Instruction: What field is the article from?

Prompt:
Title: Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue
Abstract: The emergence of large language models (LLMs) further improves the
capabilities of open-domain dialogue systems and can generate fluent, coherent,
and diverse responses. However, LLMs still lack an important ability:
communication skills, which makes them more like information seeking tools than
anthropomorphic chatbots. To make LLMs more anthropomorphic and proactive
during the conversation, we add five communication skills to the response
generation process: topic transition, proactively asking questions, concept
guidance, empathy, and summarising often. The addition of communication skills
increases the interest of users in the conversation and attracts them to chat
for longer. To enable LLMs better understand and use communication skills, we
design and add the inner monologue to LLMs. The complete process is achieved
through prompt engineering and in-context learning. To evaluate communication
skills, we construct a benchmark named Cskills for evaluating various
communication skills, which can also more comprehensively evaluate the dialogue
generation ability of the model. Experimental results show that the proposed
CSIM strategy improves the backbone models and outperforms the baselines in
both automatic and human evaluations.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Learn to Refuse: Making Large Language Models More Controllable and Reliable through Knowledge Scope Limitation and Refusal Mechanism
Abstract: Large language models (LLMs) have demonstrated impressive language
understanding and generation capabilities, enabling them to answer a wide range
of questions across various domains. However, these models are not flawless and
often produce responses that contain errors or misinformation. These
inaccuracies, commonly referred to as hallucinations, render LLMs unreliable
and even unusable in many scenarios. In this paper, our focus is on mitigating
the issue of hallucination in LLMs, particularly in the context of
question-answering. Instead of attempting to answer all questions, we explore a
refusal mechanism that instructs LLMs to refuse to answer challenging questions
in order to avoid errors. We then propose a simple yet effective solution
called Learn to Refuse (L2R), which incorporates the refusal mechanism to
enable LLMs to recognize and refuse to answer questions that they find
difficult to address. To achieve this, we utilize a structured knowledge base
to represent all the LLM's understanding of the world, enabling it to provide
traceable gold knowledge. This knowledge base is separate from the LLM and
initially empty, and it is progressively expanded with validated knowledge.
When an LLM encounters questions outside its domain, the system recognizes its
knowledge scope and determines whether it can answer the question
independently. Additionally, we introduce a method for automatically and
efficiently expanding the knowledge base of LLMs. Through qualitative and
quantitative analysis, we demonstrate that our approach enhances the
controllability and reliability of LLMs.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: A Unified Approach to Count-Based Weakly-Supervised Learning
Abstract: High-quality labels are often very scarce, whereas unlabeled data with
inferred weak labels occurs more naturally. In many cases, these weak labels
dictate the frequency of each respective class over a set of instances. In this
paper, we develop a unified approach to learning from such weakly-labeled data,
which we call count-based weakly-supervised learning. At the heart of our
approach is the ability to compute the probability of exactly k out of n
outputs being set to true. This computation is differentiable, exact, and
efficient. Building upon the previous computation, we derive a count loss
penalizing the model for deviations in its distribution from an arithmetic
constraint defined over label counts. We evaluate our approach on three common
weakly-supervised learning paradigms and observe that our proposed approach
achieves state-of-the-art or highly competitive results across all three of the
paradigms.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: In-Context Ability Transfer for Question Decomposition in Complex QA
Abstract: Answering complex questions is a challenging task that requires question
decomposition and multistep reasoning for arriving at the solution. While
existing supervised and unsupervised approaches are specialized to a certain
task and involve training, recently proposed prompt-based approaches offer
generalizable solutions to tackle a wide variety of complex question-answering
(QA) tasks. However, existing prompt-based approaches that are effective for
complex QA tasks involve expensive hand annotations from experts in the form of
rationales and are not generalizable to newer complex QA scenarios and tasks.
We propose, icat (In-Context Ability Transfer) which induces reasoning
capabilities in LLMs without any LLM fine-tuning or manual annotation of
in-context samples. We transfer the ability to decompose complex questions to
simpler questions or generate step-by-step rationales to LLMs, by careful
selection from available data sources of related tasks. We also propose an
automated uncertainty-aware exemplar selection approach for selecting examples
from transfer data sources. Finally, we conduct large-scale experiments on a
variety of complex QA tasks involving numerical reasoning, compositional
complex QA, and heterogeneous complex QA which require decomposed reasoning. We
show that ICAT convincingly outperforms existing prompt-based solutions without
involving any model training, showcasing the benefits of re-using existing
abilities.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Customize your NeRF: Adaptive Source Driven 3D Scene Editing via Local-Global Iterative Training
Abstract: In this paper, we target the adaptive source driven 3D scene editing task by
proposing a CustomNeRF model that unifies a text description or a reference
image as the editing prompt. However, obtaining desired editing results
conformed with the editing prompt is nontrivial since there exist two
significant challenges, including accurate editing of only foreground regions
and multi-view consistency given a single-view reference image. To tackle the
first challenge, we propose a Local-Global Iterative Editing (LGIE) training
scheme that alternates between foreground region editing and full-image
editing, aimed at foreground-only manipulation while preserving the background.
For the second challenge, we also design a class-guided regularization that
exploits class priors within the generation model to alleviate the
inconsistency problem among different views in image-driven editing. Extensive
experiments show that our CustomNeRF produces precise editing results under
various real scenes for both text- and image-driven settings.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: DSR-Diff: Depth Map Super-Resolution with Diffusion Model
Abstract: Color-guided depth map super-resolution (CDSR) improve the spatial resolution
of a low-quality depth map with the corresponding high-quality color map,
benefiting various applications such as 3D reconstruction, virtual reality, and
augmented reality. While conventional CDSR methods typically rely on
convolutional neural networks or transformers, diffusion models (DMs) have
demonstrated notable effectiveness in high-level vision tasks. In this work, we
present a novel CDSR paradigm that utilizes a diffusion model within the latent
space to generate guidance for depth map super-resolution. The proposed method
comprises a guidance generation network (GGN), a depth map super-resolution
network (DSRN), and a guidance recovery network (GRN). The GGN is specifically
designed to generate the guidance while managing its compactness. Additionally,
we integrate a simple but effective feature fusion module and a
transformer-style feature extraction module into the DSRN, enabling it to
leverage guided priors in the extraction, fusion, and reconstruction of
multi-model images. Taking into account both accuracy and efficiency, our
proposed method has shown superior performance in extensive experiments when
compared to state-of-the-art methods. Our codes will be made available at
https://github.com/shiyuan7/DSR-Diff.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: AFPQ: Asymmetric Floating Point Quantization for LLMs
Abstract: Large language models (LLMs) show great performance in various tasks, but
face deployment challenges from limited memory capacity and bandwidth. Low-bit
weight quantization can save memory and accelerate inference. Although
floating-point (FP) formats show good performance in LLM quantization, they
tend to perform poorly with small group sizes or sub-4 bits. We find the reason
is that the absence of asymmetry in previous FP quantization makes it
unsuitable for handling asymmetric value distribution of LLM weight tensors. In
this work, we propose asymmetric FP quantization (AFPQ), which sets separate
scales for positive and negative values. Our method leads to large accuracy
improvements and can be easily plugged into other quantization methods,
including GPTQ and AWQ, for better performance. Besides, no additional storage
is needed compared with asymmetric integer (INT) quantization. The code is
available at https://github.com/zhangsichengsjtu/AFPQ.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Keeping Users Engaged During Repeated Administration of the Same Questionnaire: Using Large Language Models to Reliably Diversify Questions
Abstract: Standardized, validated questionnaires are vital tools in HCI research and
healthcare, offering dependable self-report data. However, their repeated use
in longitudinal or pre-post studies can induce respondent fatigue, impacting
data quality via response biases and decreased response rates. We propose
utilizing large language models (LLMs) to generate diverse questionnaire
versions while retaining good psychometric properties. In a longitudinal study,
participants engaged with our agent system and responded daily for two weeks to
either a standardized depression questionnaire or one of two LLM-generated
questionnaire variants, alongside a validated depression questionnaire.
Psychometric testing revealed consistent covariation between the external
criterion and the focal measure administered across the three conditions,
demonstrating the reliability and validity of the LLM-generated variants.
Participants found the repeated administration of the standardized
questionnaire significantly more repetitive compared to the variants. Our
findings highlight the potential of LLM-generated variants to invigorate
questionnaires, fostering engagement and interest without compromising
validity.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Robust Domain Misinformation Detection via Multi-modal Feature Alignment
Abstract: Social media misinformation harms individuals and societies and is
potentialized by fast-growing multi-modal content (i.e., texts and images),
which accounts for higher "credibility" than text-only news pieces. Although
existing supervised misinformation detection methods have obtained acceptable
performances in key setups, they may require large amounts of labeled data from
various events, which can be time-consuming and tedious. In turn, directly
training a model by leveraging a publicly available dataset may fail to
generalize due to domain shifts between the training data (a.k.a. source
domains) and the data from target domains. Most prior work on domain shift
focuses on a single modality (e.g., text modality) and ignores the scenario
where sufficient unlabeled target domain data may not be readily available in
an early stage. The lack of data often happens due to the dynamic propagation
trend (i.e., the number of posts related to fake news increases slowly before
catching the public attention). We propose a novel robust domain and
cross-modal approach (\textbf{RDCM}) for multi-modal misinformation detection.
It reduces the domain shift by aligning the joint distribution of textual and
visual modalities through an inter-domain alignment module and bridges the
semantic gap between both modalities through a cross-modality alignment module.
We also propose a framework that simultaneously considers application scenarios
of domain generalization (in which the target domain data is unavailable) and
domain adaptation (in which unlabeled target domain data is available).
Evaluation results on two public multi-modal misinformation detection datasets
(Pheme and Twitter Datasets) evince the superiority of the proposed model. The
formal implementation of this paper can be found in this link:
https://github.com/less-and-less-bugs/RDCM

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Making Data Work Count
Abstract: In this paper, we examine the work of data annotation. Specifically, we focus
on the role of counting or quantification in organising annotation work. Based
on an ethnographic study of data annotation in two outsourcing centres in
India, we observe that counting practices and its associated logics are an
integral part of day-to-day annotation activities. In particular, we call
attention to the presumption of total countability observed in annotation - the
notion that everything, from tasks, datasets and deliverables, to workers, work
time, quality and performance, can be managed by applying the logics of
counting. To examine this, we draw on sociological and socio-technical
scholarship on quantification and develop the lens of a 'regime of counting'
that makes explicit the specific counts, practices, actors and structures that
underpin the pervasive counting in annotation. We find that within the AI
supply chain and data work, counting regimes aid the assertion of authority by
the AI clients (also called requesters) over annotation processes, constituting
them as reductive, standardised, and homogenous. We illustrate how this has
implications for i) how annotation work and workers get valued, ii) the role
human discretion plays in annotation, and iii) broader efforts to introduce
accountable and more just practices in AI. Through these implications, we
illustrate the limits of operating within the logic of total countability.
Instead, we argue for a view of counting as partial - located in distinct
geographies, shaped by specific interests and accountable in only limited ways.
This, we propose, sets the stage for a fundamentally different orientation to
counting and what counts in data annotation.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Closed Drafting as a Case Study for First-Principle Interpretability, Memory, and Generalizability in Deep Reinforcement Learning
Abstract: Closed drafting or "pick and pass" is a popular game mechanic where each
round players select a card or other playable element from their hand and pass
the rest to the next player. In this paper, we establish first-principle
methods for studying the interpretability, generalizability, and memory of Deep
Q-Network (DQN) models playing closed drafting games. In particular, we use a
popular family of closed drafting games called "Sushi Go Party", in which we
achieve state-of-the-art performance. We fit decision rules to interpret the
decision-making strategy of trained DRL agents by comparing them to the ranking
preferences of different types of human players. As Sushi Go Party can be
expressed as a set of closely-related games based on the set of cards in play,
we quantify the generalizability of DRL models trained on various sets of
cards, establishing a method to benchmark agent performance as a function of
environment unfamiliarity. Using the explicitly calculable memory of other
player's hands in closed drafting games, we create measures of the ability of
DRL models to learn memory.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Breaking Boundaries: Balancing Performance and Robustness in Deep Wireless Traffic Forecasting
Abstract: Balancing the trade-off between accuracy and robustness is a long-standing
challenge in time series forecasting. While most of existing robust algorithms
have achieved certain suboptimal performance on clean data, sustaining the same
performance level in the presence of data perturbations remains extremely hard.
In this paper, we study a wide array of perturbation scenarios and propose
novel defense mechanisms against adversarial attacks using real-world telecom
data. We compare our strategy against two existing adversarial training
algorithms under a range of maximal allowed perturbations, defined using
$\ell_{\infty}$-norm, $\in [0.1,0.4]$. Our findings reveal that our hybrid
strategy, which is composed of a classifier to detect adversarial examples, a
denoiser to eliminate noise from the perturbed data samples, and a standard
forecaster, achieves the best performance on both clean and perturbed data. Our
optimal model can retain up to $92.02\%$ the performance of the original
forecasting model in terms of Mean Squared Error (MSE) on clean data, while
being more robust than the standard adversarially trained models on perturbed
data. Its MSE is 2.71$\times$ and 2.51$\times$ lower than those of comparing
methods on normal and perturbed data, respectively. In addition, the components
of our models can be trained in parallel, resulting in better computational
efficiency. Our results indicate that we can optimally balance the trade-off
between the performance and robustness of forecasting models by improving the
classifier and denoiser, even in the presence of sophisticated and destructive
poisoning attacks.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Foveation in the Era of Deep Learning
Abstract: In this paper, we tackle the challenge of actively attending to visual scenes
using a foveated sensor. We introduce an end-to-end differentiable foveated
active vision architecture that leverages a graph convolutional network to
process foveated images, and a simple yet effective formulation for foveated
image sampling. Our model learns to iteratively attend to regions of the image
relevant for classification. We conduct detailed experiments on a variety of
image datasets, comparing the performance of our method with previous
approaches to foveated vision while measuring how the impact of different
choices, such as the degree of foveation, and the number of fixations the
network performs, affect object recognition performance. We find that our model
outperforms a state-of-the-art CNN and foveated vision architectures of
comparable parameters and a given pixel or computation budget

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: AI-Generated Images Introduce Invisible Relevance Bias to Text-Image Retrieval
Abstract: With the advancement of generation models, AI-generated content (AIGC) is
becoming more realistic, flooding the Internet. A recent study suggests that
this phenomenon has elevated the issue of source bias in text retrieval for web
searches. Specifically, neural retrieval models tend to rank generated texts
higher than human-written texts. In this paper, we extend the study of this
bias to cross-modal retrieval. Firstly, we successfully construct a suitable
benchmark to explore the existence of the bias. Subsequent extensive
experiments on this benchmark reveal that AI-generated images introduce an
invisible relevance bias to text-image retrieval models. Specifically, our
experiments show that text-image retrieval models tend to rank the AI-generated
images higher than the real images, even though the AI-generated images do not
exhibit more visually relevant features to the query than real images. This
invisible relevance bias is prevalent across retrieval models with varying
training data and architectures. Furthermore, our subsequent exploration
reveals that the inclusion of AI-generated images in the training data of the
retrieval models exacerbates the invisible relevance bias. The above phenomenon
triggers a vicious cycle, which makes the invisible relevance bias become more
and more serious. To elucidate the potential causes of invisible relevance and
address the aforementioned issues, we introduce an effective training method
aimed at alleviating the invisible relevance bias. Subsequently, we apply our
proposed debiasing method to retroactively identify the causes of invisible
relevance, revealing that the AI-generated images induce the image encoder to
embed additional information into their representation. This information
exhibits a certain consistency across generated images with different semantics
and can make the retriever estimate a higher relevance score.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: Cross-Domain Robustness of Transformer-based Keyphrase Generation
Abstract: Modern models for text generation show state-of-the-art results in many
natural language processing tasks. In this work, we explore the effectiveness
of abstractive text summarization models for keyphrase selection. A list of
keyphrases is an important element of a text in databases and repositories of
electronic documents. In our experiments, abstractive text summarization models
fine-tuned for keyphrase generation show quite high results for a target text
corpus. However, in most cases, the zero-shot performance on other corpora and
domains is significantly lower. We investigate cross-domain limitations of
abstractive text summarization models for keyphrase generation. We present an
evaluation of the fine-tuned BART models for the keyphrase selection task
across six benchmark corpora for keyphrase extraction including scientific
texts from two domains and news texts. We explore the role of transfer learning
between different domains to improve the BART model performance on small text
corpora. Our experiments show that preliminary fine-tuning on out-of-domain
corpora can be effective under conditions of a limited number of samples.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: SCOPE-RL: A Python Library for Offline Reinforcement Learning and Off-Policy Evaluation
Abstract: This paper introduces SCOPE-RL, a comprehensive open-source Python software
designed for offline reinforcement learning (offline RL), off-policy evaluation
(OPE), and selection (OPS). Unlike most existing libraries that focus solely on
either policy learning or evaluation, SCOPE-RL seamlessly integrates these two
key aspects, facilitating flexible and complete implementations of both offline
RL and OPE processes. SCOPE-RL put particular emphasis on its OPE modules,
offering a range of OPE estimators and robust evaluation-of-OPE protocols. This
approach enables more in-depth and reliable OPE compared to other packages. For
instance, SCOPE-RL enhances OPE by estimating the entire reward distribution
under a policy rather than its mere point-wise expected value. Additionally,
SCOPE-RL provides a more thorough evaluation-of-OPE by presenting the
risk-return tradeoff in OPE results, extending beyond mere accuracy evaluations
in existing OPE literature. SCOPE-RL is designed with user accessibility in
mind. Its user-friendly APIs, comprehensive documentation, and a variety of
easy-to-follow examples assist researchers and practitioners in efficiently
implementing and experimenting with various offline RL methods and OPE
estimators, tailored to their specific problem contexts. The documentation of
SCOPE-RL is available at https://scope-rl.readthedocs.io/en/latest/.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Enhancing Lightweight Neural Networks for Small Object Detection in IoT Applications
Abstract: Advances in lightweight neural networks have revolutionized computer vision
in a broad range of IoT applications, encompassing remote monitoring and
process automation. However, the detection of small objects, which is crucial
for many of these applications, remains an underexplored area in current
computer vision research, particularly for embedded devices. To address this
gap, the paper proposes a novel adaptive tiling method that can be used on top
of any existing object detector including the popular FOMO network for object
detection on microcontrollers. Our experimental results show that the proposed
tiling method can boost the F1-score by up to 225% while reducing the average
object count error by up to 76%. Furthermore, the findings of this work suggest
that using a soft F1 loss over the popular binary cross-entropy loss can
significantly reduce the negative impact of imbalanced data. Finally, we
validate our approach by conducting experiments on the Sony Spresense
microcontroller, showcasing the proposed method's ability to strike a balance
between detection performance, low latency, and minimal memory consumption.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Jellyfish: A Large Language Model for Data Preprocessing
Abstract: In this paper, we present Jellyfish, an open-source LLM as a universal task
solver for DP. Built on the Llama 2 13B model, Jellyfish is instruction-tuned
with the datasets of several typical DP tasks including error detection, data
imputation, schema matching, and entity matching, and delivers generalizability
to other tasks. Remarkably, Jellyfish can operate on a local, single, and
low-priced GPU with its 13 billion parameters, ensuring data security and
enabling further tuning. Its proficiency in understanding natural language
allows users to manually craft instructions for DP tasks. Unlike many existing
methods that heavily rely on prior knowledge, Jellyfish acquires domain
knowledge during its tuning process and integrates optional knowledge injection
during inference. A distinctive feature of Jellyfish is its interpreter, which
elucidates its output decisions. To construct Jellyfish, we develop a series of
pre-tuning and DP-tuning techniques. Jellyfish is equipped with an instance
serializer, which automatically translates raw data into model prompts, and a
knowledge injector, which optionally introduces task- and dataset-specific
knowledge to enhance DP performance. Our evaluation of Jellyfish, using a range
of real datasets, shows its competitiveness compared to state-of-the-art
methods and its strong generalizability to unseen tasks. Jellyfish's
performance rivals that of GPT series models, and its interpreter offers
enhanced reasoning capabilities compared to GPT-3.5. Furthermore, our
evaluation highlights the effectiveness of the techniques employed in
constructing Jellyfish. Our model is available at Hugging Face:
https://huggingface.co/NECOUDBFM/Jellyfish .

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: FormalGeo: The First Step Toward Human-like IMO-level Geometric Automated Reasoning
Abstract: This is the first paper in a series of work we have accomplished over the
past three years. In this paper, we have constructed a consistent formal plane
geometry system. This will serve as a crucial bridge between IMO-level plane
geometry challenges and readable AI automated reasoning. Within this formal
framework, we have been able to seamlessly integrate modern AI models with our
formal system. AI is now capable of providing deductive reasoning solutions to
IMO-level plane geometry problems, just like handling other natural languages,
and these proofs are readable, traceable, and verifiable. We propose the
geometry formalization theory (GFT) to guide the development of the geometry
formal system. Based on the GFT, we have established the FormalGeo, which
consists of 88 geometric predicates and 196 theorems. It can represent,
validate, and solve IMO-level geometry problems. we also have crafted the FGPS
(formal geometry problem solver) in Python. It serves as both an interactive
assistant for verifying problem-solving processes and an automated problem
solver. We've annotated the formalgeo7k and formalgeo-imo datasets. The former
contains 6,981 (expand to 133,818 through data augmentation) geometry problems,
while the latter includes 18 (expand to 2,627 and continuously increasing)
IMO-level challenging geometry problems. All annotated problems include
detailed formal language descriptions and solutions. Implementation of the
formal system and experiments validate the correctness and utility of the GFT.
The backward depth-first search method only yields a 2.42% problem-solving
failure rate, and we can incorporate deep learning techniques to achieve lower
one. The source code of FGPS and datasets are available at
https://github.com/BitSecret/FGPS.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Guided Flows for Generative Modeling and Decision Making
Abstract: Classifier-free guidance is a key component for enhancing the performance of
conditional generative models across diverse tasks. While it has previously
demonstrated remarkable improvements for the sample quality, it has only been
exclusively employed for diffusion models. In this paper, we integrate
classifier-free guidance into Flow Matching (FM) models, an alternative
simulation-free approach that trains Continuous Normalizing Flows (CNFs) based
on regressing vector fields. We explore the usage of \emph{Guided Flows} for a
variety of downstream applications. We show that Guided Flows significantly
improves the sample quality in conditional image generation and zero-shot
text-to-speech synthesis, boasting state-of-the-art performance. Notably, we
are the first to apply flow models for plan generation in the offline
reinforcement learning setting, showcasing a 10x speedup in computation
compared to diffusion models while maintaining comparable performance.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: MetaReVision: Meta-Learning with Retrieval for Visually Grounded Compositional Concept Acquisition
Abstract: Humans have the ability to learn novel compositional concepts by recalling
and generalizing primitive concepts acquired from past experiences. Inspired by
this observation, in this paper, we propose MetaReVision, a retrieval-enhanced
meta-learning model to address the visually grounded compositional concept
learning problem. The proposed MetaReVision consists of a retrieval module and
a meta-learning module which are designed to incorporate retrieved primitive
concepts as a supporting set to meta-train vision-anguage models for grounded
compositional concept recognition. Through meta-learning from episodes
constructed by the retriever, MetaReVision learns a generic compositional
representation that can be fast updated to recognize novel compositional
concepts. We create CompCOCO and CompFlickr to benchmark the grounded
compositional concept learning. Our experimental results show that MetaReVision
outperforms other competitive baselines and the retrieval module plays an
important role in this compositional learning process.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation
Abstract: Chain-of-Thought (CoT) guides large language models (LLMs) to reason
step-by-step, and can motivate their logical reasoning ability. While effective
for logical tasks, CoT is not conducive to creative problem-solving which often
requires out-of-box thoughts and is crucial for innovation advancements. In
this paper, we explore the Leap-of-Thought (LoT) abilities within LLMs -- a
non-sequential, creative paradigm involving strong associations and knowledge
leaps. To this end, we study LLMs on the popular Oogiri game which needs
participants to have good creativity and strong associative thinking for
responding unexpectedly and humorously to the given image, text, or both, and
thus is suitable for LoT study. Then to investigate LLMs' LoT ability in the
Oogiri game, we first build a multimodal and multilingual Oogiri-GO dataset
which contains over 130,000 samples from the Oogiri game, and observe the
insufficient LoT ability or failures of most existing LLMs on the Oogiri game.
Accordingly, we introduce a creative Leap-of-Thought (CLoT) paradigm to improve
LLM's LoT ability. CLoT first formulates the Oogiri-GO dataset into
LoT-oriented instruction tuning data to train pretrained LLM for achieving
certain LoT humor generation and discrimination abilities. Then CLoT designs an
explorative self-refinement that encourages the LLM to generate more creative
LoT data via exploring parallels between seemingly unrelated concepts and
selects high-quality data to train itself for self-refinement. CLoT not only
excels in humor generation in the Oogiri game but also boosts creative
abilities in various tasks like cloud guessing game and divergent association
task. These findings advance our understanding and offer a pathway to improve
LLMs' creative capacities for innovative applications across domains. The
dataset, code, and models will be released online.
https://zhongshsh.github.io/CLoT/.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Designing Long-term Group Fair Policies in Dynamical Systems
Abstract: Neglecting the effect that decisions have on individuals (and thus, on the
underlying data distribution) when designing algorithmic decision-making
policies may increase inequalities and unfairness in the long term - even if
fairness considerations were taken in the policy design process. In this paper,
we propose a novel framework for achieving long-term group fairness in
dynamical systems, in which current decisions may affect an individual's
features in the next step, and thus, future decisions. Specifically, our
framework allows us to identify a time-independent policy that converges, if
deployed, to the targeted fair stationary state of the system in the long term,
independently of the initial data distribution. We model the system dynamics
with a time-homogeneous Markov chain and optimize the policy leveraging the
Markov chain convergence theorem to ensure unique convergence. We provide
examples of different targeted fair states of the system, encompassing a range
of long-term goals for society and policymakers. Furthermore, we show how our
approach facilitates the evaluation of different long-term targets by examining
their impact on the group-conditional population distribution in the long term
and how it evolves until convergence.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedback
Abstract: Ideally, we would place a robot in a real-world environment and leave it
there improving on its own by gathering more experience autonomously. However,
algorithms for autonomous robotic learning have been challenging to realize in
the real world. While this has often been attributed to the challenge of sample
complexity, even sample-efficient techniques are hampered by two major
challenges - the difficulty of providing well "shaped" rewards, and the
difficulty of continual reset-free training. In this work, we describe a system
for real-world reinforcement learning that enables agents to show continual
improvement by training directly in the real world without requiring
painstaking effort to hand-design reward functions or reset mechanisms. Our
system leverages occasional non-expert human-in-the-loop feedback from remote
users to learn informative distance functions to guide exploration while
leveraging a simple self-supervised learning algorithm for goal-directed policy
learning. We show that in the absence of resets, it is particularly important
to account for the current "reachability" of the exploration policy when
deciding which regions of the space to explore. Based on this insight, we
instantiate a practical learning system - GEAR, which enables robots to simply
be placed in real-world environments and left to train autonomously without
interruption. The system streams robot experience to a web interface only
requiring occasional asynchronous feedback from remote, crowdsourced,
non-expert humans in the form of binary comparative feedback. We evaluate this
system on a suite of robotic tasks in simulation and demonstrate its
effectiveness at learning behaviors both in simulation and the real world.
Project website https://guided-exploration-autonomous-rl.github.io/GEAR/.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Multi-State Brain Network Discovery
Abstract: Brain network discovery aims to find nodes and edges from the spatio-temporal
signals obtained by neuroimaging data, such as fMRI scans of human brains.
Existing methods tend to derive representative or average brain networks,
assuming observed signals are generated by only a single brain activity state.
However, the human brain usually involves multiple activity states, which
jointly determine the brain activities. The brain regions and their
connectivity usually exhibit intricate patterns that are difficult to capture
with only a single-state network. Recent studies find that brain parcellation
and connectivity change according to the brain activity state. We refer to such
brain networks as multi-state, and this mixture can help us understand human
behavior. Thus, compared to a single-state network, a multi-state network can
prevent us from losing crucial information of cognitive brain network. To
achieve this, we propose a new model called MNGL (Multi-state Network Graphical
Lasso), which successfully models multi-state brain networks by combining CGL
(coherent graphical lasso) with GMM (Gaussian Mixture Model). Using both
synthetic and real world ADHD 200 fMRI datasets, we demonstrate that MNGL
outperforms recent state-of-the-art alternatives by discovering more
explanatory and realistic results.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Safe Reinforcement Learning in Tensor Reproducing Kernel Hilbert Space
Abstract: This paper delves into the problem of safe reinforcement learning (RL) in a
partially observable environment with the aim of achieving safe-reachability
objectives. In traditional partially observable Markov decision processes
(POMDP), ensuring safety typically involves estimating the belief in latent
states. However, accurately estimating an optimal Bayesian filter in POMDP to
infer latent states from observations in a continuous state space poses a
significant challenge, largely due to the intractable likelihood. To tackle
this issue, we propose a stochastic model-based approach that guarantees RL
safety almost surely in the face of unknown system dynamics and partial
observation environments. We leveraged the Predictive State Representation
(PSR) and Reproducing Kernel Hilbert Space (RKHS) to represent future
multi-step observations analytically, and the results in this context are
provable. Furthermore, we derived essential operators from the kernel Bayes'
rule, enabling the recursive estimation of future observations using various
operators. Under the assumption of \textit{undercompleness}, a polynomial
sample complexity is established for the RL algorithm for the infinite size of
observation and action spaces, ensuring an $\epsilon-$suboptimal safe policy
guarantee.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: TIAGo RL: Simulated Reinforcement Learning Environments with Tactile Data for Mobile Robots
Abstract: Tactile information is important for robust performance in robotic tasks that
involve physical interaction, such as object manipulation. However, with more
data included in the reasoning and control process, modeling behavior becomes
increasingly difficult. Deep Reinforcement Learning (DRL) produced promising
results for learning complex behavior in various domains, including
tactile-based manipulation in robotics. In this work, we present our
open-source reinforcement learning environments for the TIAGo service robot.
They produce tactile sensor measurements that resemble those of a real
sensorised gripper for TIAGo, encouraging research in transfer learning of DRL
policies. Lastly, we show preliminary training results of a learned force
control policy and compare it to a classical PI controller.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Generalized Contrastive Divergence: Joint Training of Energy-Based Model and Diffusion Model through Inverse Reinforcement Learning
Abstract: We present Generalized Contrastive Divergence (GCD), a novel objective
function for training an energy-based model (EBM) and a sampler simultaneously.
GCD generalizes Contrastive Divergence (Hinton, 2002), a celebrated algorithm
for training EBM, by replacing Markov Chain Monte Carlo (MCMC) distribution
with a trainable sampler, such as a diffusion model. In GCD, the joint training
of EBM and a diffusion model is formulated as a minimax problem, which reaches
an equilibrium when both models converge to the data distribution. The minimax
learning with GCD bears interesting equivalence to inverse reinforcement
learning, where the energy corresponds to a negative reward, the diffusion
model is a policy, and the real data is expert demonstrations. We present
preliminary yet promising results showing that joint training is beneficial for
both EBM and a diffusion model. GCD enables EBM training without MCMC while
improving the sample quality of a diffusion model.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: StochGradAdam: Accelerating Neural Networks Training with Stochastic Gradient Sampling
Abstract: In the rapidly advancing domain of deep learning optimization, this paper
unveils the StochGradAdam optimizer, a novel adaptation of the well-regarded
Adam algorithm. Central to StochGradAdam is its gradient sampling technique.
This method not only ensures stable convergence but also leverages the
advantages of selective gradient consideration, fostering robust training by
potentially mitigating the effects of noisy or outlier data and enhancing the
exploration of the loss landscape for more dependable convergence. In both
image classification and segmentation tasks, StochGradAdam has demonstrated
superior performance compared to the traditional Adam optimizer. By judiciously
sampling a subset of gradients at each iteration, the optimizer is optimized
for managing intricate models. The paper provides a comprehensive exploration
of StochGradAdam's methodology, from its mathematical foundations to bias
correction strategies, heralding a promising advancement in deep learning
training techniques.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Technical Report on the Learning of Case Relevance in Case-Based Reasoning with Abstract Argumentation
Abstract: Case-based reasoning is known to play an important role in several legal
settings. In this paper we focus on a recent approach to case-based reasoning,
supported by an instantiation of abstract argumentation whereby arguments
represent cases and attack between arguments results from outcome disagreement
between cases and a notion of relevance. In this context, relevance is
connected to a form of specificity among cases. We explore how relevance can be
learnt automatically in practice with the help of decision trees, and explore
the combination of case-based reasoning with abstract argumentation (AA-CBR)
and learning of case relevance for prediction in legal settings. Specifically,
we show that, for two legal datasets, AA-CBR and decision-tree-based learning
of case relevance perform competitively in comparison with decision trees. We
also show that AA-CBR with decision-tree-based learning of case relevance
results in a more compact representation than their decision tree counterparts,
which could be beneficial for obtaining cognitively tractable explanations.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Weakly-supervised Deep Cognate Detection Framework for Low-Resourced Languages Using Morphological Knowledge of Closely-Related Languages
Abstract: Exploiting cognates for transfer learning in under-resourced languages is an
exciting opportunity for language understanding tasks, including unsupervised
machine translation, named entity recognition and information retrieval.
Previous approaches mainly focused on supervised cognate detection tasks based
on orthographic, phonetic or state-of-the-art contextual language models, which
under-perform for most under-resourced languages. This paper proposes a novel
language-agnostic weakly-supervised deep cognate detection framework for
under-resourced languages using morphological knowledge from closely related
languages. We train an encoder to gain morphological knowledge of a language
and transfer the knowledge to perform unsupervised and weakly-supervised
cognate detection tasks with and without the pivot language for the
closely-related languages. While unsupervised, it overcomes the need for
hand-crafted annotation of cognates. We performed experiments on different
published cognate detection datasets across language families and observed not
only significant improvement over the state-of-the-art but also our method
outperformed the state-of-the-art supervised and unsupervised methods. Our
model can be extended to a wide range of languages from any language family as
it overcomes the requirement of the annotation of the cognate pairs for
training. The code and dataset building scripts can be found at
https://github.com/koustavagoswami/Weakly_supervised-Cognate_Detection

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Proving Conjectures Acquired by Composing Multiple Biases
Abstract: We present the proofs of the conjectures mentioned in the paper published in
the proceedings of the 2024 AAAI conference [1], and discovered by the
decomposition methods presented in the same paper.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated Learning
Abstract: The membership inference attack (MIA) is a popular paradigm for compromising
the privacy of a machine learning (ML) model. MIA exploits the natural
inclination of ML models to overfit upon the training data. MIAs are trained to
distinguish between training and testing prediction confidence to infer
membership information. Federated Learning (FL) is a privacy-preserving ML
paradigm that enables multiple clients to train a unified model without
disclosing their private data. In this paper, we propose an enhanced Membership
Inference Attack with the Batch-wise generated Attack Dataset (MIA-BAD), a
modification to the MIA approach. We investigate that the MIA is more accurate
when the attack dataset is generated batch-wise. This quantitatively decreases
the attack dataset while qualitatively improving it. We show how training an ML
model through FL, has some distinct advantages and investigate how the threat
introduced with the proposed MIA-BAD approach can be mitigated with FL
approaches. Finally, we demonstrate the qualitative effects of the proposed
MIA-BAD methodology by conducting extensive experiments with various target
datasets, variable numbers of federated clients, and training batch sizes.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Unifying Structure and Language Semantic for Efficient Contrastive Knowledge Graph Completion with Structured Entity Anchors
Abstract: The goal of knowledge graph completion (KGC) is to predict missing links in a
KG using trained facts that are already known. In recent, pre-trained language
model (PLM) based methods that utilize both textual and structural information
are emerging, but their performances lag behind state-of-the-art (SOTA)
structure-based methods or some methods lose their inductive inference
capabilities in the process of fusing structure embedding to text encoder. In
this paper, we propose a novel method to effectively unify structure
information and language semantics without losing the power of inductive
reasoning. We adopt entity anchors and these anchors and textual description of
KG elements are fed together into the PLM-based encoder to learn unified
representations. In addition, the proposed method utilizes additional random
negative samples which can be reused in the each mini-batch during contrastive
learning to learn a generalized entity representations. We verify the
effectiveness of the our proposed method through various experiments and
analysis. The experimental results on standard benchmark widely used in link
prediction task show that the proposed model outperforms existing the SOTA KGC
models. Especially, our method show the largest performance improvement on
FB15K-237, which is competitive to the SOTA of structure-based KGC methods.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Aiming to Minimize Alcohol-Impaired Road Fatalities: Utilizing Fairness-Aware and Domain Knowledge-Infused Artificial Intelligence
Abstract: Approximately 30% of all traffic fatalities in the United States are
attributed to alcohol-impaired driving. This means that, despite stringent laws
against this offense in every state, the frequency of drunk driving accidents
is alarming, resulting in approximately one person being killed every 45
minutes. The process of charging individuals with Driving Under the Influence
(DUI) is intricate and can sometimes be subjective, involving multiple stages
such as observing the vehicle in motion, interacting with the driver, and
conducting Standardized Field Sobriety Tests (SFSTs). Biases have been observed
through racial profiling, leading to some groups and geographical areas facing
fewer DUI tests, resulting in many actual DUI incidents going undetected,
ultimately leading to a higher number of fatalities. To tackle this issue, our
research introduces an Artificial Intelligence-based predictor that is both
fairness-aware and incorporates domain knowledge to analyze DUI-related
fatalities in different geographic locations. Through this model, we gain
intriguing insights into the interplay between various demographic groups,
including age, race, and income. By utilizing the provided information to
allocate policing resources in a more equitable and efficient manner, there is
potential to reduce DUI-related fatalities and have a significant impact on
road safety.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: BELT: Old-School Backdoor Attacks can Evade the State-of-the-Art Defense with Backdoor Exclusivity Lifting
Abstract: Deep neural networks (DNNs) are susceptible to backdoor attacks, where
malicious functionality is embedded to allow attackers to trigger incorrect
classifications. Old-school backdoor attacks use strong trigger features that
can easily be learned by victim models. Despite robustness against input
variation, the robustness however increases the likelihood of unintentional
trigger activations. This leaves traces to existing defenses, which find
approximate replacements for the original triggers that can activate the
backdoor without being identical to the original trigger via, e.g., reverse
engineering and sample overlay.
  In this paper, we propose and investigate a new characteristic of backdoor
attacks, namely, backdoor exclusivity, which measures the ability of backdoor
triggers to remain effective in the presence of input variation. Building upon
the concept of backdoor exclusivity, we propose Backdoor Exclusivity LifTing
(BELT), a novel technique which suppresses the association between the backdoor
and fuzzy triggers to enhance backdoor exclusivity for defense evasion.
Extensive evaluation on three popular backdoor benchmarks validate, our
approach substantially enhances the stealthiness of four old-school backdoor
attacks, which, after backdoor exclusivity lifting, is able to evade six
state-of-the-art backdoor countermeasures, at almost no cost of the attack
success rate and normal utility. For example, one of the earliest backdoor
attacks BadNet, enhanced by BELT, evades most of the state-of-the-art defenses
including ABS and MOTH which would otherwise recognize the backdoored model.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: EHRTutor: Enhancing Patient Understanding of Discharge Instructions
Abstract: Large language models have shown success as a tutor in education in various
fields. Educating patients about their clinical visits plays a pivotal role in
patients' adherence to their treatment plans post-discharge. This paper
presents EHRTutor, an innovative multi-component framework leveraging the Large
Language Model (LLM) for patient education through conversational
question-answering. EHRTutor first formulates questions pertaining to the
electronic health record discharge instructions. It then educates the patient
through conversation by administering each question as a test. Finally, it
generates a summary at the end of the conversation. Evaluation results using
LLMs and domain experts have shown a clear preference for EHRTutor over the
baseline. Moreover, EHRTutor also offers a framework for generating synthetic
patient education dialogues that can be used for future in-house system
training.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: ChatTraffic: Text-to-Traffic Generation via Diffusion Model
Abstract: Traffic prediction is one of the most significant foundations in Intelligent
Transportation Systems (ITS). Traditional traffic prediction methods rely only
on historical traffic data to predict traffic trends and face two main
challenges. 1) insensitivity to unusual events. 2) poor performance in
long-term prediction. In this work, we explore how generative models combined
with text describing the traffic system can be applied for traffic generation
and name the task Text-to-Traffic Generation (TTG). The key challenge of the
TTG task is how to associate text with the spatial structure of the road
network and traffic data for generating traffic situations. To this end, we
propose ChatTraffic, the first diffusion model for text-to-traffic generation.
To guarantee the consistency between synthetic and real data, we augment a
diffusion model with the Graph Convolutional Network (GCN) to extract spatial
correlations of traffic data. In addition, we construct a large dataset
containing text-traffic pairs for the TTG task. We benchmarked our model
qualitatively and quantitatively on the released dataset. The experimental
results indicate that ChatTraffic can generate realistic traffic situations
from the text. Our code and dataset are available at
https://github.com/ChyaZhang/ChatTraffic.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Text Representation Distillation via Information Bottleneck Principle
Abstract: Pre-trained language models (PLMs) have recently shown great success in text
representation field. However, the high computational cost and high-dimensional
representation of PLMs pose significant challenges for practical applications.
To make models more accessible, an effective method is to distill large models
into smaller representation models. In order to relieve the issue of
performance degradation after distillation, we propose a novel Knowledge
Distillation method called IBKD. This approach is motivated by the Information
Bottleneck principle and aims to maximize the mutual information between the
final representation of the teacher and student model, while simultaneously
reducing the mutual information between the student model's representation and
the input data. This enables the student model to preserve important learned
information while avoiding unnecessary information, thus reducing the risk of
over-fitting. Empirical studies on two main downstream applications of text
representation (Semantic Textual Similarity and Dense Retrieval tasks)
demonstrate the effectiveness of our proposed approach.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: ML-Bench: Large Language Models Leverage Open-source Libraries for Machine Learning Tasks
Abstract: Large language models have shown promising performance in code generation
benchmarks. However, a considerable divide exists between these benchmark
achievements and their practical applicability, primarily attributed to
real-world programming's reliance on pre-existing libraries. Instead of
evaluating LLMs to code from scratch, this work aims to propose a new
evaluation setup where LLMs use open-source libraries to finish machine
learning tasks. Therefore, we propose ML-Bench, an expansive benchmark
developed to assess the effectiveness of LLMs in leveraging existing functions
in open-source libraries. Consisting of 10044 samples spanning 130 tasks over
14 notable machine learning GitHub repositories. In this setting, given a
specific machine learning task instruction and the accompanying README in a
codebase, an LLM is tasked to generate code to accomplish the task. This
necessitates the comprehension of long and language-code interleaved documents,
as well as the understanding of complex cross-file code structures, introducing
new challenges. Notably, while GPT-4 exhibits remarkable improvement over other
LLMs, it manages to accomplish only 39.73\% of the tasks, leaving a huge space
for improvement. We address these challenges by proposing ML-Agent, designed to
effectively navigate the codebase, locate documentation, retrieve code, and
generate executable code. Empirical results demonstrate that ML-Agent, built
upon GPT-4, results in further improvements. Code, data, and models are
available at \url{https://ml-bench.github.io/}.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: CL-MASR: A Continual Learning Benchmark for Multilingual ASR
Abstract: Modern multilingual automatic speech recognition (ASR) systems like Whisper
have made it possible to transcribe audio in multiple languages with a single
model. However, current state-of-the-art ASR models are typically evaluated on
individual languages or in a multi-task setting, overlooking the challenge of
continually learning new languages. There is insufficient research on how to
add new languages without losing valuable information from previous data.
Furthermore, existing continual learning benchmarks focus mostly on vision and
language tasks, leaving continual learning for multilingual ASR largely
unexplored. To bridge this gap, we propose CL-MASR, a benchmark designed for
studying multilingual ASR in a continual learning setting. CL-MASR provides a
diverse set of continual learning methods implemented on top of large-scale
pretrained ASR models, along with common metrics to assess the effectiveness of
learning new languages while addressing the issue of catastrophic forgetting.
To the best of our knowledge, CL-MASR is the first continual learning benchmark
for the multilingual ASR task. The code is available at
https://github.com/speechbrain/benchmarks.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: PsyBench: a balanced and in-depth Psychological Chinese Evaluation Benchmark for Foundation Models
Abstract: As Large Language Models (LLMs) are becoming prevalent in various fields,
there is an urgent need for improved NLP benchmarks that encompass all the
necessary knowledge of individual discipline. Many contemporary benchmarks for
foundational models emphasize a broad range of subjects but often fall short in
presenting all the critical subjects and encompassing necessary professional
knowledge of them. This shortfall has led to skewed results, given that LLMs
exhibit varying performance across different subjects and knowledge areas. To
address this issue, we present psybench, the first comprehensive Chinese
evaluation suite that covers all the necessary knowledge required for graduate
entrance exams. psybench offers a deep evaluation of a model's strengths and
weaknesses in psychology through multiple-choice questions. Our findings show
significant differences in performance across different sections of a subject,
highlighting the risk of skewed results when the knowledge in test sets is not
balanced. Notably, only the ChatGPT model reaches an average accuracy above
$70\%$, indicating that there is still plenty of room for improvement. We
expect that psybench will help to conduct thorough evaluations of base models'
strengths and weaknesses and assist in practical application in the field of
psychology.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder
Abstract: Representation learning assumes that real-world data is generated by a few
semantically meaningful generative factors (i.e., sources of variation) and
aims to discover them in the latent space. These factors are expected to be
causally disentangled, meaning that distinct factors are encoded into separate
latent variables, and changes in one factor will not affect the values of the
others. Compared to statistical independence, causal disentanglement allows
more controllable data generation, improved robustness, and better
generalization. However, most existing work assumes unconfoundedness in the
discovery process, that there are no common causes to the generative factors
and thus obtain only statistical independence. In this paper, we recognize the
importance of modeling confounders in discovering causal generative factors.
Unfortunately, such factors are not identifiable without proper inductive bias.
We fill the gap by introducing a framework entitled Confounded-Disentanglement
(C-Disentanglement), the first framework that explicitly introduces the
inductive bias of confounder via labels from domain expertise. In addition, we
accordingly propose an approach to sufficiently identify the causally
disentangled factors under any inductive bias of the confounder. We conduct
extensive experiments on both synthetic and real-world datasets. Our method
demonstrates competitive results compared to various SOTA baselines in
obtaining causally disentangled features and downstream tasks under domain
shifts.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Assume-Guarantee Reinforcement Learning
Abstract: We present a modular approach to \emph{reinforcement learning} (RL) in
environments consisting of simpler components evolving in parallel. A
monolithic view of such modular environments may be prohibitively large to
learn, or may require unrealizable communication between the components in the
form of a centralized controller. Our proposed approach is based on the
assume-guarantee paradigm where the optimal control for the individual
components is synthesized in isolation by making \emph{assumptions} about the
behaviors of neighboring components, and providing \emph{guarantees} about
their own behavior. We express these \emph{assume-guarantee contracts} as
regular languages and provide automatic translations to scalar rewards to be
used in RL. By combining local probabilities of satisfaction for each
component, we provide a lower bound on the probability of satisfaction of the
complete system. By solving a Markov game for each component, RL can produce a
controller for each component that maximizes this lower bound. The controller
utilizes the information it receives through communication, observations, and
any knowledge of a coarse model of other agents. We experimentally demonstrate
the efficiency of the proposed approach on a variety of case studies.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: SDSRA: A Skill-Driven Skill-Recombination Algorithm for Efficient Policy Learning
Abstract: In this paper, we introduce a novel algorithm - the Skill-Driven Skill
Recombination Algorithm (SDSRA) - an innovative framework that significantly
enhances the efficiency of achieving maximum entropy in reinforcement learning
tasks. We find that SDSRA achieves faster convergence compared to the
traditional Soft Actor-Critic (SAC) algorithm and produces improved policies.
By integrating skill-based strategies within the robust Actor-Critic framework,
SDSRA demonstrates remarkable adaptability and performance across a wide array
of complex and diverse benchmarks.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: From Big to Small Without Losing It All: Text Augmentation with ChatGPT for Efficient Sentiment Analysis
Abstract: In the era of artificial intelligence, data is gold but costly to annotate.
The paper demonstrates a groundbreaking solution to this dilemma using ChatGPT
for text augmentation in sentiment analysis. We leverage ChatGPT's generative
capabilities to create synthetic training data that significantly improves the
performance of smaller models, making them competitive with, or even
outperforming, their larger counterparts. This innovation enables models to be
both efficient and effective, thereby reducing computational cost, inference
time, and memory usage without compromising on quality. Our work marks a key
advancement in the cost-effective development and deployment of robust
sentiment analysis models.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: ViT-Lens-2: Gateway to Omni-modal Intelligence
Abstract: Aiming to advance AI agents, large foundation models significantly improve
reasoning and instruction execution, yet the current focus on vision and
language neglects the potential of perceiving diverse modalities in open-world
environments. However, the success of data-driven vision and language models is
costly or even infeasible to be reproduced for rare modalities. In this paper,
we present ViT-Lens-2 that facilitates efficient omni-modal representation
learning by perceiving novel modalities with a pretrained ViT and aligning them
to a pre-defined space. Specifically, the modality-specific lens is tuned to
project any-modal signals to an intermediate embedding space, which are then
processed by a strong ViT with pre-trained visual knowledge. The encoded
representations are optimized toward aligning with the modal-independent space,
pre-defined by off-the-shelf foundation models. ViT-Lens-2 provides a unified
solution for representation learning of increasing modalities with two
appealing advantages: (i) Unlocking the great potential of pretrained ViTs to
novel modalities effectively with efficient data regime; (ii) Enabling emergent
downstream capabilities through modality alignment and shared ViT parameters.
We tailor ViT-Lens-2 to learn representations for 3D point cloud, depth, audio,
tactile and EEG, and set new state-of-the-art results across various
understanding tasks, such as zero-shot classification. By seamlessly
integrating ViT-Lens-2 into Multimodal Foundation Models, we enable
Any-modality to Text and Image Generation in a zero-shot manner. Code and
models are available at https://github.com/TencentARC/ViT-Lens.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Accuracy of a Vision-Language Model on Challenging Medical Cases
Abstract: Background: General-purpose large language models that utilize both text and
images have not been evaluated on a diverse array of challenging medical cases.
  Methods: Using 934 cases from the NEJM Image Challenge published between 2005
and 2023, we evaluated the accuracy of the recently released Generative
Pre-trained Transformer 4 with Vision model (GPT-4V) compared to human
respondents overall and stratified by question difficulty, image type, and skin
tone. We further conducted a physician evaluation of GPT-4V on 69 NEJM
clinicopathological conferences (CPCs). Analyses were conducted for models
utilizing text alone, images alone, and both text and images.
  Results: GPT-4V achieved an overall accuracy of 61% (95% CI, 58 to 64%)
compared to 49% (95% CI, 49 to 50%) for humans. GPT-4V outperformed humans at
all levels of difficulty and disagreement, skin tones, and image types; the
exception was radiographic images, where performance was equivalent between
GPT-4V and human respondents. Longer, more informative captions were associated
with improved performance for GPT-4V but similar performance for human
respondents. GPT-4V included the correct diagnosis in its differential for 80%
(95% CI, 68 to 88%) of CPCs when using text alone, compared to 58% (95% CI, 45
to 70%) of CPCs when using both images and text.
  Conclusions: GPT-4V outperformed human respondents on challenging medical
cases and was able to synthesize information from both images and text, but
performance deteriorated when images were added to highly informative text.
Overall, our results suggest that multimodal AI models may be useful in medical
diagnostic reasoning but that their accuracy may depend heavily on context.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models
Abstract: The lack of contextual information in text data can make the annotation
process of text-based emotion classification datasets challenging. As a result,
such datasets often contain labels that fail to consider all the relevant
emotions in the vocabulary. This misalignment between text inputs and labels
can degrade the performance of machine learning models trained on top of them.
As re-annotating entire datasets is a costly and time-consuming task that
cannot be done at scale, we propose to use the expressive capabilities of large
language models to synthesize additional context for input text to increase its
alignment with the annotated emotional labels. In this work, we propose a
formal definition of textual context to motivate a prompting strategy to
enhance such contextual information. We provide both human and empirical
evaluation to demonstrate the efficacy of the enhanced context. Our method
improves alignment between inputs and their human-annotated labels from both an
empirical and human-evaluated standpoint.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Its All Graph To Me: Foundational Topology Models with Contrastive Learning on Multiple Domains
Abstract: Representations and embeddings of graph data have been essential in many
domains of research.
  The principle benefit of learning such representations is that the
pre-trained model can be fine-tuned on smaller datasets where data or labels
are scarse.
  Existing models, however, are domain specific; for example a model trained on
molecular graphs is fine-tuned on other molecular graphs.
  This means that in many application cases the choice of pre-trained model can
be arbitrary, and novel domains may lack an appropriate pre-trained model.
  This is of particular issue where data is scarse, precluding traditional
supervised methods.
  In this work we use adversarial contrastive learning to present a \method, a
model pre-trained on many graph domains.
  We train the model only on topologies but include node labels in evaluation.
  We evaluate the efficacy of its learnt representations on various downstream
tasks.
  Against baseline models pre-trained on single domains, as well as un-trained
models and non-transferred models, we show that performance is equal or better
using our single model.
  This includes when node labels are used in evaluation, where performance is
consistently superior to single-domain or non-pre-trained models.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: ReWaRD: Retinal Waves for Pre-Training Artificial Neural Networks Mimicking Real Prenatal Development
Abstract: Computational models trained on a large amount of natural images are the
state-of-the-art to study human vision - usually adult vision. Computational
models of infant vision and its further development are gaining more and more
attention in the community. In this work we aim at the very beginning of our
visual experience - pre- and post-natal retinal waves which suggest to be a
pre-training mechanism for the primate visual system at a very early stage of
development. We see this approach as an instance of biologically plausible data
driven inductive bias through pre-training. We built a computational model that
mimics this development mechanism by pre-training different artificial
convolutional neural networks with simulated retinal wave images. The resulting
features of this biologically plausible pre-training closely match the V1
features of the primate visual system. We show that the performance gain by
pre-training with retinal waves is similar to a state-of-the art pre-training
pipeline. Our framework contains the retinal wave generator, as well as a
training strategy, which can be a first step in a curriculum learning based
training diet for various models of development. We release code, data and
trained networks to build the basis for future work on visual development and
based on a curriculum learning approach including prenatal development to
support studies of innate vs. learned properties of the primate visual system.
An additional benefit of our pre-trained networks for neuroscience or computer
vision applications is the absence of biases inherited from datasets like
ImageNet.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: A Data-driven and multi-agent decision support system for time slot management at container terminals: A case study for the Port of Rotterdam
Abstract: Controlling the departure time of the trucks from a container hub is
important to both the traffic and the logistics systems. This, however,
requires an intelligent decision support system that can control and manage
truck arrival times at terminal gates. This paper introduces an integrated
model that can be used to understand, predict, and control logistics and
traffic interactions in the port-hinterland ecosystem. This approach is
context-aware and makes use of big historical data to predict system states and
apply control policies accordingly, on truck inflow and outflow. The control
policies ensure multiple stakeholders satisfaction including those of trucking
companies, terminal operators, and road traffic agencies. The proposed method
consists of five integrated modules orchestrated to systematically steer
truckers toward choosing those time slots that are expected to result in lower
gate waiting times and more cost-effective schedules. The simulation is
supported by real-world data and shows that significant gains can be obtained
in the system.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization
Abstract: Parameter-efficient fine-tuning (PEFT) techniques make it possible to
efficiently adapt a language model to create "expert" models that specialize to
new tasks or domains. Recent techniques in model merging and compositional
generalization leverage these expert models by dynamically composing modules to
improve zero/few-shot generalization. Despite the efficiency of PEFT methods,
the size of expert models can make it onerous to retrieve expert models per
query over high-latency networks like the Internet or serve multiple experts on
a single GPU. To address these issues, we present ComPEFT, a novel method for
compressing fine-tuning residuals (task vectors) of PEFT based models. ComPEFT
employs sparsification and ternary quantization to reduce the size of the PEFT
module without performing any additional retraining while preserving or
enhancing model performance. In extensive evaluation across T5, T0, and
LLaMA-based models with 200M - 65B parameters, ComPEFT achieves compression
ratios of 8x - 50x. In particular, we show that ComPEFT improves with scale -
stronger models exhibit higher compressibility and better performance. For
example, we show that ComPEFT applied to LLaMA outperforms QLoRA by 4.16% on
MMLU with a storage size reduction of up to 26x. In addition, we show that the
compressed experts produced by ComPEFT maintain few-shot compositional
generalization capabilities, facilitate efficient communication and
computation, and exhibit enhanced performance when merged. Lastly, we provide
an analysis of different method components, compare it with other PEFT methods,
and test ComPEFT's efficacy for compressing the residual of full-finetuning.
Our code is available at https://github.com/prateeky2806/compeft.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: DreamSync: Aligning Text-to-Image Generation with Image Understanding Feedback
Abstract: Despite their wide-spread success, Text-to-Image models (T2I) still struggle
to produce images that are both aesthetically pleasing and faithful to the
user's input text. We introduce DreamSync, a model-agnostic training algorithm
by design that improves T2I models to be faithful to the text input. DreamSync
builds off a recent insight from TIFA's evaluation framework -- that large
vision-language models (VLMs) can effectively identify the fine-grained
discrepancies between generated images and the text inputs. DreamSync uses this
insight to train T2I models without any labeled data; it improves T2I models
using its own generations. First, it prompts the model to generate several
candidate images for a given input text. Then, it uses two VLMs to select the
best generation: a Visual Question Answering model that measures the alignment
of generated images to the text, and another that measures the generation's
aesthetic quality. After selection, we use LoRA to iteratively finetune the T2I
model to guide its generation towards the selected best generations. DreamSync
does not need any additional human annotation. model architecture changes, or
reinforcement learning. Despite its simplicity, DreamSync improves both the
semantic alignment and aesthetic appeal of two diffusion-based T2I models,
evidenced by multiple benchmarks (+1.7% on TIFA, +2.9% on DSG1K, +3.4% on VILA
aesthetic) and human evaluation.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Modality Plug-and-Play: Elastic Modality Adaptation in Multimodal LLMs for Embodied AI
Abstract: Large Language Models (LLMs) are capable of reasoning over diverse input data
modalities through pre-trained encoders. However, the growing diversity of
input data modalities prevents incorporating all modalities into LLMs,
especially when LLMs are deployed on resource-constrained edge devices for
embodied AI applications. Instead, a better option is to adaptively involve
only the useful modalities at runtime, depending on the current environmental
contexts and task requirements. For such modality adaptation, existing work
adopts fixed connections between encoders and the LLM's input layer, leading to
high training cost at runtime and ineffective cross-modal interaction. In this
paper, we address these limitations by presenting mPnP-LLM, a new technique
that allows fully elastic, automated and prompt runtime modality adaptation, by
connecting unimodal encoders to a flexible set of last LLM blocks and making
such latent connections fully trainable at runtime. Experiments over the
nuScenes-QA dataset show that mPnP-LLM can achieve up to 3.7x FLOPs reduction
and 30% GPU memory usage reduction, while retaining on-par accuracy with the
existing schemes. Under the same compute budget, mPnP-LLM improves the task
accuracy by up to 4% compared to the best existing scheme.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Hand Gesture Classification on Praxis Dataset: Trading Accuracy for Expense
Abstract: In this paper, we investigate hand gesture classifiers that rely upon the
abstracted 'skeletal' data recorded using the RGB-Depth sensor. We focus on
'skeletal' data represented by the body joint coordinates, from the Praxis
dataset. The PRAXIS dataset contains recordings of patients with cortical
pathologies such as Alzheimer's disease, performing a Praxis test under the
direction of a clinician. In this paper, we propose hand gesture classifiers
that are more effective with the PRAXIS dataset than previously proposed
models. Body joint data offers a compressed form of data that can be analyzed
specifically for hand gesture recognition. Using a combination of windowing
techniques with deep learning architecture such as a Recurrent Neural Network
(RNN), we achieved an overall accuracy of 70.8% using only body joint data. In
addition, we investigated a long-short-term-memory (LSTM) to extract and
analyze the movement of the joints through time to recognize the hand gestures
being performed and achieved a gesture recognition rate of 74.3% and 67.3% for
static and dynamic gestures, respectively. The proposed approach contributed to
the task of developing an automated, accurate, and inexpensive approach to
diagnosing cortical pathologies for multiple healthcare applications.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning
Abstract: In this study, we are interested in imbuing robots with the capability of
physically-grounded task planning. Recent advancements have shown that large
language models (LLMs) possess extensive knowledge useful in robotic tasks,
especially in reasoning and planning. However, LLMs are constrained by their
lack of world grounding and dependence on external affordance models to
perceive environmental information, which cannot jointly reason with LLMs. We
argue that a task planner should be an inherently grounded, unified multimodal
system. To this end, we introduce Robotic Vision-Language Planning (ViLa), a
novel approach for long-horizon robotic planning that leverages vision-language
models (VLMs) to generate a sequence of actionable steps. ViLa directly
integrates perceptual data into its reasoning and planning process, enabling a
profound understanding of commonsense knowledge in the visual world, including
spatial layouts and object attributes. It also supports flexible multimodal
goal specification and naturally incorporates visual feedback. Our extensive
evaluation, conducted in both real-robot and simulated environments,
demonstrates ViLa's superiority over existing LLM-based planners, highlighting
its effectiveness in a wide array of open-world manipulation tasks.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Elo Uncovered: Robustness and Best Practices in Language Model Evaluation
Abstract: In Natural Language Processing (NLP), the Elo rating system, originally
designed for ranking players in dynamic games such as chess, is increasingly
being used to evaluate Large Language Models (LLMs) through "A vs B" paired
comparisons. However, while popular, the system's suitability for assessing
entities with constant skill levels, such as LLMs, remains relatively
unexplored. We study two fundamental axioms that evaluation methods should
adhere to: reliability and transitivity. We conduct extensive evaluation of Elo
behaviour, illustrating that individual Elo computations exhibit volatility and
delving into the impact of varying the Elo rating system's hyperparameters. We
show that these axioms are not always satisfied raising questions about the
reliability of current comparative evaluations of LLMs. If the current use of
Elo scores is intended to substitute the costly head-to-head comparison of
LLMs, it is crucial to ensure the ranking is as robust as possible. Guided by
the axioms, our findings offer concrete guidelines for enhancing the
reliability of LLM evaluation methods, suggesting a need for reassessment of
existing comparative approaches.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Extrinsically-Focused Evaluation of Omissions in Medical Summarization
Abstract: The goal of automated summarization techniques (Paice, 1990; Kupiec et al,
1995) is to condense text by focusing on the most critical information.
Generative large language models (LLMs) have shown to be robust summarizers,
yet traditional metrics struggle to capture resulting performance (Goyal et al,
2022) in more powerful LLMs. In safety-critical domains such as medicine, more
rigorous evaluation is required, especially given the potential for LLMs to
omit important information in the resulting summary. We propose MED-OMIT, a new
omission benchmark for medical summarization. Given a doctor-patient
conversation and a generated summary, MED-OMIT categorizes the chat into a set
of facts and identifies which are omitted from the summary. We further propose
to determine fact importance by simulating the impact of each fact on a
downstream clinical task: differential diagnosis (DDx) generation. MED-OMIT
leverages LLM prompt-based approaches which categorize the importance of facts
and cluster them as supporting or negating evidence to the diagnosis. We
evaluate MED-OMIT on a publicly-released dataset of patient-doctor
conversations and find that MED-OMIT captures omissions better than alternative
metrics.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Semantic-Aware Frame-Event Fusion based Pattern Recognition via Large Vision-Language Models
Abstract: Pattern recognition through the fusion of RGB frames and Event streams has
emerged as a novel research area in recent years. Current methods typically
employ backbone networks to individually extract the features of RGB frames and
event streams, and subsequently fuse these features for pattern recognition.
However, we posit that these methods may suffer from key issues like sematic
gaps and small-scale backbone networks. In this study, we introduce a novel
pattern recognition framework that consolidates the semantic labels, RGB
frames, and event streams, leveraging pre-trained large-scale vision-language
models. Specifically, given the input RGB frames, event streams, and all the
predefined semantic labels, we employ a pre-trained large-scale vision model
(CLIP vision encoder) to extract the RGB and event features. To handle the
semantic labels, we initially convert them into language descriptions through
prompt engineering, and then obtain the semantic features using the pre-trained
large-scale language model (CLIP text encoder). Subsequently, we integrate the
RGB/Event features and semantic features using multimodal Transformer networks.
The resulting frame and event tokens are further amplified using self-attention
layers. Concurrently, we propose to enhance the interactions between text
tokens and RGB/Event tokens via cross-attention. Finally, we consolidate all
three modalities using self-attention and feed-forward layers for recognition.
Comprehensive experiments on the HARDVS and PokerEvent datasets fully
substantiate the efficacy of our proposed SAFE model. The source code will be
made available at https://github.com/Event-AHU/SAFE_LargeVLM.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Classification of retail products: From probabilistic ranking to neural networks
Abstract: Food retailing is now on an accelerated path to a success penetration into
the digital market by new ways of value creation at all stages of the consumer
decision process. One of the most important imperatives in this path is the
availability of quality data to feed all the process in digital transformation.
But the quality of data is not so obvious if we consider the variety of
products and suppliers in the grocery market. Within this context of digital
transformation of grocery industry, \textit{Midiadia} is Spanish data provider
company that works on converting data from the retailers' products into
knowledge with attributes and insights from the product labels, that is,
maintaining quality data in a dynamic market with a high dispersion of
products. Currently, they manually categorize products (groceries) according to
the information extracted directly (text processing) from the product labelling
and packaging. This paper introduces a solution to automatically categorize the
constantly changing product catalogue into a 3-level food taxonomy. Our
proposal studies three different approaches: a score-based ranking method,
traditional machine learning algorithms, and deep neural networks. Thus, we
provide four different classifiers that support a more efficient and less
error-prone maintenance of groceries catalogues, the main asset of the company.
Finally, we have compared the performance of these three alternatives,
concluding that traditional machine learning algorithms perform better, but
closely followed by the score-based approach.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion
Abstract: Learning world models can teach an agent how the world works in an
unsupervised manner. Even though it can be viewed as a special case of sequence
modeling, progress for scaling world models on robotic applications such as
autonomous driving has been somewhat less rapid than scaling language models
with Generative Pre-trained Transformers (GPT). We identify two reasons as
major bottlenecks: dealing with complex and unstructured observation space, and
having a scalable generative model. Consequently, we propose a novel world
modeling approach that first tokenizes sensor observations with VQVAE, then
predicts the future via discrete diffusion. To efficiently decode and denoise
tokens in parallel, we recast Masked Generative Image Transformer into the
discrete diffusion framework with a few simple changes, resulting in notable
improvement. When applied to learning world models on point cloud observations,
our model reduces prior SOTA Chamfer distance by more than 65% for 1s
prediction, and more than 50% for 3s prediction, across NuScenes, KITTI
Odometry, and Argoverse2 datasets. Our results demonstrate that discrete
diffusion on tokenized agent experience can unlock the power of GPT-like
unsupervised learning for robotic agents.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Finding AI-Generated Faces in the Wild
Abstract: AI-based image generation has continued to rapidly improve, producing
increasingly more realistic images with fewer obvious visual flaws.
AI-generated images are being used to create fake online profiles which in turn
are being used for spam, fraud, and disinformation campaigns. As the general
problem of detecting any type of manipulated or synthesized content is
receiving increasing attention, here we focus on a more narrow task of
distinguishing a real face from an AI-generated face. This is particularly
applicable when tackling inauthentic online accounts with a fake user profile
photo. We show that by focusing on only faces, a more resilient and
general-purpose artifact can be detected that allows for the detection of
AI-generated faces from a variety of GAN- and diffusion-based synthesis
engines, and across image resolutions (as low as 128 x 128 pixels) and
qualities.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Latent Feature-Guided Diffusion Models for Shadow Removal
Abstract: Recovering textures under shadows has remained a challenging problem due to
the difficulty of inferring shadow-free scenes from shadow images. In this
paper, we propose the use of diffusion models as they offer a promising
approach to gradually refine the details of shadow regions during the diffusion
process. Our method improves this process by conditioning on a learned latent
feature space that inherits the characteristics of shadow-free images, thus
avoiding the limitation of conventional methods that condition on degraded
images only. Additionally, we propose to alleviate potential local optima
during training by fusing noise features with the diffusion network. We
demonstrate the effectiveness of our approach which outperforms the previous
best method by 13% in terms of RMSE on the AISTD dataset. Further, we explore
instance-level shadow removal, where our model outperforms the previous best
method by 82% in terms of RMSE on the DESOBA dataset.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding
Abstract: Visually-situated languages such as charts and plots are omnipresent in
real-world documents. These graphical depictions are human-readable and are
often analyzed in visually-rich documents to address a variety of questions
that necessitate complex reasoning and common-sense responses. Despite the
growing number of datasets that aim to answer questions over charts, most only
address this task in isolation, without considering the broader context of
document-level question answering. Moreover, such datasets lack adequate
common-sense reasoning information in their questions. In this work, we
introduce a novel task named document-level chart question answering (DCQA).
The goal of this task is to conduct document-level question answering,
extracting charts or plots in the document via document layout analysis (DLA)
first and subsequently performing chart question answering (CQA). The newly
developed benchmark dataset comprises 50,010 synthetic documents integrating
charts in a wide range of styles (6 styles in contrast to 3 for PlotQA and
ChartQA) and includes 699,051 questions that demand a high degree of reasoning
ability and common-sense understanding. Besides, we present the development of
a potent question-answer generation engine that employs table data, a rich
color set, and basic question templates to produce a vast array of reasoning
question-answer pairs automatically. Based on DCQA, we devise an OCR-free
transformer for document-level chart-oriented understanding, capable of DLA and
answering complex reasoning and common-sense questions over charts in an
OCR-free manner. Our DCQA dataset is expected to foster research on
understanding visualizations in documents, especially for scenarios that
require complex reasoning for charts in the visually-rich document. We
implement and evaluate a set of baselines, and our proposed method achieves
comparable results.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: One Self-Configurable Model to Solve Many Abstract Visual Reasoning Problems
Abstract: Abstract Visual Reasoning (AVR) comprises a wide selection of various
problems similar to those used in human IQ tests. Recent years have brought
dynamic progress in solving particular AVR tasks, however, in the contemporary
literature AVR problems are largely dealt with in isolation, leading to highly
specialized task-specific methods. With the aim of developing universal
learning systems in the AVR domain, we propose the unified model for solving
Single-Choice Abstract visual Reasoning tasks (SCAR), capable of solving
various single-choice AVR tasks, without making any a priori assumptions about
the task structure, in particular the number and location of panels. The
proposed model relies on a novel Structure-Aware dynamic Layer (SAL), which
adapts its weights to the structure of the considered AVR problem. Experiments
conducted on Raven's Progressive Matrices, Visual Analogy Problems, and Odd One
Out problems show that SCAR (SAL-based models, in general) effectively solves
diverse AVR tasks, and its performance is on par with the state-of-the-art
task-specific baselines. What is more, SCAR demonstrates effective knowledge
reuse in multi-task and transfer learning settings. To our knowledge, this work
is the first successful attempt to construct a general single-choice AVR solver
relying on self-configurable architecture and unified solving method. With this
work we aim to stimulate and foster progress on task-independent research paths
in the AVR domain, with the long-term goal of development of a general AVR
solver.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: An Integrated Framework Integrating Monte Carlo Tree Search and Supervised Learning for Train Timetabling Problem
Abstract: The single-track railway train timetabling problem (TTP) is an important and
complex problem. This article proposes an integrated Monte Carlo Tree Search
(MCTS) computing framework that combines heuristic methods, unsupervised
learning methods, and supervised learning methods for solving TTP in discrete
action spaces. This article first describes the mathematical model and
simulation system dynamics of TTP, analyzes the characteristics of the solution
from the perspective of MCTS, and proposes some heuristic methods to improve
MCTS. This article considers these methods as planners in the proposed
framework. Secondly, this article utilizes deep convolutional neural networks
to approximate the value of nodes and further applies them to the MCTS search
process, referred to as learners. The experiment shows that the proposed
heuristic MCTS method is beneficial for solving TTP; The algorithm framework
that integrates planners and learners can improve the data efficiency of
solving TTP; The proposed method provides a new paradigm for solving TTP.

Output: Machine Learning