Instruction: What field is the article from?

Prompt:
Title: Detection and Defense of Unlearnable Examples
Abstract: Privacy preserving has become increasingly critical with the emergence of
social media. Unlearnable examples have been proposed to avoid leaking personal
information on the Internet by degrading generalization abilities of deep
learning models. However, our study reveals that unlearnable examples are
easily detectable. We provide theoretical results on linear separability of
certain unlearnable poisoned dataset and simple network based detection methods
that can identify all existing unlearnable examples, as demonstrated by
extensive experiments. Detectability of unlearnable examples with simple
networks motivates us to design a novel defense method. We propose using
stronger data augmentations coupled with adversarial noises generated by simple
networks, to degrade the detectability and thus provide effective defense
against unlearnable examples with a lower cost. Adversarial training with large
budgets is a widely-used defense method on unlearnable examples. We establish
quantitative criteria between the poison and adversarial budgets which
determine the existence of robust unlearnable examples or the failure of the
adversarial defense.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: An Incremental Unified Framework for Small Defect Inspection
Abstract: Artificial Intelligence (AI)-driven defect inspection is pivotal in
industrial manufacturing. Yet, many methods, tailored to specific pipelines,
grapple with diverse product portfolios and evolving processes. Addressing
this, we present the Incremental Unified Framework (IUF) that can reduce the
feature conflict problem when continuously integrating new objects in the
pipeline, making it advantageous in object-incremental learning scenarios.
Employing a state-of-the-art transformer, we introduce Object-Aware
Self-Attention (OASA) to delineate distinct semantic boundaries. Semantic
Compression Loss (SCL) is integrated to optimize non-primary semantic space,
enhancing network adaptability for novel objects. Additionally, we prioritize
retaining the features of established objects during weight updates.
Demonstrating prowess in both image and pixel-level defect inspection, our
approach achieves state-of-the-art performance, proving indispensable for
dynamic and scalable industrial inspections. Our code will be released at
https://github.com/jqtangust/IUF.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Neural Gaussian Similarity Modeling for Differential Graph Structure Learning
Abstract: Graph Structure Learning (GSL) has demonstrated considerable potential in the
analysis of graph-unknown non-Euclidean data across a wide range of domains.
However, constructing an end-to-end graph structure learning model poses a
challenge due to the impediment of gradient flow caused by the nearest neighbor
sampling strategy. In this paper, we construct a differential graph structure
learning model by replacing the non-differentiable nearest neighbor sampling
with a differentiable sampling using the reparameterization trick. Under this
framework, we argue that the act of sampling \mbox{nearest} neighbors may not
invariably be essential, particularly in instances where node features exhibit
a significant degree of similarity. To alleviate this issue, the bell-shaped
Gaussian Similarity (GauSim) modeling is proposed to sample non-nearest
neighbors. To adaptively model the similarity, we further propose Neural
Gaussian Similarity (NeuralGauSim) with learnable parameters featuring flexible
sampling behaviors. In addition, we develop a scalable method by transferring
the large-scale graph to the transition graph to significantly reduce the
complexity. Experimental results demonstrate the effectiveness of the proposed
methods.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Sim-GPT: Text Similarity via GPT Annotated Data
Abstract: Due to the lack of a large collection of high-quality labeled sentence pairs
with textual similarity scores, existing approaches for Semantic Textual
Similarity (STS) mostly rely on unsupervised techniques or training signals
that are only partially correlated with textual similarity, e.g., NLI-based
datasets. To tackle this issue, in this paper, we propose the strategy of
measuring text similarity via GPT annotated data (Sim-GPT for short). The core
idea of Sim-GPT is to generate data with STS labels using GPT-4, based on which
an STS model is trained. Sim-GPT framework utilizes LLMs to provide a
substantial amount of reliable annotated data filling the gap of the lack of
training signals for STS. Sim-GPT is trained on a one-time generated dataset
using BERT or RoBERTa as the backbone, which offers long-term savings in cost
and speed compared to repeatedly invoking LLMs for each sentence pair. Trained
on the examples from GPT-4 (371K), Sim-GPT yields SOTA performances on the
widely-used seven STS benchmarks: +0.99 over supervised-SimCSE, and +0.42 over
the current SOTA PromCSE model. To encourage further advancements of the field,
we release both models and the 371K annotated examples from GPT-4. Code, models
and annotated data are available at: https://github.com/ShuheWang1998/Sim-GPT.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: A Framework for Monitoring and Retraining Language Models in Real-World Applications
Abstract: In the Machine Learning (ML) model development lifecycle, training candidate
models using an offline holdout dataset and identifying the best model for the
given task is only the first step. After the deployment of the selected model,
continuous model monitoring and model retraining is required in many real-world
applications. There are multiple reasons for retraining, including data or
concept drift, which may be reflected on the model performance as monitored by
an appropriate metric. Another motivation for retraining is the acquisition of
increasing amounts of data over time, which may be used to retrain and improve
the model performance even in the absence of drifts. We examine the impact of
various retraining decision points on crucial factors, such as model
performance and resource utilization, in the context of Multilabel
Classification models. We explain our key decision points and propose a
reference framework for designing an effective model retraining strategy.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Automated Annotation of Scientific Texts for ML-based Keyphrase Extraction and Validation
Abstract: Advanced omics technologies and facilities generate a wealth of valuable data
daily; however, the data often lacks the essential metadata required for
researchers to find and search them effectively. The lack of metadata poses a
significant challenge in the utilization of these datasets. Machine
learning-based metadata extraction techniques have emerged as a potentially
viable approach to automatically annotating scientific datasets with the
metadata necessary for enabling effective search. Text labeling, usually
performed manually, plays a crucial role in validating machine-extracted
metadata. However, manual labeling is time-consuming; thus, there is an need to
develop automated text labeling techniques in order to accelerate the process
of scientific innovation. This need is particularly urgent in fields such as
environmental genomics and microbiome science, which have historically received
less attention in terms of metadata curation and creation of gold-standard text
mining datasets.
  In this paper, we present two novel automated text labeling approaches for
the validation of ML-generated metadata for unlabeled texts, with specific
applications in environmental genomics. Our techniques show the potential of
two new ways to leverage existing information about the unlabeled texts and the
scientific domain. The first technique exploits relationships between different
types of data sources related to the same research study, such as publications
and proposals. The second technique takes advantage of domain-specific
controlled vocabularies or ontologies. In this paper, we detail applying these
approaches for ML-generated metadata validation. Our results show that the
proposed label assignment approaches can generate both generic and
highly-specific text labels for the unlabeled texts, with up to 44% of the
labels matching with those suggested by a ML keyword extraction algorithm.

Output: Information Retrieval


Instruction: What field is the article from?

Prompt:
Title: CapsFusion: Rethinking Image-Text Data at Scale
Abstract: Large multimodal models demonstrate remarkable generalist ability to perform
diverse multimodal tasks in a zero-shot manner. Large-scale web-based
image-text pairs contribute fundamentally to this success, but suffer from
excessive noise. Recent studies use alternative captions synthesized by
captioning models and have achieved notable benchmark performance. However, our
experiments reveal significant Scalability Deficiency and World Knowledge Loss
issues in models trained with synthetic captions, which have been largely
obscured by their initial benchmark success. Upon closer examination, we
identify the root cause as the overly-simplified language structure and lack of
knowledge details in existing synthetic captions. To provide higher-quality and
more scalable multimodal pretraining data, we propose CapsFusion, an advanced
framework that leverages large language models to consolidate and refine
information from both web-based image-text pairs and synthetic captions.
Extensive experiments show that CapsFusion captions exhibit remarkable
all-round superiority over existing captions in terms of model performance
(e.g., 18.8 and 18.3 improvements in CIDEr score on COCO and NoCaps), sample
efficiency (requiring 11-16 times less computation than baselines), world
knowledge depth, and scalability. These effectiveness, efficiency and
scalability advantages position CapsFusion as a promising candidate for future
scaling of LMM training.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning
Abstract: In a data-centric era, concerns regarding privacy and ethical data handling
grow as machine learning relies more on personal information. This empirical
study investigates the privacy, generalization, and stability of deep learning
models in the presence of additive noise in federated learning frameworks. Our
main objective is to provide strategies to measure the generalization,
stability, and privacy-preserving capabilities of these models and further
improve them. To this end, five noise infusion mechanisms at varying noise
levels within centralized and federated learning settings are explored. As
model complexity is a key component of the generalization and stability of deep
learning models during training and evaluation, a comparative analysis of three
Convolutional Neural Network (CNN) architectures is provided. The paper
introduces Signal-to-Noise Ratio (SNR) as a quantitative measure of the
trade-off between privacy and training accuracy of noise-infused models, aiming
to find the noise level that yields optimal privacy and accuracy. Moreover, the
Price of Stability and Price of Anarchy are defined in the context of
privacy-preserving deep learning, contributing to the systematic investigation
of the noise infusion strategies to enhance privacy without compromising
performance. Our research sheds light on the delicate balance between these
critical factors, fostering a deeper understanding of the implications of
noise-based regularization in machine learning. By leveraging noise as a tool
for regularization and privacy enhancement, we aim to contribute to the
development of robust, privacy-aware algorithms, ensuring that AI-driven
solutions prioritize both utility and privacy.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Photorealistic Video Generation with Diffusion Models
Abstract: We present W.A.L.T, a transformer-based approach for photorealistic video
generation via diffusion modeling. Our approach has two key design decisions.
First, we use a causal encoder to jointly compress images and videos within a
unified latent space, enabling training and generation across modalities.
Second, for memory and training efficiency, we use a window attention
architecture tailored for joint spatial and spatiotemporal generative modeling.
Taken together these design decisions enable us to achieve state-of-the-art
performance on established video (UCF-101 and Kinetics-600) and image
(ImageNet) generation benchmarks without using classifier free guidance.
Finally, we also train a cascade of three models for the task of text-to-video
generation consisting of a base latent video diffusion model, and two video
super-resolution diffusion models to generate videos of $512 \times 896$
resolution at $8$ frames per second.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: LILO: Learning Interpretable Libraries by Compressing and Documenting Code
Abstract: While large language models (LLMs) now excel at code generation, a key aspect
of software development is the art of refactoring: consolidating code into
libraries of reusable and readable programs. In this paper, we introduce LILO,
a neurosymbolic framework that iteratively synthesizes, compresses, and
documents code to build libraries tailored to particular problem domains. LILO
combines LLM-guided program synthesis with recent algorithmic advances in
automated refactoring from Stitch: a symbolic compression system that
efficiently identifies optimal lambda abstractions across large code corpora.
To make these abstractions interpretable, we introduce an auto-documentation
(AutoDoc) procedure that infers natural language names and docstrings based on
contextual examples of usage. In addition to improving human readability, we
find that AutoDoc boosts performance by helping LILO's synthesizer to interpret
and deploy learned abstractions. We evaluate LILO on three inductive program
synthesis benchmarks for string editing, scene reasoning, and graphics
composition. Compared to existing neural and symbolic methods - including the
state-of-the-art library learning algorithm DreamCoder - LILO solves more
complex tasks and learns richer libraries that are grounded in linguistic
knowledge.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM
Abstract: Dense simultaneous localization and mapping (SLAM) is pivotal for embodied
scene understanding. Recent work has shown that 3D Gaussians enable
high-quality reconstruction and real-time rendering of scenes using multiple
posed cameras. In this light, we show for the first time that representing a
scene by 3D Gaussians can enable dense SLAM using a single unposed monocular
RGB-D camera. Our method, SplaTAM, addresses the limitations of prior radiance
field-based representations, including fast rendering and optimization, the
ability to determine if areas have been previously mapped, and structured map
expansion by adding more Gaussians. We employ an online tracking and mapping
pipeline while tailoring it to specifically use an underlying Gaussian
representation and silhouette-guided optimization via differentiable rendering.
Extensive experiments show that SplaTAM achieves up to 2X state-of-the-art
performance in camera pose estimation, map construction, and novel-view
synthesis, demonstrating its superiority over existing approaches, while
allowing real-time rendering of a high-resolution dense 3D map.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Scalable CP Decomposition for Tensor Learning using GPU Tensor Cores
Abstract: CP decomposition is a powerful tool for data science, especially gene
analysis, deep learning, and quantum computation. However, the application of
tensor decomposition is largely hindered by the exponential increment of the
computational complexity and storage consumption with the size of tensors.
While the data in our real world is usually presented as trillion- or even
exascale-scale tensors, existing work can only support billion-scale scale
tensors. In our work, we propose the Exascale-Tensor to mitigate the
significant gap. Specifically, we propose a compression-based tensor
decomposition framework, namely the exascale-tensor, to support exascale tensor
decomposition. Then, we carefully analyze the inherent parallelism and propose
a bag of strategies to improve computational efficiency. Last, we conduct
experiments to decompose tensors ranging from million-scale to trillion-scale
for evaluation. Compared to the baselines, the exascale-tensor supports 8,000x
larger tensors and a speedup up to 6.95x. We also apply our method to two
real-world applications, including gene analysis and tensor layer neural
networks, of which the numeric results demonstrate the scalability and
effectiveness of our method.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Learning Globally Optimized Language Structure via Adversarial Training
Abstract: Recent work has explored integrating autoregressive language models with
energy-based models (EBMs) to enhance text generation capabilities. However,
learning effective EBMs for text is challenged by the discrete nature of
language. This work proposes an adversarial training strategy to address
limitations in prior efforts. Specifically, an iterative adversarial attack
algorithm is presented to generate negative samples for training the EBM by
perturbing text from the autoregressive model. This aims to enable the EBM to
suppress spurious modes outside the support of the data distribution.
Experiments on an arithmetic sequence generation task demonstrate that the
proposed adversarial training approach can substantially enhance the quality of
generated sequences compared to prior methods. The results highlight the
promise of adversarial techniques to improve discrete EBM training. Key
contributions include: (1) an adversarial attack strategy tailored to text to
generate negative samples, circumventing MCMC limitations; (2) an adversarial
training algorithm for EBMs leveraging these attacks; (3) empirical validation
of performance improvements on a sequence generation task.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: JAB: Joint Adversarial Prompting and Belief Augmentation
Abstract: With the recent surge of language models in different applications, attention
to safety and robustness of these models has gained significant importance.
Here we introduce a joint framework in which we simultaneously probe and
improve the robustness of a black-box target model via adversarial prompting
and belief augmentation using iterative feedback loops. This framework utilizes
an automated red teaming approach to probe the target model, along with a
belief augmenter to generate instructions for the target model to improve its
robustness to those adversarial probes. Importantly, the adversarial model and
the belief generator leverage the feedback from past interactions to improve
the effectiveness of the adversarial prompts and beliefs, respectively. In our
experiments, we demonstrate that such a framework can reduce toxic content
generation both in dynamic cases where an adversary directly interacts with a
target model and static cases where we use a static benchmark dataset to
evaluate our model.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Artificial General Intelligence, Existential Risk, and Human Risk Perception
Abstract: Artificial general intelligence (AGI) does not yet exist, but given the pace
of technological development in artificial intelligence, it is projected to
reach human-level intelligence within roughly the next two decades. After that,
many experts expect it to far surpass human intelligence and to do so rapidly.
The prospect of superintelligent AGI poses an existential risk to humans
because there is no reliable method for ensuring that AGI goals stay aligned
with human goals. Drawing on publicly available forecaster and opinion data,
the author examines how experts and non-experts perceive risk from AGI. The
findings indicate that the perceived risk of a world catastrophe or extinction
from AGI is greater than for other existential risks. The increase in perceived
risk over the last year is also steeper for AGI than for other existential
threats (e.g., nuclear war or human-caused climate change). That AGI is a
pressing existential risk is something on which experts and non-experts agree,
but the basis for such agreement currently remains obscure.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination
Abstract: The hallucination issue is recognized as a fundamental deficiency of large
language models (LLMs), especially when applied to fields such as finance,
education, and law. Despite the growing concerns, there has been a lack of
empirical investigation. In this paper, we provide an empirical examination of
LLMs' hallucination behaviors in financial tasks. First, we empirically
investigate LLM model's ability of explaining financial concepts and
terminologies. Second, we assess LLM models' capacity of querying historical
stock prices. Third, to alleviate the hallucination issue, we evaluate the
efficacy of four practical methods, including few-shot learning, Decoding by
Contrasting Layers (DoLa), the Retrieval Augmentation Generation (RAG) method
and the prompt-based tool learning method for a function to generate a query
command. Finally, our major finding is that off-the-shelf LLMs experience
serious hallucination behaviors in financial tasks. Therefore, there is an
urgent need to call for research efforts in mitigating LLMs' hallucination.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Mukhyansh: A Headline Generation Dataset for Indic Languages
Abstract: The task of headline generation within the realm of Natural Language
Processing (NLP) holds immense significance, as it strives to distill the true
essence of textual content into concise and attention-grabbing summaries. While
noteworthy progress has been made in headline generation for widely spoken
languages like English, there persist numerous challenges when it comes to
generating headlines in low-resource languages, such as the rich and diverse
Indian languages. A prominent obstacle that specifically hinders headline
generation in Indian languages is the scarcity of high-quality annotated data.
To address this crucial gap, we proudly present Mukhyansh, an extensive
multilingual dataset, tailored for Indian language headline generation.
Comprising an impressive collection of over 3.39 million article-headline
pairs, Mukhyansh spans across eight prominent Indian languages, namely Telugu,
Tamil, Kannada, Malayalam, Hindi, Bengali, Marathi, and Gujarati. We present a
comprehensive evaluation of several state-of-the-art baseline models.
Additionally, through an empirical analysis of existing works, we demonstrate
that Mukhyansh outperforms all other models, achieving an impressive average
ROUGE-L score of 31.43 across all 8 languages.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Tactile Active Inference Reinforcement Learning for Efficient Robotic Manipulation Skill Acquisition
Abstract: Robotic manipulation holds the potential to replace humans in the execution
of tedious or dangerous tasks. However, control-based approaches are not
suitable due to the difficulty of formally describing open-world manipulation
in reality, and the inefficiency of existing learning methods. Thus, applying
manipulation in a wide range of scenarios presents significant challenges. In
this study, we propose a novel method for skill learning in robotic
manipulation called Tactile Active Inference Reinforcement Learning
(Tactile-AIRL), aimed at achieving efficient training. To enhance the
performance of reinforcement learning (RL), we introduce active inference,
which integrates model-based techniques and intrinsic curiosity into the RL
process. This integration improves the algorithm's training efficiency and
adaptability to sparse rewards. Additionally, we utilize a vision-based tactile
sensor to provide detailed perception for manipulation tasks. Finally, we
employ a model-based approach to imagine and plan appropriate actions through
free energy minimization. Simulation results demonstrate that our method
achieves significantly high training efficiency in non-prehensile objects
pushing tasks. It enables agents to excel in both dense and sparse reward tasks
with just a few interaction episodes, surpassing the SAC baseline. Furthermore,
we conduct physical experiments on a gripper screwing task using our method,
which showcases the algorithm's rapid learning capability and its potential for
practical applications.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
Abstract: Recent advances in computational modelling of atomic systems, spanning
molecules, proteins, and materials, represent them as geometric graphs with
atoms embedded as nodes in 3D Euclidean space. In these graphs, the geometric
attributes transform according to the inherent physical symmetries of 3D atomic
systems, including rotations and translations in Euclidean space, as well as
node permutations. In recent years, Geometric Graph Neural Networks have
emerged as the preferred machine learning architecture powering applications
ranging from protein structure prediction to molecular simulations and material
generation. Their specificity lies in the inductive biases they leverage --
such as physical symmetries and chemical properties -- to learn informative
representations of these geometric graphs. In this opinionated paper, we
provide a comprehensive and self-contained overview of the field of Geometric
GNNs for 3D atomic systems. We cover fundamental background material and
introduce a pedagogical taxonomy of Geometric GNN architectures:(1) invariant
networks, (2) equivariant networks in Cartesian basis, (3) equivariant networks
in spherical basis, and (4) unconstrained networks. Additionally, we outline
key datasets and application areas and suggest future research directions. The
objective of this work is to present a structured perspective on the field,
making it accessible to newcomers and aiding practitioners in gaining an
intuition for its mathematical abstractions.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization
Abstract: Summaries of medical text shall be faithful by being consistent and factual
with source inputs, which is an important but understudied topic for safety and
efficiency in healthcare. In this paper, we investigate and improve
faithfulness in summarization on a broad range of medical summarization tasks.
Our investigation reveals that current summarization models often produce
unfaithful outputs for medical input text. We then introduce FaMeSumm, a
framework to improve faithfulness by fine-tuning pre-trained language models
based on medical knowledge. FaMeSumm performs contrastive learning on designed
sets of faithful and unfaithful summaries, and it incorporates medical terms
and their contexts to encourage faithful generation of medical terms. We
conduct comprehensive experiments on three datasets in two languages: health
question and radiology report summarization datasets in English, and a
patient-doctor dialogue dataset in Chinese. Results demonstrate that FaMeSumm
is flexible and effective by delivering consistent improvements over mainstream
language models such as BART, T5, mT5, and PEGASUS, yielding state-of-the-art
performances on metrics for faithfulness and general quality. Human evaluation
by doctors also shows that FaMeSumm generates more faithful outputs. Our code
is available at https://github.com/psunlpgroup/FaMeSumm .

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Toward Open-ended Embodied Tasks Solving
Abstract: Empowering embodied agents, such as robots, with Artificial Intelligence (AI)
has become increasingly important in recent years. A major challenge is task
open-endedness. In practice, robots often need to perform tasks with novel
goals that are multifaceted, dynamic, lack a definitive "end-state", and were
not encountered during training. To tackle this problem, this paper introduces
\textit{Diffusion for Open-ended Goals} (DOG), a novel framework designed to
enable embodied AI to plan and act flexibly and dynamically for open-ended task
goals. DOG synergizes the generative prowess of diffusion models with
state-of-the-art, training-free guidance techniques to adaptively perform
online planning and control. Our evaluations demonstrate that DOG can handle
various kinds of novel task goals not seen during training, in both maze
navigation and robot control problems. Our work sheds light on enhancing
embodied AI's adaptability and competency in tackling open-ended goals.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
Abstract: Over the past few years, graph neural networks (GNNs) have become powerful
and practical tools for learning on (static) graph-structure data. However,
many real-world applications, such as social networks and e-commerce, involve
temporal graphs where nodes and edges are dynamically evolving. Temporal graph
neural networks (TGNNs) have progressively emerged as an extension of GNNs to
address time-evolving graphs and have gradually become a trending research
topic in both academics and industry. Advancing research and application in
such an emerging field necessitates the development of new tools to compose
TGNN models and unify their different schemes for dealing with temporal graphs.
In this work, we introduce LasTGL, an industrial framework that integrates
unified and extensible implementations of common temporal graph learning
algorithms for various advanced tasks. The purpose of LasTGL is to provide the
essential building blocks for solving temporal graph learning tasks, focusing
on the guiding principles of user-friendliness and quick prototyping on which
PyTorch is based. In particular, LasTGL provides comprehensive temporal graph
datasets, TGNN models and utilities along with well-documented tutorials,
making it suitable for both absolute beginners and expert deep learning
practitioners alike.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Open Domain Knowledge Extraction for Knowledge Graphs
Abstract: The quality of a knowledge graph directly impacts the quality of downstream
applications (e.g. the number of answerable questions using the graph). One
ongoing challenge when building a knowledge graph is to ensure completeness and
freshness of the graph's entities and facts. In this paper, we introduce ODKE,
a scalable and extensible framework that sources high-quality entities and
facts from open web at scale. ODKE utilizes a wide range of extraction models
and supports both streaming and batch processing at different latency. We
reflect on the challenges and design decisions made and share lessons learned
when building and deploying ODKE to grow an industry-scale open domain
knowledge graph.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: MaskConver: Revisiting Pure Convolution Model for Panoptic Segmentation
Abstract: In recent years, transformer-based models have dominated panoptic
segmentation, thanks to their strong modeling capabilities and their unified
representation for both semantic and instance classes as global binary masks.
In this paper, we revisit pure convolution model and propose a novel panoptic
architecture named MaskConver. MaskConver proposes to fully unify things and
stuff representation by predicting their centers. To that extent, it creates a
lightweight class embedding module that can break the ties when multiple
centers co-exist in the same location. Furthermore, our study shows that the
decoder design is critical in ensuring that the model has sufficient context
for accurate detection and segmentation. We introduce a powerful ConvNeXt-UNet
decoder that closes the performance gap between convolution- and
transformerbased models. With ResNet50 backbone, our MaskConver achieves 53.6%
PQ on the COCO panoptic val set, outperforming the modern convolution-based
model, Panoptic FCN, by 9.3% as well as transformer-based models such as
Mask2Former (+1.7% PQ) and kMaX-DeepLab (+0.6% PQ). Additionally, MaskConver
with a MobileNet backbone reaches 37.2% PQ, improving over Panoptic-DeepLab by
+6.4% under the same FLOPs/latency constraints. A further optimized version of
MaskConver achieves 29.7% PQ, while running in real-time on mobile devices. The
code and model weights will be publicly available

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language
Abstract: Counterspeech, i.e., responses to counteract potential harms of hateful
speech, has become an increasingly popular solution to address online hate
speech without censorship. However, properly countering hateful language
requires countering and dispelling the underlying inaccurate stereotypes
implied by such language. In this work, we draw from psychology and philosophy
literature to craft six psychologically inspired strategies to challenge the
underlying stereotypical implications of hateful language. We first examine the
convincingness of each of these strategies through a user study, and then
compare their usages in both human- and machine-generated counterspeech
datasets. Our results show that human-written counterspeech uses countering
strategies that are more specific to the implied stereotype (e.g., counter
examples to the stereotype, external factors about the stereotype's origins),
whereas machine-generated counterspeech uses less specific strategies (e.g.,
generally denouncing the hatefulness of speech). Furthermore, machine-generated
counterspeech often employs strategies that humans deem less convincing
compared to human-produced counterspeech. Our findings point to the importance
of accounting for the underlying stereotypical implications of speech when
generating counterspeech and for better machine reasoning about
anti-stereotypical examples.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks
Abstract: Entity and Relation Extraction (ERE) is an important task in information
extraction. Recent marker-based pipeline models achieve state-of-the-art
performance, but still suffer from the error propagation issue. Also, most of
current ERE models do not take into account higher-order interactions between
multiple entities and relations, while higher-order modeling could be
beneficial.In this work, we propose HyperGraph neural network for ERE
($\hgnn{}$), which is built upon the PL-marker (a state-of-the-art marker-based
pipleline model). To alleviate error propagation,we use a high-recall pruner
mechanism to transfer the burden of entity identification and labeling from the
NER module to the joint module of our model. For higher-order modeling, we
build a hypergraph, where nodes are entities (provided by the span pruner) and
relations thereof, and hyperedges encode interactions between two different
relations or between a relation and its associated subject and object entities.
We then run a hypergraph neural network for higher-order inference by applying
message passing over the built hypergraph. Experiments on three widely used
benchmarks (\acef{}, \ace{} and \scierc{}) for ERE task show significant
improvements over the previous state-of-the-art PL-marker.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: An attempt to generate new bridge types from latent space of variational autoencoder
Abstract: Try to generate new bridge types using generative artificial intelligence
technology. The grayscale images of the bridge facade with the change of
component width was rendered by 3dsMax animation software, and then the OpenCV
module performed an appropriate amount of geometric transformation (rotation,
horizontal scale, vertical scale) to obtain the image dataset of three-span
beam bridge, arch bridge, cable-stayed bridge and suspension bridge. Based on
Python programming language, TensorFlow and Keras deep learning platform
framework, variational autoencoder was constructed and trained, and
low-dimensional bridge-type latent space that is convenient for vector
operations was obtained. Variational autoencoder can combine two bridge types
on the basis of the original of human into one that is a new bridge type.
Generative artificial intelligence technology can assist bridge designers in
bridge-type innovation, and can be used as copilot.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Low-light Pedestrian Detection in Visible and Infrared Image Feeds: Issues and Challenges
Abstract: Pedestrian detection has become a cornerstone for several high-level tasks,
including autonomous driving, intelligent transportation, and traffic
surveillance. There are several works focussed on pedestrian detection using
visible images, mainly in the daytime. However, this task is very intriguing
when the environmental conditions change to poor lighting or nighttime.
Recently, new ideas have been spurred to use alternative sources, such as Far
InfraRed (FIR) temperature sensor feeds for detecting pedestrians in low-light
conditions. This study comprehensively reviews recent developments in low-light
pedestrian detection approaches. It systematically categorizes and analyses
various algorithms from region-based to non-region-based and graph-based
learning methodologies by highlighting their methodologies, implementation
issues, and challenges. It also outlines the key benchmark datasets that can be
used for research and development of advanced pedestrian detection algorithms,
particularly in low-light situations

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Debiasing Algorithm through Model Adaptation
Abstract: Large language models are becoming the go-to solution for various language
tasks. However, with growing capacity, models are prone to rely on spurious
correlations stemming from biases and stereotypes present in the training data.
This work proposes a novel method for detecting and mitigating gender bias in
language models. We perform causal analysis to identify problematic model
components and discover that mid-upper feed-forward layers are most prone to
convey biases. Based on the analysis results, we adapt the model by multiplying
these layers by a linear projection. Our titular method, DAMA, significantly
decreases bias as measured by diverse metrics while maintaining the model's
performance on downstream tasks. We release code for our method and models,
which retrain LLaMA's state-of-the-art performance while being significantly
less biased.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: uSF: Learning Neural Semantic Field with Uncertainty
Abstract: Recently, there has been an increased interest in NeRF methods which
reconstruct differentiable representation of three-dimensional scenes. One of
the main limitations of such methods is their inability to assess the
confidence of the model in its predictions. In this paper, we propose a new
neural network model for the formation of extended vector representations,
called uSF, which allows the model to predict not only color and semantic label
of each point, but also estimate the corresponding values of uncertainty. We
show that with a small number of images available for training, a model
quantifying uncertainty performs better than a model without such
functionality. Code of the uSF approach is publicly available at
https://github.com/sevashasla/usf/.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Causal Question Answering with Reinforcement Learning
Abstract: Causal questions inquire about causal relationships between different events
or phenomena. Specifically, they often aim to determine whether there is a
relationship between two phenomena, or to identify all causes/effects of a
phenomenon. Causal questions are important for a variety of use cases,
including virtual assistants and search engines. However, many current
approaches to causal question answering cannot provide explanations or evidence
for their answers. Hence, in this paper, we aim to answer causal questions with
CauseNet, a large-scale dataset of causal relations and their provenance data.
Inspired by recent, successful applications of reinforcement learning to
knowledge graph tasks, such as link prediction and fact-checking, we explore
the application of reinforcement learning on CauseNet for causal question
answering. We introduce an Actor-Critic based agent which learns to search
through the graph to answer causal questions. We bootstrap the agent with a
supervised learning procedure to deal with large action spaces and sparse
rewards. Our evaluation shows that the agent successfully prunes the search
space to answer binary causal questions by visiting less than 30 nodes per
question compared to over 3,000 nodes by a naive breadth-first search. Our
ablation study indicates that our supervised learning strategy provides a
strong foundation upon which our reinforcement learning agent improves. The
paths returned by our agent explain the mechanisms by which a cause produces an
effect. Moreover, for each edge on a path, CauseNet stores its original source
on the web allowing for easy verification of paths.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Prudent Silence or Foolish Babble? Examining Large Language Models' Responses to the Unknown
Abstract: Large Language Models (LLMs) often struggle when faced with situations where
they lack the prerequisite knowledge to generate a sensical response. In these
cases, models tend to fabricate and hallucinate, rather than appropriately
signaling uncertainty as humans would. This behavior misaligns with human
conversational norms and presents challenges surrounding responsible and
ethical AI development. This work aims to systematically investigate LLMs'
behaviors in such situations. We curate an adversarial question-answering
benchmark containing unanswerable questions targeting information absent from
the LLM's training data. Concretely, these unanswerable questions contain
non-existent concepts or false premises. When presented with such unanswerable
questions, an LLM should appropriately convey uncertainty, and be able to
challenge the premise and refuse to generate a response. While facing
answerable valid questions, a model should demonstrate a positive correlation
between accuracy and confidence. Using a model-agnostic unified confidence
elicitation approach, we observe that LLMs that have gone through instruction
finetuning and reinforcement learning from human feedback (RLHF) perform
significantly better than their counterparts that do not. Moreover, uncertainty
expression 1 through our elicitation method does not always stay consistent
with the perceived confidence of the direct response of an LLM. Our findings
call for further research into teaching LLMs to proactively and reliably
express uncertainty.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: VegaEdge: Edge AI Confluence Anomaly Detection for Real-Time Highway IoT-Applications
Abstract: Vehicle anomaly detection plays a vital role in highway safety applications
such as accident prevention, rapid response, traffic flow optimization, and
work zone safety. With the surge of the Internet of Things (IoT) in recent
years, there has arisen a pressing demand for Artificial Intelligence (AI)
based anomaly detection methods designed to meet the requirements of IoT
devices. Catering to this futuristic vision, we introduce a lightweight
approach to vehicle anomaly detection by utilizing the power of trajectory
prediction. Our proposed design identifies vehicles deviating from expected
paths, indicating highway risks from different camera-viewing angles from
real-world highway datasets. On top of that, we present VegaEdge - a
sophisticated AI confluence designed for real-time security and surveillance
applications in modern highway settings through edge-centric IoT-embedded
platforms equipped with our anomaly detection approach. Extensive testing
across multiple platforms and traffic scenarios showcases the versatility and
effectiveness of VegaEdge. This work also presents the Carolinas Anomaly
Dataset (CAD), to bridge the existing gap in datasets tailored for highway
anomalies. In real-world scenarios, our anomaly detection approach achieves an
AUC-ROC of 0.94, and our proposed VegaEdge design, on an embedded IoT platform,
processes 738 trajectories per second in a typical highway setting. The dataset
is available at
https://github.com/TeCSAR-UNCC/Carolinas_Dataset#chd-anomaly-test-set .

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Harnessing Synthetic Datasets: The Role of Shape Bias in Deep Neural Network Generalization
Abstract: Recent advancements in deep learning have been primarily driven by the use of
large models trained on increasingly vast datasets. While neural scaling laws
have emerged to predict network performance given a specific level of
computational resources, the growing demand for expansive datasets raises
concerns. To address this, a new research direction has emerged, focusing on
the creation of synthetic data as a substitute. In this study, we investigate
how neural networks exhibit shape bias during training on synthetic datasets,
serving as an indicator of the synthetic data quality. Specifically, our
findings indicate three key points: (1) Shape bias varies across network
architectures and types of supervision, casting doubt on its reliability as a
predictor for generalization and its ability to explain differences in model
recognition compared to human capabilities. (2) Relying solely on shape bias to
estimate generalization is unreliable, as it is entangled with diversity and
naturalism. (3) We propose a novel interpretation of shape bias as a tool for
estimating the diversity of samples within a dataset. Our research aims to
clarify the implications of using synthetic data and its associated shape bias
in deep learning, addressing concerns regarding generalization and dataset
quality.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Improving generalization in large language models by learning prefix subspaces
Abstract: This article focuses on large language models (LLMs) fine-tuning in the
scarce data regime (also known as the "few-shot" learning setting). We propose
a method to increase the generalization capabilities of LLMs based on neural
network subspaces. This optimization method, recently introduced in computer
vision, aims to improve model generalization by identifying wider local optima
through the joint optimization of an entire simplex of models in parameter
space. Its adaptation to massive, pretrained transformers, however, poses some
challenges. First, their considerable number of parameters makes it difficult
to train several models jointly, and second, their deterministic parameter
initialization schemes make them unfit for the subspace method as originally
proposed. We show in this paper that "Parameter Efficient Fine-Tuning" (PEFT)
methods, however, are perfectly compatible with this original approach, and
propose to learn entire simplex of continuous prefixes. We test our method on a
variant of the GLUE benchmark adapted to the few-shot learning setting, and
show that both our contributions jointly lead to a gain in average performances
compared to sota methods. The implementation can be found at the following
link: https://github.com/Liloulou/prefix_subspace

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: scBiGNN: Bilevel Graph Representation Learning for Cell Type Classification from Single-cell RNA Sequencing Data
Abstract: Single-cell RNA sequencing (scRNA-seq) technology provides high-throughput
gene expression data to study the cellular heterogeneity and dynamics of
complex organisms. Graph neural networks (GNNs) have been widely used for
automatic cell type classification, which is a fundamental problem to solve in
scRNA-seq analysis. However, existing methods do not sufficiently exploit both
gene-gene and cell-cell relationships, and thus the true potential of GNNs is
not realized. In this work, we propose a bilevel graph representation learning
method, named scBiGNN, to simultaneously mine the relationships at both gene
and cell levels for more accurate single-cell classification. Specifically,
scBiGNN comprises two GNN modules to identify cell types. A gene-level GNN is
established to adaptively learn gene-gene interactions and cell representations
via the self-attention mechanism, and a cell-level GNN builds on the cell-cell
graph that is constructed from the cell representations generated by the
gene-level GNN. To tackle the scalability issue for processing a large number
of cells, scBiGNN adopts an Expectation Maximization (EM) framework in which
the two modules are alternately trained via the E-step and M-step to learn from
each other. Through this interaction, the gene- and cell-level structural
information is integrated to gradually enhance the classification performance
of both GNN modules. Experiments on benchmark datasets demonstrate that our
scBiGNN outperforms a variety of existing methods for cell type classification
from scRNA-seq data.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Ask more, know better: Reinforce-Learned Prompt Questions for Decision Making with Large Language Models
Abstract: Large language models (LLMs) demonstrate their promise in tackling
complicated practical challenges by combining action-based policies with chain
of thought (CoT) reasoning. Having high-quality prompts on hand, however, is
vital to the framework's effectiveness. Currently, these prompts are
handcrafted utilizing extensive human labor, resulting in CoT policies that
frequently fail to generalize. Human intervention is also required in order to
develop grounding functions that ensure low-level controllers appropriately
process CoT reasoning. In this paper, we take the first step towards a fully
integrated end-to-end framework for task-solving in real settings employing
complicated reasoning. To that purpose, we offer a new leader-follower bilevel
framework capable of learning to ask relevant questions (prompts) and
subsequently undertaking reasoning to guide the learning of actions to be
performed in an environment. A good prompt should make introspective revisions
based on historical findings, leading the CoT to consider the anticipated
goals. A prompt-generator policy has its own aim in our system, allowing it to
adapt to the action policy and automatically root the CoT process towards
outputs that lead to decisive, high-performing actions. Meanwhile, the action
policy is learning how to use the CoT outputs to take specific actions. Our
empirical data reveal that our system outperforms leading methods in agent
learning benchmarks such as Overcooked and FourRoom.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: DeceptPrompt: Exploiting LLM-driven Code Generation via Adversarial Natural Language Instructions
Abstract: With the advancement of Large Language Models (LLMs), significant progress
has been made in code generation, enabling LLMs to transform natural language
into programming code. These Code LLMs have been widely accepted by massive
users and organizations. However, a dangerous nature is hidden in the code,
which is the existence of fatal vulnerabilities. While some LLM providers have
attempted to address these issues by aligning with human guidance, these
efforts fall short of making Code LLMs practical and robust. Without a deep
understanding of the performance of the LLMs under the practical worst cases,
it would be concerning to apply them to various real-world applications. In
this paper, we answer the critical issue: Are existing Code LLMs immune to
generating vulnerable code? If not, what is the possible maximum severity of
this issue in practical deployment scenarios? In this paper, we introduce
DeceptPrompt, a novel algorithm that can generate adversarial natural language
instructions that drive the Code LLMs to generate functionality correct code
with vulnerabilities. DeceptPrompt is achieved through a systematic
evolution-based algorithm with a fine grain loss design. The unique advantage
of DeceptPrompt enables us to find natural prefix/suffix with totally benign
and non-directional semantic meaning, meanwhile, having great power in inducing
the Code LLMs to generate vulnerable code. This feature can enable us to
conduct the almost-worstcase red-teaming on these LLMs in a real scenario,
where users are using natural language. Our extensive experiments and analyses
on DeceptPrompt not only validate the effectiveness of our approach but also
shed light on the huge weakness of LLMs in the code generation task. When
applying the optimized prefix/suffix, the attack success rate (ASR) will
improve by average 50% compared with no prefix/suffix applying.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Interactive Robot Learning from Verbal Correction
Abstract: The ability to learn and refine behavior after deployment has become ever
more important for robots as we design them to operate in unstructured
environments like households. In this work, we design a new learning system
based on large language model (LLM), OLAF, that allows everyday users to teach
a robot using verbal corrections when the robot makes mistakes, e.g., by saying
"Stop what you're doing. You should move closer to the cup." A key feature of
OLAF is its ability to update the robot's visuomotor neural policy based on the
verbal feedback to avoid repeating mistakes in the future. This is in contrast
to existing LLM-based robotic systems, which only follow verbal commands or
corrections but not learn from them. We demonstrate the efficacy of our design
in experiments where a user teaches a robot to perform long-horizon
manipulation tasks both in simulation and on physical hardware, achieving on
average 20.0% improvement in policy success rate. Videos and more results are
at https://ut-austin-rpl.github.io/olaf/

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning
Abstract: Large Language Models (LLMs), despite their remarkable progress across
various general domains, encounter significant barriers in medicine and
healthcare. This field faces unique challenges such as domain-specific
terminologies and the reasoning over specialized knowledge. To address these
obstinate issues, we propose a novel Multi-disciplinary Collaboration (MC)
framework for the medical domain that leverages role-playing LLM-based agents
who participate in a collaborative multi-round discussion, thereby enhancing
LLM proficiency and reasoning capabilities. This training-free and
interpretable framework encompasses five critical steps: gathering domain
experts, proposing individual analyses, summarising these analyses into a
report, iterating over discussions until a consensus is reached, and ultimately
making a decision. Our work particularly focuses on the zero-shot scenario, our
results on nine data sets (MedQA, MedMCQA, PubMedQA, and six subtasks from
MMLU) establish that our proposed MC framework excels at mining and harnessing
the medical expertise in LLMs, as well as extending its reasoning abilities.
Based on these outcomes, we further conduct a human evaluation to pinpoint and
categorize common errors within our method, as well as ablation studies aimed
at understanding the impact of various factors on overall performance. Our code
can be found at \url{https://github.com/gersteinlab/MedAgents}.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation
Abstract: Large Language Models (LLMs) encapsulate vast amounts of knowledge but still
remain vulnerable to external misinformation. Existing research mainly studied
this susceptibility behavior in a single-turn setting. However, belief can
change during a multi-turn conversation, especially a persuasive one.
Therefore, in this study, we delve into LLMs' susceptibility to persuasive
conversations, particularly on factual questions that they can answer
correctly. We first curate the Farm (i.e., Fact to Misinform) dataset, which
contains factual questions paired with systematically generated persuasive
misinformation. Then, we develop a testing framework to track LLMs' belief
changes in a persuasive dialogue. Through extensive experiments, we find that
LLMs' correct beliefs on factual knowledge can be easily manipulated by various
persuasive strategies.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Adventures of Trustworthy Vision-Language Models: A Survey
Abstract: Recently, transformers have become incredibly popular in computer vision and
vision-language tasks. This notable rise in their usage can be primarily
attributed to the capabilities offered by attention mechanisms and the
outstanding ability of transformers to adapt and apply themselves to a variety
of tasks and domains. Their versatility and state-of-the-art performance have
established them as indispensable tools for a wide array of applications.
However, in the constantly changing landscape of machine learning, the
assurance of the trustworthiness of transformers holds utmost importance. This
paper conducts a thorough examination of vision-language transformers,
employing three fundamental principles of responsible AI: Bias, Robustness, and
Interpretability. The primary objective of this paper is to delve into the
intricacies and complexities associated with the practical use of transformers,
with the overarching goal of advancing our comprehension of how to enhance
their reliability and accountability.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Bifurcations and loss jumps in RNN training
Abstract: Recurrent neural networks (RNNs) are popular machine learning tools for
modeling and forecasting sequential data and for inferring dynamical systems
(DS) from observed time series. Concepts from DS theory (DST) have variously
been used to further our understanding of both, how trained RNNs solve complex
tasks, and the training process itself. Bifurcations are particularly important
phenomena in DS, including RNNs, that refer to topological (qualitative)
changes in a system's dynamical behavior as one or more of its parameters are
varied. Knowing the bifurcation structure of an RNN will thus allow to deduce
many of its computational and dynamical properties, like its sensitivity to
parameter variations or its behavior during training. In particular,
bifurcations may account for sudden loss jumps observed in RNN training that
could severely impede the training process. Here we first mathematically prove
for a particular class of ReLU-based RNNs that certain bifurcations are indeed
associated with loss gradients tending toward infinity or zero. We then
introduce a novel heuristic algorithm for detecting all fixed points and
k-cycles in ReLU-based RNNs and their existence and stability regions, hence
bifurcation manifolds in parameter space. In contrast to previous numerical
algorithms for finding fixed points and common continuation methods, our
algorithm provides exact results and returns fixed points and cycles up to high
orders with surprisingly good scaling behavior. We exemplify the algorithm on
the analysis of the training process of RNNs, and find that the recently
introduced technique of generalized teacher forcing completely avoids certain
types of bifurcations in training. Thus, besides facilitating the DST analysis
of trained RNNs, our algorithm provides a powerful instrument for analyzing the
training process itself.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: LLatrieval: LLM-Verified Retrieval for Verifiable Generation
Abstract: Verifiable generation aims to let the large language model (LLM) generate
text with corresponding supporting documents, which enables the user to
flexibly verify the answer and makes it more trustworthy. Its evaluation not
only measures the correctness of the answer, but also the answer's
verifiability, i.e., how well the answer is supported by the corresponding
documents. In typical, verifiable generation adopts the retrieval-read
pipeline, which is divided into two stages: 1) retrieve relevant documents of
the question. 2) according to the documents, generate the corresponding answer.
Since the retrieved documents can supplement knowledge for the LLM to generate
the answer and serve as evidence, the retrieval stage is essential for the
correctness and verifiability of the answer. However, the widely used
retrievers become the bottleneck of the entire pipeline and limit the overall
performance. They often have fewer parameters than the large language model and
have not been proven to scale well to the size of LLMs. Since the LLM passively
receives the retrieval result, if the retriever does not correctly find the
supporting documents, the LLM can not generate the correct and verifiable
answer, which overshadows the LLM's remarkable abilities. In this paper, we
propose LLatrieval (Large Language Model Verified Retrieval), where the LLM
updates the retrieval result until it verifies that the retrieved documents can
support answering the question. Thus, the LLM can iteratively provide feedback
to retrieval and facilitate the retrieval result to sufficiently support
verifiable generation. Experimental results show that our method significantly
outperforms extensive baselines and achieves new state-of-the-art results.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Psychometric Predictive Power of Large Language Models
Abstract: Next-word probabilities from language models have been shown to successfully
simulate human reading behavior. Building on this, we show that, interestingly,
instruction-tuned large language models (LLMs) yield worse psychometric
predictive power (PPP) for human reading behavior than base LLMs with
equivalent perplexities. In other words, instruction tuning, which helps LLMs
provide human-preferred responses, does not always make them human-like from
the computational psycholinguistics perspective. In addition, we explore
prompting methodologies in simulating human reading behavior with LLMs, showing
that prompts reflecting a particular linguistic hypothesis lead LLMs to exhibit
better PPP but are still worse than base LLMs. These highlight that recent
instruction tuning and prompting do not offer better estimates than direct
probability measurements from base LLMs in cognitive modeling.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Evidence-based Interpretable Open-domain Fact-checking with Large Language Models
Abstract: Universal fact-checking systems for real-world claims face significant
challenges in gathering valid and sufficient real-time evidence and making
reasoned decisions. In this work, we introduce the Open-domain Explainable
Fact-checking (OE-Fact) system for claim-checking in real-world scenarios. The
OE-Fact system can leverage the powerful understanding and reasoning
capabilities of large language models (LLMs) to validate claims and generate
causal explanations for fact-checking decisions. To adapt the traditional
three-module fact-checking framework to the open domain setting, we first
retrieve claim-related information as relevant evidence from open websites.
After that, we retain the evidence relevant to the claim through LLM and
similarity calculation for subsequent verification. We evaluate the performance
of our adapted three-module OE-Fact system on the Fact Extraction and
Verification (FEVER) dataset. Experimental results show that our OE-Fact system
outperforms general fact-checking baseline systems in both closed- and
open-domain scenarios, ensuring stable and accurate verdicts while providing
concise and convincing real-time explanations for fact-checking decisions.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Surrogate Modelling for Sea Ice Concentration using Lightweight Neural Ensemble
Abstract: The modeling and forecasting of sea ice conditions in the Arctic region are
important tasks for ship routing, offshore oil production, and environmental
monitoring. We propose the adaptive surrogate modeling approach named LANE-SI
(Lightweight Automated Neural Ensembling for Sea Ice) that uses ensemble of
relatively simple deep learning models with different loss functions for
forecasting of spatial distribution for sea ice concentration in the specified
water area. Experimental studies confirm the quality of a long-term forecast
based on a deep learning model fitted to the specific water area is comparable
to resource-intensive physical modeling, and for some periods of the year, it
is superior. We achieved a 20% improvement against the state-of-the-art
physics-based forecast system SEAS5 for the Kara Sea.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Comparative Multi-View Language Grounding
Abstract: In this work, we consider the task of resolving object referents when given a
comparative language description. We present a Multi-view Approach to Grounding
in Context (MAGiC) that leverages transformers to pragmatically reason over
both objects given multiple image views and a language description. In contrast
to past efforts that attempt to connect vision and language for this task
without fully considering the resulting referential context, MAGiC makes use of
the comparative information by jointly reasoning over multiple views of both
object referent candidates and the referring language expression. We present an
analysis demonstrating that comparative reasoning contributes to SOTA
performance on the SNARE object reference task.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLO
Abstract: Smart glasses are rapidly gaining advanced functionality thanks to
cutting-edge computing technologies, accelerated hardware architectures, and
tiny AI algorithms. Integrating AI into smart glasses featuring a small form
factor and limited battery capacity is still challenging when targeting
full-day usage for a satisfactory user experience. This paper illustrates the
design and implementation of tiny machine-learning algorithms exploiting novel
low-power processors to enable prolonged continuous operation in smart glasses.
We explore the energy- and latency-efficient of smart glasses in the case of
real-time object detection. To this goal, we designed a smart glasses prototype
as a research platform featuring two microcontrollers, including a novel
milliwatt-power RISC-V parallel processor with a hardware accelerator for
visual AI, and a Bluetooth low-power module for communication. The smart
glasses integrate power cycling mechanisms, including image and audio sensing
interfaces. Furthermore, we developed a family of novel tiny deep-learning
models based on YOLO with sub-million parameters customized for
microcontroller-based inference dubbed TinyissimoYOLO v1.3, v5, and v8, aiming
at benchmarking object detection with smart glasses for energy and latency.
Evaluations on the prototype of the smart glasses demonstrate TinyissimoYOLO's
17ms inference latency and 1.59mJ energy consumption per inference while
ensuring acceptable detection accuracy. Further evaluation reveals an
end-to-end latency from image capturing to the algorithm's prediction of 56ms
or equivalently 18 fps, with a total power consumption of 62.9mW, equivalent to
a 9.3 hours of continuous run time on a 154mAh battery. These results
outperform MCUNet (TinyNAS+TinyEngine), which runs a simpler task (image
classification) at just 7.3 fps per second.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Rethinking Adversarial Training with Neural Tangent Kernel
Abstract: Adversarial training (AT) is an important and attractive topic in deep
learning security, exhibiting mysteries and odd properties. Recent studies of
neural network training dynamics based on Neural Tangent Kernel (NTK) make it
possible to reacquaint AT and deeply analyze its properties. In this paper, we
perform an in-depth investigation of AT process and properties with NTK, such
as NTK evolution. We uncover three new findings that are missed in previous
works. First, we disclose the impact of data normalization on AT and the
importance of unbiased estimators in batch normalization layers. Second, we
experimentally explore the kernel dynamics and propose more time-saving AT
methods. Third, we study the spectrum feature inside the kernel to address the
catastrophic overfitting problem. To the best of our knowledge, it is the first
work leveraging the observations of kernel dynamics to improve existing AT
methods.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Cost-Effective In-Context Learning for Entity Resolution: A Design Space Exploration
Abstract: Entity resolution (ER) is an important data integration task with a wide
spectrum of applications. The state-of-the-art solutions on ER rely on
pre-trained language models (PLMs), which require fine-tuning on a lot of
labeled matching/non-matching entity pairs. Recently, large languages models
(LLMs), such as GPT-4, have shown the ability to perform many tasks without
tuning model parameters, which is known as in-context learning (ICL) that
facilitates effective learning from a few labeled input context demonstrations.
However, existing ICL approaches to ER typically necessitate providing a task
description and a set of demonstrations for each entity pair and thus have
limitations on the monetary cost of interfacing LLMs. To address the problem,
in this paper, we provide a comprehensive study to investigate how to develop a
cost-effective batch prompting approach to ER. We introduce a framework BATCHER
consisting of demonstration selection and question batching and explore
different design choices that support batch prompting for ER. We also devise a
covering-based demonstration selection strategy that achieves an effective
balance between matching accuracy and monetary cost. We conduct a thorough
evaluation to explore the design space and evaluate our proposed strategies.
Through extensive experiments, we find that batch prompting is very
cost-effective for ER, compared with not only PLM-based methods fine-tuned with
extensive labeled data but also LLM-based methods with manually designed
prompting. We also provide guidance for selecting appropriate design choices
for batch prompting.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Is "A Helpful Assistant" the Best Role for Large Language Models? A Systematic Evaluation of Social Roles in System Prompts
Abstract: Prompting serves as the major way humans interact with Large Language Models
(LLM). Commercial AI systems commonly define the role of the LLM in system
prompts. For example, ChatGPT uses "You are a helpful assistant" as part of the
default system prompt. But is "a helpful assistant" the best role for LLMs? In
this study, we present a systematic evaluation of how social roles in system
prompts affect model performance. We curate a list of 162 roles covering 6
types of interpersonal relationships and 8 types of occupations. Through
extensive analysis of 3 popular LLMs and 2457 questions, we show that adding
interpersonal roles in prompts consistently improves the models' performance
over a range of questions. Moreover, while we find that using gender-neutral
roles and specifying the role as the audience leads to better performances,
predicting which role leads to the best performance remains a challenging task,
and that frequency, similarity, and perplexity do not fully explain the effect
of social roles on model performances. Our results can help inform the design
of system prompts for AI systems. Code and data are available at
https://github.com/Jiaxin-Pei/Prompting-with-Social-Roles.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: What Algorithms can Transformers Learn? A Study in Length Generalization
Abstract: Large language models exhibit surprising emergent generalization properties,
yet also struggle on many simple reasoning tasks such as arithmetic and parity.
This raises the question of if and when Transformer models can learn the true
algorithm for solving a task. We study the scope of Transformers' abilities in
the specific setting of length generalization on algorithmic tasks. Here, we
propose a unifying framework to understand when and how Transformers can
exhibit strong length generalization on a given task. Specifically, we leverage
RASP (Weiss et al., 2021) -- a programming language designed for the
computational model of a Transformer -- and introduce the RASP-Generalization
Conjecture: Transformers tend to length generalize on a task if the task can be
solved by a short RASP program which works for all input lengths. This simple
conjecture remarkably captures most known instances of length generalization on
algorithmic tasks. Moreover, we leverage our insights to drastically improve
generalization performance on traditionally hard tasks (such as parity and
addition). On the theoretical side, we give a simple example where the
"min-degree-interpolator" model of learning from Abbe et al. (2023) does not
correctly predict Transformers' out-of-distribution behavior, but our
conjecture does. Overall, our work provides a novel perspective on the
mechanisms of compositional generalization and the algorithmic capabilities of
Transformers.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Towards Learning Monocular 3D Object Localization From 2D Labels using the Physical Laws of Motion
Abstract: We present a novel method for precise 3D object localization in single images
from a single calibrated camera using only 2D labels. No expensive 3D labels
are needed. Thus, instead of using 3D labels, our model is trained with
easy-to-annotate 2D labels along with the physical knowledge of the object's
motion. Given this information, the model can infer the latent third dimension,
even though it has never seen this information during training. Our method is
evaluated on both synthetic and real-world datasets, and we are able to achieve
a mean distance error of just 6 cm in our experiments on real data. The results
indicate the method's potential as a step towards learning 3D object location
estimation, where collecting 3D data for training is not feasible.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation
Abstract: Representation Learning on Knowledge Graphs (KGs) is essential for downstream
tasks. The dominant approach, KG Embedding (KGE), represents entities with
independent vectors and faces the scalability challenge. Recent studies propose
an alternative way for parameter efficiency, which represents entities by
composing entity-corresponding codewords matched from predefined small-scale
codebooks. We refer to the process of obtaining corresponding codewords of each
entity as entity quantization, for which previous works have designed
complicated strategies. Surprisingly, this paper shows that simple random
entity quantization can achieve similar results to current strategies. We
analyze this phenomenon and reveal that entity codes, the quantization outcomes
for expressing entities, have higher entropy at the code level and Jaccard
distance at the codeword level under random entity quantization. Therefore,
different entities become more easily distinguished, facilitating effective KG
representation. The above results show that current quantization strategies are
not critical for KG representation, and there is still room for improvement in
entity distinguishability beyond current strategies. The code to reproduce our
results is available at https://github.com/JiaangL/RandomQuantization.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: LatentEditor: Text Driven Local Editing of 3D Scenes
Abstract: While neural fields have made significant strides in view synthesis and scene
reconstruction, editing them poses a formidable challenge due to their implicit
encoding of geometry and texture information from multi-view inputs. In this
paper, we introduce \textsc{LatentEditor}, an innovative framework designed to
empower users with the ability to perform precise and locally controlled
editing of neural fields using text prompts. Leveraging denoising diffusion
models, we successfully embed real-world scenes into the latent space,
resulting in a faster and more adaptable NeRF backbone for editing compared to
traditional methods. To enhance editing precision, we introduce a delta score
to calculate the 2D mask in the latent space that serves as a guide for local
modifications while preserving irrelevant regions. Our novel pixel-level
scoring approach harnesses the power of InstructPix2Pix (IP2P) to discern the
disparity between IP2P conditional and unconditional noise predictions in the
latent space. The edited latents conditioned on the 2D masks are then
iteratively updated in the training set to achieve 3D local editing. Our
approach achieves faster editing speeds and superior output quality compared to
existing 3D editing models, bridging the gap between textual instructions and
high-quality 3D scene editing in latent space. We show the superiority of our
approach on four benchmark 3D datasets, LLFF, IN2N, NeRFStudio and NeRF-Art.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Orchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach
Abstract: The efficient deployment and fine-tuning of foundation models are pivotal in
contemporary artificial intelligence. In this study, we present a
groundbreaking paradigm integrating Mobile Edge Computing (MEC) with foundation
models, specifically designed to enhance local task performance on user
equipment (UE). Central to our approach is the innovative Emulator-Adapter
architecture, segmenting the foundation model into two cohesive modules. This
design not only conserves computational resources but also ensures adaptability
and fine-tuning efficiency for downstream tasks. Additionally, we introduce an
advanced resource allocation mechanism that is fine-tuned to the needs of the
Emulator-Adapter structure in decentralized settings. To address the challenges
presented by this system, we employ a hybrid multi-agent Deep Reinforcement
Learning (DRL) strategy, adept at handling mixed discrete-continuous action
spaces, ensuring dynamic and optimal resource allocations. Our comprehensive
simulations and validations underscore the practical viability of our approach,
demonstrating its robustness, efficiency, and scalability. Collectively, this
work offers a fresh perspective on deploying foundation models and balancing
computational efficiency with task proficiency.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Interfacing Foundation Models' Embeddings
Abstract: We present FIND, a generalized interface for aligning foundation models'
embeddings. As shown in teaser figure, a lightweight transformer interface
without tuning any foundation model weights is enough for a unified image
(segmentation) and dataset-level (retrieval) understanding. The proposed
interface has the following favorable attributes: (1) Generalizable. It applies
to various tasks spanning retrieval, segmentation, \textit{etc.}, under the
same architecture and weights. (2) Prototypable. Different tasks are able to be
implemented through prototyping attention masks and embedding types. (3)
Extendable. The proposed interface is adaptive to new tasks, and new models.
(4) Interleavable. With the benefit of multi-task multi-modal training, the
proposed interface creates an interleaved shared embedding space. In light of
the interleaved embedding space, we introduce the FIND-Bench, which introduces
new training and evaluation annotations to the COCO dataset for interleave
segmentation and retrieval. Our approach achieves state-of-the-art performance
on FIND-Bench and competitive performance on standard retrieval and
segmentation settings. The training, evaluation, and demo code as well as the
dataset have been released at https://github.com/UX-Decoder/FIND.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Modeling Complex Mathematical Reasoning via Large Language Model based MathAgent
Abstract: Large language models (LLMs) face challenges in solving complex mathematical
problems that require comprehensive capacities to parse the statements,
associate domain knowledge, perform compound logical reasoning, and integrate
the intermediate rationales. Tackling all these problems once could be arduous
for LLMs, thus leading to confusion in generation. In this work, we explore the
potential of enhancing LLMs with agents by meticulous decomposition and
modeling of mathematical reasoning process. Specifically, we propose a formal
description of the mathematical solving and extend LLMs with an agent-based
zero-shot framework named
$\bf{P}$lanner-$\bf{R}$easoner-$\bf{E}$xecutor-$\bf{R}$eflector (PRER). We
further provide and implement two MathAgents that define the logical forms and
inherent relations via a pool of actions in different grains and orientations:
MathAgent-M adapts its actions to LLMs, while MathAgent-H aligns with
humankind. Experiments on miniF2F and MATH have demonstrated the effectiveness
of PRER and proposed MathAgents, achieving an increase of
$12.3\%$($53.9\%\xrightarrow{}66.2\%$) on the MiniF2F, $9.2\%$
($49.8\%\xrightarrow{}59.0\%$) on MATH, and
$13.2\%$($23.2\%\xrightarrow{}35.4\%$) for level-5 problems of MATH against
GPT-4. Further analytical results provide more insightful perspectives on
exploiting the behaviors of LLMs as agents.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Neural Structure Learning with Stochastic Differential Equations
Abstract: Discovering the underlying relationships among variables from temporal
observations has been a longstanding challenge in numerous scientific
disciplines, including biology, finance, and climate science. The dynamics of
such systems are often best described using continuous-time stochastic
processes. Unfortunately, most existing structure learning approaches assume
that the underlying process evolves in discrete-time and/or observations occur
at regular time intervals. These mismatched assumptions can often lead to
incorrect learned structures and models. In this work, we introduce a novel
structure learning method, SCOTCH, which combines neural stochastic
differential equations (SDE) with variational inference to infer a posterior
distribution over possible structures. This continuous-time approach can
naturally handle both learning from and predicting observations at arbitrary
time points. Theoretically, we establish sufficient conditions for an SDE and
SCOTCH to be structurally identifiable, and prove its consistency under
infinite data limits. Empirically, we demonstrate that our approach leads to
improved structure learning performance on both synthetic and real-world
datasets compared to relevant baselines under regular and irregular sampling
intervals.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Robustness Approaches for the Examination Timetabling Problem under Data Uncertainty
Abstract: In the literature the examination timetabling problem (ETTP) is often
considered a post-enrollment problem (PE-ETTP). In the real world, universities
often schedule their exams before students register using information from
previous terms. A direct consequence of this approach is the uncertainty
present in the resulting models. In this work we discuss several approaches
available in the robust optimization literature. We consider the implications
of each approach in respect to the examination timetabling problem and present
how the most favorable approaches can be applied to the ETTP. Afterwards we
analyze the impact of some possible implementations of the given robustness
approaches on two real world instances and several random instances generated
by our instance generation framework which we introduce in this work.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Temporal Graph Representation Learning with Adaptive Augmentation Contrastive
Abstract: Temporal graph representation learning aims to generate low-dimensional
dynamic node embeddings to capture temporal information as well as structural
and property information. Current representation learning methods for temporal
networks often focus on capturing fine-grained information, which may lead to
the model capturing random noise instead of essential semantic information.
While graph contrastive learning has shown promise in dealing with noise, it
only applies to static graphs or snapshots and may not be suitable for handling
time-dependent noise. To alleviate the above challenge, we propose a novel
Temporal Graph representation learning with Adaptive augmentation Contrastive
(TGAC) model. The adaptive augmentation on the temporal graph is made by
combining prior knowledge with temporal information, and the contrastive
objective function is constructed by defining the augmented inter-view contrast
and intra-view contrast. To complement TGAC, we propose three adaptive
augmentation strategies that modify topological features to reduce noise from
the network. Our extensive experiments on various real networks demonstrate
that the proposed model outperforms other temporal graph representation
learning methods.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Where exactly does contextualization in a PLM happen?
Abstract: Pre-trained Language Models (PLMs) have shown to be consistently successful
in a plethora of NLP tasks due to their ability to learn contextualized
representations of words (Ethayarajh, 2019). BERT (Devlin et al., 2018), ELMo
(Peters et al., 2018) and other PLMs encode word meaning via textual context,
as opposed to static word embeddings, which encode all meanings of a word in a
single vector representation. In this work, we present a study that aims to
localize where exactly in a PLM word contextualization happens. In order to
find the location of this word meaning transformation, we investigate
representations of polysemous words in the basic BERT uncased 12 layer
architecture (Devlin et al., 2018), a masked language model trained on an
additional sentence adjacency objective, using qualitative and quantitative
measures.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: StableFDG: Style and Attention Based Learning for Federated Domain Generalization
Abstract: Traditional federated learning (FL) algorithms operate under the assumption
that the data distributions at training (source domains) and testing (target
domain) are the same. The fact that domain shifts often occur in practice
necessitates equipping FL methods with a domain generalization (DG) capability.
However, existing DG algorithms face fundamental challenges in FL setups due to
the lack of samples/domains in each client's local dataset. In this paper, we
propose StableFDG, a style and attention based learning strategy for
accomplishing federated domain generalization, introducing two key
contributions. The first is style-based learning, which enables each client to
explore novel styles beyond the original source domains in its local dataset,
improving domain diversity based on the proposed style sharing, shifting, and
exploration strategies. Our second contribution is an attention-based feature
highlighter, which captures the similarities between the features of data
samples in the same class, and emphasizes the important/common characteristics
to better learn the domain-invariant characteristics of each class in data-poor
FL scenarios. Experimental results show that StableFDG outperforms existing
baselines on various DG benchmark datasets, demonstrating its efficacy.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Knowledge Editing for Large Language Models: A Survey
Abstract: Large language models (LLMs) have recently transformed both the academic and
industrial landscapes due to their remarkable capacity to understand, analyze,
and generate texts based on their vast knowledge and reasoning ability.
Nevertheless, one major drawback of LLMs is their substantial computational
cost for pre-training due to their unprecedented amounts of parameters. The
disadvantage is exacerbated when new knowledge frequently needs to be
introduced into the pre-trained model. Therefore, it is imperative to develop
effective and efficient techniques to update pre-trained LLMs. Traditional
methods encode new knowledge in pre-trained LLMs through direct fine-tuning.
However, naively re-training LLMs can be computationally intensive and risks
degenerating valuable pre-trained knowledge irrelevant to the update in the
model. Recently, Knowledge-based Model Editing (KME) has attracted increasing
attention, which aims to precisely modify the LLMs to incorporate specific
knowledge, without negatively influencing other irrelevant knowledge. In this
survey, we aim to provide a comprehensive and in-depth overview of recent
advances in the field of KME. We first introduce a general formulation of KME
to encompass different KME strategies. Afterward, we provide an innovative
taxonomy of KME techniques based on how the new knowledge is introduced into
pre-trained LLMs, and investigate existing KME strategies while analyzing key
insights, advantages, and limitations of methods from each category. Moreover,
representative metrics, datasets, and applications of KME are introduced
accordingly. Finally, we provide an in-depth analysis regarding the
practicality and remaining challenges of KME and suggest promising research
directions for further advancement in this field.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Invariance is Key to Generalization: Examining the Role of Representation in Sim-to-Real Transfer for Visual Navigation
Abstract: The data-driven approach to robot control has been gathering pace rapidly,
yet generalization to unseen task domains remains a critical challenge. We
argue that the key to generalization is representations that are (i) rich
enough to capture all task-relevant information and (ii) invariant to
superfluous variability between the training and the test domains. We
experimentally study such a representation -- containing both depth and
semantic information -- for visual navigation and show that it enables a
control policy trained entirely in simulated indoor scenes to generalize to
diverse real-world environments, both indoors and outdoors. Further, we show
that our representation reduces the A-distance between the training and test
domains, improving the generalization error bound as a result. Our proposed
approach is scalable: the learned policy improves continuously, as the
foundation models that it exploits absorb more diverse data during
pre-training.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: One-dimensional convolutional neural network model for breast cancer subtypes classification and biochemical content evaluation using micro-FTIR hyperspectral images
Abstract: Breast cancer treatment still remains a challenge, where molecular subtypes
classification plays a crucial role in selecting appropriate and specific
therapy. The four subtypes are Luminal A (LA), Luminal B (LB), HER2 subtype,
and Triple-Negative Breast Cancer (TNBC). Immunohistochemistry is the
gold-standard evaluation, although interobserver variations are reported and
molecular signatures identification is time-consuming. Fourier transform
infrared micro-spectroscopy with machine learning approaches have been used to
evaluate cancer samples, presenting biochemical-related explainability.
However, this explainability is harder when using deep learning. This study
created a 1D deep learning tool for breast cancer subtype evaluation and
biochemical contribution. Sixty hyperspectral images were acquired from a human
breast cancer microarray. K-Means clustering was applied to select tissue and
paraffin spectra. CaReNet-V1, a novel 1D convolutional neural network, was
developed to classify breast cancer (CA) and adjacent tissue (AT), and
molecular subtypes. A 1D adaptation of Grad-CAM was applied to assess the
biochemical impact to the classifications. CaReNet-V1 effectively classified CA
and AT (test accuracy of 0.89), as well as HER2 and TNBC subtypes (0.83 and
0.86), with greater difficulty for LA and LB (0.74 and 0.68). The model enabled
the evaluation of the most contributing wavenumbers to the predictions,
providing a direct relationship with the biochemical content. Therefore,
CaReNet-V1 and hyperspectral images is a potential approach for breast cancer
biopsies assessment, providing additional information to the pathology report.
Biochemical content impact feature may be used for other studies, such as
treatment efficacy evaluation and development new diagnostics and therapeutic
methods.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning
Abstract: The pre-train and fine-tune paradigm in machine learning has had dramatic
success in a wide range of domains because the use of existing data or
pre-trained models on the internet enables quick and easy learning of new
tasks. We aim to enable this paradigm in robotic reinforcement learning,
allowing a robot to learn a new task with little human effort by leveraging
data and models from the Internet. However, reinforcement learning often
requires significant human effort in the form of manual reward specification or
environment resets, even if the policy is pre-trained. We introduce RoboFuME, a
reset-free fine-tuning system that pre-trains a multi-task manipulation policy
from diverse datasets of prior experiences and self-improves online to learn a
target task with minimal human intervention. Our insights are to utilize
calibrated offline reinforcement learning techniques to ensure efficient online
fine-tuning of a pre-trained policy in the presence of distribution shifts and
leverage pre-trained vision language models (VLMs) to build a robust reward
classifier for autonomously providing reward signals during the online
fine-tuning process. In a diverse set of five real robot manipulation tasks, we
show that our method can incorporate data from an existing robot dataset
collected at a different institution and improve on a target task within as
little as 3 hours of autonomous real-world experience. We also demonstrate in
simulation experiments that our method outperforms prior works that use
different RL algorithms or different approaches for predicting rewards. Project
website: https://robofume.github.io

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: Toward autocorrection of chemical process flowsheets using large language models
Abstract: The process engineering domain widely uses Process Flow Diagrams (PFDs) and
Process and Instrumentation Diagrams (P&IDs) to represent process flows and
equipment configurations. However, the P&IDs and PFDs, hereafter called
flowsheets, can contain errors causing safety hazards, inefficient operation,
and unnecessary expenses. Correcting and verifying flowsheets is a tedious,
manual process. We propose a novel generative AI methodology for automatically
identifying errors in flowsheets and suggesting corrections to the user, i.e.,
autocorrecting flowsheets. Inspired by the breakthrough of Large Language
Models (LLMs) for grammatical autocorrection of human language, we investigate
LLMs for the autocorrection of flowsheets. The input to the model is a
potentially erroneous flowsheet and the output of the model are suggestions for
a corrected flowsheet. We train our autocorrection model on a synthetic dataset
in a supervised manner. The model achieves a top-1 accuracy of 80% and a top-5
accuracy of 84% on an independent test dataset of synthetically generated
flowsheets. The results suggest that the model can learn to autocorrect the
synthetic flowsheets. We envision that flowsheet autocorrection will become a
useful tool for chemical engineers.

Output: Machine Learning