Instruction: What field is the article from?

Input:
Title: COOL: A Constraint Object-Oriented Logic Programming Language and its Neural-Symbolic Compilation System
Abstract: This paper explores the integration of neural networks with logic
programming, addressing the longstanding challenges of combining the
generalization and learning capabilities of neural networks with the precision
of symbolic logic. Traditional attempts at this integration have been hampered
by difficulties in initial data acquisition, the reliability of undertrained
networks, and the complexity of reusing and augmenting trained models. To
overcome these issues, we introduce the COOL (Constraint Object-Oriented Logic)
programming language, an innovative approach that seamlessly combines logical
reasoning with neural network technologies. COOL is engineered to autonomously
handle data collection, mitigating the need for user-supplied initial data. It
incorporates user prompts into the coding process to reduce the risks of
undertraining and enhances the interaction among models throughout their
lifecycle to promote the reuse and augmentation of networks. Furthermore, the
foundational principles and algorithms in COOL's design and its compilation
system could provide valuable insights for future developments in programming
languages and neural network architectures.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Self Generated Wargame AI: Double Layer Agent Task Planning Based on Large Language Model
Abstract: The large language models represented by ChatGPT have a disruptive impact on
the field of artificial intelligence. But it mainly focuses on natural language
processing, speech recognition, machine learning and natural language
understanding. This paper innovatively applies the large language model to the
field of intelligent decision-making, places the large language model in the
decision-making center, and constructs an agent architecture with the large
language model as the core. Based on this, it further proposes a two-layer
agent task planning, issues and executes decision commands through the
interaction of natural language, and carries out simulation verification
through the wargame simulation environment. Through the game confrontation
simulation experiment, it is found that the intelligent decision-making ability
of the large language model is significantly stronger than the commonly used
reinforcement learning AI and rule AI, and the intelligence, understandability
and generalization are all better. And through experiments, it was found that
the intelligence of the large language model is closely related to prompt. This
work also extends the large language model from previous human-computer
interaction to the field of intelligent decision-making, which has important
reference value and significance for the development of intelligent
decision-making.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Bi-directional Adapter for Multi-modal Tracking
Abstract: Due to the rapid development of computer vision, single-modal (RGB) object
tracking has made significant progress in recent years. Considering the
limitation of single imaging sensor, multi-modal images (RGB, Infrared, etc.)
are introduced to compensate for this deficiency for all-weather object
tracking in complex environments. However, as acquiring sufficient multi-modal
tracking data is hard while the dominant modality changes with the open
environment, most existing techniques fail to extract multi-modal complementary
information dynamically, yielding unsatisfactory tracking performance. To
handle this problem, we propose a novel multi-modal visual prompt tracking
model based on a universal bi-directional adapter, cross-prompting multiple
modalities mutually. Our model consists of a universal bi-directional adapter
and multiple modality-specific transformer encoder branches with sharing
parameters. The encoders extract features of each modality separately by using
a frozen pre-trained foundation model. We develop a simple but effective light
feature adapter to transfer modality-specific information from one modality to
another, performing visual feature prompt fusion in an adaptive manner. With
adding fewer (0.32M) trainable parameters, our model achieves superior tracking
performance in comparison with both the full fine-tuning methods and the prompt
learning-based methods. Our code is available:
https://github.com/SparkTempest/BAT.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Joint-Individual Fusion Structure with Fusion Attention Module for Multi-Modal Skin Cancer Classification
Abstract: Most convolutional neural network (CNN) based methods for skin cancer
classification obtain their results using only dermatological images. Although
good classification results have been shown, more accurate results can be
achieved by considering the patient's metadata, which is valuable clinical
information for dermatologists. Current methods only use the simple joint
fusion structure (FS) and fusion modules (FMs) for the multi-modal
classification methods, there still is room to increase the accuracy by
exploring more advanced FS and FM. Therefore, in this paper, we design a new
fusion method that combines dermatological images (dermoscopy images or
clinical images) and patient metadata for skin cancer classification from the
perspectives of FS and FM. First, we propose a joint-individual fusion (JIF)
structure that learns the shared features of multi-modality data and preserves
specific features simultaneously. Second, we introduce a fusion attention (FA)
module that enhances the most relevant image and metadata features based on
both the self and mutual attention mechanism to support the decision-making
pipeline. We compare the proposed JIF-MMFA method with other state-of-the-art
fusion methods on three different public datasets. The results show that our
JIF-MMFA method improves the classification results for all tested CNN
backbones and performs better than the other fusion methods on the three public
datasets, demonstrating our method's effectiveness and robustness

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Setting the Trap: Capturing and Defeating Backdoors in Pretrained Language Models through Honeypots
Abstract: In the field of natural language processing, the prevalent approach involves
fine-tuning pretrained language models (PLMs) using local samples. Recent
research has exposed the susceptibility of PLMs to backdoor attacks, wherein
the adversaries can embed malicious prediction behaviors by manipulating a few
training samples. In this study, our objective is to develop a
backdoor-resistant tuning procedure that yields a backdoor-free model, no
matter whether the fine-tuning dataset contains poisoned samples. To this end,
we propose and integrate a honeypot module into the original PLM, specifically
designed to absorb backdoor information exclusively. Our design is motivated by
the observation that lower-layer representations in PLMs carry sufficient
backdoor features while carrying minimal information about the original tasks.
Consequently, we can impose penalties on the information acquired by the
honeypot module to inhibit backdoor creation during the fine-tuning process of
the stem network. Comprehensive experiments conducted on benchmark datasets
substantiate the effectiveness and robustness of our defensive strategy.
Notably, these results indicate a substantial reduction in the attack success
rate ranging from 10\% to 40\% when compared to prior state-of-the-art methods.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt
Abstract: In this paper, we present Neural k-Opt (NeuOpt), a novel learning-to-search
(L2S) solver for routing problems. It learns to perform flexible k-opt
exchanges based on a tailored action factorization method and a customized
recurrent dual-stream decoder. As a pioneering work to circumvent the pure
feasibility masking scheme and enable the autonomous exploration of both
feasible and infeasible regions, we then propose the Guided Infeasible Region
Exploration (GIRE) scheme, which supplements the NeuOpt policy network with
feasibility-related features and leverages reward shaping to steer
reinforcement learning more effectively. Additionally, we equip NeuOpt with
Dynamic Data Augmentation (D2A) for more diverse searches during inference.
Extensive experiments on the Traveling Salesman Problem (TSP) and Capacitated
Vehicle Routing Problem (CVRP) demonstrate that our NeuOpt not only
significantly outstrips existing (masking-based) L2S solvers, but also
showcases superiority over the learning-to-construct (L2C) and
learning-to-predict (L2P) solvers. Notably, we offer fresh perspectives on how
neural solvers can handle VRP constraints. Our code is available:
https://github.com/yining043/NeuOpt.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Adversarial Estimation of Topological Dimension with Harmonic Score Maps
Abstract: Quantification of the number of variables needed to locally explain complex
data is often the first step to better understanding it. Existing techniques
from intrinsic dimension estimation leverage statistical models to glean this
information from samples within a neighborhood. However, existing methods often
rely on well-picked hyperparameters and ample data as manifold dimension and
curvature increases. Leveraging insight into the fixed point of the score
matching objective as the score map is regularized by its Dirichlet energy, we
show that it is possible to retrieve the topological dimension of the manifold
learned by the score map. We then introduce a novel method to measure the
learned manifold's topological dimension (i.e., local intrinsic dimension)
using adversarial attacks, thereby generating useful interpretations of the
learned manifold.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: A Novel Dataset for Financial Education Text Simplification in Spanish
Abstract: Text simplification, crucial in natural language processing, aims to make
texts more comprehensible, particularly for specific groups like visually
impaired Spanish speakers, a less-represented language in this field. In
Spanish, there are few datasets that can be used to create text simplification
systems. Our research has the primary objective to develop a Spanish financial
text simplification dataset. We created a dataset with 5,314 complex and
simplified sentence pairs using established simplification rules. We also
compared our dataset with the simplifications generated from GPT-3, Tuner, and
MT5, in order to evaluate the feasibility of data augmentation using these
systems. In this manuscript we present the characteristics of our dataset and
the findings of the comparisons with other systems. The dataset is available at
Hugging face, saul1917/FEINA.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Eval-GCSC: A New Metric for Evaluating ChatGPT's Performance in Chinese Spelling Correction
Abstract: ChatGPT has demonstrated impressive performance in various downstream tasks.
However, in the Chinese Spelling Correction (CSC) task, we observe a
discrepancy: while ChatGPT performs well under human evaluation, it scores
poorly according to traditional metrics. We believe this inconsistency arises
because the traditional metrics are not well-suited for evaluating generative
models. Their overly strict length and phonics constraints may lead to
underestimating ChatGPT's correction capabilities. To better evaluate
generative models in the CSC task, this paper proposes a new evaluation metric:
Eval-GCSC. By incorporating word-level and semantic similarity judgments, it
relaxes the stringent length and phonics constraints. Experimental results show
that Eval-GCSC closely aligns with human evaluations. Under this metric,
ChatGPT's performance is comparable to traditional token-level classification
models (TCM), demonstrating its potential as a CSC tool. The source code and
scripts can be accessed at https://github.com/ktlKTL/Eval-GCSC.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Auditing and Mitigating Cultural Bias in LLMs
Abstract: Culture fundamentally shapes people's reasoning, behavior, and communication.
Generative artificial intelligence (AI) technologies may cause a shift towards
a dominant culture. As people increasingly use AI to expedite and even automate
various professional and personal tasks, cultural values embedded in AI models
may bias authentic expression. We audit large language models for cultural
bias, comparing their responses to nationally representative survey data, and
evaluate country-specific prompting as a mitigation strategy. We find that
GPT-4, 3.5 and 3 exhibit cultural values resembling English-speaking and
Protestant European countries. Our mitigation strategy reduces cultural bias in
recent models but not for all countries/territories. To avoid cultural bias in
generative AI, especially in high-stakes contexts, we suggest using culture
matching and ongoing cultural audits.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Histopathological Image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalization
Abstract: Histopathological images are essential for medical diagnosis and treatment
planning, but interpreting them accurately using machine learning can be
challenging due to variations in tissue preparation, staining and imaging
protocols. Domain generalization aims to address such limitations by enabling
the learning models to generalize to new datasets or populations. Style
transfer-based data augmentation is an emerging technique that can be used to
improve the generalizability of machine learning models for histopathological
images. However, existing style transfer-based methods can be computationally
expensive, and they rely on artistic styles, which can negatively impact model
accuracy. In this study, we propose a feature domain style mixing technique
that uses adaptive instance normalization to generate style-augmented versions
of images. We compare our proposed method with existing style transfer-based
data augmentation methods and found that it performs similarly or better,
despite requiring less computation and time. Our results demonstrate the
potential of feature domain statistics mixing in the generalization of learning
models for histopathological image analysis.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: OCGEC: One-class Graph Embedding Classification for DNN Backdoor Detection
Abstract: Deep neural networks (DNNs) have been found vulnerable to backdoor attacks,
raising security concerns about their deployment in mission-critical
applications. There are various approaches to detect backdoor attacks, however
they all make certain assumptions about the target attack to be detected and
require equal and huge numbers of clean and backdoor samples for training,
which renders these detection methods quite limiting in real-world
circumstances.
  This study proposes a novel one-class classification framework called
One-class Graph Embedding Classification (OCGEC) that uses GNNs for model-level
backdoor detection with only a little amount of clean data. First, we train
thousands of tiny models as raw datasets from a small number of clean datasets.
Following that, we design a ingenious model-to-graph method for converting the
model's structural details and weight features into graph data. We then
pre-train a generative self-supervised graph autoencoder (GAE) to better learn
the features of benign models in order to detect backdoor models without
knowing the attack strategy. After that, we dynamically combine the GAE and
one-class classifier optimization goals to form classification boundaries that
distinguish backdoor models from benign models.
  Our OCGEC combines the powerful representation capabilities of graph neural
networks with the utility of one-class classification techniques in the field
of anomaly detection. In comparison to other baselines, it achieves AUC scores
of more than 98% on a number of tasks, which far exceeds existing methods for
detection even when they rely on a huge number of positive and negative
samples. Our pioneering application of graphic scenarios for generic backdoor
detection can provide new insights that can be used to improve other backdoor
defense tasks. Code is available at https://github.com/jhy549/OCGEC.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: RLIF: Interactive Imitation Learning as Reinforcement Learning
Abstract: Although reinforcement learning methods offer a powerful framework for
automatic skill acquisition, for practical learning-based control problems in
domains such as robotics, imitation learning often provides a more convenient
and accessible alternative. In particular, an interactive imitation learning
method such as DAgger, which queries a near-optimal expert to intervene online
to collect correction data for addressing the distributional shift challenges
that afflict na\"ive behavioral cloning, can enjoy good performance both in
theory and practice without requiring manually specified reward functions and
other components of full reinforcement learning methods. In this paper, we
explore how off-policy reinforcement learning can enable improved performance
under assumptions that are similar but potentially even more practical than
those of interactive imitation learning. Our proposed method uses reinforcement
learning with user intervention signals themselves as rewards. This relaxes the
assumption that intervening experts in interactive imitation learning should be
near-optimal and enables the algorithm to learn behaviors that improve over the
potential suboptimal human expert. We also provide a unified framework to
analyze our RL method and DAgger; for which we present the asymptotic analysis
of the suboptimal gap for both methods as well as the non-asymptotic sample
complexity bound of our method. We then evaluate our method on challenging
high-dimensional continuous control simulation benchmarks as well as real-world
robotic vision-based manipulation tasks. The results show that it strongly
outperforms DAgger-like approaches across the different tasks, especially when
the intervening experts are suboptimal. Code and videos can be found on the
project website: rlif-page.github.io

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: KEEC: Embed to Control on An Equivariant Geometry
Abstract: This paper investigates how representation learning can enable optimal
control in unknown and complex dynamics, such as chaotic and non-linear
systems, without relying on prior domain knowledge of the dynamics. The core
idea is to establish an equivariant geometry that is diffeomorphic to the
manifold defined by a dynamical system and to perform optimal control within
this corresponding geometry, which is a non-trivial task. To address this
challenge, Koopman Embed to Equivariant Control (KEEC) is proposed for model
learning and control. Inspired by Lie theory, KEEC begins by learning a
non-linear dynamical system defined on a manifold and embedding trajectories
into a Lie group. Subsequently, KEEC formulates an equivariant value function
equation in reinforcement learning on the equivariant geometry, ensuring an
invariant effect as the value function on the original manifold. By deriving
analytical-form optimal actions on the equivariant value function, KEEC
theoretically achieves quadratic convergence for the optimal equivariant value
function by leveraging the differential information on the equivariant
geometry. The effectiveness of KEEC is demonstrated in challenging dynamical
systems, including chaotic ones like Lorenz-63. Notably, our results show that
isometric functions, which maintain the compactness and completeness of
geometry while preserving metric and differential information, consistently
outperform loss functions lacking these characteristics.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Variants of Tagged Sentential Decision Diagrams
Abstract: A recently proposed canonical form of Boolean functions, namely tagged
sentential decision diagrams (TSDDs), exploits both the standard and
zero-suppressed trimming rules. The standard ones minimize the size of
sentential decision diagrams (SDDs) while the zero-suppressed trimming rules
have the same objective as the standard ones but for zero-suppressed sentential
decision diagrams (ZSDDs). The original TSDDs, which we call zero-suppressed
TSDDs (ZTSDDs), firstly fully utilize the zero-suppressed trimming rules, and
then the standard ones. In this paper, we present a variant of TSDDs which we
call standard TSDDs (STSDDs) by reversing the order of trimming rules. We then
prove the canonicity of STSDDs and present the algorithms for binary operations
on TSDDs. In addition, we offer two kinds of implementations of STSDDs and
ZTSDDs and acquire three variations of the original TSDDs. Experimental
evaluations demonstrate that the four versions of TSDDs have the size advantage
over SDDs and ZSDDs.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Hyper-Relational Knowledge Graph Neural Network for Next POI
Abstract: With the advancement of mobile technology, Point of Interest (POI)
recommendation systems in Location-based Social Networks (LBSN) have brought
numerous benefits to both users and companies. Many existing works employ
Knowledge Graph (KG) to alleviate the data sparsity issue in LBSN. These
approaches primarily focus on modeling the pair-wise relations in LBSN to
enrich the semantics and thereby relieve the data sparsity issue. However,
existing approaches seldom consider the hyper-relations in LBSN, such as the
mobility relation (a 3-ary relation: user-POI-time). This makes the model hard
to exploit the semantics accurately. In addition, prior works overlook the rich
structural information inherent in KG, which consists of higher-order relations
and can further alleviate the impact of data sparsity.To this end, we propose a
Hyper-Relational Knowledge Graph Neural Network (HKGNN) model. In HKGNN, a
Hyper-Relational Knowledge Graph (HKG) that models the LBSN data is constructed
to maintain and exploit the rich semantics of hyper-relations. Then we proposed
a Hypergraph Neural Network to utilize the structural information of HKG in a
cohesive way. In addition, a self-attention network is used to leverage
sequential information and make personalized recommendations. Furthermore, side
information, essential in reducing data sparsity by providing background
knowledge of POIs, is not fully utilized in current methods. In light of this,
we extended the current dataset with available side information to further
lessen the impact of data sparsity. Results of experiments on four real-world
LBSN datasets demonstrate the effectiveness of our approach compared to
existing state-of-the-art methods.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Categorizing the Visual Environment and Analyzing the Visual Attention of Dogs
Abstract: Dogs have a unique evolutionary relationship with humans and serve many
important roles e.g. search and rescue, blind assistance, emotional support.
However, few datasets exist to categorize visual features and objects available
to dogs, as well as how dogs direct their visual attention within their
environment. We collect and study a dataset with over 11,698 gazes to
categorize the objects available to be gazed at by 11 dogs in everyday outdoor
environments i.e. a walk around a college campus and urban area. We explore the
availability of these object categories and the visual attention of dogs over
these categories using a head mounted eye tracking apparatus. A small portion
(approx. 600 images or < 20% of total dataset) of the collected data is used to
fine tune a MaskRCNN for the novel image domain to segment objects present in
the scene, enabling further statistical analysis on the visual gaze tendencies
of dogs. The MaskRCNN, with eye tracking apparatus, serves as an end to end
model for automatically classifying the visual fixations of dogs. The fine
tuned MaskRCNN performs far better than chance. There are few individual
differences between the 11 dogs and we observe greater visual fixations on
buses, plants, pavement, and construction equipment. This work takes a step
towards understanding visual behavior of dogs and their interaction with the
physical world.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: ChatGPT-3.5, ChatGPT-4, Google Bard, and Microsoft Bing to Improve Health Literacy and Communication in Pediatric Populations and Beyond
Abstract: Purpose: Enhanced health literacy has been linked to better health outcomes;
however, few interventions have been studied. We investigate whether large
language models (LLMs) can serve as a medium to improve health literacy in
children and other populations.
  Methods: We ran 288 conditions using 26 different prompts through
ChatGPT-3.5, Microsoft Bing, and Google Bard. Given constraints imposed by rate
limits, we tested a subset of 150 conditions through ChatGPT-4. The primary
outcome measurements were the reading grade level (RGL) and word counts of
output.
  Results: Across all models, output for basic prompts such as "Explain" and
"What is (are)" were at, or exceeded, a 10th-grade RGL. When prompts were
specified to explain conditions from the 1st to 12th RGL, we found that LLMs
had varying abilities to tailor responses based on RGL. ChatGPT-3.5 provided
responses that ranged from the 7th-grade to college freshmen RGL while
ChatGPT-4 outputted responses from the 6th-grade to the college-senior RGL.
Microsoft Bing provided responses from the 9th to 11th RGL while Google Bard
provided responses from the 7th to 10th RGL.
  Discussion: ChatGPT-3.5 and ChatGPT-4 did better in achieving lower-grade
level outputs. Meanwhile Bard and Bing tended to consistently produce an RGL
that is at the high school level regardless of prompt. Additionally, Bard's
hesitancy in providing certain outputs indicates a cautious approach towards
health information. LLMs demonstrate promise in enhancing health communication,
but future research should verify the accuracy and effectiveness of such tools
in this context.
  Implications: LLMs face challenges in crafting outputs below a sixth-grade
reading level. However, their capability to modify outputs above this threshold
provides a potential mechanism to improve health literacy and communication in
a pediatric population and beyond.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education
Abstract: Despite the promises of ML in education, its adoption in the classroom has
surfaced numerous issues regarding fairness, accountability, and transparency,
as well as concerns about data privacy and student consent. A root cause of
these issues is the lack of understanding of the complex dynamics of education,
including teacher-student interactions, collaborative learning, and classroom
environment. To overcome these challenges and fully utilize the potential of ML
in education, software practitioners need to work closely with educators and
students to fully understand the context of the data (the backbone of ML
applications) and collaboratively define the ML data specifications. To gain a
deeper understanding of such a collaborative process, we conduct ten co-design
sessions with ML software practitioners, educators, and students. In the
sessions, teachers and students work with ML engineers, UX designers, and legal
practitioners to define dataset characteristics for a given ML application. We
find that stakeholders contextualize data based on their domain and procedural
knowledge, proactively design data requirements to mitigate downstream harms
and data reliability concerns, and exhibit role-based collaborative strategies
and contribution patterns. Further, we find that beyond a seat at the table,
meaningful stakeholder participation in ML requires structured supports:
defined processes for continuous iteration and co-evaluation, shared contextual
data quality standards, and information scaffolds for both technical and
non-technical stakeholders to traverse expertise boundaries.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: Anatomically-aware Uncertainty for Semi-supervised Image Segmentation
Abstract: Semi-supervised learning relaxes the need of large pixel-wise labeled
datasets for image segmentation by leveraging unlabeled data. A prominent way
to exploit unlabeled data is to regularize model predictions. Since the
predictions of unlabeled data can be unreliable, uncertainty-aware schemes are
typically employed to gradually learn from meaningful and reliable predictions.
Uncertainty estimation methods, however, rely on multiple inferences from the
model predictions that must be computed for each training step, which is
computationally expensive. Moreover, these uncertainty maps capture pixel-wise
disparities and do not consider global information. This work proposes a novel
method to estimate segmentation uncertainty by leveraging global information
from the segmentation masks. More precisely, an anatomically-aware
representation is first learnt to model the available segmentation masks. The
learnt representation thereupon maps the prediction of a new segmentation into
an anatomically-plausible segmentation. The deviation from the plausible
segmentation aids in estimating the underlying pixel-level uncertainty in order
to further guide the segmentation network. The proposed method consequently
estimates the uncertainty using a single inference from our representation,
thereby reducing the total computation. We evaluate our method on two publicly
available segmentation datasets of left atria in cardiac MRIs and of multiple
organs in abdominal CTs. Our anatomically-aware method improves the
segmentation accuracy over the state-of-the-art semi-supervised methods in
terms of two commonly used evaluation metrics.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization
Abstract: In this paper, we investigate the long-term memory learning capabilities of
state-space models (SSMs) from the perspective of parameterization. We prove
that state-space models without any reparameterization exhibit a memory
limitation similar to that of traditional RNNs: the target relationships that
can be stably approximated by state-space models must have an exponential
decaying memory. Our analysis identifies this "curse of memory" as a result of
the recurrent weights converging to a stability boundary, suggesting that a
reparameterization technique can be effective. To this end, we introduce a
class of reparameterization techniques for SSMs that effectively lift its
memory limitations. Besides improving approximation capabilities, we further
illustrate that a principled choice of reparameterization scheme can also
enhance optimization stability. We validate our findings using synthetic
datasets and language models.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Large Multimodal Model Compression via Efficient Pruning and Distillation at AntGroup
Abstract: The deployment of Large Multimodal Models (LMMs) within AntGroup has
significantly advanced multimodal tasks in payment, security, and advertising,
notably enhancing advertisement audition tasks in Alipay. However, the
deployment of such sizable models introduces challenges, particularly in
increased latency and carbon emissions, which are antithetical to the ideals of
Green AI. This paper introduces a novel multi-stage compression strategy for
our proprietary LLM, AntGMM. Our methodology pivots on three main aspects:
employing small training sample sizes, addressing multi-level redundancy
through multi-stage pruning, and introducing an advanced distillation loss
design. In our research, we constructed a dataset, the Multimodal Advertisement
Audition Dataset (MAAD), from real-world scenarios within Alipay, and conducted
experiments to validate the reliability of our proposed strategy. Furthermore,
the effectiveness of our strategy is evident in its operational success in
Alipay's real-world multimodal advertisement audition for three months from
September 2023. Notably, our approach achieved a substantial reduction in
latency, decreasing it from 700ms to 90ms, while maintaining online performance
with only a slight performance decrease. Moreover, our compressed model is
estimated to reduce electricity consumption by approximately 75 million kWh
annually compared to the direct deployment of AntGMM, demonstrating our
commitment to green AI initiatives. We will publicly release our code and the
MAAD dataset after some
reviews\footnote{https://github.com/MorinW/AntGMM$\_$Pruning}.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Transfer Learning-based Real-time Handgun Detection
Abstract: Traditional surveillance systems rely on human attention, limiting their
effectiveness. This study employs convolutional neural networks and transfer
learning to develop a real-time computer vision system for automatic handgun
detection. Comprehensive analysis of online handgun detection methods is
conducted, emphasizing reducing false positives and learning time. Transfer
learning is demonstrated as an effective approach. Despite technical
challenges, the proposed system achieves a precision rate of 84.74%,
demonstrating promising performance comparable to related works, enabling
faster learning and accurate automatic handgun detection for enhanced security.
This research advances security measures by reducing human monitoring
dependence, showcasing the potential of transfer learning-based approaches for
efficient and reliable handgun detection.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: CMed-GPT: Prompt Tuning for Entity-Aware Chinese Medical Dialogue Generation
Abstract: Medical dialogue generation relies on natural language generation techniques
to enable online medical consultations. Recently, the widespread adoption of
large-scale models in the field of natural language processing has facilitated
rapid advancements in this technology. Existing medical dialogue models are
mostly based on BERT and pre-trained on English corpora, but there is a lack of
high-performing models on the task of Chinese medical dialogue generation. To
solve the above problem, this paper proposes CMed-GPT, which is the GPT
pre-training language model based on Chinese medical domain text. The model is
available in two versions, namely, base and large, with corresponding
perplexity values of 8.64 and 8.01. Additionally, we incorporate lexical and
entity embeddings into the dialogue text in a uniform manner to meet the
requirements of downstream dialogue generation tasks. By applying both
fine-tuning and p-tuning to CMed-GPT, we lowered the PPL from 8.44 to 7.35.
This study not only confirms the exceptional performance of the CMed-GPT model
in generating Chinese biomedical text but also highlights the advantages of
p-tuning over traditional fine-tuning with prefix prompts. Furthermore, we
validate the significance of incorporating external information in medical
dialogue generation, which enhances the quality of dialogue generation.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Accommodating Missing Modalities in Time-Continuous Multimodal Emotion Recognition
Abstract: Decades of research indicate that emotion recognition is more effective when
drawing information from multiple modalities. But what if some modalities are
sometimes missing? To address this problem, we propose a novel
Transformer-based architecture for recognizing valence and arousal in a
time-continuous manner even with missing input modalities. We use a coupling of
cross-attention and self-attention mechanisms to emphasize relationships
between modalities during time and enhance the learning process on weak salient
inputs. Experimental results on the Ulm-TSST dataset show that our model
exhibits an improvement of the concordance correlation coefficient evaluation
of 37% when predicting arousal values and 30% when predicting valence values,
compared to a late-fusion baseline approach.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Language Models: A Guide for the Perplexed
Abstract: Given the growing importance of AI literacy, we decided to write this
tutorial to help narrow the gap between the discourse among those who study
language models -- the core technology underlying ChatGPT and similar products
-- and those who are intrigued and want to learn more about them. In short, we
believe the perspective of researchers and educators can add some clarity to
the public's understanding of the technologies beyond what's currently
available, which tends to be either extremely technical or promotional material
generated about products by their purveyors.
  Our approach teases apart the concept of a language model from products built
on them, from the behaviors attributed to or desired from those products, and
from claims about similarity to human cognition. As a starting point, we (1)
offer a scientific viewpoint that focuses on questions amenable to study
through experimentation; (2) situate language models as they are today in the
context of the research that led to their development; and (3) describe the
boundaries of what is known about the models at this writing.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Maximal Consistent Subsystems of Max-T Fuzzy Relational Equations
Abstract: In this article, we study the inconsistency of a system of $\max-T$ fuzzy
relational equations of the form $A \Box_{T}^{\max} x = b$, where $T$ is a
t-norm among $\min$, the product or Lukasiewicz's t-norm. For an inconsistent
$\max-T$ system, we directly construct a canonical maximal consistent subsystem
(w.r.t the inclusion order). The main tool used to obtain it is the analytical
formula which compute the Chebyshev distance $\Delta = \inf_{c \in \mathcal{C}}
\Vert b - c \Vert$ associated to the inconsistent $\max-T$ system, where
$\mathcal{C}$ is the set of second members of consistent systems defined with
the same matrix $A$. Based on the same analytical formula, we give, for an
inconsistent $\max-\min$ system, an efficient method to obtain all its
consistent subsystems, and we show how to iteratively get all its maximal
consistent subsystems.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: How Far Can Fairness Constraints Help Recover From Biased Data?
Abstract: Blum & Stangl (2019) propose a data bias model to simulate
under-representation and label bias in underprivileged population. For a
stylized data distribution with i.i.d. label noise, under certain simple
conditions on the bias parameters, they show that fair classification with
equal opportunity constraints even on extremely biased distribution can recover
an optimally accurate and fair classifier on the original distribution.
Although their distribution is stylized, their result is interesting because it
demonstrates that fairness constraints can implicitly rectify data bias and
simultaneously overcome a perceived fairness-accuracy trade-off. In this paper,
we give an alternate proof of their result using threshold-based
characterization of optimal fair classifiers. Moreover, we show that their
conditions on the bias parameters are both necessary and sufficient for their
recovery result. Our technique is arguably more flexible, as it readily extends
to more general distributions, e.g., when the labels in the original
distribution have Massart noise instead of i.i.d. noise. Finally, we prove that
for any data distribution, if the optimally accurate classifier in a hypothesis
class is fair and robust, then it can be recovered through fair classification
on the biased distribution, whenever the bias parameters satisfy certain simple
conditions.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: The devil is in the fine-grained details: Evaluating open-vocabulary object detectors for fine-grained understanding
Abstract: Recent advancements in large vision-language models enabled visual object
detection in open-vocabulary scenarios, where object classes are defined in
free-text formats during inference. In this paper, we aim to probe the
state-of-the-art methods for open-vocabulary object detection to determine to
what extent they understand fine-grained properties of objects and their parts.
To this end, we introduce an evaluation protocol based on dynamic vocabulary
generation to test whether models detect, discern, and assign the correct
fine-grained description to objects in the presence of hard-negative classes.
We contribute with a benchmark suite of increasing difficulty and probing
different properties like color, pattern, and material. We further enhance our
investigation by evaluating several state-of-the-art open-vocabulary object
detectors using the proposed protocol and find that most existing solutions,
which shine in standard open-vocabulary benchmarks, struggle to accurately
capture and distinguish finer object details. We conclude the paper by
highlighting the limitations of current methodologies and exploring promising
research directions to overcome the discovered drawbacks. Data and code are
available at https://github.com/lorebianchi98/FG-OVD.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in Distributed Learning Environments
Abstract: This study presents Weighted Sampled Split Learning (WSSL), an innovative
framework tailored to bolster privacy, robustness, and fairness in distributed
machine learning systems. Unlike traditional approaches, WSSL disperses the
learning process among multiple clients, thereby safeguarding data
confidentiality. Central to WSSL's efficacy is its utilization of weighted
sampling. This approach ensures equitable learning by tactically selecting
influential clients based on their contributions. Our evaluation of WSSL
spanned various client configurations and employed two distinct datasets: Human
Gait Sensor and CIFAR-10. We observed three primary benefits: heightened model
accuracy, enhanced robustness, and maintained fairness across diverse client
compositions. Notably, our distributed frameworks consistently surpassed
centralized counterparts, registering accuracy peaks of 82.63% and 75.51% for
the Human Gait Sensor and CIFAR-10 datasets, respectively. These figures
contrast with the top accuracies of 81.12% and 58.60% achieved by centralized
systems. Collectively, our findings champion WSSL as a potent and scalable
successor to conventional centralized learning, marking it as a pivotal stride
forward in privacy-focused, resilient, and impartial distributed machine
learning.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Long-Horizon Dialogue Understanding for Role Identification in the Game of Avalon with Large Language Models
Abstract: Deception and persuasion play a critical role in long-horizon dialogues
between multiple parties, especially when the interests, goals, and motivations
of the participants are not aligned. Such complex tasks pose challenges for
current Large Language Models (LLM) as deception and persuasion can easily
mislead them, especially in long-horizon multi-party dialogues. To this end, we
explore the game of Avalon: The Resistance, a social deduction game in which
players must determine each other's hidden identities to complete their team's
objective. We introduce an online testbed and a dataset containing 20 carefully
collected and labeled games among human players that exhibit long-horizon
deception in a cooperative-competitive setting. We discuss the capabilities of
LLMs to utilize deceptive long-horizon conversations between six human players
to determine each player's goal and motivation. Particularly, we discuss the
multimodal integration of the chat between the players and the game's state
that grounds the conversation, providing further insights into the true player
identities. We find that even current state-of-the-art LLMs do not reach human
performance, making our dataset a compelling benchmark to investigate the
decision-making and language-processing capabilities of LLMs. Our dataset and
online testbed can be found at our project website:
https://sstepput.github.io/Avalon-NLU/

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: On Training Implicit Meta-Learning With Applications to Inductive Weighing in Consistency Regularization
Abstract: Meta-learning that uses implicit gradient have provided an exciting
alternative to standard techniques which depend on the trajectory of the inner
loop training. Implicit meta-learning (IML), however, require computing
$2^{nd}$ order gradients, particularly the Hessian which is impractical to
compute for modern deep learning models. Various approximations for the Hessian
were proposed but a systematic comparison of their compute cost, stability,
generalization of solution found and estimation accuracy were largely
overlooked. In this study, we start by conducting a systematic comparative
analysis of the various approximation methods and their effect when
incorporated into IML training routines. We establish situations where
catastrophic forgetting is exhibited in IML and explain their cause in terms of
the inability of the approximations to estimate the curvature at convergence
points. Sources of IML training instability are demonstrated and remedied. A
detailed analysis of the effeciency of various inverse Hessian-vector product
approximation methods is also provided. Subsequently, we use the insights
gained to propose and evaluate a novel semi-supervised learning algorithm that
learns to inductively weigh consistency regularization losses. We show how
training a "Confidence Network" to extract domain specific features can learn
to up-weigh useful images and down-weigh out-of-distribution samples. Results
outperform the baseline FixMatch performance.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: AdaptiX -- A Transitional XR Framework for Development and Evaluation of Shared Control Applications in Assistive Robotics
Abstract: With the ongoing efforts to empower people with mobility impairments and the
increase in technological acceptance by the general public, assistive
technologies, such as collaborative robotic arms, are gaining popularity. Yet,
their widespread success is limited by usability issues, specifically the
disparity between user input and software control along the autonomy continuum.
To address this, shared control concepts provide opportunities to combine the
targeted increase of user autonomy with a certain level of computer assistance.
This paper presents the free and open-source AdaptiX XR framework for
developing and evaluating shared control applications in a high-resolution
simulation environment. The initial framework consists of a simulated robotic
arm with an example scenario in Virtual Reality (VR), multiple standard control
interfaces, and a specialized recording/replay system. AdaptiX can easily be
extended for specific research needs, allowing Human-Robot Interaction (HRI)
researchers to rapidly design and test novel interaction methods, intervention
strategies, and multi-modal feedback techniques, without requiring an actual
physical robotic arm during the early phases of ideation, prototyping, and
evaluation. Also, a Robot Operating System (ROS) integration enables the
controlling of a real robotic arm in a PhysicalTwin approach without any
simulation-reality gap. Here, we review the capabilities and limitations of
AdaptiX in detail and present three bodies of research based on the framework.
AdaptiX can be accessed at https://adaptix.robot-research.de.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: Spreeze: High-Throughput Parallel Reinforcement Learning Framework
Abstract: The promotion of large-scale applications of reinforcement learning (RL)
requires efficient training computation. While existing parallel RL frameworks
encompass a variety of RL algorithms and parallelization techniques, the
excessively burdensome communication frameworks hinder the attainment of the
hardware's limit for final throughput and training effects on a single desktop.
In this paper, we propose Spreeze, a lightweight parallel framework for RL that
efficiently utilizes a single desktop hardware resource to approach the
throughput limit. We asynchronously parallelize the experience sampling,
network update, performance evaluation, and visualization operations, and
employ multiple efficient data transmission techniques to transfer various
types of data between processes. The framework can automatically adjust the
parallelization hyperparameters based on the computing ability of the hardware
device in order to perform efficient large-batch updates. Based on the
characteristics of the "Actor-Critic" RL algorithm, our framework uses dual
GPUs to independently update the network of actors and critics in order to
further improve throughput. Simulation results show that our framework can
achieve up to 15,000Hz experience sampling and 370,000Hz network update frame
rate using only a personal desktop computer, which is an order of magnitude
higher than other mainstream parallel RL frameworks, resulting in a 73%
reduction of training time. Our work on fully utilizing the hardware resources
of a single desktop computer is fundamental to enabling efficient large-scale
distributed RL training.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Complexity-Guided Curriculum Learning for Text Graphs
Abstract: Curriculum learning provides a systematic approach to training. It refines
training progressively, tailors training to task requirements, and improves
generalization through exposure to diverse examples. We present a curriculum
learning approach that builds on existing knowledge about text and graph
complexity formalisms for training with text graph data. The core part of our
approach is a novel data scheduler, which employs "spaced repetition" and
complexity formalisms to guide the training process. We demonstrate the
effectiveness of the proposed approach on several text graph tasks and graph
neural network architectures. The proposed model gains more and uses less data;
consistently prefers text over graph complexity indices throughout training,
while the best curricula derived from text and graph complexity indices are
equally effective; and it learns transferable curricula across GNN models and
datasets. In addition, we find that both node-level (local) and graph-level
(global) graph complexity indices, as well as shallow and traditional text
complexity indices play a crucial role in effective curriculum learning.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: ESG Accountability Made Easy: DocQA at Your Service
Abstract: We present Deep Search DocQA. This application enables information extraction
from documents via a question-answering conversational assistant. The system
integrates several technologies from different AI disciplines consisting of
document conversion to machine-readable format (via computer vision), finding
relevant data (via natural language processing), and formulating an eloquent
response (via large language models). Users can explore over 10,000
Environmental, Social, and Governance (ESG) disclosure reports from over 2000
corporations. The Deep Search platform can be accessed at:
https://ds4sd.github.io.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: How ChatGPT is Solving Vulnerability Management Problem
Abstract: Recently, ChatGPT has attracted great attention from the code analysis
domain. Prior works show that ChatGPT has the capabilities of processing
foundational code analysis tasks, such as abstract syntax tree generation,
which indicates the potential of using ChatGPT to comprehend code syntax and
static behaviors. However, it is unclear whether ChatGPT can complete more
complicated real-world vulnerability management tasks, such as the prediction
of security relevance and patch correctness, which require an all-encompassing
understanding of various aspects, including code syntax, program semantics, and
related manual comments.
  In this paper, we explore ChatGPT's capabilities on 6 tasks involving the
complete vulnerability management process with a large-scale dataset containing
78,445 samples. For each task, we compare ChatGPT against SOTA approaches,
investigate the impact of different prompts, and explore the difficulties. The
results suggest promising potential in leveraging ChatGPT to assist
vulnerability management. One notable example is ChatGPT's proficiency in tasks
like generating titles for software bug reports. Furthermore, our findings
reveal the difficulties encountered by ChatGPT and shed light on promising
future directions. For instance, directly providing random demonstration
examples in the prompt cannot consistently guarantee good performance in
vulnerability management. By contrast, leveraging ChatGPT in a self-heuristic
way -- extracting expertise from demonstration examples itself and integrating
the extracted expertise in the prompt is a promising research direction.
Besides, ChatGPT may misunderstand and misuse the information in the prompt.
Consequently, effectively guiding ChatGPT to focus on helpful information
rather than the irrelevant content is still an open problem.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: FP8-BERT: Post-Training Quantization for Transformer
Abstract: Transformer-based models, such as BERT, have been widely applied in a wide
range of natural language processing tasks. However, one inevitable side effect
is that they require massive memory storage and inference cost when deployed in
production. Quantization is one of the popularized ways to alleviate the cost.
However, the previous 8-bit quantization strategy based on INT8 data format
either suffers from the degradation of accuracy in a Post-Training Quantization
(PTQ) fashion or requires an expensive Quantization-Aware Training (QAT)
process. Recently, a new numeric format FP8 (i.e. floating-point of 8-bits) has
been proposed and supported in commercial AI computing platforms such as H100.
In this paper, we empirically validate the effectiveness of FP8 as a way to do
Post-Training Quantization without significant loss of accuracy, with a simple
calibration and format conversion process. We adopt the FP8 standard proposed
by NVIDIA Corp. (2022) in our extensive experiments of BERT variants on GLUE
and SQuAD v1.1 datasets, and show that PTQ with FP8 can significantly improve
the accuracy upon that with INT8, to the extent of the full-precision model.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Examining the Effect of Implementation Factors on Deep Learning Reproducibility
Abstract: Reproducing published deep learning papers to validate their conclusions can
be difficult due to sources of irreproducibility. We investigate the impact
that implementation factors have on the results and how they affect
reproducibility of deep learning studies. Three deep learning experiments were
ran five times each on 13 different hardware environments and four different
software environments. The analysis of the 780 combined results showed that
there was a greater than 6% accuracy range on the same deterministic examples
introduced from hardware or software environment variations alone. To account
for these implementation factors, researchers should run their experiments
multiple times in different hardware and software environments to verify their
conclusions are not affected.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value Extraction
Abstract: Product attribute value extraction is an important task in e-Commerce which
can help several downstream applications such as product search and
recommendation. Most previous models handle this task using sequence labeling
or question answering method which rely on the sequential position information
of values in the product text and are vulnerable to data discrepancy between
training and testing. This limits their generalization ability to real-world
scenario in which each product can have multiple descriptions across various
shopping platforms with different composition of text and style. They also have
limited zero-shot ability to new values. In this paper, we propose a multi-task
learning model with value generation/classification and attribute prediction
called JPAVE to predict values without the necessity of position information of
values in the text. Furthermore, the copy mechanism in value generator and the
value attention module in value classifier help our model address the data
discrepancy issue by only focusing on the relevant part of input text and
ignoring other information which causes the discrepancy issue such as sentence
structure in the text. Besides, two variants of our model are designed for
open-world and closed-world scenarios. In addition, copy mechanism introduced
in the first variant based on value generation can improve its zero-shot
ability for identifying unseen values. Experimental results on a public dataset
demonstrate the superiority of our model compared with strong baselines and its
generalization ability of predicting new values.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: EDA: Evolving and Distinct Anchors for Multimodal Motion Prediction
Abstract: Motion prediction is a crucial task in autonomous driving, and one of its
major challenges lands in the multimodality of future behaviors. Many
successful works have utilized mixture models which require identification of
positive mixture components, and correspondingly fall into two main lines:
prediction-based and anchor-based matching. The prediction clustering
phenomenon in prediction-based matching makes it difficult to pick
representative trajectories for downstream tasks, while the anchor-based
matching suffers from a limited regression capability. In this paper, we
introduce a novel paradigm, named Evolving and Distinct Anchors (EDA), to
define the positive and negative components for multimodal motion prediction
based on mixture models. We enable anchors to evolve and redistribute
themselves under specific scenes for an enlarged regression capacity.
Furthermore, we select distinct anchors before matching them with the ground
truth, which results in impressive scoring performance. Our approach enhances
all metrics compared to the baseline MTR, particularly with a notable relative
reduction of 13.5% in Miss Rate, resulting in state-of-the-art performance on
the Waymo Open Motion Dataset. Code is available at
https://github.com/Longzhong-Lin/EDA.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Modular Blended Attention Network for Video Question Answering
Abstract: In multimodal machine learning tasks, it is due to the complexity of the
assignments that the network structure, in most cases, is assembled in a
sophisticated way. The holistic architecture can be separated into several
logical parts according to the respective ends that the modules are devised to
achieve. As the number of modalities of information representation increases,
constructing ad hoc subnetworks for processing the data from divergent
modalities while mediating the fusion of different information types has become
a cumbersome and expensive problem. In this paper, we present an approach to
facilitate the question with a reusable and composable neural unit; by
connecting the units in series or parallel, the arduous network constructing of
multimodal machine learning tasks will be accomplished in a much
straightforward way. Additionally, through parameter sharing (weights
replication) among the units, the space complexity will be significantly
reduced. We have conducted experiments on three commonly used datasets; our
method achieves impressive performance compared to several video QA baselines.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Investigating Deep-Learning NLP for Automating the Extraction of Oncology Efficacy Endpoints from Scientific Literature
Abstract: Benchmarking drug efficacy is a critical step in clinical trial design and
planning. The challenge is that much of the data on efficacy endpoints is
stored in scientific papers in free text form, so extraction of such data is
currently a largely manual task. Our objective is to automate this task as much
as possible. In this study we have developed and optimised a framework to
extract efficacy endpoints from text in scientific papers, using a machine
learning approach. Our machine learning model predicts 25 classes associated
with efficacy endpoints and leads to high F1 scores (harmonic mean of precision
and recall) of 96.4% on the test set, and 93.9% and 93.7% on two case studies.
These methods were evaluated against - and showed strong agreement with -
subject matter experts and show significant promise in the future of automating
the extraction of clinical endpoints from free text. Clinical information
extraction from text data is currently a laborious manual task which scales
poorly and is prone to human error. Demonstrating the ability to extract
efficacy endpoints automatically shows great promise for accelerating clinical
trial design moving forwards.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Unveiling Empirical Pathologies of Laplace Approximation for Uncertainty Estimation
Abstract: In this paper, we critically evaluate Bayesian methods for uncertainty
estimation in deep learning, focusing on the widely applied Laplace
approximation and its variants. Our findings reveal that the conventional
method of fitting the Hessian matrix negatively impacts out-of-distribution
(OOD) detection efficiency. We propose a different point of view, asserting
that focusing solely on optimizing prior precision can yield more accurate
uncertainty estimates in OOD detection while preserving adequate calibration
metrics. Moreover, we demonstrate that this property is not connected to the
training stage of a model but rather to its intrinsic properties. Through
extensive experimental evaluation, we establish the superiority of our
simplified approach over traditional methods in the out-of-distribution domain.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: One Shot Learning as Instruction Data Prospector for Large Language Models
Abstract: Aligning large language models(LLMs) with human is a critical step in
effectively utilizing their pre-trained capabilities across a wide array of
language tasks. Current instruction tuning practices often rely on expanding
dataset size without a clear strategy for ensuring data quality, which can
inadvertently introduce noise and degrade model performance. To address this
challenge, we introduce Nuggets, a novel and efficient methodology that employs
one shot learning to select high-quality instruction data from expansive
datasets. Nuggets assesses the potential of individual instruction examples to
act as effective one shot examples, thereby identifying those that can
significantly enhance diverse task performance. Nuggets utilizes a scoring
system based on the impact of candidate examples on the perplexity of a diverse
anchor set, facilitating the selection of the most beneficial data for
instruction tuning. Through rigorous testing on two benchmarks, including
MT-Bench and Alpaca-Eval, we demonstrate that instruction tuning with the top
1% of Nuggets-curated examples substantially outperforms conventional methods
that use the full dataset. These findings advocate for a data selection
paradigm that prioritizes quality, offering a more efficient pathway to align
LLMs with humans.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Wide Flat Minimum Watermarking for Robust Ownership Verification of GANs
Abstract: We propose a novel multi-bit box-free watermarking method for the protection
of Intellectual Property Rights (IPR) of GANs with improved robustness against
white-box attacks like fine-tuning, pruning, quantization, and surrogate model
attacks. The watermark is embedded by adding an extra watermarking loss term
during GAN training, ensuring that the images generated by the GAN contain an
invisible watermark that can be retrieved by a pre-trained watermark decoder.
In order to improve the robustness against white-box model-level attacks, we
make sure that the model converges to a wide flat minimum of the watermarking
loss term, in such a way that any modification of the model parameters does not
erase the watermark. To do so, we add random noise vectors to the parameters of
the generator and require that the watermarking loss term is as invariant as
possible with respect to the presence of noise. This procedure forces the
generator to converge to a wide flat minimum of the watermarking loss. The
proposed method is architectureand dataset-agnostic, thus being applicable to
many different generation tasks and models, as well as to CNN-based image
processing architectures. We present the results of extensive experiments
showing that the presence of the watermark has a negligible impact on the
quality of the generated images, and proving the superior robustness of the
watermark against model modification and surrogate model attacks.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Bayesian Neural Networks: A Min-Max Game Framework
Abstract: Bayesian neural networks use random variables to describe the neural networks
rather than deterministic neural networks and are mostly trained by variational
inference which updates the mean and variance at the same time. Here, we
formulate the Bayesian neural networks as a minimax game problem. We do the
experiments on the MNIST data set and the primary result is comparable to the
existing closed-loop transcription neural network. Finally, we reveal the
connections between Bayesian neural networks and closed-loop transcription
neural networks, and show our framework is rather practical, and provide
another view of Bayesian neural networks.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: A Weighted K-Center Algorithm for Data Subset Selection
Abstract: The success of deep learning hinges on enormous data and large models, which
require labor-intensive annotations and heavy computation costs. Subset
selection is a fundamental problem that can play a key role in identifying
smaller portions of the training data, which can then be used to produce
similar models as the ones trained with full data. Two prior methods are shown
to achieve impressive results: (1) margin sampling that focuses on selecting
points with high uncertainty, and (2) core-sets or clustering methods such as
k-center for informative and diverse subsets. We are not aware of any work that
combines these methods in a principled manner. To this end, we develop a novel
and efficient factor 3-approximation algorithm to compute subsets based on the
weighted sum of both k-center and uncertainty sampling objective functions. To
handle large datasets, we show a parallel algorithm to run on multiple machines
with approximation guarantees. The proposed algorithm achieves similar or
better performance compared to other strong baselines on vision datasets such
as CIFAR-10, CIFAR-100, and ImageNet.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Mesh Neural Cellular Automata
Abstract: Modeling and synthesizing textures are essential for enhancing the realism of
virtual environments. Methods that directly synthesize textures in 3D offer
distinct advantages to the UV-mapping-based methods as they can create seamless
textures and align more closely with the ways textures form in nature. We
propose Mesh Neural Cellular Automata (MeshNCA), a method for directly
synthesizing dynamic textures on 3D meshes without requiring any UV maps.
MeshNCA is a generalized type of cellular automata that can operate on a set of
cells arranged on a non-grid structure such as vertices of a 3D mesh. While
only being trained on an Icosphere mesh, MeshNCA shows remarkable
generalization and can synthesize textures on any mesh in real time after the
training. Additionally, it accommodates multi-modal supervision and can be
trained using different targets such as images, text prompts, and motion vector
fields. Moreover, we conceptualize a way of grafting trained MeshNCA instances,
enabling texture interpolation. Our MeshNCA model enables real-time 3D texture
synthesis on meshes and allows several user interactions including texture
density/orientation control, a grafting brush, and motion speed/direction
control. Finally, we implement the forward pass of our MeshNCA model using the
WebGL shading language and showcase our trained models in an online interactive
demo which is accessible on personal computers and smartphones. Our demo and
the high resolution version of this PDF are available at
https://meshnca.github.io/.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: ArTST: Arabic Text and Speech Transformer
Abstract: We present ArTST, a pre-trained Arabic text and speech transformer for
supporting open-source speech technologies for the Arabic language. The model
architecture follows the unified-modal framework, SpeechT5, that was recently
released for English, and is focused on Modern Standard Arabic (MSA), with
plans to extend the model for dialectal and code-switched Arabic in future
editions. We pre-trained the model from scratch on MSA speech and text data,
and fine-tuned it for the following tasks: Automatic Speech Recognition (ASR),
Text-To-Speech synthesis (TTS), and spoken dialect identification. In our
experiments comparing ArTST with SpeechT5, as well as with previously reported
results in these tasks, ArTST performs on a par with or exceeding the current
state-of-the-art in all three tasks. Moreover, we find that our pre-training is
conducive for generalization, which is particularly evident in the low-resource
TTS task. The pre-trained model as well as the fine-tuned ASR and TTS models
are released for research use.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: TimelyGPT: Recurrent Convolutional Transformer for Long Time-series Representation
Abstract: Pre-trained models (PTMs) have gained prominence in Natural Language
Processing and Computer Vision domains. When it comes to time-series PTMs,
their development has been limited. Previous research on time-series
transformers has mainly been devoted to small-scale tasks, yet these models
have not consistently outperformed traditional models. Additionally, the
performance of these transformers on large-scale data remains unexplored. These
findings raise doubts about Transformer's capabilities to scale up and capture
temporal dependencies. In this study, we re-examine time-series transformers
and identify the shortcomings of prior studies. Drawing from these insights, we
then introduce a pioneering architecture called Timely Generative Pre-trained
Transformer (\model). This architecture integrates recurrent attention and
temporal convolution modules to effectively capture global-local temporal
dependencies in long sequences. The relative position embedding with time decay
can effectively deal with trend and periodic patterns from time-series. Our
experiments show that \model~excels in modeling continuously monitored
biosignal as well as irregularly-sampled time-series data commonly observed in
longitudinal electronic health records. This breakthrough suggests a priority
shift in time-series deep learning research, moving from small-scale modeling
from scratch to large-scale pre-training.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Scheming AIs: Will AIs fake alignment during training in order to get power?
Abstract: This report examines whether advanced AIs that perform well in training will
be doing so in order to gain power later -- a behavior I call "scheming" (also
sometimes called "deceptive alignment"). I conclude that scheming is a
disturbingly plausible outcome of using baseline machine learning methods to
train goal-directed AIs sophisticated enough to scheme (my subjective
probability on such an outcome, given these conditions, is roughly 25%). In
particular: if performing well in training is a good strategy for gaining power
(as I think it might well be), then a very wide variety of goals would motivate
scheming -- and hence, good training performance. This makes it plausible that
training might either land on such a goal naturally and then reinforce it, or
actively push a model's motivations towards such a goal as an easy way of
improving performance. What's more, because schemers pretend to be aligned on
tests designed to reveal their motivations, it may be quite difficult to tell
whether this has occurred. However, I also think there are reasons for comfort.
In particular: scheming may not actually be such a good strategy for gaining
power; various selection pressures in training might work against schemer-like
goals (for example, relative to non-schemers, schemers need to engage in extra
instrumental reasoning, which might harm their training performance); and we
may be able to increase such pressures intentionally. The report discusses
these and a wide variety of other considerations in detail, and it suggests an
array of empirical research directions for probing the topic further.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: A Universal Anti-Spoofing Approach for Contactless Fingerprint Biometric Systems
Abstract: With the increasing integration of smartphones into our daily lives,
fingerphotos are becoming a potential contactless authentication method. While
it offers convenience, it is also more vulnerable to spoofing using various
presentation attack instruments (PAI). The contactless fingerprint is an
emerging biometric authentication but has not yet been heavily investigated for
anti-spoofing. While existing anti-spoofing approaches demonstrated fair
results, they have encountered challenges in terms of universality and
scalability to detect any unseen/unknown spoofed samples. To address this
issue, we propose a universal presentation attack detection method for
contactless fingerprints, despite having limited knowledge of presentation
attack samples. We generated synthetic contactless fingerprints using StyleGAN
from live finger photos and integrating them to train a semi-supervised
ResNet-18 model. A novel joint loss function, combining the Arcface and Center
loss, is introduced with a regularization to balance between the two loss
functions and minimize the variations within the live samples while enhancing
the inter-class variations between the deepfake and live samples. We also
conducted a comprehensive comparison of different regularizations' impact on
the joint loss function for presentation attack detection (PAD) and explored
the performance of a modified ResNet-18 architecture with different activation
functions (i.e., leaky ReLU and RelU) in conjunction with Arcface and center
loss. Finally, we evaluate the performance of the model using unseen types of
spoof attacks and live data. Our proposed method achieves a Bona Fide
Classification Error Rate (BPCER) of 0.12\%, an Attack Presentation
Classification Error Rate (APCER) of 0.63\%, and an Average Classification
Error Rate (ACER) of 0.37\%.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Grounding for Artificial Intelligence
Abstract: A core function of intelligence is grounding, which is the process of
connecting the natural language and abstract knowledge to the internal
representation of the real world in an intelligent being, e.g., a human. Human
cognition is grounded in our sensorimotor experiences in the external world and
subjective feelings in our internal world. We use languages to communicate with
each other and the languages are grounded on our shared sensorimotor
experiences and feelings. Without this shard grounding, it is impossible for us
to understand each other because all natural languages are highly abstract and
are only able to describe a tiny portion of what has happened or is happening
in the real world. Although grounding at high or abstract levels has been
studied in different fields and applications, to our knowledge, limited
systematic work at fine-grained levels has been done. With the rapid progress
of large language models (LLMs), it is imperative that we have a sound
understanding of grounding in order to move to the next level of intelligence.
It is also believed that grounding is necessary for Artificial General
Intelligence (AGI). This paper makes an attempt to systematically study this
problem.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Efficient Open-world Reinforcement Learning via Knowledge Distillation and Autonomous Rule Discovery
Abstract: Deep reinforcement learning suffers from catastrophic forgetting and sample
inefficiency making it less applicable to the ever-changing real world.
However, the ability to use previously learned knowledge is essential for AI
agents to quickly adapt to novelties. Often, certain spatial information
observed by the agent in the previous interactions can be leveraged to infer
task-specific rules. Inferred rules can then help the agent to avoid
potentially dangerous situations in the previously unseen states and guide the
learning process increasing agent's novelty adaptation speed. In this work, we
propose a general framework that is applicable to deep reinforcement learning
agents. Our framework provides the agent with an autonomous way to discover the
task-specific rules in the novel environments and self-supervise it's learning.
We provide a rule-driven deep Q-learning agent (RDQ) as one possible
implementation of that framework. We show that RDQ successfully extracts
task-specific rules as it interacts with the world and uses them to drastically
increase its learning efficiency. In our experiments, we show that the RDQ
agent is significantly more resilient to the novelties than the baseline
agents, and is able to detect and adapt to novel situations faster.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things
Abstract: The Internet of Medical Things (IoMT) has dramatically benefited medical
professionals that patients and physicians can access from all regions.
Although the automatic detection and prediction of diseases such as melanoma
and leukemia is still being researched and studied in IoMT, existing approaches
are not able to achieve a high degree of efficiency. Thus, with a new approach
that provides better results, patients would access the adequate treatments
earlier and the death rate would be reduced. Therefore, this paper introduces
an IoMT proposal for medical images classification that may be used anywhere,
i.e. it is an ubiquitous approach. It was design in two stages: first, we
employ a Transfer Learning (TL)-based method for feature extraction, which is
carried out using MobileNetV3; second, we use the Chaos Game Optimization (CGO)
for feature selection, with the aim of excluding unnecessary features and
improving the performance, which is key in IoMT. Our methodology was evaluated
using ISIC-2016, PH2, and Blood-Cell datasets. The experimental results
indicated that the proposed approach obtained an accuracy of 88.39% on
ISIC-2016, 97.52% on PH2, and 88.79% on Blood-cell. Moreover, our approach had
successful performances for the metrics employed compared to other existing
methods.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: An Expectation-Realization Model for Metaphor Detection
Abstract: We propose a metaphor detection architecture that is structured around two
main modules: an expectation component that estimates representations of
literal word expectations given a context, and a realization component that
computes representations of actual word meanings in context. The overall
architecture is trained to learn expectation-realization (ER) patterns that
characterize metaphorical uses of words. When evaluated on three metaphor
datasets for within distribution, out of distribution, and novel metaphor
generalization, the proposed method is shown to obtain results that are
competitive or better than state-of-the art. Further increases in metaphor
detection accuracy are obtained through ensembling of ER models.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection
Abstract: Real-world social events typically exhibit a severe class-imbalance
distribution, which makes the trained detection model encounter a serious
generalization challenge. Most studies solve this problem from the frequency
perspective and emphasize the representation or classifier learning for tail
classes. While in our observation, compared to the rarity of classes, the
calibrated uncertainty estimated from well-trained evidential deep learning
networks better reflects model performance. To this end, we propose a novel
uncertainty-guided class imbalance learning framework - UCL$_{SED}$, and its
variant - UCL-EC$_{SED}$, for imbalanced social event detection tasks. We aim
to improve the overall model performance by enhancing model generalization to
those uncertain classes. Considering performance degradation usually comes from
misclassifying samples as their confusing neighboring classes, we focus on
boundary learning in latent space and classifier learning with high-quality
uncertainty estimation. First, we design a novel uncertainty-guided contrastive
learning loss, namely UCL and its variant - UCL-EC, to manipulate
distinguishable representation distribution for imbalanced data. During
training, they force all classes, especially uncertain ones, to adaptively
adjust a clear separable boundary in the feature space. Second, to obtain more
robust and accurate class uncertainty, we combine the results of multi-view
evidential classifiers via the Dempster-Shafer theory under the supervision of
an additional calibration method. We conduct experiments on three severely
imbalanced social event datasets including Events2012\_100, Events2018\_100,
and CrisisLexT\_7. Our model significantly improves social event representation
and classification tasks in almost all classes, especially those uncertain
ones.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: KirchhoffNet: A Circuit Bridging Message Passing and Continuous-Depth Models
Abstract: In this paper, we exploit a fundamental principle of analog electronic
circuitry, Kirchhoff's current law, to introduce a unique class of neural
network models that we refer to as KirchhoffNet. KirchhoffNet establishes close
connections with message passing neural networks and continuous-depth networks.
We demonstrate that even in the absence of any traditional layers (such as
convolution, pooling, or linear layers), KirchhoffNet attains 98.86% test
accuracy on the MNIST dataset, comparable with state of the art (SOTA) results.
What makes KirchhoffNet more intriguing is its potential in the realm of
hardware. Contemporary deep neural networks are conventionally deployed on
GPUs. In contrast, KirchhoffNet can be physically realized by an analog
electronic circuit. Moreover, we justify that irrespective of the number of
parameters within a KirchhoffNet, its forward calculation can always be
completed within 1/f seconds, with f representing the hardware's clock
frequency. This characteristic introduces a promising technology for
implementing ultra-large-scale neural networks.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees
Abstract: Learning graphical conditional independence structures is an important
machine learning problem and a cornerstone of causal discovery. However, the
accuracy and execution time of learning algorithms generally struggle to scale
to problems with hundreds of highly connected variables -- for instance,
recovering brain networks from fMRI data. We introduce the best order score
search (BOSS) and grow-shrink trees (GSTs) for learning directed acyclic graphs
(DAGs) in this paradigm. BOSS greedily searches over permutations of variables,
using GSTs to construct and score DAGs from permutations. GSTs efficiently
cache scores to eliminate redundant calculations. BOSS achieves
state-of-the-art performance in accuracy and execution time, comparing
favorably to a variety of combinatorial and gradient-based learning algorithms
under a broad range of conditions. To demonstrate its practicality, we apply
BOSS to two sets of resting-state fMRI data: simulated data with
pseudo-empirical noise distributions derived from randomized empirical fMRI
cortical signals and clinical data from 3T fMRI scans processed into cortical
parcels. BOSS is available for use within the TETRAD project which includes
Python and R wrappers.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Muscle volume quantification: guiding transformers with anatomical priors
Abstract: Muscle volume is a useful quantitative biomarker in sports, but also for the
follow-up of degenerative musculo-skelletal diseases. In addition to volume,
other shape biomarkers can be extracted by segmenting the muscles of interest
from medical images. Manual segmentation is still today the gold standard for
such measurements despite being very time-consuming. We propose a method for
automatic segmentation of 18 muscles of the lower limb on 3D Magnetic Resonance
Images to assist such morphometric analysis. By their nature, the tissue of
different muscles is undistinguishable when observed in MR Images. Thus, muscle
segmentation algorithms cannot rely on appearance but only on contour cues.
However, such contours are hard to detect and their thickness varies across
subjects. To cope with the above challenges, we propose a segmentation approach
based on a hybrid architecture, combining convolutional and visual transformer
blocks. We investigate for the first time the behaviour of such hybrid
architectures in the context of muscle segmentation for shape analysis.
Considering the consistent anatomical muscle configuration, we rely on
transformer blocks to capture the longrange relations between the muscles. To
further exploit the anatomical priors, a second contribution of this work
consists in adding a regularisation loss based on an adjacency matrix of
plausible muscle neighbourhoods estimated from the training data. Our
experimental results on a unique database of elite athletes show it is possible
to train complex hybrid models from a relatively small database of large
volumes, while the anatomical prior regularisation favours better predictions.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Gene-MOE: A sparsely gated prognosis and classification framework exploiting pan-cancer genomic information
Abstract: Benefiting from the advancements in deep learning, various genomic analytical
techniques, such as survival analysis, classification of tumors and their
subtypes, and exploration of specific pathways, have significantly enhanced our
understanding of the biological mechanisms driving cancer. However, the
overfitting issue, arising from the limited number of patient samples, poses a
challenge in improving the accuracy of genome analysis by deepening the neural
network. Furthermore, it remains uncertain whether novel approaches such as the
sparsely gated mixture of expert (MOE) and self-attention mechanisms can
improve the accuracy of genomic analysis. In this paper, we introduce a novel
sparsely gated RNA-seq analysis framework called Gene-MOE. This framework
exploits the potential of the MOE layers and the proposed mixture of attention
expert (MOAE) layers to enhance the analysis accuracy. Additionally, it
addresses overfitting challenges by integrating pan-cancer information from 33
distinct cancer types through pre-training.We pre-trained Gene-MOE on TCGA
pan-cancer RNA-seq dataset with 33 cancer types. Subsequently, we conducted
experiments involving cancer classification and survival analysis based on the
pre-trained Gene-MOE. According to the survival analysis results on 14 cancer
types, Gene-MOE outperformed state-of-the-art models on 12 cancer types.
Through detailed feature analysis, we found that the Gene-MOE model could learn
rich feature representations of high-dimensional genes. According to the
classification results, the total accuracy of the classification model for 33
cancer classifications reached 95.8%, representing the best performance
compared to state-of-the-art models. These results indicate that Gene-MOE holds
strong potential for use in cancer classification and survival analysis.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models
Abstract: To mitigate the potential misuse of large language models (LLMs), recent
research has developed watermarking algorithms, which restrict the generation
process to leave an invisible trace for watermark detection. Due to the
two-stage nature of the task, most studies evaluate the generation and
detection separately, thereby presenting a challenge in unbiased, thorough, and
applicable evaluations. In this paper, we introduce WaterBench, the first
comprehensive benchmark for LLM watermarks, in which we design three crucial
factors: (1) For \textbf{benchmarking procedure}, to ensure an apples-to-apples
comparison, we first adjust each watermarking method's hyper-parameter to reach
the same watermarking strength, then jointly evaluate their generation and
detection performance. (2) For \textbf{task selection}, we diversify the input
and output length to form a five-category taxonomy, covering $9$ tasks. (3) For
\textbf{evaluation metric}, we adopt the GPT4-Judge for automatically
evaluating the decline of instruction-following abilities after watermarking.
We evaluate $4$ open-source watermarks on $2$ LLMs under $2$ watermarking
strengths and observe the common struggles for current methods on maintaining
the generation quality. The code and data are available at
\url{https://github.com/THU-KEG/WaterBench}.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: CoSeR: Bridging Image and Language for Cognitive Super-Resolution
Abstract: Existing super-resolution (SR) models primarily focus on restoring local
texture details, often neglecting the global semantic information within the
scene. This oversight can lead to the omission of crucial semantic details or
the introduction of inaccurate textures during the recovery process. In our
work, we introduce the Cognitive Super-Resolution (CoSeR) framework, empowering
SR models with the capacity to comprehend low-resolution images. We achieve
this by marrying image appearance and language understanding to generate a
cognitive embedding, which not only activates prior information from large
text-to-image diffusion models but also facilitates the generation of
high-quality reference images to optimize the SR process. To further improve
image fidelity, we propose a novel condition injection scheme called
"All-in-Attention", consolidating all conditional information into a single
module. Consequently, our method successfully restores semantically correct and
photorealistic details, demonstrating state-of-the-art performance across
multiple benchmarks. Code: https://github.com/VINHYU/CoSeR

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Mixture of Weak & Strong Experts on Graphs
Abstract: Realistic graphs contain both rich self-features of nodes and informative
structures of neighborhoods, jointly handled by a GNN in the typical setup. We
propose to decouple the two modalities by mixture of weak and strong experts
(Mowst), where the weak expert is a light-weight Multi-layer Perceptron (MLP),
and the strong expert is an off-the-shelf Graph Neural Network (GNN). To adapt
the experts' collaboration to different target nodes, we propose a "confidence"
mechanism based on the dispersion of the weak expert's prediction logits. The
strong expert is conditionally activated when either the node's classification
relies on neighborhood information, or the weak expert has low model quality.
We reveal interesting training dynamics by analyzing the influence of the
confidence function on loss: our training algorithm encourages the
specialization of each expert by effectively generating soft splitting of the
graph. In addition, our "confidence" design imposes a desirable bias toward the
strong expert to benefit from GNN's better generalization capability. Mowst is
easy to optimize and achieves strong expressive power, with a computation cost
comparable to a single GNN. Empirically, Mowst shows significant accuracy
improvement on 6 standard node classification benchmarks (including both
homophilous and heterophilous graphs).

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Co-training and Co-distillation for Quality Improvement and Compression of Language Models
Abstract: Knowledge Distillation (KD) compresses computationally expensive pre-trained
language models (PLMs) by transferring their knowledge to smaller models,
allowing their use in resource-constrained or real-time settings. However, most
smaller models fail to surpass the performance of the original larger model,
resulting in sacrificing performance to improve inference speed. To address
this issue, we propose Co-Training and Co-Distillation (CTCD), a novel
framework that improves performance and inference speed together by co-training
two models while mutually distilling knowledge. The CTCD framework successfully
achieves this based on two significant findings: 1) Distilling knowledge from
the smaller model to the larger model during co-training improves the
performance of the larger model. 2) The enhanced performance of the larger
model further boosts the performance of the smaller model. The CTCD framework
shows promise as it can be combined with existing techniques like architecture
design or data augmentation, replacing one-way KD methods, to achieve further
performance improvement. Extensive ablation studies demonstrate the
effectiveness of CTCD, and the small model distilled by CTCD outperforms the
original larger model by a significant margin of 1.66 on the GLUE benchmark.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Towards A Holistic Landscape of Situated Theory of Mind in Large Language Models
Abstract: Large Language Models (LLMs) have generated considerable interest and debate
regarding their potential emergence of Theory of Mind (ToM). Several recent
inquiries reveal a lack of robust ToM in these models and pose a pressing
demand to develop new benchmarks, as current ones primarily focus on different
aspects of ToM and are prone to shortcuts and data leakage. In this position
paper, we seek to answer two road-blocking questions: (1) How can we taxonomize
a holistic landscape of machine ToM? (2) What is a more effective evaluation
protocol for machine ToM? Following psychological studies, we taxonomize
machine ToM into 7 mental state categories and delineate existing benchmarks to
identify under-explored aspects of ToM. We argue for a holistic and situated
evaluation of ToM to break ToM into individual components and treat LLMs as an
agent who is physically situated in environments and socially situated in
interactions with humans. Such situated evaluation provides a more
comprehensive assessment of mental states and potentially mitigates the risk of
shortcuts and data leakage. We further present a pilot study in a grid world
setup as a proof of concept. We hope this position paper can facilitate future
research to integrate ToM with LLMs and offer an intuitive means for
researchers to better position their work in the landscape of ToM. Project
page: https://github.com/Mars-tin/awesome-theory-of-mind

Output: Computational Linguistics