Instruction: What field is the article from?

Prompt:
Title: Large-scale Training of Foundation Models for Wearable Biosignals
Abstract: Tracking biosignals is crucial for monitoring wellness and preempting the
development of severe medical conditions. Today, wearable devices can
conveniently record various biosignals, creating the opportunity to monitor
health status without disruption to one's daily routine. Despite widespread use
of wearable devices and existing digital biomarkers, the absence of curated
data with annotated medical labels hinders the development of new biomarkers to
measure common health conditions. In fact, medical datasets are usually small
in comparison to other domains, which is an obstacle for developing neural
network models for biosignals. To address this challenge, we have employed
self-supervised learning using the unlabeled sensor data collected under
informed consent from the large longitudinal Apple Heart and Movement Study
(AHMS) to train foundation models for two common biosignals:
photoplethysmography (PPG) and electrocardiogram (ECG) recorded on Apple Watch.
We curated PPG and ECG datasets from AHMS that include data from ~141K
participants spanning ~3 years. Our self-supervised learning framework includes
participant level positive pair selection, stochastic augmentation module and a
regularized contrastive loss optimized with momentum training, and generalizes
well to both PPG and ECG modalities. We show that the pre-trained foundation
models readily encode information regarding participants' demographics and
health conditions. To the best of our knowledge, this is the first study that
builds foundation models using large-scale PPG and ECG data collected via
wearable consumer devices $\unicode{x2013}$ prior works have commonly used
smaller-size datasets collected in clinical and experimental settings. We
believe PPG and ECG foundation models can enhance future wearable devices by
reducing the reliance on labeled data and hold the potential to help the users
improve their health.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Universal Knowledge Graph Embeddings
Abstract: A variety of knowledge graph embedding approaches have been developed. Most
of them obtain embeddings by learning the structure of the knowledge graph
within a link prediction setting. As a result, the embeddings reflect only the
semantics of a single knowledge graph, and embeddings for different knowledge
graphs are not aligned, e.g., they cannot be used to find similar entities
across knowledge graphs via nearest neighbor search. However, knowledge graph
embedding applications such as entity disambiguation require a more global
representation, i.e., a representation that is valid across multiple sources.
We propose to learn universal knowledge graph embeddings from large-scale
interlinked knowledge sources. To this end, we fuse large knowledge graphs
based on the owl:sameAs relation such that every entity is represented by a
unique identity. We instantiate our idea by computing universal embeddings
based on DBpedia and Wikidata yielding embeddings for about 180 million
entities, 15 thousand relations, and 1.2 billion triples. Moreover, we develop
a convenient API to provide embeddings as a service. Experiments on link
prediction show that universal knowledge graph embeddings encode better
semantics compared to embeddings computed on a single knowledge graph. For
reproducibility purposes, we provide our source code and datasets open access
at https://github.com/dice-group/Universal_Embeddings

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Empowering Distributed Solutions in Renewable Energy Systems and Grid Optimization
Abstract: This study delves into the shift from centralized to decentralized approaches
in the electricity industry, with a particular focus on how machine learning
(ML) advancements play a crucial role in empowering renewable energy sources
and improving grid management. ML models have become increasingly important in
predicting renewable energy generation and consumption, utilizing various
techniques like artificial neural networks, support vector machines, and
decision trees. Furthermore, data preprocessing methods, such as data
splitting, normalization, decomposition, and discretization, are employed to
enhance prediction accuracy.
  The incorporation of big data and ML into smart grids offers several
advantages, including heightened energy efficiency, more effective responses to
demand, and better integration of renewable energy sources. Nevertheless,
challenges like handling large data volumes, ensuring cybersecurity, and
obtaining specialized expertise must be addressed. The research investigates
various ML applications within the realms of solar energy, wind energy, and
electric distribution and storage, illustrating their potential to optimize
energy systems. To sum up, this research demonstrates the evolving landscape of
the electricity sector as it shifts from centralized to decentralized solutions
through the application of ML innovations and distributed decision-making,
ultimately shaping a more efficient and sustainable energy future.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: SortNet: Learning To Rank By a Neural-Based Sorting Algorithm
Abstract: The problem of relevance ranking consists of sorting a set of objects with
respect to a given criterion. Since users may prefer different relevance
criteria, the ranking algorithms should be adaptable to the user needs. Two
main approaches exist in literature for the task of learning to rank: 1) a
score function, learned by examples, which evaluates the properties of each
object yielding an absolute relevance value that can be used to order the
objects or 2) a pairwise approach, where a "preference function" is learned
using pairs of objects to define which one has to be ranked first. In this
paper, we present SortNet, an adaptive ranking algorithm which orders objects
using a neural network as a comparator. The neural network training set
provides examples of the desired ordering between pairs of items and it is
constructed by an iterative procedure which, at each iteration, adds the most
informative training examples. Moreover, the comparator adopts a connectionist
architecture that is particularly suited for implementing a preference
function. We also prove that such an architecture has the universal
approximation property and can implement a wide class of functions. Finally,
the proposed algorithm is evaluated on the LETOR dataset showing promising
performances in comparison with other state of the art algorithms.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Establishing Performance Baselines in Fine-Tuning, Retrieval-Augmented Generation and Soft-Prompting for Non-Specialist LLM Users
Abstract: Research into methods for improving the performance of large language models
(LLMs) through fine-tuning, retrieval-augmented generation (RAG) and
soft-prompting has tended to focus on the use of highly technical or high-cost
techniques, making many of the newly discovered approaches comparatively
inaccessible to non-technical users. In this paper we tested an unmodified
version of GPT 3.5, a fine-tuned version, and the same unmodified model when
given access to a vectorised RAG database, both in isolation and in combination
with a basic, non-algorithmic soft prompt. In each case we tested the model's
ability to answer a set of 100 questions relating primarily to events that
occurred after September 2021 (the point at which GPT 3.5's training data set
ends). We found that if commercial platforms are used and default settings are
applied with no iteration in order to establish a baseline set of outputs, a
fine-tuned model outperforms GPT 3.5 Turbo, while the RAG approach
out-performed both. The application of a soft prompt significantly improved the
performance of each approach.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: Detrimental Contexts in Open-Domain Question Answering
Abstract: For knowledge intensive NLP tasks, it has been widely accepted that accessing
more information is a contributing factor to improvements in the model's
end-to-end performance. However, counter-intuitively, too much context can have
a negative impact on the model when evaluated on common question answering (QA)
datasets. In this paper, we analyze how passages can have a detrimental effect
on retrieve-then-read architectures used in question answering. Our empirical
evidence indicates that the current read architecture does not fully leverage
the retrieved passages and significantly degrades its performance when using
the whole passages compared to utilizing subsets of them. Our findings
demonstrate that model accuracy can be improved by 10% on two popular QA
datasets by filtering out detrimental passages. Additionally, these outcomes
are attained by utilizing existing retrieval methods without further training
or data. We further highlight the challenges associated with identifying the
detrimental passages. First, even with the correct context, the model can make
an incorrect prediction, posing a challenge in determining which passages are
most influential. Second, evaluation typically considers lexical matching,
which is not robust to variations of correct answers. Despite these
limitations, our experimental results underscore the pivotal role of
identifying and removing these detrimental passages for the context-efficient
retrieve-then-read pipeline. Code and data are available at
https://github.com/xfactlab/emnlp2023-damaging-retrieval

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: MAIRA-1: A specialised large multimodal model for radiology report generation
Abstract: We present a radiology-specific multimodal model for the task for generating
radiological reports from chest X-rays (CXRs). Our work builds on the idea that
large language model(s) can be equipped with multimodal capabilities through
alignment with pre-trained vision encoders. On natural images, this has been
shown to allow multimodal models to gain image understanding and description
capabilities. Our proposed model (MAIRA-1) leverages a CXR-specific image
encoder in conjunction with a fine-tuned large language model based on
Vicuna-7B, and text-based data augmentation, to produce reports with
state-of-the-art quality. In particular, MAIRA-1 significantly improves on the
radiologist-aligned RadCliQ metric and across all lexical metrics considered.
Manual review of model outputs demonstrates promising fluency and accuracy of
generated reports while uncovering failure modes not captured by existing
evaluation practices. More information and resources can be found on the
project website: https://aka.ms/maira.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Gen2Sim: Scaling up Robot Learning in Simulation with Generative Models
Abstract: Generalist robot manipulators need to learn a wide variety of manipulation
skills across diverse environments. Current robot training pipelines rely on
humans to provide kinesthetic demonstrations or to program simulation
environments and to code up reward functions for reinforcement learning. Such
human involvement is an important bottleneck towards scaling up robot learning
across diverse tasks and environments. We propose Generation to Simulation
(Gen2Sim), a method for scaling up robot skill learning in simulation by
automating generation of 3D assets, task descriptions, task decompositions and
reward functions using large pre-trained generative models of language and
vision. We generate 3D assets for simulation by lifting open-world 2D
object-centric images to 3D using image diffusion models and querying LLMs to
determine plausible physics parameters. Given URDF files of generated and
human-developed assets, we chain-of-thought prompt LLMs to map these to
relevant task descriptions, temporal decompositions, and corresponding python
reward functions for reinforcement learning. We show Gen2Sim succeeds in
learning policies for diverse long horizon tasks, where reinforcement learning
with non temporally decomposed reward functions fails. Gen2Sim provides a
viable path for scaling up reinforcement learning for robot manipulators in
simulation, both by diversifying and expanding task and environment
development, and by facilitating the discovery of reinforcement-learned
behaviors through temporal task decomposition in RL. Our work contributes
hundreds of simulated assets, tasks and demonstrations, taking a step towards
fully autonomous robotic manipulation skill acquisition in simulation.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors
Abstract: Video DeepFakes are fake media created with Deep Learning (DL) that
manipulate a person's expression or identity. Most current DeepFake detection
methods analyze each frame independently, ignoring inconsistencies and
unnatural movements between frames. Some newer methods employ optical flow
models to capture this temporal aspect, but they are computationally expensive.
In contrast, we propose using the related but often ignored Motion Vectors
(MVs) and Information Masks (IMs) from the H.264 video codec, to detect
temporal inconsistencies in DeepFakes. Our experiments show that this approach
is effective and has minimal computational costs, compared with per-frame
RGB-only methods. This could lead to new, real-time temporally-aware DeepFake
detection methods for video calls and streaming.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Taking it further: leveraging pseudo labels for field delineation across label-scarce smallholder regions
Abstract: Transfer learning allows for resource-efficient geographic transfer of
pre-trained field delineation models. However, the scarcity of labeled data for
complex and dynamic smallholder landscapes, particularly in Sub-Saharan Africa,
remains a major bottleneck for large-area field delineation. This study
explores opportunities of using sparse field delineation pseudo labels for
fine-tuning models across geographies and sensor characteristics. We build on a
FracTAL ResUNet trained for crop field delineation in India (median field size
of 0.24 ha) and use this pre-trained model to generate pseudo labels in
Mozambique (median field size of 0.06 ha). We designed multiple pseudo label
selection strategies and compared the quantities, area properties, seasonal
distribution, and spatial agreement of the pseudo labels against
human-annotated training labels (n = 1,512). We then used the human-annotated
labels and the pseudo labels for model fine-tuning and compared predictions
against human field annotations (n = 2,199). Our results indicate i) a good
baseline performance of the pre-trained model in both field delineation and
field size estimation, and ii) the added value of regional fine-tuning with
performance improvements in nearly all experiments. Moreover, we found iii)
substantial performance increases when using only pseudo labels (up to 77% of
the IoU increases and 68% of the RMSE decreases obtained by human labels), and
iv) additional performance increases when complementing human annotations with
pseudo labels. Pseudo labels can be efficiently generated at scale and thus
facilitate domain adaptation in label-scarce settings. The workflow presented
here is a stepping stone for overcoming the persisting data gaps in
heterogeneous smallholder agriculture of Sub-Saharan Africa, where labels are
commonly scarce.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: KwaiAgents: Generalized Information-seeking Agent System with Large Language Models
Abstract: Driven by curiosity, humans have continually sought to explore and understand
the world around them, leading to the invention of various tools to satiate
this inquisitiveness. Despite not having the capacity to process and memorize
vast amounts of information in their brains, humans excel in critical thinking,
planning, reflection, and harnessing available tools to interact with and
interpret the world, enabling them to find answers efficiently. The recent
advancements in large language models (LLMs) suggest that machines might also
possess the aforementioned human-like capabilities, allowing them to exhibit
powerful abilities even with a constrained parameter count. In this paper, we
introduce KwaiAgents, a generalized information-seeking agent system based on
LLMs. Within KwaiAgents, we propose an agent system that employs LLMs as its
cognitive core, which is capable of understanding a user's query, behavior
guidelines, and referencing external documents. The agent can also update and
retrieve information from its internal memory, plan and execute actions using a
time-aware search-browse toolkit, and ultimately provide a comprehensive
response. We further investigate the system's performance when powered by LLMs
less advanced than GPT-4, and introduce the Meta-Agent Tuning (MAT) framework,
designed to ensure even an open-sourced 7B or 13B model performs well among
many agent systems. We exploit both benchmark and human evaluations to
systematically validate these capabilities. Extensive experiments show the
superiority of our agent system compared to other autonomous agents and
highlight the enhanced generalized agent-abilities of our fine-tuned LLMs.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Efficient Toxic Content Detection by Bootstrapping and Distilling Large Language Models
Abstract: Toxic content detection is crucial for online services to remove
inappropriate content that violates community standards. To automate the
detection process, prior works have proposed varieties of machine learning (ML)
approaches to train Language Models (LMs) for toxic content detection. However,
both their accuracy and transferability across datasets are limited. Recently,
Large Language Models (LLMs) have shown promise in toxic content detection due
to their superior zero-shot and few-shot in-context learning ability as well as
broad transferability on ML tasks. However, efficiently designing prompts for
LLMs remains challenging. Moreover, the high run-time cost of LLMs may hinder
their deployments in production. To address these challenges, in this work, we
propose BD-LLM, a novel and efficient approach to Bootstrapping and Distilling
LLMs for toxic content detection. Specifically, we design a novel prompting
method named Decision-Tree-of-Thought (DToT) to bootstrap LLMs' detection
performance and extract high-quality rationales. DToT can automatically select
more fine-grained context to re-prompt LLMs when their responses lack
confidence. Additionally, we use the rationales extracted via DToT to fine-tune
student LMs. Our experimental results on various datasets demonstrate that DToT
can improve the accuracy of LLMs by up to 4.6%. Furthermore, student LMs
fine-tuned with rationales extracted via DToT outperform baselines on all
datasets with up to 16.9\% accuracy improvement, while being more than 60x
smaller than conventional LLMs. Finally, we observe that student LMs fine-tuned
with rationales exhibit better cross-dataset transferability.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Representation Learning with Large Language Models for Recommendation
Abstract: Recommender systems have seen significant advancements with the influence of
deep learning and graph neural networks, particularly in capturing complex
user-item relationships. However, these graph-based recommenders heavily depend
on ID-based data, potentially disregarding valuable textual information
associated with users and items, resulting in less informative learned
representations. Moreover, the utilization of implicit feedback data introduces
potential noise and bias, posing challenges for the effectiveness of user
preference learning. While the integration of large language models (LLMs) into
traditional ID-based recommenders has gained attention, challenges such as
scalability issues, limitations in text-only reliance, and prompt input
constraints need to be addressed for effective implementation in practical
recommender systems. To address these challenges, we propose a model-agnostic
framework RLMRec that aims to enhance existing recommenders with LLM-empowered
representation learning. It proposes a recommendation paradigm that integrates
representation learning with LLMs to capture intricate semantic aspects of user
behaviors and preferences. RLMRec incorporates auxiliary textual signals,
develops a user/item profiling paradigm empowered by LLMs, and aligns the
semantic space of LLMs with the representation space of collaborative
relational signals through a cross-view alignment framework. This work further
establish a theoretical foundation demonstrating that incorporating textual
signals through mutual information maximization enhances the quality of
representations. In our evaluation, we integrate RLMRec with state-of-the-art
recommender models, while also analyzing its efficiency and robustness to noise
data. Our implementation codes are available at
https://github.com/HKUDS/RLMRec.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: Generating Continuations in Multilingual Idiomatic Contexts
Abstract: The ability to process idiomatic or literal multiword expressions is a
crucial aspect of understanding and generating any language. The task of
generating contextually relevant continuations for narratives containing
idiomatic (or literal) expressions can allow us to test the ability of
generative language models (LMs) in understanding nuanced language containing
non-compositional figurative text. We conduct a series of experiments using
datasets in two distinct languages (English and Portuguese) under three
different training settings (zero-shot, few-shot, and fine-tuned). Our results
suggest that the models are only slightly better at generating continuations
for literal contexts than idiomatic contexts, with exceedingly small margins.
Furthermore, the models studied in this work perform equally well across both
languages, indicating the robustness of generative models in performing this
task.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: On Measuring Faithfulness of Natural Language Explanations
Abstract: Large language models (LLMs) can explain their own predictions, through
post-hoc or Chain-of-Thought (CoT) explanations. However the LLM could make up
reasonably sounding explanations that are unfaithful to its underlying
reasoning. Recent work has designed tests that aim to judge the faithfulness of
either post-hoc or CoT explanations. In this paper we argue that existing
faithfulness tests are not actually measuring faithfulness in terms of the
models' inner workings, but only evaluate their self-consistency on the output
level. The aims of our work are two-fold. i) We aim to clarify the status of
existing faithfulness tests in terms of model explainability, characterising
them as self-consistency tests instead. This assessment we underline by
constructing a Comparative Consistency Bank for self-consistency tests that for
the first time compares existing tests on a common suite of 11 open-source LLMs
and 5 datasets -- including ii) our own proposed self-consistency measure
CC-SHAP. CC-SHAP is a new fine-grained measure (not test) of LLM
self-consistency that compares a model's input contributions to answer
prediction and generated explanation. With CC-SHAP, we aim to take a step
further towards measuring faithfulness with a more interpretable and
fine-grained method. Code available at
\url{https://github.com/Heidelberg-NLP/CC-SHAP}

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Contrastive Deep Nonnegative Matrix Factorization for Community Detection
Abstract: Recently, nonnegative matrix factorization (NMF) has been widely adopted for
community detection, because of its better interpretability. However, the
existing NMF-based methods have the following three problems: 1) they directly
transform the original network into community membership space, so it is
difficult for them to capture the hierarchical information; 2) they often only
pay attention to the topology of the network and ignore its node attributes; 3)
it is hard for them to learn the global structure information necessary for
community detection. Therefore, we propose a new community detection algorithm,
named Contrastive Deep Nonnegative Matrix Factorization (CDNMF). Firstly, we
deepen NMF to strengthen its capacity for information extraction. Subsequently,
inspired by contrastive learning, our algorithm creatively constructs network
topology and node attributes as two contrasting views. Furthermore, we utilize
a debiased negative sampling layer and learn node similarity at the community
level, thereby enhancing the suitability of our model for community detection.
We conduct experiments on three public real graph datasets and the proposed
model has achieved better results than state-of-the-art methods. Code available
at https://github.com/6lyc/CDNMF.git.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Cognitive bias in large language models: Cautious optimism meets anti-Panglossian meliorism
Abstract: Traditional discussions of bias in large language models focus on a
conception of bias closely tied to unfairness, especially as affecting
marginalized groups. Recent work raises the novel possibility of assessing the
outputs of large language models for a range of cognitive biases familiar from
research in judgment and decisionmaking. My aim in this paper is to draw two
lessons from recent discussions of cognitive bias in large language models:
cautious optimism about the prevalence of bias in current models coupled with
an anti-Panglossian willingness to concede the existence of some genuine biases
and work to reduce them. I draw out philosophical implications of this
discussion for the rationality of human cognitive biases as well as the role of
unrepresentative data in driving model biases.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis
Abstract: The high dimensionality and complexity of neuroimaging data necessitate large
datasets to develop robust and high-performing deep learning models. However,
the neuroimaging field is notably hampered by the scarcity of such datasets. In
this work, we proposed a data augmentation and validation framework that
utilizes dynamic forecasting with Long Short-Term Memory (LSTM) networks to
enrich datasets. We extended multivariate time series data by predicting the
time courses of independent component networks (ICNs) in both one-step and
recursive configurations. The effectiveness of these augmented datasets was
then compared with the original data using various deep learning models
designed for chronological age prediction tasks. The results suggest that our
approach improves model performance, providing a robust solution to overcome
the challenges presented by the limited size of neuroimaging datasets.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: RDBench: ML Benchmark for Relational Databases
Abstract: Benefiting from high-quality datasets and standardized evaluation metrics,
machine learning (ML) has achieved sustained progress and widespread
applications. However, while applying machine learning to relational databases
(RDBs), the absence of a well-established benchmark remains a significant
obstacle to the development of ML. To address this issue, we introduce ML
Benchmark For Relational Databases (RDBench), a standardized benchmark that
aims to promote reproducible ML research on RDBs that include multiple tables.
RDBench offers diverse RDB datasets of varying scales, domains, and relational
structures, organized into 4 levels. Notably, to simplify the adoption of
RDBench for diverse ML domains, for any given database, RDBench exposes three
types of interfaces including tabular data, homogeneous graphs, and
heterogeneous graphs, sharing the same underlying task definition. For the
first time, RDBench enables meaningful comparisons between ML methods from
diverse domains, ranging from XGBoost to Graph Neural Networks, under RDB
prediction tasks. We design multiple classification and regression tasks for
each RDB dataset and report averaged results over the same dataset, further
enhancing the robustness of the experimental findings. RDBench is implemented
with DBGym, a user-friendly platform for ML research and application on
databases, enabling benchmarking new ML methods with RDBench at ease.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: On the Exploitability of Reinforcement Learning with Human Feedback for Large Language Models
Abstract: Reinforcement Learning with Human Feedback (RLHF) is a methodology designed
to align Large Language Models (LLMs) with human preferences, playing an
important role in LLMs alignment. Despite its advantages, RLHF relies on human
annotators to rank the text, which can introduce potential security
vulnerabilities if any adversarial annotator (i.e., attackers) manipulates the
ranking score by up-ranking any malicious text to steer the LLM adversarially.
To assess the red-teaming of RLHF against human preference data poisoning, we
propose RankPoison, a poisoning attack method on candidates' selection of
preference rank flipping to reach certain malicious behaviors (e.g., generating
longer sequences, which can increase the computational cost). With poisoned
dataset generated by RankPoison, we can perform poisoning attacks on LLMs to
generate longer tokens without hurting the original safety alignment
performance. Moreover, applying RankPoison, we also successfully implement a
backdoor attack where LLMs can generate longer answers under questions with the
trigger word. Our findings highlight critical security challenges in RLHF,
underscoring the necessity for more robust alignment methods for LLMs.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Addressing Long-Horizon Tasks by Integrating Program Synthesis and State Machines
Abstract: Deep reinforcement learning excels in various domains but lacks
generalizability and interoperability. Programmatic RL methods (Trivedi et al.,
2021; Liu et al., 2023) reformulate solving RL tasks as synthesizing
interpretable programs that can be executed in the environments. Despite
encouraging results, these methods are limited to short-horizon tasks. On the
other hand, representing RL policies using state machines (Inala et al., 2020)
can inductively generalize to long-horizon tasks; however, it struggles to
scale up to acquire diverse and complex behaviors. This work proposes Program
Machine Policies (POMPs), which bridge the advantages of programmatic RL and
state machine policies, allowing for the representation of complex behaviors
and the address of long-term tasks. Specifically, we introduce a method that
can retrieve a set of effective, diverse, compatible programs. Then, we use
these programs as modes of a state machine and learn a transition function to
transition among mode programs, allowing for capturing long-horizon repetitive
behaviors. Our proposed framework outperforms programmatic RL and deep RL
baselines on various tasks and demonstrates the ability to generalize to even
longer horizons without any fine-tuning inductively. Ablation studies justify
the effectiveness of our proposed search algorithm for retrieving a set of
programs as modes.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Non-Cross Diffusion for Semantic Consistency
Abstract: In diffusion models, deviations from a straight generative flow are a common
issue, resulting in semantic inconsistencies and suboptimal generations. To
address this challenge, we introduce `Non-Cross Diffusion', an innovative
approach in generative modeling for learning ordinary differential equation
(ODE) models. Our methodology strategically incorporates an ascending dimension
of input to effectively connect points sampled from two distributions with
uncrossed paths. This design is pivotal in ensuring enhanced semantic
consistency throughout the inference process, which is especially critical for
applications reliant on consistent generative flows, including various
distillation methods and deterministic sampling, which are fundamental in image
editing and interpolation tasks. Our empirical results demonstrate the
effectiveness of Non-Cross Diffusion, showing a substantial reduction in
semantic inconsistencies at different inference steps and a notable enhancement
in the overall performance of diffusion models.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting
Abstract: Images contain rich relational knowledge that can help machines understand
the world. Existing methods on visual knowledge extraction often rely on the
pre-defined format (e.g., sub-verb-obj tuples) or vocabulary (e.g., relation
types), restricting the expressiveness of the extracted knowledge. In this
work, we take a first exploration to a new paradigm of open visual knowledge
extraction. To achieve this, we present OpenVik which consists of an open
relational region detector to detect regions potentially containing relational
knowledge and a visual knowledge generator that generates format-free knowledge
by prompting the large multimodality model with the detected region of
interest. We also explore two data enhancement techniques for diversifying the
generated format-free visual knowledge. Extensive knowledge quality evaluations
highlight the correctness and uniqueness of the extracted open visual knowledge
by OpenVik. Moreover, integrating our extracted knowledge across various visual
reasoning applications shows consistent improvements, indicating the real-world
applicability of OpenVik.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: A Survey of Blockchain, Artificial Intelligence, and Edge Computing for Web 3.0
Abstract: Web 3.0, as the third generation of the World Wide Web, aims to solve
contemporary problems of trust, centralization, and data ownership. Driven by
the latest advances in cutting-edge technologies, Web 3.0 is moving towards a
more open, decentralized, intelligent, and interconnected network. However,
increasingly widespread data breaches have raised awareness of online privacy
and security of personal data. Additionally, since Web 3.0 is a sophisticated
and complex convergence, the technical details behind it are not as clear as
the characteristics it presents. In this survey, we conduct an in-depth
exploration of Web 3.0 from the perspectives of blockchain, artificial
intelligence, and edge computing. Specifically, we begin with summarizing the
evolution of the Internet and providing an overview of these three key
technological factors. Afterward, we provide a thorough analysis of each
technology separately, including its relevance to Web 3.0, key technology
components, and practical applications. We also propose decentralized storage
and computing solutions by exploring the integration of technologies. Finally,
we highlight the key challenges alongside potential research directions.
Through the combination and mutual complementation of multiple technologies,
Web 3.0 is expected to return more control and ownership of data and digital
assets back to users.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: RAEDiff: Denoising Diffusion Probabilistic Models Based Reversible Adversarial Examples Self-Generation and Self-Recovery
Abstract: Collected and annotated datasets, which are obtained through extensive
efforts, are effective for training Deep Neural Network (DNN) models. However,
these datasets are susceptible to be misused by unauthorized users, resulting
in infringement of Intellectual Property (IP) rights owned by the dataset
creators. Reversible Adversarial Exsamples (RAE) can help to solve the issues
of IP protection for datasets. RAEs are adversarial perturbed images that can
be restored to the original. As a cutting-edge approach, RAE scheme can serve
the purposes of preventing unauthorized users from engaging in malicious model
training, as well as ensuring the legitimate usage of authorized users.
Nevertheless, in the existing work, RAEs still rely on the embedded auxiliary
information for restoration, which may compromise their adversarial abilities.
In this paper, a novel self-generation and self-recovery method, named as
RAEDiff, is introduced for generating RAEs based on a Denoising Diffusion
Probabilistic Models (DDPM). It diffuses datasets into a Biased Gaussian
Distribution (BGD) and utilizes the prior knowledge of the DDPM for generating
and recovering RAEs. The experimental results demonstrate that RAEDiff
effectively self-generates adversarial perturbations for DNN models, including
Artificial Intelligence Generated Content (AIGC) models, while also exhibiting
significant self-recovery capabilities.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: A Bi-level Framework for Traffic Accident Duration Prediction: Leveraging Weather and Road Condition Data within a Practical Optimum Pipeline
Abstract: Due to the stochastic nature of events, predicting the duration of a traffic
incident presents a formidable challenge. Accurate duration estimation can
result in substantial advantages for commuters in selecting optimal routes and
for traffic management personnel in addressing non-recurring congestion issues.
In this study, we gathered accident duration, road conditions, and
meteorological data from a database of traffic accidents to check the
feasibility of a traffic accident duration pipeline without accident contextual
information data like accident severity and textual description. Multiple
machine learning models were employed to predict whether an accident's impact
on road traffic would be of a short-term or long-term nature, and then
utilizing a bimodal approach the precise duration of the incident's effect was
determined. Our binary classification random forest model distinguished between
short-term and long-term effects with an 83% accuracy rate, while the LightGBM
regression model outperformed other machine learning regression models with
Mean Average Error (MAE) values of 26.15 and 13.3 and RMSE values of 32.91 and
28.91 for short and long-term accident duration prediction, respectively. Using
the optimal classification and regression model identified in the preceding
section, we then construct an end-to-end pipeline to incorporate the entire
process. The results of both separate and combined approaches were comparable
with previous works, which shows the applicability of only using static
features for predicting traffic accident duration. The SHAP value analysis
identified weather conditions, wind chill and wind speed as the most
influential factors in determining the duration of an accident.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Not All Large Language Models (LLMs) Succumb to the "Reversal Curse": A Comparative Study of Deductive Logical Reasoning in BERT and GPT Models
Abstract: The "Reversal Curse" refers to the scenario where auto-regressive decoder
large language models (LLMs), such as ChatGPT, trained on "A is B" fail to
learn "B is A", demonstrating a basic failure of logical deduction. This raises
a red flag in the use of GPT models for certain general tasks such as
constructing knowledge graphs, considering their adherence to this symmetric
principle. In our study, we examined a bidirectional LLM, BERT, and found that
it is immune to the reversal curse. Driven by ongoing efforts to construct
biomedical knowledge graphs with LLMs, we also embarked on evaluating more
complex but essential deductive reasoning capabilities. This process included
first training encoder and decoder language models to master the intersection
($\cap$) and union ($\cup$) operations on two sets and then moving on to assess
their capability to infer different combinations of union ($\cup$) and
intersection ($\cap$) operations on three newly created sets. The findings
showed that while both encoder and decoder language models, trained for tasks
involving two sets (union/intersection), were proficient in such scenarios,
they encountered difficulties when dealing with operations that included three
sets (various combinations of union and intersection). Our research highlights
the distinct characteristics of encoder and decoder models in simple and
complex logical reasoning. In practice, the choice between BERT and GPT should
be guided by the specific requirements and nature of the task at hand,
leveraging their respective strengths in bidirectional context comprehension
and sequence prediction.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: A comparative analysis between Conformer-Transducer, Whisper, and wav2vec2 for improving the child speech recognition
Abstract: Automatic Speech Recognition (ASR) systems have progressed significantly in
their performance on adult speech data; however, transcribing child speech
remains challenging due to the acoustic differences in the characteristics of
child and adult voices. This work aims to explore the potential of adapting
state-of-the-art Conformer-transducer models to child speech to improve child
speech recognition performance. Furthermore, the results are compared with
those of self-supervised wav2vec2 models and semi-supervised multi-domain
Whisper models that were previously finetuned on the same data. We demonstrate
that finetuning Conformer-transducer models on child speech yields significant
improvements in ASR performance on child speech, compared to the non-finetuned
models. We also show Whisper and wav2vec2 adaptation on different child speech
datasets. Our detailed comparative analysis shows that wav2vec2 provides the
most consistent performance improvements among the three methods studied.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation
Abstract: Recent advancements in Large Language Models (LLMs) have revolutionized
decision-making by breaking down complex problems into more manageable language
sequences referred to as ``thoughts''. An effective thought design should
consider three key perspectives: performance, efficiency, and flexibility.
However, existing thought can at most exhibit two of these attributes. To
address these limitations, we introduce a novel thought prompting approach
called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle
of existing thought paradigms. XoT leverages pretrained reinforcement learning
and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge
into thoughts, thereby enhancing LLMs' capabilities and enabling them to
generalize to unseen problems efficiently. Through the utilization of the
MCTS-LLM collaborative thought revision framework, this approach autonomously
produces high-quality comprehensive cognitive mappings with minimal LLM
interactions. Additionally, XoT empowers LLMs to engage in unconstrained
thinking, allowing for flexible cognitive mappings for problems with multiple
solutions. We evaluate XoT on several challenging multi-solution
problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our
results demonstrate that XoT significantly outperforms existing approaches.
Notably, XoT can yield multiple solutions with just one LLM call, showcasing
its remarkable proficiency in addressing complex problems across diverse
domains.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Lightweight Face Recognition: An Improved MobileFaceNet Model
Abstract: This paper presents an extensive exploration and comparative analysis of
lightweight face recognition (FR) models, specifically focusing on
MobileFaceNet and its modified variant, MMobileFaceNet. The need for efficient
FR models on devices with limited computational resources has led to the
development of models with reduced memory footprints and computational demands
without sacrificing accuracy. Our research delves into the impact of dataset
selection, model architecture, and optimization algorithms on the performance
of FR models. We highlight our participation in the EFaR-2023 competition,
where our models showcased exceptional performance, particularly in categories
restricted by the number of parameters. By employing a subset of the Webface42M
dataset and integrating sharpness-aware minimization (SAM) optimization, we
achieved significant improvements in accuracy across various benchmarks,
including those that test for cross-pose, cross-age, and cross-ethnicity
performance. The results underscore the efficacy of our approach in crafting
models that are not only computationally efficient but also maintain high
accuracy in diverse conditions.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: One-shot Localization and Segmentation of Medical Images with Foundation Models
Abstract: Recent advances in Vision Transformers (ViT) and Stable Diffusion (SD) models
with their ability to capture rich semantic features of the image have been
used for image correspondence tasks on natural images. In this paper, we
examine the ability of a variety of pre-trained ViT (DINO, DINOv2, SAM, CLIP)
and SD models, trained exclusively on natural images, for solving the
correspondence problems on medical images. While many works have made a case
for in-domain training, we show that the models trained on natural images can
offer good performance on medical images across different modalities
(CT,MR,Ultrasound) sourced from various manufacturers, over multiple anatomical
regions (brain, thorax, abdomen, extremities), and on wide variety of tasks.
Further, we leverage the correspondence with respect to a template image to
prompt a Segment Anything (SAM) model to arrive at single shot segmentation,
achieving dice range of 62%-90% across tasks, using just one image as
reference. We also show that our single-shot method outperforms the recently
proposed few-shot segmentation method - UniverSeg (Dice range 47%-80%) on most
of the semantic segmentation tasks(six out of seven) across medical imaging
modalities.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction
Abstract: Predicting the trajectory of pedestrians in crowd scenarios is indispensable
in self-driving or autonomous mobile robot field because estimating the future
locations of pedestrians around is beneficial for policy decision to avoid
collision. It is a challenging issue because humans have different walking
motions and the interactions between humans and objects in the current
environment, especially between human themselves, are complex. Previous
researches have focused on how to model the human-human interactions, however,
neglecting the relative importance of interactions. In order to address this
issue, we introduce a novel mechanism based on the correntropy, which not only
can measure the relative importance of human-human interactions, but also can
build personal space for each pedestrian. We further propose an Interaction
Module including this data-driven mechanism that can effectively extract
feature representations of dynamic human-human interactions in the scene and
calculate corresponding weights to represent the importance of different
interactions. To share such social messages among pedestrians, we design an
interaction-aware architecture based on the Long Short-Term Memory (LSTM)
network for trajectory prediction. We demonstrate the performance of our model
on two public datasets and the experimental results demonstrate that our model
can achieve better performance than several latest methods with good
performance.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: A Comprehensive and Reliable Feature Attribution Method: Double-sided Remove and Reconstruct (DoRaR)
Abstract: The limited transparency of the inner decision-making mechanism in deep
neural networks (DNN) and other machine learning (ML) models has hindered their
application in several domains. In order to tackle this issue, feature
attribution methods have been developed to identify the crucial features that
heavily influence decisions made by these black box models. However, many
feature attribution methods have inherent downsides. For example, one category
of feature attribution methods suffers from the artifacts problem, which feeds
out-of-distribution masked inputs directly through the classifier that was
originally trained on natural data points. Another category of feature
attribution method finds explanations by using jointly trained feature
selectors and predictors. While avoiding the artifacts problem, this new
category suffers from the Encoding Prediction in the Explanation (EPITE)
problem, in which the predictor's decisions rely not on the features, but on
the masks that selects those features. As a result, the credibility of
attribution results is undermined by these downsides. In this research, we
introduce the Double-sided Remove and Reconstruct (DoRaR) feature attribution
method based on several improvement methods that addresses these issues. By
conducting thorough testing on MNIST, CIFAR10 and our own synthetic dataset, we
demonstrate that the DoRaR feature attribution method can effectively bypass
the above issues and can aid in training a feature selector that outperforms
other state-of-the-art feature attribution methods. Our code is available at
https://github.com/dxq21/DoRaR.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering
Abstract: Recently, the development of large language models (LLMs) has attracted wide
attention in academia and industry. Deploying LLMs to real scenarios is one of
the key directions in the current Internet industry. In this paper, we present
a novel pipeline to apply LLMs for domain-specific question answering (QA) that
incorporates domain knowledge graphs (KGs), addressing an important direction
of LLM application. As a real-world application, the content generated by LLMs
should be user-friendly to serve the customers. Additionally, the model needs
to utilize domain knowledge properly to generate reliable answers. These two
issues are the two major difficulties in the LLM application as vanilla
fine-tuning can not adequately address them. We think both requirements can be
unified as the model preference problem that needs to align with humans to
achieve practical application. Thus, we introduce Knowledgeable Preference
AlignmenT (KnowPAT), which constructs two kinds of preference set called style
preference set and knowledge preference set respectively to tackle the two
issues. Besides, we design a new alignment objective to align the LLM
preference with human preference, aiming to train a better LLM for
real-scenario domain-specific QA to generate reliable and user-friendly
answers. Adequate experiments and comprehensive with 15 baseline methods
demonstrate that our KnowPAT is an outperforming pipeline for real-scenario
domain-specific QA with LLMs. Our code is open-source at
https://github.com/zjukg/KnowPAT.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Mixture-of-Linear-Experts for Long-term Time Series Forecasting
Abstract: Long-term time series forecasting (LTSF) aims to predict future values of a
time series given the past values. The current state-of-the-art (SOTA) on this
problem is attained in some cases by linear-centric models, which primarily
feature a linear mapping layer. However, due to their inherent simplicity, they
are not able to adapt their prediction rules to periodic changes in time series
patterns. To address this challenge, we propose a Mixture-of-Experts-style
augmentation for linear-centric models and propose Mixture-of-Linear-Experts
(MoLE). Instead of training a single model, MoLE trains multiple linear-centric
models (i.e., experts) and a router model that weighs and mixes their outputs.
While the entire framework is trained end-to-end, each expert learns to
specialize in a specific temporal pattern, and the router model learns to
compose the experts adaptively. Experiments show that MoLE reduces forecasting
error of linear-centric models, including DLinear, RLinear, and RMLP, in over
78% of the datasets and settings we evaluated. By using MoLE existing
linear-centric models can achieve SOTA LTSF results in 68% of the experiments
that PatchTST reports and we compare to, whereas existing single-head
linear-centric models achieve SOTA results in only 25% of cases. Additionally,
MoLE models achieve SOTA in all settings for the newly released Weather2K
datasets.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud Registration
Abstract: The majority of point cloud registration methods currently rely on extracting
features from points. However, these methods are limited by their dependence on
information obtained from a single modality of points, which can result in
deficiencies such as inadequate perception of global features and a lack of
texture information. Actually, humans can employ visual information learned
from 2D images to comprehend the 3D world. Based on this fact, we present a
novel Cross-Modal Information-Guided Network (CMIGNet), which obtains global
shape perception through cross-modal information to achieve precise and robust
point cloud registration. Specifically, we first incorporate the projected
images from the point clouds and fuse the cross-modal features using the
attention mechanism. Furthermore, we employ two contrastive learning
strategies, namely overlapping contrastive learning and cross-modal contrastive
learning. The former focuses on features in overlapping regions, while the
latter emphasizes the correspondences between 2D and 3D features. Finally, we
propose a mask prediction module to identify keypoints in the point clouds.
Extensive experiments on several benchmark datasets demonstrate that our
network achieves superior registration performance.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Retrieval-Augmented Code Generation for Universal Information Extraction
Abstract: Information Extraction (IE) aims to extract structural knowledge (e.g.,
entities, relations, events) from natural language texts, which brings
challenges to existing methods due to task-specific schemas and complex text
expressions. Code, as a typical kind of formalized language, is capable of
describing structural knowledge under various schemas in a universal way. On
the other hand, Large Language Models (LLMs) trained on both codes and texts
have demonstrated powerful capabilities of transforming texts into codes, which
provides a feasible solution to IE tasks. Therefore, in this paper, we propose
a universal retrieval-augmented code generation framework based on LLMs, called
Code4UIE, for IE tasks. Specifically, Code4UIE adopts Python classes to define
task-specific schemas of various structural knowledge in a universal way. By so
doing, extracting knowledge under these schemas can be transformed into
generating codes that instantiate the predefined Python classes with the
information in texts. To generate these codes more precisely, Code4UIE adopts
the in-context learning mechanism to instruct LLMs with examples. In order to
obtain appropriate examples for different tasks, Code4UIE explores several
example retrieval strategies, which can retrieve examples semantically similar
to the given texts. Extensive experiments on five representative IE tasks
across nine datasets demonstrate the effectiveness of the Code4UIE framework.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions
Abstract: Large language models (LLMs) are capable of answering knowledge-intensive
complex questions with chain-of-thought (CoT) reasoning. However, they tend to
generate factually incorrect reasoning steps when the required knowledge is not
available or up-to-date in models' parameters. Recent works turn to retrieving
external knowledge to augment CoT reasoning. Despite being promising, these
chain-based methods suffer from: 1) Negative retrieval. Unnecessary or
incorrect retrieval may mislead the reasoning; 2) Limited sight. Lacking the
ability to look backward or forward, a local error in one step will propagate
along the chain.
  In this paper, we propose a novel approach: Probabilistic Tree-of-thought
Reasoning (ProbTree). First, LLMs translate a complex question into a query
tree, in which each non-root node denotes a sub-question of its parent node.
Then, probabilistic reasoning is conducted over the tree, by solving questions
from leaf to root considering the confidence of both question decomposing and
answering. During reasoning, for leaf nodes, LLMs choose a more confident
answer from Closed-book QA that employs parametric knowledge and Open-book QA
that employs retrieved external knowledge, thus eliminating the negative
retrieval problem. For non-leaf nodes, with the hierarchical structure, LLMs
have broader sights and are able to globally reason with the information from
child nodes, thus recovering from local errors. The experiments on three
Complex QA datasets under the open-domain setting show that our approach
outperforms SOTA methods significantly, demonstrating the effect of
probabilistic tree-of-thought reasoning.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: AI-assisted Learning for Electronic Engineering Courses in High Education
Abstract: This study evaluates the efficacy of ChatGPT as an AI teaching and learning
support tool in an integrated circuit systems course at a higher education
institution in an Asian country. Various question types were completed, and
ChatGPT responses were assessed to gain valuable insights for further
investigation. The objective is to assess ChatGPT's ability to provide
insights, personalized support, and interactive learning experiences in
engineering education. The study includes the evaluation and reflection of
different stakeholders: students, lecturers, and engineers. The findings of
this study shed light on the benefits and limitations of ChatGPT as an AI tool,
paving the way for innovative learning approaches in technical disciplines.
Furthermore, the study contributes to our understanding of how digital
transformation is likely to unfold in the education sector.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game
Abstract: Agents built with large language models (LLMs) have recently achieved great
advancements. However, most of the efforts focus on single-agent or cooperative
settings, leaving more general multi-agent environments underexplored. We
propose a new framework powered by reinforcement learning (RL) to develop
strategic language agents, i.e., LLM-based agents with strategic thinking
ability, for a popular language game, Werewolf. Werewolf is a social deduction
game with hidden roles that involves both cooperation and competition and
emphasizes deceptive communication and diverse gameplay. Our agent tackles this
game by first using LLMs to reason about potential deceptions and generate a
set of strategically diverse actions. Then an RL policy, which selects an
action from the candidates, is learned by population-based training to enhance
the agents' decision-making ability. By combining LLMs with the RL policy, our
agent produces a variety of emergent strategies, achieves the highest win rate
against other LLM-based agents, and stays robust against adversarial human
players in the Werewolf game.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Efficient Pre-training for Localized Instruction Generation of Videos
Abstract: Procedural videos show step-by-step demonstrations of tasks like recipe
preparation. Understanding such videos is challenging, involving the precise
localization of steps and the generation of textual instructions. Manually
annotating steps and writing instructions is costly, which limits the size of
current datasets and hinders effective learning. Leveraging large but noisy
video-transcript datasets for pre-training can boost performance, but demands
significant computational resources. Furthermore, transcripts contain
irrelevant content and exhibit style variation compared to instructions written
by human annotators. To mitigate both issues, we propose a technique,
Sieve-&-Swap, to automatically curate a smaller dataset: (i) Sieve filters
irrelevant transcripts and (ii) Swap enhances the quality of the text
instruction by automatically replacing the transcripts with human-written
instructions from a text-only recipe dataset. The curated dataset, three orders
of magnitude smaller than current web-scale datasets, enables efficient
training of large-scale models with competitive performance. We complement our
Sieve-\&-Swap approach with a Procedure Transformer (ProcX) for end-to-end step
localization and instruction generation for procedural videos. When this model
is pre-trained on our curated dataset, it achieves state-of-the-art performance
in zero-shot and finetuning settings on YouCook2 and Tasty, while using a
fraction of the computational resources.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Decision-Making for Autonomous Vehicles with Interaction-Aware Behavioral Prediction and Social-Attention Neural Network
Abstract: Autonomous vehicles need to accomplish their tasks while interacting with
human drivers in traffic. It is thus crucial to equip autonomous vehicles with
artificial reasoning to better comprehend the intentions of the surrounding
traffic, thereby facilitating the accomplishments of the tasks. In this work,
we propose a behavioral model that encodes drivers' interacting intentions into
latent social-psychological parameters. Leveraging a Bayesian filter, we
develop a receding-horizon optimization-based controller for autonomous vehicle
decision-making which accounts for the uncertainties in the interacting
drivers' intentions. For online deployment, we design a neural network
architecture based on the attention mechanism which imitates the behavioral
model with online estimated parameter priors. We also propose a decision tree
search algorithm to solve the decision-making problem online. The proposed
behavioral model is then evaluated in terms of its capabilities for real-world
trajectory prediction. We further conduct extensive evaluations of the proposed
decision-making module, in forced highway merging scenarios, using both
simulated environments and real-world traffic datasets. The results demonstrate
that our algorithms can complete the forced merging tasks in various traffic
conditions while ensuring driving safety.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Debiasing Multimodal Models via Causal Information Minimization
Abstract: Most existing debiasing methods for multimodal models, including causal
intervention and inference methods, utilize approximate heuristics to represent
the biases, such as shallow features from early stages of training or unimodal
features for multimodal tasks like VQA, etc., which may not be accurate. In
this paper, we study bias arising from confounders in a causal graph for
multimodal data and examine a novel approach that leverages causally-motivated
information minimization to learn the confounder representations. Robust
predictive features contain diverse information that helps a model generalize
to out-of-distribution data. Hence, minimizing the information content of
features obtained from a pretrained biased model helps learn the simplest
predictive features that capture the underlying data distribution. We treat
these features as confounder representations and use them via methods motivated
by causal theory to remove bias from models. We find that the learned
confounder representations indeed capture dataset biases, and the proposed
debiasing methods improve out-of-distribution (OOD) performance on multiple
multimodal datasets without sacrificing in-distribution performance.
Additionally, we introduce a novel metric to quantify the sufficiency of
spurious features in models' predictions that further demonstrates the
effectiveness of our proposed methods. Our code is available at:
https://github.com/Vaidehi99/CausalInfoMin

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark
Abstract: Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of
federated learning, an influx of approaches have delivered towards different
realistic challenges. In this survey, we provide a systematic overview of the
important and recent developments of research on federated learning. Firstly,
we introduce the study history and terminology definition of this area. Then,
we comprehensively review three basic lines of research: generalization,
robustness, and fairness, by introducing their respective background concepts,
task settings, and main challenges. We also offer a detailed overview of
representative literature on both methods and datasets. We further benchmark
the reviewed methods on several well-known datasets. Finally, we point out
several open issues in this field and suggest opportunities for further
research. We also provide a public website to continuously track developments
in this fast advancing field: https://github.com/WenkeHuang/MarsFL.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Towards Concept-Aware Large Language Models
Abstract: Concepts play a pivotal role in various human cognitive functions, including
learning, reasoning and communication. However, there is very little work on
endowing machines with the ability to form and reason with concepts. In
particular, state-of-the-art large language models (LLMs) work at the level of
tokens, not concepts.
  In this work, we analyze how well contemporary LLMs capture human concepts
and their structure. We then discuss ways to develop concept-aware LLMs, taking
place at different stages of the pipeline. We sketch a method for pretraining
LLMs using concepts, and also explore the simpler approach that uses the output
of existing LLMs. Despite its simplicity, our proof-of-concept is shown to
better match human intuition, as well as improve the robustness of predictions.
These preliminary results underscore the promise of concept-aware LLMs.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: nerblackbox: A High-level Library for Named Entity Recognition in Python
Abstract: We present nerblackbox, a python library to facilitate the use of
state-of-the-art transformer-based models for named entity recognition. It
provides simple-to-use yet powerful methods to access data and models from a
wide range of sources, for fully automated model training and evaluation as
well as versatile model inference. While many technical challenges are solved
and hidden from the user by default, nerblackbox also offers fine-grained
control and a rich set of customizable features. It is thus targeted both at
application-oriented developers as well as machine learning experts and
researchers.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: More Robots are Coming: Large Multimodal Models (ChatGPT) can Solve Visually Diverse Images of Parsons Problems
Abstract: The advent of large language models is reshaping computing education. Recent
research has demonstrated that these models can produce better explanations
than students, answer multiple-choice questions at or above the class average,
and generate code that can pass automated tests in introductory courses. These
capabilities have prompted instructors to rapidly adapt their courses and
assessment methods to accommodate changes in learning objectives and the
potential for academic integrity violations. While some scholars have advocated
for the integration of visual problems as a safeguard against the capabilities
of language models, new multimodal language models now have vision and language
capabilities that may allow them to analyze and solve visual problems. In this
paper, we evaluate the performance of two large multimodal models on visual
assignments, with a specific focus on Parsons problems presented across diverse
visual representations. Our results show that GPT-4V solved 96.7\% of these
visual problems, struggling minimally with a single Parsons problem.
Conversely, Bard performed poorly by only solving 69.2\% of problems,
struggling with common issues like hallucinations and refusals. These findings
suggest that merely transitioning to visual programming problems might not be a
panacea to issues of academic integrity in the generative AI era.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Methods to Estimate Large Language Model Confidence
Abstract: Large Language Models have difficulty communicating uncertainty, which is a
significant obstacle to applying LLMs to complex medical tasks. This study
evaluates methods to measure LLM confidence when suggesting a diagnosis for
challenging clinical vignettes. GPT4 was asked a series of challenging case
questions using Chain of Thought and Self Consistency prompting. Multiple
methods were investigated to assess model confidence and evaluated on their
ability to predict the models observed accuracy. The methods evaluated were
Intrinsic Confidence, SC Agreement Frequency and CoT Response Length. SC
Agreement Frequency correlated with observed accuracy, yielding a higher Area
under the Receiver Operating Characteristic Curve compared to Intrinsic
Confidence and CoT Length analysis. SC agreement is the most useful proxy for
model confidence, especially for medical diagnosis. Model Intrinsic Confidence
and CoT Response Length exhibit a weaker ability to differentiate between
correct and incorrect answers, preventing them from being reliable and
interpretable markers for model confidence. We conclude GPT4 has a limited
ability to assess its own diagnostic accuracy. SC Agreement Frequency is the
most useful method to measure GPT4 confidence.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Green Resilience of Cyber-Physical Systems
Abstract: Cyber-Physical System (CPS) represents systems that join both hardware and
software components to perform real-time services. Maintaining the system's
reliability is critical to the continuous delivery of these services. However,
the CPS running environment is full of uncertainties and can easily lead to
performance degradation. As a result, the need for a recovery technique is
highly needed to achieve resilience in the system, with keeping in mind that
this technique should be as green as possible. This early doctorate proposal,
suggests a game theory solution to achieve resilience and green in CPS. Game
theory has been known for its fast performance in decision-making, helping the
system to choose what maximizes its payoffs. The proposed game model is
described over a real-life collaborative artificial intelligence system (CAIS),
that involves robots with humans to achieve a common goal. It shows how the
expected results of the system will achieve the resilience of CAIS with
minimized CO2 footprint.

Output: Software Engineering


Instruction: What field is the article from?

Prompt:
Title: Quilt: Robust Data Segment Selection against Concept Drifts
Abstract: Continuous machine learning pipelines are common in industrial settings where
models are periodically trained on data streams. Unfortunately, concept drifts
may occur in data streams where the joint distribution of the data X and label
y, P(X, y), changes over time and possibly degrade model accuracy. Existing
concept drift adaptation approaches mostly focus on updating the model to the
new data possibly using ensemble techniques of previous models and tend to
discard the drifted historical data. However, we contend that explicitly
utilizing the drifted data together leads to much better model accuracy and
propose Quilt, a data-centric framework for identifying and selecting data
segments that maximize model accuracy. To address the potential downside of
efficiency, Quilt extends existing data subset selection techniques, which can
be used to reduce the training data without compromising model accuracy. These
techniques cannot be used as is because they only assume virtual drifts where
the posterior probabilities P(y|X) are assumed not to change. In contrast, a
key challenge in our setup is to also discard undesirable data segments with
concept drifts. Quilt thus discards drifted data segments and selects data
segment subsets holistically for accurate and efficient model training. The two
operations use gradient-based scores, which have little computation overhead.
In our experiments, we show that Quilt outperforms state-of-the-art drift
adaptation and data selection baselines on synthetic and real datasets.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Coordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneity
Abstract: Federated Learning (FL) is a well-known framework for successfully performing
a learning task in an edge computing scenario where the devices involved have
limited resources and incomplete data representation. The basic assumption of
FL is that the devices communicate directly or indirectly with a parameter
server that centrally coordinates the whole process, overcoming several
challenges associated with it. However, in highly pervasive edge scenarios, the
presence of a central controller that oversees the process cannot always be
guaranteed, and the interactions (i.e., the connectivity graph) between devices
might not be predetermined, resulting in a complex network structure. Moreover,
the heterogeneity of data and devices further complicates the learning process.
This poses new challenges from a learning standpoint that we address by
proposing a communication-efficient Decentralised Federated Learning (DFL)
algorithm able to cope with them. Our solution allows devices communicating
only with their direct neighbours to train an accurate model, overcoming the
heterogeneity induced by data and different training histories. Our results
show that the resulting local models generalise better than those trained with
competing approaches, and do so in a more communication-efficient way.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Kuro Siwo: 12.1 billion $m^2$ under the water. A global multi-temporal satellite dataset for rapid flood mapping
Abstract: Global floods, exacerbated by climate change, pose severe threats to human
life, infrastructure, and the environment. This urgency is highlighted by
recent catastrophic events in Pakistan and New Zealand, underlining the
critical need for precise flood mapping for guiding restoration efforts,
understanding vulnerabilities, and preparing for future events. While Synthetic
Aperture Radar (SAR) offers day-and-night, all-weather imaging capabilities,
harnessing it for deep learning is hindered by the absence of a large annotated
dataset. To bridge this gap, we introduce Kuro Siwo, a meticulously curated
multi-temporal dataset, spanning 32 flood events globally. Our dataset maps
more than 63 billion m2 of land, with 12.1 billion of them being either a
flooded area or a permanent water body. Kuro Siwo stands out for its
unparalleled annotation quality to facilitate rapid flood mapping in a
supervised setting. We also augment learning by including a large unlabeled set
of SAR samples, aimed at self-supervised pretraining. We provide an extensive
benchmark and strong baselines for a diverse set of flood events from Europe,
America, Africa and Australia. Our benchmark demonstrates the quality of Kuro
Siwo annotations, training models that can achieve $\approx$ 85% and $\approx$
87% in F1-score for flooded areas and general water detection respectively.
This work calls on the deep learning community to develop solution-driven
algorithms for rapid flood mapping, with the potential to aid civil protection
and humanitarian agencies amid climate change challenges. Our code and data
will be made available at https://github.com/Orion-AI-Lab/KuroSiwo

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Masking Hyperspectral Imaging Data with Pretrained Models
Abstract: The presence of undesired background areas associated with potential noise
and unknown spectral characteristics degrades the performance of hyperspectral
data processing. Masking out unwanted regions is key to addressing this issue.
Processing only regions of interest yields notable improvements in terms of
computational costs, required memory, and overall performance. The proposed
processing pipeline encompasses two fundamental parts: regions of interest mask
generation, followed by the application of hyperspectral data processing
techniques solely on the newly masked hyperspectral cube. The novelty of our
work lies in the methodology adopted for the preliminary image segmentation. We
employ the Segment Anything Model (SAM) to extract all objects within the
dataset, and subsequently refine the segments with a zero-shot Grounding Dino
object detector, followed by intersection and exclusion filtering steps,
without the need for fine-tuning or retraining. To illustrate the efficacy of
the masking procedure, the proposed method is deployed on three challenging
applications scenarios that demand accurate masking; shredded plastics
characterization, drill core scanning, and litter monitoring. The numerical
evaluation of the proposed masking method on the three applications is provided
along with the used hyperparameters. The scripts for the method will be
available at https://github.com/hifexplo/Masking.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: How to Bridge the Gap between Modalities: A Comprehensive Survey on Multimodal Large Language Model
Abstract: This review paper explores Multimodal Large Language Models (MLLMs), which
integrate Large Language Models (LLMs) like GPT-4 to handle multimodal data
such as text and vision. MLLMs demonstrate capabilities like generating image
narratives and answering image-based questions, bridging the gap towards
real-world human-computer interactions and hinting at a potential pathway to
artificial general intelligence. However, MLLMs still face challenges in
processing the semantic gap in multimodality, which may lead to erroneous
generation, posing potential risks to society. Choosing the appropriate
modality alignment method is crucial, as improper methods might require more
parameters with limited performance improvement. This paper aims to explore
modality alignment methods for LLMs and their existing capabilities.
Implementing modality alignment allows LLMs to address environmental issues and
enhance accessibility. The study surveys existing modal alignment methods in
MLLMs into four groups: (1) Multimodal Converters that change data into
something LLMs can understand; (2) Multimodal Perceivers to improve how LLMs
perceive different types of data; (3) Tools Assistance for changing data into
one common format, usually text; and (4) Data-Driven methods that teach LLMs to
understand specific types of data in a dataset. This field is still in a phase
of exploration and experimentation, and we will organize and update various
existing research methods for multimodal information alignment.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Adaptive Interventions with User-Defined Goals for Health Behavior Change
Abstract: Physical inactivity remains a major public health concern, having
associations with adverse health outcomes such as cardiovascular disease and
type-2 diabetes. Mobile health applications present a promising avenue for
low-cost, scalable physical activity promotion, yet often suffer from small
effect sizes and low adherence rates, particularly in comparison to human
coaching. Goal-setting is a critical component of health coaching that has been
underutilized in adaptive algorithms for mobile health interventions. This
paper introduces a modification to the Thompson sampling algorithm that places
emphasis on individualized goal-setting by optimizing personalized reward
functions. As a step towards supporting goal-setting, this paper offers a
balanced approach that can leverage shared structure while optimizing
individual preferences and goals. We prove that our modification incurs only a
constant penalty on the cumulative regret while preserving the sample
complexity benefits of data sharing. In a physical activity simulator, we
demonstrate that our algorithm achieves substantial improvements in cumulative
regret compared to baselines that do not share data or do not optimize for
individualized rewards.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Roles of Scaling and Instruction Tuning in Language Perception: Model vs. Human Attention
Abstract: Recent large language models (LLMs) have revealed strong abilities to
understand natural language. Since most of them share the same basic structure,
i.e. the transformer block, possible contributors to their success in the
training process are scaling and instruction tuning. However, how these factors
affect the models' language perception is unclear. This work compares the
self-attention of several existing LLMs (LLaMA, Alpaca and Vicuna) in different
sizes (7B, 13B, 30B, 65B), together with eye saccade, an aspect of human
reading attention, to assess the effect of scaling and instruction tuning on
language perception. Results show that scaling enhances the human resemblance
and improves the effective attention by reducing the trivial pattern reliance,
while instruction tuning does not. However, instruction tuning significantly
enhances the models' sensitivity to instructions. We also find that current
LLMs are consistently closer to non-native than native speakers in attention,
suggesting a sub-optimal language perception of all models. Our code and data
used in the analysis is available on GitHub.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Culturally Responsive Artificial Intelligence -- Problems, Challenges and Solutions
Abstract: In the contemporary interconnected world, the concept of cultural
responsibility occupies paramount importance. As the lines between nations
become less distinct, it is incumbent upon individuals, communities, and
institutions to assume the responsibility of safeguarding and valuing the
landscape of diverse cultures that constitute our global society. This paper
explores the socio-cultural and ethical challenges stemming from the
implementation of AI algorithms and highlights the necessity for their
culturally responsive development. It also offers recommendations on essential
elements required to enhance AI systems' adaptability to meet the demands of
contemporary multicultural societies. The paper highlights the need for further
multidisciplinary research to create AI models that effectively address these
challenges. It also advocates the significance of AI enculturation and
underlines the importance of regulatory measures to promote cultural
responsibility in AI systems.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Deep Reinforcement Learning for Community Battery Scheduling under Uncertainties of Load, PV Generation, and Energy Prices
Abstract: In response to the growing uptake of distributed energy resources (DERs),
community batteries have emerged as a promising solution to support renewable
energy integration, reduce peak load, and enhance grid reliability. This paper
presents a deep reinforcement learning (RL) strategy, centered around the soft
actor-critic (SAC) algorithm, to schedule a community battery system in the
presence of uncertainties, such as solar photovoltaic (PV) generation, local
demand, and real-time energy prices. We position the community battery to play
a versatile role, in integrating local PV energy, reducing peak load, and
exploiting energy price fluctuations for arbitrage, thereby minimizing the
system cost. To improve exploration and convergence during RL training, we
utilize the noisy network technique. This paper conducts a comparative study of
different RL algorithms, including proximal policy optimization (PPO) and deep
deterministic policy gradient (DDPG) algorithms, to evaluate their
effectiveness in the community battery scheduling problem. The results
demonstrate the potential of RL in addressing community battery scheduling
challenges and show that the SAC algorithm achieves the best performance
compared to RL and optimization benchmarks.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Artificial Intelligence Ethics Education in Cybersecurity: Challenges and Opportunities: a focus group report
Abstract: The emergence of AI tools in cybersecurity creates many opportunities and
uncertainties. A focus group with advanced graduate students in cybersecurity
revealed the potential depth and breadth of the challenges and opportunities.
The salient issues are access to open source or free tools, documentation,
curricular diversity, and clear articulation of ethical principles for AI
cybersecurity education. Confronting the "black box" mentality in AI
cybersecurity work is also of the greatest importance, doubled by deeper and
prior education in foundational AI work. Systems thinking and effective
communication were considered relevant areas of educational improvement. Future
AI educators and practitioners need to address these issues by implementing
rigorous technical training curricula, clear documentation, and frameworks for
ethically monitoring AI combined with critical and system's thinking and
communication skills.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning
Abstract: Modular and composable transfer learning is an emerging direction in the
field of Parameter Efficient Fine-Tuning, as it enables neural networks to
better organize various aspects of knowledge, leading to improved cross-task
generalization. In this paper, we introduce a novel approach Customized
Polytropon C-Poly that combines task-common skills and task-specific skills,
while the skill parameters being highly parameterized using low-rank
techniques. Each task is associated with a customizable number of exclusive
specialized skills and also benefits from skills shared with peer tasks. A
skill assignment matrix is jointly learned. To evaluate our approach, we
conducted extensive experiments on the Super-NaturalInstructions and the
SuperGLUE benchmarks. Our findings demonstrate that C-Poly outperforms
fully-shared, task-specific, and skill-indistinguishable baselines,
significantly enhancing the sample efficiency in multi-task learning scenarios.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos
Abstract: Out-of-distribution (OOD) detection is essential to improve the reliability
of machine learning models by detecting samples that do not belong to the
training distribution. Detecting OOD samples effectively in certain tasks can
pose a challenge because of the substantial heterogeneity within the
in-distribution (ID), and the high structural similarity between ID and OOD
classes. For instance, when detecting heart views in fetal ultrasound videos
there is a high structural similarity between the heart and other anatomies
such as the abdomen, and large in-distribution variance as a heart has 5
distinct views and structural variations within each view. To detect OOD
samples in this context, the resulting model should generalise to the
intra-anatomy variations while rejecting similar OOD samples. In this paper, we
introduce dual-conditioned diffusion models (DCDM) where we condition the model
on in-distribution class information and latent features of the input image for
reconstruction-based OOD detection. This constrains the generative manifold of
the model to generate images structurally and semantically similar to those
within the in-distribution. The proposed model outperforms reference methods
with a 12% improvement in accuracy, 22% higher precision, and an 8% better F1
score.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Unsupervised Lexical Simplification with Context Augmentation
Abstract: We propose a new unsupervised lexical simplification method that uses only
monolingual data and pre-trained language models. Given a target word and its
context, our method generates substitutes based on the target context and also
additional contexts sampled from monolingual data. We conduct experiments in
English, Portuguese, and Spanish on the TSAR-2022 shared task, and show that
our model substantially outperforms other unsupervised systems across all
languages. We also establish a new state-of-the-art by ensembling our model
with GPT-3.5. Lastly, we evaluate our model on the SWORDS lexical substitution
data set, achieving a state-of-the-art result.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: RankAug: Augmented data ranking for text classification
Abstract: Research on data generation and augmentation has been focused majorly on
enhancing generation models, leaving a notable gap in the exploration and
refinement of methods for evaluating synthetic data. There are several text
similarity metrics within the context of generated data filtering which can
impact the performance of specific Natural Language Understanding (NLU) tasks,
specifically focusing on intent and sentiment classification. In this study, we
propose RankAug, a text-ranking approach that detects and filters out the top
augmented texts in terms of being most similar in meaning with lexical and
syntactical diversity. Through experiments conducted on multiple datasets, we
demonstrate that the judicious selection of filtering techniques can yield a
substantial improvement of up to 35% in classification accuracy for
under-represented classes.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Unleashing the Creative Mind: Language Model As Hierarchical Policy For Improved Exploration on Challenging Problem Solving
Abstract: Large Language Models (LLMs) have achieved tremendous progress, yet they
still often struggle with challenging reasoning problems. Current approaches
address this challenge by sampling or searching detailed and low-level
reasoning chains. However, these methods are still limited in their exploration
capabilities, making it challenging for correct solutions to stand out in the
huge solution space. In this work, we unleash LLMs' creative potential for
exploring multiple diverse problem solving strategies by framing an LLM as a
hierarchical policy via in-context learning. This policy comprises of a
visionary leader that proposes multiple diverse high-level problem-solving
tactics as hints, accompanied by a follower that executes detailed
problem-solving processes following each of the high-level instruction. The
follower uses each of the leader's directives as a guide and samples multiple
reasoning chains to tackle the problem, generating a solution group for each
leader proposal. Additionally, we propose an effective and efficient
tournament-based approach to select among these explored solution groups to
reach the final answer. Our approach produces meaningful and inspiring hints,
enhances problem-solving strategy exploration, and improves the final answer
accuracy on challenging problems in the MATH dataset. Code will be released at
https://github.com/lz1oceani/LLM-As-Hierarchical-Policy.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Empowering remittance management in the digitised landscape: A real-time Data-Driven Decision Support with predictive abilities for financial transactions
Abstract: The advent of Blockchain technology (BT) revolutionised the way remittance
transactions are recorded. Banks and remittance organisations have shown a
growing interest in exploring blockchain's potential advantages over
traditional practices. This paper presents a data-driven predictive decision
support approach as an innovative artefact designed for the blockchain-oriented
remittance industry. Employing a theory-generating Design Science Research
(DSR) approach, we have uncovered the emergence of predictive capabilities
driven by transactional big data. The artefact integrates predictive analytics
and Machine Learning (ML) to enable real-time remittance monitoring, empowering
management decision-makers to address challenges in the uncertain digitised
landscape of blockchain-oriented remittance companies. Bridging the gap between
theory and practice, this research not only enhances the security of the
remittance ecosystem but also lays the foundation for future predictive
decision support solutions, extending the potential of predictive analytics to
other domains. Additionally, the generated theory from the artifact's
implementation enriches the DSR approach and fosters grounded and stakeholder
theory development in the information systems domain.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Dexterous Functional Grasping
Abstract: While there have been significant strides in dexterous manipulation, most of
it is limited to benchmark tasks like in-hand reorientation which are of
limited utility in the real world. The main benefit of dexterous hands over
two-fingered ones is their ability to pickup tools and other objects (including
thin ones) and grasp them firmly to apply force. However, this task requires
both a complex understanding of functional affordances as well as precise
low-level control. While prior work obtains affordances from human data this
approach doesn't scale to low-level control. Similarly, simulation training
cannot give the robot an understanding of real-world semantics. In this paper,
we aim to combine the best of both worlds to accomplish functional grasping for
in-the-wild objects. We use a modular approach. First, affordances are obtained
by matching corresponding regions of different objects and then a low-level
policy trained in sim is run to grasp it. We propose a novel application of
eigengrasps to reduce the search space of RL using a small amount of human data
and find that it leads to more stable and physically realistic motion. We find
that eigengrasp action space beats baselines in simulation and outperforms
hardcoded grasping in real and matches or outperforms a trained human
teleoperator. Results visualizations and videos at https://dexfunc.github.io/

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Histopathologic Cancer Detection
Abstract: Early diagnosis of the cancer cells is necessary for making an effective
treatment plan and for the health and safety of a patient. Nowadays, doctors
usually use a histological grade that pathologists determine by performing a
semi-quantitative analysis of the histopathological and cytological features of
hematoxylin-eosin (HE) stained histopathological images. This research
contributes a potential classification model for cancer prognosis to
efficiently utilize the valuable information underlying the HE-stained
histopathological images. This work uses the PatchCamelyon benchmark datasets
and trains them in a multi-layer perceptron and convolution model to observe
the model's performance in terms of precision, Recall, F1 Score, Accuracy, and
AUC Score. The evaluation result shows that the baseline convolution model
outperforms the baseline MLP model. Also, this paper introduced ResNet50 and
InceptionNet models with data augmentation, where ResNet50 is able to beat the
state-of-the-art model. Furthermore, the majority vote and concatenation
ensemble were evaluated and provided the future direction of using transfer
learning and segmentation to understand the specific features.

Output: Computer Vision