May 2024 arXiv papers — page 25

Alexey Kuznetsov, Zhao Wu, Sergey Anfinogentov, Yang Su

We investigated in detail the M5.8 class solar flare that occurred on 2023-03-06. This flare was one of the first strong flares observed by the Siberian Radioheliograph in the microwave range and the Advanced Space-based Solar Observatory in the X-ray range. The flare consisted of two separate flaring events (a "thermal" and a "cooler" ones), and was associa

Jiaze Wang, Hao Chen, Hongcan Xu, Jinpeng Li

Weather forecasting plays a critical role in various sectors, driving decision-making and risk management. However, traditional methods often struggle to capture the complex dynamics of meteorological systems, particularly in the presence of high-resolution data. In this paper, we propose the Spatial-Frequency Attention Network (SFANet), a novel deep learnin

Alain Ryser, Thomas M. Sutter, Alexander Marx, Julia E. Vogt

Anomaly detection focuses on identifying samples that deviate from the norm. Discovering informative representations of normal samples is crucial to detecting anomalies effectively. Recent self-supervised methods have successfully learned such representations by employing prior knowledge about anomalies to create synthetic outliers during training. However,

Amna Pir Muhammad, Eric Knauss, Odzaya Batsaikhan, Nassiba El Haskouri

Research shows that many of the challenges currently encountered with agile development are related to requirements engineering. Based on design science research, this paper investigates critical challenges that arise in agile development from an undefined requirements strategy. We explore potential ways to address these challenges and synthesize the key bui

Taketo Inaba, Futoshi Takahashi

In this paper, we study a nonlocal boundary blow up problem on an interval and obtain the precise asymptotic formula for solutions when the bifurcation parameter in the problem is large.

Silvia García Méndez, Fátima Leal, Benedita Malheiro, Juan Carlos Burguillo Rial

Data crowdsourcing is a data acquisition process where groups of voluntary contributors feed platforms with highly relevant data ranging from news, comments, and media to knowledge and classifications. It typically processes user-generated data streams to provide and refine popular services such as wikis, collaborative maps, e-commerce sites, and social netw

Shiyuan Sun, Fang Yang, Weidong Mei, Jian Song

Optical intelligent reflecting surface (OIRS) offers a new and effective approach to resolving the line-of-sight blockage issue in visible light communication (VLC) by enabling redirection of light to bypass obstacles, thereby dramatically enhancing indoor VLC coverage and reliability. This article provides a comprehensive overview of OIRS for VLC, including

Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley, Sunil Aryal

In this era of advanced manufacturing, it's now more crucial than ever to diagnose machine faults as early as possible to guarantee their safe and efficient operation. With the massive surge in industrial big data and advancement in sensing and computational technologies, data-driven Machinery Fault Diagnosis (MFD) solutions based on machine/deep learning ap

Zhiyuan You, Jinjin Gu, Xin Cai, Zheyuan Li

With the rapid advancement of Vision Language Models (VLMs), VLM-based Image Quality Assessment (IQA) seeks to describe image quality linguistically to align with human expression and capture the multifaceted nature of IQA tasks. However, current methods are still far from practical usage. First, prior works focus narrowly on specific sub-tasks or settings,

Amna Pir Muhammad, Eric Knauss, Jonas Bärgman, Alessia Knauss

Due to the technical complexity and social impact, automated vehicle (AV) development challenges the current state of automotive engineering practice. Research shows that it is important to consider human factors (HF) knowledge when developing AVs to make them safe and accepted. This study explores the current practices and challenges of the automotive indus

Zelin Peng, Zhengqin Xu, Zhilin Zeng, Yaoming Wang

Open-vocabulary semantic segmentation seeks to label each pixel in an image with arbitrary text descriptions. Vision-language foundation models, especially CLIP, have recently emerged as powerful tools for acquiring open-vocabulary capabilities. However, fine-tuning CLIP to equip it with pixel-level prediction ability often suffers three issues: 1) high comp

Guénolé Fiche, Simon Leglaive, Xavier Alameda-Pineda, Francesc Moreno-Noguer

Human Mesh Recovery (HMR) from a single RGB image is a highly ambiguous problem, as an infinite set of 3D interpretations can explain the 2D observation equally well. Nevertheless, most HMR methods overlook this issue and make a single prediction without accounting for this ambiguity. A few approaches generate a distribution of human meshes, enabling the sam

Amna Pir Muhammad, Alessia Knauss, Eric Knauss, Jonas Bärgman

The integration of human factors (HF) knowledge is crucial when developing safety-critical systems, such as automated vehicles (AVs). Ensuring that HF knowledge is considered continuously throughout the AV development process is essential for several reasons, including efficacy, safety, and acceptance of these advanced systems. However, it is challenging to

Fei Zhang, Jie Li, Zhimin He, Haozhen Situ

With the exponentially faster computation for certain problems, quantum computing has garnered significant attention in recent years. Variational quantum algorithms are crucial methods to implement quantum computing, and an appropriate task-specific quantum circuit ansatz can effectively enhance the quantum advantage of VQAs. However, the vast search space m

Siyuan Guo, Chi Zhang, Karthika Mohan, Ferenc Huszár

We study causal effect estimation in a setting where the data are not i.i.d. (independent and identically distributed). We focus on exchangeable data satisfying an assumption of independent causal mechanisms. Traditional causal effect estimation frameworks, e.g., relying on structural causal models and do-calculus, are typically limited to i.i.d. data and do

A. K. Alauadinov, B. B. Yusupov

The present paper is devoted to study local super-derivations of the super Schr\"{o}dinger algebras. We prove that every local super-derivation on the super Schr\"{o}dinger algebra is a super-derivation.

A. Hammad, P. Ko, Chih-Ting Lu, Myeonghun Park

The Standard Model (SM) Higgs boson, the most recently discovered elementary particle, may still serve as a mediator between the SM sector and a new physics sector related to dark matter (DM). The Large Hadron Collider (LHC) has not yet fully constrained the physics associated with the Higgs boson, leaving room for such possibilities. Among the various poten

Sujoy Bhore, Sarfaraz Equbal, Rohit Gurjar

In this work, we study the parallel complexity of the Euclidean minimum-weight perfect matching (EWPM) problem. Here our graph is the complete bipartite graph $G$ on two sets of points $A$ and $B$ in $\mathbb{R}^2$ and the weight of each edge is the Euclidean distance between the corresponding points. The weighted perfect matching problem on general bipartit

Taehyun Kim, Kwanseok Choi, Youngmock Cho, Jaehoon Cho

Mixture-of-Experts (MoE) large language models (LLM) have memory requirements that often exceed the GPU memory capacity, requiring costly parameter movement from secondary memories to the GPU for expert computation. In this work, we present Mixture of Near-Data Experts (MoNDE), a near-data computing solution that efficiently enables MoE LLM inference. MoNDE

Simranjit Singh, Georgios Pavlakos, Dimitrios Stamoulis

As interest in "reformulating" the 3D Visual Question Answering (VQA) problem in the context of foundation models grows, it is imperative to assess how these new paradigms influence existing closed-vocabulary datasets. In this case study, we evaluate the zero-shot performance of foundational models (GPT-4 Vision and GPT-4) on well-established 3D VQA benchmar

Antonio Rosales, Tapio Heikkilä, Markku Suomalainen

This paper proposes a feature-based Visual Servoing (VS) method for insertion task skills. A camera mounted on the robot's end-effector provides the pose relative to a cylinder (hole), allowing a contact-free and damage-free search of the hole and avoiding uncertainties emerging when the pose is computed via robot kinematics. Two points located on the hole's

Guillaume Ferriere

We consider an infinite ferromagnetic nanowire, with an energy functional $E$ with easy-axis in the direction $e_1$ and a constant external magnetic field $H_{ext} = h_0 e_1$ along the same direction. The evolution of its magnetization is governed by the Landau-Lifshitz-Gilbert equation (LLG) associated to $E$. Under some assumptions on $h_0$, we prove the e

Sophie Jaffard, Samuel Vaiter, Patricia Reynaud-Bouret

The present work aims at proving mathematically that a neural network inspired by biology can learn a classification task thanks to local transformations only. In this purpose, we propose a spiking neural network named CHANI (Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration), whose neurons activity is modeled by Hawkes processes. Synaptic

Tomasz Rybotycki, Tomasz Białecki, Josep Batle, Adam Bednorz

We construct a null test of the two-level space of a qubit, which is both device independent and needs a small number of different experiments. We demonstrate its feasibility on IBM Quantum, with most qubits failing the test by more than 10 standard deviations. The robustness of the test against common technical imperfections, like decoherence and phase shif

San-Dong Guo, Liguo Zhang, Xiao-Shu Guo, Gangqiang Zhu

Exploring combination of antiferromagnetic (AFM) spintronics and anomalous valley Hall effect (AVHE) is one of the most important questions for valleytronic applications. The key to address this issue is to achieve spin splitting around the valleys in AFM systems. Here, we propose a possible way for achieving AVHE in hexagonal AFM monolayer, which involves t

Khaleel Al-Adhami, Dev Chheda

The tango tree is the first proven $O(\lg \lg n)$-competitive binary search tree (BST). We present the first ever experimental implementation of tango trees and compare the running time of the tango tree with the multi-splay tree and the splay tree on a variety of families of access sequences. We construct access sequences that are intended to test specific

Peican Zhu, Zechen Pan, Keke Tang, Xiaodong Cui

Graph Neural Network (GNN) has achieved remarkable success in various graph learning tasks, such as node classification, link prediction and graph classification. The key to the success of GNN lies in its effective structure information representation through neighboring aggregation. However, the attacker can easily perturb the aggregation process through in

Hallah Shahid Butt, Benjamin Schäfer

With economic development, the complexity of infrastructure has increased drastically. Similarly, with the shift from fossil fuels to renewable sources of energy, there is a dire need for such systems that not only predict and forecast with accuracy but also help in understanding the process of predictions. Artificial intelligence and machine learning techni

Sanghati Saha, Ertan Güdekli, Surajit Chattopadhyay

The holographic cut-off in generalized dark energy (HDE) formalism depends on its cut-off. Following this, a four-parameter generalized entropy has recently been developed. It reduces to various known entropies for appropriate parameter limits in the study of Odintsov, S. D., S. DOnofrio, and T. Paul. (2023) Physics of the Dark Universe, 42 pp: 101277. In th

Zhanhao Hu, Julien Piet, Geng Zhao, Jiantao Jiao

Current LLMs are generally aligned to follow safety requirements and tend to refuse toxic prompts. However, LLMs can fail to refuse toxic prompts or be overcautious and refuse benign examples. In addition, state-of-the-art toxicity detectors have low TPRs at low FPR, incurring high costs in real-world applications where toxic examples are rare. In this paper

Arkady Berenstein, Jacob Greenstein, Jian-Rong Li

The aim of the present work is to systematically study homomorphisms of Hecke and Artin monoids and thus to develop their comprehensive theory. Our original motivation was the striking observation that parabolic projections of Hecke monoids respect all parabolic elements. We found other classes of homomorphisms of Hecke monoids with the same property and dis

Mathieu Carriere, Marc Theveneau, Théo Lacombe

Topological Data Analysis (TDA) provides a pipeline to extract quantitative topological descriptors from structured objects. This enables the definition of topological loss functions, which assert to what extent a given object exhibits some topological properties. These losses can then be used to perform topological optimizationvia gradient descent routines.

Yilin Wang

Kagome metals exhibit rich quantum states by the intertwining of lattice, charge, orbital and spin degrees of freedom. Recently, a novel charge density wave (CDW) ground state was discovered in kagome magnet FeGe and was revealed to be driven by lowering magnetic energy via large Ge1-dimerization. Here, based on DFT calculations, we show that such mechanism

V. A. Filippov, R. M. Iakhibbaev, D. I. Kazakov, D. M. Tolkachev

In this paper we calculate quantum corrections to the effective potential in different models of inflationary cosmology. We show that quantum corrections lead to a modification of the initial potential uplifting its value at the minimum, which can be interpreted as a cosmological constant/dark energy. We concentrate on the models of $\alpha$-attractors and s

Yoichi Nii, Kei Yamamoto, Masaki Kanno, Sadamichi Maekawa

Rectification phenomenon caused by the simultaneous breaking of time reversal and spatial inversion symmetries has been extended to a wide range of (quasi)particles and waves; however, the nonreciprocal diffraction, which is the imbalance of upward and downward deflections, was previously observed only for photons and remained to be extended to other (quasi)

Yasi Zhang, Peiyu Yu, Yaxuan Zhu, Yingshan Chang

Generative models based on flow matching have attracted significant attention for their simplicity and superior performance in high-resolution image synthesis. By leveraging the instantaneous change-of-variables formula, one can directly compute image likelihoods from a learned flow, making them enticing candidates as priors for downstream tasks such as inve

Dev Chheda, Ram Goel, Eddie Qiao

We review the progress made on bounding the number of independent sets in $d$-regular and irregular graphs over the last 31 years. We particularly focus on contributions from Kahn, Zhao, and Sah et al. in incrementally proving stronger and more general versions of the upper bound. We reproduce the main results of these works, particularly focusing on the unw

Daniel I. Lucas, Théo Guillaume, Dwayne E. Heard, Julia H. Lehman

We present the development of a new astrochemical research tool HILTRAC, the Highly Instrumented Low Temperature ReAction Chamber. The instrument is based on a pulsed form of the CRESU (Cin\'etique de R\'eaction en \'Ecoulement Supersonique Uniforme, meaning reaction kinetics in a uniform supersonic flow) apparatus, with the aim of collecting kinetics and sp

Tetsutaro Higaki, Tatsuo Kobayashi, Kaito Nasu, Hajime Otsuka

We study the realization of spontaneous CP violation through moduli stabilization. In modular flavor models, the source of CP violation is the vacuum expectation values of the complex structure moduli of toroidal compact space. We demonstrate that the combined effects of Type IIB flux compactifications with modular invariant couplings between the moduli and

Ziqi Ren, Jie Li, Xuetong Xue, Xin Li

Deciphering the human visual experience through brain activities captured by fMRI represents a compelling and cutting-edge challenge in the field of neuroscience research. Compared to merely predicting the viewed image itself, decoding brain activity into meaningful captions provides a higher-level interpretation and summarization of visual information, whic

Debashish Mukherji

Thermal transport coefficient $\kappa$ is an important property that often dictates broad applications of a polymeric material, while at the same time its computation remains challenging. In particular, classical simulations overestimate $\kappa$ than the experimentally measured $\kappa^{\rm exp}$ and thus hinder their meaningful comparison. This is even whe

Jingjing Xie, Yuxin Zhang, Mingbao Lin, Zhihang Lin

Post-training Sparsity (PTS) is a recently emerged avenue that chases efficient network sparsity with limited data in need. Existing PTS methods, however, undergo significant performance degradation compared with traditional methods that retrain the sparse networks via the whole dataset, especially at high sparsity ratios. In this paper, we attempt to reconc

Ta Duy Nguyen, Alina Ene

We study the densest subgraph problem and give algorithms via multiplicative weights update and area convexity that converge in $O\left(\frac{\log m}{\epsilon^{2}}\right)$ and $O\left(\frac{\log m}{\epsilon}\right)$ iterations, respectively, both with nearly-linear time per iteration. Compared with the work by Bahmani et al. (2014), our MWU algorithm uses a

Xuan-Bac Nguyen, Hojin Jang, Xin Li, Samee U. Khan

The human brain is a highly efficient processing unit, and understanding how it works can inspire new algorithms and architectures in machine learning. In this work, we introduce a novel framework named Brain Activation Network (BRACTIVE), a transformer-based approach to studying the human visual brain. The primary objective of BRACTIVE is to align the visua

Mathilde Lacroix, Bart Smeets, Carlès Blanch-Mercader, Samuel Bell

Directed collective cell migration is central in morphogenesis, wound healing and cancer progression1,2. Although it is well-accepted that the molecular anisotropy of the micro-environment guides this migration3,4, its impact on the pattern of the cell flows remains largely unexplored. Studying confluent human bronchial epithelial cells (HBECs) in vitro, we

David Kapanadze, Zurab Vashakidze

This paper examines the propagation of time-harmonic waves in a two-dimensional triangular lattice with a lattice constant $a = 1$. The sources are positioned along line segments within the lattice. Specifically, we investigate the discrete Helmholtz equation with a wavenumber $k \in \left( 0,2\sqrt{2} \right)$, where input data is prescribed on finite rows

Bart M. N. Smets, Peter D. Donker, Jim W. Portegies

We introduce a class of trainable nonlinear operators based on semirings that are suitable for use in neural networks. These operators generalize the traditional alternation of linear operators with activation functions in neural networks. Semirings are algebraic structures that describe a generalised notation of linearity, greatly expanding the range of tra

Zilin Si, Kevin Lee Zhang, Zeynep Temel, Oliver Kroemer

Dexterous robotic manipulation remains a challenging domain due to its strict demands for precision and robustness on both hardware and software. While dexterous robotic hands have demonstrated remarkable capabilities in complex tasks, efficiently learning adaptive control policies for hands still presents a significant hurdle given the high dimensionalities

Minyu Feng, Ziyan Zeng, Qin Li, Matjaž Perc

Dynamic processes in complex networks are crucial for better understanding collective behavior in human societies, biological systems, and the internet. In this paper, we first focus on the continuous Markov-based modeling of evolving networks with the birth-death of individuals. A new individual arrives at the group by the Poisson process, while new links a

Wenjie Li, Kai Fan, Jingyuan Zhang, Hui Li

Federated Learning (FL) is a promising privacy-preserving machine learning paradigm that allows data owners to collaboratively train models while keeping their data localized. Despite its potential, FL faces challenges related to the trustworthiness of both clients and servers, particularly against curious or malicious adversaries. In this paper, we introduc

Zhenbei Wu, Qiang Wang, Jie Yang

The scarcity of free-hand sketch presents a challenging problem. Despite the emergence of some large-scale sketch datasets, these datasets primarily consist of sketches at the single-object level. There continues to be a lack of large-scale paired datasets for scene sketches. In this paper, we propose a self-supervised method for scene sketch generation that

Zhenhua Zhao, Ji Chen, Zhicheng Lin, Haojiang Ying

Whether face processing depends on unique, domain-specific neurocognitive mechanisms or domain-general object recognition mechanisms has long been debated. Directly testing these competing hypotheses in humans has proven challenging due to extensive exposure to both faces and objects. Here, we systematically test these hypotheses by capitalizing on recent pr

Ali Waqar Azim, Ahmad Bazzi, Roberto Bomfin, Raed Shubair

This article examines two chirp spread spectrum techniques specifically devised for low-power wide-area networks (LPWANs) to optimize energy and spectral efficiency (SE). These methods referred to as layered CSS (LCSS) and layered dual-mode CSS (LDMCSS), involves utilizing multiple layers for multiplexing symbols with varying chirp rates. These waveform desi

Mohammad Ali S. Afshar, Jafar Sadeghi

In this paper, we base our analysis on the assumption that the existence of a photon sphere is an intrinsic feature of any ultra-compact gravitational structure with spherical symmetry. Utilizing the concept of a topological photon sphere, we categorize the behaviors of various gravitational models based on the structure of their photon spheres. This innovat

Miao Dai, Gang Sun, Hongfang Yu, Sheng Wang

With the proliferation of mobile terminals and the continuous upgrading of services, 4G LTE networks are showing signs of weakness. To enhance the capacity of wireless networks, millimeter waves are introduced to drive the evolution of networks towards multi-band 5G heterogeneous networks. The distinct propagation characteristics of mmWaves and microwaves, a

Zhong Zheng, Haochen Zhang, Lingzhou Xue

In this paper, we consider model-free federated reinforcement learning for tabular episodic Markov decision processes. Under the coordination of a central server, multiple agents collaboratively explore the environment and learn an optimal policy without sharing their raw data. Despite recent advances in federated Q-learning algorithms achieving near-linear

Earl Almazan, Anthony Affolder, Ian Dyckes, Vitaliy Fadeyev

Future tracking systems in High Energy Physics experiments will require large instrumented areas with low radiation length. Crystalline silicon sensors have been used in tracking systems for decades, but are difficult to manufacture and costly to produce for large areas. We are exploring alternative sensor materials that are amenable to fast fabrication tech

Avik Kar, Rahul Singh

We study the infinite-horizon average-reward reinforcement learning (RL) for continuous space Lipschitz MDPs in which an agent can play policies from a given set $\Phi$. The proposed algorithms efficiently explore the policy space by ''zooming'' into the ''promising regions'' of $\Phi$, thereby achieving adaptivity gains in the performance. We upper bound th

Haanvid Lee, Tri Wahyu Guntara, Jongmin Lee, Yung-Kyun Noh

We consider off-policy evaluation (OPE) of deterministic target policies for reinforcement learning (RL) in environments with continuous action spaces. While it is common to use importance sampling for OPE, it suffers from high variance when the behavior policy deviates significantly from the target policy. In order to address this issue, some recent works o

Alwani Liyana Ahmad, Jose Sanchez-Bornot, Roberto C. Sotero, Damien Coyle

Alzheimer Disease poses a significant challenge, necessitating early detection for effective intervention. MRI is a key neuroimaging tool due to its ease of use and cost effectiveness. This study analyzes machine learning methods for MRI based biomarker selection and classification to distinguish between healthy controls and those who develop mild cognitive

Shouwei Hui, Michael Zhang

This paper presents a novel approach to coordinated vehicle platooning, where the platoon followers communicate solely with the platoon leader. A dynamic model is proposed to account for driving safety under communication delays. General linear stability results are mathematically proven, and numerical simulations are performed to analyze the impact of model

Zhangkai Ni, Yue Liu, Keyan Ding, Wenhan Yang

Deep learning-based methods have significantly influenced the blind image quality assessment (BIQA) field, however, these methods often require training using large amounts of human rating data. In contrast, traditional knowledge-based methods are cost-effective for training but face challenges in effectively extracting features aligned with human visual per

Nodari Vakhania, Frank Werner, Kevin Johedan Ramírez-Fuentes, Víctor Pacheco-Valencia

Since the publication of the first scheduling paper in 1954, a huge number of works dealing with different types of single machine problems appeared. They addressed many heuristics and enumerative procedures, complexity results or structural properties of certain problems. Regarding surveys, often particular subjects like special objective functions are disc

Ignacio Martín, Gorka Zamora, Jan Fousek, Michael Schirner

This paper introduces TVB C++, a streamlined and fast C++ Back-End for The Virtual Brain (TVB), a renowned platform and a benchmark tool for full-brain simulation. TVB C++ is engineered with speed as a primary focus while retaining the flexibility and ease of use characteristic of the original TVB platform. Positioned as a complementary tool, TVB serves as a

Vijay Reddy Vundela, Vijay Muralidharan

In this paper, a new under-actuated Bi-Quadcopter Unmanned Aerial Vehicle is introduced. The proposed drone configuration can be controlled similar to a Bicopter. The dynamics of the proposed Bi-Quadcopter is developed using the Newton-Euler approach. Using the force decomposition technique, a mapping between the control wrench and actuator inputs is develop

Hongduan Tian, Feng Liu, Tongliang Liu, Bo Du

In cross-domain few-shot classification, \emph{nearest centroid classifier} (NCC) aims to learn representations to construct a metric space where few-shot classification can be performed by measuring the similarities between samples and the prototype of each class. An intuition behind NCC is that each sample is pulled closer to the class centroid it belongs

Gary J Mooney

Quantum computing promises breakthroughs in simulating and solving complex, classically intractable problems. However, current noisy intermediate-scale quantum (NISQ) devices are relatively small and error-prone, prohibiting large-scale computations. To achieve quantum advantage in this regime, it is crucial to minimise the impact of noise from qubit decoher

Zhaoliang Zhang, Tianchen Song, Yongjae Lee, Li Yang

Recently, 3D Gaussian Splatting (3DGS) has become one of the mainstream methodologies for novel view synthesis (NVS) due to its high quality and fast rendering speed. However, as a point-based scene representation, 3DGS potentially generates a large number of Gaussians to fit the scene, leading to high memory usage. Improvements that have been proposed requi

Seung Park, Kyunghyun Baek, Seungjin Lee, Mahn-Soo Choi

We present a global optimization routine for the variational quantum algorithms, which utilizes the dynamic tunneling flow. Originally designed to leverage information gathered by a gradient-based optimizer around local minima, we adapt the conventional dynamic tunneling flow to exploit the distance measure of quantum states, resolving issues of extrinsic de

Zihui Wu, Yu Sun, Yifan Chen, Bingliang Zhang

Diffusion models (DMs) have recently shown outstanding capabilities in modeling complex image distributions, making them expressive image priors for solving Bayesian inverse problems. However, most existing DM-based methods rely on approximations in the generative process to be generic to different inverse problems, leading to inaccurate sample distributions

Xinyi Wu, Amir Ajorlou, Yifei Wang, Stefanie Jegelka

Self-attention is the key mechanism of transformers, which are the essential building blocks of modern foundation models. Recent studies have shown that pure self-attention suffers from an increasing degree of rank collapse as depth increases, limiting model expressivity and further utilization of model depth. The existing literature on rank collapse, howeve

Isha Chaudhary, Qian Hu, Manoj Kumar, Morteza Ziyadi

Large Language Models (LLMs) can produce biased responses that can cause representational harms. However, conventional studies are insufficient to thoroughly evaluate biases across LLM responses for different demographic groups (a.k.a. counterfactual bias), as they do not scale to large number of inputs and do not provide guarantees. Therefore, we propose th

Guanao Yan, Shuo Harper Hua, Jingyi Jessica Li

In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 33 state-of-the-art methods, categorizing SVGs into three types: overall, cell-type-specific, and spatial-domain-marker SV

Yuichiro Toma

We consider the moment of quadratic twists of the M\"obius function of the form \[ S_k(X,Y) = \sum_{d\leq X} \left( \sum_{n\leq Y} \left(\frac{8d}{n}\right) \mu(n)\right)^k, \] where $\left(\frac{8d}{\cdot}\right)$ is the Kronecker symbol and $d$ runs over positive, odd and square-free integers. We give unconditional results for their asymptotic behaviors.

Tianshu Chu, Dachuan Xu, Wei Yao, Jin Zhang

While stochastic bilevel optimization methods have been extensively studied for addressing large-scale nested optimization problems in machine learning, it remains an open question whether the optimal complexity bounds for solving bilevel optimization are the same as those in single-level optimization. Our main result resolves this question: SPABA, an adapta

Qin Yang, Meisam Mohammad, Han Wang, Ali Payani

Differentially Private Stochastic Gradient Descent (DP-SGD) and its variants have been proposed to ensure rigorous privacy for fine-tuning large-scale pre-trained language models. However, they rely heavily on the Gaussian mechanism, which may overly perturb the gradients and degrade the accuracy, especially in stronger privacy regimes (e.g., the privacy bud

Qihao Peng, Hong Ren, Zhendong Peng, Cunhua Pan

Carrier frequency and timing synchronization play the fundamental roles in cooperative integrating communication and sensing (ISAC). To mitigate the effects of synchronization error, this paper develops a novel synchronization scheme in cell-free massive multiple-input multiple-output (mMIMO) systems. First, we characterize the impacts of pilot contamination

Mingrui Ma, Yu Yang

Medical image registration is an essential topic in medical image analysis. In this paper, we propose a method for medical image registration using a pretrained large language model. We find that using the pretrained large language model to encode deep features of the medical images in the registration model can effectively improve image registration accurac

Mohd Shariq, Praveen Mathil, Jitender Kumar

The upper ideal relation graph $\Gamma_{U}(R)$ of a commutative ring $R$ with unity is a simple undirected graph with the set of all non-unit elements of $R$ as a vertex set and two vertices $x$, $y$ are adjacent if and only if the principal ideals $(x)$ and $(y)$ are contained in the principal ideal $(z)$ for some non-unit element $z\in R$. This manuscript

Saba Sadeghi Ahouei, Jacob de Nobel, Aneta Neumann, Thomas Bäck

Chance-constrained problems involve stochastic components in the constraints which can be violated with a small probability. We investigate the impact of different types of chance constraints on the performance of iterative search algorithms and study the classical maximum coverage problem in graphs with chance constraints. Our goal is to evolve reliable cha

Junjie Yang, Zhi-Hao Cui, Ankit Mahajan, Huanchen Zhai

Polarons are quasiparticles formed as a result of lattice distortions induced by charge carriers. The single-electron Holstein model captures the fundamentals of single polaron physics. We examine the power of the exponential ansatz for the polaron ground-state wavefunction in its coupled cluster, canonical transformation, and (canonically transformed) pertu

Futa Waseda, Antonio Tejero-de-Pablos, Isao Echizen

Pre-trained vision-language (VL) models are highly vulnerable to adversarial attacks. However, existing defense methods primarily focus on image classification, overlooking two key aspects of VL tasks: multimodal attacks, where both image and text can be perturbed, and the one-to-many relationship of images and texts, where a single image can correspond to m

Xinji Mai, Haoran Wang, Zeng Tao, Junxiong Lin

Dynamic Facial Expression Recognition (DFER) is crucial for affective computing but often overlooks the impact of scene context. We have identified a significant issue in current DFER tasks: human annotators typically integrate emotions from various angles, including environmental cues and body language, whereas existing DFER methods tend to consider the sce

Divya Nori, Wengong Jin

The growing significance of RNA engineering in diverse biological applications has spurred interest in developing AI methods for structure-based RNA design. While diffusion models have excelled in protein design, adapting them for RNA presents new challenges due to RNA's conformational flexibility and the computational cost of fine-tuning large structure pre

X. Zhao, X. D. Tang, C. Henkel, Y. Gong

The kinetic temperature structure of the massive filament DR21 has been mapped using the IRAM 30 m telescope. This mapping employed the para-H$_2$CO triplet ($J_{\rm K_aK_c}$ = 3$_{03}$--2$_{02}$, 3$_{22}$--2$_{21}$, and 3$_{21}$--2$_{20}$) on a scale of $\sim$0.1 pc. By modeling the averaged line ratios of para-H$_{2}$CO with RADEX under non-LTE assumptions

William Q. Erickson, Markus Hunziker

The Bernstein degree ($\operatorname{Deg}$) is a fundamental invariant of admissible representations of a real reductive Lie group $G_{\mathbb{R}}$. Our main result concerns the classical dual pairs $(G_{\mathbb{R}}, H_{\mathbb{R}}(k))$, namely $(\operatorname{U}(p,q), \: \operatorname{U}(k))$, $(\operatorname{Mp}(2n, \mathbb{R}), \: \operatorname{O}(k))$, a

Wei-Bang Jiang, Li-Ming Zhao, Bao-Liang Lu

The current electroencephalogram (EEG) based deep learning models are typically designed for specific datasets and applications in brain-computer interaction (BCI), limiting the scale of the models and thus diminishing their perceptual capabilities and generalizability. Recently, Large Language Models (LLMs) have achieved unprecedented success in text proces

Florian Ludwig, Hartmut G. Roskos, Raul Borsche

In the past decade, detection of THz radiation by plasma-wave-assisted frequency mixing in antenna-coupled field-effect transistors (TeraFETs) -- implemented in various semiconductor material systems (Si CMOS, GaN/AlGaN, GaAs/AlGaAs, graphene, etc.) -- has matured and led to a practically applied detector technology. This has been supported by the developmen

Han L. Gan, Maite Wilke-Berenguer

We consider two finite population Markov chain models, the two-island Wright-Fisher model with mutation, and the seed-bank model with mutation. Despite the relatively simple descriptions of the two processes, the the exact form of their stationary distributions is in general intractable. For each of the two models we provide two approximation theorems with e

Ilia Binder, Tomas Kojar

In this article we study the decoupling structure and multipoint moment of the inverse of the Gaussian multiplicative chaos. It is also the second part of preliminary work for extending the work in "Random conformal weldings" (by K. Astala, P. Jones, A. Kupiainen, E. Saksman) to the existence of Lehto welding for the inverse. In particular, we prove that the

Jiyoon Myung, Jihyeon Park

This paper examines the limitations of advanced text-to-image models in accurately rendering unconventional concepts which are scarcely represented or absent in their training datasets. We identify how these limitations not only confine the creative potential of these models but also pose risks of reinforcing stereotypes. To address these challenges, we intr

Prabhath Reddy Gujavarthy

In reinforcement learning, it is often difficult to automate high-dimensional, rapid decision-making in dynamic environments, especially when domains require real-time online interaction and adaptive strategies such as web-based games. This work proposes a state-of-the-art Flexible Deep Q-Network (FDQN) framework that can address this challenge with a selfad

Wangbo Yu, Chaoran Feng, Jiye Tang, Jiashu Yang

3D Gaussian Splatting (3D-GS) has demonstrated exceptional capabilities in 3D scene reconstruction and novel view synthesis. However, its training heavily depends on high-quality, sharp images and accurate camera poses. Fulfilling these requirements can be challenging in non-ideal real-world scenarios, where motion-blurred images are commonly encountered in

Irina A. Mizeva, Natalia P. Podolyan, Oleg V. Mamontov, Anastasiia V. Sakovskaia

Low-frequency oscillations in the human circulatory system is important for basic physiology and practical applications in clinical medicine. Our objective was to study which mechanism (central or local) is responsible for changes in blood flow fluctuations at around 0.1 Hz. We used the method of imaging photoplethysmography synchronized with electrocardiogr

James R. Young, Pierre-Alexandre Gourdain

The well-known ponderomotive force, arising from the interaction of matter and light, has critical implications across a broad range of fields from laser fusion and astrophysics to laser diagnostics and even pulsed-power experiments. This pseudo-potential pushes electrons, which through coulomb forces causes ion density modulations that can steepen with prof

Soochan Lee, Hyeonseong Jeon, Jaehyeon Son, Gunhee Kim

In the present era of deep learning, continual learning research is mainly focused on mitigating forgetting when training a neural network with stochastic gradient descent on a non-stationary stream of data. On the other hand, in the more classical literature of statistical machine learning, many models have sequential Bayesian update rules that yield the sa

Erik W. Lentz, Ryan C. Felton

Recent research has proposed that advanced propulsion mechanisms such as warp drives are more physically feasible than previously thought, using positive energy sources potentially sourced by known classical physics. Motivated by this, we hypothesize that an advanced inter-planetary or interstellar civilization using warp drives at sub-luminal or super-lumin

Jiawei Fu, Yonghao Long, Kai Chen, Wang Wei

Surgical robot task automation has been a promising research topic for improving surgical efficiency and quality. Learning-based methods have been recognized as an interesting paradigm and been increasingly investigated. However, existing approaches encounter difficulties in long-horizon goal-conditioned tasks due to the intricate compositional structure, wh

Yichen Wen, Zhiquan Tan, Kaipeng Zheng, Chuanlong Xie

Continual learning requires learning incremental tasks with dynamic data distributions. So far, it has been observed that employing a combination of contrastive loss and distillation loss for training in continual learning yields strong performance. To the best of our knowledge, however, this contrastive continual learning framework lacks convincing theoreti

Giampiero Passarino

In this work we discuss the connection between Feynman integrals and Fox functions. Illustrative examples are given.