Instruction: What field is the article from?

Input:
Title: Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models
Abstract: The environmental impacts of global warming driven by methane (CH4) emissions
have catalyzed significant research initiatives in developing novel
technologies that enable proactive and rapid detection of CH4. Several
data-driven machine learning (ML) models were tested to determine how well they
identified fugitive CH4 and its related intensity in the affected areas.
Various meteorological characteristics, including wind speed, temperature,
pressure, relative humidity, water vapor, and heat flux, were included in the
simulation. We used the ensemble learning method to determine the
best-performing weighted ensemble ML models built upon several weaker
lower-layer ML models to (i) detect the presence of CH4 as a classification
problem and (ii) predict the intensity of CH4 as a regression problem.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Discret2Di -- Deep Learning based Discretization for Model-based Diagnosis
Abstract: Consistency-based diagnosis is an established approach to diagnose technical
applications, but suffers from significant modeling efforts, especially for
dynamic multi-modal time series. Machine learning seems to be an obvious
solution, which becomes less obvious when looking at details: Which notion of
consistency can be used? If logical calculi are still to be used, how can
dynamic time series be transferred into the discrete world?
  This paper presents the methodology Discret2Di for automated learning of
logical expressions for consistency-based diagnosis. While these logical
calculi have advantages by providing a clear notion of consistency, they have
the key problem of relying on a discretization of the dynamic system. The
solution presented combines machine learning from both the time series and the
symbolic domain to automate the learning of logical rules for consistency-based
diagnosis.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: DEFT: Dexterous Fine-Tuning for Real-World Hand Policies
Abstract: Dexterity is often seen as a cornerstone of complex manipulation. Humans are
able to perform a host of skills with their hands, from making food to
operating tools. In this paper, we investigate these challenges, especially in
the case of soft, deformable objects as well as complex, relatively
long-horizon tasks. However, learning such behaviors from scratch can be data
inefficient. To circumvent this, we propose a novel approach, DEFT (DExterous
Fine-Tuning for Hand Policies), that leverages human-driven priors, which are
executed directly in the real world. In order to improve upon these priors,
DEFT involves an efficient online optimization procedure. With the integration
of human-based learning and online fine-tuning, coupled with a soft robotic
hand, DEFT demonstrates success across various tasks, establishing a robust,
data-efficient pathway toward general dexterous manipulation. Please see our
website at https://dexterous-finetuning.github.io for video results.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: TaskDiff: A Similarity Metric for Task-Oriented Conversations
Abstract: The popularity of conversational digital assistants has resulted in the
availability of large amounts of conversational data which can be utilized for
improved user experience and personalized response generation. Building these
assistants using popular large language models like ChatGPT also require
additional emphasis on prompt engineering and evaluation methods. Textual
similarity metrics are a key ingredient for such analysis and evaluations.
While many similarity metrics have been proposed in the literature, they have
not proven effective for task-oriented conversations as they do not take
advantage of unique conversational features. To address this gap, we present
TaskDiff, a novel conversational similarity metric that utilizes different
dialogue components (utterances, intents, and slots) and their distributions to
compute similarity. Extensive experimental evaluation of TaskDiff on a
benchmark dataset demonstrates its superior performance and improved robustness
over other related approaches.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Semi-Synthetic Dataset Augmentation for Application-Specific Gaze Estimation
Abstract: Although the number of gaze estimation datasets is growing, the application
of appearance-based gaze estimation methods is mostly limited to estimating the
point of gaze on a screen. This is in part because most datasets are generated
in a similar fashion, where the gaze target is on a screen close to camera's
origin. In other applications such as assistive robotics or marketing research,
the 3D point of gaze might not be close to the camera's origin, meaning models
trained on current datasets do not generalize well to these tasks. We therefore
suggest generating a textured tridimensional mesh of the face and rendering the
training images from a virtual camera at a specific position and orientation
related to the application as a mean of augmenting the existing datasets. In
our tests, this lead to an average 47% decrease in gaze estimation angular
error.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Sparsify-then-Classify: From Internal Neurons of Large Language Models To Efficient Text Classifiers
Abstract: Among the many tasks that Large Language Models (LLMs) have revolutionized is
text classification. However, existing approaches for applying pretrained LLMs
to text classification predominantly rely on using single token outputs from
only the last layer of hidden states. As a result, they suffer from limitations
in efficiency, task-specificity, and interpretability. In our work, we
contribute an approach that uses all internal representations by employing
multiple pooling strategies on all activation and hidden states. Our novel
lightweight strategy, Sparsify-then-Classify (STC) first sparsifies
task-specific features layer-by-layer, then aggregates across layers for text
classification. STC can be applied as a seamless plug-and-play module on top of
existing LLMs. Our experiments on a comprehensive set of models and datasets
demonstrate that STC not only consistently improves the classification
performance of pretrained and fine-tuned models, but is also more efficient for
both training and inference, and is more intrinsically interpretable.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: An Interdisciplinary Outlook on Large Language Models for Scientific Research
Abstract: In this paper, we describe the capabilities and constraints of Large Language
Models (LLMs) within disparate academic disciplines, aiming to delineate their
strengths and limitations with precision. We examine how LLMs augment
scientific inquiry, offering concrete examples such as accelerating literature
review by summarizing vast numbers of publications, enhancing code development
through automated syntax correction, and refining the scientific writing
process. Simultaneously, we articulate the challenges LLMs face, including
their reliance on extensive and sometimes biased datasets, and the potential
ethical dilemmas stemming from their use. Our critical discussion extends to
the varying impacts of LLMs across fields, from the natural sciences, where
they help model complex biological sequences, to the social sciences, where
they can parse large-scale qualitative data. We conclude by offering a nuanced
perspective on how LLMs can be both a boon and a boundary to scientific
progress.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting
Abstract: In the hydrology field, time series forecasting is crucial for efficient
water resource management, improving flood and drought control and increasing
the safety and quality of life for the general population. However, predicting
long-term streamflow is a complex task due to the presence of extreme events.
It requires the capture of long-range dependencies and the modeling of rare but
important extreme values. Existing approaches often struggle to tackle these
dual challenges simultaneously. In this paper, we specifically delve into these
issues and propose Distance-weighted Auto-regularized Neural network (DAN), a
novel extreme-adaptive model for long-range forecasting of stremflow enhanced
by polar representation learning. DAN utilizes a distance-weighted multi-loss
mechanism and stackable blocks to dynamically refine indicator sequences from
exogenous data, while also being able to handle uni-variate time-series by
employing Gaussian Mixture probability modeling to improve robustness to severe
events. We also introduce Kruskal-Wallis sampling and gate control vectors to
handle imbalanced extreme data. On four real-life hydrologic streamflow
datasets, we demonstrate that DAN significantly outperforms both
state-of-the-art hydrologic time series prediction methods and general methods
designed for long-term time series prediction.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: OSM vs HD Maps: Map Representations for Trajectory Prediction
Abstract: While High Definition (HD) Maps have long been favored for their precise
depictions of static road elements, their accessibility constraints and
susceptibility to rapid environmental changes impede the widespread deployment
of autonomous driving, especially in the motion forecasting task. In this
context, we propose to leverage OpenStreetMap (OSM) as a promising alternative
to HD Maps for long-term motion forecasting. The contributions of this work are
threefold: firstly, we extend the application of OSM to long-horizon
forecasting, doubling the forecasting horizon compared to previous studies.
Secondly, through an expanded receptive field and the integration of
intersection priors, our OSM-based approach exhibits competitive performance,
narrowing the gap with HD Map-based models. Lastly, we conduct an exhaustive
context-aware analysis, providing deeper insights in motion forecasting across
diverse scenarios as well as conducting class-aware comparisons. This research
not only advances long-term motion forecasting with coarse map representations
but additionally offers a potential scalable solution within the domain of
autonomous driving.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Interpretable Geoscience Artificial Intelligence (XGeoS-AI): Application to Demystify Image Recognition
Abstract: As Earth science enters the era of big data, artificial intelligence (AI) not
only offers great potential for solving geoscience problems, but also plays a
critical role in accelerating the understanding of the complex, interactive,
and multiscale processes of Earth's behavior. As geoscience AI models are
progressively utilized for significant predictions in crucial situations,
geoscience researchers are increasingly demanding their interpretability and
versatility. This study proposes an interpretable geoscience artificial
intelligence (XGeoS-AI) framework to unravel the mystery of image recognition
in the Earth sciences, and its effectiveness and versatility is demonstrated by
taking computed tomography (CT) image recognition as an example. Inspired by
the mechanism of human vision, the proposed XGeoS-AI framework generates a
threshold value from a local region within the whole image to complete the
recognition. Different kinds of artificial intelligence (AI) methods, such as
Support Vector Regression (SVR), Multilayer Perceptron (MLP), Convolutional
Neural Network (CNN), can be adopted as the AI engines of the proposed XGeoS-AI
framework to efficiently complete geoscience image recognition tasks.
Experimental results demonstrate that the effectiveness, versatility, and
heuristics of the proposed framework have great potential in solving geoscience
image recognition problems. Interpretable AI should receive more and more
attention in the field of the Earth sciences, which is the key to promoting
more rational and wider applications of AI in the field of Earth sciences. In
addition, the proposed interpretable framework may be the forerunner of
technological innovation in the Earth sciences.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Unveiling the Limits of Learned Local Search Heuristics: Are You the Mightiest of the Meek?
Abstract: In recent years, combining neural networks with local search heuristics has
become popular in the field of combinatorial optimization. Despite its
considerable computational demands, this approach has exhibited promising
outcomes with minimal manual engineering. However, we have identified three
critical limitations in the empirical evaluation of these integration attempts.
Firstly, instances with moderate complexity and weak baselines pose a challenge
in accurately evaluating the effectiveness of learning-based approaches.
Secondly, the absence of an ablation study makes it difficult to quantify and
attribute improvements accurately to the deep learning architecture. Lastly,
the generalization of learned heuristics across diverse distributions remains
underexplored. In this study, we conduct a comprehensive investigation into
these identified limitations. Surprisingly, we demonstrate that a simple
learned heuristic based on Tabu Search surpasses state-of-the-art (SOTA)
learned heuristics in terms of performance and generalizability. Our findings
challenge prevailing assumptions and open up exciting avenues for future
research and innovation in combinatorial optimization.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference
Abstract: In causal inference, it is a fundamental task to estimate the causal effect
from observational data. However, latent confounders pose major challenges in
causal inference in observational data, for example, confounding bias and
M-bias. Recent data-driven causal effect estimators tackle the confounding bias
problem via balanced representation learning, but assume no M-bias in the
system, thus they fail to handle the M-bias. In this paper, we identify a
challenging and unsolved problem caused by a variable that leads to confounding
bias and M-bias simultaneously. To address this problem with co-occurring
M-bias and confounding bias, we propose a novel Disentangled Latent
Representation learning framework for learning latent representations from
proxy variables for unbiased Causal effect Estimation (DLRCE) from
observational data. Specifically, DLRCE learns three sets of latent
representations from the measured proxy variables to adjust for the confounding
bias and M-bias. Extensive experiments on both synthetic and three real-world
datasets demonstrate that DLRCE significantly outperforms the state-of-the-art
estimators in the case of the presence of both confounding bias and M-bias.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Harmonics of Learning: Universal Fourier Features Emerge in Invariant Networks
Abstract: In this work, we formally prove that, under certain conditions, if a neural
network is invariant to a finite group then its weights recover the Fourier
transform on that group. This provides a mathematical explanation for the
emergence of Fourier features -- a ubiquitous phenomenon in both biological and
artificial learning systems. The results hold even for non-commutative groups,
in which case the Fourier transform encodes all the irreducible unitary group
representations. Our findings have consequences for the problem of symmetry
discovery. Specifically, we demonstrate that the algebraic structure of an
unknown group can be recovered from the weights of a network that is at least
approximately invariant within certain bounds. Overall, this work contributes
to a foundation for an algebraic learning theory of invariant neural network
representations.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: LMDrive: Closed-Loop End-to-End Driving with Large Language Models
Abstract: Despite significant recent progress in the field of autonomous driving,
modern methods still struggle and can incur serious accidents when encountering
long-tail unforeseen events and challenging urban scenarios. On the one hand,
large language models (LLM) have shown impressive reasoning capabilities that
approach "Artificial General Intelligence". On the other hand, previous
autonomous driving methods tend to rely on limited-format inputs (e.g. sensor
data and navigation waypoints), restricting the vehicle's ability to understand
language information and interact with humans. To this end, this paper
introduces LMDrive, a novel language-guided, end-to-end, closed-loop autonomous
driving framework. LMDrive uniquely processes and integrates multi-modal sensor
data with natural language instructions, enabling interaction with humans and
navigation software in realistic instructional settings. To facilitate further
research in language-based closed-loop autonomous driving, we also publicly
release the corresponding dataset which includes approximately 64K
instruction-following data clips, and the LangAuto benchmark that tests the
system's ability to handle complex instructions and challenging driving
scenarios. Extensive closed-loop experiments are conducted to demonstrate
LMDrive's effectiveness. To the best of our knowledge, we're the very first
work to leverage LLMs for closed-loop end-to-end autonomous driving. Codes can
be found at https://github.com/opendilab/LMDrive

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: The Memory Perturbation Equation: Understanding Model's Sensitivity to Data
Abstract: Understanding model's sensitivity to its training data is crucial but can
also be challenging and costly, especially during training. To simplify such
issues, we present the Memory-Perturbation Equation (MPE) which relates model's
sensitivity to perturbation in its training data. Derived using Bayesian
principles, the MPE unifies existing sensitivity measures, generalizes them to
a wide-variety of models and algorithms, and unravels useful properties
regarding sensitivities. Our empirical results show that sensitivity estimates
obtained during training can be used to faithfully predict generalization on
unseen test data. The proposed equation is expected to be useful for future
research on robust and adaptive learning.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Better Neural PDE Solvers Through Data-Free Mesh Movers
Abstract: Recently, neural networks have been extensively employed to solve partial
differential equations (PDEs) in physical system modeling. While major studies
focus on learning system evolution on predefined static mesh discretizations,
some methods utilize reinforcement learning or supervised learning techniques
to create adaptive and dynamic meshes, due to the dynamic nature of these
systems. However, these approaches face two primary challenges: (1) the need
for expensive optimal mesh data, and (2) the change of the solution space's
degree of freedom and topology during mesh refinement. To address these
challenges, this paper proposes a neural PDE solver with a neural mesh adapter.
To begin with, we introduce a novel data-free neural mesh adaptor, called
Data-free Mesh Mover (DMM), with two main innovations. Firstly, it is an
operator that maps the solution to adaptive meshes and is trained using the
Monge-Ampere equation without optimal mesh data. Secondly, it dynamically
changes the mesh by moving existing nodes rather than adding or deleting nodes
and edges. Theoretical analysis shows that meshes generated by DMM have the
lowest interpolation error bound. Based on DMM, to efficiently and accurately
model dynamic systems, we develop a moving mesh based neural PDE solver
(MM-PDE) that embeds the moving mesh with a two-branch architecture and a
learnable interpolation framework to preserve information within the data.
Empirical experiments demonstrate that our method generates suitable meshes and
considerably enhances accuracy when modeling widely considered PDE systems.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: User Friendly and Adaptable Discriminative AI: Using the Lessons from the Success of LLMs and Image Generation Models
Abstract: While there is significant interest in using generative AI tools as
general-purpose models for specific ML applications, discriminative models are
much more widely deployed currently. One of the key shortcomings of these
discriminative AI tools that have been already deployed is that they are not
adaptable and user-friendly compared to generative AI tools (e.g., GPT4, Stable
Diffusion, Bard, etc.), where a non-expert user can iteratively refine model
inputs and give real-time feedback that can be accounted for immediately,
allowing users to build trust from the start. Inspired by this emerging
collaborative workflow, we develop a new system architecture that enables users
to work with discriminative models (such as for object detection, sentiment
classification, etc.) in a fashion similar to generative AI tools, where they
can easily provide immediate feedback as well as adapt the deployed models as
desired. Our approach has implications on improving trust, user-friendliness,
and adaptability of these versatile but traditional prediction models.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Evaluating the Impact of Flaky Simulators on Testing Autonomous Driving Systems
Abstract: Simulators are widely used to test Autonomous Driving Systems (ADS), but
their potential flakiness can lead to inconsistent test results. We investigate
test flakiness in simulation-based testing of ADS by addressing two key
questions: (1) How do flaky ADS simulations impact automated testing that
relies on randomized algorithms? and (2) Can machine learning (ML) effectively
identify flaky ADS tests while decreasing the required number of test reruns?
Our empirical results, obtained from two widely-used open-source ADS simulators
and five diverse ADS test setups, show that test flakiness in ADS is a common
occurrence and can significantly impact the test results obtained by randomized
algorithms. Further, our ML classifiers effectively identify flaky ADS tests
using only a single test run, achieving F1-scores of $85$%, $82$% and $96$% for
three different ADS test setups. Our classifiers significantly outperform our
non-ML baseline, which requires executing tests at least twice, by $31$%,
$21$%, and $13$% in F1-score performance, respectively. We conclude with a
discussion on the scope, implications and limitations of our study. We provide
our complete replication package in a Github repository.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: Cross-modal Prompts: Adapting Large Pre-trained Models for Audio-Visual Downstream Tasks
Abstract: In recent years, the deployment of large-scale pre-trained models in
audio-visual downstream tasks has yielded remarkable outcomes. However, these
models, primarily trained on single-modality unconstrained datasets, still
encounter challenges in feature extraction for multi-modal tasks, leading to
suboptimal performance. This limitation arises due to the introduction of
irrelevant modality-specific information during encoding, which adversely
affects the performance of downstream tasks. To address this challenge, this
paper proposes a novel Dual-Guided Spatial-Channel-Temporal (DG-SCT) attention
mechanism. This mechanism leverages audio and visual modalities as soft prompts
to dynamically adjust the parameters of pre-trained models based on the current
multi-modal input features. Specifically, the DG-SCT module incorporates
trainable cross-modal interaction layers into pre-trained audio-visual
encoders, allowing adaptive extraction of crucial information from the current
modality across spatial, channel, and temporal dimensions, while preserving the
frozen parameters of large-scale pre-trained models. Experimental evaluations
demonstrate that our proposed model achieves state-of-the-art results across
multiple downstream tasks, including AVE, AVVP, AVS, and AVQA. Furthermore, our
model exhibits promising performance in challenging few-shot and zero-shot
scenarios. The source code and pre-trained models are available at
https://github.com/haoyi-duan/DG-SCT.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Latent Skill Discovery for Chain-of-Thought Reasoning
Abstract: Recent advances in Large Language Models (LLMs) have led to an emergent
ability of chain-of-thought (CoT) prompting, a prompt reasoning strategy that
adds intermediate rationale steps between questions and answers to construct
prompts. Conditioned on these prompts, LLMs can effectively learn in context to
generate rationales that lead to more accurate answers than when answering the
same question directly. To design LLM prompts, one important setting, called
demonstration selection, considers selecting demonstrations from an example
bank. Existing methods use various heuristics for this selection, but for CoT
prompting, which involves unique rationales, it is essential to base the
selection upon the intrinsic skills that CoT rationales need, for instance, the
skills of addition or subtraction for math word problems.
  To address this requirement, we introduce a novel approach named Reasoning
Skill Discovery (RSD) that use unsupervised learning to create a latent space
representation of rationales, called a reasoning skill. Simultaneously, RSD
learns a reasoning policy to determine the required reasoning skill for a given
question. This can then guide the selection of examples that demonstrate the
required reasoning skills. Our approach offers several desirable properties: it
is (1) theoretically grounded, (2) sample-efficient, requiring no LLM inference
or manual prompt design, and (3) LLM-agnostic. Empirically, RSD outperforms
existing methods by up to 6% in terms of the answer accuracy across multiple
reasoning tasks.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Diversity and Diffusion: Observations on Synthetic Image Distributions with Stable Diffusion
Abstract: Recent progress in text-to-image (TTI) systems, such as StableDiffusion,
Imagen, and DALL-E 2, have made it possible to create realistic images with
simple text prompts. It is tempting to use these systems to eliminate the
manual task of obtaining natural images for training a new machine learning
classifier. However, in all of the experiments performed to date, classifiers
trained solely with synthetic images perform poorly at inference, despite the
images used for training appearing realistic. Examining this apparent
incongruity in detail gives insight into the limitations of the underlying
image generation processes. Through the lens of diversity in image creation
vs.accuracy of what is created, we dissect the differences in semantic
mismatches in what is modeled in synthetic vs. natural images. This will
elucidate the roles of the image-languag emodel, CLIP, and the image generation
model, diffusion. We find four issues that limit the usefulness of TTI systems
for this task: ambiguity, adherence to prompt, lack of diversity, and inability
to represent the underlying concept. We further present surprising insights
into the geometry of CLIP embeddings.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Improving Denoising Diffusion Models via Simultaneous Estimation of Image and Noise
Abstract: This paper introduces two key contributions aimed at improving the speed and
quality of images generated through inverse diffusion processes. The first
contribution involves reparameterizing the diffusion process in terms of the
angle on a quarter-circular arc between the image and noise, specifically
setting the conventional $\displaystyle \sqrt{\bar{\alpha}}=\cos(\eta)$. This
reparameterization eliminates two singularities and allows for the expression
of diffusion evolution as a well-behaved ordinary differential equation (ODE).
In turn, this allows higher order ODE solvers such as Runge-Kutta methods to be
used effectively. The second contribution is to directly estimate both the
image ($\mathbf{x}_0$) and noise ($\mathbf{\epsilon}$) using our network, which
enables more stable calculations of the update step in the inverse diffusion
steps, as accurate estimation of both the image and noise are crucial at
different stages of the process. Together with these changes, our model
achieves faster generation, with the ability to converge on high-quality images
more quickly, and higher quality of the generated images, as measured by
metrics such as Frechet Inception Distance (FID), spatial Frechet Inception
Distance (sFID), precision, and recall.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Redefining Developer Assistance: Through Large Language Models in Software Ecosystem
Abstract: In this paper, we delve into the advancement of domain-specific Large
Language Models (LLMs) with a focus on their application in software
development. We introduce DevAssistLlama, a model developed through instruction
tuning, to assist developers in processing software-related natural language
queries. This model, a variant of instruction tuned LLM, is particularly adept
at handling intricate technical documentation, enhancing developer capability
in software specific tasks. The creation of DevAssistLlama involved
constructing an extensive instruction dataset from various software systems,
enabling effective handling of Named Entity Recognition (NER), Relation
Extraction (RE), and Link Prediction (LP). Our results demonstrate
DevAssistLlama's superior capabilities in these tasks, in comparison with other
models including ChatGPT. This research not only highlights the potential of
specialized LLMs in software development also the pioneer LLM for this domain.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: Large Scale Foundation Models for Intelligent Manufacturing Applications: A Survey
Abstract: Although the applications of artificial intelligence especially deep learning
had greatly improved various aspects of intelligent manufacturing, they still
face challenges for wide employment due to the poor generalization ability,
difficulties to establish high-quality training datasets, and unsatisfactory
performance of deep learning methods. The emergence of large scale foundational
models(LSFMs) had triggered a wave in the field of artificial intelligence,
shifting deep learning models from single-task, single-modal, limited data
patterns to a paradigm encompassing diverse tasks, multimodal, and pre-training
on massive datasets. Although LSFMs had demonstrated powerful generalization
capabilities, automatic high-quality training dataset generation and superior
performance across various domains, applications of LSFMs on intelligent
manufacturing were still in their nascent stage. A systematic overview of this
topic was lacking, especially regarding which challenges of deep learning can
be addressed by LSFMs and how these challenges can be systematically tackled.
To fill this gap, this paper systematically expounded current statue of LSFMs
and their advantages in the context of intelligent manufacturing. and compared
comprehensively with the challenges faced by current deep learning models in
various intelligent manufacturing applications. We also outlined the roadmaps
for utilizing LSFMs to address these challenges. Finally, case studies of
applications of LSFMs in real-world intelligent manufacturing scenarios were
presented to illustrate how LSFMs could help industries, improve their
efficiency.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: A Study on Altering the Latent Space of Pretrained Text to Speech Models for Improved Expressiveness
Abstract: This report explores the challenge of enhancing expressiveness control in
Text-to-Speech (TTS) models by augmenting a frozen pretrained model with a
Diffusion Model that is conditioned on joint semantic audio/text embeddings.
The paper identifies the challenges encountered when working with a VAE-based
TTS model and evaluates different image-to-image methods for altering latent
speech features. Our results offer valuable insights into the complexities of
adding expressiveness control to TTS systems and open avenues for future
research in this direction.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Robust Graph Clustering via Meta Weighting for Noisy Graphs
Abstract: How can we find meaningful clusters in a graph robustly against noise edges?
Graph clustering (i.e., dividing nodes into groups of similar ones) is a
fundamental problem in graph analysis with applications in various fields.
Recent studies have demonstrated that graph neural network (GNN) based
approaches yield promising results for graph clustering. However, we observe
that their performance degenerates significantly on graphs with noise edges,
which are prevalent in practice. In this work, we propose MetaGC for robust
GNN-based graph clustering. MetaGC employs a decomposable clustering loss
function, which can be rephrased as a sum of losses over node pairs. We add a
learnable weight to each node pair, and MetaGC adaptively adjusts the weights
of node pairs using meta-weighting so that the weights of meaningful node pairs
increase and the weights of less-meaningful ones (e.g., noise edges) decrease.
We show empirically that MetaGC learns weights as intended and consequently
outperforms the state-of-the-art GNN-based competitors, even when they are
equipped with separate denoising schemes, on five real-world graphs under
varying levels of noise. Our code and datasets are available at
https://github.com/HyeonsooJo/MetaGC.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Model Evaluation for Domain Identification of Unknown Classes in Open-World Recognition: A Proposal
Abstract: Open-World Recognition (OWR) is an emerging field that makes a machine
learning model competent in rejecting the unknowns, managing them, and
incrementally adding novel samples to the base knowledge. However, this broad
objective is not practical for an agent that works on a specific task. Not all
rejected samples will be used for learning continually in the future. Some
novel images in the open environment may not belong to the domain of interest.
Hence, identifying the unknown in the domain of interest is essential for a
machine learning model to learn merely the important samples. In this study, we
propose an evaluation protocol for estimating a model's capability in
separating unknown in-domain (ID) and unknown out-of-domain (OOD). We evaluated
using three approaches with an unknown domain and demonstrated the possibility
of identifying the domain of interest using the pre-trained parameters through
traditional transfer learning, Automated Machine Learning (AutoML), and Nearest
Class Mean (NCM) classifier with First Integer Neighbor Clustering Hierarchy
(FINCH). We experimented with five different domains: garbage, food, dogs,
plants, and birds. The results show that all approaches can be used as an
initial baseline yielding a good accuracy. In addition, a Balanced Accuracy
(BACCU) score from a pre-trained model indicates a tendency to excel in one or
more domains of interest. We observed that MobileNetV3 yielded the highest
BACCU score for the garbage domain and surpassed complex models such as the
transformer network. Meanwhile, our results also suggest that a strong
representation in the pre-trained model is important for identifying unknown
classes in the same domain. This study could open the bridge toward open-world
recognition in domain-specific tasks where the relevancy of the unknown classes
is vital.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: UniTSFace: Unified Threshold Integrated Sample-to-Sample Loss for Face Recognition
Abstract: Sample-to-class-based face recognition models can not fully explore the
cross-sample relationship among large amounts of facial images, while
sample-to-sample-based models require sophisticated pairing processes for
training. Furthermore, neither method satisfies the requirements of real-world
face verification applications, which expect a unified threshold separating
positive from negative facial pairs. In this paper, we propose a unified
threshold integrated sample-to-sample based loss (USS loss), which features an
explicit unified threshold for distinguishing positive from negative pairs.
Inspired by our USS loss, we also derive the sample-to-sample based softmax and
BCE losses, and discuss their relationship. Extensive evaluation on multiple
benchmark datasets, including MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace,
demonstrates that the proposed USS loss is highly efficient and can work
seamlessly with sample-to-class-based losses. The embedded loss (USS and
sample-to-class Softmax loss) overcomes the pitfalls of previous approaches and
the trained facial model UniTSFace exhibits exceptional performance,
outperforming state-of-the-art methods, such as CosFace, ArcFace, VPL,
AnchorFace, and UNPG. Our code is available.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Fuse Your Latents: Video Editing with Multi-source Latent Diffusion Models
Abstract: Latent Diffusion Models (LDMs) are renowned for their powerful capabilities
in image and video synthesis. Yet, video editing methods suffer from
insufficient pre-training data or video-by-video re-training cost. In
addressing this gap, we propose FLDM (Fused Latent Diffusion Model), a
training-free framework to achieve text-guided video editing by applying
off-the-shelf image editing methods in video LDMs. Specifically, FLDM fuses
latents from an image LDM and an video LDM during the denoising process. In
this way, temporal consistency can be kept with video LDM while high-fidelity
from the image LDM can also be exploited. Meanwhile, FLDM possesses high
flexibility since both image LDM and video LDM can be replaced so advanced
image editing methods such as InstructPix2Pix and ControlNet can be exploited.
To the best of our knowledge, FLDM is the first method to adapt off-the-shelf
image editing methods into video LDMs for video editing. Extensive quantitative
and qualitative experiments demonstrate that FLDM can improve the textual
alignment and temporal consistency of edited videos.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Extraction of Atypical Aspects from Customer Reviews: Datasets and Experiments with Language Models
Abstract: A restaurant dinner may become a memorable experience due to an unexpected
aspect enjoyed by the customer, such as an origami-making station in the
waiting area. If aspects that are atypical for a restaurant experience were
known in advance, they could be leveraged to make recommendations that have the
potential to engender serendipitous experiences, further increasing user
satisfaction. Although relatively rare, whenever encountered, atypical aspects
often end up being mentioned in reviews due to their memorable quality.
Correspondingly, in this paper we introduce the task of detecting atypical
aspects in customer reviews. To facilitate the development of extraction
models, we manually annotate benchmark datasets of reviews in three domains -
restaurants, hotels, and hair salons, which we use to evaluate a number of
language models, ranging from fine-tuning the instruction-based text-to-text
transformer Flan-T5 to zero-shot and few-shot prompting of GPT-3.5.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Emergent Communication in Interactive Sketch Question Answering
Abstract: Vision-based emergent communication (EC) aims to learn to communicate through
sketches and demystify the evolution of human communication. Ironically,
previous works neglect multi-round interaction, which is indispensable in human
communication. To fill this gap, we first introduce a novel Interactive Sketch
Question Answering (ISQA) task, where two collaborative players are interacting
through sketches to answer a question about an image in a multi-round manner.
To accomplish this task, we design a new and efficient interactive EC system,
which can achieve an effective balance among three evaluation factors,
including the question answering accuracy, drawing complexity and human
interpretability. Our experimental results including human evaluation
demonstrate that multi-round interactive mechanism facilitates targeted and
efficient communication between intelligent agents with decent human
interpretability.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Understanding Teacher Perspectives and Experiences after Deployment of AI Literacy Curriculum in Middle-school Classrooms
Abstract: Artificial Intelligence (AI) and its associated applications are ubiquitous
in today's world, making it imperative that students and their teachers
understand how it works and the ramifications arising from its usage. In this
study, we investigate the experiences of seven teachers following their
implementation of modules from the MIT RAICA (Responsible AI for Computational
Action) curriculum. Through semi-structured interviews, we investigated their
instructional strategies as they engaged with the AI curriculum in their
classroom, how their teaching and learning beliefs about AI evolved with the
curriculum as well as how those beliefs impacted their implementation of the
curriculum. Our analysis suggests that the AI modules not only expanded our
teachers' knowledge in the field, but also prompted them to recognize its daily
applications and their ethical and societal implications, so that they could
better engage with the content they deliver to students. Teachers were able to
leverage their own interdisciplinary backgrounds to creatively introduce
foundational AI topics to students to maximize engagement and playful learning.
Our teachers advocated their need for better external support when navigating
technological resources, additional time for preparation given the novelty of
the curriculum, more flexibility within curriculum timelines, and additional
accommodations for students of determination. Our findings provide valuable
insights for enhancing future iterations of AI literacy curricula and teacher
professional development (PD) resources.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: Distilling Large Language Models for Matching Patients to Clinical Trials
Abstract: The recent success of large language models (LLMs) has paved the way for
their adoption in the high-stakes domain of healthcare. Specifically, the
application of LLMs in patient-trial matching, which involves assessing patient
eligibility against clinical trial's nuanced inclusion and exclusion criteria,
has shown promise. Recent research has shown that GPT-3.5, a widely recognized
LLM developed by OpenAI, can outperform existing methods with minimal 'variable
engineering' by simply comparing clinical trial information against patient
summaries. However, there are significant challenges associated with using
closed-source proprietary LLMs like GPT-3.5 in practical healthcare
applications, such as cost, privacy and reproducibility concerns. To address
these issues, this study presents the first systematic examination of the
efficacy of both proprietary (GPT-3.5, and GPT-4) and open-source LLMs (LLAMA
7B,13B, and 70B) for the task of patient-trial matching. Employing a
multifaceted evaluation framework, we conducted extensive automated and
human-centric assessments coupled with a detailed error analysis for each
model. To enhance the adaptability of open-source LLMs, we have created a
specialized synthetic dataset utilizing GPT-4, enabling effective fine-tuning
under constrained data conditions. Our findings reveal that open-source LLMs,
when fine-tuned on this limited and synthetic dataset, demonstrate performance
parity with their proprietary counterparts. This presents a massive opportunity
for their deployment in real-world healthcare applications. To foster further
research and applications in this field, we release both the annotated
evaluation dataset along with the fine-tuned LLM -- Trial-LLAMA -- for public
use.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Towards Robust Text Retrieval with Progressive Learning
Abstract: Retrieval augmentation has become an effective solution to empower large
language models (LLMs) with external and verified knowledge sources from the
database, which overcomes the limitations and hallucinations of LLMs in
handling up-to-date and domain-specific information. However, existing
embedding models for text retrieval usually have three non-negligible
limitations. First, the number and diversity of samples in a batch are too
restricted to supervise the modeling of textual nuances at scale. Second, the
high proportional noise are detrimental to the semantic correctness and
consistency of embeddings. Third, the equal treatment to easy and difficult
samples would cause sub-optimum convergence of embeddings with poorer
generalization. In this paper, we propose the PEG, a progressively learned
embeddings for robust text retrieval. Specifically, we increase the training
in-batch negative samples to 80,000, and for each query, we extracted five hard
negatives. Concurrently, we incorporated a progressive learning mechanism,
enabling the model to dynamically modulate its attention to the samples
throughout the entire training process. Additionally, PEG is trained on more
than 100 million data, encompassing a wide range of domains (e.g., finance,
medicine, and tourism) and covering various tasks (e.g., question-answering,
machine reading comprehension, and similarity matching). Extensive experiments
conducted on C-MTEB and DuReader demonstrate that PEG surpasses
state-of-the-art embeddings in retrieving true positives, highlighting its
significant potential for applications in LLMs. Our model is publicly available
at https://huggingface.co/TownsWu/PEG.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: Model-Based Epistemic Variance of Values for Risk-Aware Policy Optimization
Abstract: We consider the problem of quantifying uncertainty over expected cumulative
rewards in model-based reinforcement learning. In particular, we focus on
characterizing the variance over values induced by a distribution over MDPs.
Previous work upper bounds the posterior variance over values by solving a
so-called uncertainty Bellman equation (UBE), but the over-approximation may
result in inefficient exploration. We propose a new UBE whose solution
converges to the true posterior variance over values and leads to lower regret
in tabular exploration problems. We identify challenges to apply the UBE theory
beyond tabular problems and propose a suitable approximation. Based on this
approximation, we introduce a general-purpose policy optimization algorithm,
Q-Uncertainty Soft Actor-Critic (QU-SAC), that can be applied for either
risk-seeking or risk-averse policy optimization with minimal changes.
Experiments in both online and offline RL demonstrate improved performance
compared to other uncertainty estimation methods.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Enhancing Scene Graph Generation with Hierarchical Relationships and Commonsense Knowledge
Abstract: This work presents an enhanced approach to generating scene graphs by
incorporating a relationship hierarchy and commonsense knowledge. Specifically,
we propose a Bayesian classification head that exploits an informative
hierarchical structure. It jointly predicts the super-category or type of
relationship between the two objects, along with the detailed relationship
under each super-category. We design a commonsense validation pipeline that
uses a large language model to critique the results from the scene graph
prediction system and then use that feedback to enhance the model performance.
The system requires no external large language model assistance at test time,
making it more convenient for practical applications. Experiments on the Visual
Genome and the OpenImage V6 datasets demonstrate that harnessing hierarchical
relationships enhances the model performance by a large margin. The proposed
Bayesian head can also be incorporated as a portable module in existing scene
graph generation algorithms to improve their results. In addition, the
commonsense validation enables the model to generate an extensive set of
reasonable predictions beyond dataset annotations.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Working Backwards: Learning to Place by Picking
Abstract: We present Learning to Place by Picking (LPP), a method capable of
autonomously collecting demonstrations for a family of placing tasks in which
objects must be manipulated to specific locations. With LPP, we approach the
learning of robotic object placement policies by reversing the grasping process
and exploiting the inherent symmetry of the pick and place problems.
Specifically, we obtain placing demonstrations from a set of grasp sequences of
objects that are initially located at their target placement locations. Our
system is capable of collecting hundreds of demonstrations without human
intervention by using a combination of tactile sensing and compliant control
for grasps. We train a policy directly from visual observations through
behaviour cloning, using the autonomously-collected demonstrations. By doing
so, the policy can generalize to object placement scenarios outside of the
training environment without privileged information (e.g., placing a plate
picked up from a table and not at the original placement location). We validate
our approach on home robotic scenarios that include dishwasher loading and
table setting. Our approach yields robotic placing policies that outperform
policies trained with kinesthetic teaching, both in terms of performance and
data efficiency, while requiring no human supervision.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Solving the Team Orienteering Problem with Transformers
Abstract: Route planning for a fleet of vehicles is an important task in applications
such as package delivery, surveillance, or transportation. This problem is
usually modeled as a Combinatorial Optimization problem named as Team
Orienteering Problem. The most popular Team Orienteering Problem solvers are
mainly based on either linear programming, which provides accurate solutions by
employing a large computation time that grows with the size of the problem, or
heuristic methods, which usually find suboptimal solutions in a shorter amount
of time. In this paper, a multi-agent route planning system capable of solving
the Team Orienteering Problem in a very fast and accurate manner is presented.
The proposed system is based on a centralized Transformer neural network that
can learn to encode the scenario (modeled as a graph) and the context of the
agents to provide fast and accurate solutions. Several experiments have been
performed to demonstrate that the presented system can outperform most of the
state-of-the-art works in terms of computation speed. In addition, the code is
publicly available at http://gti.ssr.upm.es/data.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Sparse but Strong: Crafting Adversarially Robust Graph Lottery Tickets
Abstract: Graph Lottery Tickets (GLTs), comprising a sparse adjacency matrix and a
sparse graph neural network (GNN), can significantly reduce the inference
latency and compute footprint compared to their dense counterparts. Despite
these benefits, their performance against adversarial structure perturbations
remains to be fully explored. In this work, we first investigate the resilience
of GLTs against different structure perturbation attacks and observe that they
are highly vulnerable and show a large drop in classification accuracy. Based
on this observation, we then present an adversarially robust graph
sparsification (ARGS) framework that prunes the adjacency matrix and the GNN
weights by optimizing a novel loss function capturing the graph homophily
property and information associated with both the true labels of the train
nodes and the pseudo labels of the test nodes. By iteratively applying ARGS to
prune both the perturbed graph adjacency matrix and the GNN model weights, we
can find adversarially robust graph lottery tickets that are highly sparse yet
achieve competitive performance under different untargeted training-time
structure attacks. Evaluations conducted on various benchmarks, considering
different poisoning structure attacks, namely, PGD, MetaAttack, Meta-PGD, and
PR-BCD demonstrate that the GLTs generated by ARGS can significantly improve
the robustness, even when subjected to high levels of sparsity.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Weighted Ensemble Models Are Strong Continual Learners
Abstract: In this work, we study the problem of continual learning (CL) where the goal
is to learn a model on a sequence of tasks, such that the data from the
previous tasks becomes unavailable while learning on the current task data. CL
is essentially a balancing act between being able to learn on the new task
(i.e., plasticity) and maintaining the performance on the previously learned
concepts (i.e., stability). With an aim to address the stability-plasticity
trade-off, we propose to perform weight-ensembling of the model parameters of
the previous and current task. This weight-ensembled model, which we call
Continual Model Averaging (or CoMA), attains high accuracy on the current task
by leveraging plasticity, while not deviating too far from the previous weight
configuration, ensuring stability. We also propose an improved variant of CoMA,
named Continual Fisher-weighted Model Averaging (or CoFiMA), that selectively
weighs each parameter in the weight ensemble by leveraging the Fisher
information of the weights of the model. Both the variants are conceptually
simple, easy to implement, and effective in attaining state-of-the-art
performance on several standard CL benchmarks.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Explaining Deep Learning Models for Age-related Gait Classification based on time series acceleration
Abstract: Gait analysis holds significant importance in monitoring daily health,
particularly among older adults. Advancements in sensor technology enable the
capture of movement in real-life environments and generate big data. Machine
learning, notably deep learning (DL), shows promise to use these big data in
gait analysis. However, the inherent black-box nature of these models poses
challenges for their clinical application. This study aims to enhance
transparency in DL-based gait classification for aged-related gait patterns
using Explainable Artificial Intelligence, such as SHAP.
  A total of 244 subjects, comprising 129 adults and 115 older adults (age>65),
were included. They performed a 3-minute walking task while accelerometers were
affixed to the lumbar segment L3. DL models, convolutional neural network (CNN)
and gated recurrent unit (GRU), were trained using 1-stride and 8-stride
accelerations, respectively, to classify adult and older adult groups. SHAP was
employed to explain the models' predictions.
  CNN achieved a satisfactory performance with an accuracy of 81.4% and an AUC
of 0.89, and GRU demonstrated promising results with an accuracy of 84.5% and
an AUC of 0.94. SHAP analysis revealed that both CNN and GRU assigned higher
SHAP values to the data from vertical and walking directions, particularly
emphasizing data around heel contact, spanning from the terminal swing to
loading response phases. Furthermore, SHAP values indicated that GRU did not
treat every stride equally.
  CNN accurately distinguished between adults and older adults based on the
characteristics of a single stride's data. GRU achieved accurate classification
by considering the relationships and subtle differences between strides. In
both models, data around heel contact emerged as most critical, suggesting
differences in acceleration and deceleration patterns during walking between
different age groups.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals
Abstract: Counterfactual explanations offer an intuitive and straightforward way to
explain black-box models and offer algorithmic recourse to individuals. To
address the need for plausible explanations, existing work has primarily relied
on surrogate models to learn how the input data is distributed. This
effectively reallocates the task of learning realistic explanations for the
data from the model itself to the surrogate. Consequently, the generated
explanations may seem plausible to humans but need not necessarily describe the
behaviour of the black-box model faithfully. We formalise this notion of
faithfulness through the introduction of a tailored evaluation metric and
propose a novel algorithmic framework for generating Energy-Constrained
Conformal Counterfactuals that are only as plausible as the model permits.
Through extensive empirical studies, we demonstrate that ECCCo reconciles the
need for faithfulness and plausibility. In particular, we show that for models
with gradient access, it is possible to achieve state-of-the-art performance
without the need for surrogate models. To do so, our framework relies solely on
properties defining the black-box model itself by leveraging recent advances in
energy-based modelling and conformal prediction. To our knowledge, this is the
first venture in this direction for generating faithful counterfactual
explanations. Thus, we anticipate that ECCCo can serve as a baseline for future
research. We believe that our work opens avenues for researchers and
practitioners seeking tools to better distinguish trustworthy from unreliable
models.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Data-driven Semi-supervised Machine Learning with Surrogate Safety Measures for Abnormal Driving Behavior Detection
Abstract: Detecting abnormal driving behavior is critical for road traffic safety and
the evaluation of drivers' behavior. With the advancement of machine learning
(ML) algorithms and the accumulation of naturalistic driving data, many ML
models have been adopted for abnormal driving behavior detection. Most existing
ML-based detectors rely on (fully) supervised ML methods, which require
substantial labeled data. However, ground truth labels are not always available
in the real world, and labeling large amounts of data is tedious. Thus, there
is a need to explore unsupervised or semi-supervised methods to make the
anomaly detection process more feasible and efficient. To fill this research
gap, this study analyzes large-scale real-world data revealing several abnormal
driving behaviors (e.g., sudden acceleration, rapid lane-changing) and develops
a Hierarchical Extreme Learning Machines (HELM) based semi-supervised ML method
using partly labeled data to accurately detect the identified abnormal driving
behaviors. Moreover, previous ML-based approaches predominantly utilize basic
vehicle motion features (such as velocity and acceleration) to label and detect
abnormal driving behaviors, while this study seeks to introduce Surrogate
Safety Measures (SSMs) as the input features for ML models to improve the
detection performance. Results from extensive experiments demonstrate the
effectiveness of the proposed semi-supervised ML model with the introduced SSMs
serving as important features. The proposed semi-supervised ML method
outperforms other baseline semi-supervised or unsupervised methods regarding
various metrics, e.g., delivering the best accuracy at 99.58% and the best F-1
measure at 0.9913. The ablation study further highlights the significance of
SSMs for advancing detection performance.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: How to ensure a safe control strategy? Towards a SRL for urban transit autonomous operation
Abstract: Deep reinforcement learning has gradually shown its latent decision-making
ability in urban rail transit autonomous operation. However, since
reinforcement learning can not neither guarantee safety during learning nor
execution, this is still one of the major obstacles to the practical
application of reinforcement learning. Given this drawback, reinforcement
learning applied in the safety-critical autonomous operation domain remains
challenging without generating a safe control command sequence that avoids
overspeed operations. Therefore, a SSA-DRL framework is proposed in this paper
for safe intelligent control of urban rail transit autonomous operation trains.
The proposed framework is combined with linear temporal logic, reinforcement
learning and Monte Carlo tree search and consists of four mainly module: a
post-posed shielding, a searching tree module, a DRL framework and an
additional actor. Furthermore, the output of the framework can meet speed
constraint, schedule constraint and optimize the operation process. Finally,
the proposed SSA-DRL framework for decision-making in urban rail transit
autonomous operation is evaluated in sixteen different sections, and its
effectiveness is demonstrated through an ablation experiment and comparison
with the scheduled operation plan.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Improving Interpersonal Communication by Simulating Audiences with Language Models
Abstract: How do we communicate with others to achieve our goals? We use our prior
experience or advice from others, or construct a candidate utterance by
predicting how it will be received. However, our experiences are limited and
biased, and reasoning about potential outcomes can be difficult and cognitively
challenging. In this paper, we explore how we can leverage Large Language Model
(LLM) simulations to help us communicate better. We propose the
Explore-Generate-Simulate (EGS) framework, which takes as input any scenario
where an individual is communicating to an audience with a goal they want to
achieve. EGS (1) explores the solution space by producing a diverse set of
advice relevant to the scenario, (2) generates communication candidates
conditioned on subsets of the advice, and (3) simulates the reactions from
various audiences to determine both the best candidate and advice to use. We
evaluate the framework on eight scenarios spanning the ten fundamental
processes of interpersonal communication. For each scenario, we collect a
dataset of human evaluations across candidates and baselines, and showcase that
our framework's chosen candidate is preferred over popular generation
mechanisms including Chain-of-Thought. We also find that audience simulations
achieve reasonably high agreement with human raters across 5 of the 8
scenarios. Finally, we demonstrate the generality of our framework by applying
it to real-world scenarios described by users on web forums. Through
evaluations and demonstrations, we show that EGS enhances the effectiveness and
outcomes of goal-oriented communication across a variety of situations, thus
opening up new possibilities for the application of large language models in
revolutionizing communication and decision-making processes.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modeling
Abstract: We study the tipping point collective dynamics of an adaptive
susceptible-infected-susceptible (SIS) epidemiological network in a
data-driven, machine learning-assisted manner. We identify a
parameter-dependent effective stochastic differential equation (eSDE) in terms
of physically meaningful coarse mean-field variables through a deep-learning
ResNet architecture inspired by numerical stochastic integrators. We construct
an approximate effective bifurcation diagram based on the identified drift term
of the eSDE and contrast it with the mean-field SIS model bifurcation diagram.
We observe a subcritical Hopf bifurcation in the evolving network's effective
SIS dynamics, that causes the tipping point behavior; this takes the form of
large amplitude collective oscillations that spontaneously -- yet rarely --
arise from the neighborhood of a (noisy) stationary state. We study the
statistics of these rare events both through repeated brute force simulations
and by using established mathematical/computational tools exploiting the
right-hand-side of the identified SDE. We demonstrate that such a collective
SDE can also be identified (and the rare events computations also performed) in
terms of data-driven coarse observables, obtained here via manifold learning
techniques, in particular Diffusion Maps. The workflow of our study is
straightforwardly applicable to other complex dynamics problems exhibiting
tipping point dynamics.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Open World Object Detection in the Era of Foundation Models
Abstract: Object detection is integral to a bevy of real-world applications, from
robotics to medical image analysis. To be used reliably in such applications,
models must be capable of handling unexpected - or novel - objects. The open
world object detection (OWD) paradigm addresses this challenge by enabling
models to detect unknown objects and learn discovered ones incrementally.
However, OWD method development is hindered due to the stringent benchmark and
task definitions. These definitions effectively prohibit foundation models.
Here, we aim to relax these definitions and investigate the utilization of
pre-trained foundation models in OWD. First, we show that existing benchmarks
are insufficient in evaluating methods that utilize foundation models, as even
naive integration methods nearly saturate these benchmarks. This result
motivated us to curate a new and challenging benchmark for these models.
Therefore, we introduce a new benchmark that includes five real-world
application-driven datasets, including challenging domains such as aerial and
surgical images, and establish baselines. We exploit the inherent connection
between classes in application-driven datasets and introduce a novel method,
Foundation Object detection Model for the Open world, or FOMO, which identifies
unknown objects based on their shared attributes with the base known objects.
FOMO has ~3x unknown object mAP compared to baselines on our benchmark.
However, our results indicate a significant place for improvement - suggesting
a great research opportunity in further scaling object detection methods to
real-world domains. Our code and benchmark are available at
https://orrzohar.github.io/projects/fomo/.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Radiology Report Generation Using Transformers Conditioned with Non-imaging Data
Abstract: Medical image interpretation is central to most clinical applications such as
disease diagnosis, treatment planning, and prognostication. In clinical
practice, radiologists examine medical images and manually compile their
findings into reports, which can be a time-consuming process. Automated
approaches to radiology report generation, therefore, can reduce radiologist
workload and improve efficiency in the clinical pathway. While recent
deep-learning approaches for automated report generation from medical images
have seen some success, most studies have relied on image-derived features
alone, ignoring non-imaging patient data. Although a few studies have included
the word-level contexts along with the image, the use of patient demographics
is still unexplored. This paper proposes a novel multi-modal transformer
network that integrates chest x-ray (CXR) images and associated patient
demographic information, to synthesise patient-specific radiology reports. The
proposed network uses a convolutional neural network to extract visual features
from CXRs and a transformer-based encoder-decoder network that combines the
visual features with semantic text embeddings of patient demographic
information, to synthesise full-text radiology reports. Data from two public
databases were used to train and evaluate the proposed approach. CXRs and
reports were extracted from the MIMIC-CXR database and combined with
corresponding patients' data MIMIC-IV. Based on the evaluation metrics used
including patient demographic information was found to improve the quality of
reports generated using the proposed approach, relative to a baseline network
trained using CXRs alone. The proposed approach shows potential for enhancing
radiology report generation by leveraging rich patient metadata and combining
semantic text embeddings derived thereof, with medical image-derived visual
features.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Iterative missing value imputation based on feature importance
Abstract: Many datasets suffer from missing values due to various reasons,which not
only increases the processing difficulty of related tasks but also reduces the
accuracy of classification. To address this problem, the mainstream approach is
to use missing value imputation to complete the dataset. Existing imputation
methods estimate the missing parts based on the observed values in the original
feature space, and they treat all features as equally important during data
completion, while in fact different features have different importance.
Therefore, we have designed an imputation method that considers feature
importance. This algorithm iteratively performs matrix completion and feature
importance learning, and specifically, matrix completion is based on a filling
loss that incorporates feature importance. Our experimental analysis involves
three types of datasets: synthetic datasets with different noisy features and
missing values, real-world datasets with artificially generated missing values,
and real-world datasets originally containing missing values. The results on
these datasets consistently show that the proposed method outperforms the
existing five imputation algorithms.To the best of our knowledge, this is the
first work that considers feature importance in the imputation model.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: A Pipeline For Discourse Circuits From CCG
Abstract: There is a significant disconnect between linguistic theory and modern NLP
practice, which relies heavily on inscrutable black-box architectures.
DisCoCirc is a newly proposed model for meaning that aims to bridge this
divide, by providing neuro-symbolic models that incorporate linguistic
structure. DisCoCirc represents natural language text as a `circuit' that
captures the core semantic information of the text. These circuits can then be
interpreted as modular machine learning models. Additionally, DisCoCirc fulfils
another major aim of providing an NLP model that can be implemented on
near-term quantum computers.
  In this paper we describe a software pipeline that converts English text to
its DisCoCirc representation. The pipeline achieves coverage over a large
fragment of the English language. It relies on Combinatory Categorial Grammar
(CCG) parses of the input text as well as coreference resolution information.
This semantic and syntactic information is used in several steps to convert the
text into a simply-typed $\lambda$-calculus term, and then into a circuit
diagram. This pipeline will enable the application of the DisCoCirc framework
to NLP tasks, using both classical and quantum approaches.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: DeepQC: A Deep Learning System for Automatic Quality Control of In-situ Soil Moisture Sensor Time Series Data
Abstract: Amidst changing climate, real-time soil moisture monitoring is vital for the
development of in-season decision support tools to help farmers manage weather
related risks. Precision Sustainable Agriculture (PSA) recently established a
real-time soil moisture monitoring network across the central, Midwest, and
eastern U.S., but field-scale sensor observations often come with data gaps and
anomalies. To maintain the data quality needed for development of decision
tools, a quality control system is necessary. The International Soil Moisture
Network (ISMN) introduced the Flagit module for anomaly detection in soil
moisture observations. However, under certain conditions, Flagit's quality
control approaches may underperform in identifying anomalies. Recently deep
learning methods have been successfully applied to detect anomalies in time
series data in various disciplines. However, their use in agriculture has not
been yet investigated. This study focuses on developing a Bi-directional Long
Short-Term Memory (LSTM) model, referred to as DeepQC, to identify anomalies in
soil moisture data. Manual flagged PSA observations were used for training,
validation, and testing the model, following an 80:10:10 split. The study then
compared the DeepQC and Flagit based estimates to assess their relative
performance. Flagit corrected flagged 95.5% of the corrected observations and
50.3% of the anomaly observations, indicating its limitations in identifying
anomalies. On the other hand, the DeepQC correctly flagged 99.7% of the correct
observations and 95.6% of the anomalies in significantly less time,
demonstrating its superiority over Flagit approach. Importantly, DeepQC's
performance remained consistent regardless of the number of anomalies. Given
the promising results obtained with the DeepQC, future studies will focus on
implementing this model on national and global soil moisture networks.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Self-Supervised Pre-Training for Precipitation Post-Processor
Abstract: Obtaining a sufficient forecast lead time for local precipitation is
essential in preventing hazardous weather events. Global warming-induced
climate change increases the challenge of accurately predicting severe
precipitation events, such as heavy rainfall. In this paper, we propose a deep
learning-based precipitation post-processor for numerical weather prediction
(NWP) models. The precipitation post-processor consists of (i) employing
self-supervised pre-training, where the parameters of the encoder are
pre-trained on the reconstruction of the masked variables of the atmospheric
physics domain; and (ii) conducting transfer learning on precipitation
segmentation tasks (the target domain) from the pre-trained encoder. In
addition, we introduced a heuristic labeling approach to effectively train
class-imbalanced datasets. Our experiments on precipitation correction for
regional NWP show that the proposed method outperforms other approaches.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: GGNNs : Generalizing GNNs using Residual Connections and Weighted Message Passing
Abstract: Many real-world phenomena can be modeled as a graph, making them extremely
valuable due to their ubiquitous presence. GNNs excel at capturing those
relationships and patterns within these graphs, enabling effective learning and
prediction tasks. GNNs are constructed using Multi-Layer Perceptrons (MLPs) and
incorporate additional layers for message passing to facilitate the flow of
features among nodes. It is commonly believed that the generalizing power of
GNNs is attributed to the message-passing mechanism between layers, where nodes
exchange information with their neighbors, enabling them to effectively capture
and propagate information across the nodes of a graph. Our technique builds on
these results, modifying the message-passing mechanism further: one by weighing
the messages before accumulating at each node and another by adding Residual
connections. These two mechanisms show significant improvements in learning and
faster convergence

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Establishing Central Sensitization Inventory Cut-off Values in patients with Chronic Low Back Pain by Unsupervised Machine Learning
Abstract: Human Assumed Central Sensitization is involved in the development and
maintenance of chronic low back pain (CLBP). The Central Sensitization
Inventory (CSI) was developed to evaluate the presence of HACS, with a cut-off
value of 40/100 based on patients with chronic pain. However, various factors
including pain conditions (e.g., CLBP), and gender may influence this cut-off
value. For chronic pain condition such as CLBP, unsupervised clustering
approaches can take these factors into consideration and automatically learn
the HACS-related patterns. Therefore, this study aimed to determine the cut-off
values for a Dutch-speaking population with CLBP, considering the total group
and stratified by gender based on unsupervised machine learning. In this study,
questionnaire data covering pain, physical, and psychological aspects were
collected from patients with CLBP and aged-matched pain-free adults (referred
to as healthy controls, HC). Four clustering approaches were applied to
identify HACS-related clusters based on the questionnaire data and gender. The
clustering performance was assessed using internal and external indicators.
Subsequently, receiver operating characteristic analysis was conducted on the
best clustering results to determine the optimal cut-off values. The study
included 151 subjects, consisting of 63 HCs and 88 patients with CLBP.
Hierarchical clustering yielded the best results, identifying three clusters:
healthy group, CLBP with low HACS level, and CLBP with high HACS level groups.
Based on the low HACS levels group (including HC and CLBP with low HACS level)
and high HACS level group, the cut-off value for the overall groups were 35, 34
for females, and 35 for. The findings suggest that the optimal cut-off values
for CLBP is 35. The gender-related cut-off values should be interpreted with
caution due to the unbalanced gender distribution in the sample.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Negotiated Representations to Prevent Forgetting in Machine Learning Applications
Abstract: Catastrophic forgetting is a significant challenge in the field of machine
learning, particularly in neural networks. When a neural network learns to
perform well on a new task, it often forgets its previously acquired knowledge
or experiences. This phenomenon occurs because the network adjusts its weights
and connections to minimize the loss on the new task, which can inadvertently
overwrite or disrupt the representations that were crucial for the previous
tasks. As a result, the the performance of the network on earlier tasks
deteriorates, limiting its ability to learn and adapt to a sequence of tasks.
In this paper, we propose a novel method for preventing catastrophic forgetting
in machine learning applications, specifically focusing on neural networks. Our
approach aims to preserve the knowledge of the network across multiple tasks
while still allowing it to learn new information effectively. We demonstrate
the effectiveness of our method by conducting experiments on various benchmark
datasets, including Split MNIST, Split CIFAR10, Split Fashion MNIST, and Split
CIFAR100. These datasets are created by dividing the original datasets into
separate, non overlapping tasks, simulating a continual learning scenario where
the model needs to learn multiple tasks sequentially without forgetting the
previous ones. Our proposed method tackles the catastrophic forgetting problem
by incorporating negotiated representations into the learning process, which
allows the model to maintain a balance between retaining past experiences and
adapting to new tasks. By evaluating our method on these challenging datasets,
we aim to showcase its potential for addressing catastrophic forgetting and
improving the performance of neural networks in continual learning settings.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Successive Model-Agnostic Meta-Learning for Few-Shot Fault Time Series Prognosis
Abstract: Meta learning is a promising technique for solving few-shot fault prediction
problems, which have attracted the attention of many researchers in recent
years. Existing meta-learning methods for time series prediction, which
predominantly rely on random and similarity matching-based task partitioning,
face three major limitations: (1) feature exploitation inefficiency; (2)
suboptimal task data allocation; and (3) limited robustness with small samples.
To overcome these limitations, we introduce a novel 'pseudo meta-task'
partitioning scheme that treats a continuous time period of a time series as a
meta-task, composed of multiple successive short time periods. Employing
continuous time series as pseudo meta-tasks allows our method to extract more
comprehensive features and relationships from the data, resulting in more
accurate predictions. Moreover, we introduce a differential algorithm to
enhance the robustness of our method across different datasets. Through
extensive experiments on several fault and time series prediction datasets, we
demonstrate that our approach substantially enhances prediction performance and
generalization capability under both few-shot and general conditions.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: RIGHT: Retrieval-augmented Generation for Mainstream Hashtag Recommendation
Abstract: Automatic mainstream hashtag recommendation aims to accurately provide users
with concise and popular topical hashtags before publication. Generally,
mainstream hashtag recommendation faces challenges in the comprehensive
difficulty of newly posted tweets in response to new topics, and the accurate
identification of mainstream hashtags beyond semantic correctness. However,
previous retrieval-based methods based on a fixed predefined mainstream hashtag
list excel in producing mainstream hashtags, but fail to understand the
constant flow of up-to-date information. Conversely, generation-based methods
demonstrate a superior ability to comprehend newly posted tweets, but their
capacity is constrained to identifying mainstream hashtags without additional
features. Inspired by the recent success of the retrieval-augmented technique,
in this work, we attempt to adopt this framework to combine the advantages of
both approaches. Meantime, with the help of the generator component, we could
rethink how to further improve the quality of the retriever component at a low
cost. Therefore, we propose RetrIeval-augmented Generative Mainstream HashTag
Recommender (RIGHT), which consists of three components: 1) a retriever seeks
relevant hashtags from the entire tweet-hashtags set; 2) a selector enhances
mainstream identification by introducing global signals; and 3) a generator
incorporates input tweets and selected hashtags to directly generate the
desired hashtags. The experimental results show that our method achieves
significant improvements over state-of-the-art baselines. Moreover, RIGHT can
be easily integrated into large language models, improving the performance of
ChatGPT by more than 10%.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Tree of Attacks: Jailbreaking Black-Box LLMs Automatically
Abstract: While Large Language Models (LLMs) display versatile functionality, they
continue to generate harmful, biased, and toxic content, as demonstrated by the
prevalence of human-designed jailbreaks. In this work, we present Tree of
Attacks with Pruning (TAP), an automated method for generating jailbreaks that
only requires black-box access to the target LLM. TAP utilizes an LLM to
iteratively refine candidate (attack) prompts using tree-of-thoughts reasoning
until one of the generated prompts jailbreaks the target. Crucially, before
sending prompts to the target, TAP assesses them and prunes the ones unlikely
to result in jailbreaks. Using tree-of-thought reasoning allows TAP to navigate
a large search space of prompts and pruning reduces the total number of queries
sent to the target. In empirical evaluations, we observe that TAP generates
prompts that jailbreak state-of-the-art LLMs (including GPT4 and GPT4-Turbo)
for more than 80% of the prompts using only a small number of queries. This
significantly improves upon the previous state-of-the-art black-box method for
generating jailbreaks.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: MotionCtrl: A Unified and Flexible Motion Controller for Video Generation
Abstract: Motions in a video primarily consist of camera motion, induced by camera
movement, and object motion, resulting from object movement. Accurate control
of both camera and object motion is essential for video generation. However,
existing works either mainly focus on one type of motion or do not clearly
distinguish between the two, limiting their control capabilities and diversity.
Therefore, this paper presents MotionCtrl, a unified and flexible motion
controller for video generation designed to effectively and independently
control camera and object motion. The architecture and training strategy of
MotionCtrl are carefully devised, taking into account the inherent properties
of camera motion, object motion, and imperfect training data. Compared to
previous methods, MotionCtrl offers three main advantages: 1) It effectively
and independently controls camera motion and object motion, enabling more
fine-grained motion control and facilitating flexible and diverse combinations
of both types of motion. 2) Its motion conditions are determined by camera
poses and trajectories, which are appearance-free and minimally impact the
appearance or shape of objects in generated videos. 3) It is a relatively
generalizable model that can adapt to a wide array of camera poses and
trajectories once trained. Extensive qualitative and quantitative experiments
have been conducted to demonstrate the superiority of MotionCtrl over existing
methods.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Agent as Cerebrum, Controller as Cerebellum: Implementing an Embodied LMM-based Agent on Drones
Abstract: In this study, we present a novel paradigm for industrial robotic embodied
agents, encapsulating an 'agent as cerebrum, controller as cerebellum'
architecture. Our approach harnesses the power of Large Multimodal Models
(LMMs) within an agent framework known as AeroAgent, tailored for drone
technology in industrial settings. To facilitate seamless integration with
robotic systems, we introduce ROSchain, a bespoke linkage framework connecting
LMM-based agents to the Robot Operating System (ROS). We report findings from
extensive empirical research, including simulated experiments on the Airgen and
real-world case study, particularly in individual search and rescue operations.
The results demonstrate AeroAgent's superior performance in comparison to
existing Deep Reinforcement Learning (DRL)-based agents, highlighting the
advantages of the embodied LMM in complex, real-world scenarios.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Generative Models: What do they know? Do they know things? Let's find out!
Abstract: Generative models have been shown to be capable of synthesizing highly
detailed and realistic images. It is natural to suspect that they implicitly
learn to model some image intrinsics such as surface normals, depth, or
shadows. In this paper, we present compelling evidence that generative models
indeed internally produce high-quality scene intrinsic maps. We introduce
Intrinsic LoRA (I LoRA), a universal, plug-and-play approach that transforms
any generative model into a scene intrinsic predictor, capable of extracting
intrinsic scene maps directly from the original generator network without
needing additional decoders or fully fine-tuning the original network. Our
method employs a Low-Rank Adaptation (LoRA) of key feature maps, with newly
learned parameters that make up less than 0.6% of the total parameters in the
generative model. Optimized with a small set of labeled images, our
model-agnostic approach adapts to various generative architectures, including
Diffusion models, GANs, and Autoregressive models. We show that the scene
intrinsic maps produced by our method compare well with, and in some cases
surpass those generated by leading supervised techniques.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: SCStory: Self-supervised and Continual Online Story Discovery
Abstract: We present a framework SCStory for online story discovery, that helps people
digest rapidly published news article streams in real-time without human
annotations. To organize news article streams into stories, existing approaches
directly encode the articles and cluster them based on representation
similarity. However, these methods yield noisy and inaccurate story discovery
results because the generic article embeddings do not effectively reflect the
story-indicative semantics in an article and cannot adapt to the rapidly
evolving news article streams. SCStory employs self-supervised and continual
learning with a novel idea of story-indicative adaptive modeling of news
article streams. With a lightweight hierarchical embedding module that first
learns sentence representations and then article representations, SCStory
identifies story-relevant information of news articles and uses them to
discover stories. The embedding module is continuously updated to adapt to
evolving news streams with a contrastive learning objective, backed up by two
unique techniques, confidence-aware memory replay and prioritized-augmentation,
employed for label absence and data scarcity problems. Thorough experiments on
real and the latest news data sets demonstrate that SCStory outperforms
existing state-of-the-art algorithms for unsupervised online story discovery.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Hierarchical Mutual Information Analysis: Towards Multi-view Clustering in The Wild
Abstract: Multi-view clustering (MVC) can explore common semantics from unsupervised
views generated by different sources, and thus has been extensively used in
applications of practical computer vision. Due to the spatio-temporal
asynchronism, multi-view data often suffer from view missing and are unaligned
in real-world applications, which makes it difficult to learn consistent
representations. To address the above issues, this work proposes a deep MVC
framework where data recovery and alignment are fused in a hierarchically
consistent way to maximize the mutual information among different views and
ensure the consistency of their latent spaces. More specifically, we first
leverage dual prediction to fill in missing views while achieving the
instance-level alignment, and then take the contrastive reconstruction to
achieve the class-level alignment. To the best of our knowledge, this could be
the first successful attempt to handle the missing and unaligned data problem
separately with different learning paradigms. Extensive experiments on public
datasets demonstrate that our method significantly outperforms state-of-the-art
methods on multi-view clustering even in the cases of view missing and
unalignment.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: LineConGraphs: Line Conversation Graphs for Effective Emotion Recognition using Graph Neural Networks
Abstract: Emotion Recognition in Conversations (ERC) is a critical aspect of affective
computing, and it has many practical applications in healthcare, education,
chatbots, and social media platforms. Earlier approaches for ERC analysis
involved modeling both speaker and long-term contextual information using graph
neural network architectures. However, it is ideal to deploy
speaker-independent models for real-world applications. Additionally, long
context windows can potentially create confusion in recognizing the emotion of
an utterance in a conversation. To overcome these limitations, we propose novel
line conversation graph convolutional network (LineConGCN) and graph attention
(LineConGAT) models for ERC analysis. These models are speaker-independent and
built using a graph construction strategy for conversations -- line
conversation graphs (LineConGraphs). The conversational context in
LineConGraphs is short-term -- limited to one previous and future utterance,
and speaker information is not part of the graph. We evaluate the performance
of our proposed models on two benchmark datasets, IEMOCAP and MELD, and show
that our LineConGAT model outperforms the state-of-the-art methods with an
F1-score of 64.58% and 76.50%. Moreover, we demonstrate that embedding
sentiment shift information into line conversation graphs further enhances the
ERC performance in the case of GCN models.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: STraceBERT: Source Code Retrieval using Semantic Application Traces
Abstract: Software reverse engineering is an essential task in software engineering and
security, but it can be a challenging process, especially for adversarial
artifacts. To address this challenge, we present STraceBERT, a novel approach
that utilizes a Java dynamic analysis tool to record calls to core Java
libraries, and pretrain a BERT-style model on the recorded application traces
for effective method source code retrieval from a candidate set. Our
experiments demonstrate the effectiveness of STraceBERT in retrieving the
source code compared to existing approaches. Our proposed approach offers a
promising solution to the problem of code retrieval in software reverse
engineering and opens up new avenues for further research in this area.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: The BLA Benchmark: Investigating Basic Language Abilities of Pre-Trained Multimodal Models
Abstract: Despite the impressive performance achieved by pre-trained
language-and-vision models in downstream tasks, it remains an open question
whether this reflects a proper understanding of image-text interaction. In this
work, we explore to what extent they handle basic linguistic constructions --
active-passive voice, coordination, and relative clauses -- that even preschool
children can typically master. We present BLA, a novel, automatically
constructed benchmark to evaluate multimodal models on these Basic Language
Abilities. We show that different types of Transformer-based systems, such as
CLIP, ViLBERT, and BLIP2, generally struggle with BLA in a zero-shot setting,
in line with previous findings. Our experiments, in particular, show that most
of the tested models only marginally benefit when fine-tuned or prompted with
construction-specific samples. Yet, the generative BLIP2 shows promising
trends, especially in an in-context learning setting. This opens the door to
using BLA not only as an evaluation benchmark but also to improve models' basic
language abilities.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Toward Scalable and Transparent Multimodal Analytics to Study Standard Medical Procedures: Linking Hand Movement, Proximity, and Gaze Data
Abstract: This study employed multimodal learning analytics (MMLA) to analyze
behavioral dynamics during the ABCDE procedure in nursing education, focusing
on gaze entropy, hand movement velocities, and proximity measures. Utilizing
accelerometers and eye-tracking techniques, behaviorgrams were generated to
depict various procedural phases. Results identified four primary phases
characterized by distinct patterns of visual attention, hand movements, and
proximity to the patient or instruments. The findings suggest that MMLA can
offer valuable insights into procedural competence in medical education. This
research underscores the potential of MMLA to provide detailed, objective
evaluations of clinical procedures and their inherent complexities.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Assessing the Impact of Noise on Quantum Neural Networks: An Experimental Analysis
Abstract: In the race towards quantum computing, the potential benefits of quantum
neural networks (QNNs) have become increasingly apparent. However, Noisy
Intermediate-Scale Quantum (NISQ) processors are prone to errors, which poses a
significant challenge for the execution of complex algorithms or quantum
machine learning. To ensure the quality and security of QNNs, it is crucial to
explore the impact of noise on their performance. This paper provides a
comprehensive analysis of the impact of noise on QNNs, examining the Mottonen
state preparation algorithm under various noise models and studying the
degradation of quantum states as they pass through multiple layers of QNNs.
Additionally, the paper evaluates the effect of noise on the performance of
pre-trained QNNs and highlights the challenges posed by noise models in quantum
computing. The findings of this study have significant implications for the
development of quantum software, emphasizing the importance of prioritizing
stability and noise-correction measures when developing QNNs to ensure reliable
and trustworthy results. This paper contributes to the growing body of
literature on quantum computing and quantum machine learning, providing new
insights into the impact of noise on QNNs and paving the way towards the
development of more robust and efficient quantum algorithms.

Output: Artificial Intelligence