December 2024 arXiv papers — page 29

Zhong-Heng Tan, Tiexiang Li, Wen-Wei Lin, Shing-Tung Yau

In this paper, we propose a novel parameterization method for genus-one and multiply connected genus-zero surfaces, called periodic conformal flattening. The conformal energy minimization technique is utilized to compute the desired conformal map, which is characterised as an easily solvable quadratic functional minimization problem, yielding a sparse linear

Harsh Kumar, R. Govindarajan

Even in the era of Deep Learning based methods, traditional machine learning methods with large data sets continue to attract significant attention. However, we find an apparent lack of a detailed performance characterization of these methods in the context of large training datasets. In this work, we study the system's behavior of a number of traditional ML

Jian-hao Kang, Zhun Gou, Nan-jing Huang

This study employs expected certainty equivalents to explore the reinsurance and investment issue pertaining to an insurer that aims to maximize the expected utility while being subject to random risk aversion. The insurer's surplus process is modeled approximately by a drifted Brownian motion, and the financial market is comprised of a risk-free asset and a

Seokcheon Lee

The $3+1$ formalism provides a structured approach to analyzing spacetime by separating it into spatial and temporal components. When applied to the Robertson-Walker metric, it simplifies the analysis of cosmological evolution by dividing the Einstein field equations into constraint and evolution equations. It introduces the lapse function $N$ and the shift

Dun Zhang, Jiacheng Li, Ziyang Zeng, Fulong Wang

A crucial component in many deep learning applications, such as Frequently Asked Questions (FAQ) and Retrieval-Augmented Generation (RAG), is dense retrieval. In this process, embedding models transform raw text into numerical vectors. However, the embedding models that currently excel on text embedding benchmarks, like the Massive Text Embedding Benchmark (

Akira Yamada

The Fourier transform and its inverse are well-known to have complex conjugate integral kernels. S.~Saitoh demonstrated that this relationship extends to the theory of integral transforms of Hilbert spaces of functions under certain conditions. In this paper, we derive a necessary and sufficient condition for the inverse of an integral transform of a Hilbert

Haoyang Fu, Wentai Luo, Xiangpan Ji, Shaomin Chen

The determination of experimental sensitivity is a key step in the search for neutrinoless double beta decay ($0\nu\beta\beta$), providing a quantitative benchmark for detector design. Two commonly used statistical approaches are the counting method, which estimates sensitivity from the number of events in a predefined region of interest, and the fitting met

Vinicius Leitão, Onofre Rojas, Moises Rojas

In this paper, we investigate the thermal quantum correlations in a semiconductor double quantum dot system. The device comprises a single electron in a double quantum dot subjected to a longitudinal magnetic field and a transverse magnetic field gradient. The thermal entanglement of the single electron is driven by the charge and spin qubits. Utilizing the

Aku Antikainen, Siddharth Ramachandran

To accelerate ultra-relativistic charged particles, such as electrons, using an electromagnetic pulse along a hollow-core waveguide, the pulse needs to have a longitudinal electric field component and a phase velocity of $c$, the speed of light in vacuum. We derive an approximate closed-form expression for the wavelength at which the phase velocity of the TM

Hongyinping Feng, Hai-Li Du

In this paper, we develop a novel and simple adaptive control scheme for a one-dimensional unstable heat equation with unknown control coefficient. A new state observer is designed to estimate the system state, while a new update law is devised to estimate the reciprocal of the control coefficient. In contrast with the conventional state observer which is us

Ahmad Alfani Handoyo, Chung Tran, Dessi Puji Lestari, Sakriani Sakti

Multilingual text-to-speech systems convert text into speech across multiple languages. In many cases, text sentences may contain segments in different languages, a phenomenon known as code-switching. This is particularly common in Indonesia, especially between Indonesian and English. Despite its significance, no research has yet developed a multilingual TTS

Sammy Luo, Zixuan Xu

A graph is $(t_1, t_2)$-Ramsey if any red-blue coloring of its edges contains either a red copy of $K_{t_1}$ or a blue copy of $K_{t_2}$. The size Ramsey number is the minimum number of edges contained in a $(t_1,t_2)$-Ramsey graph. Generalizing the notion of size Ramsey numbers, the $F$-Ramsey number $r_F(t_1, t_2)$ is defined to be the minimum number of co

Md Mirajul Islam, Md Nahiyan Uddin, Maoyejatun Hasana, Debojit Pandit

People exhibit unique emotional responses. In the same scenario, the emotional reactions of two individuals can be either similar or vastly different. For instance, consider one person's reaction to an invitation to smoke versus another person's response to a query about their sleep quality. The identification of these individual traits through the observati

Norihiro Hanihara

We study graded and ungraded singularity categories of some commutative Gorenstein toric singularities, namely, Veronese subrings of polynomial rings, and Segre products of some copies of polynomial rings. We show that the graded singularity category has a tilting object whose endomorphism ring is higher representation infinite. Moreover, we construct the ti

Elira Shaska

An irreducible quintic equation is solvable by radicals if and only if its Galois group is solvable. In this work, we provide necessary and sufficient conditions for solvability, expressed in terms of invariants of the quintic.

Soichiro Fujii, Kei Kimura, Yuta Nozaki

Given finite simple graphs $G$ and $H$, the Hom complex $\mathrm{Hom}(G,H)$ is a polyhedral complex having the graph homomorphisms $G\to H$ as the vertices. We determine the homotopy type of each connected component of $\mathrm{Hom}(G,H)$ when $H$ is square-free, meaning that it does not contain the $4$-cycle graph $C_4$ as a subgraph. Specifically, for a co

Kensuke Egami, Akira Masuoka, Kenta Suzuki

Hopf algebras, most generally in a semisimple abelian symmetric monoidal category, are here supposed to be commutative but not to be of finite-type, and their (equivariant) smoothness are discussed. Given a Hopf algebra $H$ in a category such as above, it is proved that the following are equivalent: (i) $H$ is smooth as an algebra; (ii) $H$ is smooth as an $

Jingyi Zheng, Tianyi Hu, Tianshuo Cong, Xinlei He

Backdoor attacks significantly compromise the security of large language models by triggering them to output specific and controlled content. Currently, triggers for textual backdoor attacks fall into two categories: fixed-token triggers and sentence-pattern triggers. However, the former are typically easy to identify and filter, while the latter, such as sy

Weizhen Ma, Zhongliang Jing, Peng Dong, Henry Leung

This paper proposes a unified tree-reweighted belief propagation (BP) and mean field (MF) approach for scalable detection and tracking of extended targets within the framework of factor graph. The factor graph is partitioned into a BP region and an MF region so that the messages in each region are updated according to the corresponding region rules. The BP r

Youjie Chen, Pranathi Iyer, Rene F. Kizilcec

College students routinely use online course catalogs to explore a variety of academic offerings. Course catalogs may therefore be an effective place to encourage reflection on academic choices and interests. To test this, we embedded a psychological intervention in an online course catalog to encourage students to reflect on their purpose during course expl

Dongyi Wang, Ruoyang Zhang, Chinghua Lee, Kun Ding

Flat band and non-Hermitian are both significant conceptions in modern physics. In this study, we delve into the behaviours of flat bands in non-Hermitian systems, focusing on the interplay between the flat band and its dispersive counterparts, investigating the exceptional points (EPs) formed by them together, and the non-Hermitian skin effect (NHSE) on the

Shuntuo Xu, Zhou Yu

This paper investigates the connection between neural networks and sufficient dimension reduction (SDR), demonstrating that neural networks inherently perform SDR in regression tasks under appropriate rank regularizations. Specifically, the weights in the first layer span the central mean subspace. We establish the statistical consistency of the neural netwo

Nobuyoshi Komatsu

The first and second laws of thermodynamics should lead to a consistent scenario for discussing the cosmological constant problem. In the present study, to establish such a thermodynamic scenario, cosmological equations in a flat Friedmann-Lema\^{i}tre-Robertson-Walker universe were derived from the first law, using an arbitrary entropy $S_{H}$ on a cosmolog

Zexiong Ma, Shengnan An, Zeqi Lin, Yanzhen Zou

Language models have been applied to various software development tasks, but the performance varies according to the scale of the models. Large Language Models (LLMs) outperform Small Language Models (SLMs) in complex tasks like repository-level issue resolving, but raise concerns about privacy and cost. In contrast, SLMs are more accessible but under-perfor

Enrique Diaz, Oscar Meza-Aldama

We present phenomenological signatures for a modified gravity model f(R), constructed with linear, quadratic, cubic and quartic terms. The obtained signatures satisfy current phenomenological bounds reported by PLANCK and BICEP3. Furthermore, two of the model solutions $\sigma_1$ and $\sigma_2$ seem to favor a much lower value for the tensor-to-scalar ratio

Fuzhou Gong, Yong Liu, Yuan Liu, Ziyu Liu

This paper investigates the ergodicity of Markov--Feller semigroups on Polish spaces, focusing on very weak regularity conditions, particularly the Ces\`aro eventual continuity. First, it is showed that the Ces\`aro average of such semigroups weakly converges to an ergodic measure when starting from its support. This leads to a characterization of the relati

Zhi-Hao Bian, Jia-Qi Sun, Yi Shen

Multi-party quantum steering is an important concept in quantum information theory and quantum mechanics, typically related to quantum entanglement and quantum nonlocality. It enables precise manipulation of large quantum systems, which is essential for large-scale quantum computing, simulations, and quantum communication. Recently, a quantum steering certif

Yuwen Chen, Danny Tse, Parth Nobel, Paul Goulart

We present the GPU implementation of the general-purpose interior-point solver Clarabel for convex optimization problems with conic constraints. We introduce a mixed parallel computing strategy that processes linear constraints first, then handles other conic constraints in parallel. The GPU solver currently supports linear equality and inequality constraint

Yixin Chen, Lin Gao, Yajuan Gao, Rui Wang

The integration of deep learning in medical imaging has shown great promise for enhancing diagnostic, therapeutic, and research outcomes. However, applying universal models across multiple modalities remains challenging due to the inherent variability in data characteristics. This study aims to introduce and evaluate a Modality Projection Universal Model (MP

Xiqiang Qu, Ruibin Li, Jun Chen, Lei Yu

Optimal transport has numerous applications, particularly in machine learning tasks involving generative models. In practice, the transportation process often encounters an information bottleneck, typically arising from the conversion of a communication channel into a rate-limited bit pipeline using error correction codes. While this conversion enables a cha

Lu-Qi Zhang, Yao Ma, Yong-Liang Ma

Chiral effective theory has become a powerful tool for studying the low-energy properties of QCD. In this work, we apply an extended chiral effective theory -- chiral-scale effective theory -- including a dilatonic scalar meson to study nuclear matter and find that the properties around saturation density can be well reproduced. Compared to the traditionally

Sitan Chen, Weiyuan Gong, Zhihan Zhang

In recent years there has been significant interest in understanding the statistical complexity of learning from quantum data under the constraint that one can only make unentangled measurements. While a key challenge in establishing tight lower bounds in this setting is to deal with the fact that the measurements can be chosen in an adaptive fashion, a recu

Tao Liu, Rongjie Li, Chongyu Wang, Xuming He

Open-vocabulary Scene Graph Generation (OV-SGG) overcomes the limitations of the closed-set assumption by aligning visual relationship representations with open-vocabulary textual representations. This enables the identification of novel visual relationships, making it applicable to real-world scenarios with diverse relationships. However, existing OV-SGG me

Jorge P. Zubelli, Kuldeep Singh, Vinicius Albani, Ioannis Kourakis

The Black-Scholes framework is crucial in pricing a vast number of financial instruments that permeate the complex dynamics of world markets. Associated with this framework, we consider a second-order differential operator $L(x, {\partial_x}) := v^2(x,t) (\partial_x^2 -\partial_x)$ that carries a variable volatility term $v(x,t)$ and which is dependent on th

Ling Hong, Yuning Zhang, Yuanyuan Chen, Lixiang Chen

High-dimensional quantum entanglement is an essential resource in quantum technology since it provides benefits in increasing the information capacity and processing speed. Thus, the controlled harnessing of high-dimensional entanglement has long been hailed as a necessary prerequisite towards practical quantum applications. By using a deterministic quantum

Ruixi Lin, Yang You

Large language models (LLMs) often struggle with balanced class accuracy in text classification tasks using in-context learning (ICL), hindering some practical uses due to user dissatisfaction or safety risks caused by misclassifications. Retraining LLMs to address root causes in data or model priors is neither easy nor cost-effective. This paper delves deep

Saadat Behzadi, Danial Sharifrazi, Roohallah Alizadehsani, Mojtaba Lotfaliany

Brain aging is a complex and dynamic process, leading to functional and structural changes in the brain. These changes could lead to the increased risk of neurodegenerative diseases and cognitive decline. Accurate brain-age estimation utilizing neuroimaging data has become necessary for detecting initial signs of neurodegeneration. Here, we propose a novel d

Abhijit Pendse, Sebastian Wüster, Matthew T. Eiles, Alexander Eisfeld

We show theoretically that it is possible to coherently transfer vibrational excitation between trapped neutral atoms over a micrometer apart. To this end we consider three atoms, where two are in the electronic ground state and one is excited to a Rydberg state whose electronic orbital overlaps with the positional wave functions of the two ground-state atom

Keke Tang, Weiyao Ke, Weilong Peng, Xiaofei Wang

Adversarial attacks on point clouds are crucial for assessing and improving the adversarial robustness of 3D deep learning models. Traditional solutions strictly limit point displacement during attacks, making it challenging to balance imperceptibility with adversarial effectiveness. In this paper, we attribute the inadequate imperceptibility of adversarial

Lingqiang Li, Qiu Jin

This paper investigates a novel structure of stratified L-convex groups, defined as groups possessing stratified L-convex structures, in which the group operations are L-convexity-preserving mappings. It is verified that stratified L-convex groups serve as objects, while L-convexity-preserving group homomorphisms serve as morphisms, together forming a concre

Sunho Kim, Chunhe Xiong, Junde Wu

A central problem in quantum resource theory is to give operational meaning to quantum resources that can provide clear advantages in certain physical tasks compared to the convex set of resource-free states. We propose to extend this basic principle by defining the relative superiority of resources over a specific convex set of resource states, also provide

Runbing Zheng, Avanti Athreya, Marta Zlatic, Michael Clayton

The classification of different patterns of network evolution, for example in brain connectomes or social networks, is a key problem in network inference and modern data science. Building on the notion of a network's Euclidean mirror, which captures its evolution as a curve in Euclidean space, we develop the Dynamic Network Clustering through Mirror Distance

Shu-Yung Liu, Mei-Heng Yueh

We propose a novel method, called spherical authalic energy minimization (SAEM), for computing spherical area-preserving parameterizations of genus-zero closed surfaces, with strong theoretical foundations. The global convergence of the associated computational algorithm is theoretically guaranteed. In addition, we introduce a Riemannian bijective correction

Joel Z. Leibo, Alexander Sasha Vezhnevets, Manfred Diaz, John P. Agapiou

What is appropriateness? Humans navigate a multi-scale mosaic of interlocking notions of what is appropriate for different situations. We act one way with our friends, another with our family, and yet another in the office. Likewise for AI, appropriate behavior for a comedy-writing assistant is not the same as appropriate behavior for a customer-service repr

Wanglong Lu, Jikai Wang, Xiaogang Jin, Xianta Jiang

Existing facial editing methods have achieved remarkable results, yet they often fall short in supporting multimodal conditional local facial editing. One of the significant evidences is that their output image quality degrades dramatically after several iterations of incremental editing, as they do not support local editing. In this paper, we present a nove

Xinyu Li

Motivated by Beilinson-Bernstein's proof of the Jantzen conjectures, we define the minimal parabolic induction functor for Kac-Moody algebras, and establish some basic properties. As applications of the formal theory, we examine first extension groups between simple highest weight modules in the category of weight modules, and analyze the annihilators of som

Hengyi Li

We show that the continuity property of Lyapunov exponents proved in \cite{BCS-Exponents} for smooth surface diffeomorphisms extends to smooth interval maps, in the case when the map only has non-flat critical points and the entropies converging to the topological entropy. The result we obtained is stronger than the continuity of Lyapunov exponents. In parti

Shivam Dangwal, Kaveh Edalati

High-entropy alloys (HEAs) are potential candidates for applications as refractory materials. While dual-phase refractory HEAs containing an ordered phase exhibit high hardness, there is high interest in developing intermetallic-free and single-phase refractory HEAs with high hardness. In this study, a new equiatomic HEA ZrHfNbTaW with an ultrahigh hardness

Yihan Wu, Yichen Lu, Yifan Peng, Xihua Wang

Audiovisual Automatic Speech Recognition (AV-ASR) aims to improve speech recognition accuracy by leveraging visual signals. It is particularly challenging in unconstrained real-world scenarios across various domains due to noisy acoustic environments, spontaneous speech, and the uncertain use of visual information. Most previous works fine-tune audio-only AS

Jeremy Oguamalam, Peter Filzmoser, Karel Hron, Alessandra Menafoglio

This paper introduces a robust approach to functional principal component analysis (FPCA) for relative data, particularly density functions. While recent papers have studied density data within the Bayes space framework, there has been limited focus on developing robust methods to effectively handle anomalous observations and large noise. To address this, we

Riccardo Droghei

We introduce a novel class of coherent states, termed $\mathcal{W}^{(\bar{\alpha},\bar{\nu})}(z)$-coherent states, constructed using a deformed boson algebra based on the generalized factorial $[n]_{\alpha,\beta,\nu}!$. This algebra extends conventional factorials, incorporating advanced special functions such as the Mittag-Leffler and Wright functions, enab

Ritu, Rajat, Arko Roy, Sandeep Gautam

We implement Hartree-Fock-Bogoliubov theory with Popov approximation for a homogeneous Raman-induced spin-orbit-coupled spin-1 Bose-Einstein condensate and investigate the effects of finite temperature ($T$) on the ground-state phase diagram. We calculate the roton gap as a function of Raman coupling ($Ω$) or quadratic Zeeman field strength ($ε$) to extract

Zhao-Yang Wang, Hao-Chen Tian, Yun-Feng Liang

We study the axion-photon conversion process in pulsar binary systems with high Shapiro time delay. In these binary systems, the orbital plane is nearly parallel to the line of sight. When the companion star is positioned between the pulsar and the Earth, the pulsar radiation beam will pass through the companion's magnetic field, potentially leading to a

Illych Alvarez

This study examines second-order dynamical systems incorporating Tikhonov regularization. It focuses on how nonlinearities induce bifurcations and chaotic dynamics. By using Lyapunov functions, bifurcation theory, and numerical simulations, we identify critical transitions that lead to complex behaviors like strange attractors and chaos. The findings provide

Th. A. Rijken

In these notes, while focusing on the meson-nucleon vertices, we give a derivation of the nucleon-nucleon 9NN) potentials from meson-exchange between quarks. To establish such a relation the quark-quark-meson (QQM) interactions are properly defined. Hitherto, the coefficients in the Pauli-spinor expansion of the meson-nucleon-nucleon (NNM) vertices are equat

Prabhu Vellaisamy, Harideep Nair, Thomas Kang, Yichen Ni

The increasing complexity of deep neural networks (DNNs) poses significant challenges for edge inference deployment due to resource and power constraints of edge devices. Recent works on unary-based matrix multiplication hardware aim to leverage data sparsity and low-precision values to enhance hardware efficiency. However, the adoption and integration of su

Jean Alexandre, Nick E. Mavromatos, Vasiliki A. Mitsou, Emanuela Musumeci

A one-loop Dyson-Schwinger-like resummation scheme is applied to scalar High-Electric-Charge compact Objects (HECOs), extending previous work on spin-1/2 case. The electromagnetic interactions of HECOs are considered within the framework of strongly coupled scalar Quantun Electrodynamics. The resummation amounts to determining non-trivial ultraviolet (UV) fi

Nafiz Al Asad, Md. Appel Mahmud Pranto, Shbiruzzaman Shiam, Musaddeq Mahmud Akand

Image inpainting is a widely used technique in computer vision for reconstructing missing or damaged pixels in images. Recent advancements with Generative Adversarial Networks (GANs) have demonstrated superior performance over traditional methods due to their deep learning capabilities and adaptability across diverse image domains. Residual Networks (ResNet)

Yunze Wei, Maria Attarian, Igor Gilitschenski

Despite recent progress on multi-finger dexterous grasping, current methods focus on single grippers and unseen objects, and even the ones that explore cross-embodiment, often fail to generalize well to unseen end-effectors. This work addresses the problem of dexterous grasping generalization to unseen end-effectors via a unified policy that learns correlati

A. A. Kirillov, B. S. Murygin, V. V. Nikulin

The formation of composite solitons produced by scalar fields without thermal phase transitions in the early Universe is considered. We present numerical simulations of the formation and evolution of soliton structures at the post-inflationary stage. The realistic initial conditions are obtained through the simulation of multiple quantum fluctuations during

Ganesh Aryal

Molecular beam epitaxy (MBE) is an epitaxy method for thin film deposition of single crystals. MBE is widely used in the manufacture of semiconductor devices, including transistors, and it is considered one of the fundamental tools for the development of the nanotechnologies. We have used Kinetic Monte Carlo (KMC) simulation technique to study the crystal gr

Yue Shi, Liangxiu Han, Darren Dancy, Lianghao Han

Deep neural networks have recently achieved significant advancements in remote sensing superresolu-tion (SR). However, most existing methods are limited to low magnification rates (e.g., 2 or 4) due to the escalating ill-posedness at higher magnification scales. To tackle this challenge, we redefine high-magnification SR as the ultra-resolution (UR) problem,

Theresa Chen, Yao-Yi Chiang

Climate change poses an extreme threat to biodiversity, making it imperative to efficiently model the geographical range of different species. The availability of large-scale remote sensing images and environmental data has facilitated the use of machine learning in Species Distribution Models (SDMs), which aim to predict the presence of a species at any giv

Sajjad Afroosheh, Mohammadreza Askari

This study explores the integration of Lidar, Synthetic Aperture Radar (SAR), and optical imagery through advanced artificial intelligence techniques for enhanced urban mapping. By fusing these diverse geospatial datasets, we aim to overcome the limitations associated with single-sensor data, achieving a more comprehensive representation of urban environment

Nathaniel Bottman, Katrin Wehrheim

This paper provides a blueprint for the construction of a symplectic $(A_\infty,2)$-category, $\mathsf{Symp}$. We develop two ways of encoding the information in $\mathsf{Symp}$ -- one topological, one algebraic. The topological encoding is as an $(A_\infty,2)$-flow category, which we define here. The algebraic encoding is as a linear $(A_\infty,2)$-category

Sajjad Afroosheh, Mohammadreza Askari

This paper presents an innovative framework for remote sensing image analysis by fusing deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, with Geographic Information Systems (GIS). The primary objective is to enhance the accuracy and efficiency of spatial data analysis by overcoming challe

Tony Cai, Abhinav Chakraborty, Lasse Vuursteen

Federated learning (FL) is a distributed machine learning technique designed to preserve data privacy and security, and it has gained significant importance due to its broad range of applications. This paper addresses the problem of optimal functional mean estimation from discretely sampled data in a federated setting. We consider a heterogeneous framework w

Thomas F. Kent, Keng Meng Ng, Andrea Sorbi

Answering an open question raised by Cooper, we show that there exist $\Delta^0_2$ sets $D$ and $E$ such that the singleton degree of $E$ is a minimal cover of the singleton degree of $D$. This shows that the $\Sigma^{0}_{2}$ singleton degrees, and the $\Delta^{0}_{2}$ singleton degrees, are not dense (and consequently the $\Pi^0_2$ $Q$-degrees, and the $\De

Dmytro Tymoshchuk, Oleh Yasniy, Mykola Mytnyk, Nataliya Zagorodna

This study focuses on a method for detecting and classifying distributed denial of service (DDoS) attacks, such as SYN Flooding, ACK Flooding, HTTP Flooding, and UDP Flooding, using neural networks. Machine learning, particularly neural networks, is highly effective in detecting malicious traffic. A dataset containing normal traffic and various DDoS attacks

Alejandro Velasco, Daniel Rodriguez-Cardenas, Luftar Rahman Alif, David N. Palacio

Large Language Models (LLMs) have shown significant potential in automating software engineering tasks, particularly in code generation. However, current evaluation benchmarks, which primarily focus on accuracy, fall short in assessing the quality of the code generated by these models, specifically their tendency to produce code smells. To address this limit

Souvik Dutta, Suri Brahmaroutu

Job scheduling under various constraints to achieve global optimization is a well-studied problem. However, in scenarios that involve time-dependent constraints, such as scheduling backup jobs, achieving global optimization may not always be desirable. This paper presents a framework for scheduling new backup jobs in the presence of existing job schedules, f

Peihao Xiang, Kaida Wu, Ou Bai

This paper expands the cascaded network branch of the autoencoder-based multi-task learning (MTL) framework for dynamic facial expression recognition, namely Multi-Task Cascaded Autoencoder for Dynamic Facial Expression Recognition (MTCAE-DFER). MTCAE-DFER builds a plug-and-play cascaded decoder module, which is based on the Vision Transformer (ViT) architec

Jialiang He, Jintao Luo, David Schrittesser, Hang Zhang

We study $\mathcal I$-maximal eventually different families of functions from the set of natural numbers into itself where $\mathcal I$ is an arbitrary ideal on the set of natural numbers that includes the ideal of all finite sets $\mathrm{fin}$. We introduce the class of uniformly weak Ramsey ideals and prove that there exists a closed $\mathcal I$-maximal

T. Ramburuth-Hurt, A. De Cia, J. -K. Krogager, C. Ledoux

The interstellar medium (ISM) is a fundamental component of the Milky Way. Studying its chemical composition and the level of its chemical diversity gives us insight into the evolution of the Milky Way and the role of gas in the Galactic environment. In this paper, we use a novel simulation technique to model the distribution of total hydrogen between gas co

Ariel Noyman, Kai Hu, Kent Larson

Understanding human behavior in built environments is critical for designing functional, user centered urban spaces. Traditional approaches, such as manual observations, surveys, and simplified simulations, often fail to capture the complexity and dynamics of real world behavior. To address these limitations, we introduce TravelAgent, a novel simulation plat

Igor Pak, Colleen Robichaux

Schubert coefficients $c_{u,v}^w$ are structure constants describing multiplication of Schubert polynomials. Deciding positivity of Schubert coefficients is a major open problem in Algebraic Combinatorics. We prove a positive rule for this problem based on two standard assumptions.

Shuo Sun, Chong Huang, Gaojie Chen, Pei Xiao

Advances in wireless technology have significantly increased the number of wireless connections, leading to higher energy consumption in networks. Among these, base stations (BSs) in radio access networks (RANs) account for over half of the total energy usage. To address this, we propose a multi-cell sleep strategy combined with adaptive cell zooming, user a

Khulod Almontashery, Vinicius de Oliveira Rodrigues, Paul J. Szeptycki

We discuss the proximal game and semi-proximality in $\Psi$-spaces of almost disjoint families over an infinite countable set and $\Psi$-spaces of ladder systems on $\omega_1$. We show that a semi-proximal almost disjoint families must be nowhere MAD, anti-Luzin and characterize semi-proximality for a class of ${\mathbb R}$-embeddable almost disjoint familie

Mohammed Hamdan, Abderrahmane Rahiche, Mohamed Cheriet

Handwritten document recognition (HDR) is one of the most challenging tasks in the field of computer vision, due to the various writing styles and complex layouts inherent in handwritten texts. Traditionally, this problem has been approached as two separate tasks, handwritten text recognition and layout analysis, and struggled to integrate the two processes

Jessica Babyak, Kevin Buck, Leah Dichter, David Jiang

We study the issue introduced by Buck-Lee-Platnick-Wheeler-Zumbrun of synchronous vs. asynchronous coalitions in multiplayer games, that is, the difference between coalitions with full and partial communication, with a specific interest in the context of continuous Guts poker where this problem was originally formulated. We observe for general symmetric mult

Lucas Lamata

Quantum machine learning may permit to realize more efficient machine learning calculations with near-term quantum devices. Among the diverse quantum machine learning paradigms which are currently being considered, quantum memristors are promising as a way of combining, in the same quantum hardware, a unitary evolution with the nonlinearity provided by the m

Guilherme B. Kanegae, Marcelo L. Pereira Junior, Douglas S. Galvão, Luiz A. Ribeiro Junior

The global emphasis on sustainable technologies has become a paramount concern for nations worldwide. Specifically, numerous sustainable methods are being explored as promising alternatives to the well-established vapor-compression technologies in cooling and heating devices. One such avenue gaining traction within the scientific community is the elastocalor

Annamaria Chiarini, Julia Sammet, Masoud Shokri

We explore various aspects of semi-classical spin hydrodynamics, where hydrodynamic currents are derived from an expansion in the reduced Planck constant $\hbar$, incorporating both flat and curved spacetimes. After establishing covariant definitions for angular momentum currents, we demonstrate that the conservation of the energy-momentum tensor requires mo

Chenxi Zhang, Qing Zhang, Jiayun Wu, Youwei Pang

Camouflaged Object Detection (COD) aims to identify objects that blend seamlessly into their surroundings. The inherent visual complexity of camouflaged objects, including their low contrast with the background, diverse textures, and subtle appearance variations, often obscures semantic cues, making accurate segmentation highly challenging. Existing methods

Anna Doležalová, Stanislav Hencl, Jani Onninen

Let $f \colon \Omega \to \Omega' $ be a Sobolev mapping of finite distortion between planar domains $\Omega $ and $\Omega'$, satisfying the $(INV)$ condition and coinciding with a homeomorphism near $\partial\Omega $. We show that $f$ admits a generalized inverse mapping $h \colon \Omega' \to \Omega$, which is also a Sobolev mapping of finite distortion and

A. Dilara Yavuz, M. Emre Gursoy

The rapid growth of natural language processing (NLP) and pre-trained language models have enabled accurate text classification in a variety of settings. However, text classification models are susceptible to backdoor attacks, where an attacker embeds a trigger into the victim model to make the model predict attacker-desired labels in targeted scenarios. In

Abdelrahman Hussein, Byungki Kim, Tom Depover, Kim Verbeken

Hydrogen grain boundary (GB) trapping is widely accepted as the main cause for hydrogen induced intergranular failure. Several studies were conducted to unveil the role of GBs on hydrogen transport; however, a clear understanding is yet to be attained. This is due to the limitations of the state-of-the-art experimental procedures for such highly kinetic proc

Katherine Van Kirk, Christian Kokail, Jonathan Kunjummen, Hong-Ye Hu

Efficiently estimating large numbers of non-commuting observables is an important subroutine of many quantum science tasks. We present the derandomized shallow shadows (DSS) algorithm for efficiently learning a large set of non-commuting observables, using shallow circuits to rotate into measurement bases. Exploiting tensor network techniques to ensure polyn

Parth V. Patil, Wenxin Jiang, Huiyun Peng, Daniel Lugo

The availability of pre-trained models (PTMs) has enabled faster deployment of machine learning across applications by reducing the need for extensive training. Techniques like quantization and distillation have further expanded PTM applicability to resource-constrained IoT hardware. Given the many PTM options for any given task, engineers often find it too

Pegah Ahadian, Wei Xu, Sherry Wang, Qiang Guan

Sleep disorders have a major impact on both lifestyle and health. Effective sleep disorder prediction from lifestyle and physiological data can provide essential details for early intervention. This research utilizes three deep time series models and facilitates them with explainability approaches for sleep disorder prediction. Specifically, our approach ado

Zhaobing Fan, Ziqi Xin

This paper mainly considers the center of two-parameter quantum group U_{v,t} of finite type via an analogue of the Harish-Chandra homomorphism. Through combining the connection between one-parameter quantum group case and two-parameter quantum group case, we get the description of center in the sense of Harish-Chandra homomorphism.

Daniela Pagliero, Rohma Khan, Kapila Elkaduwe, Ankit Bhardwaj

Nanoscale confinement of molecules in a fluid can result in enhanced viscosity, local fluidic order, or collective motion. Confinement also affects ion transport and/or the rate and equilibrium concentration in a chemical reaction, all of which makes it the subject of broad interest. Studying these effects, however, is notoriously difficult, mainly due to th

Indranil Chowdhury, N. N. Dattatreya

This article studies large solutions, for a class of quasi-linear equations involving p-Laplacian on the infinite cylindrical domains. We study the wellposedness of weak large solutions on infinite cylinders by the convergence of large solutions on finite cylinders and observe that any such solution coincides with the large solution on its cross-section. Fin

Balbeer Singh, Varun Vaidya

Employing the recently developed open quantum system Effective Field Theory framework, we investigate jet production and evolution in a dense nuclear medium in electron-ion/heavy-ion collisions. We confirm that the frequent monitoring of the jet by the medium leads to the emergence of a perturbative transverse momentum scale, often referred to as the saturat

Zhefan Rao, Liya Ji, Yazhou Xing, Runtao Liu

Text-to-video (T2V) generation has gained significant attention recently. However, the costs of training a T2V model from scratch remain persistently high, and there is considerable room for improving the generation performance, especially under limited computation resources. This work explores the continual general pre-training of text-to-video models, enab

Nisa Ara, Aritra Banerjee, Rudranil Basu, Bhagya Krishnan

We show how Carrollian symmetries become important in the construction of one-dimensional fermionic systems with all flat-band spectra from first principles. The key ingredient of this construction is the identification of Compact Localised States (CLSs), which appear naturally by demanding $\textit{supertranslation}$ invariance of the system. We use CLS bas

Yifan Peng, Siyao Yang, Yuehaw Khoo, Daren Wang

We propose a linear algebraic framework for performing density estimation. It consists of three simple steps: convolving the empirical distribution with certain smoothing kernels to remove the exponentially large variance; compressing the empirical distribution after convolution as a tensor train, with efficient tensor decomposition algorithms; and finally,

Zheyu Chen, Jinfeng Xu, Haibo Hu

The rapid expansion of multimedia contents has led to the emergence of multimodal recommendation systems. It has attracted increasing attention in recommendation systems because its full utilization of data from different modalities alleviates the persistent data sparsity problem. As such, multimodal recommendation models can learn personalized information a

Vedant Subhash

A stationary axisymmetric traversable wormhole with a throat-localized conical factor is developed. The conical factor produces two genuine conical tips at the poles of each angular cross-section, interpreted as cosmic-string cores along the rotation axis. A single consistent background geometry is used throughout. The metric is written in proper radial dist

Nader Alfares, George Kesidis

We introduce a Unity based benchmark XRFlux for evaluating Virtual Reality (VR) delivery systems using edge-cloud caching. As VR applications and systems progress, the need to meet strict latency and Quality of Experience (QoE) requirements is increasingly evident. In the context of VR, traditional cloud architectures (e.g., remote AWS S3 for content deliver