Instruction: What field is the article from?

Prompt:
Title: A Survey of Temporal Credit Assignment in Deep Reinforcement Learning
Abstract: The Credit Assignment Problem (CAP) refers to the longstanding challenge of
Reinforcement Learning (RL) agents to associate actions with their long-term
consequences. Solving the CAP is a crucial step towards the successful
deployment of RL in the real world since most decision problems provide
feedback that is noisy, delayed, and with little or no information about the
causes. These conditions make it hard to distinguish serendipitous outcomes
from those caused by informed decision-making. However, the mathematical nature
of credit and the CAP remains poorly understood and defined. In this survey, we
review the state of the art of Temporal Credit Assignment (CA) in deep RL. We
propose a unifying formalism for credit that enables equitable comparisons of
state of the art algorithms and improves our understanding of the trade-offs
between the various methods. We cast the CAP as the problem of learning the
influence of an action over an outcome from a finite amount of experience. We
discuss the challenges posed by delayed effects, transpositions, and a lack of
action influence, and analyse how existing methods aim to address them.
Finally, we survey the protocols to evaluate a credit assignment method, and
suggest ways to diagnoses the sources of struggle for different credit
assignment methods. Overall, this survey provides an overview of the field for
new-entry practitioners and researchers, it offers a coherent perspective for
scholars looking to expedite the starting stages of a new study on the CAP, and
it suggests potential directions for future research

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: An Ambiguity Measure for Recognizing the Unknowns in Deep Learning
Abstract: We study the understanding of deep neural networks from the scope in which
they are trained on. While the accuracy of these models is usually impressive
on the aggregate level, they still make mistakes, sometimes on cases that
appear to be trivial. Moreover, these models are not reliable in realizing what
they do not know leading to failures such as adversarial vulnerability and
out-of-distribution failures. Here, we propose a measure for quantifying the
ambiguity of inputs for any given model with regard to the scope of its
training. We define the ambiguity based on the geometric arrangements of the
decision boundaries and the convex hull of training set in the feature space
learned by the trained model, and demonstrate that a single ambiguity measure
may detect a considerable portion of mistakes of a model on in-distribution
samples, adversarial inputs, as well as out-of-distribution inputs. Using our
ambiguity measure, a model may abstain from classification when it encounters
ambiguous inputs leading to a better model accuracy not just on a given testing
set, but on the inputs it may encounter at the world at large. In pursuit of
this measure, we develop a theoretical framework that can identify the unknowns
of the model in relation to its scope. We put this in perspective with the
confidence of the model and develop formulations to identify the regions of the
domain which are unknown to the model, yet the model is guaranteed to have high
confidence.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models
Abstract: Traditional 3D content creation tools empower users to bring their
imagination to life by giving them direct control over a scene's geometry,
appearance, motion, and camera path. Creating computer-generated videos,
however, is a tedious manual process, which can be automated by emerging
text-to-video diffusion models. Despite great promise, video diffusion models
are difficult to control, hindering a user to apply their own creativity rather
than amplifying it. To address this challenge, we present a novel approach that
combines the controllability of dynamic 3D meshes with the expressivity and
editability of emerging diffusion models. For this purpose, our approach takes
an animated, low-fidelity rendered mesh as input and injects the ground truth
correspondence information obtained from the dynamic mesh into various stages
of a pre-trained text-to-image generation model to output high-quality and
temporally consistent frames. We demonstrate our approach on various examples
where motion can be obtained by animating rigged assets or changing the camera
path.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Implement services for business scenarios by combining basic emulators
Abstract: This article mainly introduces how to use various basic emulators to form a
combined emulator in the Jiutian Intelligence Network Simulation Platform to
realize simulation service functions in different business scenarios. Among
them, the combined emulator is included. The business scenarios include
different practical applications such as multi-objective antenna optimization,
high traffic of business, CSI (channel state information) compression feedback,
etc.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Training on Synthetic Data Beats Real Data in Multimodal Relation Extraction
Abstract: The task of multimodal relation extraction has attracted significant research
attention, but progress is constrained by the scarcity of available training
data. One natural thought is to extend existing datasets with cross-modal
generative models. In this paper, we consider a novel problem setting, where
only unimodal data, either text or image, are available during training. We aim
to train a multimodal classifier from synthetic data that perform well on real
multimodal test data. However, training with synthetic data suffers from two
obstacles: lack of data diversity and label information loss. To alleviate the
issues, we propose Mutual Information-aware Multimodal Iterated Relational dAta
GEneration (MI2RAGE), which applies Chained Cross-modal Generation (CCG) to
promote diversity in the generated data and exploits a teacher network to
select valuable training samples with high mutual information with the
ground-truth labels. Comparing our method to direct training on synthetic data,
we observed a significant improvement of 24.06% F1 with synthetic text and
26.42% F1 with synthetic images. Notably, our best model trained on completely
synthetic images outperforms prior state-of-the-art models trained on real
multimodal data by a margin of 3.76% in F1. Our codebase will be made available
upon acceptance.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Taiyi: A Bilingual Fine-Tuned Large Language Model for Diverse Biomedical Tasks
Abstract: Objective: Most existing fine-tuned biomedical large language models (LLMs)
focus on enhancing performance in monolingual biomedical question answering and
conversation tasks. To investigate the effectiveness of the fine-tuned LLMs on
diverse biomedical NLP tasks in different languages, We present Taiyi, a
bilingual fine-tuned LLM for diverse biomedical tasks. Materials and Methods:
We first curated a comprehensive collection of 140 existing biomedical text
mining datasets (102 English and 38 Chinese datasets) across over 10 task
types. Subsequently, a two-stage strategy is proposed for supervised
fine-tuning to optimize the model performance across varied tasks. Results:
Experimental results on 13 test sets covering named entity recognition,
relation extraction, text classification, question answering tasks demonstrate
that Taiyi achieves superior performance compared to general LLMs. The case
study involving additional biomedical NLP tasks further shows Taiyi's
considerable potential for bilingual biomedical multi-tasking. Conclusion:
Leveraging rich high-quality biomedical corpora and developing effective
fine-tuning strategies can significantly improve the performance of LLMs within
the biomedical domain. Taiyi shows the bilingual multi-tasking capability
through supervised fine-tuning. However, those tasks such as information
extraction that are not generation tasks in nature remain challenging for
LLM-based generative approaches, and they still underperform the conventional
discriminative approaches of smaller language models.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Alquist 5.0: Dialogue Trees Meet Generative Models. A Novel Approach for Enhancing SocialBot Conversations
Abstract: We present our SocialBot -- Alquist~5.0 -- developed for the Alexa Prize
SocialBot Grand Challenge~5. Building upon previous versions of our system, we
introduce the NRG Barista and outline several innovative approaches for
integrating Barista into our SocialBot, improving the overall conversational
experience. Additionally, we extend our SocialBot to support multimodal
devices. This paper offers insights into the development of Alquist~5.0, which
meets evolving user expectations while maintaining empathetic and knowledgeable
conversational abilities across diverse topics.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Multiple View Geometry Transformers for 3D Human Pose Estimation
Abstract: In this work, we aim to improve the 3D reasoning ability of Transformers in
multi-view 3D human pose estimation. Recent works have focused on end-to-end
learning-based transformer designs, which struggle to resolve geometric
information accurately, particularly during occlusion. Instead, we propose a
novel hybrid model, MVGFormer, which has a series of geometric and appearance
modules organized in an iterative manner. The geometry modules are
learning-free and handle all viewpoint-dependent 3D tasks geometrically which
notably improves the model's generalization ability. The appearance modules are
learnable and are dedicated to estimating 2D poses from image signals
end-to-end which enables them to achieve accurate estimates even when occlusion
occurs, leading to a model that is both accurate and generalizable to new
cameras and geometries. We evaluate our approach for both in-domain and
out-of-domain settings, where our model consistently outperforms
state-of-the-art methods, and especially does so by a significant margin in the
out-of-domain setting. We will release the code and models:
https://github.com/XunshanMan/MVGFormer.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Modeling Choice via Self-Attention
Abstract: Models of choice are a fundamental input to many now-canonical optimization
problems in the field of Operations Management, including assortment,
inventory, and price optimization. Naturally, accurate estimation of these
models from data is a critical step in the application of these optimization
problems in practice, and so it is perhaps surprising that such choice
estimation has to now been accomplished almost exclusively, both in theory and
in practice, (a) without the use of deep learning in any meaningful way, and
(b) via evaluation on limited data with constantly-changing metrics. This is in
stark contrast to the vast majority of similar learning applications, for which
the practice of machine learning suggests that (a) neural network-based models
are typically state-of-the-art, and (b) strict standardization on evaluation
procedures (datasets, metrics, etc.) is crucial. Thus motivated, we first
propose a choice model that is the first to successfully (both theoretically
and practically) leverage a modern neural network architectural concept
(self-attention). Theoretically, we show that our attention-based choice model
is a low-rank generalization of the Halo Multinomial Logit model, a recent
model that parsimoniously captures irrational choice effects and has seen
empirical success. We prove that whereas the Halo-MNL requires $\Omega(m^2)$
data samples to estimate, where $m$ is the number of products, our model
supports a natural nonconvex estimator (in particular, that which a standard
neural network implementation would apply) which admits a near-optimal
stationary point with $O(m)$ samples. We then establish the first
realistic-scale benchmark for choice estimation on real data and use this
benchmark to run the largest evaluation of existing choice models to date. We
find that the model we propose is dominant over both short-term and long-term
data periods.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Clover: Closed-Loop Verifiable Code Generation
Abstract: The use of large language models for code generation is a rapidly growing
trend in software development. However, without effective methods for ensuring
the correctness of generated code, this trend could lead to any number of
undesirable outcomes. In this paper, we lay out a vision for addressing this
challenge: the Clover paradigm, short for Closed-Loop Verifiable Code
Generation, which reduces correctness checking to the more accessible problem
of consistency checking. At the core of Clover lies a checker that performs
consistency checks among code, docstrings, and formal annotations. The checker
is implemented using a novel integration of formal verification tools and large
language models. We provide a theoretical analysis to support our thesis that
Clover should be effective at consistency checking. We also empirically
investigate its feasibility on a hand-designed dataset (CloverBench) featuring
annotated Dafny programs at a textbook level of difficulty. Experimental
results show that for this dataset, (i) LLMs are reasonably successful at
automatically generating formal specifications; and (ii) our consistency
checker achieves a promising acceptance rate (up to 87%) for correct instances
while maintaining zero tolerance for incorrect ones (no false positives).

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing
Abstract: Model-based reinforcement learning (MBRL) has gained much attention for its
ability to learn complex behaviors in a sample-efficient way: planning actions
by generating imaginary trajectories with predicted rewards. Despite its
success, we found that surprisingly, reward prediction is often a bottleneck of
MBRL, especially for sparse rewards that are challenging (or even ambiguous) to
predict. Motivated by the intuition that humans can learn from rough reward
estimates, we propose a simple yet effective reward smoothing approach,
DreamSmooth, which learns to predict a temporally-smoothed reward, instead of
the exact reward at the given timestep. We empirically show that DreamSmooth
achieves state-of-the-art performance on long-horizon sparse-reward tasks both
in sample efficiency and final performance without losing performance on common
benchmarks, such as Deepmind Control Suite and Atari benchmarks.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: A Framework of Defining, Modeling, and Analyzing Cognition Mechanisms
Abstract: Cognition is a core part of and a common topic among philosophy of mind,
psychology, neuroscience, AI, and cognitive science. Through a mechanistic
lens, I propose a framework of defining, modeling, and analyzing cognition
mechanisms. Firstly, appropriate terms are introduced and used in explanations
related to the framework and within the definition of a mechanism. I implicitly
contend that this terminology essentially characterizes a conceptual world
required for discussions in this paper. Secondly, a mathematical model of a
mechanism based on directed graphs is proposed. Thirdly, the definition of a
base necessary for a mechanism to be classified as a cognition mechanism is
proposed. I argue that the cognition base has the features of the cognition
self of humans. Fourthly, three ways to mechanistically look at mechanisms is
defined and specific instances of them are suggested. Fifthly, standards for
visualization and presentation of mechanisms, cognition mechanisms, and the
instances to mechanistically look at them are suggested and used to analyze
cognition mechanisms through appropriate examples. Finally, the features of
this paper are discussed and prospects of further development of the proposed
framework are briefly expressed.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: One Strike, You're Out: Detecting Markush Structures in Low Signal-to-Noise Ratio Images
Abstract: Modern research increasingly relies on automated methods to assist
researchers. An example of this is Optical Chemical Structure Recognition
(OCSR), which aids chemists in retrieving information about chemicals from
large amounts of documents. Markush structures are chemical structures that
cannot be parsed correctly by OCSR and cause errors. The focus of this research
was to propose and test a novel method for classifying Markush structures.
Within this method, a comparison was made between fixed-feature extraction and
end-to-end learning (CNN). The end-to-end method performed significantly better
than the fixed-feature method, achieving 0.928 (0.035 SD) Macro F1 compared to
the fixed-feature method's 0.701 (0.052 SD). Because of the nature of the
experiment, these figures are a lower bound and can be improved further. These
results suggest that Markush structures can be filtered out effectively and
accurately using the proposed method. When implemented into OCSR pipelines,
this method can improve their performance and use to other researchers.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Combatting Human Trafficking in the Cyberspace: A Natural Language Processing-Based Methodology to Analyze the Language in Online Advertisements
Abstract: This project tackles the pressing issue of human trafficking in online C2C
marketplaces through advanced Natural Language Processing (NLP) techniques. We
introduce a novel methodology for generating pseudo-labeled datasets with
minimal supervision, serving as a rich resource for training state-of-the-art
NLP models. Focusing on tasks like Human Trafficking Risk Prediction (HTRP) and
Organized Activity Detection (OAD), we employ cutting-edge Transformer models
for analysis. A key contribution is the implementation of an interpretability
framework using Integrated Gradients, providing explainable insights crucial
for law enforcement. This work not only fills a critical gap in the literature
but also offers a scalable, machine learning-driven approach to combat human
exploitation online. It serves as a foundation for future research and
practical applications, emphasizing the role of machine learning in addressing
complex social issues.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Enhanced E-Commerce Attribute Extraction: Innovating with Decorative Relation Correction and LLAMA 2.0-Based Annotation
Abstract: The rapid proliferation of e-commerce platforms accentuates the need for
advanced search and retrieval systems to foster a superior user experience.
Central to this endeavor is the precise extraction of product attributes from
customer queries, enabling refined search, comparison, and other crucial
e-commerce functionalities. Unlike traditional Named Entity Recognition (NER)
tasks, e-commerce queries present a unique challenge owing to the intrinsic
decorative relationship between product types and attributes. In this study, we
propose a pioneering framework that integrates BERT for classification, a
Conditional Random Fields (CRFs) layer for attribute value extraction, and
Large Language Models (LLMs) for data annotation, significantly advancing
attribute recognition from customer inquiries. Our approach capitalizes on the
robust representation learning of BERT, synergized with the sequence decoding
prowess of CRFs, to adeptly identify and extract attribute values. We introduce
a novel decorative relation correction mechanism to further refine the
extraction process based on the nuanced relationships between product types and
attributes inherent in e-commerce data. Employing LLMs, we annotate additional
data to expand the model's grasp and coverage of diverse attributes. Our
methodology is rigorously validated on various datasets, including Walmart,
BestBuy's e-commerce NER dataset, and the CoNLL dataset, demonstrating
substantial improvements in attribute recognition performance. Particularly,
the model showcased promising results during a two-month deployment in
Walmart's Sponsor Product Search, underscoring its practical utility and
effectiveness.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Fair Enough? A map of the current limitations of the requirements to have "fair" algorithms
Abstract: In the recent years, the raise in the usage and efficiency of Artificial
Intelligence and, more in general, of Automated Decision-Making systems has
brought with it an increasing and welcome awareness of the risks associated
with such systems. One of such risks is that of perpetuating or even amplifying
bias and unjust disparities present in the data from which many of these
systems learn to adjust and optimise their decisions. This awareness has on one
side encouraged several scientific communities to come up with more and more
appropriate ways and methods to assess, quantify, and possibly mitigate such
biases and disparities. On the other hand, it has prompted more and more layers
of society, including policy makers, to call for "fair" algorithms. We believe
that while a lot of excellent and multidisciplinary research is currently being
conducted, what is still fundamentally missing is the awareness that having
"fair" algorithms is per se a nearly meaningless requirement, that needs to be
complemented with a lot of additional societal choices to become actionable.
Namely, there is a hiatus between what the society is demanding from Automated
Decision-Making systems, and what this demand actually means in real-world
scenarios. In this work, we outline the key features of such a hiatus, and
pinpoint a list of fundamental ambiguities and attention points that we as a
society must address in order to give a concrete meaning to the increasing
demand of fairness in Automated Decision-Making systems.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D
Abstract: In the realm of text-to-3D generation, utilizing 2D diffusion models through
score distillation sampling (SDS) frequently leads to issues such as blurred
appearances and multi-faced geometry, primarily due to the intrinsically noisy
nature of the SDS loss. Our analysis identifies the core of these challenges as
the interaction among noise levels in the 2D diffusion process, the
architecture of the diffusion network, and the 3D model representation. To
overcome these limitations, we present StableDreamer, a methodology
incorporating three advances. First, inspired by InstructNeRF2NeRF, we
formalize the equivalence of the SDS generative prior and a simple supervised
L2 reconstruction loss. This finding provides a novel tool to debug SDS, which
we use to show the impact of time-annealing noise levels on reducing
multi-faced geometries. Second, our analysis shows that while image-space
diffusion contributes to geometric precision, latent-space diffusion is crucial
for vivid color rendition. Based on this observation, StableDreamer introduces
a two-stage training strategy that effectively combines these aspects,
resulting in high-fidelity 3D models. Third, we adopt an anisotropic 3D
Gaussians representation, replacing Neural Radiance Fields (NeRFs), to enhance
the overall quality, reduce memory usage during training, and accelerate
rendering speeds, and better capture semi-transparent objects. StableDreamer
reduces multi-face geometries, generates fine details, and converges stably.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Using Cooperative Game Theory to Prune Neural Networks
Abstract: We show how solution concepts from cooperative game theory can be used to
tackle the problem of pruning neural networks.
  The ever-growing size of deep neural networks (DNNs) increases their
performance, but also their computational requirements. We introduce a method
called Game Theory Assisted Pruning (GTAP), which reduces the neural network's
size while preserving its predictive accuracy. GTAP is based on eliminating
neurons in the network based on an estimation of their joint impact on the
prediction quality through game theoretic solutions. Specifically, we use a
power index akin to the Shapley value or Banzhaf index, tailored using a
procedure similar to Dropout (commonly used to tackle overfitting problems in
machine learning).
  Empirical evaluation of both feedforward networks and convolutional neural
networks shows that this method outperforms existing approaches in the achieved
tradeoff between the number of parameters and model accuracy.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Proceedings of the 2023 XCSP3 Competition
Abstract: This document represents the proceedings of the 2023 XCSP3 Competition. The
results of this competition of constraint solvers were presented at CP'23 (the
29th International Conference on Principles and Practice of Constraint
Programming, held in Toronto, Canada from 27th to 31th August, 2023).

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs
Abstract: Adapting a language model into a specific domain, a.k.a `domain adaption', is
a common practice when specialized knowledge, e.g. medicine, is not
encapsulated in a general language model like Llama2. The challenge lies in the
heterogeneity of data across the two training stages, as it varies in
languages, genres, or formats. To tackle this and simplify the learning
protocol, we propose to transform heterogeneous data, from the both
pre-training and supervised stages, into a unified, simple input-output pair
format. We validate the new protocol in the domains where proprietary LLMs like
ChatGPT perform relatively poorly, such as Traditional Chinese Medicine. The
developed model, HuatuoGPT-II, has shown state-of-the-art performance in
Chinese medicine domain on a number of benchmarks, e.g. medical licensing
exams. It even outperforms proprietary models like ChatGPT and GPT-4 in some
aspects, especially in Traditional Chinese Medicine. Expert manual evaluations
further validate HuatuoGPT-II's advantages over existing LLMs. Notably,
HuatuoGPT-II was benchmarked in a fresh Chinese National Medical Licensing
Examination where it achieved the best performance, showcasing not only its
effectiveness but also its generalization capabilities.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: AdsorbRL: Deep Multi-Objective Reinforcement Learning for Inverse Catalysts Design
Abstract: A central challenge of the clean energy transition is the development of
catalysts for low-emissions technologies. Recent advances in Machine Learning
for quantum chemistry drastically accelerate the computation of catalytic
activity descriptors such as adsorption energies. Here we introduce AdsorbRL, a
Deep Reinforcement Learning agent aiming to identify potential catalysts given
a multi-objective binding energy target, trained using offline learning on the
Open Catalyst 2020 and Materials Project data sets. We experiment with Deep
Q-Network agents to traverse the space of all ~160,000 possible unary, binary
and ternary compounds of 55 chemical elements, with very sparse rewards based
on adsorption energy known for only between 2,000 and 3,000 catalysts per
adsorbate. To constrain the actions space, we introduce Random Edge Traversal
and train a single-objective DQN agent on the known states subgraph, which we
find strengthens target binding energy by an average of 4.1 eV. We extend this
approach to multi-objective, goal-conditioned learning, and train a DQN agent
to identify materials with the highest (respectively lowest) adsorption
energies for multiple simultaneous target adsorbates. We experiment with
Objective Sub-Sampling, a novel training scheme aimed at encouraging
exploration in the multi-objective setup, and demonstrate simultaneous
adsorption energy improvement across all target adsorbates, by an average of
0.8 eV. Overall, our results suggest strong potential for Deep Reinforcement
Learning applied to the inverse catalysts design problem.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: When the Few Outweigh the Many: Illicit Content Recognition with Few-Shot Learning
Abstract: The anonymity and untraceability benefits of the Dark web account for the
exponentially-increased potential of its popularity while creating a suitable
womb for many illicit activities, to date. Hence, in collaboration with
cybersecurity and law enforcement agencies, research has provided approaches
for recognizing and classifying illicit activities with most exploiting textual
dark web markets' content recognition; few such approaches use images that
originated from dark web content. This paper investigates this alternative
technique for recognizing illegal activities from images. In particular, we
investigate label-agnostic learning techniques like One-Shot and Few-Shot
learning featuring the use Siamese neural networks, a state-of-the-art approach
in the field. Our solution manages to handle small-scale datasets with
promising accuracy. In particular, Siamese neural networks reach 90.9% on
20-Shot experiments over a 10-class dataset; this leads us to conclude that
such models are a promising and cheaper alternative to the definition of
automated law-enforcing machinery over the dark web.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Fraud Analytics Using Machine-learning & Engineering on Big Data (FAME) for Telecom
Abstract: Telecom industries lose globally 46.3 Billion USD due to fraud. Data mining
and machine learning techniques (apart from rules oriented approach) have been
used in past, but efficiency has been low as fraud pattern changes very
rapidly. This paper presents an industrialized solution approach with self
adaptive data mining technique and application of big data technologies to
detect fraud and discover novel fraud patterns in accurate, efficient and cost
effective manner. Solution has been successfully demonstrated to detect
International Revenue Share Fraud with <5% false positive. More than 1 Terra
Bytes of Call Detail Record from a reputed wholesale carrier and overseas
telecom transit carrier has been used to conduct this study.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models
Abstract: With LLMs shifting their role from statistical modeling of language to
serving as general-purpose AI agents, how should LLM evaluations change?
Arguably, a key ability of an AI agent is to flexibly combine, as needed, the
basic skills it has learned. The capability to combine skills plays an
important role in (human) pedagogy and also in a paper on emergence phenomena
(Arora & Goyal, 2023).
  This work introduces Skill-Mix, a new evaluation to measure ability to
combine skills. Using a list of $N$ skills the evaluator repeatedly picks
random subsets of $k$ skills and asks the LLM to produce text combining that
subset of skills. Since the number of subsets grows like $N^k$, for even modest
$k$ this evaluation will, with high probability, require the LLM to produce
text significantly different from any text in the training set. The paper
develops a methodology for (a) designing and administering such an evaluation,
and (b) automatic grading (plus spot-checking by humans) of the results using
GPT-4 as well as the open LLaMA-2 70B model.
  Administering a version of to popular chatbots gave results that, while
generally in line with prior expectations, contained surprises. Sizeable
differences exist among model capabilities that are not captured by their
ranking on popular LLM leaderboards ("cramming for the leaderboard").
Furthermore, simple probability calculations indicate that GPT-4's reasonable
performance on $k=5$ is suggestive of going beyond "stochastic parrot" behavior
(Bender et al., 2021), i.e., it combines skills in ways that it had not seen
during training.
  We sketch how the methodology can lead to a Skill-Mix based eco-system of
open evaluations for AI capabilities of future models.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure
Abstract: Graph learning methods help utilize implicit relationships among data items,
thereby reducing training label requirements and improving task performance.
However, determining the optimal graph structure for a particular learning task
remains a challenging research problem.
  In this work, we introduce the Graph Lottery Ticket (GLT) Hypothesis - that
there is an extremely sparse backbone for every graph, and that graph learning
algorithms attain comparable performance when trained on that subgraph as on
the full graph. We identify and systematically study 8 key metrics of interest
that directly influence the performance of graph learning algorithms.
Subsequently, we define the notion of a "winning ticket" for graph structure -
an extremely sparse subset of edges that can deliver a robust approximation of
the entire graph's performance. We propose a straightforward and efficient
algorithm for finding these GLTs in arbitrary graphs. Empirically, we observe
that performance of different graph learning algorithms can be matched or even
exceeded on graphs with the average degree as low as 5.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Removing RLHF Protections in GPT-4 via Fine-Tuning
Abstract: As large language models (LLMs) have increased in their capabilities, so does
their potential for dual use. To reduce harmful outputs, produces and vendors
of LLMs have used reinforcement learning with human feedback (RLHF). In tandem,
LLM vendors have been increasingly enabling fine-tuning of their most powerful
models. However, concurrent work has shown that fine-tuning can remove RLHF
protections. We may expect that the most powerful models currently available
(GPT-4) are less susceptible to fine-tuning attacks.
  In this work, we show the contrary: fine-tuning allows attackers to remove
RLHF protections with as few as 340 examples and a 95% success rate. These
training examples can be automatically generated with weaker models. We further
show that removing RLHF protections does not decrease usefulness on
non-censored outputs, providing evidence that our fine-tuning strategy does not
decrease usefulness despite using weaker models to generate training data. Our
results show the need for further research on protections on LLMs.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Automated Behavioral Analysis Using Instance Segmentation
Abstract: Animal behavior analysis plays a crucial role in various fields, such as life
science and biomedical research. However, the scarcity of available data and
the high cost associated with obtaining a large number of labeled datasets pose
significant challenges. In this research, we propose a novel approach that
leverages instance segmentation-based transfer learning to address these
issues. By capitalizing on fine-tuning the classification head of the instance
segmentation network, we enable the tracking of multiple animals and facilitate
behavior analysis in laboratory-recorded videos. To demonstrate the
effectiveness of our method, we conducted a series of experiments, revealing
that our approach achieves exceptional performance levels, comparable to human
capabilities, across a diverse range of animal behavior analysis tasks.
Moreover, we emphasize the practicality of our solution, as it requires only a
small number of labeled images for training. To facilitate the adoption and
further development of our method, we have developed an open-source
implementation named Annolid (An annotation and instance segmentation-based
multiple animal tracking and behavior analysis package). The codebase is
publicly available on GitHub at https://github.com/cplab/annolid. This resource
serves as a valuable asset for researchers and practitioners interested in
advancing animal behavior analysis through state-of-the-art techniques.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Transformers in Unsupervised Structure-from-Motion
Abstract: Transformers have revolutionized deep learning based computer vision with
improved performance as well as robustness to natural corruptions and
adversarial attacks. Transformers are used predominantly for 2D vision tasks,
including image classification, semantic segmentation, and object detection.
However, robots and advanced driver assistance systems also require 3D scene
understanding for decision making by extracting structure-from-motion (SfM). We
propose a robust transformer-based monocular SfM method that learns to predict
monocular pixel-wise depth, ego vehicle's translation and rotation, as well as
camera's focal length and principal point, simultaneously. With experiments on
KITTI and DDAD datasets, we demonstrate how to adapt different vision
transformers and compare them against contemporary CNN-based methods. Our study
shows that transformer-based architecture, though lower in run-time efficiency,
achieves comparable performance while being more robust against natural
corruptions, as well as untargeted and targeted attacks.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: SplitNeRF: Split Sum Approximation Neural Field for Joint Geometry, Illumination, and Material Estimation
Abstract: We present a novel approach for digitizing real-world objects by estimating
their geometry, material properties, and environmental lighting from a set of
posed images with fixed lighting. Our method incorporates into Neural Radiance
Field (NeRF) pipelines the split sum approximation used with image-based
lighting for real-time physical-based rendering. We propose modeling the
scene's lighting with a single scene-specific MLP representing pre-integrated
image-based lighting at arbitrary resolutions. We achieve accurate modeling of
pre-integrated lighting by exploiting a novel regularizer based on efficient
Monte Carlo sampling. Additionally, we propose a new method of supervising
self-occlusion predictions by exploiting a similar regularizer based on Monte
Carlo sampling. Experimental results demonstrate the efficiency and
effectiveness of our approach in estimating scene geometry, material
properties, and lighting. Our method is capable of attaining state-of-the-art
relighting quality after only ${\sim}1$ hour of training in a single NVIDIA
A100 GPU.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep Centers
Abstract: While machine learning (ML) includes a valuable array of tools for analyzing
biomedical data, significant time and expertise is required to assemble
effective, rigorous, and unbiased pipelines. Automated ML (AutoML) tools seek
to facilitate ML application by automating a subset of analysis pipeline
elements. In this study we develop and validate a Simple, Transparent,
End-to-end Automated Machine Learning Pipeline (STREAMLINE) and apply it to
investigate the added utility of photography-based phenotypes for predicting
obstructive sleep apnea (OSA); a common and underdiagnosed condition associated
with a variety of health, economic, and safety consequences. STREAMLINE is
designed to tackle biomedical binary classification tasks while adhering to
best practices and accommodating complexity, scalability, reproducibility,
customization, and model interpretation. Benchmarking analyses validated the
efficacy of STREAMLINE across data simulations with increasingly complex
patterns of association. Then we applied STREAMLINE to evaluate the utility of
demographics (DEM), self-reported comorbidities (DX), symptoms (SYM), and
photography-based craniofacial (CF) and intraoral (IO) anatomy measures in
predicting any OSA or moderate/severe OSA using 3,111 participants from Sleep
Apnea Global Interdisciplinary Consortium (SAGIC). OSA analyses identified a
significant increase in ROC-AUC when adding CF to DEM+DX+SYM to predict
moderate/severe OSA. A consistent but non-significant increase in PRC-AUC was
observed with the addition of each subsequent feature set to predict any OSA,
with CF and IO yielding minimal improvements. Application of STREAMLINE to OSA
data suggests that CF features provide additional value in predicting
moderate/severe OSA, but neither CF nor IO features meaningfully improved the
prediction of any OSA beyond established demographics, comorbidity and symptom
characteristics.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Causal disentanglement of multimodal data
Abstract: Causal representation learning algorithms discover lower-dimensional
representations of data that admit a decipherable interpretation of cause and
effect; as achieving such interpretable representations is challenging, many
causal learning algorithms utilize elements indicating prior information, such
as (linear) structural causal models, interventional data, or weak supervision.
Unfortunately, in exploratory causal representation learning, such elements and
prior information may not be available or warranted. Alternatively, scientific
datasets often have multiple modalities or physics-based constraints, and the
use of such scientific, multimodal data has been shown to improve
disentanglement in fully unsupervised settings. Consequently, we introduce a
causal representation learning algorithm (causalPIMA) that can use multimodal
data and known physics to discover important features with causal
relationships. Our innovative algorithm utilizes a new differentiable
parametrization to learn a directed acyclic graph (DAG) together with a latent
space of a variational autoencoder in an end-to-end differentiable framework
via a single, tractable evidence lower bound loss function. We place a Gaussian
mixture prior on the latent space and identify each of the mixtures with an
outcome of the DAG nodes; this novel identification enables feature discovery
with causal relationships. Tested against a synthetic and a scientific dataset,
our results demonstrate the capability of learning an interpretable causal
structure while simultaneously discovering key features in a fully unsupervised
setting.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision
Abstract: Explainability is an aspect of modern AI that is vital for impact and
usability in the real world. The main objective of this paper is to emphasise
the need to understand the predictions of Computer Vision models, specifically
Convolutional Neural Network (CNN) based models. Existing methods of explaining
CNN predictions are mostly based on Gradient-weighted Class Activation Maps
(Grad-CAM) and solely focus on a single target class. We show that from the
point of the target class selection, we make an assumption on the prediction
process, hence neglecting a large portion of the predictor CNN model's thinking
process. In this paper, we present an exhaustive methodology called Fused
Multi-class Gradient-weighted Class Activation Map (FM-G-CAM) that considers
multiple top predicted classes, which provides a holistic explanation of the
predictor CNN's thinking rationale. We also provide a detailed and
comprehensive mathematical and algorithmic description of our method.
Furthermore, along with a concise comparison of existing methods, we compare
FM-G-CAM with Grad-CAM, highlighting its benefits through real-world practical
use cases. Finally, we present an open-source Python library with FM-G-CAM
implementation to conveniently generate saliency maps for CNN-based model
predictions.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Grouping Local Process Models
Abstract: In recent years, process mining emerged as a proven technology to analyze and
improve operational processes. An expanding range of organizations using
process mining in their daily operation brings a broader spectrum of processes
to be analyzed. Some of these processes are highly unstructured, making it
difficult for traditional process discovery approaches to discover a
start-to-end model describing the entire process. Therefore, the subdiscipline
of Local Process Model (LPM) discovery tries to build a set of LPMs, i.e.,
smaller models that explain sub-behaviors of the process. However, like other
pattern mining approaches, LPM discovery algorithms also face the problems of
model explosion and model repetition, i.e., the algorithms may create hundreds
if not thousands of models, and subsets of them are close in structure or
behavior. This work proposes a three-step pipeline for grouping similar LPMs
using various process model similarity measures. We demonstrate the usefulness
of grouping through a real-life case study, and analyze the impact of different
measures, the gravity of repetition in the discovered LPMs, and how it improves
after grouping on multiple real event logs.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Fairness-Aware Domain Generalization under Covariate and Dependence Shifts
Abstract: Achieving the generalization of an invariant classifier from source domains
to shifted target domains while simultaneously considering model fairness is a
substantial and complex challenge in machine learning. Existing domain
generalization research typically attributes domain shifts to concept shift,
which relates to alterations in class labels, and covariate shift, which
pertains to variations in data styles. In this paper, by introducing another
form of distribution shift, known as dependence shift, which involves
variations in fair dependence patterns across domains, we propose a novel
domain generalization approach that addresses domain shifts by considering both
covariate and dependence shifts. We assert the existence of an underlying
transformation model can transform data from one domain to another. By
generating data in synthetic domains through the model, a fairness-aware
invariant classifier is learned that enforces both model accuracy and fairness
in unseen domains. Extensive empirical studies on four benchmark datasets
demonstrate that our approach surpasses state-of-the-art methods.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: GeoSAM: Fine-tuning SAM with Sparse and Dense Visual Prompting for Automated Segmentation of Mobility Infrastructure
Abstract: The Segment Anything Model (SAM) has shown impressive performance when
applied to natural image segmentation. However, it struggles with geographical
images like aerial and satellite imagery, especially when segmenting mobility
infrastructure including roads, sidewalks, and crosswalks. This inferior
performance stems from the narrow features of these objects, their textures
blending into the surroundings, and interference from objects like trees,
buildings, vehicles, and pedestrians - all of which can disorient the model to
produce inaccurate segmentation maps. To address these challenges, we propose
Geographical SAM (GeoSAM), a novel SAM-based framework that implements a
fine-tuning strategy using the dense visual prompt from zero-shot learning, and
the sparse visual prompt from a pre-trained CNN segmentation model. The
proposed GeoSAM outperforms existing approaches for geographical image
segmentation, specifically by 20%, 14.29%, and 17.65% for road infrastructure,
pedestrian infrastructure, and on average, respectively, representing a
momentous leap in leveraging foundation models to segment mobility
infrastructure including both road and pedestrian infrastructure in
geographical images.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: What Lies beyond the Pareto Front? A Survey on Decision-Support Methods for Multi-Objective Optimization
Abstract: We present a review that unifies decision-support methods for exploring the
solutions produced by multi-objective optimization (MOO) algorithms. As MOO is
applied to solve diverse problems, approaches for analyzing the trade-offs
offered by MOO algorithms are scattered across fields. We provide an overview
of the advances on this topic, including methods for visualization, mining the
solution set, and uncertainty exploration as well as emerging research
directions, including interactivity, explainability, and ethics. We synthesize
these methods drawing from different fields of research to build a unified
approach, independent of the application. Our goals are to reduce the entry
barrier for researchers and practitioners on using MOO algorithms and to
provide novel research directions.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Foundational propositions of hesitant fuzzy sets and parameter reductions of hesitant fuzzy information systems
Abstract: Hesitant fuzzy sets are widely used in the instances of uncertainty and
hesitation. The inclusion relationship is an important and foundational
definition for sets. Hesitant fuzzy set, as a kind of set, needs explicit
definition of inclusion relationship. Base on the hesitant fuzzy membership
degree of discrete form, several kinds of inclusion relationships for hesitant
fuzzy sets are proposed. And then some foundational propositions of hesitant
fuzzy sets and the families of hesitant fuzzy sets are presented. Finally, some
foundational propositions of hesitant fuzzy information systems with respect to
parameter reductions are put forward, and an example and an algorithm are given
to illustrate the processes of parameter reductions.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: ADT: Agent-based Dynamic Thresholding for Anomaly Detection
Abstract: The complexity and scale of IT systems are increasing dramatically, posing
many challenges to real-world anomaly detection. Deep learning anomaly
detection has emerged, aiming at feature learning and anomaly scoring, which
has gained tremendous success. However, little work has been done on the
thresholding problem despite it being a critical factor for the effectiveness
of anomaly detection. In this paper, we model thresholding in anomaly detection
as a Markov Decision Process and propose an agent-based dynamic thresholding
(ADT) framework based on a deep Q-network. The proposed method can be
integrated into many systems that require dynamic thresholding. An auto-encoder
is utilized in this study to obtain feature representations and produce anomaly
scores for complex input data. ADT can adjust thresholds adaptively by
utilizing the anomaly scores from the auto-encoder and significantly improve
anomaly detection performance. The properties of ADT are studied through
experiments on three real-world datasets and compared with benchmarks, hence
demonstrating its thresholding capability, data-efficient learning, stability,
and robustness. Our study validates the effectiveness of reinforcement learning
in optimal thresholding control in anomaly detection.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Towards a Transportable Causal Network Model Based on Observational Healthcare Data
Abstract: Over the last decades, many prognostic models based on artificial
intelligence techniques have been used to provide detailed predictions in
healthcare. Unfortunately, the real-world observational data used to train and
validate these models are almost always affected by biases that can strongly
impact the outcomes validity: two examples are values missing not-at-random and
selection bias. Addressing them is a key element in achieving transportability
and in studying the causal relationships that are critical in clinical decision
making, going beyond simpler statistical approaches based on probabilistic
association.
  In this context, we propose a novel approach that combines selection
diagrams, missingness graphs, causal discovery and prior knowledge into a
single graphical model to estimate the cardiovascular risk of adolescent and
young females who survived breast cancer. We learn this model from data
comprising two different cohorts of patients. The resulting causal network
model is validated by expert clinicians in terms of risk assessment, accuracy
and explainability, and provides a prognostic model that outperforms competing
machine learning methods.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Privacy Threats in Stable Diffusion Models
Abstract: This paper introduces a novel approach to membership inference attacks (MIA)
targeting stable diffusion computer vision models, specifically focusing on the
highly sophisticated Stable Diffusion V2 by StabilityAI. MIAs aim to extract
sensitive information about a model's training data, posing significant privacy
concerns. Despite its advancements in image synthesis, our research reveals
privacy vulnerabilities in the stable diffusion models' outputs. Exploiting
this information, we devise a black-box MIA that only needs to query the victim
model repeatedly. Our methodology involves observing the output of a stable
diffusion model at different generative epochs and training a classification
model to distinguish when a series of intermediates originated from a training
sample or not. We propose numerous ways to measure the membership features and
discuss what works best. The attack's efficacy is assessed using the ROC AUC
method, demonstrating a 60\% success rate in inferring membership information.
This paper contributes to the growing body of research on privacy and security
in machine learning, highlighting the need for robust defenses against MIAs.
Our findings prompt a reevaluation of the privacy implications of stable
diffusion models, urging practitioners and developers to implement enhanced
security measures to safeguard against such attacks.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Pixel-Level Clustering Network for Unsupervised Image Segmentation
Abstract: While image segmentation is crucial in various computer vision applications,
such as autonomous driving, grasping, and robot navigation, annotating all
objects at the pixel-level for training is nearly impossible. Therefore, the
study of unsupervised image segmentation methods is essential. In this paper,
we present a pixel-level clustering framework for segmenting images into
regions without using ground truth annotations. The proposed framework includes
feature embedding modules with an attention mechanism, a feature statistics
computing module, image reconstruction, and superpixel segmentation to achieve
accurate unsupervised segmentation. Additionally, we propose a training
strategy that utilizes intra-consistency within each superpixel,
inter-similarity/dissimilarity between neighboring superpixels, and structural
similarity between images. To avoid potential over-segmentation caused by
superpixel-based losses, we also propose a post-processing method. Furthermore,
we present an extension of the proposed method for unsupervised semantic
segmentation. We conducted experiments on three publicly available datasets
(Berkeley segmentation dataset, PASCAL VOC 2012 dataset, and COCO-Stuff
dataset) to demonstrate the effectiveness of the proposed framework. The
experimental results show that the proposed framework outperforms previous
state-of-the-art methods.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Enhancing Malware Detection by Integrating Machine Learning with Cuckoo Sandbox
Abstract: In the modern era, malware is experiencing a significant increase in both its
variety and quantity, aligning with the widespread adoption of the digital
world. This surge in malware has emerged as a critical challenge in the realm
of cybersecurity, prompting numerous research endeavors and contributions to
address the issue. Machine learning algorithms have been leveraged for malware
detection due to their ability to uncover concealed patterns within vast
datasets. However, deep learning algorithms, characterized by their
multi-layered structure, surpass the limitations of traditional machine
learning approaches. By employing deep learning techniques such as CNN
(Convolutional Neural Network) and RNN (Recurrent Neural Network), this study
aims to classify and identify malware extracted from a dataset containing API
call sequences. The performance of these algorithms is compared with that of
conventional machine learning methods, including SVM (Support Vector Machine),
RF (Random Forest), KNN (K-Nearest Neighbors), XGB (Extreme Gradient Boosting),
and GBC (Gradient Boosting Classifier), all using the same dataset. The
outcomes of this research demonstrate that both deep learning and machine
learning algorithms achieve remarkably high levels of accuracy, reaching up to
99% in certain cases.

Output: Cryptography and Security


Instruction: What field is the article from?

Prompt:
Title: Short-term prediction of construction waste transport activities using AI-Truck
Abstract: Construction waste hauling trucks (or `slag trucks') are among the most
commonly seen heavy-duty vehicles in urban streets, which not only produce
significant NOx and PM emissions but are also a major source of on-road and
on-site fugitive dust. Slag trucks are subject to a series of spatial and
temporal access restrictions by local traffic and environmental policies. This
paper addresses the practical problem of predicting slag truck activity at a
city scale during heavy pollution episodes, such that environmental law
enforcement units can take timely and proactive measures against localized
truck aggregation. A deep ensemble learning framework (coined AI-Truck) is
designed, which employs a soft vote integrator that utilizes BI-LSTM, TCN,
STGCN, and PDFormer as base classifiers to predict the level of slag truck
activities at a resolution of 1km$\times$1km, in a 193 km$^2$ area in Chengdu,
China. As a classifier, AI-Truck yields a Macro f1 close to 80\% for 0.5h- and
1h-prediction.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA
Abstract: Multi-hop Knowledge Graph Question Answering (KGQA) is a task that involves
retrieving nodes from a knowledge graph (KG) to answer natural language
questions. Recent GNN-based approaches formulate this task as a KG path
searching problem, where messages are sequentially propagated from the seed
node towards the answer nodes. However, these messages are past-oriented, and
they do not consider the full KG context. To make matters worse, KG nodes often
represent proper noun entities and are sometimes encrypted, being uninformative
in selecting between paths. To address these problems, we propose Neural Tree
Search (NuTrea), a tree search-based GNN model that incorporates the broader KG
context. Our model adopts a message-passing scheme that probes the unreached
subtree regions to boost the past-oriented embeddings. In addition, we
introduce the Relation Frequency-Inverse Entity Frequency (RF-IEF) node
embedding that considers the global KG context to better characterize ambiguous
KG nodes. The general effectiveness of our approach is demonstrated through
experiments on three major multi-hop KGQA benchmark datasets, and our extensive
analyses further validate its expressiveness and robustness. Overall, NuTrea
provides a powerful means to query the KG with complex natural language
questions. Code is available at https://github.com/mlvlab/NuTrea.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: See and Think: Embodied Agent in Virtual Environment
Abstract: Large language models (LLMs) have achieved impressive progress on several
open-world tasks. Recently, using LLMs to build embodied agents has been a
hotspot. In this paper, we propose STEVE, a comprehensive and visionary
embodied agent in the Minecraft virtual environment. STEVE consists of three
key components: vision perception, language instruction, and code action.
Vision perception involves the interpretation of visual information in the
environment, which is then integrated into the LLMs component with agent state
and task instruction. Language instruction is responsible for iterative
reasoning and decomposing complex tasks into manageable guidelines. Code action
generates executable skill actions based on retrieval in skill database,
enabling the agent to interact effectively within the Minecraft environment. We
also collect STEVE-21K dataset, which includes 600$+$ vision-environment pairs,
20K knowledge question-answering pairs, and 200$+$ skill-code pairs. We conduct
continuous block search, knowledge question and answering, and tech tree
mastery to evaluate the performance. Extensive experiments show that STEVE
achieves at most $1.5 \times$ faster unlocking key tech trees and $2.5 \times$
quicker in block search tasks compared to previous state-of-the-art methods.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Stochastic Vision Transformers with Wasserstein Distance-Aware Attention
Abstract: Self-supervised learning is one of the most promising approaches to acquiring
knowledge from limited labeled data. Despite the substantial advancements made
in recent years, self-supervised models have posed a challenge to
practitioners, as they do not readily provide insight into the model's
confidence and uncertainty. Tackling this issue is no simple feat, primarily
due to the complexity involved in implementing techniques that can make use of
the latent representations learned during pre-training without relying on
explicit labels. Motivated by this, we introduce a new stochastic vision
transformer that integrates uncertainty and distance awareness into
self-supervised learning (SSL) pipelines. Instead of the conventional
deterministic vector embedding, our novel stochastic vision transformer encodes
image patches into elliptical Gaussian distributional embeddings. Notably, the
attention matrices of these stochastic representational embeddings are computed
using Wasserstein distance-based attention, effectively capitalizing on the
distributional nature of these embeddings. Additionally, we propose a
regularization term based on Wasserstein distance for both pre-training and
fine-tuning processes, thereby incorporating distance awareness into latent
representations. We perform extensive experiments across different tasks such
as in-distribution generalization, out-of-distribution detection, dataset
corruption, semi-supervised settings, and transfer learning to other datasets
and tasks. Our proposed method achieves superior accuracy and calibration,
surpassing the self-supervised baseline in a wide range of experiments on a
variety of datasets.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Generative AI and US Intellectual Property Law
Abstract: The rapidity with which generative AI has been adopted and advanced has
raised legal and ethical questions related to the impact on artists rights,
content production, data collection, privacy, accuracy of information, and
intellectual property rights. Recent administrative and case law challenges
have shown that generative AI software systems do not have independent
intellectual property rights in the content that they generate. It remains to
be seen whether human content creators can retain their intellectual property
rights against generative AI software, its developers, operators, and owners
for the misappropriation of the work of human creatives, given the metes and
bounds of existing law. Early signs from various courts are mixed as to whether
and to what degree the results generated by AI models meet the legal standards
of infringement under existing law.

Output: Computers and Society


Instruction: What field is the article from?

Prompt:
Title: Accurate Differential Operators for Hybrid Neural Fields
Abstract: Neural fields have become widely used in various fields, from shape
representation to neural rendering, and for solving partial differential
equations (PDEs). With the advent of hybrid neural field representations like
Instant NGP that leverage small MLPs and explicit representations, these models
train quickly and can fit large scenes. Yet in many applications like rendering
and simulation, hybrid neural fields can cause noticeable and unreasonable
artifacts. This is because they do not yield accurate spatial derivatives
needed for these downstream applications. In this work, we propose two ways to
circumvent these challenges. Our first approach is a post hoc operator that
uses local polynomial-fitting to obtain more accurate derivatives from
pre-trained hybrid neural fields. Additionally, we also propose a
self-supervised fine-tuning approach that refines the neural field to yield
accurate derivatives directly while preserving the initial signal. We show the
application of our method on rendering, collision simulation, and solving PDEs.
We observe that using our approach yields more accurate derivatives, reducing
artifacts and leading to more accurate simulations in downstream applications.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Diff-GO: Diffusion Goal-Oriented Communications to Achieve Ultra-High Spectrum Efficiency
Abstract: The latest advances in artificial intelligence (AI) present many
unprecedented opportunities to achieve much improved bandwidth saving in
communications. Unlike conventional communication systems focusing on packet
transport, rich datasets and AI makes it possible to efficiently transfer only
the information most critical to the goals of message recipients. One of the
most exciting advances in generative AI known as diffusion model presents a
unique opportunity for designing ultra-fast communication systems well beyond
language-based messages. This work presents an ultra-efficient communication
design by utilizing generative AI-based on diffusion models as a specific
example of the general goal-oriented communication framework. To better control
the regenerated message at the receiver output, our diffusion system design
includes a local regeneration module with finite dimensional noise latent. The
critical significance of noise latent control and sharing residing on our
Diff-GO is the ability to introduce the concept of "local generative feedback"
(Local-GF), which enables the transmitter to monitor the quality and gauge the
quality or accuracy of the message recovery at the semantic system receiver. To
this end, we propose a new low-dimensional noise space for the training of
diffusion models, which significantly reduces the communication overhead and
achieves satisfactory message recovery performance. Our experimental results
demonstrate that the proposed noise space and the diffusion-based generative
model achieve ultra-high spectrum efficiency and accurate recovery of
transmitted image signals. By trading off computation for bandwidth efficiency
(C4BE), this new framework provides an important avenue to achieve exceptional
computation-bandwidth tradeoff.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion
Abstract: The widespread deployment of Graph Neural Networks (GNNs) sparks significant
interest in their explainability, which plays a vital role in model auditing
and ensuring trustworthy graph learning. The objective of GNN explainability is
to discern the underlying graph structures that have the most significant
impact on model predictions. Ensuring that explanations generated are reliable
necessitates consideration of the in-distribution property, particularly due to
the vulnerability of GNNs to out-of-distribution data. Unfortunately,
prevailing explainability methods tend to constrain the generated explanations
to the structure of the original graph, thereby downplaying the significance of
the in-distribution property and resulting in explanations that lack
reliability. To address these challenges, we propose D4Explainer, a novel
approach that provides in-distribution GNN explanations for both counterfactual
and model-level explanation scenarios. The proposed D4Explainer incorporates
generative graph distribution learning into the optimization objective, which
accomplishes two goals: 1) generate a collection of diverse counterfactual
graphs that conform to the in-distribution property for a given instance, and
2) identify the most discriminative graph patterns that contribute to a
specific class prediction, thus serving as model-level explanations. It is
worth mentioning that D4Explainer is the first unified framework that combines
both counterfactual and model-level explanations. Empirical evaluations
conducted on synthetic and real-world datasets provide compelling evidence of
the state-of-the-art performance achieved by D4Explainer in terms of
explanation accuracy, faithfulness, diversity, and robustness.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Dynamic Adjustment of Matching Radii under the Broadcasting Mode: A Novel Multitask Learning Strategy and Temporal Modeling Approach
Abstract: As ride-hailing services have experienced significant growth, the majority of
research has concentrated on the dispatching mode, where drivers must adhere to
the platform's assigned routes. However, the broadcasting mode, in which
drivers can freely choose their preferred orders from those broadcast by the
platform, has received less attention. One important but challenging task in
such a system is the determination of the optimal matching radius, which
usually varies across space, time, and real-time supply/demand characteristics.
This study develops a Transformer-Encoder-Based (TEB) model that predicts key
system performance metrics for a range of matching radii, which enables the
ride-hailing platform to select an optimal matching radius that maximizes
overall system performance according to real-time supply and demand
information. To simultaneously maximize multiple system performance metrics for
matching radius determination, we devise a novel multi-task learning algorithm
that enhances convergence speed of each task (corresponding to the optimization
of one metric) and delivers more accurate overall predictions. We evaluate our
methods in a simulation environment specifically designed for
broadcasting-mode-based ride-hailing service. Our findings reveal that
dynamically adjusting matching radii based on our proposed
predict-then-optimize approach significantly improves system performance, e.g.,
increasing platform revenue by 7.55% and enhancing order fulfillment rate by
13% compared to benchmark algorithms.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Anytime-Valid Confidence Sequences for Consistent Uncertainty Estimation in Early-Exit Neural Networks
Abstract: Early-exit neural networks (EENNs) facilitate adaptive inference by producing
predictions at multiple stages of the forward pass. In safety-critical
applications, these predictions are only meaningful when complemented with
reliable uncertainty estimates. Yet, due to their sequential structure, an
EENN's uncertainty estimates should also be consistent: labels that are deemed
improbable at one exit should not reappear within the confidence interval / set
of later exits. We show that standard uncertainty quantification techniques,
like Bayesian methods or conformal prediction, can lead to inconsistency across
exits. We address this problem by applying anytime-valid confidence sequences
(AVCSs) to the exits of EENNs. By design, AVCSs maintain consistency across
exits. We examine the theoretical and practical challenges of applying AVCSs to
EENNs and empirically validate our approach on both regression and
classification tasks.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Deceiving Semantic Shortcuts on Reasoning Chains: How Far Can Models Go without Hallucination?
Abstract: Despite the recent advancement in large language models (LLMs) and their high
performances across numerous benchmarks, recent research has unveiled that LLMs
suffer from hallucinations and unfaithful reasoning. This work studies a
specific type of hallucination induced by semantic associations. Specifically,
we investigate to what extent LLMs take shortcuts from certain keyword/entity
biases in the prompt instead of following the correct reasoning path. To
quantify this phenomenon, we propose a novel probing method and benchmark
called EureQA. We start from questions that LLMs will answer correctly with
utmost certainty, and mask the important entity with evidence sentence
recursively, asking models to find masked entities according to a chain of
evidence before answering the question.
  During the construction of the evidence, we purposefully replace semantic
clues (entities) that may lead to the correct answer with distractor clues
(evidence) that will not directly lead to the correct answer but require a
chain-like reasoning process. We evaluate if models can follow the correct
reasoning chain instead of short-cutting through distractor clues. We find that
existing LLMs lack the necessary capabilities to follow correct reasoning paths
and resist the attempt of greedy shortcuts. We show that the distractor
semantic associations often lead to model hallucination, which is strong
evidence that questions the validity of current LLM reasoning.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: ProtoArgNet: Interpretable Image Classification with Super-Prototypes and Argumentation [Technical Report]
Abstract: We propose ProtoArgNet, a novel interpretable deep neural architecture for
image classification in the spirit of prototypical-part-learning as found, e.g.
in ProtoPNet. While earlier approaches associate every class with multiple
prototypical-parts, ProtoArgNet uses super-prototypes that combine
prototypical-parts into single prototypical class representations. Furthermore,
while earlier approaches use interpretable classification layers, e.g. logistic
regression in ProtoPNet, ProtoArgNet improves accuracy with multi-layer
perceptrons while relying upon an interpretable reading thereof based on a form
of argumentation. ProtoArgNet is customisable to user cognitive requirements by
a process of sparsification of the multi-layer perceptron/argumentation
component. Also, as opposed to other prototypical-part-learning approaches,
ProtoArgNet can recognise spatial relations between different
prototypical-parts that are from different regions in images, similar to how
CNNs capture relations between patterns recognized in earlier layers.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: VideoCon: Robust Video-Language Alignment via Contrast Captions
Abstract: Despite being (pre)trained on a massive amount of data, state-of-the-art
video-language alignment models are not robust to semantically-plausible
contrastive changes in the video captions. Our work addresses this by
identifying a broad spectrum of contrast misalignments, such as replacing
entities, actions, and flipping event order, which alignment models should be
robust against. To this end, we introduce the VideoCon, a video-language
alignment dataset constructed by a large language model that generates
plausible contrast video captions and explanations for differences between
original and contrast video captions. Then, a generative video-language model
is finetuned with VideoCon to assess video-language entailment and generate
explanations. Our VideoCon-based alignment model significantly outperforms
current models. It exhibits a 12-point increase in AUC for the video-language
alignment task on human-generated contrast captions. Finally, our model sets
new state of the art zero-shot performance in temporally-extensive
video-language tasks such as text-to-video retrieval (SSv2-Temporal) and video
question answering (ATP-Hard). Moreover, our model shows superior performance
on novel videos and human-crafted captions and explanations. Our code and data
are available at https://github.com/Hritikbansal/videocon.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources
Abstract: To address the data scarcity issue in Conversational question answering
(ConvQA), a dialog inpainting method, which utilizes documents to generate
ConvQA datasets, has been proposed. However, the original dialog inpainting
model is trained solely on the dialog reconstruction task, resulting in the
generation of questions with low contextual relevance due to insufficient
learning of question-answer alignment. To overcome this limitation, we propose
a novel framework called Dialogizer, which has the capability to automatically
generate ConvQA datasets with high contextual relevance from textual sources.
The framework incorporates two training tasks: question-answer matching (QAM)
and topic-aware dialog generation (TDG). Moreover, re-ranking is conducted
during the inference phase based on the contextual relevance of the generated
questions. Using our framework, we produce four ConvQA datasets by utilizing
documents from multiple domains as the primary source. Through automatic
evaluation using diverse metrics, as well as human evaluation, we validate that
our proposed framework exhibits the ability to generate datasets of higher
quality compared to the baseline dialog inpainting model.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Generating Pragmatic Examples to Train Neural Program Synthesizers
Abstract: Programming-by-example is the task of synthesizing a program that is
consistent with a set of user-provided input-output examples. As examples are
often an under-specification of one's intent, a good synthesizer must choose
the intended program from the many that are consistent with the given set of
examples. Prior work frames program synthesis as a cooperative game between a
listener (that synthesizes programs) and a speaker (a user choosing examples),
and shows that models of computational pragmatic inference are effective in
choosing the user intended programs. However, these models require
counterfactual reasoning over a large set of programs and examples, which is
infeasible in realistic program spaces. In this paper, we propose a novel way
to amortize this search with neural networks. We sample pairs of programs and
examples via self-play between listener and speaker models, and use pragmatic
inference to choose informative training examples from this sample.We then use
the informative dataset to train models to improve the synthesizer's ability to
disambiguate user-provided examples without human supervision. We validate our
method on the challenging task of synthesizing regular expressions from example
strings, and find that our method (1) outperforms models trained without
choosing pragmatic examples by 23% (a 51% relative increase) (2) matches the
performance of supervised learning on a dataset of pragmatic examples provided
by humans, despite using no human data in training.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: 3DCoMPaT$^{++}$: An improved Large-scale 3D Vision Dataset for Compositional Recognition
Abstract: In this work, we present 3DCoMPaT$^{++}$, a multimodal 2D/3D dataset with 160
million rendered views of more than 10 million stylized 3D shapes carefully
annotated at the part-instance level, alongside matching RGB point clouds, 3D
textured meshes, depth maps, and segmentation masks. 3DCoMPaT$^{++}$ covers 41
shape categories, 275 fine-grained part categories, and 293 fine-grained
material classes that can be compositionally applied to parts of 3D objects. We
render a subset of one million stylized shapes from four equally spaced views
as well as four randomized views, leading to a total of 160 million renderings.
Parts are segmented at the instance level, with coarse-grained and fine-grained
semantic levels. We introduce a new task, called Grounded CoMPaT Recognition
(GCR), to collectively recognize and ground compositions of materials on parts
of 3D objects. Additionally, we report the outcomes of a data challenge
organized at CVPR2023, showcasing the winning method's utilization of a
modified PointNet$^{++}$ model trained on 6D inputs, and exploring alternative
techniques for GCR enhancement. We hope our work will help ease future research
on compositional 3D Vision.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: DiffDub: Person-generic Visual Dubbing Using Inpainting Renderer with Diffusion Auto-encoder
Abstract: Generating high-quality and person-generic visual dubbing remains a
challenge. Recent innovation has seen the advent of a two-stage paradigm,
decoupling the rendering and lip synchronization process facilitated by
intermediate representation as a conduit. Still, previous methodologies rely on
rough landmarks or are confined to a single speaker, thus limiting their
performance. In this paper, we propose DiffDub: Diffusion-based dubbing. We
first craft the Diffusion auto-encoder by an inpainting renderer incorporating
a mask to delineate editable zones and unaltered regions. This allows for
seamless filling of the lower-face region while preserving the remaining parts.
Throughout our experiments, we encountered several challenges. Primarily, the
semantic encoder lacks robustness, constricting its ability to capture
high-level features. Besides, the modeling ignored facial positioning, causing
mouth or nose jitters across frames. To tackle these issues, we employ
versatile strategies, including data augmentation and supplementary eye
guidance. Moreover, we encapsulated a conformer-based reference encoder and
motion generator fortified by a cross-attention mechanism. This enables our
model to learn person-specific textures with varying references and reduces
reliance on paired audio-visual data. Our rigorous experiments comprehensively
highlight that our ground-breaking approach outpaces existing methods with
considerable margins and delivers seamless, intelligible videos in
person-generic and multilingual scenarios.

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: A Unified Sampling Framework for Solver Searching of Diffusion Probabilistic Models
Abstract: Recent years have witnessed the rapid progress and broad application of
diffusion probabilistic models (DPMs). Sampling from DPMs can be viewed as
solving an ordinary differential equation (ODE). Despite the promising
performance, the generation of DPMs usually consumes much time due to the large
number of function evaluations (NFE). Though recent works have accelerated the
sampling to around 20 steps with high-order solvers, the sample quality with
less than 10 NFE can still be improved. In this paper, we propose a unified
sampling framework (USF) to study the optional strategies for solver. Under
this framework, we further reveal that taking different solving strategies at
different timesteps may help further decrease the truncation error, and a
carefully designed \emph{solver schedule} has the potential to improve the
sample quality by a large margin. Therefore, we propose a new sampling
framework based on the exponential integral formulation that allows free
choices of solver strategy at each step and design specific decisions for the
framework. Moreover, we propose $S^3$, a predictor-based search method that
automatically optimizes the solver schedule to get a better time-quality
trade-off of sampling. We demonstrate that $S^3$ can find outstanding solver
schedules which outperform the state-of-the-art sampling methods on CIFAR-10,
CelebA, ImageNet, and LSUN-Bedroom datasets. Specifically, we achieve 2.69 FID
with 10 NFE and 6.86 FID with 5 NFE on CIFAR-10 dataset, outperforming the SOTA
method significantly. We further apply $S^3$ to Stable-Diffusion model and get
an acceleration ratio of 2$\times$, showing the feasibility of sampling in very
few steps without retraining the neural network.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: An AI-Guided Data Centric Strategy to Detect and Mitigate Biases in Healthcare Datasets
Abstract: The adoption of diagnosis and prognostic algorithms in healthcare has led to
concerns about the perpetuation of bias against disadvantaged groups of
individuals. Deep learning methods to detect and mitigate bias have revolved
around modifying models, optimization strategies, and threshold calibration
with varying levels of success. Here, we generate a data-centric,
model-agnostic, task-agnostic approach to evaluate dataset bias by
investigating the relationship between how easily different groups are learned
at small sample sizes (AEquity). We then apply a systematic analysis of AEq
values across subpopulations to identify and mitigate manifestations of racial
bias in two known cases in healthcare - Chest X-rays diagnosis with deep
convolutional neural networks and healthcare utilization prediction with
multivariate logistic regression. AEq is a novel and broadly applicable metric
that can be applied to advance equity by diagnosing and remediating bias in
healthcare datasets.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Expressive Modeling Is Insufficient for Offline RL: A Tractable Inference Perspective
Abstract: A popular paradigm for offline Reinforcement Learning (RL) tasks is to first
fit the offline trajectories to a sequence model, and then prompt the model for
actions that lead to high expected return. While a common consensus is that
more expressive sequence models imply better performance, this paper highlights
that tractability, the ability to exactly and efficiently answer various
probabilistic queries, plays an equally important role. Specifically, due to
the fundamental stochasticity from the offline data-collection policies and the
environment dynamics, highly non-trivial conditional/constrained generation is
required to elicit rewarding actions. While it is still possible to approximate
such queries, we observe that such crude estimates significantly undermine the
benefits brought by expressive sequence models. To overcome this problem, this
paper proposes Trifle (Tractable Inference for Offline RL), which leverages
modern Tractable Probabilistic Models (TPMs) to bridge the gap between good
sequence models and high expected returns at evaluation time. Empirically,
Trifle achieves the most state-of-the-art scores in 9 Gym-MuJoCo benchmarks
against strong baselines. Further, owing to its tractability, Trifle
significantly outperforms prior approaches in stochastic environments and safe
RL tasks (e.g. with action constraints) with minimum algorithmic modifications.

Output: Machine Learning


Instruction: What field is the article from?

Prompt:
Title: Safe Reinforcement Learning in a Simulated Robotic Arm
Abstract: Reinforcement learning (RL) agents need to explore their environments in
order to learn optimal policies. In many environments and tasks, safety is of
critical importance. The widespread use of simulators offers a number of
advantages, including safe exploration which will be inevitable in cases when
RL systems need to be trained directly in the physical environment (e.g. in
human-robot interaction). The popular Safety Gym library offers three mobile
agent types that can learn goal-directed tasks while considering various safety
constraints. In this paper, we extend the applicability of safe RL algorithms
by creating a customized environment with Panda robotic arm where Safety Gym
algorithms can be tested. We performed pilot experiments with the popular PPO
algorithm comparing the baseline with the constrained version and show that the
constrained version is able to learn the equally good policy while better
complying with safety constraints and taking longer training time as expected.

Output: Robotics


Instruction: What field is the article from?

Prompt:
Title: BED: Bi-Encoder-Decoder Model for Canonical Relation Extraction
Abstract: Canonical relation extraction aims to extract relational triples from
sentences, where the triple elements (entity pairs and their relationship) are
mapped to the knowledge base. Recently, methods based on the encoder-decoder
architecture are proposed and achieve promising results. However, these methods
cannot well utilize the entity information, which is merely used as augmented
training data. Moreover, they are incapable of representing novel entities,
since no embeddings have been learned for them. In this paper, we propose a
novel framework, Bi-Encoder-Decoder (BED), to solve the above issues.
Specifically, to fully utilize entity information, we employ an encoder to
encode semantics of this information, leading to high-quality entity
representations. For novel entities, given a trained entity encoder, their
representations can be easily generated. Experimental results on two datasets
show that, our method achieves a significant performance improvement over the
previous state-of-the-art and handle novel entities well without retraining.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Sparse Beats Dense: Rethinking Supervision in Radar-Camera Depth Completion
Abstract: It is widely believed that the dense supervision is better than the sparse
supervision in the field of depth completion, but the underlying reasons for
this are rarely discussed. In this paper, we find that the challenge of using
sparse supervision for training Radar-Camera depth prediction models is the
Projection Transformation Collapse (PTC). The PTC implies that sparse
supervision leads the model to learn unexpected collapsed projection
transformations between Image/Radar/LiDAR spaces. Building on this insight, we
propose a novel ``Disruption-Compensation" framework to handle the PTC, thereby
relighting the use of sparse supervision in depth completion tasks. The
disruption part deliberately discards position correspondences among
Image/Radar/LiDAR, while the compensation part leverages 3D spatial and 2D
semantic information to compensate for the discarded beneficial position
correspondence. Extensive experimental results demonstrate that our framework
(sparse supervision) outperforms the state-of-the-art (dense supervision) with
11.6$\%$ improvement in mean absolute error and $1.6 \times$ speedup. The code
is available at ...

Output: Computer Vision


Instruction: What field is the article from?

Prompt:
Title: Predicting recovery following stroke: deep learning, multimodal data and feature selection using explainable AI
Abstract: Machine learning offers great potential for automated prediction of
post-stroke symptoms and their response to rehabilitation. Major challenges for
this endeavour include the very high dimensionality of neuroimaging data, the
relatively small size of the datasets available for learning, and how to
effectively combine neuroimaging and tabular data (e.g. demographic information
and clinical characteristics). This paper evaluates several solutions based on
two strategies. The first is to use 2D images that summarise MRI scans. The
second is to select key features that improve classification accuracy.
Additionally, we introduce the novel approach of training a convolutional
neural network (CNN) on images that combine regions-of-interest extracted from
MRIs, with symbolic representations of tabular data. We evaluate a series of
CNN architectures (both 2D and a 3D) that are trained on different
representations of MRI and tabular data, to predict whether a composite measure
of post-stroke spoken picture description ability is in the aphasic or
non-aphasic range. MRI and tabular data were acquired from 758 English speaking
stroke survivors who participated in the PLORAS study. The classification
accuracy for a baseline logistic regression was 0.678 for lesion size alone,
rising to 0.757 and 0.813 when initial symptom severity and recovery time were
successively added. The highest classification accuracy 0.854 was observed when
8 regions-of-interest was extracted from each MRI scan and combined with lesion
size, initial severity and recovery time in a 2D Residual Neural Network.Our
findings demonstrate how imaging and tabular data can be combined for high
post-stroke classification accuracy, even when the dataset is small in machine
learning terms. We conclude by proposing how the current models could be
improved to achieve even higher levels of accuracy using images from hospital
scanners.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Language-assisted Vision Model Debugger: A Sample-Free Approach to Finding Bugs
Abstract: Vision models with high overall accuracy often exhibit systematic errors in
specific scenarios, posing potential serious safety concerns. Diagnosing bugs
of vision models is gaining increased attention, however traditional diagnostic
approaches require annotation efforts (\eg rich metadata accompanying each
samples of CelebA). To address this issue,We propose a language-assisted
diagnostic method that uses texts instead of images to diagnose bugs in vision
models based on multi-modal models (\eg CLIP). Our approach connects the
embedding space of CLIP with the buggy vision model to be diagnosed; meanwhile,
utilizing a shared classifier and the cross-modal transferability of embedding
space from CLIP, the text-branch of CLIP become a proxy model to find bugs in
the buggy model. The proxy model can classify texts paired with images. During
the diagnosis, a Large Language Model (LLM) is employed to obtain task-relevant
corpora, and this corpora is used to extract keywords. Descriptions constructed
with templates containing these keywords serve as input text to probe errors in
the proxy model. Finally, we validate the ability to diagnose existing visual
models using language on the Waterbirds and CelebA datasets, we can identify
bugs comprehensible to human experts, uncovering not only known bugs but also
previously unknown ones.

Output: Artificial Intelligence


Instruction: What field is the article from?

Prompt:
Title: Prompt Cache: Modular Attention Reuse for Low-Latency Inference
Abstract: We present Prompt Cache, an approach for accelerating inference for large
language models (LLM) by reusing attention states across different LLM prompts.
Many input prompts have overlapping text segments, such as system messages,
prompt templates, and documents provided for context. Our key insight is that
by precomputing and storing the attention states of these frequently occurring
text segments on the inference server, we can efficiently reuse them when these
segments appear in user prompts. Prompt Cache employs a schema to explicitly
define such reusable text segments, called prompt modules. The schema ensures
positional accuracy during attention state reuse and provides users with an
interface to access cached states in their prompt. Using a prototype
implementation, we evaluate Prompt Cache across several LLMs. We show that
Prompt Cache significantly reduce latency in time-to-first-token, especially
for longer prompts such as document-based question answering and
recommendations. The improvements range from 8x for GPU-based inference to 60x
for CPU-based inference, all while maintaining output accuracy and without the
need for model parameter modifications.

Output: Computational Linguistics


Instruction: What field is the article from?

Prompt:
Title: Locating Cross-Task Sequence Continuation Circuits in Transformers
Abstract: While transformer models exhibit strong capabilities on linguistic tasks,
their complex architectures make them difficult to interpret. Recent work has
aimed to reverse engineer transformer models into human-readable
representations called circuits that implement algorithmic functions. We extend
this research by analyzing and comparing circuits for similar sequence
continuation tasks, which include increasing sequences of digits, number words,
and months. Through the application of circuit analysis techniques, we identify
key sub-circuits responsible for detecting sequence members and for predicting
the next member in a sequence. Our analysis reveals that semantically related
sequences rely on shared circuit subgraphs with analogous roles. Overall,
documenting shared computational structures enables better prediction of model
behaviors, identification of errors, and safer editing procedures. This
mechanistic understanding of transformers is a critical step towards building
more robust, aligned, and interpretable language models.

Output: Computational Linguistics