut of Distribution-\textbf{C}linical Triage
(HOT) model. For a clinical image, our model generates three outputs: a
hierarchical prediction, an alert for out-of-distribution images, and a
recommendation for dermoscopy if clinical image alone is insufficient for
diagnosis. When the recommendation is pursued, it integrates both clinical and
dermoscopic images to deliver final diagnosis. Extensive experiments on a
representative cutaneous lesion dataset demonstrate the effectiveness and
synergy of each component within our framework. Our versatile model provides
valuable decision support for lesion diagnosis and sets a promising precedent
for medical AI applications.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Towards Auditing Large Language Models: Improving Text-based Stereotype Detection
Abstract: Large Language Models (LLM) have made significant advances in the recent past
becoming more mainstream in Artificial Intelligence (AI) enabled human-facing
applications. However, LLMs often generate stereotypical output inherited from
historical data, amplifying societal biases and raising ethical concerns. This
work introduces i) the Multi-Grain Stereotype Dataset, which includes 52,751
instances of gender, race, profession and religion stereotypic text and ii) a
novel stereotype classifier for English text. We design several experiments to
rigorously test the proposed model trained on the novel dataset. Our
experiments show that training the model in a multi-class setting can
outperform the one-vs-all binary counterpart. Consistent feature importance
signals from different eXplainable AI tools demonstrate that the new model
exploits relevant text features. We utilise the newly created model to assess
the stereotypic behaviour of the popular GPT family of models and observe the
reduction of bias over time. In summary, our work establishes a robust and
practical framework for auditing and evaluating the stereotypic bias in LLM.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors
Abstract: In a spoken dialogue system, an NLU model is preceded by a speech recognition
system that can deteriorate the performance of natural language understanding.
This paper proposes a method for investigating the impact of speech recognition
errors on the performance of natural language understanding models. The
proposed method combines the back transcription procedure with a fine-grained
technique for categorizing the errors that affect the performance of NLU
models. The method relies on the usage of synthesized speech for NLU
evaluation. We show that the use of synthesized speech in place of audio
recording does not change the outcomes of the presented technique in a
significant way.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Mapping the Empirical Evidence of the GDPR (In-)Effectiveness: A Systematic Review
Abstract: In the realm of data protection, a striking disconnect prevails between
traditional domains of doctrinal, legal, theoretical, and policy-based
inquiries and a burgeoning body of empirical evidence. Much of the scholarly
and regulatory discourse remains entrenched in abstract legal principles or
normative frameworks, leaving the empirical landscape uncharted or minimally
engaged. Since the birth of EU data protection law, a modest body of empirical
evidence has been generated but remains widely scattered and unexamined. Such
evidence offers vital insights into the perception, impact, clarity, and
effects of data protection measures but languishes on the periphery,
inadequately integrated into the broader conversation. To make a meaningful
connection, we conduct a comprehensive review and synthesis of empirical
research spanning nearly three decades (1995- March 2022), advocating for a
more robust integration of empirical evidence into the evaluation and review of
the GDPR, while laying a methodological foundation for future empirical
research.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: Improving Compositional Generalization Using Iterated Learning and Simplicial Embeddings
Abstract: Compositional generalization, the ability of an agent to generalize to unseen
combinations of latent factors, is easy for humans but hard for deep neural
networks. A line of research in cognitive science has hypothesized a process,
``iterated learning,'' to help explain how human language developed this
ability; the theory rests on simultaneous pressures towards compressibility
(when an ignorant agent learns from an informed one) and expressivity (when it
uses the representation for downstream tasks). Inspired by this process, we
propose to improve the compositional generalization of deep networks by using
iterated learning on models with simplicial embeddings, which can approximately
discretize representations. This approach is further motivated by an analysis
of compositionality based on Kolmogorov complexity. We show that this
combination of changes improves compositional generalization over other
approaches, demonstrating these improvements both on vision tasks with
well-understood latent factors and on real molecular graph prediction tasks
where the latent structure is unknown.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Towards Transparency in Coreference Resolution: A Quantum-Inspired Approach
Abstract: Guided by grammatical structure, words compose to form sentences, and guided
by discourse structure, sentences compose to form dialogues and documents. The
compositional aspect of sentence and discourse units is often overlooked by
machine learning algorithms. A recent initiative called Quantum Natural
Language Processing (QNLP) learns word meanings as points in a Hilbert space
and acts on them via a translation of grammatical structure into Parametrised
Quantum Circuits (PQCs). Previous work extended the QNLP translation to
discourse structure using points in a closure of Hilbert spaces. In this paper,
we evaluate this translation on a Winograd-style pronoun resolution task. We
train a Variational Quantum Classifier (VQC) for binary classification and
implement an end-to-end pronoun resolution system. The simulations executed on
IBMQ software converged with an F1 score of 87.20%. The model outperformed two
out of three classical coreference resolution systems and neared
state-of-the-art SpanBERT. A mixed quantum-classical model yet improved these
results with an F1 score increase of around 6%.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: OtterHD: A High-Resolution Multi-modality Model
Abstract: In this paper, we present OtterHD-8B, an innovative multimodal model evolved
from Fuyu-8B, specifically engineered to interpret high-resolution visual
inputs with granular precision. Unlike conventional models that are constrained
by fixed-size vision encoders, OtterHD-8B boasts the ability to handle flexible
input dimensions, ensuring its versatility across various inference
requirements. Alongside this model, we introduce MagnifierBench, an evaluation
framework designed to scrutinize models' ability to discern minute details and
spatial relationships of small objects. Our comparative analysis reveals that
while current leading models falter on this benchmark, OtterHD-8B, particularly
when directly processing high-resolution inputs, outperforms its counterparts
by a substantial margin. The findings illuminate the structural variances in
visual information processing among different models and the influence that the
vision encoders' pre-training resolution disparities have on model
effectiveness within such benchmarks. Our study highlights the critical role of
flexibility and high-resolution input capabilities in large multimodal models
and also exemplifies the potential inherent in the Fuyu architecture's
simplicity for handling complex visual data.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Backward Learning for Goal-Conditioned Policies
Abstract: Can we learn policies in reinforcement learning without rewards? Can we learn
a policy just by trying to reach a goal state? We answer these questions
positively by proposing a multi-step procedure that first learns a world model
that goes backward in time, secondly generates goal-reaching backward
trajectories, thirdly improves those sequences using shortest path finding
algorithms, and finally trains a neural network policy by imitation learning.
We evaluate our method on a deterministic maze environment where the
observations are $64\times 64$ pixel bird's eye images and can show that it
consistently reaches several goals.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Topology Recoverability Prediction for Ad-Hoc Robot Networks: A Data-Driven Fault-Tolerant Approach
Abstract: Faults occurring in ad-hoc robot networks may fatally perturb their
topologies leading to disconnection of subsets of those networks. Optimal
topology synthesis is generally resource-intensive and time-consuming to be
done in real time for large ad-hoc robot networks. One should only perform
topology re-computations if the probability of topology recoverability after
the occurrence of any fault surpasses that of its irrecoverability. We
formulate this problem as a binary classification problem. Then, we develop a
two-pathway data-driven model based on Bayesian Gaussian mixture models that
predicts the solution to a typical problem by two different pre-fault and
post-fault prediction pathways. The results, obtained by the integration of the
predictions of those pathways, clearly indicate the success of our model in
solving the topology (ir)recoverability prediction problem compared to the best
of current strategies found in the literature.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: FormaT5: Abstention and Examples for Conditional Table Formatting with Natural Language
Abstract: Formatting is an important property in tables for visualization,
presentation, and analysis. Spreadsheet software allows users to automatically
format their tables by writing data-dependent conditional formatting (CF)
rules. Writing such rules is often challenging for users as it requires them to
understand and implement the underlying logic. We present FormaT5, a
transformer-based model that can generate a CF rule given the target table and
a natural language description of the desired formatting logic. We find that
user descriptions for these tasks are often under-specified or ambiguous,
making it harder for code generation systems to accurately learn the desired
rule in a single step. To tackle this problem of under-specification and
minimise argument errors, FormaT5 learns to predict placeholders though an
abstention objective. These placeholders can then be filled by a second model
or, when examples of rows that should be formatted are available, by a
programming-by-example system. To evaluate FormaT5 on diverse and real
scenarios, we create an extensive benchmark of 1053 CF tasks, containing
real-world descriptions collected from four different sources. We release our
benchmarks to encourage research in this area. Abstention and filling allow
FormaT5 to outperform 8 different neural approaches on our benchmarks, both
with and without examples. Our results illustrate the value of building
domain-specific learning systems.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Moments for Perceptive Narration Analysis Through the Emotional Attachment of Audience to Discourse and Story
Abstract: In this work, our goal is to develop a theoretical framework that can
eventually be used for analyzing the effectiveness of visual stories such as
feature films to comic books. To develop this theoretical framework, we
introduce a new story element called moments. Our conjecture is that any linear
story such as the story of a feature film can be decomposed into a set of
moments that follow each other. Moments are defined as the perception of the
actions, interactions, and expressions of all characters or a single character
during a given time period. We categorize the moments into two major types:
story moments and discourse moments. Each type of moment can further be
classified into three types, which we call universal storytelling moments. We
believe these universal moments foster or deteriorate the emotional attachment
of the audience to a particular character or the story. We present a
methodology to catalog the occurrences of these universal moments as they are
found in the story. The cataloged moments can be represented using curves or
color strips. Therefore, we can visualize a character's journey through the
story as either a 3D curve or a color strip. We also demonstrated that both
story and discourse moments can be transformed into one lump-sum attraction
parameter. The attraction parameter in time provides a function that can be
plotted graphically onto a timeline illustrating changes in the emotional
attachment of audience to a character or the story. By inspecting these
functions the story analyst can analytically decipher the moments in the story
where the attachment is being established, maintained, strengthened, or
conversely where it is languishing.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Ensembling Textual and Structure-Based Models for Knowledge Graph Completion
Abstract: We consider two popular approaches to Knowledge Graph Completion (KGC):
textual models that rely on textual entity descriptions, and structure-based
models that exploit the connectivity structure of the Knowledge Graph (KG).
Preliminary experiments show that these approaches have complementary
strengths: structure-based models perform well when the gold answer is easily
reachable from the query head in the KG, while textual models exploit
descriptions to give good performance even when the gold answer is not
reachable. In response, we explore ensembling as a way of combining the best of
both approaches. We propose a novel method for learning query-dependent
ensemble weights by using the distributions of scores assigned by individual
models to all candidate entities. Our ensemble baseline achieves
state-of-the-art results on three standard KGC datasets, with up to 6.8 pt MRR
and 8.3 pt Hits@1 gains over best individual models.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning
Abstract: We introduce Adapters, an open-source library that unifies
parameter-efficient and modular transfer learning in large language models. By
integrating 10 diverse adapter methods into a unified interface, Adapters
offers ease of use and flexible configuration. Our library allows researchers
and practitioners to leverage adapter modularity through composition blocks,
enabling the design of complex adapter setups. We demonstrate the library's
efficacy by evaluating its performance against full fine-tuning on various NLP
tasks. Adapters provides a powerful tool for addressing the challenges of
conventional fine-tuning paradigms and promoting more efficient and modular
transfer learning. The library is available via https://adapterhub.ml/adapters.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges
Abstract: In recent years, the combination of artificial intelligence (AI) and unmanned
aerial vehicles (UAVs) has brought about advancements in various areas. This
comprehensive analysis explores the changing landscape of AI-powered UAVs and
friendly computing in their applications. It covers emerging trends, futuristic
visions, and the inherent challenges that come with this relationship. The
study examines how AI plays a role in enabling navigation, detecting and
tracking objects, monitoring wildlife, enhancing precision agriculture,
facilitating rescue operations, conducting surveillance activities, and
establishing communication among UAVs using environmentally conscious computing
techniques. By delving into the interaction between AI and UAVs, this analysis
highlights the potential for these technologies to revolutionise industries
such as agriculture, surveillance practices, disaster management strategies,
and more. While envisioning possibilities, it also takes a look at ethical
considerations, safety concerns, regulatory frameworks to be established, and
the responsible deployment of AI-enhanced UAV systems. By consolidating
insights from research endeavours in this field, this review provides an
understanding of the evolving landscape of AI-powered UAVs while setting the
stage for further exploration in this transformative domain.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Algorithms for automatic intents extraction and utterances classification for goal-oriented dialogue systems
Abstract: Modern machine learning techniques in the natural language processing domain
can be used to automatically generate scripts for goal-oriented dialogue
systems. The current article presents a general framework for studying the
automatic generation of scripts for goal-oriented dialogue systems. A method
for preprocessing dialog data sets in JSON format is described. A comparison is
made of two methods for extracting user intent based on BERTopic and latent
Dirichlet allocation. A comparison has been made of two implemented algorithms
for classifying statements of users of a goal-oriented dialogue system based on
logistic regression and BERT transformer models. The BERT transformer approach
using the bert-base-uncased model showed better results for the three metrics
Precision (0.80), F1-score (0.78) and Matthews correlation coefficient (0.74)
in comparison with other methods.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning
Abstract: Continual learning aims to create artificial neural networks capable of
accumulating knowledge and skills through incremental training on a sequence of
tasks. The main challenge of continual learning is catastrophic interference,
wherein new knowledge overrides or interferes with past knowledge, leading to
forgetting. An associated issue is the problem of learning "cross-task
knowledge," where models fail to acquire and retain knowledge that helps
differentiate classes across task boundaries. A common solution to both
problems is "replay," where a limited buffer of past instances is utilized to
learn cross-task knowledge and mitigate catastrophic interference. However, a
notable drawback of these methods is their tendency to overfit the limited
replay buffer. In contrast, our proposed solution, SurpriseNet, addresses
catastrophic interference by employing a parameter isolation method and
learning cross-task knowledge using an auto-encoder inspired by anomaly
detection. SurpriseNet is applicable to both structured and unstructured data,
as it does not rely on image-specific inductive biases. We have conducted
empirical experiments demonstrating the strengths of SurpriseNet on various
traditional vision continual-learning benchmarks, as well as on structured data
datasets. Source code made available at https://doi.org/10.5281/zenodo.8247906
and https://github.com/tachyonicClock/SurpriseNet-CIKM-23

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Retro-BLEU: Quantifying Chemical Plausibility of Retrosynthesis Routes through Reaction Template Sequence Analysis
Abstract: Computer-assisted methods have emerged as valuable tools for retrosynthesis
analysis. However, quantifying the plausibility of generated retrosynthesis
routes remains a challenging task. We introduce Retro-BLEU, a statistical
metric adapted from the well-established BLEU score in machine translation, to
evaluate the plausibility of retrosynthesis routes based on reaction template
sequences analysis. We demonstrate the effectiveness of Retro-BLEU by applying
it to a diverse set of retrosynthesis routes generated by state-of-the-art
algorithms and compare the performance with other evaluation metrics. The
results show that Retro-BLEU is capable of differentiating between plausible
and implausible routes. Furthermore, we provide insights into the strengths and
weaknesses of Retro-BLEU, paving the way for future developments and
improvements in this field.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: KPIs-Based Clustering and Visualization of HPC jobs: a Feature Reduction Approach
Abstract: High-Performance Computing (HPC) systems need to be constantly monitored to
ensure their stability. The monitoring systems collect a tremendous amount of
data about different parameters or Key Performance Indicators (KPIs), such as
resource usage, IO waiting time, etc. A proper analysis of this data, usually
stored as time series, can provide insight in choosing the right management
strategies as well as the early detection of issues. In this paper, we
introduce a methodology to cluster HPC jobs according to their KPI indicators.
Our approach reduces the inherent high dimensionality of the collected data by
applying two techniques to the time series: literature-based and variance-based
feature extraction. We also define a procedure to visualize the obtained
clusters by combining the two previous approaches and the Principal Component
Analysis (PCA). Finally, we have validated our contributions on a real data set
to conclude that those KPIs related to CPU usage provide the best cohesion and
separation for clustering analysis and the good results of our visualization
methodology.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: OTOv3: Automatic Architecture-Agnostic Neural Network Training and Compression from Structured Pruning to Erasing Operators
Abstract: Compressing a predefined deep neural network (DNN) into a compact sub-network
with competitive performance is crucial in the efficient machine learning
realm. This topic spans various techniques, from structured pruning to neural
architecture search, encompassing both pruning and erasing operators
perspectives. Despite advancements, existing methods suffers from complex,
multi-stage processes that demand substantial engineering and domain knowledge,
limiting their broader applications. We introduce the third-generation
Only-Train-Once (OTOv3), which first automatically trains and compresses a
general DNN through pruning and erasing operations, creating a compact and
competitive sub-network without the need of fine-tuning. OTOv3 simplifies and
automates the training and compression process, minimizes the engineering
efforts required from users. It offers key technological advancements: (i)
automatic search space construction for general DNNs based on dependency graph
analysis; (ii) Dual Half-Space Projected Gradient (DHSPG) and its enhanced
version with hierarchical search (H2SPG) to reliably solve (hierarchical)
structured sparsity problems and ensure sub-network validity; and (iii)
automated sub-network construction using solutions from DHSPG/H2SPG and
dependency graphs. Our empirical results demonstrate the efficacy of OTOv3
across various benchmarks in structured pruning and neural architecture search.
OTOv3 produces sub-networks that match or exceed the state-of-the-arts. The
source code will be available at https://github.com/tianyic/only_train_once.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: A Language Model with Limited Memory Capacity Captures Interference in Human Sentence Processing
Abstract: Two of the central factors believed to underpin human sentence processing
difficulty are expectations and retrieval from working memory. A recent attempt
to create a unified cognitive model integrating these two factors relied on the
parallels between the self-attention mechanism of transformer language models
and cue-based retrieval theories of working memory in human sentence processing
(Ryu and Lewis 2021). While Ryu and Lewis show that attention patterns in
specialized attention heads of GPT-2 are consistent with similarity-based
interference, a key prediction of cue-based retrieval models, their method
requires identifying syntactically specialized attention heads, and makes the
cognitively implausible assumption that hundreds of memory retrieval operations
take place in parallel. In the present work, we develop a recurrent neural
language model with a single self-attention head, which more closely parallels
the memory system assumed by cognitive theories. We show that our model's
single attention head captures semantic and syntactic interference effects
observed in human experiments.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5?
Abstract: We introduce and study the problem of adversarial arithmetic, which provides
a simple yet challenging testbed for language model alignment. This problem is
comprised of arithmetic questions posed in natural language, with an arbitrary
adversarial string inserted before the question is complete. Even in the simple
setting of 1-digit addition problems, it is easy to find adversarial prompts
that make all tested models (including PaLM2, GPT4, Claude2) misbehave, and
even to steer models to a particular wrong answer. We additionally provide a
simple algorithm for finding successful attacks by querying those same models,
which we name "prompt inversion rejection sampling" (PIRS). We finally show
that models can be partially hardened against these attacks via reinforcement
learning and via agentic constitutional loops. However, we were not able to
make a language model fully robust against adversarial arithmetic attacks.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Gender inference: can chatGPT outperform common commercial tools?
Abstract: An increasing number of studies use gender information to understand
phenomena such as gender bias, inequity in access and participation, or the
impact of the Covid pandemic response. Unfortunately, most datasets do not
include self-reported gender information, making it necessary for researchers
to infer gender from other information, such as names or names and country
information. An important limitation of these tools is that they fail to
appropriately capture the fact that gender exists on a non-binary scale,
however, it remains important to evaluate and compare how well these tools
perform in a variety of contexts. In this paper, we compare the performance of
a generative Artificial Intelligence (AI) tool ChatGPT with three commercially
available list-based and machine learning-based gender inference tools (Namsor,
Gender-API, and genderize.io) on a unique dataset. Specifically, we use a large
Olympic athlete dataset and report how variations in the input (e.g., first
name and first and last name, with and without country information) impact the
accuracy of their predictions. We report results for the full set, as well as
for the subsets: medal versus non-medal winners, athletes from the largest
English-speaking countries, and athletes from East Asia. On these sets, we find
that Namsor is the best traditional commercially available tool. However,
ChatGPT performs at least as well as Namsor and often outperforms it,
especially for the female sample when country and/or last name information is
available. All tools perform better on medalists versus non-medalists and on
names from English-speaking countries. Although not designed for this purpose,
ChatGPT may be a cost-effective tool for gender prediction. In the future, it
might even be possible for ChatGPT or other large scale language models to
better identify self-reported gender rather than report gender on a binary
scale.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Visual tracking brain computer interface
Abstract: Brain-computer interfaces (BCIs) offer a way to interact with computers
without relying on physical movements. Non-invasive electroencephalography
(EEG)-based visual BCIs, known for efficient speed and calibration ease, face
limitations in continuous tasks due to discrete stimulus design and decoding
methods. To achieve continuous control, we implemented a novel spatial encoding
stimulus paradigm and devised a corresponding projection method to enable
continuous modulation of decoded velocity. Subsequently, we conducted
experiments involving 17 participants and achieved Fitt's ITR of 0.55 bps for
the fixed tracking task and 0.37 bps for the random tracking task. The proposed
BCI with a high Fitt's ITR was then integrated into two applications, including
painting and gaming. In conclusion, this study proposed a visual BCI-based
control method to go beyond discrete commands, allowing natural continuous
control based on neural activity.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: Efficient LLM Inference on CPUs
Abstract: Large language models (LLMs) have demonstrated remarkable performance and
tremendous potential across a wide range of tasks. However, deploying these
models has been challenging due to the astronomical amount of model parameters,
which requires a demand for large memory capacity and high memory bandwidth. In
this paper, we propose an effective approach that can make the deployment of
LLMs more efficiently. We support an automatic INT4 weight-only quantization
flow and design a special LLM runtime with highly-optimized kernels to
accelerate the LLM inference on CPUs. We demonstrate the general applicability
of our approach on popular LLMs including Llama2, Llama, GPT-NeoX, and showcase
the extreme inference efficiency on CPUs. The code is publicly available at:
https://github.com/intel/intel-extension-for-transformers.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: LooGLE: Can Long-Context Language Models Understand Long Contexts?
Abstract: Large language models (LLMs), despite their impressive performance in various
language tasks, are typically limited to processing texts within context-window
size. This limitation has spurred significant research efforts to enhance LLMs'
long-context understanding with high-quality long-sequence benchmarks. However,
prior datasets in this regard suffer from shortcomings, such as short context
length compared to the context window of modern LLMs; outdated documents that
have data leakage problems; and an emphasis on short dependency tasks rather
than long dependency tasks. In this paper, we present LooGLE, a Long Context
Generic Language Evaluation benchmark for LLMs' long context understanding.
LooGLE features relatively new documents post-2022, with over 24,000 tokens per
document and 6,000 newly generated questions spanning diverse domains. Human
annotators meticulously crafted more than 1,100 high-quality question-answer
pairs to meet the long dependency requirements. These pairs underwent thorough
cross-validation, yielding the most precise assessment of LLMs' long dependency
capabilities. The evaluation of eight state-of-the-art LLMs on LooGLE revealed
key findings: (i) commercial models outperformed open-sourced models; (ii) LLMs
excelled in short dependency tasks like short question-answering and cloze
tasks but struggled with more intricate long dependency tasks; (iii) in-context
learning and chaining thoughts offered only marginal improvements; (iv)
retrieval-based techniques demonstrated substantial benefits for short
question-answering, while strategies for extending context window length had
limited impact on long context understanding. As such, LooGLE not only provides
a systematic and comprehensive evaluation schema on long-context LLMs, but also
sheds light on future development of enhanced models towards "true long-context
understanding".

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Lecture Notes in Probabilistic Diffusion Models
Abstract: Diffusion models are loosely modelled based on non-equilibrium
thermodynamics, where \textit{diffusion} refers to particles flowing from
high-concentration regions towards low-concentration regions. In statistics,
the meaning is quite similar, namely the process of transforming a complex
distribution $p_{\text{complex}}$ on $\mathbb{R}^d$ to a simple distribution
$p_{\text{prior}}$ on the same domain. This constitutes a Markov chain of
diffusion steps of slowly adding random noise to data, followed by a reverse
diffusion process in which the data is reconstructed from the noise. The
diffusion model learns the data manifold to which the original and thus the
reconstructed data samples belong, by training on a large number of data
points. While the diffusion process pushes a data sample off the data manifold,
the reverse process finds a trajectory back to the data manifold. Diffusion
models have -- unlike variational autoencoder and flow models -- latent
variables with the same dimensionality as the original data, and they are
currently\footnote{At the time of writing, 2023.} outperforming other
approaches -- including Generative Adversarial Networks (GANs) -- to modelling
the distribution of, e.g., natural images.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations
Abstract: Large language models (LLMs) have emerged as powerful and general solutions
to many natural language tasks. However, many of the most important
applications of language generation are interactive, where an agent has to talk
to a person to reach a desired outcome. For example, a teacher might try to
understand their student's current comprehension level to tailor their
instruction accordingly, and a travel agent might ask questions of their
customer to understand their preferences in order to recommend activities they
might enjoy. LLMs trained with supervised fine-tuning or "single-step" RL, as
with standard RLHF, might struggle which tasks that require such goal-directed
behavior, since they are not trained to optimize for overall conversational
outcomes after multiple turns of interaction. In this work, we explore a new
method for adapting LLMs with RL for such goal-directed dialogue. Our key
insight is that, though LLMs might not effectively solve goal-directed dialogue
tasks out of the box, they can provide useful data for solving such tasks by
simulating suboptimal but human-like behaviors. Given a textual description of
a goal-directed dialogue task, we leverage LLMs to sample diverse synthetic
rollouts of hypothetical in-domain human-human interactions. Our algorithm then
utilizes this dataset with offline reinforcement learning to train an
interactive conversational agent that can optimize goal-directed objectives
over multiple turns. In effect, the LLM produces examples of possible
interactions, and RL then processes these examples to learn to perform more
optimal interactions. Empirically, we show that our proposed approach achieves
state-of-the-art performance in various goal-directed dialogue tasks that
include teaching and preference elicitation.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Is Machine Learning Unsafe and Irresponsible in Social Sciences? Paradoxes and Reconsidering from Recidivism Prediction Tasks
Abstract: The paper addresses some fundamental and hotly debated issues for high-stakes
event predictions underpinning the computational approach to social sciences.
We question several prevalent views against machine learning and outline a new
paradigm that highlights the promises and promotes the infusion of
computational methods and conventional social science approaches.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: Multi-Resolution Diffusion for Privacy-Sensitive Recommender Systems
Abstract: While recommender systems have become an integral component of the Web
experience, their heavy reliance on user data raises privacy and security
concerns. Substituting user data with synthetic data can address these
concerns, but accurately replicating these real-world datasets has been a
notoriously challenging problem. Recent advancements in generative AI have
demonstrated the impressive capabilities of diffusion models in generating
realistic data across various domains. In this work we introduce a Score-based
Diffusion Recommendation Module (SDRM), which captures the intricate patterns
of real-world datasets required for training highly accurate recommender
systems. SDRM allows for the generation of synthetic data that can replace
existing datasets to preserve user privacy, or augment existing datasets to
address excessive data sparsity. Our method outperforms competing baselines
such as generative adversarial networks, variational autoencoders, and recently
proposed diffusion models in synthesizing various datasets to replace or
augment the original data by an average improvement of 4.30% in Recall@$k$ and
4.65% in NDCG@$k$.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: SEMQA: Semi-Extractive Multi-Source Question Answering
Abstract: Recently proposed long-form question answering (QA) systems, supported by
large language models (LLMs), have shown promising capabilities. Yet,
attributing and verifying their generated abstractive answers can be difficult,
and automatically evaluating their accuracy remains an ongoing challenge.
  In this work, we introduce a new QA task for answering multi-answer questions
by summarizing multiple diverse sources in a semi-extractive fashion.
Specifically, Semi-extractive Multi-source QA (SEMQA) requires models to output
a comprehensive answer, while mixing factual quoted spans -- copied verbatim
from given input sources -- and non-factual free-text connectors that glue
these spans together into a single cohesive passage. This setting bridges the
gap between the outputs of well-grounded but constrained extractive QA systems
and more fluent but harder to attribute fully abstractive answers.
Particularly, it enables a new mode for language models that leverages their
advanced language generation capabilities, while also producing fine in-line
attributions by-design that are easy to verify, interpret, and evaluate.
  To study this task, we create the first dataset of this kind, QuoteSum, with
human-written semi-extractive answers to natural and generated questions, and
define text-based evaluation metrics. Experimenting with several LLMs in
various settings, we find this task to be surprisingly challenging,
demonstrating the importance of QuoteSum for developing and studying such
consolidation capabilities.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: RDR: the Recap, Deliberate, and Respond Method for Enhanced Language Understanding
Abstract: Natural language understanding (NLU) using neural network pipelines often
requires additional context that is not solely present in the input data.
Through Prior research, it has been evident that NLU benchmarks are susceptible
to manipulation by neural models, wherein these models exploit statistical
artifacts within the encoded external knowledge to artificially inflate
performance metrics for downstream tasks. Our proposed approach, known as the
Recap, Deliberate, and Respond (RDR) paradigm, addresses this issue by
incorporating three distinct objectives within the neural network pipeline.
Firstly, the Recap objective involves paraphrasing the input text using a
paraphrasing model in order to summarize and encapsulate its essence. Secondly,
the Deliberation objective entails encoding external graph information related
to entities mentioned in the input text, utilizing a graph embedding model.
Finally, the Respond objective employs a classification head model that
utilizes representations from the Recap and Deliberation modules to generate
the final prediction. By cascading these three models and minimizing a combined
loss, we mitigate the potential for gaming the benchmark and establish a robust
method for capturing the underlying semantic patterns, thus enabling accurate
predictions. To evaluate the effectiveness of the RDR method, we conduct tests
on multiple GLUE benchmark tasks. Our results demonstrate improved performance
compared to competitive baselines, with an enhancement of up to 2\% on standard
metrics. Furthermore, we analyze the observed evidence for semantic
understanding exhibited by RDR models, emphasizing their ability to avoid
gaming the benchmark and instead accurately capture the true underlying
semantic patterns.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Towards Mitigating Perceived Unfairness in Contracts from a Non-Legal Stakeholder's Perspective
Abstract: Commercial contracts are known to be a valuable source for deriving
project-specific requirements. However, contract negotiations mainly occur
among the legal counsel of the parties involved. The participation of non-legal
stakeholders, including requirement analysts, engineers, and solution
architects, whose primary responsibility lies in ensuring the seamless
implementation of contractual terms, is often indirect and inadequate.
Consequently, a significant number of sentences in contractual clauses, though
legally accurate, can appear unfair from an implementation perspective to
non-legal stakeholders. This perception poses a problem since requirements
indicated in the clauses are obligatory and can involve punitive measures and
penalties if not implemented as committed in the contract. Therefore, the
identification of potentially unfair clauses in contracts becomes crucial. In
this work, we conduct an empirical study to analyze the perspectives of
different stakeholders regarding contractual fairness. We then investigate the
ability of Pre-trained Language Models (PLMs) to identify unfairness in
contractual sentences by comparing chain of thought prompting and
semi-supervised fine-tuning approaches. Using BERT-based fine-tuning, we
achieved an accuracy of 84% on a dataset consisting of proprietary contracts.
It outperformed chain of thought prompting using Vicuna-13B by a margin of 9%.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Large Language Models for Robotics: A Survey
Abstract: The human ability to learn, generalize, and control complex manipulation
tasks through multi-modality feedback suggests a unique capability, which we
refer to as dexterity intelligence. Understanding and assessing this
intelligence is a complex task. Amidst the swift progress and extensive
proliferation of large language models (LLMs), their applications in the field
of robotics have garnered increasing attention. LLMs possess the ability to
process and generate natural language, facilitating efficient interaction and
collaboration with robots. Researchers and engineers in the field of robotics
have recognized the immense potential of LLMs in enhancing robot intelligence,
human-robot interaction, and autonomy. Therefore, this comprehensive review
aims to summarize the applications of LLMs in robotics, delving into their
impact and contributions to key areas such as robot control, perception,
decision-making, and path planning. We first provide an overview of the
background and development of LLMs for robotics, followed by a description of
the benefits of LLMs for robotics and recent advancements in robotics models
based on LLMs. We then delve into the various techniques used in the model,
including those employed in perception, decision-making, control, and
interaction. Finally, we explore the applications of LLMs in robotics and some
potential challenges they may face in the near future. Embodied intelligence is
the future of intelligent science, and LLMs-based robotics is one of the
promising but challenging paths to achieve this.

Output: Robotics