Instruction: What field is the article from?

Prompt:
Title: MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning
Abstract: Code LLMs have emerged as a specialized research field, with remarkable
studies dedicated to enhancing model's coding capabilities through fine-tuning
on pre-trained models. Previous fine-tuning approaches were typically tailored
to specific downstream tasks or scenarios, which meant separate fine-tuning for
each task, requiring extensive training resources and posing challenges in
terms of deployment and maintenance. Furthermore, these approaches failed to
leverage the inherent interconnectedness among different code-related tasks. To
overcome these limitations, we present a multi-task fine-tuning framework,
MFTcoder, that enables simultaneous and parallel fine-tuning on multiple tasks.
By incorporating various loss functions, we effectively address common
challenges in multi-task learning, such as data imbalance, varying difficulty
levels, and inconsistent convergence speeds. Extensive experiments have
conclusively demonstrated that our multi-task fine-tuning approach outperforms
both individual fine-tuning on single tasks and fine-tuning on a mixed ensemble
of tasks. Moreover, MFTcoder offers efficient training capabilities, including
efficient data tokenization modes and PEFT fine-tuning, resulting in
significantly improved speed compared to traditional fine-tuning methods.
MFTcoder seamlessly integrates with several mainstream open-source LLMs, such
as CodeLLama and Qwen. Leveraging the CodeLLama foundation, our MFTcoder
fine-tuned model, \textsc{CodeFuse-CodeLLama-34B}, achieves an impressive
pass@1 score of 74.4\% on the HumaneEval benchmark, surpassing GPT-4
performance (67\%, zero-shot). MFTCoder is open-sourced at
\url{https://github.com/codefuse-ai/MFTCOder}

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: XFEVER: Exploring Fact Verification across Languages
Abstract: This paper introduces the Cross-lingual Fact Extraction and VERification
(XFEVER) dataset designed for benchmarking the fact verification models across
different languages. We constructed it by translating the claim and evidence
texts of the Fact Extraction and VERification (FEVER) dataset into six
languages. The training and development sets were translated using machine
translation, whereas the test set includes texts translated by professional
translators and machine-translated texts. Using the XFEVER dataset, two
cross-lingual fact verification scenarios, zero-shot learning and
translate-train learning, are defined, and baseline models for each scenario
are also proposed in this paper. Experimental results show that the
multilingual language model can be used to build fact verification models in
different languages efficiently. However, the performance varies by language
and is somewhat inferior to the English case. We also found that we can
effectively mitigate model miscalibration by considering the prediction
similarity between the English and target languages. The XFEVER dataset, code,
and model checkpoints are available at
https://github.com/nii-yamagishilab/xfever.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: From Knowledge Representation to Knowledge Organization and Back
Abstract: Knowledge Representation (KR) and facet-analytical Knowledge Organization
(KO) have been the two most prominent methodologies of data and knowledge
modelling in the Artificial Intelligence community and the Information Science
community, respectively. KR boasts of a robust and scalable ecosystem of
technologies to support knowledge modelling while, often, underemphasizing the
quality of its models (and model-based data). KO, on the other hand, is less
technology-driven but has developed a robust framework of guiding principles
(canons) for ensuring modelling (and model-based data) quality. This paper
elucidates both the KR and facet-analytical KO methodologies in detail and
provides a functional mapping between them. Out of the mapping, the paper
proposes an integrated KO-enriched KR methodology with all the standard
components of a KR methodology plus the guiding canons of modelling quality
provided by KO. The practical benefits of the methodological integration has
been exemplified through a prominent case study of KR-based image annotation
exercise.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Adversarial Attacks to Reward Machine-based Reinforcement Learning
Abstract: In recent years, Reward Machines (RMs) have stood out as a simple yet
effective automata-based formalism for exposing and exploiting task structure
in reinforcement learning settings. Despite their relevance, little to no
attention has been directed to the study of their security implications and
robustness to adversarial scenarios, likely due to their recent appearance in
the literature. With my thesis, I aim to provide the first analysis of the
security of RM-based reinforcement learning techniques, with the hope of
motivating further research in the field, and I propose and evaluate a novel
class of attacks on RM-based techniques: blinding attacks.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: HKTGNN: Hierarchical Knowledge Transferable Graph Neural Network-based Supply Chain Risk Assessment
Abstract: The strength of a supply chain is an important measure of a country's or
region's technical advancement and overall competitiveness. Establishing supply
chain risk assessment models for effective management and mitigation of
potential risks has become increasingly crucial. As the number of businesses
grows, the important relationships become more complicated and difficult to
measure. This emphasizes the need of extracting relevant information from graph
data. Previously, academics mostly employed knowledge inference to increase the
visibility of links between nodes in the supply chain. However, they have not
solved the data hunger problem of single node feature characteristics. We
propose a hierarchical knowledge transferable graph neural network-based
(HKTGNN) supply chain risk assessment model to address these issues. Our
approach is based on current graph embedding methods for assessing corporate
investment risk assessment. We embed the supply chain network corresponding to
individual goods in the supply chain using the graph embedding module,
resulting in a directed homogeneous graph with just product nodes. This reduces
the complicated supply chain network into a basic product network. It addresses
difficulties using the domain difference knowledge transferable module based on
centrality, which is presented by the premise that supply chain feature
characteristics may be biased in the actual world. Meanwhile, the feature
complement and message passing will alleviate the data hunger problem, which is
driven by domain differences. Our model outperforms in experiments on a
real-world supply chain dataset. We will give an equation to prove that our
comparative experiment is both effective and fair.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Multi Loss-based Feature Fusion and Top Two Voting Ensemble Decision Strategy for Facial Expression Recognition in the Wild
Abstract: Facial expression recognition (FER) in the wild is a challenging task
affected by the image quality and has attracted broad interest in computer
vision. There is no research using feature fusion and ensemble strategy for FER
simultaneously. Different from previous studies, this paper applies both
internal feature fusion for a single model and feature fusion among multiple
networks, as well as the ensemble strategy. This paper proposes one novel
single model named R18+FAML, as well as one ensemble model named
R18+FAML-FGA-T2V to improve the performance of the FER in the wild. Based on
the structure of ResNet18 (R18), R18+FAML combines internal Feature fusion and
three Attention blocks using Multiple Loss functions (FAML) to improve the
diversity of the feature extraction. To improve the performance of R18+FAML, we
propose a Feature fusion among networks based on the Genetic Algorithm (FGA),
which can fuse the convolution kernels for feature extraction of multiple
networks. On the basis of R18+FAML and FGA, we propose one ensemble strategy,
i.e., the Top Two Voting (T2V) to support the classification of FER, which can
consider more classification information comprehensively. Combining the above
strategies, R18+FAML-FGA-T2V can focus on the main expression-aware areas.
Extensive experiments demonstrate that our single model R18+FAML and the
ensemble model R18+FAML-FGA-T2V achieve the accuracies of $\left( 90.32, 62.17,
65.83 \right)\%$ and $\left( 91.59, 63.27, 66.63 \right)\%$ on three
challenging unbalanced FER datasets RAF-DB, AffectNet-8 and AffectNet-7
respectively, both outperforming the state-of-the-art results.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning
Abstract: Pre-trained language models (PLMs) have shown impressive performance in
various language tasks. However, they are prone to spurious correlations, and
often generate illusory information. In real-world applications, PLMs should
justify decisions with formalized, coherent reasoning chains, but this
challenge remains under-explored. Cognitive psychology theorizes that humans
are capable of utilizing fast and intuitive heuristic thinking to make
decisions based on past experience, then rationalizing the decisions through
slower and deliberative analytic reasoning. We incorporate these interlinked
dual processes in fine-tuning and in-context learning with PLMs, applying them
to two language understanding tasks that require coherent physical commonsense
reasoning. We show that our proposed Heuristic-Analytic Reasoning (HAR)
strategies drastically improve the coherence of rationalizations for model
decisions, yielding state-of-the-art results on Tiered Reasoning for Intuitive
Physics (TRIP). We also find that this improved coherence is a direct result of
more faithful attention to relevant language context in each step of reasoning.
Our findings suggest that human-like reasoning strategies can effectively
improve the coherence and reliability of PLM reasoning.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Exploring Causal Learning through Graph Neural Networks: An In-depth Review
Abstract: In machine learning, exploring data correlations to predict outcomes is a
fundamental task. Recognizing causal relationships embedded within data is
pivotal for a comprehensive understanding of system dynamics, the significance
of which is paramount in data-driven decision-making processes. Beyond
traditional methods, there has been a surge in the use of graph neural networks
(GNNs) for causal learning, given their capabilities as universal data
approximators. Thus, a thorough review of the advancements in causal learning
using GNNs is both relevant and timely. To structure this review, we introduce
a novel taxonomy that encompasses various state-of-the-art GNN methods employed
in studying causality. GNNs are further categorized based on their applications
in the causality domain. We further provide an exhaustive compilation of
datasets integral to causal learning with GNNs to serve as a resource for
practical study. This review also touches upon the application of causal
learning across diverse sectors. We conclude the review with insights into
potential challenges and promising avenues for future exploration in this
rapidly evolving field of machine learning.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Bias in Evaluation Processes: An Optimization-Based Model
Abstract: Biases with respect to socially-salient attributes of individuals have been
well documented in evaluation processes used in settings such as admissions and
hiring. We view such an evaluation process as a transformation of a
distribution of the true utility of an individual for a task to an observed
distribution and model it as a solution to a loss minimization problem subject
to an information constraint. Our model has two parameters that have been
identified as factors leading to biases: the resource-information trade-off
parameter in the information constraint and the risk-averseness parameter in
the loss function. We characterize the distributions that arise from our model
and study the effect of the parameters on the observed distribution. The
outputs of our model enrich the class of distributions that can be used to
capture variation across groups in the observed evaluations. We empirically
validate our model by fitting real-world datasets and use it to study the
effect of interventions in a downstream selection task. These results
contribute to an understanding of the emergence of bias in evaluation processes
and provide tools to guide the deployment of interventions to mitigate biases.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Vision-Language Integration in Multimodal Video Transformers (Partially) Aligns with the Brain
Abstract: Integrating information from multiple modalities is arguably one of the
essential prerequisites for grounding artificial intelligence systems with an
understanding of the real world. Recent advances in video transformers that
jointly learn from vision, text, and sound over time have made some progress
toward this goal, but the degree to which these models integrate information
from modalities still remains unclear. In this work, we present a promising
approach for probing a pre-trained multimodal video transformer model by
leveraging neuroscientific evidence of multimodal information processing in the
brain. Using brain recordings of participants watching a popular TV show, we
analyze the effects of multi-modal connections and interactions in a
pre-trained multi-modal video transformer on the alignment with uni- and
multi-modal brain regions. We find evidence that vision enhances masked
prediction performance during language processing, providing support that
cross-modal representations in models can benefit individual modalities.
However, we don't find evidence of brain-relevant information captured by the
joint multi-modal transformer representations beyond that captured by all of
the individual modalities. We finally show that the brain alignment of the
pre-trained joint representation can be improved by fine-tuning using a task
that requires vision-language inferences. Overall, our results paint an
optimistic picture of the ability of multi-modal transformers to integrate
vision and language in partially brain-relevant ways but also show that
improving the brain alignment of these models may require new approaches.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Large Knowledge Model: Perspectives and Challenges
Abstract: Humankind's understanding of the world is fundamentally linked to our
perception and cognition, with \emph{human languages} serving as one of the
major carriers of \emph{world knowledge}. In this vein, \emph{Large Language
Models} (LLMs) like ChatGPT epitomize the pre-training of extensive,
sequence-based world knowledge into neural networks, facilitating the
processing and manipulation of this knowledge in a parametric space. This
article explores large models through the lens of ``knowledge''. We initially
investigate the role of symbolic knowledge such as Knowledge Graphs (KGs) in
enhancing LLMs, covering aspects like knowledge-augmented language model,
structure-inducing pre-training, knowledgeable prompts, structured CoT,
knowledge editing, semantic tools for LLM and knowledgeable AI agents.
Subsequently, we examine how LLMs can amplify traditional symbolic knowledge
bases, encompassing aspects like using LLM as KG builder and controller,
structured knowledge pretraining, LLM-enhanced symbolic reasoning, and the
amalgamation of perception with cognition. Considering the intricate nature of
human knowledge, we advocate for the creation of \emph{Large Knowledge Models}
(LKM), specifically engineered to manage diversified spectrum of knowledge
structures. This ambitious undertaking could entail several key challenges,
such as disentangling knowledge representation from language models,
restructuring pre-training with structured knowledge, and building large
commonsense models, among others. We finally propose a five-``A'' principle to
distinguish the concept of LKM.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Compensation Sampling for Improved Convergence in Diffusion Models
Abstract: Diffusion models achieve remarkable quality in image generation, but at a
cost. Iterative denoising requires many time steps to produce high fidelity
images. We argue that the denoising process is crucially limited by an
accumulation of the reconstruction error due to an initial inaccurate
reconstruction of the target data. This leads to lower quality outputs, and
slower convergence. To address this issue, we propose compensation sampling to
guide the generation towards the target domain. We introduce a compensation
term, implemented as a U-Net, which adds negligible computation overhead during
training and, optionally, inference. Our approach is flexible and we
demonstrate its application in unconditional generation, face inpainting, and
face de-occlusion using benchmark datasets CIFAR-10, CelebA, CelebA-HQ,
FFHQ-256, and FSG. Our approach consistently yields state-of-the-art results in
terms of image quality, while accelerating the denoising process to converge
during training by up to an order of magnitude.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Fingerprint Matching with Localized Deep Representation
Abstract: Compared to minutia-based fingerprint representations, fixed-length
representations are attractive due to simple and efficient matching. However,
fixed-length fingerprint representations are limited in accuracy when matching
fingerprints with different visible areas, which can occur due to different
finger poses or acquisition methods. To address this issue, we propose a
localized deep representation of fingerprint, named LDRF. By focusing on the
discriminative characteristics within local regions, LDRF provides a more
robust and accurate fixed-length representation for fingerprints with variable
visible areas. LDRF can be adapted to retain information within any valid area,
making it highly flexible. The matching scores produced by LDRF also exhibit
intuitive statistical characteristics, which led us to propose a matching score
normalization technique to mitigate the uncertainty in the cases of very small
overlapping area. With this new technique, we can maintain a high level of
accuracy and reliability in our fingerprint matching, even as the size of the
database grows rapidly. Our experimental results on 21 datasets containing over
140K fingerprints of various finger poses and impression types show that LDRF
outperforms other fixed-length representations and is robust to sensing
technologies and impression types. Besides, the proposed matching score
normalization effectively reduces the false match rate (FMR) in large-scale
identification experiments comprising over 5.11 million fingerprints.
Specifically, this technique results in a reduction of two orders of magnitude
compared to matching without matching score normalization and five orders of
magnitude compared to prior works.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach
Abstract: Lattice reduction is a combinatorial optimization problem aimed at finding
the most orthogonal basis in a given lattice. In this work, we address lattice
reduction via deep learning methods. We design a deep neural model outputting
factorized unimodular matrices and train it in a self-supervised manner by
penalizing non-orthogonal lattice bases. We incorporate the symmetries of
lattice reduction into the model by making it invariant and equivariant with
respect to appropriate continuous and discrete groups.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: An Evaluation Framework for Mapping News Headlines to Event Classes in a Knowledge Graph
Abstract: Mapping ongoing news headlines to event-related classes in a rich knowledge
base can be an important component in a knowledge-based event analysis and
forecasting solution. In this paper, we present a methodology for creating a
benchmark dataset of news headlines mapped to event classes in Wikidata, and
resources for the evaluation of methods that perform the mapping. We use the
dataset to study two classes of unsupervised methods for this task: 1)
adaptations of classic entity linking methods, and 2) methods that treat the
problem as a zero-shot text classification problem. For the first approach, we
evaluate off-the-shelf entity linking systems. For the second approach, we
explore a) pre-trained natural language inference (NLI) models, and b)
pre-trained large generative language models. We present the results of our
evaluation, lessons learned, and directions for future work. The dataset and
scripts for evaluation are made publicly available.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Artificial Intelligence Studies in Cartography: A Review and Synthesis of Methods, Applications, and Ethics
Abstract: The past decade has witnessed the rapid development of geospatial artificial
intelligence (GeoAI) primarily due to the ground-breaking achievements in deep
learning and machine learning. A growing number of scholars from cartography
have demonstrated successfully that GeoAI can accelerate previously complex
cartographic design tasks and even enable cartographic creativity in new ways.
Despite the promise of GeoAI, researchers and practitioners have growing
concerns about the ethical issues of GeoAI for cartography. In this paper, we
conducted a systematic content analysis and narrative synthesis of research
studies integrating GeoAI and cartography to summarize current research and
development trends regarding the usage of GeoAI for cartographic design. Based
on this review and synthesis, we first identify dimensions of GeoAI methods for
cartography such as data sources, data formats, map evaluations, and six
contemporary GeoAI models, each of which serves a variety of cartographic
tasks. These models include decision trees, knowledge graph and semantic web
technologies, deep convolutional neural networks, generative adversarial
networks, graph neural networks, and reinforcement learning. Further, we
summarize seven cartographic design applications where GeoAI have been
effectively employed: generalization, symbolization, typography, map reading,
map interpretation, map analysis, and map production. We also raise five
potential ethical challenges that need to be addressed in the integration of
GeoAI for cartography: commodification, responsibility, privacy, bias, and
(together) transparency, explainability, and provenance. We conclude by
identifying four potential research directions for future cartographic research
with GeoAI: GeoAI-enabled active cartographic symbolism, human-in-the-loop
GeoAI for cartography, GeoAI-based mapping-as-a-service, and generative GeoAI
for cartography.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: Towards Sample-specific Backdoor Attack with Clean Labels via Attribute Trigger
Abstract: Currently, sample-specific backdoor attacks (SSBAs) are the most advanced and
malicious methods since they can easily circumvent most of the current backdoor
defenses. In this paper, we reveal that SSBAs are not sufficiently stealthy due
to their poisoned-label nature, where users can discover anomalies if they
check the image-label relationship. In particular, we demonstrate that it is
ineffective to directly generalize existing SSBAs to their clean-label variants
by poisoning samples solely from the target class. We reveal that it is
primarily due to two reasons, including \textbf{(1)} the `antagonistic effects'
of ground-truth features and \textbf{(2)} the learning difficulty of
sample-specific features. Accordingly, trigger-related features of existing
SSBAs cannot be effectively learned under the clean-label setting due to their
mild trigger intensity required for ensuring stealthiness. We argue that the
intensity constraint of existing SSBAs is mostly because their trigger patterns
are `content-irrelevant' and therefore act as `noises' for both humans and
DNNs. Motivated by this understanding, we propose to exploit content-relevant
features, $a.k.a.$ (human-relied) attributes, as the trigger patterns to design
clean-label SSBAs. This new attack paradigm is dubbed backdoor attack with
attribute trigger (BAAT). Extensive experiments are conducted on benchmark
datasets, which verify the effectiveness of our BAAT and its resistance to
existing defenses.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Castor: Causal Temporal Regime Structure Learning
Abstract: The task of uncovering causal relationships among multivariate time series
data stands as an essential and challenging objective that cuts across a broad
array of disciplines ranging from climate science to healthcare. Such data
entails linear or non-linear relationships, and usually follow multiple a
priori unknown regimes. Existing causal discovery methods can infer summary
causal graphs from heterogeneous data with known regimes, but they fall short
in comprehensively learning both regimes and the corresponding causal graph. In
this paper, we introduce CASTOR, a novel framework designed to learn causal
relationships in heterogeneous time series data composed of various regimes,
each governed by a distinct causal graph. Through the maximization of a score
function via the EM algorithm, CASTOR infers the number of regimes and learns
linear or non-linear causal relationships in each regime. We demonstrate the
robust convergence properties of CASTOR, specifically highlighting its
proficiency in accurately identifying unique regimes. Empirical evidence,
garnered from exhaustive synthetic experiments and two real-world benchmarks,
confirm CASTOR's superior performance in causal discovery compared to baseline
methods. By learning a full temporal causal graph for each regime, CASTOR
establishes itself as a distinctly interpretable method for causal discovery in
heterogeneous time series.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks
Abstract: Graphs have emerged as a natural choice to represent and analyze the
intricate patterns and rich information of the Web, enabling applications such
as online page classification and social recommendation. The prevailing
"pre-train, fine-tune" paradigm has been widely adopted in graph machine
learning tasks, particularly in scenarios with limited labeled nodes. However,
this approach often exhibits a misalignment between the training objectives of
pretext tasks and those of downstream tasks. This gap can result in the
"negative transfer" problem, wherein the knowledge gained from pre-training
adversely affects performance in the downstream tasks. The surge in
prompt-based learning within Natural Language Processing (NLP) suggests the
potential of adapting a "pre-train, prompt" paradigm to graphs as an
alternative. However, existing graph prompting techniques are tailored to
homogeneous graphs, neglecting the inherent heterogeneity of Web graphs. To
bridge this gap, we propose HetGPT, a general post-training prompting framework
to improve the predictive performance of pre-trained heterogeneous graph neural
networks (HGNNs). The key is the design of a novel prompting function that
integrates a virtual class prompt and a heterogeneous feature prompt, with the
aim to reformulate downstream tasks to mirror pretext tasks. Moreover, HetGPT
introduces a multi-view neighborhood aggregation mechanism, capturing the
complex neighborhood structure in heterogeneous graphs. Extensive experiments
on three benchmark datasets demonstrate HetGPT's capability to enhance the
performance of state-of-the-art HGNNs on semi-supervised node classification.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Cooperative AI via Decentralized Commitment Devices
Abstract: Credible commitment devices have been a popular approach for robust
multi-agent coordination. However, existing commitment mechanisms face
limitations like privacy, integrity, and susceptibility to mediator or user
strategic behavior. It is unclear if the cooperative AI techniques we study are
robust to real-world incentives and attack vectors. However, decentralized
commitment devices that utilize cryptography have been deployed in the wild,
and numerous studies have shown their ability to coordinate algorithmic agents
facing adversarial opponents with significant economic incentives, currently in
the order of several million to billions of dollars. In this paper, we use
examples in the decentralization and, in particular, Maximal Extractable Value
(MEV) (arXiv:1904.05234) literature to illustrate the potential security issues
in cooperative AI. We call for expanded research into decentralized commitments
to advance cooperative AI capabilities for secure coordination in open
environments and empirical testing frameworks to evaluate multi-agent
coordination ability given real-world commitment constraints.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Variational Autoencoders for Feature Exploration and Malignancy Prediction of Lung Lesions
Abstract: Lung cancer is responsible for 21% of cancer deaths in the UK and five-year
survival rates are heavily influenced by the stage the cancer was identified
at. Recent studies have demonstrated the capability of AI methods for accurate
and early diagnosis of lung cancer from routine scans. However, this evidence
has not translated into clinical practice with one barrier being a lack of
interpretable models. This study investigates the application Variational
Autoencoders (VAEs), a type of generative AI model, to lung cancer lesions.
Proposed models were trained on lesions extracted from 3D CT scans in the
LIDC-IDRI public dataset. Latent vector representations of 2D slices produced
by the VAEs were explored through clustering to justify their quality and used
in an MLP classifier model for lung cancer diagnosis, the best model achieved
state-of-the-art metrics of AUC 0.98 and 93.1% accuracy. Cluster analysis shows
the VAE latent space separates the dataset of malignant and benign lesions
based on meaningful feature components including tumour size, shape, patient
and malignancy class. We also include a comparative analysis of the standard
Gaussian VAE (GVAE) and the more recent Dirichlet VAE (DirVAE), which replaces
the prior with a Dirichlet distribution to encourage a more explainable latent
space with disentangled feature representation. Finally, we demonstrate the
potential for latent space traversals corresponding to clinically meaningful
feature changes.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Non-Autoregressive Diffusion-based Temporal Point Processes for Continuous-Time Long-Term Event Prediction
Abstract: Continuous-time long-term event prediction plays an important role in many
application scenarios. Most existing works rely on autoregressive frameworks to
predict event sequences, which suffer from error accumulation, thus
compromising prediction quality. Inspired by the success of denoising diffusion
probabilistic models, we propose a diffusion-based non-autoregressive temporal
point process model for long-term event prediction in continuous time. Instead
of generating events one at a time in an autoregressive way, our model predicts
the future event sequence entirely as a whole. In order to perform diffusion
processes on event sequences, we develop a bidirectional map between target
event sequences and the Euclidean vector space. Furthermore, we design a novel
denoising network to capture both sequential and contextual features for better
sample quality. Extensive experiments are conducted to prove the superiority of
our proposed model over state-of-the-art methods on long-term event prediction
in continuous time. To the best of our knowledge, this is the first work to
apply diffusion methods to long-term event prediction problems.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images
Abstract: Recent advances in generative deep learning have enabled the creation of
high-quality synthetic images in text-to-image generation. Prior work shows
that fine-tuning a pretrained diffusion model on ImageNet and generating
synthetic training images from the finetuned model can enhance an ImageNet
classifier's performance. However, performance degrades as synthetic images
outnumber real ones. In this paper, we explore whether generative fine-tuning
is essential for this improvement and whether it is possible to further scale
up training using more synthetic data. We present a new framework leveraging
off-the-shelf generative models to generate synthetic training images,
addressing multiple challenges: class name ambiguity, lack of diversity in
naive prompts, and domain shifts. Specifically, we leverage large language
models (LLMs) and CLIP to resolve class name ambiguity. To diversify images, we
propose contextualized diversification (CD) and stylized diversification (SD)
methods, also prompted by LLMs. Finally, to mitigate domain shifts, we leverage
domain adaptation techniques with auxiliary batch normalization for synthetic
images. Our framework consistently enhances recognition model performance with
more synthetic data, up to 6x of original ImageNet size showcasing the
potential of synthetic data for improved recognition models and strong
out-of-domain generalization.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysis
Abstract: Game theory, as an analytical tool, is frequently utilized to analyze human
behavior in social science research. With the high alignment between the
behavior of Large Language Models (LLMs) and humans, a promising research
direction is to employ LLMs as substitutes for humans in game experiments,
enabling social science research. However, despite numerous empirical
researches on the combination of LLMs and game theory, the capability
boundaries of LLMs in game theory remain unclear. In this research, we endeavor
to systematically analyze LLMs in the context of game theory. Specifically,
rationality, as the fundamental principle of game theory, serves as the metric
for evaluating players' behavior -- building a clear desire, refining belief
about uncertainty, and taking optimal actions. Accordingly, we select three
classical games (dictator game, Rock-Paper-Scissors, and ring-network game) to
analyze to what extent LLMs can achieve rationality in these three aspects. The
experimental results indicate that even the current state-of-the-art LLM
(GPT-4) exhibits substantial disparities compared to humans in game theory. For
instance, LLMs struggle to build desires based on uncommon preferences, fail to
refine belief from many simple patterns, and may overlook or modify refined
belief when taking actions. Therefore, we consider that introducing LLMs into
game experiments in the field of social science should be approached with
greater caution.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Cone Ranking for Multi-Criteria Decision Making
Abstract: Recently introduced cone distribution functions from statistics are turned
into multi-criteria decision making (MCDM) tools. It is demonstrated that this
procedure can be considered as an upgrade of the weighted sum scalarization
insofar as it absorbs a whole collection of weighted sum scalarizations at once
instead of fixing a particular one in advance. Moreover, situations are
characterized in which different types of rank reversal occur, and it is
explained why this might even be useful for analyzing the ranking procedure. A
few examples will be discussed and a potential application in machine learning
is outlined.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Wired Perspectives: Multi-View Wire Art Embraces Generative AI
Abstract: Creating multi-view wire art (MVWA), a static 3D sculpture with diverse
interpretations from different viewpoints, is a complex task even for skilled
artists. In response, we present DreamWire, an AI system enabling everyone to
craft MVWA easily. Users express their vision through text prompts or
scribbles, freeing them from intricate 3D wire organisation. Our approach
synergises 3D B\'ezier curves, Prim's algorithm, and knowledge distillation
from diffusion models or their variants (e.g., ControlNet). This blend enables
the system to represent 3D wire art, ensuring spatial continuity and overcoming
data scarcity. Extensive evaluation and analysis are conducted to shed insight
on the inner workings of the proposed system, including the trade-off between
connectivity and visual aesthetics.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons
Abstract: With the advancement of maritime unmanned aerial vehicles (UAVs) and deep
learning technologies, the application of UAV-based object detection has become
increasingly significant in the fields of maritime industry and ocean
engineering. Endowed with intelligent sensing capabilities, the maritime UAVs
enable effective and efficient maritime surveillance. To further promote the
development of maritime UAV-based object detection, this paper provides a
comprehensive review of challenges, relative methods, and UAV aerial datasets.
Specifically, in this work, we first briefly summarize four challenges for
object detection on maritime UAVs, i.e., object feature diversity, device
limitation, maritime environment variability, and dataset scarcity. We then
focus on computational methods to improve maritime UAV-based object detection
performance in terms of scale-aware, small object detection, view-aware,
rotated object detection, lightweight methods, and others. Next, we review the
UAV aerial image/video datasets and propose a maritime UAV aerial dataset named
MS2ship for ship detection. Furthermore, we conduct a series of experiments to
present the performance evaluation and robustness analysis of object detection
methods on maritime datasets. Eventually, we give the discussion and outlook on
future works for maritime UAV-based object detection. The MS2ship dataset is
available at
\href{https://github.com/zcj234/MS2ship}{https://github.com/zcj234/MS2ship}.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency
Abstract: Previous entity disambiguation (ED) methods adopt a discriminative paradigm,
where prediction is made based on matching scores between mention context and
candidate entities using length-limited encoders. However, these methods often
struggle to capture explicit discourse-level dependencies, resulting in
incoherent predictions at the abstract level (e.g. topic or category). We
propose CoherentED, an ED system equipped with novel designs aimed at enhancing
the coherence of entity predictions. Our method first introduces an
unsupervised variational autoencoder (VAE) to extract latent topic vectors of
context sentences. This approach not only allows the encoder to handle longer
documents more effectively, conserves valuable input space, but also keeps a
topic-level coherence. Additionally, we incorporate an external category
memory, enabling the system to retrieve relevant categories for undecided
mentions. By employing step-by-step entity decisions, this design facilitates
the modeling of entity-entity interactions, thereby maintaining maximum
coherence at the category level. We achieve new state-of-the-art results on
popular ED benchmarks, with an average improvement of 1.3 F1 points. Our model
demonstrates particularly outstanding performance on challenging long-text
scenarios.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Graph-based Prediction and Planning Policy Network (GP3Net) for scalable self-driving in dynamic environments using Deep Reinforcement Learning
Abstract: Recent advancements in motion planning for Autonomous Vehicles (AVs) show
great promise in using expert driver behaviors in non-stationary driving
environments. However, learning only through expert drivers needs more
generalizability to recover from domain shifts and near-failure scenarios due
to the dynamic behavior of traffic participants and weather conditions. A deep
Graph-based Prediction and Planning Policy Network (GP3Net) framework is
proposed for non-stationary environments that encodes the interactions between
traffic participants with contextual information and provides a decision for
safe maneuver for AV. A spatio-temporal graph models the interactions between
traffic participants for predicting the future trajectories of those
participants. The predicted trajectories are utilized to generate a future
occupancy map around the AV with uncertainties embedded to anticipate the
evolving non-stationary driving environments. Then the contextual information
and future occupancy maps are input to the policy network of the GP3Net
framework and trained using Proximal Policy Optimization (PPO) algorithm. The
proposed GP3Net performance is evaluated on standard CARLA benchmarking
scenarios with domain shifts of traffic patterns (urban, highway, and mixed).
The results show that the GP3Net outperforms previous state-of-the-art
imitation learning-based planning models for different towns. Further, in
unseen new weather conditions, GP3Net completes the desired route with fewer
traffic infractions. Finally, the results emphasize the advantage of including
the prediction module to enhance safety measures in non-stationary
environments.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: DeepLearningBrasil@LT-EDI-2023: Exploring Deep Learning Techniques for Detecting Depression in Social Media Text
Abstract: In this paper, we delineate the strategy employed by our team,
DeepLearningBrasil, which secured us the first place in the shared task
DepSign-LT-EDI@RANLP-2023, achieving a 47.0% Macro F1-Score and a notable 2.4%
advantage. The task was to classify social media texts into three distinct
levels of depression - "not depressed," "moderately depressed," and "severely
depressed." Leveraging the power of the RoBERTa and DeBERTa models, we further
pre-trained them on a collected Reddit dataset, specifically curated from
mental health-related Reddit's communities (Subreddits), leading to an enhanced
understanding of nuanced mental health discourse. To address lengthy textual
data, we used truncation techniques that retained the essence of the content by
focusing on its beginnings and endings. Our model was robust against unbalanced
data by incorporating sample weights into the loss. Cross-validation and
ensemble techniques were then employed to combine our k-fold trained models,
delivering an optimal solution. The accompanying code is made available for
transparency and further development.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: UniTeam: Open Vocabulary Mobile Manipulation Challenge
Abstract: This report introduces our UniTeam agent - an improved baseline for the
"HomeRobot: Open Vocabulary Mobile Manipulation" challenge. The challenge poses
problems of navigation in unfamiliar environments, manipulation of novel
objects, and recognition of open-vocabulary object classes. This challenge aims
to facilitate cross-cutting research in embodied AI using recent advances in
machine learning, computer vision, natural language, and robotics. In this
work, we conducted an exhaustive evaluation of the provided baseline agent;
identified deficiencies in perception, navigation, and manipulation skills; and
improved the baseline agent's performance. Notably, enhancements were made in
perception - minimizing misclassifications; navigation - preventing infinite
loop commitments; picking - addressing failures due to changing object
visibility; and placing - ensuring accurate positioning for successful object
placement.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: IG Captioner: Information Gain Captioners are Strong Zero-shot Classifiers
Abstract: Generative training has been demonstrated to be powerful for building
visual-language models. However, on zero-shot discriminative benchmarks, there
is still a performance gap between models trained with generative and
discriminative objectives. In this paper, we aim to narrow this gap by
improving the efficacy of generative training on classification tasks, without
any finetuning processes or additional modules.
  Specifically, we focus on narrowing the gap between the generative captioner
and the CLIP classifier. We begin by analysing the predictions made by the
captioner and classifier and observe that the caption generation inherits the
distribution bias from the language model trained with pure text modality,
making it less grounded on the visual signal. To tackle this problem, we
redesign the scoring objective for the captioner to alleviate the
distributional bias and focus on measuring the gain of information brought by
the visual inputs. We further design a generative training objective to match
the evaluation objective. We name our model trained and evaluated from the
novel procedures as Information Gain (IG) captioner. We pretrain the models on
the public Laion-5B dataset and perform a series of discriminative evaluations.
For the zero-shot classification on ImageNet, IG captioner achieves $> 18\%$
improvements over the standard captioner, achieving comparable performances
with the CLIP classifier. IG captioner also demonstrated strong performance on
zero-shot image-text retrieval tasks on MSCOCO and Flickr30K. We hope this
paper inspires further research towards unifying generative and discriminative
training procedures for visual-language models.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: WAVER: Writing-style Agnostic Video Retrieval via Distilling Vision-Language Models Through Open-Vocabulary Knowledge
Abstract: Text-video retrieval, a prominent sub-field within the broader domain of
multimedia content management, has witnessed remarkable growth and innovation
over the past decade. However, existing methods assume the video scenes are
consistent and the description annotators are unbiased. These limitations fail
to align with fluid real-world scenarios, and descriptions can be influenced by
annotator biases, diverse writing styles, and varying textual perspectives. To
overcome the aforementioned problems, we introduce WAVER, a cross-domain
knowledge distillation mechanism designed to tackle the challenge of handling
writing-style agnostics. WAVER capitalizes on the open-vocabulary properties
inherent in pre-trained vision-language models and employs an implicit
knowledge distillation approach to transfer text-based knowledge from a teacher
model to a vision-based student. Empirical studies conducted across four
standard benchmark datasets, encompassing various settings, provide compelling
evidence that \WAVER can achieve state-of-the-art performance in text-video
retrieval tasks while handling writing-style variations.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models
Abstract: Language models have shown promise in various tasks but can be affected by
undesired data during training, fine-tuning, or alignment. For example, if some
unsafe conversations are wrongly annotated as safe ones, the model fine-tuned
on these samples may be harmful. Therefore, the correctness of annotations,
i.e., the credibility of the dataset, is important. This study focuses on the
credibility of real-world datasets, including the popular benchmarks Jigsaw
Civil Comments, Anthropic Harmless & Red Team, PKU BeaverTails & SafeRLHF, that
can be used for training a harmless language model. Given the cost and
difficulty of cleaning these datasets by humans, we introduce a systematic
framework for evaluating the credibility of datasets, identifying label errors,
and evaluating the influence of noisy labels in the curated language data,
specifically focusing on unsafe comments and conversation classification. With
the framework, we find and fix an average of 6.16% label errors in 11 datasets
constructed from the above benchmarks. The data credibility and downstream
learning performance can be remarkably improved by directly fixing label
errors, indicating the significance of cleaning existing real-world datasets.
Open-source: https://github.com/Docta-ai/docta.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: The Case for Scalable, Data-Driven Theory: A Paradigm for Scientific Progress in NLP
Abstract: I propose a paradigm for scientific progress in NLP centered around
developing scalable, data-driven theories of linguistic structure. The idea is
to collect data in tightly scoped, carefully defined ways which allow for
exhaustive annotation of behavioral phenomena of interest, and then use machine
learning to construct explanatory theories of these phenomena which can form
building blocks for intelligible AI systems. After laying some conceptual
groundwork, I describe several investigations into data-driven theories of
shallow semantic structure using Question-Answer driven Semantic Role Labeling
(QA-SRL), a schema for annotating verbal predicate-argument relations using
highly constrained question-answer pairs. While this only scratches the surface
of the complex language behaviors of interest in AI, I outline principles for
data collection and theoretical modeling which can inform future scientific
progress. This note summarizes and draws heavily on my PhD thesis.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: The Hidden Linear Structure in Score-Based Models and its Application
Abstract: Score-based models have achieved remarkable results in the generative
modeling of many domains. By learning the gradient of smoothed data
distribution, they can iteratively generate samples from complex distribution
e.g. natural images.
  However, is there any universal structure in the gradient field that will
eventually be learned by any neural network? Here, we aim to find such
structures through a normative analysis of the score function.
  First, we derived the closed-form solution to the scored-based model with a
Gaussian score. We claimed that for well-trained diffusion models, the learned
score at a high noise scale is well approximated by the linear score of
Gaussian. We demonstrated this through empirical validation of pre-trained
images diffusion model and theoretical analysis of the score function. This
finding enabled us to precisely predict the initial diffusion trajectory using
the analytical solution and to accelerate image sampling by 15-30\% by skipping
the initial phase without sacrificing image quality. Our finding of the linear
structure in the score-based model has implications for better model design and
data pre-processing.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Multi Time Scale World Models
Abstract: Intelligent agents use internal world models to reason and make predictions
about different courses of their actions at many scales. Devising learning
paradigms and architectures that allow machines to learn world models that
operate at multiple levels of temporal abstractions while dealing with complex
uncertainty predictions is a major technical hurdle. In this work, we propose a
probabilistic formalism to learn multi-time scale world models which we call
the Multi Time Scale State Space (MTS3) model. Our model uses a computationally
efficient inference scheme on multiple time scales for highly accurate
long-horizon predictions and uncertainty estimates over several seconds into
the future. Our experiments, which focus on action conditional long horizon
future predictions, show that MTS3 outperforms recent methods on several system
identification benchmarks including complex simulated and real-world dynamical
systems. Code is available at this repository: https://github.com/ALRhub/MTS3.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approach
Abstract: Phishing and spam detection is long standing challenge that has been the
subject of much academic research. Large Language Models (LLM) have vast
potential to transform society and provide new and innovative approaches to
solve well-established challenges. Phishing and spam have caused financial
hardships and lost time and resources to email users all over the world and
frequently serve as an entry point for ransomware threat actors. While
detection approaches exist, especially heuristic-based approaches, LLMs offer
the potential to venture into a new unexplored area for understanding and
solving this challenge. LLMs have rapidly altered the landscape from business,
consumers, and throughout academia and demonstrate transformational potential
for the potential of society. Based on this, applying these new and innovative
approaches to email detection is a rational next step in academic research. In
this work, we present IPSDM, our model based on fine-tuning the BERT family of
models to specifically detect phishing and spam email. We demonstrate our
fine-tuned version, IPSDM, is able to better classify emails in both unbalanced
and balanced datasets. This work serves as an important first step towards
employing LLMs to improve the security of our information systems.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation
Abstract: Data augmentation is a crucial component in training neural networks to
overcome the limitation imposed by data size, and several techniques have been
studied for time series. Although these techniques are effective in certain
tasks, they have yet to be generalized to time series benchmarks. We find that
current data augmentation techniques ruin the core information contained within
the frequency domain. To address this issue, we propose a simple strategy to
preserve spectral information (SimPSI) in time series data augmentation. SimPSI
preserves the spectral information by mixing the original and augmented input
spectrum weighted by a preservation map, which indicates the importance score
of each frequency. Specifically, our experimental contributions are to build
three distinct preservation maps: magnitude spectrum, saliency map, and
spectrum-preservative map. We apply SimPSI to various time series data
augmentations and evaluate its effectiveness across a wide range of time series
benchmarks. Our experimental results support that SimPSI considerably enhances
the performance of time series data augmentations by preserving core spectral
information. The source code used in the paper is available at
https://github.com/Hyun-Ryu/simpsi.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Sequential Planning in Large Partially Observable Environments guided by LLMs
Abstract: Sequential planning in large state space and action space quickly becomes
intractable due to combinatorial explosion of the search space. Heuristic
methods, like monte-carlo tree search, though effective for large state space,
but struggle if action space is large. Pure reinforcement learning methods,
relying only on reward signals, needs prohibitively large interactions with the
environment to device a viable plan. If the state space, observations and
actions can be represented in natural language then Large Language models (LLM)
can be used to generate action plans. Recently several such goal-directed
agents like Reflexion, CLIN, SayCan were able to surpass the performance of
other state-of-the-art methods with minimum or no task specific training. But
they still struggle with exploration and get stuck in local optima. Their
planning capabilities are limited by the limited reasoning capability of the
foundational LLMs on text data. We propose a hybrid agent "neoplanner", that
synergizes both state space search with queries to foundational LLM to get the
best action plan. The reward signals are quantitatively used to drive the
search. A balance of exploration and exploitation is maintained by maximizing
upper confidence bounds of values of states. In places where random exploration
is needed, the LLM is queried to generate an action plan. Learnings from each
trial are stored as entity relationships in text format. Those are used in
future queries to the LLM for continual improvement. Experiments in the
Scienceworld environment reveals a 124% improvement from the current best
method in terms of average reward gained across multiple tasks.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Quantum learning and essential cognition under the traction of meta-characteristics in an open world
Abstract: Artificial intelligence has made significant progress in the Close World
problem, being able to accurately recognize old knowledge through training and
classification. However, AI faces significant challenges in the Open World
problem, as it involves a new and unknown exploration journey. AI is not
inherently proactive in exploration, and its challenge lies in not knowing how
to approach and adapt to the unknown world. How do humans acquire knowledge of
the unknown world. Humans identify new knowledge through intrinsic cognition.
In the process of recognizing new colors, the cognitive cues are different from
known color features and involve hue, saturation, brightness, and other
characteristics. When AI encounters objects with different features in the new
world, it faces another challenge: where are the distinguishing features
between influential features of new and old objects? AI often mistakes a new
world's brown bear for a known dog because it has not learned the differences
in feature distributions between knowledge systems. This is because things in
the new and old worlds have different units and dimensions for their features.
This paper proposes an open-world model and elemental feature system that
focuses on fundamentally recognizing the distribution differences in objective
features between the new and old worlds. The quantum tunneling effect of
learning ability in the new and old worlds is realized through the tractive
force of meta-characteristic. The outstanding performance of the model system
in learning new knowledge (using pedestrian re-identification datasets as an
example) demonstrates that AI has acquired the ability to recognize the new
world with an accuracy of $96.71\%$ at most and has gained the capability to
explore new knowledge, similar to humans.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Evaluative Item-Contrastive Explanations in Rankings
Abstract: The remarkable success of Artificial Intelligence in advancing automated
decision-making is evident both in academia and industry. Within the plethora
of applications, ranking systems hold significant importance in various
domains. This paper advocates for the application of a specific form of
Explainable AI -- namely, contrastive explanations -- as particularly
well-suited for addressing ranking problems. This approach is especially potent
when combined with an Evaluative AI methodology, which conscientiously
evaluates both positive and negative aspects influencing a potential ranking.
Therefore, the present work introduces Evaluative Item-Contrastive Explanations
tailored for ranking systems and illustrates its application and
characteristics through an experiment conducted on publicly available data.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: SAGE: Smart home Agent with Grounded Execution
Abstract: This article introduces SAGE (Smart home Agent with Grounded Execution), a
framework designed to maximize the flexibility of smart home assistants by
replacing manually-defined inference logic with an LLM-powered autonomous agent
system. SAGE integrates information about user preferences, device states, and
external factors (such as weather and TV schedules) through the orchestration
of a collection of tools. SAGE's capabilities include learning user preferences
from natural-language utterances, interacting with devices by reading their API
documentation, writing code to continuously monitor devices, and understanding
natural device references. To evaluate SAGE, we develop a benchmark of 43
highly challenging smart home tasks, where SAGE successfully achieves 23 tasks,
significantly outperforming existing LLM-enabled baselines (5/43).

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Performance Trade-offs of Watermarking Large Language Models
Abstract: Amidst growing concerns of large language models (LLMs) being misused for
generating misinformation or completing homework assignments, watermarking has
emerged as an effective solution for distinguishing human-written and
LLM-generated text. A prominent watermarking strategy is to embed a signal into
generated text by upsampling a (pseudorandomly-chosen) subset of tokens at
every generation step. Although this signal is imperceptible to a human reader,
it is detectable through statistical testing. However, implanting such signals
alters the model's output distribution and can have unintended effects when
watermarked LLMs are used for downstream applications. In this work, we
evaluate the performance of watermarked LLMs on a diverse suite of tasks,
including text classification, textual entailment, reasoning, question
answering, translation, summarization, and language modeling. We find that
watermarking has negligible impact on the performance of tasks posed as k-class
classification problems in the average case. However, the accuracy can plummet
to that of a random classifier for some scenarios (that occur with
non-negligible probability). Tasks that are cast as multiple-choice questions
and short-form generation are surprisingly unaffected by watermarking. For
long-form generation tasks, including summarization and translation, we see a
drop of 15-20% in the performance due to watermarking. Our findings highlight
the trade-offs that users should be cognizant of when using watermarked models,
and point to cases where future research could improve existing trade-offs.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: VideoLCM: Video Latent Consistency Model
Abstract: Consistency models have demonstrated powerful capability in efficient image
generation and allowed synthesis within a few sampling steps, alleviating the
high computational cost in diffusion models. However, the consistency model in
the more challenging and resource-consuming video generation is still less
explored. In this report, we present the VideoLCM framework to fill this gap,
which leverages the concept of consistency models from image generation to
efficiently synthesize videos with minimal steps while maintaining high
quality. VideoLCM builds upon existing latent video diffusion models and
incorporates consistency distillation techniques for training the latent
consistency model. Experimental results reveal the effectiveness of our
VideoLCM in terms of computational efficiency, fidelity and temporal
consistency. Notably, VideoLCM achieves high-fidelity and smooth video
synthesis with only four sampling steps, showcasing the potential for real-time
synthesis. We hope that VideoLCM can serve as a simple yet effective baseline
for subsequent research. The source code and models will be publicly available.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Transforming organic chemistry research paradigms: moving from manual efforts to the intersection of automation and artificial intelligence
Abstract: Organic chemistry is undergoing a major paradigm shift, moving from a
labor-intensive approach to a new era dominated by automation and artificial
intelligence (AI). This transformative shift is being driven by technological
advances, the ever-increasing demand for greater research efficiency and
accuracy, and the burgeoning growth of interdisciplinary research. AI models,
supported by computational power and algorithms, are drastically reshaping
synthetic planning and introducing groundbreaking ways to tackle complex
molecular synthesis. In addition, autonomous robotic systems are rapidly
accelerating the pace of discovery by performing tedious tasks with
unprecedented speed and precision. This article examines the multiple
opportunities and challenges presented by this paradigm shift and explores its
far-reaching implications. It provides valuable insights into the future
trajectory of organic chemistry research, which is increasingly defined by the
synergistic interaction of automation and AI.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Career Path Prediction using Resume Representation Learning and Skill-based Matching
Abstract: The impact of person-job fit on job satisfaction and performance is widely
acknowledged, which highlights the importance of providing workers with next
steps at the right time in their career. This task of predicting the next step
in a career is known as career path prediction, and has diverse applications
such as turnover prevention and internal job mobility. Existing methods to
career path prediction rely on large amounts of private career history data to
model the interactions between job titles and companies. We propose leveraging
the unexplored textual descriptions that are part of work experience sections
in resumes. We introduce a structured dataset of 2,164 anonymized career
histories, annotated with ESCO occupation labels. Based on this dataset, we
present a novel representation learning approach, CareerBERT, specifically
designed for work history data. We develop a skill-based model and a text-based
model for career path prediction, which achieve 35.24% and 39.61% recall@10
respectively on our dataset. Finally, we show that both approaches are
complementary as a hybrid approach achieves the strongest result with 43.01%
recall@10.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: A Foundational Framework and Methodology for Personalized Early and Timely Diagnosis
Abstract: Early diagnosis of diseases holds the potential for deep transformation in
healthcare by enabling better treatment options, improving long-term survival
and quality of life, and reducing overall cost. With the advent of medical big
data, advances in diagnostic tests as well as in machine learning and
statistics, early or timely diagnosis seems within reach. Early diagnosis
research often neglects the potential for optimizing individual diagnostic
paths. To enable personalized early diagnosis, a foundational framework is
needed that delineates the diagnosis process and systematically identifies the
time-dependent value of various diagnostic tests for an individual patient
given their unique characteristics. Here, we propose the first foundational
framework for early and timely diagnosis. It builds on decision-theoretic
approaches to outline the diagnosis process and integrates machine learning and
statistical methodology for estimating the optimal personalized diagnostic
path. To describe the proposed framework as well as possibly other frameworks,
we provide essential definitions.
  The development of a foundational framework is necessary for several reasons:
1) formalism provides clarity for the development of decision support tools; 2)
observed information can be complemented with estimates of the future patient
trajectory; 3) the net benefit of counterfactual diagnostic paths and
associated uncertainties can be modeled for individuals 4) 'early' and 'timely'
diagnosis can be clearly defined; 5) a mechanism emerges for assessing the
value of technologies in terms of their impact on personalized early diagnosis,
resulting health outcomes and incurred costs.
  Finally, we hope that this foundational framework will unlock the
long-awaited potential of timely diagnosis and intervention, leading to
improved outcomes for patients and higher cost-effectiveness for healthcare
systems.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: MuST: Multimodal Spatiotemporal Graph-Transformer for Hospital Readmission Prediction
Abstract: Hospital readmission prediction is considered an essential approach to
decreasing readmission rates, which is a key factor in assessing the quality
and efficacy of a healthcare system. Previous studies have extensively utilized
three primary modalities, namely electronic health records (EHR), medical
images, and clinical notes, to predict hospital readmissions. However, the
majority of these studies did not integrate information from all three
modalities or utilize the spatiotemporal relationships present in the dataset.
This study introduces a novel model called the Multimodal Spatiotemporal
Graph-Transformer (MuST) for predicting hospital readmissions. By employing
Graph Convolution Networks and temporal transformers, we can effectively
capture spatial and temporal dependencies in EHR and chest radiographs. We then
propose a fusion transformer to combine the spatiotemporal features from the
two modalities mentioned above with the features from clinical notes extracted
by a pre-trained, domain-specific transformer. We assess the effectiveness of
our methods using the latest publicly available dataset, MIMIC-IV. The
experimental results indicate that the inclusion of multimodal features in MuST
improves its performance in comparison to unimodal methods. Furthermore, our
proposed pipeline outperforms the current leading methods in the prediction of
hospital readmissions.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: MetaSymNet: A Dynamic Symbolic Regression Network Capable of Evolving into Arbitrary Formulations
Abstract: Mathematical formulas serve as the means of communication between humans and
nature, encapsulating the operational laws governing natural phenomena. The
concise formulation of these laws is a crucial objective in scientific research
and an important challenge for artificial intelligence (AI). While traditional
artificial neural networks (MLP) excel at data fitting, they often yield
uninterpretable black box results that hinder our understanding of the
relationship between variables x and predicted values y. Moreover, the fixed
network architecture in MLP often gives rise to redundancy in both network
structure and parameters. To address these issues, we propose MetaSymNet, a
novel neural network that dynamically adjusts its structure in real-time,
allowing for both expansion and contraction. This adaptive network employs the
PANGU meta function as its activation function, which is a unique type capable
of evolving into various basic functions during training to compose
mathematical formulas tailored to specific needs. We then evolve the neural
network into a concise, interpretable mathematical expression. To evaluate
MetaSymNet's performance, we compare it with four state-of-the-art symbolic
regression algorithms across more than 10 public datasets comprising 222
formulas. Our experimental results demonstrate that our algorithm outperforms
others consistently regardless of noise presence or absence. Furthermore, we
assess MetaSymNet against MLP and SVM regarding their fitting ability and
extrapolation capability, these are two essential aspects of machine learning
algorithms. The findings reveal that our algorithm excels in both areas.
Finally, we compared MetaSymNet with MLP using iterative pruning in network
structure complexity. The results show that MetaSymNet's network structure
complexity is obviously less than MLP under the same goodness of fit.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Aggregate, Decompose, and Fine-Tune: A Simple Yet Effective Factor-Tuning Method for Vision Transformer
Abstract: Recent advancements have illuminated the efficacy of some
tensorization-decomposition Parameter-Efficient Fine-Tuning methods like LoRA
and FacT in the context of Vision Transformers (ViT). However, these methods
grapple with the challenges of inadequately addressing inner- and cross-layer
redundancy. To tackle this issue, we introduce EFfective Factor-Tuning (EFFT),
a simple yet effective fine-tuning method. Within the VTAB-1K dataset, our EFFT
surpasses all baselines, attaining state-of-the-art performance with a
categorical average of 75.9% in top-1 accuracy with only 0.28% of the
parameters for full fine-tuning. Considering the simplicity and efficacy of
EFFT, it holds the potential to serve as a foundational benchmark. The code and
model are now available at
https://github.com/Dongping-Chen/EFFT-EFfective-Factor-Tuning.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Privacy-Aware Document Visual Question Answering
Abstract: Document Visual Question Answering (DocVQA) is a fast growing branch of
document understanding. Despite the fact that documents contain sensitive or
copyrighted information, none of the current DocVQA methods offers strong
privacy guarantees.
  In this work, we explore privacy in the domain of DocVQA for the first time.
We highlight privacy issues in state of the art multi-modal LLM models used for
DocVQA, and explore possible solutions.
  Specifically, we focus on the invoice processing use case as a realistic,
widely used scenario for document understanding, and propose a large scale
DocVQA dataset comprising invoice documents and associated questions and
answers. We employ a federated learning scheme, that reflects the real-life
distribution of documents in different businesses, and we explore the use case
where the ID of the invoice issuer is the sensitive information to be
protected.
  We demonstrate that non-private models tend to memorise, behaviour that can
lead to exposing private information. We then evaluate baseline training
schemes employing federated learning and differential privacy in this
multi-modal scenario, where the sensitive information might be exposed through
any of the two input modalities: vision (document image) or language (OCR
tokens).
  Finally, we design an attack exploiting the memorisation effect of the model,
and demonstrate its effectiveness in probing different DocVQA models.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: New Epochs in AI Supervision: Design and Implementation of an Autonomous Radiology AI Monitoring System
Abstract: With the increasingly widespread adoption of AI in healthcare, maintaining
the accuracy and reliability of AI models in clinical practice has become
crucial. In this context, we introduce novel methods for monitoring the
performance of radiology AI classification models in practice, addressing the
challenges of obtaining real-time ground truth for performance monitoring. We
propose two metrics - predictive divergence and temporal stability - to be used
for preemptive alerts of AI performance changes. Predictive divergence,
measured using Kullback-Leibler and Jensen-Shannon divergences, evaluates model
accuracy by comparing predictions with those of two supplementary models.
Temporal stability is assessed through a comparison of current predictions
against historical moving averages, identifying potential model decay or data
drift. This approach was retrospectively validated using chest X-ray data from
a single-center imaging clinic, demonstrating its effectiveness in maintaining
AI model reliability. By providing continuous, real-time insights into model
performance, our system ensures the safe and effective use of AI in clinical
decision-making, paving the way for more robust AI integration in healthcare

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Linear Mode Connectivity in Sparse Neural Networks
Abstract: With the rise in interest of sparse neural networks, we study how neural
network pruning with synthetic data leads to sparse networks with unique
training properties. We find that distilled data, a synthetic summarization of
the real data, paired with Iterative Magnitude Pruning (IMP) unveils a new
class of sparse networks that are more stable to SGD noise on the real data,
than either the dense model, or subnetworks found with real data in IMP. That
is, synthetically chosen subnetworks often train to the same minima, or exhibit
linear mode connectivity. We study this through linear interpolation, loss
landscape visualizations, and measuring the diagonal of the hessian. While
dataset distillation as a field is still young, we find that these properties
lead to synthetic subnetworks matching the performance of traditional IMP with
up to 150x less training points in settings where distilled data applies.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Bridging the Gap: A Unified Video Comprehension Framework for Moment Retrieval and Highlight Detection
Abstract: Video Moment Retrieval (MR) and Highlight Detection (HD) have attracted
significant attention due to the growing demand for video analysis. Recent
approaches treat MR and HD as similar video grounding problems and address them
together with transformer-based architecture. However, we observe that the
emphasis of MR and HD differs, with one necessitating the perception of local
relationships and the other prioritizing the understanding of global contexts.
Consequently, the lack of task-specific design will inevitably lead to
limitations in associating the intrinsic specialty of two tasks. To tackle the
issue, we propose a Unified Video COMprehension framework (UVCOM) to bridge the
gap and jointly solve MR and HD effectively. By performing progressive
integration on intra and inter-modality across multi-granularity, UVCOM
achieves the comprehensive understanding in processing a video. Moreover, we
present multi-aspect contrastive learning to consolidate the local relation
modeling and global knowledge accumulation via well aligned multi-modal space.
Extensive experiments on QVHighlights, Charades-STA, TACoS , YouTube Highlights
and TVSum datasets demonstrate the effectiveness and rationality of UVCOM which
outperforms the state-of-the-art methods by a remarkable margin.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: User Persona Identification and New Service Adaptation Recommendation
Abstract: Providing a personalized user experience on information dense webpages helps
users in reaching their end-goals sooner. We explore an automated approach to
identifying user personas by leveraging high dimensional trajectory information
from user sessions on webpages. While neural collaborative filtering (NCF)
approaches pay little attention to token semantics, our method introduces
SessionBERT, a Transformer-backed language model trained from scratch on the
masked language modeling (mlm) objective for user trajectories (pages,
metadata, billing in a session) aiming to capture semantics within them. Our
results show that representations learned through SessionBERT are able to
consistently outperform a BERT-base model providing a 3% and 1% relative
improvement in F1-score for predicting page links and next services. We
leverage SessionBERT and extend it to provide recommendations (top-5) for the
next most-relevant services that a user would be likely to use. We achieve a
HIT@5 of 58% from our recommendation model.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: Towards Learning a Generalist Model for Embodied Navigation
Abstract: Building a generalist agent that can interact with the world is the
intriguing target of AI systems, thus spurring the research for embodied
navigation, where an agent is required to navigate according to instructions or
respond to queries. Despite the major progress attained, previous works
primarily focus on task-specific agents and lack generalizability to unseen
scenarios. Recently, LLMs have presented remarkable capabilities across various
fields, and provided a promising opportunity for embodied navigation. Drawing
on this, we propose the first generalist model for embodied navigation,
NaviLLM. It adapts LLMs to embodied navigation by introducing schema-based
instruction. The schema-based instruction flexibly casts various tasks into
generation problems, thereby unifying a wide range of tasks. This approach
allows us to integrate diverse data sources from various datasets into the
training, equipping NaviLLM with a wide range of capabilities required by
embodied navigation. We conduct extensive experiments to evaluate the
performance and generalizability of our model. The experimental results
demonstrate that our unified model achieves state-of-the-art performance on
CVDN, SOON, and ScanQA. Specifically, it surpasses the previous
stats-of-the-art method by a significant margin of 29% in goal progress on
CVDN. Moreover, our model also demonstrates strong generalizability and
presents impressive results on unseen tasks, e.g., embodied question answering
and 3D captioning.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Automated Process Planning Based on a Semantic Capability Model and SMT
Abstract: In research of manufacturing systems and autonomous robots, the term
capability is used for a machine-interpretable specification of a system
function. Approaches in this research area develop information models that
capture all information relevant to interpret the requirements, effects and
behavior of functions. These approaches are intended to overcome the
heterogeneity resulting from the various types of processes and from the large
number of different vendors. However, these models and associated methods do
not offer solutions for automated process planning, i.e. finding a sequence of
individual capabilities required to manufacture a certain product or to
accomplish a mission using autonomous robots. Instead, this is a typical task
for AI planning approaches, which unfortunately require a high effort to create
the respective planning problem descriptions. In this paper, we present an
approach that combines these two topics: Starting from a semantic capability
model, an AI planning problem is automatically generated. The planning problem
is encoded using Satisfiability Modulo Theories and uses an existing solver to
find valid capability sequences including required parameter values. The
approach also offers possibilities to integrate existing human expertise and to
provide explanations for human operators in order to help understand planning
decisions.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network
Abstract: Ball mills play a critical role in modern mining operations, making their
bearing failures a significant concern due to the potential loss of production
efficiency and economic consequences. This paper presents an anomaly detection
method based on Deep Convolutional Auto-encoding Neural Networks (DCAN) for
addressing the issue of ball mill bearing fault detection. The proposed
approach leverages vibration data collected during normal operation for
training, overcoming challenges such as labeling issues and data imbalance
often encountered in supervised learning methods. DCAN includes the modules of
convolutional feature extraction and transposed convolutional feature
reconstruction, demonstrating exceptional capabilities in signal processing and
feature extraction. Additionally, the paper describes the practical deployment
of the DCAN-based anomaly detection model for bearing fault detection,
utilizing data from the ball mill bearings of Wuhan Iron & Steel Resources
Group and fault data from NASA's bearing vibration dataset. Experimental
results validate the DCAN model's reliability in recognizing fault vibration
patterns. This method holds promise for enhancing bearing fault detection
efficiency, reducing production interruptions, and lowering maintenance costs.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: A density estimation perspective on learning from pairwise human preferences
Abstract: Learning from human feedback (LHF) -- and in particular learning from
pairwise preferences -- has recently become a crucial ingredient in training
large language models (LLMs), and has been the subject of much research. Most
recent works frame it as a reinforcement learning problem, where a reward
function is learned from pairwise preference data and the LLM is treated as a
policy which is adapted to maximize the rewards, often under additional
regularization constraints. We propose an alternative interpretation which
centers on the generative process for pairwise preferences and treats LHF as a
density estimation problem. We provide theoretical and empirical results
showing that for a family of generative processes defined via preference
behavior distribution equations, training a reward function on pairwise
preferences effectively models an annotator's implicit preference distribution.
Finally, we discuss and present findings on "annotator misspecification" --
failure cases where wrong modeling assumptions are made about annotator
behavior, resulting in poorly-adapted models -- suggesting that approaches that
learn from pairwise human preferences could have trouble learning from a
population of annotators with diverse viewpoints.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Deep Tensor Network
Abstract: In this paper, we delve into the foundational principles of tensor
categories, harnessing the universal property of the tensor product to pioneer
novel methodologies in deep network architectures. Our primary contribution is
the introduction of the Tensor Attention and Tensor Interaction Mechanism, a
groundbreaking approach that leverages the tensor category to enhance the
computational efficiency and the expressiveness of deep networks, and can even
be generalized into the quantum realm.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: On The Relationship Between Universal Adversarial Attacks And Sparse Representations
Abstract: The prominent success of neural networks, mainly in computer vision tasks, is
increasingly shadowed by their sensitivity to small, barely perceivable
adversarial perturbations in image input.
  In this work, we aim at explaining this vulnerability through the framework
of sparsity.
  We show the connection between adversarial attacks and sparse
representations, with a focus on explaining the universality and
transferability of adversarial examples in neural networks.
  To this end, we show that sparse coding algorithms, and the neural
network-based learned iterative shrinkage thresholding algorithm (LISTA) among
them, suffer from this sensitivity, and that common attacks on neural networks
can be expressed as attacks on the sparse representation of the input image.
The phenomenon that we observe holds true also when the network is agnostic to
the sparse representation and dictionary, and thus can provide a possible
explanation for the universality and transferability of adversarial attacks.
  The code is available at
https://github.com/danawr/adversarial_attacks_and_sparse_representations.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Multicoated and Folded Graph Neural Networks with Strong Lottery Tickets
Abstract: The Strong Lottery Ticket Hypothesis (SLTH) demonstrates the existence of
high-performing subnetworks within a randomly initialized model, discoverable
through pruning a convolutional neural network (CNN) without any weight
training. A recent study, called Untrained GNNs Tickets (UGT), expanded SLTH
from CNNs to shallow graph neural networks (GNNs). However, discrepancies
persist when comparing baseline models with learned dense weights.
Additionally, there remains an unexplored area in applying SLTH to deeper GNNs,
which, despite delivering improved accuracy with additional layers, suffer from
excessive memory requirements. To address these challenges, this work utilizes
Multicoated Supermasks (M-Sup), a scalar pruning mask method, and implements it
in GNNs by proposing a strategy for setting its pruning thresholds adaptively.
In the context of deep GNNs, this research uncovers the existence of untrained
recurrent networks, which exhibit performance on par with their trained
feed-forward counterparts. This paper also introduces the Multi-Stage Folding
and Unshared Masks methods to expand the search space in terms of both
architecture and parameters. Through the evaluation of various datasets,
including the Open Graph Benchmark (OGB), this work establishes a triple-win
scenario for SLTH-based GNNs: by achieving high sparsity, competitive
performance, and high memory efficiency with up to 98.7\% reduction, it
demonstrates suitability for energy-efficient graph processing.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: MultiIoT: Towards Large-scale Multisensory Learning for the Internet of Things
Abstract: The Internet of Things (IoT), the network integrating billions of smart
physical devices embedded with sensors, software, and communication
technologies for the purpose of connecting and exchanging data with other
devices and systems, is a critical and rapidly expanding component of our
modern world. The IoT ecosystem provides a rich source of real-world modalities
such as motion, thermal, geolocation, imaging, depth, sensors, video, and audio
for prediction tasks involving the pose, gaze, activities, and gestures of
humans as well as the touch, contact, pose, 3D of physical objects. Machine
learning presents a rich opportunity to automatically process IoT data at
scale, enabling efficient inference for impact in understanding human
wellbeing, controlling physical devices, and interconnecting smart cities. To
develop machine learning technologies for IoT, this paper proposes MultiIoT,
the most expansive IoT benchmark to date, encompassing over 1.15 million
samples from 12 modalities and 8 tasks. MultiIoT introduces unique challenges
involving (1) learning from many sensory modalities, (2) fine-grained
interactions across long temporal ranges, and (3) extreme heterogeneity due to
unique structure and noise topologies in real-world sensors. We also release a
set of strong modeling baselines, spanning modality and task-specific methods
to multisensory and multitask models to encourage future research in
multisensory representation learning for IoT.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Sparse Training of Discrete Diffusion Models for Graph Generation
Abstract: Generative models for graphs often encounter scalability challenges due to
the inherent need to predict interactions for every node pair. Despite the
sparsity often exhibited by real-world graphs, the unpredictable sparsity
patterns of their adjacency matrices, stemming from their unordered nature,
leads to quadratic computational complexity. In this work, we introduce
SparseDiff, a denoising diffusion model for graph generation that is able to
exploit sparsity during its training phase. At the core of SparseDiff is a
message-passing neural network tailored to predict only a subset of edges
during each forward pass. When combined with a sparsity-preserving noise model,
this model can efficiently work with edge lists representations of graphs,
paving the way for scalability to much larger structures. During the sampling
phase, SparseDiff iteratively populates the adjacency matrix from its prior
state, ensuring prediction of the full graph while controlling memory
utilization. Experimental results show that SparseDiff simultaneously matches
state-of-the-art in generation performance on both small and large graphs,
highlighting the versatility of our method.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Workflow-Guided Response Generation for Task-Oriented Dialogue
Abstract: Task-oriented dialogue (TOD) systems aim to achieve specific goals through
interactive dialogue. Such tasks usually involve following specific workflows,
i.e. executing a sequence of actions in a particular order. While prior work
has focused on supervised learning methods to condition on past actions, they
do not explicitly optimize for compliance to a desired workflow. In this paper,
we propose a novel framework based on reinforcement learning (RL) to generate
dialogue responses that are aligned with a given workflow. Our framework
consists of ComplianceScorer, a metric designed to evaluate how well a
generated response executes the specified action, combined with an RL
opimization process that utilizes an interactive sampling technique. We
evaluate our approach on two TOD datasets, Action-Based Conversations Dataset
(ABCD) (Chen et al., 2021a) and MultiWOZ 2.2 (Zang et al., 2020) on a range of
automated and human evaluation metrics. Our findings indicate that our RL-based
framework outperforms baselines and is effective at enerating responses that
both comply with the intended workflows while being expressed in a natural and
fluent manner.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Calibration-free online test-time adaptation for electroencephalography motor imagery decoding
Abstract: Providing a promising pathway to link the human brain with external devices,
Brain-Computer Interfaces (BCIs) have seen notable advancements in decoding
capabilities, primarily driven by increasingly sophisticated techniques,
especially deep learning. However, achieving high accuracy in real-world
scenarios remains a challenge due to the distribution shift between sessions
and subjects. In this paper we will explore the concept of online test-time
adaptation (OTTA) to continuously adapt the model in an unsupervised fashion
during inference time. Our approach guarantees the preservation of privacy by
eliminating the requirement to access the source data during the adaptation
process. Additionally, OTTA achieves calibration-free operation by not
requiring any session- or subject-specific data. We will investigate the task
of electroencephalography (EEG) motor imagery decoding using a lightweight
architecture together with different OTTA techniques like alignment, adaptive
batch normalization, and entropy minimization. We examine two datasets and
three distinct data settings for a comprehensive analysis. Our adaptation
methods produce state-of-the-art results, potentially instigating a shift in
transfer learning for BCI decoding towards online adaptation.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Prompt:
Title: AI Alignment and Social Choice: Fundamental Limitations and Policy Implications
Abstract: Aligning AI agents to human intentions and values is a key bottleneck in
building safe and deployable AI applications. But whose values should AI agents
be aligned with? Reinforcement learning with human feedback (RLHF) has emerged
as the key framework for AI alignment. RLHF uses feedback from human
reinforcers to fine-tune outputs; all widely deployed large language models
(LLMs) use RLHF to align their outputs to human values. It is critical to
understand the limitations of RLHF and consider policy challenges arising from
these limitations. In this paper, we investigate a specific challenge in
building RLHF systems that respect democratic norms. Building on impossibility
results in social choice theory, we show that, under fairly broad assumptions,
there is no unique voting protocol to universally align AI systems using RLHF
through democratic processes. Further, we show that aligning AI agents with the
values of all individuals will always violate certain private ethical
preferences of an individual user i.e., universal AI alignment using RLHF is
impossible. We discuss policy implications for the governance of AI systems
built using RLHF: first, the need for mandating transparent voting rules to
hold model builders accountable. Second, the need for model builders to focus
on developing AI agents that are narrowly aligned to specific user groups.

Output: Artificial Intelligence