Instruction: What field is the article from?

Input:
Title: Gauge-optimal approximate learning for small data classification problems
Abstract: Small data learning problems are characterized by a significant discrepancy
between the limited amount of response variable observations and the large
feature space dimension. In this setting, the common learning tools struggle to
identify the features important for the classification task from those that
bear no relevant information, and cannot derive an appropriate learning rule
which allows to discriminate between different classes. As a potential solution
to this problem, here we exploit the idea of reducing and rotating the feature
space in a lower-dimensional gauge and propose the Gauge-Optimal Approximate
Learning (GOAL) algorithm, which provides an analytically tractable joint
solution to the dimension reduction, feature segmentation and classification
problems for small data learning problems. We prove that the optimal solution
of the GOAL algorithm consists in piecewise-linear functions in the Euclidean
space, and that it can be approximated through a monotonically convergent
algorithm which presents -- under the assumption of a discrete segmentation of
the feature space -- a closed-form solution for each optimization substep and
an overall linear iteration cost scaling. The GOAL algorithm has been compared
to other state-of-the-art machine learning (ML) tools on both synthetic data
and challenging real-world applications from climate science and bioinformatics
(i.e., prediction of the El Nino Southern Oscillation and inference of
epigenetically-induced gene-activity networks from limited experimental data).
The experimental results show that the proposed algorithm outperforms the
reported best competitors for these problems both in learning performance and
computational cost.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning
Abstract: In real-world reinforcement learning problems, the state information is often
only partially observable, which breaks the basic assumption in Markov decision
processes, and thus, leads to inferior performances. Partially Observable
Markov Decision Processes have been introduced to explicitly take the issue
into account for learning, exploration, and planning, but presenting
significant computational and statistical challenges. To address these
difficulties, we exploit the representation view, which leads to a coherent
design framework for a practically tractable reinforcement learning algorithm
upon partial observations. We provide a theoretical analysis for justifying the
statistical efficiency of the proposed algorithm. We also empirically
demonstrate the proposed algorithm can surpass state-of-the-art performance
with partial observations across various benchmarks, therefore, pushing
reliable reinforcement learning towards more practical applications.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Quantifying Divergence for Human-AI Collaboration and Cognitive Trust
Abstract: Predicting the collaboration likelihood and measuring cognitive trust to AI
systems is more important than ever. To do that, previous research mostly focus
solely on the model features (e.g., accuracy, confidence) and ignore the human
factor. To address that, we propose several decision-making similarity measures
based on divergence metrics (e.g., KL, JSD) calculated over the labels acquired
from humans and a wide range of models. We conduct a user study on a textual
entailment task, where the users are provided with soft labels from various
models and asked to pick the closest option to them. The users are then shown
the similarities/differences to their most similar model and are surveyed for
their likelihood of collaboration and cognitive trust to the selected system.
Finally, we qualitatively and quantitatively analyze the relation between the
proposed decision-making similarity measures and the survey results. We find
that people tend to collaborate with their most similar models -- measured via
JSD -- yet this collaboration does not necessarily imply a similar level of
cognitive trust. We release all resources related to the user study (e.g.,
design, outputs), models, and metrics at our repo.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Do personality tests generalize to Large Language Models?
Abstract: With large language models (LLMs) appearing to behave increasingly human-like
in text-based interactions, it has become popular to attempt to evaluate
various properties of these models using tests originally designed for humans.
While re-using existing tests is a resource-efficient way to evaluate LLMs,
careful adjustments are usually required to ensure that test results are even
valid across human sub-populations. Thus, it is not clear to what extent
different tests' validity generalizes to LLMs. In this work, we provide
evidence that LLMs' responses to personality tests systematically deviate from
typical human responses, implying that these results cannot be interpreted in
the same way as human test results. Concretely, reverse-coded items (e.g. "I am
introverted" vs "I am extraverted") are often both answered affirmatively by
LLMs. In addition, variation across different prompts designed to "steer" LLMs
to simulate particular personality types does not follow the clear separation
into five independent personality factors from human samples. In light of these
results, we believe it is important to pay more attention to tests' validity
for LLMs before drawing strong conclusions about potentially ill-defined
concepts like LLMs' "personality".

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Domain Knowledge Injection in Bayesian Search for New Materials
Abstract: In this paper we propose DKIBO, a Bayesian optimization (BO) algorithm that
accommodates domain knowledge to tune exploration in the search space. Bayesian
optimization has recently emerged as a sample-efficient optimizer for many
intractable scientific problems. While various existing BO frameworks allow the
input of prior beliefs to accelerate the search by narrowing down the space,
incorporating such knowledge is not always straightforward and can often
introduce bias and lead to poor performance. Here we propose a simple approach
to incorporate structural knowledge in the acquisition function by utilizing an
additional deterministic surrogate model to enrich the approximation power of
the Gaussian process. This is suitably chosen according to structural
information of the problem at hand and acts a corrective term towards a
better-informed sampling. We empirically demonstrate the practical utility of
the proposed method by successfully injecting domain knowledge in a materials
design task. We further validate our method's performance on different
experimental settings and ablation analyses.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Manifold Preserving Guided Diffusion
Abstract: Despite the recent advancements, conditional image generation still faces
challenges of cost, generalizability, and the need for task-specific training.
In this paper, we propose Manifold Preserving Guided Diffusion (MPGD), a
training-free conditional generation framework that leverages pretrained
diffusion models and off-the-shelf neural networks with minimal additional
inference cost for a broad range of tasks. Specifically, we leverage the
manifold hypothesis to refine the guided diffusion steps and introduce a
shortcut algorithm in the process. We then propose two methods for on-manifold
training-free guidance using pre-trained autoencoders and demonstrate that our
shortcut inherently preserves the manifolds when applied to latent diffusion
models. Our experiments show that MPGD is efficient and effective for solving a
variety of conditional generation applications in low-compute settings, and can
consistently offer up to 3.8x speed-ups with the same number of diffusion steps
while maintaining high sample quality compared to the baselines.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: FLIP: Towards Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction
Abstract: Click-through rate (CTR) prediction plays as a core function module in
various personalized online services. The traditional ID-based models for CTR
prediction take as inputs the one-hot encoded ID features of tabular modality,
which capture the collaborative signals via feature interaction modeling. But
the one-hot encoding discards the semantic information conceived in the
original feature texts. Recently, the emergence of Pretrained Language Models
(PLMs) has given rise to another paradigm, which takes as inputs the sentences
of textual modality obtained by hard prompt templates and adopts PLMs to
extract the semantic knowledge. However, PLMs generally tokenize the input text
data into subword tokens and ignore field-wise collaborative signals.
Therefore, these two lines of research focus on different characteristics of
the same input data (i.e., textual and tabular modalities), forming a distinct
complementary relationship with each other. In this paper, we propose to
conduct Fine-grained feature-level ALignment between ID-based Models and
Pretrained Language Models (FLIP) for CTR prediction. We design a novel joint
reconstruction pretraining task for both masked language and tabular modeling.
Specifically, the masked data of one modality (i.e., tokens or features) has to
be recovered with the help of the other modality, which establishes the
feature-level interaction and alignment via sufficient mutual information
extraction between dual modalities. Moreover, we propose to jointly finetune
the ID-based model and PLM for downstream CTR prediction tasks, thus achieving
superior performance by combining the advantages of both models. Extensive
experiments on three real-world datasets demonstrate that FLIP outperforms SOTA
baselines, and is highly compatible for various ID-based models and PLMs.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: Erasing Self-Supervised Learning Backdoor by Cluster Activation Masking
Abstract: Researchers have recently found that Self-Supervised Learning (SSL) is
vulnerable to backdoor attacks. The attacker can embed hidden SSL backdoors via
a few poisoned examples in the training dataset and maliciously manipulate the
behavior of downstream models. To defend against SSL backdoor attacks, a
feasible route is to detect and remove the poisonous samples in the training
set. However, the existing SSL backdoor defense method fails to detect the
poisonous samples precisely. In this paper, we propose to erase the SSL
backdoor by cluster activation masking and propose a novel PoisonCAM method.
After obtaining the threat model trained on the poisoned dataset, our method
can precisely detect poisonous samples based on the assumption that masking the
backdoor trigger can effectively change the activation of a downstream
clustering model. In experiments, our PoisonCAM achieves 96% accuracy for
backdoor trigger detection compared to 3% of the state-of-the-art method on
poisoned ImageNet-100. Moreover, our proposed PoisonCAM significantly improves
the performance of the trained SSL model under backdoor attacks compared to the
state-of-the-art method. Our code will be available at
https://github.com/LivXue/PoisonCAM.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Rosetta Stone at the Arabic Reverse Dictionary Shared Task: A Hop From Language Modeling To Word--Definition Alignment
Abstract: A Reverse Dictionary is a tool enabling users to discover a word based on its
provided definition, meaning, or description. Such a technique proves valuable
in various scenarios, aiding language learners who possess a description of a
word without its identity, and benefiting writers seeking precise terminology.
These scenarios often encapsulate what is referred to as the
"Tip-of-the-Tongue" (TOT) phenomena. In this work, we present our winning
solution for the Arabic Reverse Dictionary shared task. This task focuses on
deriving a vector representation of an Arabic word from its accompanying
description. The shared task encompasses two distinct subtasks: the first
involves an Arabic definition as input, while the second employs an English
definition. For the first subtask, our approach relies on an ensemble of
finetuned Arabic BERT-based models, predicting the word embedding for a given
definition. The final representation is obtained through averaging the output
embeddings from each model within the ensemble. In contrast, the most effective
solution for the second subtask involves translating the English test
definitions into Arabic and applying them to the finetuned models originally
trained for the first subtask. This straightforward method achieves the highest
score across both subtasks.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: A Taxonomy of Rater Disagreements: Surveying Challenges & Opportunities from the Perspective of Annotating Online Toxicity
Abstract: Toxicity is an increasingly common and severe issue in online spaces.
Consequently, a rich line of machine learning research over the past decade has
focused on computationally detecting and mitigating online toxicity. These
efforts crucially rely on human-annotated datasets that identify toxic content
of various kinds in social media texts. However, such annotations historically
yield low inter-rater agreement, which was often dealt with by taking the
majority vote or other such approaches to arrive at a single ground truth
label. Recent research has pointed out the importance of accounting for the
subjective nature of this task when building and utilizing these datasets, and
this has triggered work on analyzing and better understanding rater
disagreements, and how they could be effectively incorporated into the machine
learning developmental pipeline. While these efforts are filling an important
gap, there is a lack of a broader framework about the root causes of rater
disagreement, and therefore, we situate this work within that broader
landscape. In this survey paper, we analyze a broad set of literature on the
reasons behind rater disagreements focusing on online toxicity, and propose a
detailed taxonomy for the same. Further, we summarize and discuss the potential
solutions targeting each reason for disagreement. We also discuss several open
issues, which could promote the future development of online toxicity research.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks
Abstract: The MineRL BASALT competition has served to catalyze advances in learning
from human feedback through four hard-to-specify tasks in Minecraft, such as
create and photograph a waterfall. Given the completion of two years of BASALT
competitions, we offer to the community a formalized benchmark through the
BASALT Evaluation and Demonstrations Dataset (BEDD), which serves as a resource
for algorithm development and performance assessment. BEDD consists of a
collection of 26 million image-action pairs from nearly 14,000 videos of human
players completing the BASALT tasks in Minecraft. It also includes over 3,000
dense pairwise human evaluations of human and algorithmic agents. These
comparisons serve as a fixed, preliminary leaderboard for evaluating
newly-developed algorithms. To enable this comparison, we present a streamlined
codebase for benchmarking new algorithms against the leaderboard. In addition
to presenting these datasets, we conduct a detailed analysis of the data from
both datasets to guide algorithm development and evaluation. The released code
and data are available at https://github.com/minerllabs/basalt-benchmark .

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Investigating Responsible AI for Scientific Research: An Empirical Study
Abstract: Scientific research organizations that are developing and deploying
Artificial Intelligence (AI) systems are at the intersection of technological
progress and ethical considerations. The push for Responsible AI (RAI) in such
institutions underscores the increasing emphasis on integrating ethical
considerations within AI design and development, championing core values like
fairness, accountability, and transparency. For scientific research
organizations, prioritizing these practices is paramount not just for
mitigating biases and ensuring inclusivity, but also for fostering trust in AI
systems among both users and broader stakeholders. In this paper, we explore
the practices at a research organization concerning RAI practices, aiming to
assess the awareness and preparedness regarding the ethical risks inherent in
AI design and development. We have adopted a mixed-method research approach,
utilising a comprehensive survey combined with follow-up in-depth interviews
with selected participants from AI-related projects. Our results have revealed
certain knowledge gaps concerning ethical, responsible, and inclusive AI, with
limitations in awareness of the available AI ethics frameworks. This revealed
an overarching underestimation of the ethical risks that AI technologies can
present, especially when implemented without proper guidelines and governance.
Our findings reveal the need for a holistic and multi-tiered strategy to uplift
capabilities and better support science research teams for responsible,
ethical, and inclusive AI development and deployment.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey
Abstract: As artificial intelligence (AI) continues to rapidly evolve, the realm of
Earth and atmospheric sciences is increasingly adopting data-driven models,
powered by progressive developments in deep learning (DL). Specifically, DL
techniques are extensively utilized to decode the chaotic and nonlinear aspects
of Earth systems, and to address climate challenges via understanding weather
and climate data. Cutting-edge performance on specific tasks within narrower
spatio-temporal scales has been achieved recently through DL. The rise of large
models, specifically large language models (LLMs), has enabled fine-tuning
processes that yield remarkable outcomes across various downstream tasks,
thereby propelling the advancement of general AI. However, we are still
navigating the initial stages of crafting general AI for weather and climate.
In this survey, we offer an exhaustive, timely overview of state-of-the-art AI
methodologies specifically engineered for weather and climate data, with a
special focus on time series and text data. Our primary coverage encompasses
four critical aspects: types of weather and climate data, principal model
architectures, model scopes and applications, and datasets for weather and
climate. Furthermore, in relation to the creation and application of foundation
models for weather and climate data understanding, we delve into the field's
prevailing challenges, offer crucial insights, and propose detailed avenues for
future research. This comprehensive approach equips practitioners with the
requisite knowledge to make substantial progress in this domain. Our survey
encapsulates the most recent breakthroughs in research on large, data-driven
models for weather and climate data understanding, emphasizing robust
foundations, current advancements, practical applications, crucial resources,
and prospective research opportunities.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: On the Initialization of Graph Neural Networks
Abstract: Graph Neural Networks (GNNs) have displayed considerable promise in graph
representation learning across various applications. The core learning process
requires the initialization of model weight matrices within each GNN layer,
which is typically accomplished via classic initialization methods such as
Xavier initialization. However, these methods were originally motivated to
stabilize the variance of hidden embeddings and gradients across layers of
Feedforward Neural Networks (FNNs) and Convolutional Neural Networks (CNNs) to
avoid vanishing gradients and maintain steady information flow. In contrast,
within the GNN context classical initializations disregard the impact of the
input graph structure and message passing on variance. In this paper, we
analyze the variance of forward and backward propagation across GNN layers and
show that the variance instability of GNN initializations comes from the
combined effect of the activation function, hidden dimension, graph structure
and message passing. To better account for these influence factors, we propose
a new initialization method for Variance Instability Reduction within GNN
Optimization (Virgo), which naturally tends to equate forward and backward
variances across successive layers. We conduct comprehensive experiments on 15
datasets to show that Virgo can lead to superior model performance and more
stable variance at initialization on node classification, link prediction and
graph classification tasks. Codes are in
https://github.com/LspongebobJH/virgo_icml2023.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Authoring Worked Examples for Java Programming with Human-AI Collaboration
Abstract: Worked examples (solutions to typical programming problems presented as a
source code in a certain language and are used to explain the topics from a
programming class) are among the most popular types of learning content in
programming classes. Most approaches and tools for presenting these examples to
students are based on line-by-line explanations of the example code. However,
instructors rarely have time to provide line-by-line explanations for a large
number of examples typically used in a programming class. In this paper, we
explore and assess a human-AI collaboration approach to authoring worked
examples for Java programming. We introduce an authoring system for creating
Java worked examples that generates a starting version of code explanations and
presents it to the instructor to edit if necessary. We also present a study
that assesses the quality of explanations created with this approach.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: Deep Learning-Empowered Semantic Communication Systems with a Shared Knowledge Base
Abstract: Deep learning-empowered semantic communication is regarded as a promising
candidate for future 6G networks. Although existing semantic communication
systems have achieved superior performance compared to traditional methods, the
end-to-end architecture adopted by most semantic communication systems is
regarded as a black box, leading to the lack of explainability. To tackle this
issue, in this paper, a novel semantic communication system with a shared
knowledge base is proposed for text transmissions. Specifically, a textual
knowledge base constructed by inherently readable sentences is introduced into
our system. With the aid of the shared knowledge base, the proposed system
integrates the message and corresponding knowledge from the shared knowledge
base to obtain the residual information, which enables the system to transmit
fewer symbols without semantic performance degradation. In order to make the
proposed system more reliable, the semantic self-information and the source
entropy are mathematically defined based on the knowledge base. Furthermore,
the knowledge base construction algorithm is developed based on a
similarity-comparison method, in which a pre-configured threshold can be
leveraged to control the size of the knowledge base. Moreover, the simulation
results have demonstrated that the proposed approach outperforms existing
baseline methods in terms of transmitted data size and sentence similarity.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Reward Certification for Policy Smoothed Reinforcement Learning
Abstract: Reinforcement Learning (RL) has achieved remarkable success in
safety-critical areas, but it can be weakened by adversarial attacks. Recent
studies have introduced "smoothed policies" in order to enhance its robustness.
Yet, it is still challenging to establish a provable guarantee to certify the
bound of its total reward. Prior methods relied primarily on computing bounds
using Lipschitz continuity or calculating the probability of cumulative reward
above specific thresholds. However, these techniques are only suited for
continuous perturbations on the RL agent's observations and are restricted to
perturbations bounded by the $l_2$-norm. To address these limitations, this
paper proposes a general black-box certification method capable of directly
certifying the cumulative reward of the smoothed policy under various
$l_p$-norm bounded perturbations. Furthermore, we extend our methodology to
certify perturbations on action spaces. Our approach leverages f-divergence to
measure the distinction between the original distribution and the perturbed
distribution, subsequently determining the certification bound by solving a
convex optimisation problem. We provide a comprehensive theoretical analysis
and run sufficient experiments in multiple environments. Our results show that
our method not only improves the certified lower bound of mean cumulative
reward but also demonstrates better efficiency than state-of-the-art
techniques.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: SCADI: Self-supervised Causal Disentanglement in Latent Variable Models
Abstract: Causal disentanglement has great potential for capturing complex situations.
However, there is a lack of practical and efficient approaches. It is already
known that most unsupervised disentangling methods are unable to produce
identifiable results without additional information, often leading to randomly
disentangled output. Therefore, most existing models for disentangling are
weakly supervised, providing information about intrinsic factors, which incurs
excessive costs. Therefore, we propose a novel model, SCADI(SElf-supervised
CAusal DIsentanglement), that enables the model to discover semantic factors
and learn their causal relationships without any supervision. This model
combines a masked structural causal model (SCM) with a pseudo-label generator
for causal disentanglement, aiming to provide a new direction for
self-supervised causal disentanglement models.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: The perpetual motion machine of AI-generated data and the distraction of ChatGPT-as-scientist
Abstract: Since ChatGPT works so well, are we on the cusp of solving science with AI?
Is not AlphaFold2 suggestive that the potential of LLMs in biology and the
sciences more broadly is limitless? Can we use AI itself to bridge the lack of
data in the sciences in order to then train an AI? Herein we present a
discussion of these topics.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Explainable artificial intelligence for Healthcare applications using Random Forest Classifier with LIME and SHAP
Abstract: With the advances in computationally efficient artificial Intelligence (AI)
techniques and their numerous applications in our everyday life, there is a
pressing need to understand the computational details hidden in black box AI
techniques such as most popular machine learning and deep learning techniques;
through more detailed explanations. The origin of explainable AI (xAI) is
coined from these challenges and recently gained more attention by the
researchers by adding explainability comprehensively in traditional AI systems.
This leads to develop an appropriate framework for successful applications of
xAI in real life scenarios with respect to innovations, risk mitigation,
ethical issues and logical values to the users. In this book chapter, an
in-depth analysis of several xAI frameworks and methods including LIME (Local
Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive
exPlanations) are provided. Random Forest Classifier as black box AI is used on
a publicly available Diabetes symptoms dataset with LIME and SHAP for better
interpretations. The results obtained are interesting in terms of transparency,
valid and trustworthiness in diabetes disease prediction.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: FRDiff: Feature Reuse for Exquisite Zero-shot Acceleration of Diffusion Models
Abstract: The substantial computational costs of diffusion models, particularly due to
the repeated denoising steps crucial for high-quality image generation, present
a major obstacle to their widespread adoption. While several studies have
attempted to address this issue by reducing the number of score function
evaluations using advanced ODE solvers without fine-tuning, the decreased
number of denoising iterations misses the opportunity to update fine details,
resulting in noticeable quality degradation. In our work, we introduce an
advanced acceleration technique that leverages the temporal redundancy inherent
in diffusion models. Reusing feature maps with high temporal similarity opens
up a new opportunity to save computation without sacrificing output quality. To
realize the practical benefits of this intuition, we conduct an extensive
analysis and propose a novel method, FRDiff. FRDiff is designed to harness the
advantages of both reduced NFE and feature reuse, achieving a Pareto frontier
that balances fidelity and latency trade-offs in various generative tasks.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Evaluating the Utility of Model Explanations for Model Development
Abstract: One of the motivations for explainable AI is to allow humans to make better
and more informed decisions regarding the use and deployment of AI models. But
careful evaluations are needed to assess whether this expectation has been
fulfilled. Current evaluations mainly focus on algorithmic properties of
explanations, and those that involve human subjects often employ subjective
questions to test human's perception of explanation usefulness, without being
grounded in objective metrics and measurements. In this work, we evaluate
whether explanations can improve human decision-making in practical scenarios
of machine learning model development. We conduct a mixed-methods user study
involving image data to evaluate saliency maps generated by SmoothGrad,
GradCAM, and an oracle explanation on two tasks: model selection and
counterfactual simulation. To our surprise, we did not find evidence of
significant improvement on these tasks when users were provided with any of the
saliency maps, even the synthetic oracle explanation designed to be simple to
understand and highly indicative of the answer. Nonetheless, explanations did
help users more accurately describe the models. These findings suggest caution
regarding the usefulness and potential for misunderstanding in saliency-based
explanations.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning
Abstract: Climate models have been key for assessing the impact of climate change and
simulating future climate scenarios. The machine learning (ML) community has
taken an increased interest in supporting climate scientists' efforts on
various tasks such as climate model emulation, downscaling, and prediction
tasks. Many of those tasks have been addressed on datasets created with single
climate models. However, both the climate science and ML communities have
suggested that to address those tasks at scale, we need large, consistent, and
ML-ready climate model datasets. Here, we introduce ClimateSet, a dataset
containing the inputs and outputs of 36 climate models from the Input4MIPs and
CMIP6 archives. In addition, we provide a modular dataset pipeline for
retrieving and preprocessing additional climate models and scenarios. We
showcase the potential of our dataset by using it as a benchmark for ML-based
climate model emulation. We gain new insights about the performance and
generalization capabilities of the different ML models by analyzing their
performance across different climate models. Furthermore, the dataset can be
used to train an ML emulator on several climate models instead of just one.
Such a "super emulator" can quickly project new climate change scenarios,
complementing existing scenarios already provided to policymakers. We believe
ClimateSet will create the basis needed for the ML community to tackle
climate-related tasks at scale.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Learning-based Scheduling for Information Accuracy and Freshness in Wireless Networks
Abstract: We consider a system of multiple sources, a single communication channel, and
a single monitoring station. Each source measures a time-varying quantity with
varying levels of accuracy and one of them sends its update to the monitoring
station via the channel. The probability of success of each attempted
communication is a function of the source scheduled for transmitting its
update. Both the probability of correct measurement and the probability of
successful transmission of all the sources are unknown to the scheduler. The
metric of interest is the reward received by the system which depends on the
accuracy of the last update received by the destination and the
Age-of-Information (AoI) of the system. We model our scheduling problem as a
variant of the multi-arm bandit problem with sources as different arms. We
compare the performance of all $4$ standard bandit policies, namely, ETC,
$\epsilon$-greedy, UCB, and TS suitably adjusted to our system model via
simulations. In addition, we provide analytical guarantees of $2$ of these
policies, ETC, and $\epsilon$-greedy. Finally, we characterize the lower bound
on the cumulative regret achievable by any policy.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Ransomware Detection and Classification using Machine Learning
Abstract: Vicious assaults, malware, and various ransomware pose a cybersecurity
threat, causing considerable damage to computer structures, servers, and mobile
and web apps across various industries and businesses. These safety concerns
are important and must be addressed immediately. Ransomware detection and
classification are critical for guaranteeing rapid reaction and prevention.
This study uses the XGBoost classifier and Random Forest (RF) algorithms to
detect and classify ransomware attacks. This approach involves analyzing the
behaviour of ransomware and extracting relevant features that can help
distinguish between different ransomware families. The models are evaluated on
a dataset of ransomware attacks and demonstrate their effectiveness in
accurately detecting and classifying ransomware. The results show that the
XGBoost classifier, Random Forest Classifiers, can effectively detect and
classify different ransomware attacks with high accuracy, thereby providing a
valuable tool for enhancing cybersecurity.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: STEP CATFormer: Spatial-Temporal Effective Body-Part Cross Attention Transformer for Skeleton-based Action Recognition
Abstract: Graph convolutional networks (GCNs) have been widely used and achieved
remarkable results in skeleton-based action recognition. We think the key to
skeleton-based action recognition is a skeleton hanging in frames, so we focus
on how the Graph Convolutional Convolution networks learn different topologies
and effectively aggregate joint features in the global temporal and local
temporal. In this work, we propose three Channel-wise Tolopogy Graph
Convolution based on Channel-wise Topology Refinement Graph Convolution
(CTR-GCN). Combining CTR-GCN with two joint cross-attention modules can capture
the upper-lower body part and hand-foot relationship skeleton features. After
that, to capture features of human skeletons changing in frames we design the
Temporal Attention Transformers to extract skeletons effectively. The Temporal
Attention Transformers can learn the temporal features of human skeleton
sequences. Finally, we fuse the temporal features output scale with MLP and
classification. We develop a powerful graph convolutional network named Spatial
Temporal Effective Body-part Cross Attention Transformer which notably
high-performance on the NTU RGB+D, NTU RGB+D 120 datasets. Our code and models
are available at https://github.com/maclong01/STEP-CATFormer

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: On the Effects of Randomness on Stability of Learning with Limited Labelled Data: A Systematic Literature Review
Abstract: Learning with limited labelled data, such as few-shot learning, meta-learning
or transfer learning, aims to effectively train a model using only small amount
of labelled samples. However, these approaches were observed to be excessively
sensitive to the effects of uncontrolled randomness caused by non-determinism
in the training process. The randomness negatively affects the stability of the
models, leading to large variance in results across training runs. When such
instability is disregarded, it can unintentionally, but unfortunately also
intentionally, create an imaginary perception of research progress. Recently,
this area started to attract a research attention and the number of relevant
studies is continuously growing. In this survey, we provide a comprehensive
overview of 134 papers addressing the effects of randomness on the stability of
learning with limited labelled data. We distinguish between four main tasks
addressed in the papers (investigate/evaluate; determine; mitigate;
benchmark/compare/report randomness effects), providing findings for each one.
Furthermore, we identify and discuss seven challenges and open problems
together with possible directions to facilitate further research. The ultimate
goal of this survey is to emphasise the importance of this growing research
area, which so far has not received appropriate level of attention.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Stacking the Odds: Transformer-Based Ensemble for AI-Generated Text Detection
Abstract: This paper reports our submission under the team name `SynthDetectives' to
the ALTA 2023 Shared Task. We use a stacking ensemble of Transformers for the
task of AI-generated text detection. Our approach is novel in terms of its
choice of models in that we use accessible and lightweight models in the
ensemble. We show that ensembling the models results in an improved accuracy in
comparison with using them individually. Our approach achieves an accuracy
score of 0.9555 on the official test data provided by the shared task
organisers.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Is one brick enough to break the wall of spoken dialogue state tracking?
Abstract: In Task-Oriented Dialogue (TOD) systems, correctly updating the system's
understanding of the user's needs (a.k.a dialogue state tracking) is key to a
smooth interaction. Traditionally, TOD systems perform this update in three
steps: transcription of the user's utterance, semantic extraction of the key
concepts, and contextualization with the previously identified concepts. Such
cascade approaches suffer from cascading errors and separate optimization.
End-to-End approaches have been proved helpful up to the semantic extraction
step. This paper goes one step further paving the path towards completely
neural spoken dialogue state tracking by comparing three approaches: (1) a
state of the art cascade approach, (2) a locally E2E approach with rule-based
contextualization and (3) a completely neural approach.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: LDM$^2$: A Large Decision Model Imitating Human Cognition with Dynamic Memory Enhancement
Abstract: With the rapid development of large language models (LLMs), it is highly
demanded that LLMs can be adopted to make decisions to enable the artificial
general intelligence. Most approaches leverage manually crafted examples to
prompt the LLMs to imitate the decision process of human. However, designing
optimal prompts is difficult and the patterned prompts can hardly be
generalized to more complex environments. In this paper, we propose a novel
model named Large Decision Model with Memory (LDM$^2$), which leverages a
dynamic memory mechanism to construct dynamic prompts, guiding the LLMs in
making proper decisions according to the faced state. LDM$^2$ consists of two
stages: memory formation and memory refinement. In the former stage, human
behaviors are decomposed into state-action tuples utilizing the powerful
summarizing ability of LLMs. Then, these tuples are stored in the memory, whose
indices are generated by the LLMs, to facilitate the retrieval of the most
relevant subset of memorized tuples based on the current state. In the latter
stage, our LDM$^2$ employs tree exploration to discover more suitable decision
processes and enrich the memory by adding valuable state-action tuples. The
dynamic circle of exploration and memory enhancement provides LDM$^2$ a better
understanding of the global environment. Extensive experiments conducted in two
interactive environments have shown that our LDM$^2$ outperforms the baselines
in terms of both score and success rate, which demonstrates its effectiveness.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Human Machine Co-Creation. A Complementary Cognitive Approach to Creative Character Design Process Using GANs
Abstract: Recent advances in Generative Adversarial Networks GANs applications continue
to attract the attention of researchers in different fields. In such a
framework, two neural networks compete adversely to generate new visual
contents indistinguishable from the original dataset. The objective of this
research is to create a complementary codesign process between humans and
machines to augment character designers abilities in visualizing and creating
new characters for multimedia projects such as games and animation. Driven by
design cognitive scaffolding, the proposed approach aims to inform the process
of perceiving, knowing, and making. The machine generated concepts are used as
a launching platform for character designers to conceptualize new characters. A
labelled dataset of 22,000 characters was developed for this work and deployed
using different GANs to evaluate the most suited for the context, followed by
mixed methods evaluation for the machine output and human derivations. The
discussed results substantiate the value of the proposed cocreation framework
and elucidate how the generated concepts are used as cognitive substances that
interact with designers competencies in a versatile manner to influence the
creative processes of conceptualizing novel characters.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: A Large-Scale Car Parts (LSCP) Dataset for Lightweight Fine-Grained Detection
Abstract: Automotive related datasets have previously been used for training autonomous
driving systems or vehicle classification tasks. However, there is a lack of
datasets in the field of automotive AI for car parts detection, and most
available datasets are limited in size and scope, struggling to cover diverse
scenarios. To address this gap, this paper presents a large-scale and
fine-grained automotive dataset consisting of 84,162 images for detecting 12
different types of car parts. This dataset was collected from natural cameras
and online websites which covers various car brands, scenarios, and shooting
angles. To alleviate the burden of manual annotation, we propose a novel
semi-supervised auto-labeling method that leverages state-of-the-art
pre-trained detectors. Moreover, we study the limitations of the Grounding DINO
approach for zero-shot labeling. Finally, we evaluate the effectiveness of our
proposed dataset through fine-grained car parts detection by training several
lightweight YOLO-series detectors.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Prompt Me Up: Unleashing the Power of Alignments for Multimodal Entity and Relation Extraction
Abstract: How can we better extract entities and relations from text? Using multimodal
extraction with images and text obtains more signals for entities and
relations, and aligns them through graphs or hierarchical fusion, aiding in
extraction. Despite attempts at various fusions, previous works have overlooked
many unlabeled image-caption pairs, such as NewsCLIPing. This paper proposes
innovative pre-training objectives for entity-object and relation-image
alignment, extracting objects from images and aligning them with entity and
relation prompts for soft pseudo-labels. These labels are used as
self-supervised signals for pre-training, enhancing the ability to extract
entities and relations. Experiments on three datasets show an average 3.41% F1
improvement over prior SOTA. Additionally, our method is orthogonal to previous
multimodal fusions, and using it on prior SOTA fusions further improves 5.47%
F1.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale
Abstract: Neural Architecture Search (NAS) has demonstrated its efficacy in computer
vision and potential for ranking systems. However, prior work focused on
academic problems, which are evaluated at small scale under well-controlled
fixed baselines. In industry system, such as ranking system in Meta, it is
unclear whether NAS algorithms from the literature can outperform production
baselines because of: (1) scale - Meta ranking systems serve billions of users,
(2) strong baselines - the baselines are production models optimized by
hundreds to thousands of world-class engineers for years since the rise of deep
learning, (3) dynamic baselines - engineers may have established new and
stronger baselines during NAS search, and (4) efficiency - the search pipeline
must yield results quickly in alignment with the productionization life cycle.
In this paper, we present Rankitect, a NAS software framework for ranking
systems at Meta. Rankitect seeks to build brand new architectures by composing
low level building blocks from scratch. Rankitect implements and improves
state-of-the-art (SOTA) NAS methods for comprehensive and fair comparison under
the same search space, including sampling-based NAS, one-shot NAS, and
Differentiable NAS (DNAS). We evaluate Rankitect by comparing to multiple
production ranking models at Meta. We find that Rankitect can discover new
models from scratch achieving competitive tradeoff between Normalized Entropy
loss and FLOPs. When utilizing search space designed by engineers, Rankitect
can generate better models than engineers, achieving positive offline
evaluation and online A/B test at Meta scale.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Deep Dynamics: Vehicle Dynamics Modeling with a Physics-Informed Neural Network for Autonomous Racing
Abstract: Autonomous racing is a critical research area for autonomous driving,
presenting significant challenges in vehicle dynamics modeling, such as
balancing model precision and computational efficiency at high speeds
(>280kmph), where minor errors in modeling have severe consequences. Existing
physics-based models for vehicle dynamics require elaborate testing setups and
tuning, which are hard to implement, time-intensive, and cost-prohibitive.
Conversely, purely data-driven approaches do not generalize well and cannot
adequately ensure physical constraints on predictions. This paper introduces
Deep Dynamics, a physics-informed neural network (PINN) for vehicle dynamics
modeling of an autonomous racecar. It combines physics coefficient estimation
and dynamical equations to accurately predict vehicle states at high speeds and
includes a unique Physics Guard layer to ensure internal coefficient estimates
remain within their nominal physical ranges. Open-loop and closed-loop
performance assessments, using a physics-based simulator and full-scale
autonomous Indy racecar data, highlight Deep Dynamics as a promising approach
for modeling racecar vehicle dynamics.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Comparative Analysis of Transformers for Modeling Tabular Data: A Casestudy using Industry Scale Dataset
Abstract: We perform a comparative analysis of transformer-based models designed for
modeling tabular data, specifically on an industry-scale dataset. While earlier
studies demonstrated promising outcomes on smaller public or synthetic
datasets, the effectiveness did not extend to larger industry-scale datasets.
The challenges identified include handling high-dimensional data, the necessity
for efficient pre-processing of categorical and numerical features, and
addressing substantial computational requirements.
  To overcome the identified challenges, the study conducts an extensive
examination of various transformer-based models using both synthetic datasets
and the default prediction Kaggle dataset (2022) from American Express. The
paper presents crucial insights into optimal data pre-processing, compares
pre-training and direct supervised learning methods, discusses strategies for
managing categorical and numerical features, and highlights trade-offs between
computational resources and performance. Focusing on temporal financial data
modeling, the research aims to facilitate the systematic development and
deployment of transformer-based models in real-world scenarios, emphasizing
scalability.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Digital Life Project: Autonomous 3D Characters with Social Intelligence
Abstract: In this work, we present Digital Life Project, a framework utilizing language
as the universal medium to build autonomous 3D characters, who are capable of
engaging in social interactions and expressing with articulated body motions,
thereby simulating life in a digital environment. Our framework comprises two
primary components: 1) SocioMind: a meticulously crafted digital brain that
models personalities with systematic few-shot exemplars, incorporates a
reflection process based on psychology principles, and emulates autonomy by
initiating dialogue topics; 2) MoMat-MoGen: a text-driven motion synthesis
paradigm for controlling the character's digital body. It integrates motion
matching, a proven industry technique to ensure motion quality, with
cutting-edge advancements in motion generation for diversity. Extensive
experiments demonstrate that each module achieves state-of-the-art performance
in its respective domain. Collectively, they enable virtual characters to
initiate and sustain dialogues autonomously, while evolving their
socio-psychological states. Concurrently, these characters can perform
contextually relevant bodily movements. Additionally, a motion captioning
module further allows the virtual character to recognize and appropriately
respond to human players' actions. Homepage: https://digital-life-project.com/

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Towards Garment Sewing Pattern Reconstruction from a Single Image
Abstract: Garment sewing pattern represents the intrinsic rest shape of a garment, and
is the core for many applications like fashion design, virtual try-on, and
digital avatars. In this work, we explore the challenging problem of recovering
garment sewing patterns from daily photos for augmenting these applications. To
solve the problem, we first synthesize a versatile dataset, named SewFactory,
which consists of around 1M images and ground-truth sewing patterns for model
training and quantitative evaluation. SewFactory covers a wide range of human
poses, body shapes, and sewing patterns, and possesses realistic appearances
thanks to the proposed human texture synthesis network. Then, we propose a
two-level Transformer network called Sewformer, which significantly improves
the sewing pattern prediction performance. Extensive experiments demonstrate
that the proposed framework is effective in recovering sewing patterns and well
generalizes to casually-taken human photos. Code, dataset, and pre-trained
models are available at: https://sewformer.github.io.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Identifying Semantic Component for Robust Molecular Property Prediction
Abstract: Although graph neural networks have achieved great success in the task of
molecular property prediction in recent years, their generalization ability
under out-of-distribution (OOD) settings is still under-explored. Different
from existing methods that learn discriminative representations for prediction,
we propose a generative model with semantic-components identifiability, named
SCI. We demonstrate that the latent variables in this generative model can be
explicitly identified into semantic-relevant (SR) and semantic-irrelevant (SI)
components, which contributes to better OOD generalization by involving minimal
change properties of causal mechanisms. Specifically, we first formulate the
data generation process from the atom level to the molecular level, where the
latent space is split into SI substructures, SR substructures, and SR atom
variables. Sequentially, to reduce misidentification, we restrict the minimal
changes of the SR atom variables and add a semantic latent substructure
regularization to mitigate the variance of the SR substructure under augmented
domain changes. Under mild assumptions, we prove the block-wise identifiability
of the SR substructure and the comment-wise identifiability of SR atom
variables. Experimental studies achieve state-of-the-art performance and show
general improvement on 21 datasets in 3 mainstream benchmarks. Moreover, the
visualization results of the proposed SCI method provide insightful case
studies and explanations for the prediction results. The code is available at:
https://github.com/DMIRLAB-Group/SCI.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Voice Recognition Robot with Real-Time Surveillance and Automation
Abstract: Voice recognition technology enables the execution of real-world operations
through a single voice command. This paper introduces a voice recognition
system that involves converting input voice signals into corresponding text
using an Android application. The text messages are then transmitted through
Bluetooth connectivity, serving as a communication platform. Simultaneously, a
controller circuit, equipped with a Bluetooth module, receives the text signal
and, following a coding mechanism, executes real-world operations. The paper
extends the application of voice recognition to real-time surveillance and
automation, incorporating obstacle detection and avoidance mechanisms, as well
as control over lighting and horn functions through predefined voice commands.
The proposed technique not only serves as an assistive tool for individuals
with disabilities but also finds utility in industrial automation, enabling
robots to perform specific tasks with precision.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: The Power of the Senses: Generalizable Manipulation from Vision and Touch through Masked Multimodal Learning
Abstract: Humans rely on the synergy of their senses for most essential tasks. For
tasks requiring object manipulation, we seamlessly and effectively exploit the
complementarity of our senses of vision and touch. This paper draws inspiration
from such capabilities and aims to find a systematic approach to fuse visual
and tactile information in a reinforcement learning setting. We propose Masked
Multimodal Learning (M3L), which jointly learns a policy and visual-tactile
representations based on masked autoencoding. The representations jointly
learned from vision and touch improve sample efficiency, and unlock
generalization capabilities beyond those achievable through each of the senses
separately. Remarkably, representations learned in a multimodal setting also
benefit vision-only policies at test time. We evaluate M3L on three simulated
environments with both visual and tactile observations: robotic insertion, door
opening, and dexterous in-hand manipulation, demonstrating the benefits of
learning a multimodal policy. Code and videos of the experiments are available
at https://sferrazza.cc/m3l_site.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Sparse4D v3: Advancing End-to-End 3D Detection and Tracking
Abstract: In autonomous driving perception systems, 3D detection and tracking are the
two fundamental tasks. This paper delves deeper into this field, building upon
the Sparse4D framework. We introduce two auxiliary training tasks (Temporal
Instance Denoising and Quality Estimation) and propose decoupled attention to
make structural improvements, leading to significant enhancements in detection
performance. Additionally, we extend the detector into a tracker using a
straightforward approach that assigns instance ID during inference, further
highlighting the advantages of query-based algorithms. Extensive experiments
conducted on the nuScenes benchmark validate the effectiveness of the proposed
improvements. With ResNet50 as the backbone, we witnessed enhancements of
3.0\%, 2.2\%, and 7.6\% in mAP, NDS, and AMOTA, achieving 46.9\%, 56.1\%, and
49.0\%, respectively. Our best model achieved 71.9\% NDS and 67.7\% AMOTA on
the nuScenes test set. Code will be released at
\url{https://github.com/linxuewu/Sparse4D}.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: On the Inadequacy of Similarity-based Privacy Metrics: Reconstruction Attacks against "Truly Anonymous Synthetic Data''
Abstract: Training generative models to produce synthetic data is meant to provide a
privacy-friendly approach to data release. However, we get robust guarantees
only when models are trained to satisfy Differential Privacy (DP). Alas, this
is not the standard in industry as many companies use ad-hoc strategies to
empirically evaluate privacy based on the statistical similarity between
synthetic and real data. In this paper, we review the privacy metrics offered
by leading companies in this space and shed light on a few critical flaws in
reasoning about privacy entirely via empirical evaluations. We analyze the
undesirable properties of the most popular metrics and filters and demonstrate
their unreliability and inconsistency through counter-examples. We then present
a reconstruction attack, ReconSyn, which successfully recovers (i.e., leaks all
attributes of) at least 78% of the low-density train records (or outliers) with
only black-box access to a single fitted generative model and the privacy
metrics. Finally, we show that applying DP only to the model or using
low-utility generators does not mitigate ReconSyn as the privacy leakage
predominantly comes from the metrics. Overall, our work serves as a warning to
practitioners not to deviate from established privacy-preserving mechanisms.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: MARRS: Multimodal Reference Resolution System
Abstract: Successfully handling context is essential for any dialog understanding task.
This context maybe be conversational (relying on previous user queries or
system responses), visual (relying on what the user sees, for example, on their
screen), or background (based on signals such as a ringing alarm or playing
music). In this work, we present an overview of MARRS, or Multimodal Reference
Resolution System, an on-device framework within a Natural Language
Understanding system, responsible for handling conversational, visual and
background context. In particular, we present different machine learning models
to enable handing contextual queries; specifically, one to enable reference
resolution, and one to handle context via query rewriting. We also describe how
these models complement each other to form a unified, coherent, lightweight
system that can understand context while preserving user privacy.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Enhancing Sentiment Analysis Results through Outlier Detection Optimization
Abstract: When dealing with text data containing subjective labels like speaker
emotions, inaccuracies or discrepancies among labelers are not uncommon. Such
discrepancies can significantly affect the performance of machine learning
algorithms. This study investigates the potential of identifying and addressing
outliers in text data with subjective labels, aiming to enhance classification
outcomes. We utilized the Deep SVDD algorithm, a one-class classification
method, to detect outliers in nine text-based emotion and sentiment analysis
datasets. By employing both a small-sized language model (DistilBERT base model
with 66 million parameters) and non-deep learning machine learning algorithms
(decision tree, KNN, Logistic Regression, and LDA) as the classifier, our
findings suggest that the removal of outliers can lead to enhanced results in
most cases. Additionally, as outliers in such datasets are not necessarily
unlearnable, we experienced utilizing a large language model -- DeBERTa v3
large with 131 million parameters, which can capture very complex patterns in
data. We continued to observe performance enhancements across multiple
datasets.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Ensemble Federated Learning: an approach for collaborative pneumonia diagnosis
Abstract: Federated learning is a very convenient approach for scenarios where (i) the
exchange of data implies privacy concerns and/or (ii) a quick reaction is
needed. In smart healthcare systems, both aspects are usually required. In this
paper, we work on the first scenario, where preserving privacy is key and,
consequently, building a unique and massive medical image data set by fusing
different data sets from different medical institutions or research centers
(computation nodes) is not an option. We propose an ensemble federated learning
(EFL) approach that is based on the following characteristics: First, each
computation node works with a different data set (but of the same type). They
work locally and apply an ensemble approach combining eight well-known CNN
models (densenet169, mobilenetv2, xception, inceptionv3, vgg16, resnet50,
densenet121, and resnet152v2) on Chest X-ray images. Second, the best two local
models are used to create a local ensemble model that is shared with a central
node. Third, the ensemble models are aggregated to obtain a global model, which
is shared with the computation nodes to continue with a new iteration. This
procedure continues until there are no changes in the best local models. We
have performed different experiments to compare our approach with centralized
ones (with or without an ensemble approach)\color{black}. The results conclude
that our proposal outperforms these ones in Chest X-ray images (achieving an
accuracy of 96.63\%) and offers very competitive results compared to other
proposals in the literature.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy
Abstract: Graph neural networks (GNNs) have shown advantages in graph-based analysis
tasks. However, most existing methods have the homogeneity assumption and show
poor performance on heterophilic graphs, where the linked nodes have dissimilar
features and different class labels, and the semantically related nodes might
be multi-hop away. To address this limitation, this paper presents GraphRARE, a
general framework built upon node relative entropy and deep reinforcement
learning, to strengthen the expressive capability of GNNs. An innovative node
relative entropy, which considers node features and structural similarity, is
used to measure mutual information between node pairs. In addition, to avoid
the sub-optimal solutions caused by mixing useful information and noises of
remote nodes, a deep reinforcement learning-based algorithm is developed to
optimize the graph topology. This algorithm selects informative nodes and
discards noisy nodes based on the defined node relative entropy. Extensive
experiments are conducted on seven real-world datasets. The experimental
results demonstrate the superiority of GraphRARE in node classification and its
capability to optimize the original graph topology.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Accelerating Reinforcement Learning of Robotic Manipulations via Feedback from Large Language Models
Abstract: Reinforcement Learning (RL) plays an important role in the robotic
manipulation domain since it allows self-learning from trial-and-error
interactions with the environment. Still, sample efficiency and reward
specification seriously limit its potential. One possible solution involves
learning from expert guidance. However, obtaining a human expert is impractical
due to the high cost of supervising an RL agent, and developing an automatic
supervisor is a challenging endeavor. Large Language Models (LLMs) demonstrate
remarkable abilities to provide human-like feedback on user inputs in natural
language. Nevertheless, they are not designed to directly control low-level
robotic motions, as their pretraining is based on vast internet data rather
than specific robotics data. In this paper, we introduce the Lafite-RL
(Language agent feedback interactive Reinforcement Learning) framework, which
enables RL agents to learn robotic tasks efficiently by taking advantage of
LLMs' timely feedback. Our experiments conducted on RLBench tasks illustrate
that, with simple prompt design in natural language, the Lafite-RL agent
exhibits improved learning capabilities when guided by an LLM. It outperforms
the baseline in terms of both learning efficiency and success rate,
underscoring the efficacy of the rewards provided by an LLM.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: The Claire French Dialogue Dataset
Abstract: We present the Claire French Dialogue Dataset (CFDD), a resource created by
members of LINAGORA Labs in the context of the OpenLLM France initiative. CFDD
is a corpus containing roughly 160 million words from transcripts and stage
plays in French that we have assembled and publicly released in an effort to
further the development of multilingual, open source language models. This
paper describes the 24 individual corpora of which CFDD is composed and
provides links and citations to their original sources. It also provides our
proposed breakdown of the full CFDD dataset into eight categories of subcorpora
and describes the process we followed to standardize the format of the final
dataset. We conclude with a discussion of similar work and future directions.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Speak Like a Native: Prompting Large Language Models in a Native Style
Abstract: Existing work has found that the prompt engineering heavily influences the
performance of large language models (LLMs). Chain-of-thought (CoT), as a
popular prompt engineering technique, prompted LLMs using in-context examples
with reasoning steps. In current studies, the few-shot examples of CoT are
generally handcrafted by humans. However, how the text style of in-context
examples influence the outputs of LLMs still remains under-explored. This paper
presents a novel and effective approach, named \textbf{AlignCoT}, to improve
the reasoning capability of LLMs by aligning the in-context examples with the
native style of LLMs. ``Native'' refers to the inherent characteristic style of
LLMs which can be probed by original zero-shot scenarios. AlignCoT is
orthogonal to other prompt engineering methods, making it easy to combine with
state-of-the-art techniques to further improve the LLMs' performance. We
conduct extensive and comprehensive experiments on several benchmarks. The
empirical results demonstrate that our AlignCoTsignificantly improves
performance over the carefully handcrafted in-context examples. For instance,
with GPT-3.5-turbo, we observed a +2.5\% improvement on GSM8K. Furthermore, our
AlignCoT consistently improve the performance when combined with other
state-of-the-art prompt engineering methods. The source code and dataset will
be available at
\href{https://github.com/yangzhch6/AlignCoT}{https://github.com/yangzhch6/AlignCoT}.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam Prediction
Abstract: Recently, spam on online social networks has attracted attention in the
research and business world. Twitter has become the preferred medium to spread
spam content. Many research efforts attempted to encounter social networks
spam. Twitter brought extra challenges represented by the feature space size,
and imbalanced data distributions. Usually, the related research works focus on
part of these main challenges or produce black-box models. In this paper, we
propose a modified genetic algorithm for simultaneous dimensionality reduction
and hyper parameter optimization over imbalanced datasets. The algorithm
initialized an eXtreme Gradient Boosting classifier and reduced the features
space of tweets dataset; to generate a spam prediction model. The model is
validated using a 50 times repeated 10-fold stratified cross-validation, and
analyzed using nonparametric statistical tests. The resulted prediction model
attains on average 82.32\% and 92.67\% in terms of geometric mean and accuracy
respectively, utilizing less than 10\% of the total feature space. The
empirical results show that the modified genetic algorithm outperforms $Chi^2$
and $PCA$ feature selection methods. In addition, eXtreme Gradient Boosting
outperforms many machine learning algorithms, including BERT-based deep
learning model, in spam prediction. Furthermore, the proposed approach is
applied to SMS spam modeling and compared to related works.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
Abstract: Offline-to-online reinforcement learning (RL) is a training paradigm that
combines pre-training on a pre-collected dataset with fine-tuning in an online
environment. However, the incorporation of online fine-tuning can intensify the
well-known distributional shift problem. Existing solutions tackle this problem
by imposing a policy constraint on the policy improvement objective in both
offline and online learning. They typically advocate a single balance between
policy improvement and constraints across diverse data collections. This
one-size-fits-all manner may not optimally leverage each collected sample due
to the significant variation in data quality across different states. To this
end, we introduce Family Offline-to-Online RL (FamO2O), a simple yet effective
framework that empowers existing algorithms to determine state-adaptive
improvement-constraint balances. FamO2O utilizes a universal model to train a
family of policies with different improvement/constraint intensities, and a
balance model to select a suitable policy for each state. Theoretically, we
prove that state-adaptive balances are necessary for achieving a higher policy
performance upper bound. Empirically, extensive experiments show that FamO2O
offers a statistically significant improvement over various existing methods,
achieving state-of-the-art performance on the D4RL benchmark. Codes are
available at https://github.com/LeapLabTHU/FamO2O.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Regions are Who Walk Them: a Large Pre-trained Spatiotemporal Model Based on Human Mobility for Ubiquitous Urban Sensing
Abstract: User profiling and region analysis are two tasks of significant commercial
value. However, in practical applications, modeling different features
typically involves four main steps: data preparation, data processing, model
establishment, evaluation, and optimization. This process is time-consuming and
labor-intensive. Repeating this workflow for each feature results in abundant
development time for tasks and a reduced overall volume of task development.
Indeed, human mobility data contains a wealth of information. Several
successful cases suggest that conducting in-depth analysis of population
movement data could potentially yield meaningful profiles about users and
areas. Nonetheless, most related works have not thoroughly utilized the
semantic information within human mobility data and trained on a fixed number
of the regions. To tap into the rich information within population movement,
based on the perspective that Regions Are Who walk them, we propose a large
spatiotemporal model based on trajectories (RAW). It possesses the following
characteristics: 1) Tailored for trajectory data, introducing a GPT-like
structure with a parameter count of up to 1B; 2) Introducing a spatiotemporal
fine-tuning module, interpreting trajectories as collection of users to derive
arbitrary region embedding. This framework allows rapid task development based
on the large spatiotemporal model. We conducted extensive experiments to
validate the effectiveness of our proposed large spatiotemporal model. It's
evident that our proposed method, relying solely on human mobility data without
additional features, exhibits a certain level of relevance in user profiling
and region analysis. Moreover, our model showcases promising predictive
capabilities in trajectory generation tasks based on the current state,
offering the potential for further innovative work utilizing this large
spatiotemporal model.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: APoLLo: Unified Adapter and Prompt Learning for Vision Language Models
Abstract: The choice of input text prompt plays a critical role in the performance of
Vision-Language Pretrained (VLP) models such as CLIP. We present APoLLo, a
unified multi-modal approach that combines Adapter and Prompt learning for
Vision-Language models. Our method is designed to substantially improve the
generalization capabilities of VLP models when they are fine-tuned in a
few-shot setting. We introduce trainable cross-attention-based adapter layers
in conjunction with vision and language encoders to strengthen the alignment
between the two modalities. We enforce consistency between the respective
encoder branches (receiving augmented inputs) to prevent overfitting in
downstream tasks. Our method is evaluated on three representative tasks:
generalization to novel classes, cross-dataset evaluation, and unseen domain
shifts. In practice, APoLLo achieves a relative gain up to 6.03% over MaPLe
(SOTA) on novel classes for 10 diverse image recognition datasets.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: A Review of the Evidence for Existential Risk from AI via Misaligned Power-Seeking
Abstract: Rapid advancements in artificial intelligence (AI) have sparked growing
concerns among experts, policymakers, and world leaders regarding the potential
for increasingly advanced AI systems to pose existential risks. This paper
reviews the evidence for existential risks from AI via misalignment, where AI
systems develop goals misaligned with human values, and power-seeking, where
misaligned AIs actively seek power. The review examines empirical findings,
conceptual arguments and expert opinion relating to specification gaming, goal
misgeneralization, and power-seeking. The current state of the evidence is
found to be concerning but inconclusive regarding the existence of extreme
forms of misaligned power-seeking. Strong empirical evidence of specification
gaming combined with strong conceptual evidence for power-seeking make it
difficult to dismiss the possibility of existential risk from misaligned
power-seeking. On the other hand, to date there are no public empirical
examples of misaligned power-seeking in AI systems, and so arguments that
future systems will pose an existential risk remain somewhat speculative. Given
the current state of the evidence, it is hard to be extremely confident either
that misaligned power-seeking poses a large existential risk, or that it poses
no existential risk. The fact that we cannot confidently rule out existential
risk from AI via misaligned power-seeking is cause for serious concern.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: Accelerating Exploration with Unlabeled Prior Data
Abstract: Learning to solve tasks from a sparse reward signal is a major challenge for
standard reinforcement learning (RL) algorithms. However, in the real world,
agents rarely need to solve sparse reward tasks entirely from scratch. More
often, we might possess prior experience to draw on that provides considerable
guidance about which actions and outcomes are possible in the world, which we
can use to explore more effectively for new tasks. In this work, we study how
prior data without reward labels may be used to guide and accelerate
exploration for an agent solving a new sparse reward task. We propose a simple
approach that learns a reward model from online experience, labels the
unlabeled prior data with optimistic rewards, and then uses it concurrently
alongside the online data for downstream policy and critic optimization. This
general formula leads to rapid exploration in several challenging sparse-reward
domains where tabula rasa exploration is insufficient, including the AntMaze
domain, Adroit hand manipulation domain, and a visual simulated robotic
manipulation domain. Our results highlight the ease of incorporating unlabeled
prior data into existing online RL algorithms, and the (perhaps surprising)
effectiveness of doing so.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability, Explainability, and Safety
Abstract: Explainability and Safety engender Trust. These require a model to exhibit
consistency and reliability. To achieve these, it is necessary to use and
analyze data and knowledge with statistical and symbolic AI methods relevant to
the AI application - neither alone will do. Consequently, we argue and seek to
demonstrate that the NeuroSymbolic AI approach is better suited for making AI a
trusted AI system. We present the CREST framework that shows how Consistency,
Reliability, user-level Explainability, and Safety are built on NeuroSymbolic
methods that use data and knowledge to support requirements for critical
applications such as health and well-being. This article focuses on Large
Language Models (LLMs) as the chosen AI system within the CREST framework. LLMs
have garnered substantial attention from researchers due to their versatility
in handling a broad array of natural language processing (NLP) scenarios. For
example, ChatGPT and Google's MedPaLM have emerged as highly promising
platforms for providing information in general and health-related queries,
respectively. Nevertheless, these models remain black boxes despite
incorporating human feedback and instruction-guided tuning. For instance,
ChatGPT can generate unsafe responses despite instituting safety guardrails.
CREST presents a plausible approach harnessing procedural and graph-based
knowledge within a NeuroSymbolic framework to shed light on the challenges
associated with LLMs.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph
Abstract: Local Attention-guided Message Passing Mechanism (LAMP) adopted in Graph
Attention Networks (GATs) is designed to adaptively learn the importance of
neighboring nodes for better local aggregation on the graph, which can bring
the representations of similar neighbors closer effectively, thus showing
stronger discrimination ability. However, existing GATs suffer from a
significant discrimination ability decline in heterophilic graphs because the
high proportion of dissimilar neighbors can weaken the self-attention of the
central node, jointly resulting in the deviation of the central node from
similar nodes in the representation space. This kind of effect generated by
neighboring nodes is called the Distraction Effect (DE) in this paper. To
estimate and weaken the DE of neighboring nodes, we propose a Causally graph
Attention network for Trimming heterophilic graph (CAT). To estimate the DE,
since the DE are generated through two paths (grab the attention assigned to
neighbors and reduce the self-attention of the central node), we use Total
Effect to model DE, which is a kind of causal estimand and can be estimated
from intervened data; To weaken the DE, we identify the neighbors with the
highest DE (we call them Distraction Neighbors) and remove them. We adopt three
representative GATs as the base model within the proposed CAT framework and
conduct experiments on seven heterophilic datasets in three different sizes.
Comparative experiments show that CAT can improve the node classification
accuracy of all base GAT models. Ablation experiments and visualization further
validate the enhancement of discrimination ability brought by CAT. The source
code is available at https://github.com/GeoX-Lab/CAT.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Harmonic Mobile Manipulation
Abstract: Recent advancements in robotics have enabled robots to navigate complex
scenes or manipulate diverse objects independently. However, robots are still
impotent in many household tasks requiring coordinated behaviors such as
opening doors. The factorization of navigation and manipulation, while
effective for some tasks, fails in scenarios requiring coordinated actions. To
address this challenge, we introduce, HarmonicMM, an end-to-end learning method
that optimizes both navigation and manipulation, showing notable improvement
over existing techniques in everyday tasks. This approach is validated in
simulated and real-world environments and adapts to novel unseen settings
without additional tuning. Our contributions include a new benchmark for mobile
manipulation and the successful deployment in a real unseen apartment,
demonstrating the potential for practical indoor robot deployment in daily
life. More results are on our project site:
https://rchalyang.github.io/HarmonicMM/

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion
Abstract: Existing pedestrian attribute recognition (PAR) algorithms adopt pre-trained
CNN (e.g., ResNet) as their backbone network for visual feature learning, which
might obtain sub-optimal results due to the insufficient employment of the
relations between pedestrian images and attribute labels. In this paper, we
formulate PAR as a vision-language fusion problem and fully exploit the
relations between pedestrian images and attribute labels. Specifically, the
attribute phrases are first expanded into sentences, and then the pre-trained
vision-language model CLIP is adopted as our backbone for feature embedding of
visual images and attribute descriptions. The contrastive learning objective
connects the vision and language modalities well in the CLIP-based feature
space, and the Transformer layers used in CLIP can capture the long-range
relations between pixels. Then, a multi-modal Transformer is adopted to fuse
the dual features effectively and feed-forward network is used to predict
attributes. To optimize our network efficiently, we propose the region-aware
prompt tuning technique to adjust very few parameters (i.e., only the prompt
vectors and classification heads) and fix both the pre-trained VL model and
multi-modal Transformer. Our proposed PAR algorithm only adjusts 0.75%
learnable parameters compared with the fine-tuning strategy. It also achieves
new state-of-the-art performance on both standard and zero-shot settings for
PAR, including RAPv1, RAPv2, WIDER, PA100K, and PETA-ZS, RAP-ZS datasets. The
source code and pre-trained models will be released on
https://github.com/Event-AHU/OpenPAR.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Lite-Mind: Towards Efficient and Versatile Brain Representation Network
Abstract: Research in decoding visual information from the brain, particularly through
the non-invasive fMRI method, is rapidly progressing. The challenge arises from
the limited data availability and the low signal-to-noise ratio of fMRI
signals, leading to a low-precision task of fMRI-to-image retrieval.
State-of-the-art MindEye remarkably improves fMRI-to-image retrieval
performance by leveraging a deep MLP with a high parameter count orders of
magnitude, i.e., a 996M MLP Backbone per subject, to align fMRI embeddings to
the final hidden layer of CLIP's vision transformer. However, significant
individual variations exist among subjects, even within identical experimental
setups, mandating the training of subject-specific models. The substantial
parameters pose significant challenges in deploying fMRI decoding on practical
devices, especially with the necessitating of specific models for each subject.
To this end, we propose Lite-Mind, a lightweight, efficient, and versatile
brain representation network based on discrete Fourier transform, that
efficiently aligns fMRI voxels to fine-grained information of CLIP. Our
experiments demonstrate that Lite-Mind achieves an impressive 94.3%
fMRI-to-image retrieval accuracy on the NSD dataset for Subject 1, with 98.7%
fewer parameters than MindEye. Lite-Mind is also proven to be able to be
migrated to smaller brain datasets and establishes a new state-of-the-art for
zero-shot classification on the GOD dataset. The code is available at
https://github.com/gongzix/Lite-Mind.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Guardians of Trust: Navigating Data Security in AIOps through Vendor Partnerships
Abstract: Artificial Intelligence for IT Operations (AIOps) is a rapidly growing field
that applies artificial intelligence and machine learning to automate and
optimize IT operations. AIOps vendors provide services that ingest end-to-end
logs, traces, and metrics to offer a full stack observability of IT systems.
However, these data sources may contain sensitive information such as internal
IP addresses, hostnames, HTTP headers, SQLs, method/argument return values,
URLs, personal identifiable information (PII), or confidential business data.
Therefore, data security is a crucial concern when working with AIOps vendors.
In this article, we will discuss the security features offered by different
vendors and how we can adopt best practices to ensure data protection and
privacy.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: Unlearn What You Want to Forget: Efficient Unlearning for LLMs
Abstract: Large language models (LLMs) have achieved significant progress from
pre-training on and memorizing a wide range of textual data, however, this
process might suffer from privacy issues and violations of data protection
regulations. As a result, the ability to easily remove data related to
individual users from such models while not deteriorating their predictive
quality after the removal becomes increasingly important. To address these
issues, in this work, we propose an efficient unlearning framework that could
efficiently update LLMs without having to retrain the whole model after data
removals, by introducing lightweight unlearning layers learned with a selective
teacher-student objective into the transformers. In addition, we introduce a
fusion mechanism to effectively combine different unlearning layers that learns
to forget different sets of data to handle a sequence of forgetting operations.
Experiments on classification and generation tasks demonstrate the
effectiveness of our proposed methods compared to the state-of-the-art
baselines.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: FedTruth: Byzantine-Robust and Backdoor-Resilient Federated Learning Framework
Abstract: Federated Learning (FL) enables collaborative machine learning model training
across multiple parties without sharing raw data. However, FL's distributed
nature allows malicious clients to impact model training through Byzantine or
backdoor attacks, using erroneous model updates. Existing defenses measure the
deviation of each update from a 'ground-truth model update.' They often rely on
a benign root dataset on the server or use trimmed mean or median for clipping,
both methods having limitations.
  We introduce FedTruth, a robust defense against model poisoning in FL.
FedTruth doesn't assume specific data distributions nor requires a benign root
dataset. It estimates a global model update with dynamic aggregation weights,
considering contributions from all benign clients. Empirical studies
demonstrate FedTruth's efficacy in mitigating the impacts of poisoned updates
from both Byzantine and backdoor attacks.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: LLM A*: Human in the Loop Large Language Models Enabled A* Search for Robotics
Abstract: This research focuses on how Large Language Models (LLMs) can help with path
planning for mobile embodied agents such as robots, in a human-in-the-loop and
interactive manner. A novel framework named LLM A*, aims to leverage the
commonsense of LLMs, and the utility-optimal A* is proposed to facilitate
few-shot near-optimal path planning. Prompts are used to 1) provide LLMs with
essential information like environment, cost, heuristics, etc.; 2) communicate
human feedback to LLMs on intermediate planning results. This makes the whole
path planning process a `white box' and human feedback guides LLM A* to
converge quickly compared to other data-driven methods such as reinforcement
learning-based (RL) path planning. In addition, it makes code-free path
planning practical, henceforth promoting the inclusiveness of artificial
intelligence techniques. Comparative analysis against A* and RL shows that LLM
A* is more efficient in terms of search space and achieves an on-a-par path
with A* and a better path than RL. The interactive nature of LLM A* also makes
it a promising tool for deployment in collaborative human-robot tasks.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss
Abstract: Several natural phenomena and complex systems are often represented as
networks. Discovering their community structure is a fundamental task for
understanding these networks. Many algorithms have been proposed, but recently,
Graph Neural Networks (GNN) have emerged as a compelling approach for enhancing
this task.In this paper, we introduce a simple, efficient, and
clustering-oriented model based on unsupervised \textbf{G}raph Attention
\textbf{A}uto\textbf{E}ncoder for community detection in attributed networks
(GAECO). The proposed model adeptly learns representations from both the
network's topology and attribute information, simultaneously addressing dual
objectives: reconstruction and community discovery. It places a particular
emphasis on discovering compact communities by robustly minimizing clustering
errors. The model employs k-means as an objective function and utilizes a
multi-head Graph Attention Auto-Encoder for decoding the representations.
Experiments conducted on three datasets of attributed networks show that our
method surpasses state-of-the-art algorithms in terms of NMI and ARI.
Additionally, our approach scales effectively with the size of the network,
making it suitable for large-scale applications. The implications of our
findings extend beyond biological network interpretation and social network
analysis, where knowledge of the fundamental community structure is essential.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning
Abstract: We present a novel multi-agent RL approach, Selective Multi-Agent Prioritized
Experience Relay, in which agents share with other agents a limited number of
transitions they observe during training. The intuition behind this is that
even a small number of relevant experiences from other agents could help each
agent learn. Unlike many other multi-agent RL algorithms, this approach allows
for largely decentralized training, requiring only a limited communication
channel between agents. We show that our approach outperforms baseline
no-sharing decentralized training and state-of-the art multi-agent RL
algorithms. Further, sharing only a small number of highly relevant experiences
outperforms sharing all experiences between agents, and the performance uplift
from selective experience sharing is robust across a range of hyperparameters
and DQN variants. A reference implementation of our algorithm is available at
https://github.com/mgerstgrasser/super.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Unify Change Point Detection and Segment Classification in a Regression Task for Transportation Mode Identification
Abstract: Identifying travelers' transportation modes is important in transportation
science and location-based services. It's appealing for researchers to leverage
GPS trajectory data to infer transportation modes with the popularity of
GPS-enabled devices, e.g., smart phones. Existing studies frame this problem as
classification task. The dominant two-stage studies divide the trip into
single-one mode segments first and then categorize these segments. The over
segmentation strategy and inevitable error propagation bring difficulties to
classification stage and make optimizing the whole system hard. The recent
one-stage works throw out trajectory segmentation entirely to avoid these by
directly conducting point-wise classification for the trip, whereas leaving
predictions dis-continuous. To solve above-mentioned problems, inspired by YOLO
and SSD in object detection, we propose to reframe change point detection and
segment classification as a unified regression task instead of the existing
classification task. We directly regress coordinates of change points and
classify associated segments. In this way, our method divides the trip into
segments under a supervised manner and leverage more contextual information,
obtaining predictions with high accuracy and continuity. Two frameworks,
TrajYOLO and TrajSSD, are proposed to solve the regression task and various
feature extraction backbones are exploited. Exhaustive experiments on GeoLife
dataset show that the proposed method has competitive overall identification
accuracy of 0.853 when distinguishing five modes: walk, bike, bus, car, train.
As for change point detection, our method increases precision at the cost of
drop in recall. All codes are available at
https://github.com/RadetzkyLi/TrajYOLO-SSD.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: PotholeGuard: A Pothole Detection Approach by Point Cloud Semantic Segmentation
Abstract: Pothole detection is crucial for road safety and maintenance, traditionally
relying on 2D image segmentation. However, existing 3D Semantic Pothole
Segmentation research often overlooks point cloud sparsity, leading to
suboptimal local feature capture and segmentation accuracy. Our research
presents an innovative point cloud-based pothole segmentation architecture. Our
model efficiently identifies hidden features and uses a feedback mechanism to
enhance local characteristics, improving feature presentation. We introduce a
local relationship learning module to understand local shape relationships,
enhancing structural insights. Additionally, we propose a lightweight adaptive
structure for refining local point features using the K nearest neighbor
algorithm, addressing point cloud density differences and domain selection.
Shared MLP Pooling is integrated to learn deep aggregation features,
facilitating semantic data exploration and segmentation guidance. Extensive
experiments on three public datasets confirm PotholeGuard's superior
performance over state-of-the-art methods. Our approach offers a promising
solution for robust and accurate 3D pothole segmentation, with applications in
road maintenance and safety.

Output: Computer Vision