Instruction: What field is the article from?

Prompt:
Title: Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Abstract: Foundation models, now powering most of the exciting applications in deep
learning, are almost universally based on the Transformer architecture and its
core attention module. Many subquadratic-time architectures such as linear
attention, gated convolution and recurrent models, and structured state space
models (SSMs) have been developed to address Transformers' computational
inefficiency on long sequences, but they have not performed as well as
attention on important modalities such as language. We identify that a key
weakness of such models is their inability to perform content-based reasoning,
and make several improvements. First, simply letting the SSM parameters be
functions of the input addresses their weakness with discrete modalities,
allowing the model to selectively propagate or forget information along the
sequence length dimension depending on the current token. Second, even though
this change prevents the use of efficient convolutions, we design a
hardware-aware parallel algorithm in recurrent mode. We integrate these
selective SSMs into a simplified end-to-end neural network architecture without
attention or even MLP blocks (Mamba). Mamba enjoys fast inference (5$\times$
higher throughput than Transformers) and linear scaling in sequence length, and
its performance improves on real data up to million-length sequences. As a
general sequence model backbone, Mamba achieves state-of-the-art performance
across several modalities such as language, audio, and genomics. On language
modeling, our Mamba-3B model outperforms Transformers of the same size and
matches Transformers twice its size, both in pretraining and downstream
evaluation.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Adversarial Imitation Learning On Aggregated Data
Abstract: Inverse Reinforcement Learning (IRL) learns an optimal policy, given some
expert demonstrations, thus avoiding the need for the tedious process of
specifying a suitable reward function. However, current methods are constrained
by at least one of the following requirements. The first one is the need to
fully solve a forward Reinforcement Learning (RL) problem in the inner loop of
the algorithm, which might be prohibitively expensive in many complex
environments. The second one is the need for full trajectories from the
experts, which might not be easily available. The third one is the assumption
that the expert data is homogeneous rather than a collection from various
experts or possibly alternative solutions to the same task. Such constraints
make IRL approaches either not scalable or not usable on certain existing
systems. In this work we propose an approach which removes these requirements
through a dynamic, adaptive method called Adversarial Imitation Learning on
Aggregated Data (AILAD). It learns conjointly both a non linear reward function
and the associated optimal policy using an adversarial framework. The reward
learner only uses aggregated data. Moreover, it generates diverse behaviors
producing a distribution over the aggregated data matching that of the experts.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Cattle Identification Using Muzzle Images and Deep Learning Techniques
Abstract: Traditional animal identification methods such as ear-tagging, ear notching,
and branding have been effective but pose risks to the animal and have
scalability issues. Electrical methods offer better tracking and monitoring but
require specialized equipment and are susceptible to attacks. Biometric
identification using time-immutable dermatoglyphic features such as muzzle
prints and iris patterns is a promising solution. This project explores cattle
identification using 4923 muzzle images collected from 268 beef cattle. Two
deep learning classification models are implemented - wide ResNet50 and
VGG16\_BN and image compression is done to lower the image quality and adapt
the models to work for the African context. From the experiments run, a maximum
accuracy of 99.5\% is achieved while using the wide ResNet50 model with a
compression retaining 25\% of the original image. From the study, it is noted
that the time required by the models to train and converge as well as
recognition time are dependent on the machine used to run the model.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Moral Responsibility for AI Systems
Abstract: As more and more decisions that have a significant ethical dimension are
being outsourced to AI systems, it is important to have a definition of moral
responsibility that can be applied to AI systems. Moral responsibility for an
outcome of an agent who performs some action is commonly taken to involve both
a causal condition and an epistemic condition: the action should cause the
outcome, and the agent should have been aware -- in some form or other -- of
the possible moral consequences of their action. This paper presents a formal
definition of both conditions within the framework of causal models. I compare
my approach to the existing approaches of Braham and van Hees (BvH) and of
Halpern and Kleiman-Weiner (HK). I then generalize my definition into a degree
of responsibility.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Learning to Holistically Detect Bridges from Large-Size VHR Remote Sensing Imagery
Abstract: Bridge detection in remote sensing images (RSIs) plays a crucial role in
various applications, but it poses unique challenges compared to the detection
of other objects. In RSIs, bridges exhibit considerable variations in terms of
their spatial scales and aspect ratios. Therefore, to ensure the visibility and
integrity of bridges, it is essential to perform holistic bridge detection in
large-size very-high-resolution (VHR) RSIs. However, the lack of datasets with
large-size VHR RSIs limits the deep learning algorithms' performance on bridge
detection. Due to the limitation of GPU memory in tackling large-size images,
deep learning-based object detection methods commonly adopt the cropping
strategy, which inevitably results in label fragmentation and discontinuous
prediction. To ameliorate the scarcity of datasets, this paper proposes a
large-scale dataset named GLH-Bridge comprising 6,000 VHR RSIs sampled from
diverse geographic locations across the globe. These images encompass a wide
range of sizes, varying from 2,048*2,048 to 16,38*16,384 pixels, and
collectively feature 59,737 bridges. Furthermore, we present an efficient
network for holistic bridge detection (HBD-Net) in large-size RSIs. The HBD-Net
presents a separate detector-based feature fusion (SDFF) architecture and is
optimized via a shape-sensitive sample re-weighting (SSRW) strategy. Based on
the proposed GLH-Bridge dataset, we establish a bridge detection benchmark
including the OBB and HBB tasks, and validate the effectiveness of the proposed
HBD-Net. Additionally, cross-dataset generalization experiments on two publicly
available datasets illustrate the strong generalization capability of the
GLH-Bridge dataset.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: KNVQA: A Benchmark for evaluation knowledge-based VQA
Abstract: Within the multimodal field, large vision-language models (LVLMs) have made
significant progress due to their strong perception and reasoning capabilities
in the visual and language systems. However, LVLMs are still plagued by the two
critical issues of object hallucination and factual accuracy, which limit the
practicality of LVLMs in different scenarios. Furthermore, previous evaluation
methods focus more on the comprehension and reasoning of language content but
lack a comprehensive evaluation of multimodal interactions, thereby resulting
in potential limitations. To this end, we propose a novel KNVQA-Eval, which is
devoted to knowledge-based VQA task evaluation to reflect the factuality of
multimodal LVLMs. To ensure the robustness and scalability of the evaluation,
we develop a new KNVQA dataset by incorporating human judgment and perception,
aiming to evaluate the accuracy of standard answers relative to AI-generated
answers in knowledge-based VQA. This work not only comprehensively evaluates
the contextual information of LVLMs using reliable human annotations, but also
further analyzes the fine-grained capabilities of current methods to reveal
potential avenues for subsequent optimization of LVLMs-based estimators. Our
proposed VQA-Eval and corresponding dataset KNVQA will facilitate the
development of automatic evaluation tools with the advantages of low cost,
privacy protection, and reproducibility. Our code will be released upon
publication.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Sentiment analysis in Tourism: Fine-tuning BERT or sentence embeddings concatenation?
Abstract: Undoubtedly that the Bidirectional Encoder representations from Transformers
is the most powerful technique in making Natural Language Processing tasks such
as Named Entity Recognition, Question & Answers or Sentiment Analysis, however,
the use of traditional techniques remains a major potential for the improvement
of recent models, in particular word tokenization techniques and embeddings,
but also the improvement of neural network architectures which are now the core
of each architecture. recent. In this paper, we conduct a comparative study
between Fine-Tuning the Bidirectional Encoder Representations from Transformers
and a method of concatenating two embeddings to boost the performance of a
stacked Bidirectional Long Short-Term Memory-Bidirectional Gated Recurrent
Units model; these two approaches are applied in the context of sentiment
analysis of shopping places in Morocco. A search for the best learning rate was
made at the level of the two approaches, and a comparison of the best
optimizers was made for each sentence embedding combination with regard to the
second approach.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: "You Are An Expert Linguistic Annotator": Limits of LLMs as Analyzers of Abstract Meaning Representation
Abstract: Large language models (LLMs) show amazing proficiency and fluency in the use
of language. Does this mean that they have also acquired insightful linguistic
knowledge about the language, to an extent that they can serve as an "expert
linguistic annotator"? In this paper, we examine the successes and limitations
of the GPT-3, ChatGPT, and GPT-4 models in analysis of sentence meaning
structure, focusing on the Abstract Meaning Representation (AMR; Banarescu et
al. 2013) parsing formalism, which provides rich graphical representations of
sentence meaning structure while abstracting away from surface forms. We
compare models' analysis of this semantic structure across two settings: 1)
direct production of AMR parses based on zero- and few-shot prompts, and 2)
indirect partial reconstruction of AMR via metalinguistic natural language
queries (e.g., "Identify the primary event of this sentence, and the predicate
corresponding to that event."). Across these settings, we find that models can
reliably reproduce the basic format of AMR, and can often capture core event,
argument, and modifier structure -- however, model outputs are prone to
frequent and major errors, and holistic analysis of parse acceptability shows
that even with few-shot demonstrations, models have virtually 0% success in
producing fully accurate parses. Eliciting natural language responses produces
similar patterns of errors. Overall, our findings indicate that these models
out-of-the-box can capture aspects of semantic structure, but there remain key
limitations in their ability to support fully accurate semantic analyses or
parses.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: VQPy: An Object-Oriented Approach to Modern Video Analytics
Abstract: Video analytics is widely used in contemporary systems and services. At the
forefront of video analytics are video queries that users develop to find
objects of particular interest. Building upon the insight that video objects
(e.g., human, animals, cars, etc.), the center of video analytics, are similar
in spirit to objects modeled by traditional object-oriented languages, we
propose to develop an object-oriented approach to video analytics. This
approach, named VQPy, consists of a frontend$\unicode{x2015}$a Python variant
with constructs that make it easy for users to express video objects and their
interactions$\unicode{x2015}$as well as an extensible backend that can
automatically construct and optimize pipelines based on video objects. We have
implemented and open-sourced VQPy, which has been productized in Cisco as part
of its DeepVision framework.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Japanese Tort-case Dataset for Rationale-supported Legal Judgment Prediction
Abstract: This paper presents the first dataset for Japanese Legal Judgment Prediction
(LJP), the Japanese Tort-case Dataset (JTD), which features two tasks: tort
prediction and its rationale extraction. The rationale extraction task
identifies the court's accepting arguments from alleged arguments by plaintiffs
and defendants, which is a novel task in the field. JTD is constructed based on
annotated 3,477 Japanese Civil Code judgments by 41 legal experts, resulting in
7,978 instances with 59,697 of their alleged arguments from the involved
parties. Our baseline experiments show the feasibility of the proposed two
tasks, and our error analysis by legal experts identifies sources of errors and
suggests future directions of the LJP research.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Optimal Hyperparameter $ε$ for Adaptive Stochastic Optimizers through Gradient Histograms
Abstract: Optimizers are essential components for successfully training deep neural
network models. In order to achieve the best performance from such models,
designers need to carefully choose the optimizer hyperparameters. However, this
can be a computationally expensive and time-consuming process. Although it is
known that all optimizer hyperparameters must be tuned for maximum performance,
there is still a lack of clarity regarding the individual influence of minor
priority hyperparameters, including the safeguard factor $\epsilon$ and
momentum factor $\beta$, in leading adaptive optimizers (specifically, those
based on the Adam optimizers). In this manuscript, we introduce a new framework
based on gradient histograms to analyze and justify important attributes of
adaptive optimizers, such as their optimal performance and the relationships
and dependencies among hyperparameters. Furthermore, we propose a novel
gradient histogram-based algorithm that automatically estimates a reduced and
accurate search space for the safeguard hyperparameter $\epsilon$, where the
optimal value can be easily found.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Dynamic Data-Driven Digital Twins for Blockchain Systems
Abstract: In recent years, we have seen an increase in the adoption of blockchain-based
systems in non-financial applications, looking to benefit from what the
technology has to offer. Although many fields have managed to include
blockchain in their core functionalities, the adoption of blockchain, in
general, is constrained by the so-called trilemma trade-off between
decentralization, scalability, and security. In our previous work, we have
shown that using a digital twin for dynamically managing blockchain systems
during runtime can be effective in managing the trilemma trade-off. Our Digital
Twin leverages DDDAS feedback loop, which is responsible for getting the data
from the system to the digital twin, conducting optimisation, and updating the
physical system. This paper examines how leveraging DDDAS feedback loop can
support the optimisation component of the trilemma benefiting from
Reinforcement Learning agents and a simulation component to augment the quality
of the learned model while reducing the computational overhead required for
decision-making.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: ALGNet: Attention Light Graph Memory Network for Medical Recommendation System
Abstract: Medication recommendation is a vital task for improving patient care and
reducing adverse events. However, existing methods often fail to capture the
complex and dynamic relationships among patient medical records, drug efficacy
and safety, and drug-drug interactions (DDI). In this paper, we propose ALGNet,
a novel model that leverages light graph convolutional networks (LGCN) and
augmentation memory networks (AMN) to enhance medication recommendation. LGCN
can efficiently encode the patient records and the DDI graph into
low-dimensional embeddings, while AMN can augment the patient representation
with external knowledge from a memory module. We evaluate our model on the
MIMIC-III dataset and show that it outperforms several baselines in terms of
recommendation accuracy and DDI avoidance. We also conduct an ablation study to
analyze the effects of different components of our model. Our results
demonstrate that ALGNet can achieve superior performance with less computation
and more interpretability. The implementation of this paper can be found at:
https://github.com/huyquoctrinh/ALGNet.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Empowering Multi-step Reasoning across Languages via Tree-of-Thoughts
Abstract: Chain-of-Thought (CoT) prompting empowers the reasoning abilities of Large
Language Models (LLMs), eliciting them to solve complex reasoning tasks
step-by-step. However, with the success of CoT methods, the ability to deliver
multi-step reasoning remains limited to English due to the imbalance in the
distribution of the pre-training data, making the other languages a barrier.
  In this work, we propose a Cross-lingual multi-step reasoning approach,
aiming to align reasoning processes across different languages. In particular,
our method, through a Self-consistent Cross-lingual prompting mechanism
inspired by the Tree-of-Thoughts approach, delivers multi-step reasoning paths
in different languages that, during the steps, lead to the final solution. Our
experimental evaluations show that our method significantly outperforms
existing prompting methods, reducing the number of interactions and achieving
state-of-the-art performance.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Federated Active Learning for Target Domain Generalisation
Abstract: In this paper, we introduce Active Learning framework in Federated Learning
for Target Domain Generalisation, harnessing the strength from both learning
paradigms. Our framework, FEDALV, composed of Active Learning (AL) and
Federated Domain Generalisation (FDG), enables generalisation of an image
classification model trained from limited source domain client's data without
sharing images to an unseen target domain. To this end, our FDG, FEDA, consists
of two optimisation updates during training, one at the client and another at
the server level. For the client, the introduced losses aim to reduce feature
complexity and condition alignment, while in the server, the regularisation
limits free energy biases between source and target obtained by the global
model. The remaining component of FEDAL is AL with variable budgets, which
queries the server to retrieve and sample the most informative local data for
the targeted client. We performed multiple experiments on FDG w/ and w/o AL and
compared with both conventional FDG baselines and Federated Active Learning
baselines. Our extensive quantitative experiments demonstrate the superiority
of our method in accuracy and efficiency compared to the multiple contemporary
methods. FEDALV manages to obtain the performance of the full training target
accuracy while sampling as little as 5% of the source client's data.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: This Reads Like That: Deep Learning for Interpretable Natural Language Processing
Abstract: Prototype learning, a popular machine learning method designed for inherently
interpretable decisions, leverages similarities to learned prototypes for
classifying new data. While it is mainly applied in computer vision, in this
work, we build upon prior research and further explore the extension of
prototypical networks to natural language processing. We introduce a learned
weighted similarity measure that enhances the similarity computation by
focusing on informative dimensions of pre-trained sentence embeddings.
Additionally, we propose a post-hoc explainability mechanism that extracts
prediction-relevant words from both the prototype and input sentences. Finally,
we empirically demonstrate that our proposed method not only improves
predictive performance on the AG News and RT Polarity datasets over a previous
prototype-based approach, but also improves the faithfulness of explanations
compared to rationale-based recurrent convolutions.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Revolutionizing Global Food Security: Empowering Resilience through Integrated AI Foundation Models and Data-Driven Solutions
Abstract: Food security, a global concern, necessitates precise and diverse data-driven
solutions to address its multifaceted challenges. This paper explores the
integration of AI foundation models across various food security applications,
leveraging distinct data types, to overcome the limitations of current deep and
machine learning methods. Specifically, we investigate their utilization in
crop type mapping, cropland mapping, field delineation and crop yield
prediction. By capitalizing on multispectral imagery, meteorological data, soil
properties, historical records, and high-resolution satellite imagery, AI
foundation models offer a versatile approach. The study demonstrates that AI
foundation models enhance food security initiatives by providing accurate
predictions, improving resource allocation, and supporting informed
decision-making. These models serve as a transformative force in addressing
global food security limitations, marking a significant leap toward a
sustainable and secure food future.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Risk-Controlling Model Selection via Guided Bayesian Optimization
Abstract: Adjustable hyperparameters of machine learning models typically impact
various key trade-offs such as accuracy, fairness, robustness, or inference
cost. Our goal in this paper is to find a configuration that adheres to
user-specified limits on certain risks while being useful with respect to other
conflicting metrics. We solve this by combining Bayesian Optimization (BO) with
rigorous risk-controlling procedures, where our core idea is to steer BO
towards an efficient testing strategy. Our BO method identifies a set of Pareto
optimal configurations residing in a designated region of interest. The
resulting candidates are statistically verified and the best-performing
configuration is selected with guaranteed risk levels. We demonstrate the
effectiveness of our approach on a range of tasks with multiple desiderata,
including low error rates, equitable predictions, handling spurious
correlations, managing rate and distortion in generative models, and reducing
computational costs.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: GADY: Unsupervised Anomaly Detection on Dynamic Graphs
Abstract: Anomaly detection on dynamic graphs refers to detecting entities whose
behaviors obviously deviate from the norms observed within graphs and their
temporal information. This field has drawn increasing attention due to its
application in finance, network security, social networks, and more. However,
existing methods face two challenges: dynamic structure constructing challenge
- difficulties in capturing graph structure with complex time information and
negative sampling challenge - unable to construct excellent negative samples
for unsupervised learning. To address these challenges, we propose Unsupervised
Generative Anomaly Detection on Dynamic Graphs (GADY). To tackle the first
challenge, we propose a continuous dynamic graph model to capture the
fine-grained information, which breaks the limit of existing discrete methods.
Specifically, we employ a message-passing framework combined with positional
features to get edge embeddings, which are decoded to identify anomalies. For
the second challenge, we pioneer the use of Generative Adversarial Networks to
generate negative interactions. Moreover, we design a loss function to alter
the training goal of the generator while ensuring the diversity and quality of
generated samples. Extensive experiments demonstrate that our proposed GADY
significantly outperforms the previous state-of-the-art method on three
real-world datasets. Supplementary experiments further validate the
effectiveness of our model design and the necessity of each module.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Adapting Segment Anything Model (SAM) through Prompt-based Learning for Enhanced Protein Identification in Cryo-EM Micrographs
Abstract: Cryo-electron microscopy (cryo-EM) remains pivotal in structural biology, yet
the task of protein particle picking, integral for 3D protein structure
construction, is laden with manual inefficiencies. While recent AI tools such
as Topaz and crYOLO are advancing the field, they do not fully address the
challenges of cryo-EM images, including low contrast, complex shapes, and
heterogeneous conformations. This study explored prompt-based learning to adapt
the state-of-the-art image segmentation foundation model Segment Anything Model
(SAM) for cryo-EM. This focus was driven by the desire to optimize model
performance with a small number of labeled data without altering pre-trained
parameters, aiming for a balance between adaptability and foundational
knowledge retention. Through trials with three prompt-based learning
strategies, namely head prompt, prefix prompt, and encoder prompt, we observed
enhanced performance and reduced computational requirements compared to the
fine-tuning approach. This work not only highlights the potential of prompting
SAM in protein identification from cryo-EM micrographs but also suggests its
broader promise in biomedical image segmentation and object detection.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: LDM3D-VR: Latent Diffusion Model for 3D VR
Abstract: Latent diffusion models have proven to be state-of-the-art in the creation
and manipulation of visual outputs. However, as far as we know, the generation
of depth maps jointly with RGB is still limited. We introduce LDM3D-VR, a suite
of diffusion models targeting virtual reality development that includes
LDM3D-pano and LDM3D-SR. These models enable the generation of panoramic RGBD
based on textual prompts and the upscaling of low-resolution inputs to
high-resolution RGBD, respectively. Our models are fine-tuned from existing
pretrained models on datasets containing panoramic/high-resolution RGB images,
depth maps and captions. Both models are evaluated in comparison to existing
related methods.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024
Abstract: The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime
computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface
Vehicles (USV). Three challenges categories are considered: (i) UAV-based
Maritime Object Tracking with Re-identification, (ii) USV-based Maritime
Obstacle Segmentation and Detection, (iii) USV-based Maritime Boat Tracking.
The USV-based Maritime Obstacle Segmentation and Detection features three
sub-challenges, including a new embedded challenge addressing efficicent
inference on real-world embedded devices. This report offers a comprehensive
overview of the findings from the challenges. We provide both statistical and
qualitative analyses, evaluating trends from over 195 submissions. All
datasets, evaluation code, and the leaderboard are available to the public at
https://macvi.org/workshop/macvi24.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Emergence and Function of Abstract Representations in Self-Supervised Transformers
Abstract: Human intelligence relies in part on our brains' ability to create abstract
mental models that succinctly capture the hidden blueprint of our reality. Such
abstract world models notably allow us to rapidly navigate novel situations by
generalizing prior knowledge, a trait deep learning systems have historically
struggled to replicate. However, the recent shift from supervised to
self-supervised objectives, combined with expressive transformer-based
architectures, have yielded powerful foundation models that appear to learn
versatile representations that can support a wide range of downstream tasks.
This promising development raises the intriguing possibility of such models
developing in silico abstract world models. We test this hypothesis by studying
the inner workings of small-scale transformers trained to reconstruct partially
masked visual scenes generated from a simple blueprint. We show that the
network develops intermediate abstract representations, or abstractions, that
encode all semantic features of the dataset. These abstractions manifest as
low-dimensional manifolds where the embeddings of semantically related tokens
transiently converge, thus allowing for the generalization of downstream
computations. Using precise manipulation experiments, we demonstrate that
abstractions are central to the network's decision-making process. Our research
also suggests that these abstractions are compositionally structured,
exhibiting features like contextual independence and part-whole relationships
that mirror the compositional nature of the dataset. Finally, we introduce a
Language-Enhanced Architecture (LEA) designed to encourage the network to
articulate its computations. We find that LEA develops an abstraction-centric
language that can be easily interpreted, allowing us to more readily access and
steer the network's decision-making process.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: How Contentious Terms About People and Cultures are Used in Linked Open Data
Abstract: Web resources in linked open data (LOD) are comprehensible to humans through
literal textual values attached to them, such as labels, notes, or comments.
Word choices in literals may not always be neutral. When outdated and
culturally stereotyping terminology is used in literals, they may appear as
offensive to users in interfaces and propagate stereotypes to algorithms
trained on them. We study how frequently and in which literals contentious
terms about people and cultures occur in LOD and whether there are attempts to
mark the usage of such terms. For our analysis, we reuse English and Dutch
terms from a knowledge graph that provides opinions of experts from the
cultural heritage domain about terms' contentiousness. We inspect occurrences
of these terms in four widely used datasets: Wikidata, The Getty Art &
Architecture Thesaurus, Princeton WordNet, and Open Dutch WordNet. Some terms
are ambiguous and contentious only in particular senses. Applying word sense
disambiguation, we generate a set of literals relevant to our analysis. We
found that outdated, derogatory, stereotyping terms frequently appear in
descriptive and labelling literals, such as preferred labels that are usually
displayed in interfaces and used for indexing. In some cases, LOD contributors
mark contentious terms with words and phrases in literals (implicit markers) or
properties linked to resources (explicit markers). However, such marking is
rare and non-consistent in all datasets. Our quantitative and qualitative
insights could be helpful in developing more systematic approaches to address
the propagation of stereotypes via LOD.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Clustering Students According to their Academic Achievement Using Fuzzy Logic
Abstract: The software for clustering students according to their educational
achievements using fuzzy logic was developed in Python using the Google Colab
cloud service. In the process of analyzing educational data, the problems of
Data Mining are solved, since only some characteristics of the educational
process are obtained from a large sample of data. Data clustering was performed
using the classic K-Means method, which is characterized by simplicity and high
speed. Cluster analysis was performed in the space of two features using the
machine learning library scikit-learn (Python). The obtained clusters are
described by fuzzy triangular membership functions, which allowed to correctly
determine the membership of each student to a certain cluster. Creation of
fuzzy membership functions is done using the scikit-fuzzy library. The
development of fuzzy functions of objects belonging to clusters is also useful
for educational purposes, as it allows a better understanding of the principles
of using fuzzy logic. As a result of processing test educational data using the
developed software, correct results were obtained. It is shown that the use of
fuzzy membership functions makes it possible to correctly determine the
belonging of students to certain clusters, even if such clusters are not
clearly separated. Due to this, it is possible to more accurately determine the
recommended level of difficulty of tasks for each student, depending on his
previous evaluations.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Large Language Models in Education: Vision and Opportunities
Abstract: With the rapid development of artificial intelligence technology, large
language models (LLMs) have become a hot research topic. Education plays an
important role in human social development and progress. Traditional education
faces challenges such as individual student differences, insufficient
allocation of teaching resources, and assessment of teaching effectiveness.
Therefore, the applications of LLMs in the field of digital/smart education
have broad prospects. The research on educational large models (EduLLMs) is
constantly evolving, providing new methods and approaches to achieve
personalized learning, intelligent tutoring, and educational assessment goals,
thereby improving the quality of education and the learning experience. This
article aims to investigate and summarize the application of LLMs in smart
education. It first introduces the research background and motivation of LLMs
and explains the essence of LLMs. It then discusses the relationship between
digital education and EduLLMs and summarizes the current research status of
educational large models. The main contributions are the systematic summary and
vision of the research background, motivation, and application of large models
for education (LLM4Edu). By reviewing existing research, this article provides
guidance and insights for educators, researchers, and policy-makers to gain a
deep understanding of the potential and challenges of LLM4Edu. It further
provides guidance for further advancing the development and application of
LLM4Edu, while still facing technical, ethical, and practical challenges
requiring further research and exploration.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Enhancing Numeric-SAM for Learning with Few Observations
Abstract: A significant challenge in applying planning technology to real-world
problems lies in obtaining a planning model that accurately represents the
problem's dynamics. Numeric Safe Action Models Learning (N-SAM) is a recently
proposed algorithm that addresses this challenge. It is an algorithm designed
to learn the preconditions and effects of actions from observations in domains
that may involve both discrete and continuous state variables. N-SAM has
several attractive properties. It runs in polynomial time and is guaranteed to
output an action model that is safe, in the sense that plans generated by it
are applicable and will achieve their intended goals. To preserve this safety
guarantee, N-SAM must observe a substantial number of examples for each action
before it is included in the learned action model. We address this limitation
of N-SAM and propose N-SAM*, an enhanced version of N-SAM that always returns
an action model where every observed action is applicable at least in some
state, even if it was only observed once. N-SAM* does so without compromising
the safety of the returned action model. We prove that N-SAM* is optimal in
terms of sample complexity compared to any other algorithm that guarantees
safety. An empirical study on a set of benchmark domains shows that the action
models returned by N-SAM* enable solving significantly more problems compared
to the action models returned by N-SAM.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Serverless Federated Learning with flwr-serverless
Abstract: Federated learning is becoming increasingly relevant and popular as we
witness a surge in data collection and storage of personally identifiable
information. Alongside these developments there have been many proposals from
governments around the world to provide more protections for individuals' data
and a heightened interest in data privacy measures. As deep learning continues
to become more relevant in new and existing domains, it is vital to develop
strategies like federated learning that can effectively train data from
different sources, such as edge devices, without compromising security and
privacy. Recently, the Flower (\texttt{Flwr}) Python package was introduced to
provide a scalable, flexible, and easy-to-use framework for implementing
federated learning. However, to date, Flower is only able to run synchronous
federated learning which can be costly and time-consuming to run because the
process is bottlenecked by client-side training jobs that are slow or fragile.
Here, we introduce \texttt{flwr-serverless}, a wrapper around the Flower
package that extends its functionality to allow for both synchronous and
asynchronous federated learning with minimal modification to Flower's design
paradigm. Furthermore, our approach to federated learning allows the process to
run without a central server, which increases the domains of application and
accessibility of its use. This paper presents the design details and usage of
this approach through a series of experiments that were conducted using public
datasets. Overall, we believe that our approach decreases the time and cost to
run federated training and provides an easier way to implement and experiment
with federated learning systems.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Innovations in Agricultural Forecasting: A Multivariate Regression Study on Global Crop Yield Prediction
Abstract: The prediction of crop yields internationally is a crucial objective in
agricultural research. Thus, this study implements 6 regression models (Linear,
Tree, Gradient Descent, Gradient Boosting, K- Nearest Neighbors, and Random
Forest) to predict crop yields in 196 countries. Given 4 key training
parameters, pesticides (tonnes), rainfall (mm), temperature (Celsius), and
yield (hg/ha), it was found that our Random Forest Regression model achieved a
determination coefficient (r^2) of 0.94, with a margin of error (ME) of .03.
The models were trained and tested using the Food and Agricultural Organization
of the United Nations data, along with the World Bank Climate Change Data
Catalog. Furthermore, each parameter was analyzed to understand how varying
factors could impact overall yield. We used unconventional models, contrary to
generally used Deep Learning (DL) and Machine Learning (ML) models, combined
with recently collected data to implement a unique approach in our research.
Existing scholarship would benefit from understanding the most optimal model
for agricultural research, specifically using the United Nations data.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Physics-Informed Data Denoising for Real-Life Sensing Systems
Abstract: Sensors measuring real-life physical processes are ubiquitous in today's
interconnected world. These sensors inherently bear noise that often adversely
affects performance and reliability of the systems they support. Classic
filtering-based approaches introduce strong assumptions on the time or
frequency characteristics of sensory measurements, while learning-based
denoising approaches typically rely on using ground truth clean data to train a
denoising model, which is often challenging or prohibitive to obtain for many
real-world applications. We observe that in many scenarios, the relationships
between different sensor measurements (e.g., location and acceleration) are
analytically described by laws of physics (e.g., second-order differential
equation). By incorporating such physics constraints, we can guide the
denoising process to improve even in the absence of ground truth data. In light
of this, we design a physics-informed denoising model that leverages the
inherent algebraic relationships between different measurements governed by the
underlying physics. By obviating the need for ground truth clean data, our
method offers a practical denoising solution for real-world applications. We
conducted experiments in various domains, including inertial navigation, CO2
monitoring, and HVAC control, and achieved state-of-the-art performance
compared with existing denoising methods. Our method can denoise data in real
time (4ms for a sequence of 1s) for low-cost noisy sensors and produces results
that closely align with those from high-precision, high-cost alternatives,
leading to an efficient, cost-effective approach for more accurate sensor-based
systems.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Source Prompt: Coordinated Pre-training of Language Models on Diverse Corpora from Multiple Sources
Abstract: Pre-trained language models (PLMs) have established the new paradigm in the
field of NLP. For more powerful PLMs, one of the most popular and successful
way is to continuously scale up sizes of the models and the pre-training
corpora. These large corpora are generally obtained by converging smaller ones
from multiple sources, they are thus growing increasingly diverse. However, the
side-effects of these colossal converged corpora remain understudied. In this
paper, we identify the disadvantage of heterogeneous corpora from multiple
sources for pre-training PLMs. Towards coordinated pre-training on diverse
corpora, we further propose source prompts (SP), which explicitly prompt the
model of the data source at the pre-training and fine-tuning stages. Results of
extensive experiments demonstrate that PLMs pre-trained with SP on diverse
corpora gain significant improvement in various downstream tasks.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Enhancing the Performance of Neural Networks Through Causal Discovery and Integration of Domain Knowledge
Abstract: In this paper, we develop a generic methodology to encode hierarchical
causality structure among observed variables into a neural network in order to
improve its predictive performance. The proposed methodology, called
causality-informed neural network (CINN), leverages three coherent steps to
systematically map the structural causal knowledge into the layer-to-layer
design of neural network while strictly preserving the orientation of every
causal relationship. In the first step, CINN discovers causal relationships
from observational data via directed acyclic graph (DAG) learning, where causal
discovery is recast as a continuous optimization problem to avoid the
combinatorial nature. In the second step, the discovered hierarchical causality
structure among observed variables is systematically encoded into neural
network through a dedicated architecture and customized loss function. By
categorizing variables in the causal DAG as root, intermediate, and leaf nodes,
the hierarchical causal DAG is translated into CINN with a one-to-one
correspondence between nodes in the causal DAG and units in the CINN while
maintaining the relative order among these nodes. Regarding the loss function,
both intermediate and leaf nodes in the DAG graph are treated as target outputs
during CINN training so as to drive co-learning of causal relationships among
different types of nodes. As multiple loss components emerge in CINN, we
leverage the projection of conflicting gradients to mitigate gradient
interference among the multiple learning tasks. Computational experiments
across a broad spectrum of UCI data sets demonstrate substantial advantages of
CINN in predictive performance over other state-of-the-art methods. In
addition, an ablation study underscores the value of integrating structural and
quantitative causal knowledge in enhancing the neural network's predictive
performance incrementally.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Vehicle Lane Change Prediction based on Knowledge Graph Embeddings and Bayesian Inference
Abstract: Prediction of vehicle lane change maneuvers has gained a lot of momentum in
the last few years. Some recent works focus on predicting a vehicle's intention
by predicting its trajectory first. This is not enough, as it ignores the
context of the scene and the state of the surrounding vehicles (as they might
be risky to the target vehicle). Other works assessed the risk made by the
surrounding vehicles only by considering their existence around the target
vehicle, or by considering the distance and relative velocities between them
and the target vehicle as two separate numerical features. In this work, we
propose a solution that leverages Knowledge Graphs (KGs) to anticipate lane
changes based on linguistic contextual information in a way that goes well
beyond the capabilities of current perception systems. Our solution takes the
Time To Collision (TTC) with surrounding vehicles as input to assess the risk
on the target vehicle. Moreover, our KG is trained on the HighD dataset using
the TransE model to obtain the Knowledge Graph Embeddings (KGE). Then, we apply
Bayesian inference on top of the KG using the embeddings learned during
training. Finally, the model can predict lane changes two seconds ahead with
97.95% f1-score, which surpassed the state of the art, and three seconds before
changing lanes with 93.60% f1-score.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression
Abstract: Large-scale pre-trained language models (LLMs) have demonstrated exceptional
performance in various natural language processing (NLP) tasks. However, the
massive size of these models poses huge challenges for their deployment in
real-world applications. While numerous model compression techniques have been
proposed, most of them are not well-suited for achieving extreme model
compression when there is a significant gap in model scale. In this paper, we
introduce a novel compression paradigm called Retrieval-based Knowledge
Transfer (RetriKT), which effectively transfers the knowledge of LLMs to
extremely small-scale models (e.g., 1%). In particular, our approach extracts
knowledge from LLMs to construct a knowledge store, from which the small-scale
model can retrieve relevant information and leverage it for effective
inference. To improve the quality of the model, soft prompt tuning and Proximal
Policy Optimization (PPO) reinforcement learning techniques are employed.
Extensive experiments are conducted on low-resource tasks from SuperGLUE and
GLUE benchmarks. The results demonstrate that the proposed approach
significantly enhances the performance of small-scale models by leveraging the
knowledge from LLMs.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Local Differential Privacy for Smart Meter Data Sharing
Abstract: Energy disaggregation techniques, which use smart meter data to infer
appliance energy usage, can provide consumers and energy companies valuable
insights into energy management. However, these techniques also present privacy
risks, such as the potential for behavioral profiling. Local differential
privacy (LDP) methods provide strong privacy guarantees with high efficiency in
addressing privacy concerns. However, existing LDP methods focus on protecting
aggregated energy consumption data rather than individual appliances.
Furthermore, these methods do not consider the fact that smart meter data are a
form of streaming data, and its processing methods should account for time
windows. In this paper, we propose a novel LDP approach (named LDP-SmartEnergy)
that utilizes randomized response techniques with sliding windows to facilitate
the sharing of appliance-level energy consumption data over time while not
revealing individual users' appliance usage patterns. Our evaluations show that
LDP-SmartEnergy runs efficiently compared to baseline methods. The results also
demonstrate that our solution strikes a balance between protecting privacy and
maintaining the utility of data for effective analysis.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: AtomXR: Streamlined XR Prototyping with Natural Language and Immersive Physical Interaction
Abstract: As technological advancements in extended reality (XR) amplify the demand for
more XR content, traditional development processes face several challenges: 1)
a steep learning curve for inexperienced developers, 2) a disconnect between 2D
development environments and 3D user experiences inside headsets, and 3) slow
iteration cycles due to context switching between development and testing
environments. To address these challenges, we introduce AtomXR, a streamlined,
immersive, no-code XR prototyping tool designed to empower both experienced and
inexperienced developers in creating applications using natural language,
eye-gaze, and touch interactions. AtomXR consists of: 1) AtomScript, a
high-level human-interpretable scripting language for rapid prototyping, 2) a
natural language interface that integrates LLMs and multimodal inputs for
AtomScript generation, and 3) an immersive in-headset authoring environment.
Empirical evaluation through two user studies offers insights into natural
language-based and immersive prototyping, and shows AtomXR provides significant
improvements in speed and user experience compared to traditional systems.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Does GPT-4 Pass the Turing Test?
Abstract: We evaluated GPT-4 in a public online Turing Test. The best-performing GPT-4
prompt passed in 41% of games, outperforming baselines set by ELIZA (27%) and
GPT-3.5 (14%), but falling short of chance and the baseline set by human
participants (63%). Participants' decisions were based mainly on linguistic
style (35%) and socio-emotional traits (27%), supporting the idea that
intelligence is not sufficient to pass the Turing Test. Participants'
demographics, including education and familiarity with LLMs, did not predict
detection rate, suggesting that even those who understand systems deeply and
interact with them frequently may be susceptible to deception. Despite known
limitations as a test of intelligence, we argue that the Turing Test continues
to be relevant as an assessment of naturalistic communication and deception. AI
models with the ability to masquerade as humans could have widespread societal
consequences, and we analyse the effectiveness of different strategies and
criteria for judging humanlikeness.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: healthAIChain: Improving security and safety using Blockchain Technology applications in AI-based healthcare systems
Abstract: Blockchain as a digital ledger for keeping records of digital transactions
and other information, it is secure and decentralized technology. The globally
growing number of digital population every day possesses a significant threat
to online data including the medical and patients data. After bitcoin,
blockchain technology has emerged into a general-purpose technology with
applications in medical industries and healthcare. Blockchain can promote
highly configurable openness while retaining the highest security standards for
critical data of medical patients. Referred to as distributed record keeping
for healthcare systems which makes digital assets unalterable and transparent
via a cryptographic hash and decentralized network. The study delves into the
security and safety improvement associated with implementing blockchain in
AI-based healthcare systems. Blockchain-enabled AI tackles the existing issues
related to security, performance efficiencies, and safety in healthcare
systems. We have also examined the Artificial Intelligence in healthcare and
medical industry, potential areas, open questions concerning the blockchain in
healthcare systems. Finally, the article proposed an AI-based healthcare
blockchain model (healthAIChain) to improve patients data and security.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey
Abstract: Due to the greatly improved capabilities of devices, massive data, and
increasing concern about data privacy, Federated Learning (FL) has been
increasingly considered for applications to wireless communication networks
(WCNs). Wireless FL (WFL) is a distributed method of training a global deep
learning model in which a large number of participants each train a local model
on their training datasets and then upload the local model updates to a central
server. However, in general, non-independent and identically distributed
(non-IID) data of WCNs raises concerns about robustness, as a malicious
participant could potentially inject a "backdoor" into the global model by
uploading poisoned data or models over WCN. This could cause the model to
misclassify malicious inputs as a specific target class while behaving normally
with benign inputs. This survey provides a comprehensive review of the latest
backdoor attacks and defense mechanisms. It classifies them according to their
targets (data poisoning or model poisoning), the attack phase (local data
collection, training, or aggregation), and defense stage (local training,
before aggregation, during aggregation, or after aggregation). The strengths
and limitations of existing attack strategies and defense mechanisms are
analyzed in detail. Comparisons of existing attack methods and defense designs
are carried out, pointing to noteworthy findings, open challenges, and
potential future research directions related to security and privacy of WFL.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Elijah: Eliminating Backdoors Injected in Diffusion Models via Distribution Shift
Abstract: Diffusion models (DM) have become state-of-the-art generative models because
of their capability to generate high-quality images from noises without
adversarial training. However, they are vulnerable to backdoor attacks as
reported by recent studies. When a data input (e.g., some Gaussian noise) is
stamped with a trigger (e.g., a white patch), the backdoored model always
generates the target image (e.g., an improper photo). However, effective
defense strategies to mitigate backdoors from DMs are underexplored. To bridge
this gap, we propose the first backdoor detection and removal framework for
DMs. We evaluate our framework Elijah on hundreds of DMs of 3 types including
DDPM, NCSN and LDM, with 13 samplers against 3 existing backdoor attacks.
Extensive experiments show that our approach can have close to 100% detection
accuracy and reduce the backdoor effects to close to zero without significantly
sacrificing the model utility.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: META4: Semantically-Aligned Generation of Metaphoric Gestures Using Self-Supervised Text and Speech Representation
Abstract: Image Schemas are repetitive cognitive patterns that influence the way we
conceptualize and reason about various concepts present in speech. These
patterns are deeply embedded within our cognitive processes and are reflected
in our bodily expressions including gestures. Particularly, metaphoric gestures
possess essential characteristics and semantic meanings that align with Image
Schemas, to visually represent abstract concepts. The shape and form of
gestures can convey abstract concepts, such as extending the forearm and hand
or tracing a line with hand movements to visually represent the image schema of
PATH. Previous behavior generation models have primarily focused on utilizing
speech (acoustic features and text) to drive the generation model of virtual
agents. They have not considered key semantic information as those carried by
Image Schemas to effectively generate metaphoric gestures. To address this
limitation, we introduce META4, a deep learning approach that generates
metaphoric gestures from both speech and Image Schemas. Our approach has two
primary goals: computing Image Schemas from input text to capture the
underlying semantic and metaphorical meaning, and generating metaphoric
gestures driven by speech and the computed image schemas. Our approach is the
first method for generating speech driven metaphoric gestures while leveraging
the potential of Image Schemas. We demonstrate the effectiveness of our
approach and highlight the importance of both speech and image schemas in
modeling metaphoric gestures.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: A Multi-Agent Reinforcement Learning Framework for Evaluating the U.S. Ending the HIV Epidemic Plan
Abstract: Human immunodeficiency virus (HIV) is a major public health concern in the
United States, with about 1.2 million people living with HIV and 35,000 newly
infected each year. There are considerable geographical disparities in HIV
burden and care access across the U.S. The 2019 Ending the HIV Epidemic (EHE)
initiative aims to reduce new infections by 90% by 2030, by improving coverage
of diagnoses, treatment, and prevention interventions and prioritizing
jurisdictions with high HIV prevalence. Identifying optimal scale-up of
intervention combinations will help inform resource allocation. Existing HIV
decision analytic models either evaluate specific cities or the overall
national population, thus overlooking jurisdictional interactions or
differences. In this paper, we propose a multi-agent reinforcement learning
(MARL) model, that enables jurisdiction-specific decision analyses but in an
environment with cross-jurisdictional epidemiological interactions. In
experimental analyses, conducted on jurisdictions within California and
Florida, optimal policies from MARL were significantly different than those
generated from single-agent RL, highlighting the influence of jurisdictional
variations and interactions. By using comprehensive modeling of HIV and
formulations of state space, action space, and reward functions, this work
helps demonstrate the strengths and applicability of MARL for informing public
health policies, and provides a framework for expanding to the national-level
to inform the EHE.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data
Abstract: The most challenging, yet practical, setting of semi-supervised federated
learning (SSFL) is where a few clients have fully labeled data whereas the
other clients have fully unlabeled data. This is particularly common in
healthcare settings where collaborating partners (typically hospitals) may have
images but not annotations. The bottleneck in this setting is the joint
training of labeled and unlabeled clients as the objective function for each
client varies based on the availability of labels. This paper investigates an
alternative way for effective training with labeled and unlabeled clients in a
federated setting. We propose a novel learning scheme specifically designed for
SSFL which we call Isolated Federated Learning (IsoFed) that circumvents the
problem by avoiding simple averaging of supervised and semi-supervised models
together. In particular, our training approach consists of two parts - (a)
isolated aggregation of labeled and unlabeled client models, and (b) local
self-supervised pretraining of isolated global models in all clients. We
evaluate our model performance on medical image datasets of four different
modalities publicly available within the biomedical image classification
benchmark MedMNIST. We further vary the proportion of labeled clients and the
degree of heterogeneity to demonstrate the effectiveness of the proposed method
under varied experimental settings.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Automatized Self-Supervised Learning for Skin Lesion Screening
Abstract: The incidence rates of melanoma, the deadliest form of skin cancer, have been
increasing steadily worldwide, presenting a significant challenge to
dermatologists. Early detection of melanoma is crucial for improving patient
survival rates, but identifying suspicious lesions through ugly duckling (UD)
screening, the current method used for skin cancer screening, can be
challenging and often requires expertise in pigmented lesions. To address these
challenges and improve patient outcomes, an artificial intelligence (AI)
decision support tool was developed to assist dermatologists in identifying UD
from wide-field patient images. The tool uses a state-of-the-art object
detection algorithm to identify and extract all skin lesions from patient
images, which are then sorted by suspiciousness using a self-supervised AI
algorithm. A clinical validation study was conducted to evaluate the tool's
performance, which demonstrated an average sensitivity of 93% for the top-10
AI-identified UDs on skin lesions selected by the majority of experts in
pigmented skin lesions. The study also found that dermatologists confidence
increased, and the average majority agreement with the top-10 AI-identified UDs
improved to 100% when assisted by AI. The development of this AI decision
support tool aims to address the shortage of specialists, enable at-risk
patients to receive faster consultations and understand the impact of
AI-assisted screening. The tool's automation can assist dermatologists in
identifying suspicious lesions and provide a more objective assessment,
reducing subjectivity in the screening process. The future steps for this
project include expanding the dataset to include histologically confirmed
melanoma cases and increasing the number of participants for clinical
validation to strengthen the tool's reliability and adapt it for real-world
consultation.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: A Novel Variational Lower Bound for Inverse Reinforcement Learning
Abstract: Inverse reinforcement learning (IRL) seeks to learn the reward function from
expert trajectories, to understand the task for imitation or collaboration
thereby removing the need for manual reward engineering. However, IRL in the
context of large, high-dimensional problems with unknown dynamics has been
particularly challenging. In this paper, we present a new Variational Lower
Bound for IRL (VLB-IRL), which is derived under the framework of a
probabilistic graphical model with an optimality node. Our method
simultaneously learns the reward function and policy under the learned reward
function by maximizing the lower bound, which is equivalent to minimizing the
reverse Kullback-Leibler divergence between an approximated distribution of
optimality given the reward function and the true distribution of optimality
given trajectories. This leads to a new IRL method that learns a valid reward
function such that the policy under the learned reward achieves expert-level
performance on several known domains. Importantly, the method outperforms the
existing state-of-the-art IRL algorithms on these domains by demonstrating
better reward from the learned policy.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data
Abstract: The efficiency of business processes relies on business key performance
indicators (Biz-KPIs), that can be negatively impacted by IT failures. Business
and IT Observability (BizITObs) data fuses both Biz-KPIs and IT event channels
together as multivariate time series data. Forecasting Biz-KPIs in advance can
enhance efficiency and revenue through proactive corrective measures. However,
BizITObs data generally exhibit both useful and noisy inter-channel
interactions between Biz-KPIs and IT events that need to be effectively
decoupled. This leads to suboptimal forecasting performance when existing
multivariate forecasting models are employed. To address this, we introduce
AutoMixer, a time-series Foundation Model (FM) approach, grounded on the novel
technique of channel-compressed pretrain and finetune workflows. AutoMixer
leverages an AutoEncoder for channel-compressed pretraining and integrates it
with the advanced TSMixer model for multivariate time series forecasting. This
fusion greatly enhances the potency of TSMixer for accurate forecasts and also
generalizes well across several downstream tasks. Through detailed experiments
and dashboard analytics, we show AutoMixer's capability to consistently improve
the Biz-KPI's forecasting accuracy (by 11-15\%) which directly translates to
actionable business insights.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Multimodal Large Language Models: A Survey
Abstract: The exploration of multimodal language models integrates multiple data types,
such as images, text, language, audio, and other heterogeneity. While the
latest large language models excel in text-based tasks, they often struggle to
understand and process other data types. Multimodal models address this
limitation by combining various modalities, enabling a more comprehensive
understanding of diverse data. This paper begins by defining the concept of
multimodal and examining the historical development of multimodal algorithms.
Furthermore, we introduce a range of multimodal products, focusing on the
efforts of major technology companies. A practical guide is provided, offering
insights into the technical aspects of multimodal models. Moreover, we present
a compilation of the latest algorithms and commonly used datasets, providing
researchers with valuable resources for experimentation and evaluation. Lastly,
we explore the applications of multimodal models and discuss the challenges
associated with their development. By addressing these aspects, this paper aims
to facilitate a deeper understanding of multimodal models and their potential
in various domains.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: The Falcon Series of Open Language Models
Abstract: We introduce the Falcon series: 7B, 40B, and 180B parameters causal
decoder-only models trained on a diverse high-quality corpora predominantly
assembled from web data. The largest model, Falcon-180B, has been trained on
over 3.5 trillion tokens of text--the largest openly documented pretraining
run. Falcon-180B significantly outperforms models such as PaLM or Chinchilla,
and improves upon concurrently developed models such as LLaMA 2 or
Inflection-1. It nears the performance of PaLM-2-Large at a reduced pretraining
and inference cost, making it, to our knowledge, one of the three best language
models in the world along with GPT-4 and PaLM-2-Large. We report detailed
evaluations, as well as a deep dive into the methods and custom tooling
employed to pretrain Falcon. Notably, we report on our custom distributed
training codebase, allowing us to efficiently pretrain these models on up to
4,096 A100s on cloud AWS infrastructure with limited interconnect. We release a
600B tokens extract of our web dataset, as well as the Falcon-7/40/180B models
under a permissive license to foster open-science and accelerate the
development of an open ecosystem of large language models.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Cybersecurity threats in FinTech: A systematic review
Abstract: The rapid evolution of the Smart-everything movement and Artificial
Intelligence (AI) advancements have given rise to sophisticated cyber threats
that traditional methods cannot counteract. Cyber threats are extremely
critical in financial technology (FinTech) as a data-centric sector expected to
provide 24/7 services. This paper introduces a novel and refined taxonomy of
security threats in FinTech and conducts a comprehensive systematic review of
defensive strategies. Through PRISMA methodology applied to 74 selected studies
and topic modeling, we identified 11 central cyber threats, with 43 papers
detailing them, and pinpointed 9 corresponding defense strategies, as covered
in 31 papers. This in-depth analysis offers invaluable insights for
stakeholders ranging from banks and enterprises to global governmental bodies,
highlighting both the current challenges in FinTech and effective
countermeasures, as well as directions for future research.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Progression and Challenges of IoT in Healthcare: A Short Review
Abstract: Smart healthcare, an integral element of connected living, plays a pivotal
role in fulfilling a fundamental human need. The burgeoning field of smart
healthcare is poised to generate substantial revenue in the foreseeable future.
Its multifaceted framework encompasses vital components such as the Internet of
Things (IoT), medical sensors, artificial intelligence (AI), edge and cloud
computing, as well as next-generation wireless communication technologies. Many
research papers discuss smart healthcare and healthcare more broadly. Numerous
nations have strategically deployed the Internet of Medical Things (IoMT)
alongside other measures to combat the propagation of COVID-19. This combined
effort has not only enhanced the safety of frontline healthcare workers but has
also augmented the overall efficacy in managing the pandemic, subsequently
reducing its impact on human lives and mortality rates. Remarkable strides have
been made in both applications and technology within the IoMT domain. However,
it is imperative to acknowledge that this technological advancement has
introduced certain challenges, particularly in the realm of security. The rapid
and extensive adoption of IoMT worldwide has magnified issues related to
security and privacy. These encompass a spectrum of concerns, ranging from
replay attacks, man-in-the-middle attacks, impersonation, privileged insider
threats, remote hijacking, password guessing, and denial of service (DoS)
attacks, to malware incursions. In this comprehensive review, we undertake a
comparative analysis of existing strategies designed for the detection and
prevention of malware in IoT environments.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Human-in-the-Loop Task and Motion Planning for Imitation Learning
Abstract: Imitation learning from human demonstrations can teach robots complex
manipulation skills, but is time-consuming and labor intensive. In contrast,
Task and Motion Planning (TAMP) systems are automated and excel at solving
long-horizon tasks, but they are difficult to apply to contact-rich tasks. In
this paper, we present Human-in-the-Loop Task and Motion Planning (HITL-TAMP),
a novel system that leverages the benefits of both approaches. The system
employs a TAMP-gated control mechanism, which selectively gives and takes
control to and from a human teleoperator. This enables the human teleoperator
to manage a fleet of robots, maximizing data collection efficiency. The
collected human data is then combined with an imitation learning framework to
train a TAMP-gated policy, leading to superior performance compared to training
on full task demonstrations. We compared HITL-TAMP to a conventional
teleoperation system -- users gathered more than 3x the number of demos given
the same time budget. Furthermore, proficient agents (75\%+ success) could be
trained from just 10 minutes of non-expert teleoperation data. Finally, we
collected 2.1K demos with HITL-TAMP across 12 contact-rich, long-horizon tasks
and show that the system often produces near-perfect agents. Videos and
additional results at https://hitltamp.github.io .

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Contrastive Difference Predictive Coding
Abstract: Predicting and reasoning about the future lie at the heart of many
time-series questions. For example, goal-conditioned reinforcement learning can
be viewed as learning representations to predict which states are likely to be
visited in the future. While prior methods have used contrastive predictive
coding to model time series data, learning representations that encode
long-term dependencies usually requires large amounts of data. In this paper,
we introduce a temporal difference version of contrastive predictive coding
that stitches together pieces of different time series data to decrease the
amount of data required to learn predictions of future events. We apply this
representation learning method to derive an off-policy algorithm for
goal-conditioned RL. Experiments demonstrate that, compared with prior RL
methods, ours achieves $2 \times$ median improvement in success rates and can
better cope with stochastic environments. In tabular settings, we show that our
method is about $20 \times$ more sample efficient than the successor
representation and $1500 \times$ more sample efficient than the standard (Monte
Carlo) version of contrastive predictive coding.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Neural Graph Collaborative Filtering Using Variational Inference
Abstract: The customization of recommended content to users holds significant
importance in enhancing user experiences across a wide spectrum of applications
such as e-commerce, music, and shopping. Graph-based methods have achieved
considerable performance by capturing user-item interactions. However, these
methods tend to utilize randomly constructed embeddings in the dataset used for
training the recommender, which lacks any user preferences. Here, we propose
the concept of variational embeddings as a means of pre-training the
recommender system to improve the feature propagation through the layers of
graph convolutional networks (GCNs). The graph variational embedding
collaborative filtering (GVECF) is introduced as a novel framework to
incorporate representations learned through a variational graph auto-encoder
which are embedded into a GCN-based collaborative filtering. This approach
effectively transforms latent high-order user-item interactions into more
trainable vectors, ultimately resulting in better performance in terms of
recall and normalized discounted cumulative gain(NDCG) metrics. The experiments
conducted on benchmark datasets demonstrate that our proposed method achieves
up to 13.78% improvement in the recall over the test data.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: SynthScribe: Deep Multimodal Tools for Synthesizer Sound Retrieval and Exploration
Abstract: Synthesizers are powerful tools that allow musicians to create dynamic and
original sounds. Existing commercial interfaces for synthesizers typically
require musicians to interact with complex low-level parameters or to manage
large libraries of premade sounds. To address these challenges, we implement
SynthScribe -- a fullstack system that uses multimodal deep learning to let
users express their intentions at a much higher level. We implement features
which address a number of difficulties, namely 1) searching through existing
sounds, 2) creating completely new sounds, 3) making meaningful modifications
to a given sound. This is achieved with three main features: a multimodal
search engine for a large library of synthesizer sounds; a user centered
genetic algorithm by which completely new sounds can be created and selected
given the users preferences; a sound editing support feature which highlights
and gives examples for key control parameters with respect to a text or audio
based query. The results of our user studies show SynthScribe is capable of
reliably retrieving and modifying sounds while also affording the ability to
create completely new sounds that expand a musicians creative horizon.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Inherently Interpretable Time Series Classification via Multiple Instance Learning
Abstract: Conventional Time Series Classification (TSC) methods are often black boxes
that obscure inherent interpretation of their decision-making processes. In
this work, we leverage Multiple Instance Learning (MIL) to overcome this issue,
and propose a new framework called MILLET: Multiple Instance Learning for
Locally Explainable Time series classification. We apply MILLET to existing
deep learning TSC models and show how they become inherently interpretable
without compromising (and in some cases, even improving) predictive
performance. We evaluate MILLET on 85 UCR TSC datasets and also present a novel
synthetic dataset that is specially designed to facilitate interpretability
evaluation. On these datasets, we show MILLET produces sparse explanations
quickly that are of higher quality than other well-known interpretability
methods. To the best of our knowledge, our work with MILLET, which is available
on GitHub (https://github.com/JAEarly/MILTimeSeriesClassification), is the
first to develop general MIL methods for TSC and apply them to an extensive
variety of domains

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion?
Abstract: Knowledge graphs (KGs) consist of links that describe relationships between
entities. Due to the difficulty of manually enumerating all relationships
between entities, automatically completing them is essential for KGs. Knowledge
Graph Completion (KGC) is a task that infers unseen relationships between
entities in a KG. Traditional embedding-based KGC methods, such as RESCAL,
TransE, DistMult, ComplEx, RotatE, HAKE, HousE, etc., infer missing links using
only the knowledge from training data. In contrast, the recent Pre-trained
Language Model (PLM)-based KGC utilizes knowledge obtained during pre-training.
Therefore, PLM-based KGC can estimate missing links between entities by reusing
memorized knowledge from pre-training without inference. This approach is
problematic because building KGC models aims to infer unseen links between
entities. However, conventional evaluations in KGC do not consider inference
and memorization abilities separately. Thus, a PLM-based KGC method, which
achieves high performance in current KGC evaluations, may be ineffective in
practical applications. To address this issue, we analyze whether PLM-based KGC
methods make inferences or merely access memorized knowledge. For this purpose,
we propose a method for constructing synthetic datasets specified in this
analysis and conclude that PLMs acquire the inference abilities required for
KGC through pre-training, even though the performance improvements mostly come
from textual information of entities and relations.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition
Abstract: Human Activity Recognition (HAR) models often suffer from performance
degradation in real-world applications due to distribution shifts in activity
patterns across individuals. Test-Time Adaptation (TTA) is an emerging learning
paradigm that aims to utilize the test stream to adjust predictions in
real-time inference, which has not been explored in HAR before. However, the
high computational cost of optimization-based TTA algorithms makes it
intractable to run on resource-constrained edge devices. In this paper, we
propose an Optimization-Free Test-Time Adaptation (OFTTA) framework for
sensor-based HAR. OFTTA adjusts the feature extractor and linear classifier
simultaneously in an optimization-free manner. For the feature extractor, we
propose Exponential DecayTest-time Normalization (EDTN) to replace the
conventional batch normalization (CBN) layers. EDTN combines CBN and Test-time
batch Normalization (TBN) to extract reliable features against domain shifts
with TBN's influence decreasing exponentially in deeper layers. For the
classifier, we adjust the prediction by computing the distance between the
feature and the prototype, which is calculated by a maintained support set. In
addition, the update of the support set is based on the pseudo label, which can
benefit from reliable features extracted by EDTN. Extensive experiments on
three public cross-person HAR datasets and two different TTA settings
demonstrate that OFTTA outperforms the state-of-the-art TTA approaches in both
classification performance and computational efficiency. Finally, we verify the
superiority of our proposed OFTTA on edge devices, indicating possible
deployment in real applications. Our code is available at
\href{https://github.com/Claydon-Wang/OFTTA}{this https URL}.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Improving Word Sense Disambiguation in Neural Machine Translation with Salient Document Context
Abstract: Lexical ambiguity is a challenging and pervasive problem in machine
translation (\mt). We introduce a simple and scalable approach to resolve
translation ambiguity by incorporating a small amount of extra-sentential
context in neural \mt. Our approach requires no sense annotation and no change
to standard model architectures. Since actual document context is not available
for the vast majority of \mt training data, we collect related sentences for
each input to construct pseudo-documents. Salient words from pseudo-documents
are then encoded as a prefix to each source sentence to condition the
generation of the translation. To evaluate, we release \docmucow, a challenge
set for translation disambiguation based on the English-German \mucow
\cite{raganato-etal-2020-evaluation} augmented with document IDs. Extensive
experiments show that our method translates ambiguous source words better than
strong sentence-level baselines and comparable document-level baselines while
reducing training costs.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Spacecraft Autonomous Decision-Planning for Collision Avoidance: a Reinforcement Learning Approach
Abstract: The space environment around the Earth is becoming increasingly populated by
both active spacecraft and space debris. To avoid potential collision events,
significant improvements in Space Situational Awareness (SSA) activities and
Collision Avoidance (CA) technologies are allowing the tracking and maneuvering
of spacecraft with increasing accuracy and reliability. However, these
procedures still largely involve a high level of human intervention to make the
necessary decisions. For an increasingly complex space environment, this
decision-making strategy is not likely to be sustainable. Therefore, it is
important to successfully introduce higher levels of automation for key Space
Traffic Management (STM) processes to ensure the level of reliability needed
for navigating a large number of spacecraft. These processes range from
collision risk detection to the identification of the appropriate action to
take and the execution of avoidance maneuvers. This work proposes an
implementation of autonomous CA decision-making capabilities on spacecraft
based on Reinforcement Learning (RL) techniques. A novel methodology based on a
Partially Observable Markov Decision Process (POMDP) framework is developed to
train the Artificial Intelligence (AI) system on board the spacecraft,
considering epistemic and aleatory uncertainties. The proposed framework
considers imperfect monitoring information about the status of the debris in
orbit and allows the AI system to effectively learn stochastic policies to
perform accurate Collision Avoidance Maneuvers (CAMs). The objective is to
successfully delegate the decision-making process for autonomously implementing
a CAM to the spacecraft without human intervention. This approach would allow
for a faster response in the decision-making process and for highly
decentralized operations.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: PolyIE: A Dataset of Information Extraction from Polymer Material Scientific Literature
Abstract: Scientific information extraction (SciIE), which aims to automatically
extract information from scientific literature, is becoming more important than
ever. However, there are no existing SciIE datasets for polymer materials,
which is an important class of materials used ubiquitously in our daily lives.
To bridge this gap, we introduce POLYIE, a new SciIE dataset for polymer
materials. POLYIE is curated from 146 full-length polymer scholarly articles,
which are annotated with different named entities (i.e., materials, properties,
values, conditions) as well as their N-ary relations by domain experts. POLYIE
presents several unique challenges due to diverse lexical formats of entities,
ambiguity between entities, and variable-length relations. We evaluate
state-of-the-art named entity extraction and relation extraction models on
POLYIE, analyze their strengths and weaknesses, and highlight some difficult
cases for these models. To the best of our knowledge, POLYIE is the first SciIE
benchmark for polymer materials, and we hope it will lead to more research
efforts from the community on this challenging task. Our code and data are
available on: https://github.com/jerry3027/PolyIE.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Learning to Denoise Unreliable Interactions for Link Prediction on Biomedical Knowledge Graph
Abstract: Link prediction in biomedical knowledge graphs (KGs) aims at predicting
unknown interactions between entities, including drug-target interaction (DTI)
and drug-drug interaction (DDI), which is critical for drug discovery and
therapeutics. Previous methods prefer to utilize the rich semantic relations
and topological structure of the KG to predict missing links, yielding
promising outcomes. However, all these works only focus on improving the
predictive performance without considering the inevitable noise and unreliable
interactions existing in the KGs, which limits the development of KG-based
computational methods. To address these limitations, we propose a Denoised Link
Prediction framework, called DenoisedLP. DenoisedLP obtains reliable
interactions based on the local subgraph by denoising noisy links in a
learnable way, providing a universal module for mining underlying task-relevant
relations. To collaborate with the smoothed semantic information, DenoisedLP
introduces the semantic subgraph by blurring conflict relations around the
predicted link. By maximizing the mutual information between the reliable
structure and smoothed semantic relations, DenoisedLP emphasizes the
informative interactions for predicting relation-specific links. Experimental
results on real-world datasets demonstrate that DenoisedLP outperforms
state-of-the-art methods on DTI and DDI prediction tasks, and verify the
effectiveness and robustness of denoising unreliable interactions on the
contaminated KGs.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search
Abstract: This work studies a central extremal graph theory problem inspired by a 1975
conjecture of Erd\H{o}s, which aims to find graphs with a given size (number of
nodes) that maximize the number of edges without having 3- or 4-cycles. We
formulate this problem as a sequential decision-making problem and compare
AlphaZero, a neural network-guided tree search, with tabu search, a heuristic
local search method. Using either method, by introducing a curriculum --
jump-starting the search for larger graphs using good graphs found at smaller
sizes -- we improve the state-of-the-art lower bounds for several sizes. We
also propose a flexible graph-generation environment and a
permutation-invariant network architecture for learning to search in the space
of graphs.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Autoregressive Renaissance in Neural PDE Solvers
Abstract: Recent developments in the field of neural partial differential equation
(PDE) solvers have placed a strong emphasis on neural operators. However, the
paper "Message Passing Neural PDE Solver" by Brandstetter et al. published in
ICLR 2022 revisits autoregressive models and designs a message passing graph
neural network that is comparable with or outperforms both the state-of-the-art
Fourier Neural Operator and traditional classical PDE solvers in its
generalization capabilities and performance. This blog post delves into the key
contributions of this work, exploring the strategies used to address the common
problem of instability in autoregressive models and the design choices of the
message passing graph neural network architecture.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Building Domain-Specific LLMs Faithful To The Islamic Worldview: Mirage or Technical Possibility?
Abstract: Large Language Models (LLMs) have demonstrated remarkable performance across
numerous natural language understanding use cases. However, this impressive
performance comes with inherent limitations, such as the tendency to perpetuate
stereotypical biases or fabricate non-existent facts. In the context of Islam
and its representation, accurate and factual representation of its beliefs and
teachings rooted in the Quran and Sunnah is key. This work focuses on the
challenge of building domain-specific LLMs faithful to the Islamic worldview
and proposes ways to build and evaluate such systems. Firstly, we define this
open-ended goal as a technical problem and propose various solutions.
Subsequently, we critically examine known challenges inherent to each approach
and highlight evaluation methodologies that can be used to assess such systems.
This work highlights the need for high-quality datasets, evaluations, and
interdisciplinary work blending machine learning with Islamic scholarship.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: GPT vs Human for Scientific Reviews: A Dual Source Review on Applications of ChatGPT in Science
Abstract: The new polymath Large Language Models (LLMs) can speed-up greatly scientific
reviews, possibly using more unbiased quantitative metrics, facilitating
cross-disciplinary connections, and identifying emerging trends and research
gaps by analyzing large volumes of data. However, at the present time, they
lack the required deep understanding of complex methodologies, they have
difficulty in evaluating innovative claims, and they are unable to assess
ethical issues and conflicts of interest. Herein, we consider 13 GPT-related
papers across different scientific domains, reviewed by a human reviewer and
SciSpace, a large language model, with the reviews evaluated by three distinct
types of evaluators, namely GPT-3.5, a crowd panel, and GPT-4. We found that
50% of SciSpace's responses to objective questions align with those of a human
reviewer, with GPT-4 (informed evaluator) often rating the human reviewer
higher in accuracy, and SciSpace higher in structure, clarity, and
completeness. In subjective questions, the uninformed evaluators (GPT-3.5 and
crowd panel) showed varying preferences between SciSpace and human responses,
with the crowd panel showing a preference for the human responses. However,
GPT-4 rated them equally in accuracy and structure but favored SciSpace for
completeness.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation
Abstract: Recent studies in reinforcement learning (RL) have made significant progress
by leveraging function approximation to alleviate the sample complexity hurdle
for better performance. Despite the success, existing provably efficient
algorithms typically rely on the accessibility of immediate feedback upon
taking actions. The failure to account for the impact of delay in observations
can significantly degrade the performance of real-world systems due to the
regret blow-up. In this work, we tackle the challenge of delayed feedback in RL
with linear function approximation by employing posterior sampling, which has
been shown to empirically outperform the popular UCB algorithms in a wide range
of regimes. We first introduce Delayed-PSVI, an optimistic value-based
algorithm that effectively explores the value function space via noise
perturbation with posterior sampling. We provide the first analysis for
posterior sampling algorithms with delayed feedback in RL and show our
algorithm achieves $\widetilde{O}(\sqrt{d^3H^3 T} + d^2H^2 E[\tau])$ worst-case
regret in the presence of unknown stochastic delays. Here $E[\tau]$ is the
expected delay. To further improve its computational efficiency and to expand
its applicability in high-dimensional RL problems, we incorporate a
gradient-based approximate sampling scheme via Langevin dynamics for
Delayed-LPSVI, which maintains the same order-optimal regret guarantee with
$\widetilde{O}(dHK)$ computational cost. Empirical evaluations are performed to
demonstrate the statistical and computational efficacy of our algorithms.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Self-Supervised Deconfounding Against Spatio-Temporal Shifts: Theory and Modeling
Abstract: As an important application of spatio-temporal (ST) data, ST traffic
forecasting plays a crucial role in improving urban travel efficiency and
promoting sustainable development. In practice, the dynamics of traffic data
frequently undergo distributional shifts attributed to external factors such as
time evolution and spatial differences. This entails forecasting models to
handle the out-of-distribution (OOD) issue where test data is distributed
differently from training data. In this work, we first formalize the problem by
constructing a causal graph of past traffic data, future traffic data, and
external ST contexts. We reveal that the failure of prior arts in OOD traffic
data is due to ST contexts acting as a confounder, i.e., the common cause for
past data and future ones. Then, we propose a theoretical solution named
Disentangled Contextual Adjustment (DCA) from a causal lens. It differentiates
invariant causal correlations against variant spurious ones and deconfounds the
effect of ST contexts. On top of that, we devise a Spatio-Temporal
sElf-superVised dEconfounding (STEVE) framework. It first encodes traffic data
into two disentangled representations for associating invariant and variant ST
contexts. Then, we use representative ST contexts from three conceptually
different perspectives (i.e., temporal, spatial, and semantic) as
self-supervised signals to inject context information into both
representations. In this way, we improve the generalization ability of the
learned context-oriented representations to OOD ST traffic forecasting.
Comprehensive experiments on four large-scale benchmark datasets demonstrate
that our STEVE consistently outperforms the state-of-the-art baselines across
various ST OOD scenarios.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning
Abstract: We consider the problem of safe multi-agent motion planning for drones in
uncertain, cluttered workspaces. For this problem, we present a tractable
motion planner that builds upon the strengths of reinforcement learning and
constrained-control-based trajectory planning. First, we use single-agent
reinforcement learning to learn motion plans from data that reach the target
but may not be collision-free. Next, we use a convex optimization, chance
constraints, and set-based methods for constrained control to ensure safety,
despite the uncertainty in the workspace, agent motion, and sensing. The
proposed approach can handle state and control constraints on the agents, and
enforce collision avoidance among themselves and with static obstacles in the
workspace with high probability. The proposed approach yields a safe, real-time
implementable, multi-agent motion planner that is simpler to train than methods
based solely on learning. Numerical simulations and experiments show the
efficacy of the approach.

Output: Robotics