Instruction: What field is the article from?

Input:
Title: Machine learning-based malware detection for IoT devices using control-flow data
Abstract: Embedded devices are specialised devices designed for one or only a few
purposes. They are often part of a larger system, through wired or wireless
connection. Those embedded devices that are connected to other computers or
embedded systems through the Internet are called Internet of Things (IoT for
short) devices.
  With their widespread usage and their insufficient protection, these devices
are increasingly becoming the target of malware attacks. Companies often cut
corners to save manufacturing costs or misconfigure when producing these
devices. This can be lack of software updates, ports left open or security
defects by design. Although these devices may not be as powerful as a regular
computer, their large number makes them suitable candidates for botnets. Other
types of IoT devices can even cause health problems since there are even
pacemakers connected to the Internet. This means, that without sufficient
defence, even directed assaults are possible against people.
  The goal of this thesis project is to provide better security for these
devices with the help of machine learning algorithms and reverse engineering
tools. Specifically, I study the applicability of control-flow related data of
executables for malware detection. I present a malware detection method with
two phases. The first phase extracts control-flow related data using static
binary analysis. The second phase classifies binary executables as either
malicious or benign using a neural network model. I train the model using a
dataset of malicious and benign ARM applications.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Simple Weak Coresets for Non-Decomposable Classification Measures
Abstract: While coresets have been growing in terms of their application, barring few
exceptions, they have mostly been limited to unsupervised settings. We consider
supervised classification problems, and non-decomposable evaluation measures in
such settings. We show that stratified uniform sampling based coresets have
excellent empirical performance that are backed by theoretical guarantees too.
We focus on the F1 score and Matthews Correlation Coefficient, two widely used
non-decomposable objective functions that are nontrivial to optimize for and
show that uniform coresets attain a lower bound for coreset size, and have good
empirical performance, comparable with ``smarter'' coreset construction
strategies.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Forecasting Lithium-Ion Battery Longevity with Limited Data Availability: Benchmarking Different Machine Learning Algorithms
Abstract: As the use of Lithium-ion batteries continues to grow, it becomes
increasingly important to be able to predict their remaining useful life. This
work aims to compare the relative performance of different machine learning
algorithms, both traditional machine learning and deep learning, in order to
determine the best-performing algorithms for battery cycle life prediction
based on minimal data. We investigated 14 different machine learning models
that were fed handcrafted features based on statistical data and split into 3
feature groups for testing. For deep learning models, we tested a variety of
neural network models including different configurations of standard Recurrent
Neural Networks, Gated Recurrent Units, and Long Short Term Memory with and
without attention mechanism. Deep learning models were fed multivariate time
series signals based on the raw data for each battery across the first 100
cycles. Our experiments revealed that the machine learning algorithms on
handcrafted features performed particularly well, resulting in 10-20% average
mean absolute percentage error. The best-performing algorithm was the Random
Forest Regressor, which gave a minimum 9.8% mean absolute percentage error.
Traditional machine learning models excelled due to their capability to
comprehend general data set trends. In comparison, deep learning models were
observed to perform particularly poorly on raw, limited data. Algorithms like
GRU and RNNs that focused on capturing medium-range data dependencies were less
adept at recognizing the gradual, slow trends critical for this task. Our
investigation reveals that implementing machine learning models with
hand-crafted features proves to be more effective than advanced deep learning
models for predicting the remaining useful Lithium-ion battery life with
limited data availability.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Contrastive Denoising Score for Text-guided Latent Diffusion Image Editing
Abstract: With the remarkable advent of text-to-image diffusion models, image editing
methods have become more diverse and continue to evolve. A promising recent
approach in this realm is Delta Denoising Score (DDS) - an image editing
technique based on Score Distillation Sampling (SDS) framework that leverages
the rich generative prior of text-to-image diffusion models. However, relying
solely on the difference between scoring functions is insufficient for
preserving specific structural elements from the original image, a crucial
aspect of image editing. Inspired by the similarity and importance differences
between DDS and the contrastive learning for unpaired image-to-image
translation (CUT), here we present an embarrassingly simple yet very powerful
modification of DDS, called Contrastive Denoising Score (CDS), for latent
diffusion models (LDM). Specifically, to enforce structural correspondence
between the input and output while maintaining the controllability of contents,
we introduce a straightforward approach to regulate structural consistency
using CUT loss within the DDS framework. To calculate this loss, instead of
employing auxiliary networks, we utilize the intermediate features of LDM, in
particular, those from the self-attention layers, which possesses rich spatial
information. Our approach enables zero-shot image-to-image translation and
neural radiance field (NeRF) editing, achieving a well-balanced interplay
between maintaining the structural details and transforming content.
Qualitative results and comparisons demonstrates the effectiveness of our
proposed method. Project page with code is available at
https://hyelinnam.github.io/CDS/.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: On Functional Activations in Deep Neural Networks
Abstract: Background: Deep neural networks have proven to be powerful computational
tools for modeling, prediction, and generation. However, the workings of these
models have generally been opaque. Recent work has shown that the performance
of some models are modulated by overlapping functional networks of connections
within the models. Here the techniques of functional neuroimaging are applied
to an exemplary large language model to probe its functional structure.
Methods: A series of block-designed task-based prompt sequences were generated
to probe the Facebook Galactica-125M model. Tasks included prompts relating to
political science, medical imaging, paleontology, archeology, pathology, and
random strings presented in an off/on/off pattern with prompts about other
random topics. For the generation of each output token, all layer output values
were saved to create an effective time series. General linear models were fit
to the data to identify layer output values which were active with the tasks.
Results: Distinct, overlapping networks were identified with each task. Most
overlap was observed between medical imaging and pathology networks. These
networks were repeatable across repeated performance of related tasks, and
correspondence of identified functional networks and activation in tasks not
used to define the functional networks was shown to accurately identify the
presented task. Conclusion: The techniques of functional neuroimaging can be
applied to deep neural networks as a means to probe their workings. Identified
functional networks hold the potential for use in model alignment, modulation
of model output, and identifying weights to target in fine-tuning.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Localizing Lying in Llama: Understanding Instructed Dishonesty on True-False Questions Through Prompting, Probing, and Patching
Abstract: Large language models (LLMs) demonstrate significant knowledge through their
outputs, though it is often unclear whether false outputs are due to a lack of
knowledge or dishonesty. In this paper, we investigate instructed dishonesty,
wherein we explicitly prompt LLaMA-2-70b-chat to lie. We perform prompt
engineering to find which prompts best induce lying behavior, and then use
mechanistic interpretability approaches to localize where in the network this
behavior occurs. Using linear probing and activation patching, we localize five
layers that appear especially important for lying. We then find just 46
attention heads within these layers that enable us to causally intervene such
that the lying model instead answers honestly. We show that these interventions
work robustly across many prompts and dataset splits. Overall, our work
contributes a greater understanding of dishonesty in LLMs so that we may hope
to prevent it.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: DIRECT: Deep Active Learning under Imbalance and Label Noise
Abstract: Class imbalance is a prevalent issue in real world machine learning
applications, often leading to poor performance in rare and minority classes.
With an abundance of wild unlabeled data, active learning is perhaps the most
effective technique in solving the problem at its root -- collecting a more
balanced and informative set of labeled examples during annotation. In this
work, we propose a novel algorithm that first identifies the class separation
threshold and then annotate the most uncertain examples from the minority
classes, close to the separation threshold. Through a novel reduction to
one-dimensional active learning, our algorithm DIRECT is able to leverage the
classic active learning literature to address issues such as batch labeling and
tolerance towards label noise. Compared to existing algorithms, our algorithm
saves more than 15\% of the annotation budget compared to state-of-art active
learning algorithm and more than 90\% of annotation budget compared to random
sampling.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: CustomNet: Zero-shot Object Customization with Variable-Viewpoints in Text-to-Image Diffusion Models
Abstract: Incorporating a customized object into image generation presents an
attractive feature in text-to-image generation. However, existing
optimization-based and encoder-based methods are hindered by drawbacks such as
time-consuming optimization, insufficient identity preservation, and a
prevalent copy-pasting effect. To overcome these limitations, we introduce
CustomNet, a novel object customization approach that explicitly incorporates
3D novel view synthesis capabilities into the object customization process.
This integration facilitates the adjustment of spatial position relationships
and viewpoints, yielding diverse outputs while effectively preserving object
identity. Moreover, we introduce delicate designs to enable location control
and flexible background control through textual descriptions or specific
user-defined images, overcoming the limitations of existing 3D novel view
synthesis methods. We further leverage a dataset construction pipeline that can
better handle real-world objects and complex backgrounds. Equipped with these
designs, our method facilitates zero-shot object customization without
test-time optimization, offering simultaneous control over the viewpoints,
location, and background. As a result, our CustomNet ensures enhanced identity
preservation and generates diverse, harmonious outputs.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Energy-based Potential Games for Joint Motion Forecasting and Control
Abstract: This work uses game theory as a mathematical framework to address interaction
modeling in multi-agent motion forecasting and control. Despite its
interpretability, applying game theory to real-world robotics, like automated
driving, faces challenges such as unknown game parameters. To tackle these, we
establish a connection between differential games, optimal control, and
energy-based models, demonstrating how existing approaches can be unified under
our proposed Energy-based Potential Game formulation. Building upon this, we
introduce a new end-to-end learning application that combines neural networks
for game-parameter inference with a differentiable game-theoretic optimization
layer, acting as an inductive bias. The analysis provides empirical evidence
that the game-theoretic layer adds interpretability and improves the predictive
performance of various neural network backbones using two simulations and two
real-world driving datasets.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Two Complementary Perspectives to Continual Learning: Ask Not Only What to Optimize, But Also How
Abstract: Recent years have seen considerable progress in the continual training of
deep neural networks, predominantly thanks to approaches that add replay or
regularization terms to the loss function to approximate the joint loss over
all tasks so far. However, we show that even with a perfect approximation to
the joint loss, these approaches still suffer from temporary but substantial
forgetting when starting to train on a new task. Motivated by this 'stability
gap', we propose that continual learning strategies should focus not only on
the optimization objective, but also on the way this objective is optimized.
While there is some continual learning work that alters the optimization
trajectory (e.g., using gradient projection techniques), this line of research
is positioned as alternative to improving the optimization objective, while we
argue it should be complementary. To evaluate the merits of our proposition, we
plan to combine replay-approximated joint objectives with gradient
projection-based optimization routines to test whether the addition of the
latter provides benefits in terms of (1) alleviating the stability gap, (2)
increasing the learning efficiency and (3) improving the final learning
outcome.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Human-Guided Complexity-Controlled Abstractions
Abstract: Neural networks often learn task-specific latent representations that fail to
generalize to novel settings or tasks. Conversely, humans learn discrete
representations (i.e., concepts or words) at a variety of abstraction levels
(e.g., "bird" vs. "sparrow") and deploy the appropriate abstraction based on
task. Inspired by this, we train neural models to generate a spectrum of
discrete representations, and control the complexity of the representations
(roughly, how many bits are allocated for encoding inputs) by tuning the
entropy of the distribution over representations. In finetuning experiments,
using only a small number of labeled examples for a new task, we show that (1)
tuning the representation to a task-appropriate complexity level supports the
highest finetuning performance, and (2) in a human-participant study, users
were able to identify the appropriate complexity level for a downstream task
using visualizations of discrete representations. Our results indicate a
promising direction for rapid model finetuning by leveraging human insight.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Object-Centric Learning with Slot Mixture Module
Abstract: Object-centric architectures usually apply a differentiable module to the
entire feature map to decompose it into sets of entity representations called
slots. Some of these methods structurally resemble clustering algorithms, where
the cluster's center in latent space serves as a slot representation. Slot
Attention is an example of such a method, acting as a learnable analog of the
soft k-means algorithm. Our work employs a learnable clustering method based on
the Gaussian Mixture Model. Unlike other approaches, we represent slots not
only as centers of clusters but also incorporate information about the distance
between clusters and assigned vectors, leading to more expressive slot
representations. Our experiments demonstrate that using this approach instead
of Slot Attention improves performance in object-centric scenarios, achieving
state-of-the-art results in the set property prediction task.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosis
Abstract: Synthesizing information from multiple data sources plays a crucial role in
the practice of modern medicine. Current applications of artificial
intelligence in medicine often focus on single-modality data due to a lack of
publicly available, multimodal medical datasets. To address this limitation, we
introduce INSPECT, which contains de-identified longitudinal records from a
large cohort of patients at risk for pulmonary embolism (PE), along with ground
truth labels for multiple outcomes. INSPECT contains data from 19,402 patients,
including CT images, radiology report impression sections, and structured
electronic health record (EHR) data (i.e. demographics, diagnoses, procedures,
vitals, and medications). Using INSPECT, we develop and release a benchmark for
evaluating several baseline modeling approaches on a variety of important PE
related tasks. We evaluate image-only, EHR-only, and multimodal fusion models.
Trained models and the de-identified dataset are made available for
non-commercial use under a data use agreement. To the best of our knowledge,
INSPECT is the largest multimodal dataset integrating 3D medical imaging and
EHR for reproducible methods evaluation and research.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Automated Camera Calibration via Homography Estimation with GNNs
Abstract: Over the past few decades, a significant rise of camera-based applications
for traffic monitoring has occurred. Governments and local administrations are
increasingly relying on the data collected from these cameras to enhance road
safety and optimize traffic conditions. However, for effective data
utilization, it is imperative to ensure accurate and automated calibration of
the involved cameras. This paper proposes a novel approach to address this
challenge by leveraging the topological structure of intersections. We propose
a framework involving the generation of a set of synthetic intersection
viewpoint images from a bird's-eye-view image, framed as a graph of virtual
cameras to model these images. Using the capabilities of Graph Neural Networks,
we effectively learn the relationships within this graph, thereby facilitating
the estimation of a homography matrix. This estimation leverages the
neighbourhood representation for any real-world camera and is enhanced by
exploiting multiple images instead of a single match. In turn, the homography
matrix allows the retrieval of extrinsic calibration parameters. As a result,
the proposed framework demonstrates superior performance on both synthetic
datasets and real-world cameras, setting a new state-of-the-art benchmark.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: MultiLoRA: Democratizing LoRA for Better Multi-Task Learning
Abstract: LoRA achieves remarkable resource efficiency and comparable performance when
adapting LLMs for specific tasks. Since ChatGPT demonstrated superior
performance on various tasks, there has been a growing desire to adapt one
model for all tasks. However, the explicit low-rank of LoRA limits the
adaptation performance in complex multi-task scenarios. LoRA is dominated by a
small number of top singular vectors while fine-tuning decomposes into a set of
less important unitary transforms. In this paper, we propose MultiLoRA for
better multi-task adaptation by reducing the dominance of top singular vectors
observed in LoRA. MultiLoRA scales LoRA modules horizontally and change
parameter initialization of adaptation matrices to reduce parameter dependency,
thus yields more balanced unitary subspaces. We unprecedentedly construct
specialized training data by mixing datasets of instruction follow, natural
language understanding, world knowledge, to cover semantically and
syntactically different samples. With only 2.5% of additional parameters,
MultiLoRA outperforms single LoRA counterparts and fine-tuning on multiple
benchmarks and model scales. Further investigation into weight update matrices
of MultiLoRA exhibits reduced dependency on top singular vectors and more
democratic unitary transform contributions.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Understanding Tool Discovery and Tool Innovation Using Active Inference
Abstract: The ability to invent new tools has been identified as an important facet of
our ability as a species to problem solve in dynamic and novel environments.
While the use of tools by artificial agents presents a challenging task and has
been widely identified as a key goal in the field of autonomous robotics, far
less research has tackled the invention of new tools by agents. In this paper,
(1) we articulate the distinction between tool discovery and tool innovation by
providing a minimal description of the two concepts under the formalism of
active inference. We then (2) apply this description to construct a toy model
of tool innovation by introducing the notion of tool affordances into the
hidden states of the agent's probabilistic generative model. This particular
state factorisation facilitates the ability to not just discover tools but
invent them through the offline induction of an appropriate tool property. We
discuss the implications of these preliminary results and outline future
directions of research.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Control Risk for Potential Misuse of Artificial Intelligence in Science
Abstract: The expanding application of Artificial Intelligence (AI) in scientific
fields presents unprecedented opportunities for discovery and innovation.
However, this growth is not without risks. AI models in science, if misused,
can amplify risks like creation of harmful substances, or circumvention of
established regulations. In this study, we aim to raise awareness of the
dangers of AI misuse in science, and call for responsible AI development and
use in this domain. We first itemize the risks posed by AI in scientific
contexts, then demonstrate the risks by highlighting real-world examples of
misuse in chemical science. These instances underscore the need for effective
risk management strategies. In response, we propose a system called SciGuard to
control misuse risks for AI models in science. We also propose a red-teaming
benchmark SciMT-Safety to assess the safety of different systems. Our proposed
SciGuard shows the least harmful impact in the assessment without compromising
performance in benign tests. Finally, we highlight the need for a
multidisciplinary and collaborative effort to ensure the safe and ethical use
of AI models in science. We hope that our study can spark productive
discussions on using AI ethically in science among researchers, practitioners,
policymakers, and the public, to maximize benefits and minimize the risks of
misuse.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Symptom-based Machine Learning Models for the Early Detection of COVID-19: A Narrative Review
Abstract: Despite the widespread testing protocols for COVID-19, there are still
significant challenges in early detection of the disease, which is crucial for
preventing its spread and optimizing patient outcomes. Owing to the limited
testing capacity in resource-strapped settings and the limitations of the
available traditional methods of testing, it has been established that a fast
and efficient strategy is important to fully stop the virus. Machine learning
models can analyze large datasets, incorporating patient-reported symptoms,
clinical data, and medical imaging. Symptom-based detection methods have been
developed to predict COVID-19, and they have shown promising results. In this
paper, we provide an overview of the landscape of symptoms-only machine
learning models for predicting COVID-19, including their performance and
limitations. The review will also examine the performance of symptom-based
models when compared to image-based models. Because different studies used
varying datasets, methodologies, and performance metrics. Selecting the model
that performs best relies on the context and objectives of the research.
However, based on the results, we observed that ensemble classifier performed
exceptionally well in predicting the occurrence of COVID-19 based on patient
symptoms with the highest overall accuracy of 97.88%. Gradient Boosting
Algorithm achieved an AUC (Area Under the Curve) of 0.90 and identified key
features contributing to the decision-making process. Image-based models, as
observed in the analyzed studies, have consistently demonstrated higher
accuracy than symptom-based models, often reaching impressive levels ranging
from 96.09% to as high as 99%.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Using a Large Language Model to generate a Design Structure Matrix
Abstract: The Design Structure Matrix (DSM) is an established method used in dependency
modelling, especially in the design of complex engineering systems. The
generation of DSM is traditionally carried out through manual means and can
involve interviewing experts to elicit critical system elements and the
relationships between them. Such manual approaches can be time-consuming and
costly. This paper presents a workflow that uses a Large Language Model (LLM)
to support the generation of DSM and improve productivity. A prototype of the
workflow was developed in this work and applied on a diesel engine DSM
published previously. It was found that the prototype could reproduce 357 out
of 462 DSM entries published (i.e. 77.3%), suggesting that the work can aid DSM
generation. A no-code version of the prototype is made available online to
support future research.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Revisiting Non-separable Binary Classification and its Applications in Anomaly Detection
Abstract: The inability to linearly classify XOR has motivated much of deep learning.
We revisit this age-old problem and show that linear classification of XOR is
indeed possible. Instead of separating data between halfspaces, we propose a
slightly different paradigm, equality separation, that adapts the SVM objective
to distinguish data within or outside the margin. Our classifier can then be
integrated into neural network pipelines with a smooth approximation. From its
properties, we intuit that equality separation is suitable for anomaly
detection. To formalize this notion, we introduce closing numbers, a
quantitative measure on the capacity for classifiers to form closed decision
regions for anomaly detection. Springboarding from this theoretical connection
between binary classification and anomaly detection, we test our hypothesis on
supervised anomaly detection experiments, showing that equality separation can
detect both seen and unseen anomalies.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Can language agents be alternatives to PPO? A Preliminary Empirical Study On OpenAI Gym
Abstract: The formidable capacity for zero- or few-shot decision-making in language
agents encourages us to pose a compelling question: Can language agents be
alternatives to PPO agents in traditional sequential decision-making tasks? To
investigate this, we first take environments collected in OpenAI Gym as our
testbeds and ground them to textual environments that construct the TextGym
simulator. This allows for straightforward and efficient comparisons between
PPO agents and language agents, given the widespread adoption of OpenAI Gym. To
ensure a fair and effective benchmarking, we introduce $5$ levels of scenario
for accurate domain-knowledge controlling and a unified RL-inspired framework
for language agents. Additionally, we propose an innovative
explore-exploit-guided language (EXE) agent to solve tasks within TextGym.
Through numerical experiments and ablation studies, we extract valuable
insights into the decision-making capabilities of language agents and make a
preliminary evaluation of their potential to be alternatives to PPO in
classical sequential decision-making problems. This paper sheds light on the
performance of language agents and paves the way for future research in this
exciting domain. Our code is publicly available
at~\url{https://github.com/mail-ecnu/Text-Gym-Agents}.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops
Abstract: Recommendation models trained on the user feedback collected from deployed
recommendation systems are commonly biased. User feedback is considerably
affected by the exposure mechanism, as users only provide feedback on the items
exposed to them and passively ignore the unexposed items, thus producing
numerous false negative samples. Inevitably, biases caused by such user
feedback are inherited by new models and amplified via feedback loops.
Moreover, the presence of false negative samples makes negative sampling
difficult and introduces spurious information in the user preference modeling
process of the model. Recent work has investigated the negative impact of
feedback loops and unknown exposure mechanisms on recommendation quality and
user experience, essentially treating them as independent factors and ignoring
their cross-effects. To address these issues, we deeply analyze the data
exposure mechanism from the perspective of data iteration and feedback loops
with the Missing Not At Random (\textbf{MNAR}) assumption, theoretically
demonstrating the existence of an available stabilization factor in the
transformation of the exposure mechanism under the feedback loops. We further
propose Dynamic Personalized Ranking (\textbf{DPR}), an unbiased algorithm that
uses dynamic re-weighting to mitigate the cross-effects of exposure mechanisms
and feedback loops without additional information. Furthermore, we design a
plugin named Universal Anti-False Negative (\textbf{UFN}) to mitigate the
negative impact of the false negative problem. We demonstrate theoretically
that our approach mitigates the negative effects of feedback loops and unknown
exposure mechanisms. Experimental results on real-world datasets demonstrate
that models using DPR can better handle bias accumulation and the universality
of UFN in mainstream loss methods.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: Optimally Teaching a Linear Behavior Cloning Agent
Abstract: We study optimal teaching of Linear Behavior Cloning (LBC) learners. In this
setup, the teacher can select which states to demonstrate to an LBC learner.
The learner maintains a version space of infinite linear hypotheses consistent
with the demonstration. The goal of the teacher is to teach a realizable target
policy to the learner using minimum number of state demonstrations. This number
is known as the Teaching Dimension(TD). We present a teaching algorithm called
``Teach using Iterative Elimination(TIE)" that achieves instance optimal TD.
However, we also show that finding optimal teaching set computationally is
NP-hard. We further provide an approximation algorithm that guarantees an
approximation ratio of $\log(|A|-1)$ on the teaching dimension. Finally, we
provide experimental results to validate the efficiency and effectiveness of
our algorithm.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data
Abstract: Unsupervised anomaly detection methods are at the forefront of industrial
anomaly detection efforts and have made notable progress. Previous work
primarily used 2D information as input, but multi-modal industrial anomaly
detection based on 3D point clouds and RGB images is just beginning to emerge.
The regular approach involves utilizing large pre-trained models for feature
representation and storing them in memory banks. However, the above methods
require a longer inference time and higher memory usage, which cannot meet the
real-time requirements of the industry. To overcome these issues, we propose a
lightweight dual-branch reconstruction network(DBRN) based on RGB-D input,
learning the decision boundary between normal and abnormal examples. The
requirement for alignment between the two modalities is eliminated by using
depth maps instead of point cloud input. Furthermore, we introduce an
importance scoring module in the discriminative network to assist in fusing
features from these two modalities, thereby obtaining a comprehensive
discriminative result. DBRN achieves 92.8% AUROC with high inference efficiency
on the MVTec 3D-AD dataset without large pre-trained models and memory banks.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: MCAD: Multi-teacher Cross-modal Alignment Distillation for efficient image-text retrieval
Abstract: With the success of large-scale visual-language pretraining models and the
wide application of image-text retrieval in industry areas, reducing the model
size and streamlining their terminal-device deployment have become urgently
necessary. The mainstream model structures for image-text retrieval are
single-stream and dual-stream, both aiming to close the semantic gap between
visual and textual modalities. Dual-stream models excel at offline indexing and
fast inference, while single-stream models achieve more accurate cross-model
alignment by employing adequate feature fusion. We propose a multi-teacher
cross-modality alignment distillation (MCAD) technique to integrate the
advantages of single-stream and dual-stream models. By incorporating the fused
single-stream features into the image and text features of the dual-stream
model, we formulate new modified teacher features and logits. Then, we conduct
both logit and feature distillation to boost the capability of the student
dual-stream model, achieving high retrieval performance without increasing
inference complexity. Extensive experiments demonstrate the remarkable
performance and high efficiency of MCAD on image-text retrieval tasks.
Furthermore, we implement a mobile CLIP model on Snapdragon clips with only 93M
running memory and 30ms search latency, without apparent performance
degradation of the original large CLIP.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection
Abstract: Out-of-distribution (OOD) detection methods often exploit auxiliary outliers
to train model identifying OOD samples, especially discovering challenging
outliers from auxiliary outliers dataset to improve OOD detection. However,
they may still face limitations in effectively distinguishing between the most
challenging OOD samples that are much like in-distribution (ID) data, i.e.,
ID-like samples. To this end, we propose a novel OOD detection framework that
discovers ID-like outliers using CLIP from the vicinity space of the ID
samples, thus helping to identify these most challenging OOD samples. Then a
prompt learning framework is proposed that utilizes the identified ID-like
outliers to further leverage the capabilities of CLIP for OOD detection.
Benefiting from the powerful CLIP, we only need a small number of ID samples to
learn the prompts of the model without exposing other auxiliary outlier
datasets. By focusing on the most challenging ID-like OOD samples and elegantly
exploiting the capabilities of CLIP, our method achieves superior few-shot
learning performance on various real-world image datasets (e.g., in 4-shot OOD
detection on the ImageNet-1k dataset, our method reduces the average FPR95 by
12.16% and improves the average AUROC by 2.76%, compared to state-of-the-art
methods).

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: TOD-Flow: Modeling the Structure of Task-Oriented Dialogues
Abstract: Task-Oriented Dialogue (TOD) systems have become crucial components in
interactive artificial intelligence applications. While recent advances have
capitalized on pre-trained language models (PLMs), they exhibit limitations
regarding transparency and controllability. To address these challenges, we
propose a novel approach focusing on inferring the TOD-Flow graph from dialogue
data annotated with dialog acts, uncovering the underlying task structure in
the form of a graph. The inferred TOD-Flow graph can be easily integrated with
any dialogue model to improve its prediction performance, transparency, and
controllability. Our TOD-Flow graph learns what a model can, should, and should
not predict, effectively reducing the search space and providing a rationale
for the model's prediction. We show that the proposed TOD-Flow graph better
resembles human-annotated graphs compared to prior approaches. Furthermore,
when combined with several dialogue policies and end-to-end dialogue models, we
demonstrate that our approach significantly improves dialog act classification
and end-to-end response generation performance in the MultiWOZ and SGD
benchmarks. Code available at: https://github.com/srsohn/TOD-Flow

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Goals are Enough: Inducing AdHoc cooperation among unseen Multi-Agent systems in IMFs
Abstract: Intent-based management will play a critical role in achieving customers'
expectations in the next-generation mobile networks. Traditional methods cannot
perform efficient resource management since they tend to handle each
expectation independently. Existing approaches, e.g., based on multi-agent
reinforcement learning (MARL) allocate resources in an efficient fashion when
there are conflicting expectations on the network slice. However, in reality,
systems are often far more complex to be addressed by a standalone MARL
formulation. Often there exists a hierarchical structure of intent fulfilment
where multiple pre-trained, self-interested agents may need to be further
orchestrated by a supervisor or controller agent. Such agents may arrive in the
system adhoc, which then needs to be orchestrated along with other available
agents. Retraining the whole system every time is often infeasible given the
associated time and cost. Given the challenges, such adhoc coordination of
pre-trained systems could be achieved through an intelligent supervisor agent
which incentivizes pre-trained RL/MARL agents through sets of dynamic contracts
(goals or bonuses) and encourages them to act as a cohesive unit towards
fulfilling a global expectation. Some approaches use a rule-based supervisor
agent and deploy the hierarchical constituent agents sequentially, based on
human-coded rules.
  In the current work, we propose a framework whereby pre-trained agents can be
orchestrated in parallel leveraging an AI-based supervisor agent. For this, we
propose to use Adhoc-Teaming approaches which assign optimal goals to the MARL
agents and incentivize them to exhibit certain desired behaviours. Results on
the network emulator show that the proposed approach results in faster and
improved fulfilment of expectations when compared to rule-based approaches and
even generalizes to changes in environments.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Architecture of Smart Certificates for Web3 Applications Against Cyberthreats in Financial Industry
Abstract: This study addresses the security challenges associated with the current
internet transformations, specifically focusing on emerging technologies such
as blockchain and decentralized storage. It also investigates the role of Web3
applications in shaping the future of the internet. The primary objective is to
propose a novel design for 'smart certificates,' which are digital certificates
that can be programmatically enforced. Utilizing such certificates, an
enterprise can better protect itself from cyberattacks and ensure the security
of its data and systems. Web3 recent security solutions by companies and
projects like Certik, Forta, Slither, and Securify are the equivalent of code
scanning tool that were originally developed for Web1 and Web2 applications,
and definitely not like certificates to help enterprises feel safe against
cyberthreats. We aim to improve the resilience of enterprises' digital
infrastructure by building on top of Web3 application and put methodologies in
place for vulnerability analysis and attack correlation, focusing on
architecture of different layers, Wallet/Client, Application and Smart
Contract, where specific components are provided to identify and predict
threats and risks. Furthermore, Certificate Transparency is used for enhancing
the security, trustworthiness and decentralized management of the certificates,
and detecting misuses, compromises, and malfeasances.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: Recent Advances in Multi-modal 3D Scene Understanding: A Comprehensive Survey and Evaluation
Abstract: Multi-modal 3D scene understanding has gained considerable attention due to
its wide applications in many areas, such as autonomous driving and
human-computer interaction. Compared to conventional single-modal 3D
understanding, introducing an additional modality not only elevates the
richness and precision of scene interpretation but also ensures a more robust
and resilient understanding. This becomes especially crucial in varied and
challenging environments where solely relying on 3D data might be inadequate.
While there has been a surge in the development of multi-modal 3D methods over
past three years, especially those integrating multi-camera images (3D+2D) and
textual descriptions (3D+language), a comprehensive and in-depth review is
notably absent. In this article, we present a systematic survey of recent
progress to bridge this gap. We begin by briefly introducing a background that
formally defines various 3D multi-modal tasks and summarizes their inherent
challenges. After that, we present a novel taxonomy that delivers a thorough
categorization of existing methods according to modalities and tasks, exploring
their respective strengths and limitations. Furthermore, comparative results of
recent approaches on several benchmark datasets, together with insightful
analysis, are offered. Finally, we discuss the unresolved issues and provide
several potential avenues for future research.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Towards Improving Robustness Against Common Corruptions using Mixture of Class Specific Experts
Abstract: Neural networks have demonstrated significant accuracy across various
domains, yet their vulnerability to subtle input alterations remains a
persistent challenge. Conventional methods like data augmentation, while
effective to some extent, fall short in addressing unforeseen corruptions,
limiting the adaptability of neural networks in real-world scenarios. In
response, this paper introduces a novel paradigm known as the Mixture of
Class-Specific Expert Architecture. The approach involves disentangling feature
learning for individual classes, offering a nuanced enhancement in scalability
and overall performance. By training dedicated network segments for each class
and subsequently aggregating their outputs, the proposed architecture aims to
mitigate vulnerabilities associated with common neural network structures. The
study underscores the importance of comprehensive evaluation methodologies,
advocating for the incorporation of benchmarks like the common corruptions
benchmark. This inclusion provides nuanced insights into the vulnerabilities of
neural networks, especially concerning their generalization capabilities and
robustness to unforeseen distortions. The research aligns with the broader
objective of advancing the development of highly robust learning systems
capable of nuanced reasoning across diverse and challenging real-world
scenarios. Through this contribution, the paper aims to foster a deeper
understanding of neural network limitations and proposes a practical approach
to enhance their resilience in the face of evolving and unpredictable
conditions.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: FERGI: Automatic Annotation of User Preferences for Text-to-Image Generation from Spontaneous Facial Expression Reaction
Abstract: Researchers have proposed to use data of human preference feedback to
fine-tune text-to-image generative models. However, the scalability of human
feedback collection has been limited by its reliance on manual annotation.
Therefore, we develop and test a method to automatically annotate user
preferences from their spontaneous facial expression reaction to the generated
images. We collect a dataset of Facial Expression Reaction to Generated Images
(FERGI) and show that the activations of multiple facial action units (AUs) are
highly correlated with user evaluations of the generated images. Specifically,
AU4 (brow lowerer) is most consistently reflective of negative evaluations of
the generated image. This can be useful in two ways. Firstly, we can
automatically annotate user preferences between image pairs with substantial
difference in AU4 responses to them with an accuracy significantly
outperforming state-of-the-art scoring models. Secondly, directly integrating
the AU4 responses with the scoring models improves their consistency with human
preferences. Additionally, the AU4 response best reflects the user's evaluation
of the image fidelity, making it complementary to the state-of-the-art scoring
models, which are generally better at reflecting image-text alignment. Finally,
this method of automatic annotation with facial expression analysis can be
potentially generalized to other generation tasks. The code is available at
https://github.com/ShuangquanFeng/FERGI, and the dataset is also available at
the same link for research purposes.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Deep Reinforcement Learning for Weapons to Targets Assignment in a Hypersonic strike
Abstract: We use deep reinforcement learning (RL) to optimize a weapons to target
assignment (WTA) policy for multi-vehicle hypersonic strike against multiple
targets. The objective is to maximize the total value of destroyed targets in
each episode. Each randomly generated episode varies the number and initial
conditions of the hypersonic strike weapons (HSW) and targets, the value
distribution of the targets, and the probability of a HSW being intercepted. We
compare the performance of this WTA policy to that of a benchmark WTA policy
derived using non-linear integer programming (NLIP), and find that the RL WTA
policy gives near optimal performance with a 1000X speedup in computation time,
allowing real time operation that facilitates autonomous decision making in the
mission end game.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Fusion of Deep and Shallow Features for Face Kinship Verification
Abstract: Kinship verification from face images is a novel and formidable challenge in
the realms of pattern recognition and computer vision. This work makes notable
contributions by incorporating a preprocessing technique known as Multiscale
Retinex (MSR), which enhances image quality. Our approach harnesses the
strength of complementary deep (VGG16) and shallow texture descriptors (BSIF)
by combining them at the score level using Logistic Regression (LR) technique.
We assess the effectiveness of our approach by conducting comprehensive
experiments on three challenging kinship datasets: Cornell Kin Face, UB Kin
Face and TS Kin Face

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: AI Chatbot for Generating Episodic Future Thinking (EFT) Cue Texts for Health
Abstract: We describe an AI-powered chatbot to aid with health improvement by
generating Episodic Future Thinking (EFT) cue texts that should reduce delay
discounting. In prior studies, EFT has been shown to address maladaptive health
behaviors. Those studies involved participants, working with researchers,
vividly imagining future events, and writing a description that they
subsequently will frequently review, to ensure a shift from an inclination
towards immediate rewards. That should promote behavior change, aiding in
health tasks such as treatment adherence and lifestyle modifications. The AI
chatbot is designed to guide users in generating personalized EFTs, automating
the current labor-intensive interview-based process. This can enhance the
efficiency of EFT interventions and make them more accessible, targeting
specifically those with limited educational backgrounds or communication
challenges. By leveraging AI for EFT intervention, we anticipate broadened
access and improved health outcomes across diverse populations

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: AWEQ: Post-Training Quantization with Activation-Weight Equalization for Large Language Models
Abstract: Large language models(LLMs) exhibit excellent performance across a variety of
tasks, but they come with significant computational and storage costs.
Quantizing these models is an effective way to alleviate this issue. However,
existing methods struggle to strike a balance between model accuracy and
hardware efficiency. This is where we introduce AWEQ, a post-training method
that requires no additional training overhead. AWEQ excels in both
ultra-low-bit quantization and 8-bit weight and activation (W8A8) quantization.
There is an observation that weight quantization is less challenging than
activation quantization. AWEQ transfers the difficulty of activation
quantization to weights using channel equalization, achieving a balance between
the quantization difficulties of both, and thereby maximizing performance. We
have further refined the equalization method to mitigate quantization bias
error, ensuring the robustness of the model. Extensive experiments on popular
models such as LLaMA and OPT demonstrate that AWEQ outperforms all existing
post-training quantization methods for large models.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: ChatGPT Application In Summarizing An Evolution Of Deep Learning Techniques In Imaging: A Qualitative Study
Abstract: The pursuit of article or text summarization has captured the attention of
natural language processing (NLP) practitioners, presenting itself as a
formidable challenge. ChatGPT 3.5 exhibits the capacity to condense the content
of up to 3000 tokens into a single page, aiming to retain pivotal information
from a given text across diverse themes. In a conducted qualitative research
endeavor, we selected seven scientific articles and employed the publicly
available ChatGPT service to generate summaries of these articles.
Subsequently, we engaged six co-authors of the articles in a survey, presenting
five questions to evaluate the quality of the summaries compared to the
original content. The findings revealed that the summaries produced by ChatGPT
effectively encapsulated the crucial information present in the articles,
preserving the principal message of each manuscript. Nonetheless, there was a
slight diminishment in the technical depth of the summaries as opposed to the
original articles. As a result, our conclusion underscores ChatGPT's text
summarization capability as a potent tool for extracting essential insights in
a manner more aligned with reporting than purely scientific discourse.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Advances in 3D Neural Stylization: A Survey
Abstract: Modern artificial intelligence provides a novel way of producing digital art
in styles. The expressive power of neural networks enables the realm of visual
style transfer methods, which can be used to edit images, videos, and 3D data
to make them more artistic and diverse. This paper reports on recent advances
in neural stylization for 3D data. We provide a taxonomy for neural stylization
by considering several important design choices, including scene
representation, guidance data, optimization strategies, and output styles.
Building on such taxonomy, our survey first revisits the background of neural
stylization on 2D images, and then provides in-depth discussions on recent
neural stylization methods for 3D data, where we also provide a mini-benchmark
on artistic stylization methods. Based on the insights gained from the survey,
we then discuss open challenges, future research, and potential applications
and impacts of neural stylization.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Transformers as Graph-to-Graph Models
Abstract: We argue that Transformers are essentially graph-to-graph models, with
sequences just being a special case. Attention weights are functionally
equivalent to graph edges. Our Graph-to-Graph Transformer architecture makes
this ability explicit, by inputting graph edges into the attention weight
computations and predicting graph edges with attention-like functions, thereby
integrating explicit graphs into the latent graphs learned by pretrained
Transformers. Adding iterative graph refinement provides a joint embedding of
input, output, and latent graphs, allowing non-autoregressive graph prediction
to optimise the complete graph without any bespoke pipeline or decoding
strategy. Empirical results show that this architecture achieves
state-of-the-art accuracies for modelling a variety of linguistic structures,
integrating very effectively with the latent linguistic representations learned
by pretraining.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Explainable Fraud Detection with Deep Symbolic Classification
Abstract: There is a growing demand for explainable, transparent, and data-driven
models within the domain of fraud detection. Decisions made by fraud detection
models need to be explainable in the event of a customer dispute. Additionally,
the decision-making process in the model must be transparent to win the trust
of regulators and business stakeholders. At the same time, fraud detection
solutions can benefit from data due to the noisy, dynamic nature of fraud and
the availability of large historical data sets. Finally, fraud detection is
notorious for its class imbalance: there are typically several orders of
magnitude more legitimate transactions than fraudulent ones. In this paper, we
present Deep Symbolic Classification (DSC), an extension of the Deep Symbolic
Regression framework to classification problems. DSC casts classification as a
search problem in the space of all analytic functions composed of a vocabulary
of variables, constants, and operations and optimizes for an arbitrary
evaluation metric directly. The search is guided by a deep neural network
trained with reinforcement learning. Because the functions are mathematical
expressions that are in closed-form and concise, the model is inherently
explainable both at the level of a single classification decision and the
model's decision process. Furthermore, the class imbalance problem is
successfully addressed by optimizing for metrics that are robust to class
imbalance such as the F1 score. This eliminates the need for oversampling and
undersampling techniques that plague traditional approaches. Finally, the model
allows to explicitly balance between the prediction accuracy and the
explainability. An evaluation on the PaySim data set demonstrates competitive
predictive performance with state-of-the-art models, while surpassing them in
terms of explainability. This establishes DSC as a promising model for fraud
detection systems.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning
Abstract: Despite the dramatic progress in Large Language Model (LLM) development, LLMs
often provide seemingly plausible but not factual information, often referred
to as hallucinations. Retrieval-augmented LLMs provide a non-parametric
approach to solve these issues by retrieving relevant information from external
data sources and augment the training process. These models help to trace
evidence from an externally provided knowledge base allowing the model
predictions to be better interpreted and verified. In this work, we critically
evaluate these models in their ability to perform in scientific document
reasoning tasks. To this end, we tuned multiple such model variants with
science-focused instructions and evaluated them on a scientific document
reasoning benchmark for the usefulness of the retrieved document passages. Our
findings suggest that models justify predictions in science tasks with
fabricated evidence and leveraging scientific corpus as pretraining data does
not alleviate the risk of evidence fabrication.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
Abstract: Time series forecasting has played the key role in different industrial,
including finance, traffic, energy, and healthcare domains. While existing
literatures have designed many sophisticated architectures based on RNNs, GNNs,
or Transformers, another kind of approaches based on multi-layer perceptrons
(MLPs) are proposed with simple structure, low complexity, and {superior
performance}. However, most MLP-based forecasting methods suffer from the
point-wise mappings and information bottleneck, which largely hinders the
forecasting performance. To overcome this problem, we explore a novel direction
of applying MLPs in the frequency domain for time series forecasting. We
investigate the learned patterns of frequency-domain MLPs and discover their
two inherent characteristic benefiting forecasting, (i) global view: frequency
spectrum makes MLPs own a complete view for signals and learn global
dependencies more easily, and (ii) energy compaction: frequency-domain MLPs
concentrate on smaller key part of frequency components with compact signal
energy. Then, we propose FreTS, a simple yet effective architecture built upon
Frequency-domain MLPs for Time Series forecasting. FreTS mainly involves two
stages, (i) Domain Conversion, that transforms time-domain signals into complex
numbers of frequency domain; (ii) Frequency Learning, that performs our
redesigned MLPs for the learning of real and imaginary part of frequency
components. The above stages operated on both inter-series and intra-series
scales further contribute to channel-wise and time-wise dependency learning.
Extensive experiments on 13 real-world benchmarks (including 7 benchmarks for
short-term forecasting and 6 benchmarks for long-term forecasting) demonstrate
our consistent superiority over state-of-the-art methods.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classification
Abstract: Chest X-rays are widely used to diagnose thoracic diseases, but the lack of
detailed information about these abnormalities makes it challenging to develop
accurate automated diagnosis systems, which is crucial for early detection and
effective treatment. To address this challenge, we employed deep learning
techniques to identify patterns in chest X-rays that correspond to different
diseases. We conducted experiments on the "ChestX-ray14" dataset using various
pre-trained CNNs, transformers, hybrid(CNN+Transformer) models and classical
models. The best individual model was the CoAtNet, which achieved an area under
the receiver operating characteristic curve (AUROC) of 84.2%. By combining the
predictions of all trained models using a weighted average ensemble where the
weight of each model was determined using differential evolution, we further
improved the AUROC to 85.4%, outperforming other state-of-the-art methods in
this field. Our findings demonstrate the potential of deep learning techniques,
particularly ensemble deep learning, for improving the accuracy of automatic
diagnosis of thoracic diseases from chest X-rays.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: From External to Swap Regret 2.0: An Efficient Reduction and Oblivious Adversary for Large Action Spaces
Abstract: We provide a novel reduction from swap-regret minimization to external-regret
minimization, which improves upon the classical reductions of Blum-Mansour
[BM07] and Stolz-Lugosi [SL05] in that it does not require finiteness of the
space of actions. We show that, whenever there exists a no-external-regret
algorithm for some hypothesis class, there must also exist a no-swap-regret
algorithm for that same class. For the problem of learning with expert advice,
our result implies that it is possible to guarantee that the swap regret is
bounded by {\epsilon} after $\log(N)^{O(1/\epsilon)}$ rounds and with $O(N)$
per iteration complexity, where $N$ is the number of experts, while the
classical reductions of Blum-Mansour and Stolz-Lugosi require $O(N/\epsilon^2)$
rounds and at least $\Omega(N^2)$ per iteration complexity. Our result comes
with an associated lower bound, which -- in contrast to that in [BM07] -- holds
for oblivious and $\ell_1$-constrained adversaries and learners that can employ
distributions over experts, showing that the number of rounds must be
$\tilde\Omega(N/\epsilon^2)$ or exponential in $1/\epsilon$.
  Our reduction implies that, if no-regret learning is possible in some game,
then this game must have approximate correlated equilibria, of arbitrarily good
approximation. This strengthens the folklore implication of no-regret learning
that approximate coarse correlated equilibria exist. Importantly, it provides a
sufficient condition for the existence of correlated equilibrium which vastly
extends the requirement that the action set is finite, thus answering a
question left open by [DG22; Ass+23]. Moreover, it answers several outstanding
questions about equilibrium computation and learning in games.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Improving Biomedical Entity Linking with Retrieval-enhanced Learning
Abstract: Biomedical entity linking (BioEL) has achieved remarkable progress with the
help of pre-trained language models. However, existing BioEL methods usually
struggle to handle rare and difficult entities due to long-tailed distribution.
To address this limitation, we introduce a new scheme $k$NN-BioEL, which
provides a BioEL model with the ability to reference similar instances from the
entire training corpus as clues for prediction, thus improving the
generalization capabilities. Moreover, we design a contrastive learning
objective with dynamic hard negative sampling (DHNS) that improves the quality
of the retrieved neighbors during inference. Extensive experimental results
show that $k$NN-BioEL outperforms state-of-the-art baselines on several
datasets.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: On Mask-based Image Set Desensitization with Recognition Support
Abstract: In recent years, Deep Neural Networks (DNN) have emerged as a practical
method for image recognition. The raw data, which contain sensitive
information, are generally exploited within the training process. However, when
the training process is outsourced to a third-party organization, the raw data
should be desensitized before being transferred to protect sensitive
information. Although masks are widely applied to hide important sensitive
information, preventing inpainting masked images is critical, which may restore
the sensitive information. The corresponding models should be adjusted for the
masked images to reduce the degradation of the performance for recognition or
classification tasks due to the desensitization of images. In this paper, we
propose a mask-based image desensitization approach while supporting
recognition. This approach consists of a mask generation algorithm and a model
adjustment method. We propose exploiting an interpretation algorithm to
maintain critical information for the recognition task in the mask generation
algorithm. In addition, we propose a feature selection masknet as the model
adjustment method to improve the performance based on the masked images.
Extensive experimentation results based on multiple image datasets reveal
significant advantages (up to 9.34% in terms of accuracy) of our approach for
image desensitization while supporting recognition.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Loss Balancing for Fair Supervised Learning
Abstract: Supervised learning models have been used in various domains such as lending,
college admission, face recognition, natural language processing, etc. However,
they may inherit pre-existing biases from training data and exhibit
discrimination against protected social groups. Various fairness notions have
been proposed to address unfairness issues. In this work, we focus on Equalized
Loss (EL), a fairness notion that requires the expected loss to be
(approximately) equalized across different groups. Imposing EL on the learning
process leads to a non-convex optimization problem even if the loss function is
convex, and the existing fair learning algorithms cannot properly be adopted to
find the fair predictor under the EL constraint. This paper introduces an
algorithm that can leverage off-the-shelf convex programming tools (e.g.,
CVXPY) to efficiently find the global optimum of this non-convex optimization.
In particular, we propose the ELminimizer algorithm, which finds the optimal
fair predictor under EL by reducing the non-convex optimization to a sequence
of convex optimization problems. We theoretically prove that our algorithm
finds the global optimal solution under certain conditions. Then, we support
our theoretical results through several empirical studies.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Radar Perception in Autonomous Driving: Exploring Different Data Representations
Abstract: With the rapid advancements of sensor technology and deep learning,
autonomous driving systems are providing safe and efficient access to
intelligent vehicles as well as intelligent transportation. Among these
equipped sensors, the radar sensor plays a crucial role in providing robust
perception information in diverse environmental conditions. This review focuses
on exploring different radar data representations utilized in autonomous
driving systems. Firstly, we introduce the capabilities and limitations of the
radar sensor by examining the working principles of radar perception and signal
processing of radar measurements. Then, we delve into the generation process of
five radar representations, including the ADC signal, radar tensor, point
cloud, grid map, and micro-Doppler signature. For each radar representation, we
examine the related datasets, methods, advantages and limitations. Furthermore,
we discuss the challenges faced in these data representations and propose
potential research directions. Above all, this comprehensive review offers an
in-depth insight into how these representations enhance autonomous system
capabilities, providing guidance for radar perception researchers. To
facilitate retrieval and comparison of different data representations, datasets
and methods, we provide an interactive website at
https://radar-camera-fusion.github.io/radar.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Non-autoregressive Streaming Transformer for Simultaneous Translation
Abstract: Simultaneous machine translation (SiMT) models are trained to strike a
balance between latency and translation quality. However, training these models
to achieve high quality while maintaining low latency often leads to a tendency
for aggressive anticipation. We argue that such issue stems from the
autoregressive architecture upon which most existing SiMT models are built. To
address those issues, we propose non-autoregressive streaming Transformer
(NAST) which comprises a unidirectional encoder and a non-autoregressive
decoder with intra-chunk parallelism. We enable NAST to generate the blank
token or repetitive tokens to adjust its READ/WRITE strategy flexibly, and
train it to maximize the non-monotonic latent alignment with an alignment-based
latency loss. Experiments on various SiMT benchmarks demonstrate that NAST
outperforms previous strong autoregressive SiMT baselines.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Recognize Any Regions
Abstract: Understanding the semantics of individual regions or patches within
unconstrained images, such as in open-world object detection, represents a
critical yet challenging task in computer vision. Building on the success of
powerful image-level vision-language (ViL) foundation models like CLIP, recent
efforts have sought to harness their capabilities by either training a
contrastive model from scratch with an extensive collection of region-label
pairs or aligning the outputs of a detection model with image-level
representations of region proposals. Despite notable progress, these approaches
are plagued by computationally intensive training requirements, susceptibility
to data noise, and deficiency in contextual information. To address these
limitations, we explore the synergistic potential of off-the-shelf foundation
models, leveraging their respective strengths in localization and semantics. We
introduce a novel, generic, and efficient region recognition architecture,
named RegionSpot, designed to integrate position-aware localization knowledge
from a localization foundation model (e.g., SAM) with semantic information
extracted from a ViL model (e.g., CLIP). To fully exploit pretrained knowledge
while minimizing training overhead, we keep both foundation models frozen,
focusing optimization efforts solely on a lightweight attention-based knowledge
integration module. Through extensive experiments in the context of open-world
object recognition, our RegionSpot demonstrates significant performance
improvements over prior alternatives, while also providing substantial
computational savings. For instance, training our model with 3 million data in
a single day using 8 V100 GPUs. Our model outperforms GLIP by 6.5 % in mean
average precision (mAP), with an even larger margin by 14.8 % for more
challenging and rare categories.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems
Abstract: Artificial Intelligence (AI) systems such as autonomous vehicles, facial
recognition, and speech recognition systems are increasingly integrated into
our daily lives. However, despite their utility, these AI systems are
vulnerable to a wide range of attacks such as adversarial, backdoor, data
poisoning, membership inference, model inversion, and model stealing attacks.
In particular, numerous attacks are designed to target a particular model or
system, yet their effects can spread to additional targets, referred to as
transferable attacks. Although considerable efforts have been directed toward
developing transferable attacks, a holistic understanding of the advancements
in transferable attacks remains elusive. In this paper, we comprehensively
explore learning-based attacks from the perspective of transferability,
particularly within the context of cyber-physical security. We delve into
different domains -- the image, text, graph, audio, and video domains -- to
highlight the ubiquitous and pervasive nature of transferable attacks. This
paper categorizes and reviews the architecture of existing attacks from various
viewpoints: data, process, model, and system. We further examine the
implications of transferable attacks in practical scenarios such as autonomous
driving, speech recognition, and large language models (LLMs). Additionally, we
outline the potential research directions to encourage efforts in exploring the
landscape of transferable attacks. This survey offers a holistic understanding
of the prevailing transferable attacks and their impacts across different
domains.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: Large Language Model is a Good Policy Teacher for Training Reinforcement Learning Agents
Abstract: Recent studies have shown that Large Language Models (LLMs) can be utilized
for solving complex sequential decision-making tasks by providing high-level
instructions. However, LLM-based agents face limitations in real-time dynamic
environments due to their lack of specialization in solving specific target
problems. Moreover, the deployment of such LLM-based agents is both costly and
time-consuming in practical scenarios. In this paper, we introduce a novel
framework that addresses these challenges by training a smaller scale
specialized student agent using instructions from an LLM-based teacher agent.
By leveraging guided actions provided by the teachers, the prior knowledge of
the LLM is distilled into the local student model. Consequently, the student
agent can be trained with significantly less data. Furthermore, subsequent
training with environment feedback empowers the student agents to surpass the
capabilities of their teachers. We conducted experiments on three challenging
MiniGrid environments to evaluate the effectiveness of our framework. The
results demonstrate that our approach enhances sample efficiency and achieves
superior performance compared to baseline methods. Our code is available at
https://github.com/ZJLAB-AMMI/LLM4Teach.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition
Abstract: Large Language Models (LLMs) are deployed in interactive contexts with direct
user engagement, such as chatbots and writing assistants. These deployments are
vulnerable to prompt injection and jailbreaking (collectively, prompt hacking),
in which models are manipulated to ignore their original instructions and
follow potentially malicious ones. Although widely acknowledged as a
significant security threat, there is a dearth of large-scale resources and
quantitative studies on prompt hacking. To address this lacuna, we launch a
global prompt hacking competition, which allows for free-form human input
attacks. We elicit 600K+ adversarial prompts against three state-of-the-art
LLMs. We describe the dataset, which empirically verifies that current LLMs can
indeed be manipulated via prompt hacking. We also present a comprehensive
taxonomical ontology of the types of adversarial prompts.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: LSTM-CNN: An efficient diagnostic network for Parkinson's disease utilizing dynamic handwriting analysis
Abstract: Background and objectives: Dynamic handwriting analysis, due to its
non-invasive and readily accessible nature, has recently emerged as a vital
adjunctive method for the early diagnosis of Parkinson's disease. In this
study, we design a compact and efficient network architecture to analyse the
distinctive handwriting patterns of patients' dynamic handwriting signals,
thereby providing an objective identification for the Parkinson's disease
diagnosis.
  Methods: The proposed network is based on a hybrid deep learning approach
that fully leverages the advantages of both long short-term memory (LSTM) and
convolutional neural networks (CNNs). Specifically, the LSTM block is adopted
to extract the time-varying features, while the CNN-based block is implemented
using one-dimensional convolution for low computational cost. Moreover, the
hybrid model architecture is continuously refined under ablation studies for
superior performance. Finally, we evaluate the proposed method with its
generalization under a five-fold cross-validation, which validates its
efficiency and robustness.
  Results: The proposed network demonstrates its versatility by achieving
impressive classification accuracies on both our new DraWritePD dataset
($96.2\%$) and the well-established PaHaW dataset ($90.7\%$). Moreover, the
network architecture also stands out for its excellent lightweight design,
occupying a mere $0.084$M of parameters, with a total of only $0.59$M
floating-point operations. It also exhibits near real-time CPU inference
performance, with inference times ranging from $0.106$ to $0.220$s.
  Conclusions: We present a series of experiments with extensive analysis,
which systematically demonstrate the effectiveness and efficiency of the
proposed hybrid neural network in extracting distinctive handwriting patterns
for precise diagnosis of Parkinson's disease.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: CommunityAI: Towards Community-based Federated Learning
Abstract: Federated Learning (FL) has emerged as a promising paradigm to train machine
learning models collaboratively while preserving data privacy. However, its
widespread adoption faces several challenges, including scalability,
heterogeneous data and devices, resource constraints, and security concerns.
Despite its promise, FL has not been specifically adapted for community
domains, primarily due to the wide-ranging differences in data types and
context, devices and operational conditions, environmental factors, and
stakeholders. In response to these challenges, we present a novel framework for
Community-based Federated Learning called CommunityAI. CommunityAI enables
participants to be organized into communities based on their shared interests,
expertise, or data characteristics. Community participants collectively
contribute to training and refining learning models while maintaining data and
participant privacy within their respective groups. Within this paper, we
discuss the conceptual architecture, system requirements, processes, and future
challenges that must be solved. Finally, our goal within this paper is to
present our vision regarding enabling a collaborative learning process within
various communities.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Toxicity Detection is NOT all you Need: Measuring the Gaps to Supporting Volunteer Content Moderators
Abstract: Extensive efforts in automated approaches for content moderation have been
focused on developing models to identify toxic, offensive, and hateful content
-- with the aim of lightening the load for moderators. Yet, it remains
uncertain whether improvements on those tasks truly address the needs that
moderators have in accomplishing their work. In this paper, we surface the gaps
between past research efforts that have aimed to provide automation for aspects
of the content moderation task, and the needs of volunteer content moderators.
To do so, we conduct a model review on Hugging Face to reveal the availability
of models to cover various moderation rules and guidelines. We further put
state-of-the-art LLMs to the test (GPT-4 and Llama-2), evaluating how well
these models perform in flagging violations of platform rules. Overall, we
observe a non-trivial gap, as missing developed models and LLMs exhibit low
recall on a significant portion of the rules.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Multimodal Stress Detection Using Facial Landmarks and Biometric Signals
Abstract: The development of various sensing technologies is improving measurements of
stress and the well-being of individuals. Although progress has been made with
single signal modalities like wearables and facial emotion recognition,
integrating multiple modalities provides a more comprehensive understanding of
stress, given that stress manifests differently across different people.
Multi-modal learning aims to capitalize on the strength of each modality rather
than relying on a single signal. Given the complexity of processing and
integrating high-dimensional data from limited subjects, more research is
needed. Numerous research efforts have been focused on fusing stress and
emotion signals at an early stage, e.g., feature-level fusion using basic
machine learning methods and 1D-CNN Methods. This paper proposes a multi-modal
learning approach for stress detection that integrates facial landmarks and
biometric signals. We test this multi-modal integration with various
early-fusion and late-fusion techniques to integrate the 1D-CNN model from
biometric signals and 2-D CNN using facial landmarks. We evaluate these
architectures using a rigorous test of models' generalizability using the
leave-one-subject-out mechanism, i.e., all samples related to a single subject
are left out to train the model. Our findings show that late-fusion achieved
94.39\% accuracy, and early-fusion surpassed it with a 98.38\% accuracy rate.
This research contributes valuable insights into enhancing stress detection
through a multi-modal approach. The proposed research offers important
knowledge in improving stress detection using a multi-modal approach.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: 3D Hand Pose Estimation in Egocentric Images in the Wild
Abstract: We present WildHands, a method for 3D hand pose estimation in egocentric
images in the wild. This is challenging due to (a) lack of 3D hand pose
annotations for images in the wild, and (b) a form of perspective
distortion-induced shape ambiguity that arises in the analysis of crops around
hands. For the former, we use auxiliary supervision on in-the-wild data in the
form of segmentation masks & grasp labels in addition to 3D supervision
available in lab datasets. For the latter, we provide spatial cues about the
location of the hand crop in the camera's field of view. Our approach achieves
the best 3D hand pose on the ARCTIC leaderboard and outperforms FrankMocap, a
popular and robust approach for estimating hand pose in the wild, by 45.3% when
evaluated on 2D hand pose on our EPIC-HandKps dataset.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Domain-Specific Deep Learning Feature Extractor for Diabetic Foot Ulcer Detection
Abstract: Diabetic Foot Ulcer (DFU) is a condition requiring constant monitoring and
evaluations for treatment. DFU patient population is on the rise and will soon
outpace the available health resources. Autonomous monitoring and evaluation of
DFU wounds is a much-needed area in health care. In this paper, we evaluate and
identify the most accurate feature extractor that is the core basis for
developing a deep-learning wound detection network. For the evaluation, we used
mAP and F1-score on the publicly available DFU2020 dataset. A combination of
UNet and EfficientNetb3 feature extractor resulted in the best evaluation among
the 14 networks compared. UNet and Efficientnetb3 can be used as the classifier
in the development of a comprehensive DFU domain-specific autonomous wound
detection pipeline.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Improving Source-Free Target Adaptation with Vision Transformers Leveraging Domain Representation Images
Abstract: Unsupervised Domain Adaptation (UDA) methods facilitate knowledge transfer
from a labeled source domain to an unlabeled target domain, navigating the
obstacle of domain shift. While Convolutional Neural Networks (CNNs) are a
staple in UDA, the rise of Vision Transformers (ViTs) provides new avenues for
domain generalization. This paper presents an innovative method to bolster ViT
performance in source-free target adaptation, beginning with an evaluation of
how key, query, and value elements affect ViT outcomes. Experiments indicate
that altering the key component has negligible effects on Transformer
performance. Leveraging this discovery, we introduce Domain Representation
Images (DRIs), feeding embeddings through the key element. DRIs act as
domain-specific markers, effortlessly merging with the training regimen. To
assess our method, we perform target adaptation tests on the Cross Instance DRI
source-only (SO) control. We measure the efficacy of target adaptation with and
without DRIs, against existing benchmarks like SHOT-B* and adaptations via
CDTrans. Findings demonstrate that excluding DRIs offers limited gains over
SHOT-B*, while their inclusion in the key segment boosts average precision
promoting superior domain generalization. This research underscores the vital
role of DRIs in enhancing ViT efficiency in UDA scenarios, setting a precedent
for further domain adaptation explorations.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Exploring Post-Training Quantization of Protein Language Models
Abstract: Recent advancements in unsupervised protein language models (ProteinLMs),
like ESM-1b and ESM-2, have shown promise in different protein prediction
tasks. However, these models face challenges due to their high computational
demands, significant memory needs, and latency, restricting their usage on
devices with limited resources. To tackle this, we explore post-training
quantization (PTQ) for ProteinLMs, focusing on ESMFold, a simplified version of
AlphaFold based on ESM-2 ProteinLM. Our study is the first attempt to quantize
all weights and activations of ProteinLMs. We observed that the typical uniform
quantization method performs poorly on ESMFold, causing a significant drop in
TM-Score when using 8-bit quantization. We conducted extensive quantization
experiments, uncovering unique challenges associated with ESMFold, particularly
highly asymmetric activation ranges before Layer Normalization, making
representation difficult using low-bit fixed-point formats. To address these
challenges, we propose a new PTQ method for ProteinLMs, utilizing piecewise
linear quantization for asymmetric activation values to ensure accurate
approximation. We demonstrated the effectiveness of our method in protein
structure prediction tasks, demonstrating that ESMFold can be accurately
quantized to low-bit widths without compromising accuracy. Additionally, we
applied our method to the contact prediction task, showcasing its versatility.
In summary, our study introduces an innovative PTQ method for ProteinLMs,
addressing specific quantization challenges and potentially leading to the
development of more efficient ProteinLMs with significant implications for
various protein-related applications.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Toward Reinforcement Learning-based Rectilinear Macro Placement Under Human Constraints
Abstract: Macro placement is a critical phase in chip design, which becomes more
intricate when involving general rectilinear macros and layout areas.
Furthermore, macro placement that incorporates human-like constraints, such as
design hierarchy and peripheral bias, has the potential to significantly reduce
the amount of additional manual labor required from designers. This study
proposes a methodology that leverages an approach suggested by Google's Circuit
Training (G-CT) to provide a learning-based macro placer that not only supports
placing rectilinear cases, but also adheres to crucial human-like design
principles. Our experimental results demonstrate the effectiveness of our
framework in achieving power-performance-area (PPA) metrics and in obtaining
placements of high quality, comparable to those produced with human
intervention. Additionally, our methodology shows potential as a generalized
model to address diverse macro shapes and layout areas.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: A Multi-Center Study on the Adaptability of a Shared Foundation Model for Electronic Health Records
Abstract: Foundation models hold promise for transforming AI in healthcare by providing
modular components that are easily adaptable to downstream healthcare tasks,
making AI development more scalable and cost-effective. Structured EHR
foundation models, trained on coded medical records from millions of patients,
demonstrated benefits including increased performance with fewer training
labels, and improved robustness to distribution shifts. However, questions
remain on the feasibility of sharing these models across different hospitals
and their performance for local task adaptation. This multi-center study
examined the adaptability of a recently released structured EHR foundation
model ($FM_{SM}$), trained on longitudinal medical record data from 2.57M
Stanford Medicine patients. Experiments were conducted using EHR data at The
Hospital for Sick Children and MIMIC-IV. We assessed both adaptability via
continued pretraining on local data, and task adaptability compared to
baselines of training models from scratch at each site, including a local
foundation model. We evaluated the performance of these models on 8 clinical
prediction tasks. In both datasets, adapting the off-the-shelf $FM_{SM}$
matched the performance of GBM models locally trained on all data while
providing a 13% improvement in settings with few task-specific training labels.
With continued pretraining on local data, label efficiency substantially
improved, such that $FM_{SM}$ required fewer than 1% of training examples to
match the fully trained GBM's performance. Continued pretraining was also 60 to
90% more sample-efficient than training local foundation models from scratch.
Our findings show that adapting shared EHR foundation models across hospitals
provides improved prediction performance at less cost, underscoring the utility
of base foundation models as modular components to streamline the development
of healthcare AI.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Enabling Decision-Support Systems through Automated Cell Tower Detection
Abstract: Cell phone coverage and high-speed service gaps persist in rural areas in
sub-Saharan Africa, impacting public access to mobile-based financial,
educational, and humanitarian services. Improving maps of telecommunications
infrastructure can help inform strategies to eliminate gaps in mobile coverage.
Deep neural networks, paired with remote sensing images, can be used for object
detection of cell towers and eliminate the need for inefficient and burdensome
manual mapping to find objects over large geographic regions. In this study, we
demonstrate a partially automated workflow to train an object detection model
to locate cell towers using OpenStreetMap (OSM) features and high-resolution
Maxar imagery. For model fine-tuning and evaluation, we curated a diverse
dataset of over 6,000 unique images of cell towers in 26 countries in eastern,
southern, and central Africa using automatically generated annotations from OSM
points. Our model achieves an average precision at 50% Intersection over Union
(IoU) (AP@50) of 81.2 with good performance across different geographies and
out-of-sample testing. Accurate localization of cell towers can yield more
accurate cell coverage maps, in turn enabling improved delivery of digital
services for decision-support applications.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: REST: Retrieval-Based Speculative Decoding
Abstract: We introduce Retrieval-Based Speculative Decoding (REST), a novel algorithm
designed to speed up language model generation. The key insight driving the
development of REST is the observation that the process of text generation
often includes certain common phases and patterns. Unlike previous methods that
rely on a draft language model for speculative decoding, REST harnesses the
power of retrieval to generate draft tokens. This method draws from the
reservoir of existing knowledge, retrieving and employing relevant tokens based
on the current context. Its plug-and-play nature allows for seamless
integration and acceleration of any language models, all without necessitating
additional training. When benchmarked on 7B and 13B language models in a
single-batch setting, REST achieves a significant speedup of 1.62X to 2.36X on
code or text generation. The code of REST is available at
https://github.com/FasterDecoding/REST.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation
Abstract: Vision Transformers (ViTs) have revolutionized the field of computer vision,
yet their deployments on resource-constrained devices remain challenging due to
high computational demands. To expedite pre-trained ViTs, token pruning and
token merging approaches have been developed, which aim at reducing the number
of tokens involved in the computation. However, these methods still have some
limitations, such as image information loss from pruned tokens and inefficiency
in the token-matching process. In this paper, we introduce a novel Graph-based
Token Propagation (GTP) method to resolve the challenge of balancing model
efficiency and information preservation for efficient ViTs. Inspired by graph
summarization algorithms, GTP meticulously propagates less significant tokens'
information to spatially and semantically connected tokens that are of greater
importance. Consequently, the remaining few tokens serve as a summarization of
the entire token graph, allowing the method to reduce computational complexity
while preserving essential information of eliminated tokens. Combined with an
innovative token selection strategy, GTP can efficiently identify image tokens
to be propagated. Extensive experiments have validated GTP's effectiveness,
demonstrating both efficiency and performance improvements. Specifically, GTP
decreases the computational complexity of both DeiT-S and DeiT-B by up to 26%
with only a minimal 0.3% accuracy drop on ImageNet-1K without finetuning, and
remarkably surpasses the state-of-the-art token merging method on various
backbones at an even faster inference speed. The source code is available at
https://github.com/Ackesnal/GTP-ViT.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Differentially Private Pre-Trained Model Fusion using Decentralized Federated Graph Matching
Abstract: Model fusion is becoming a crucial component in the context of
model-as-a-service scenarios, enabling the delivery of high-quality model
services to local users. However, this approach introduces privacy risks and
imposes certain limitations on its applications. Ensuring secure model exchange
and knowledge fusion among users becomes a significant challenge in this
setting. To tackle this issue, we propose PrivFusion, a novel architecture that
preserves privacy while facilitating model fusion under the constraints of
local differential privacy. PrivFusion leverages a graph-based structure,
enabling the fusion of models from multiple parties without necessitating
retraining. By employing randomized mechanisms, PrivFusion ensures privacy
guarantees throughout the fusion process. To enhance model privacy, our
approach incorporates a hybrid local differentially private mechanism and
decentralized federated graph matching, effectively protecting both activation
values and weights. Additionally, we introduce a perturbation filter adapter to
alleviate the impact of randomized noise, thereby preserving the utility of the
fused model. Through extensive experiments conducted on diverse image datasets
and real-world healthcare applications, we provide empirical evidence
showcasing the effectiveness of PrivFusion in maintaining model performance
while preserving privacy. Our contributions offer valuable insights and
practical solutions for secure and collaborative data analysis within the
domain of privacy-preserving model fusion.

Output: Machine Learning