December 2024 arXiv papers — page 208

Girmaw Abebe Tadesse, Caleb Robinson, Charles Mwangi, Esther Maina

In 2023, 58.0% of the African population experienced moderate to severe food insecurity, with 21.6% facing severe food insecurity. Land-use and land-cover maps provide crucial insights for addressing food insecurity by improving agricultural efforts, including mapping and monitoring crop types and estimating yield. The development of global land-cover maps h

Chongyang Zhao, Dong Gong

Continual learning (CL) learns from a non-stationary data stream without storing or re-training on all seen samples. Meta-continual learning (MCL) casts CL as sequence prediction and meta-learns the continual learner itself as a sequence model, with Transformers as natural choices. However, despite decent performance, a Transformer learner relies on a linear

Maximilian Löw, Martin Ibrügger, Gerhard Rempe, Martin Zeppenfeld

Polar polyatomic molecules provide an ideal but largely unexplored platform to encode qubits in rotational states. Here, we trap cold (100-600 mK) formaldehyde (H$_2$CO) inside an electric box and perform a Ramsey-type experiment to observe long-lived (~100 $\mu$s) coherences between symmetry-protected molecular states with opposite rotation but identical or

Sankha Das, Amit Dua

The COVID-19 pandemic has severely affected the world in terms of health, economy and peace. Fortunately, the countries are trying to overcome the situation by actively carrying out vaccinations. However, like any other massive operation involving humans such as human resource management, elections, surveys, etc., the vaccination process raises several quest

Haoyang Long, Yan Wang, Wendong Wang

Video deblurring presents a considerable challenge owing to the complexity of blur, which frequently results from a combination of camera shakes, and object motions. In the field of video deblurring, many previous works have primarily concentrated on distortion-based metrics, such as PSNR. However, this approach often results in a weak correlation with human

Xiangkai Ma, Xiaobin Hong, Wenzhong Li, Sanglu Lu

Time series analysis is a fundamental data mining task that supervised training methods based on empirical risk minimization have proven their effectiveness on specific tasks and datasets. However, the acquisition of well-annotated data is costly and a large amount of unlabeled series data is under-utilized. Due to distributional shifts across various domain

Arash Tirandaz, Abolfazl Ramezanpour, Vivi Rottschäfer, Mehrad Babaei

Living cells presumably employ optimized information transfer methods, enabling efficient communication even in noisy environments. As expected, the efficiency of chemical communications between cells depends on the properties of the molecular messenger. Evidence suggests that proteins from narrow ranges of molecular masses have been naturally selected to me

Anthony Acciavatti, Sarthak Arora, Michael Warner, Ariel Chamberlain

Surface water mixed with wastewater creates serious environmental concerns, particularly in densely populated urban areas with inadequate infrastructure. Such contamination threatens to cause major public health crises in the Ganga Basin where monsoonal flooding converges with 6 billion liters of untreated sewage that is discharged daily into the basin by 65

Raynor Kirkson E. Chavez, Kyle Gabriel M. Reynoso

This paper investigates the impact of sampling and pretraining using datasets with different image characteristics on the performance of self-supervised learning (SSL) models for object classification. To do this, we sample two apartment datasets from the Omnidata platform based on modality, luminosity, image size, and camera field of view and use them to pr

Ednaldo L. B. Junior, José Tarciso S. S. Junior, Francisco S. N. Lobo, Manuel E. Rodrigues

In this paper, we study time-like geodesics around a spherically symmetric black hole in Kalb-Ramond (KR) gravity, characterized by the parameter $l$, which induces spontaneous Lorentz symmetry breaking. The geodesic equations and effective potential are derived to investigate the influence of $l$. We calculate the marginally bound orbits and innermost stabl

Linhai Zhuo, Zheng Wang, Yuqian Fu, Tianwen Qian

The source-free cross-domain few-shot learning (CD-FSL) task aims to transfer pretrained models to target domains utilizing minimal samples, eliminating the need for source domain data. Addressing this issue requires models to have robust generalization abilities and strong feature representation, aligning with the characteristics of large-scale pretrained m

Eunice Hoo Qingyi, Lee-Peng Teo

In this work, we show that for all $t\geq e$, \[|\zeta(1+it)|\leq 0.6443 \log t. \] The equality is achieved when $t=17.7477$. We also use the Riemann-Siegel formula and numerical computations to show that \[|\zeta(1+it)|\leq\frac{1}{2}\log t+0.6633\hspace{1cm}\text{when}\;t\geq e.\]When $t\geq 100$, the bound $\frac{1}{2}\log t+0.6633$ is better than the bo

Aihua Pei, Zehua Yang, Shunan Zhu, Ruoxi Cheng

Traditional methods for evaluating the robustness of large language models (LLMs) often rely on standardized benchmarks, which can escalate costs and limit evaluations across varied domains. This paper introduces a novel framework designed to autonomously evaluate the robustness of LLMs by incorporating refined adversarial prompts and domain-constrained know

Edmundo J. Huertas, Alberto Lastra, Francisco Marcellán, Víctor Soto-Larrosa

We define the family of symmetric truncated Freud polynomials $P_n(x;z)$, orthogonal with respect to the linear functional $\mathbf{u}$ defined by \begin{equation*} \langle \mathbf{u}, p(x)\rangle = \int_{-z}^z p(x)e^{-x^4}dx, \quad p\in \mathbb{P}, \quad z>0. \end{equation*} The semiclassical character of $P_n (x; z)$ as polynomials of class $4$ is stated.

Hao Dong, Wei Wei

Recently, generative pre-training based models have demonstrated remarkable results on Aspect-based Sentiment Analysis (ABSA) task. However, previous works overemphasize crafting various templates to paraphrase training targets for enhanced decoding, ignoring the internal optimizations on generative models. Despite notable results achieved by these target-or

Cong Wang, Jiabao Su

In this paper we confirm that $2^*(\gamma)=\frac{2(N+\gamma)}{N-2}$ with $\gamma>0$ is exactly the critical exponent for the embedding from $H_r^1(\mathbb{R}^N)$ into $L^q(\mathbb{R}^N;|x|^\gamma)$($N\geqslant 3$) (see \cite{2007SWW-1,2007SWW-2}) and name it as the upper H\'enon-Sobolev critical exponent. Based on this fact we study the ground state solution

Jose Miguel Lara Rangel, Stefan Schoepf, Jack Foster, David Krueger

Machine unlearning is gaining increasing attention as a way to remove adversarial data poisoning attacks from already trained models and to comply with privacy and AI regulations. The objective is to unlearn the effect of undesired data from a trained model while maintaining performance on the remaining data. This paper introduces HyperForget, a novel machin

Firdavs Nasriddinov, Rafal Kocielnik, Arushi Gupta, Cherine Yang

This work introduces the first framework for reconstructing surgical dialogue from unstructured real-world recordings, which is crucial for characterizing teaching tasks. In surgical training, the formative verbal feedback that trainers provide to trainees during live surgeries is crucial for ensuring safety, correcting behavior immediately, and facilitating

Xin Xie, Dong Gong

Text-to-image diffusion model alignment is critical for improving the alignment between the generated images and human preferences. While training-based methods are constrained by high computational costs and dataset requirements, training-free alignment methods remain underexplored and are often limited by inaccurate guidance. We propose a plug-and-play tra

Gijs Vermariën, Serena Viti, Rahul Ravichandran, Thomas G. Bisbas

We present a novel dataset of simulations of the photodissociation region (PDR) in the Orion Bar and provide benchmarks of emulators for the dataset. Numerical models of PDRs are computationally expensive since the modeling of these changing regions requires resolving the thermal balance and chemical composition along a line-of-sight into an interstellar clo

Changle Liu, Guijing Duan, Rong Yu

Shastry-Sutherland magnet is a typical frustrated spin system hosting rich phases. While the Heisenberg limit has been extensively studied, the role of spin-orbit coupling is not well explored. Motivated by newly discovered rare-earth Shastry-Sutherland magnets, we construct a generic effective-spin model that describes the interactions between Kramers doubl

Diandian Guo, Cong Cao, Fangfang Yuan, Yanbing Liu

Multimodal sarcasm detection (MSD) is essential for various downstream tasks. Existing MSD methods tend to rely on spurious correlations. These methods often mistakenly prioritize non-essential features yet still make correct predictions, demonstrating poor generalizability beyond training environments. Regarding this phenomenon, this paper undertakes severa

Sanjit Biswas, Prashanta Garain

This article proves the existence and regularity of weak solutions for a class of mixed local-nonlocal problems with singular nonlinearities. We examine both the purely singular problem and perturbed singular problems. A central contribution of this work is the inclusion of a variable singular exponent in the context of measure-valued data. Another notable f

Marin Jezidžić, Matej Mihelčić

We explore diverse representations of speech audio, and their effect on a performance of late fusion ensemble of E-Branchformer models, applied to Automatic Speech Recognition (ASR) task. Although it is generally known that ensemble methods often improve the performance of the system even for speech recognition, it is very interesting to explore how ensemble

Jian Liu, Zhen Yu

The neural radiance field (NERF) advocates learning the continuous representation of 3D geometry through a multilayer perceptron (MLP). By integrating this into a generative model, the generative neural radiance field (GRAF) is capable of producing images from random noise z without 3D supervision. In practice, the shape and appearance are modeled by z_s and

Dylan Waldner, Shyamal Mitra

This study takes a preliminary step toward teaching computers to recognize human emotions through Facial Emotion Recognition (FER). Transfer learning is applied using ResNeXt, EfficientNet models, and an ArcFace model originally trained on the facial verification task, leveraging the AffectNet database, a collection of human face images annotated with corres

Marieke Wesselkamp, Jakob Albrecht, Ewan Pinnington, William J. Castillo

Ecological forecasts are model-based statements about currently unknown ecosystem states in time or space. For a model forecast to be useful to inform decision makers, model validation and verification determine adequateness. The measure of forecast goodness that can be translated into a limit up to which a forecast is acceptable is known as the 'forecast li

Xiao Han, Hai-Feng Wang, Giovanni Carraro, Martín López-Corredoira

We present an analysis of the structure, kinematics, and chemo-dynamical properties of the Milky Way bulge using RR Lyrae stars from OGLE, and giant stars from APOGEE and Gaia that have distances placing them in the inner Galaxy. Firstly, using a sample of 1,879 ab-type RR Lyrae stars (RRabs) from OGLE-IV, we identified three populations: central bulge RRabs

Gordana Dodig-Crnkovic

This study aims to place Lorenzo Magnanis Eco-Cognitive Computationalism within the broader context of current work on information, computation, and cognition. Traditionally, cognition was believed to be exclusive to humans and a result of brain activity. However, recent studies reveal it as a fundamental characteristic of all life forms, ranging from single

I. M. Akimov, P. O. Kazinski, A. A. Sokolov

The explicit expression for the photon polarization operator in the presence of a single electron is found in the $in$-$in$ formalism in the one-loop approximation out of the photon mass-shell. This polarization operator describes the dielectric permittivity of a single electron wave packet in coherent scattering processes. The plasmons and plasmon-polariton

Yue Chen, Haoyang Yuan

Let $k$ be an algebraically closed field of characteristic $p\neq 0$. Let $G$ be a connected reductive group over $k$, $P \subseteq G$ be a parabolic subgroup and $\lambda: P \longrightarrow \mathbb G_m$ be a strictly anti-dominant character. Let $C$ be a projective smooth curve over $k$ with function field $K=k(C)$ and $F$ be a principal $G$-bundle on $C$.

Kaixin Zhang, Hongzhi Wang, Kunkai Gu, Ziqi Li

With the growing demand for massive data analysis, many DBMSs have adopted complex underlying query execution mechanisms, including vectorized operators, parallel execution, and dynamic pipeline modifications. However, there remains a lack of targeted Query Performance Prediction (QPP) methods for these complex execution mechanisms and their interactions, as

Gordana Dodig-Crnkovic

Traditionally, cognition has been considered a uniquely human capability involving perception, memory, learning, reasoning, and problem-solving. However, recent research shows that cognition is a fundamental ability shared by all living beings, from single cells to complex organisms. This chapter takes an info-computational approach (ICON), viewing natural s

Marco Laudato, Luca Manzari, Khemraj Shukla

The goal of this work is to investigate the capability of a neural operator (DeepONet) to accurately capture the complex deformation of a platelet's membrane under shear flow. The surrogate model approximated by the neural operator predicts the deformed membrane configuration based on its initial configuration and the shear stress exerted by the blood flow.

Yisong Xiao, Aishan Liu, Xinwei Zhang, Tianyuan Zhang

Pre-trained large deep learning models are now serving as the dominant component for downstream middleware users and have revolutionized the learning paradigm, replacing the traditional approach of training from scratch locally. To reduce development costs, developers often integrate third-party pre-trained deep neural networks (DNNs) into their intelligent

Jia-Ding Chen, Ya-Nan Dai, Kai-Hong Zhuang, Jing-Jing Jiang

Vacuum polarization is numerically investigated for the interaction between a GeV electron beam and a counterpropagating ultraintense laser pulse in the quantum radiation dominated-regime (QRDR). We identify a signal of vacuum polarization in pair density using a straightforward one-stage setup, circumventing the challenge of preparations of highly polarized

Haowei Sun, Jinwu Hu, Zhirui Zhang, Haoyuan Tian

Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations, providing a more practical solution for effective tracking in dynamic environments. However, accurate Drone Visual Active Tracking using reinforcement learning remains challenging due to the absence of a unified benchmark an

Ting Xie, Chuan-Cun Shu

xploiting quantum interference remains a significant challenge in ultracold inelastic scattering. In this work, we propose a method to enable detectable quantum interference within the two-body loss rate resulting from various inelastic scattering channels. Our approach utilizes a ``ring-coupling" configuration, achieved by combining external radio-frequency

Ahmed ElGazzar, Marcel van Gerven

We propose a probabilistic framework for developing computational models of biological neural systems. In this framework, physiological recordings are viewed as discrete-time partial observations of an underlying continuous-time stochastic dynamical system which implements computations through its state evolution. To model this dynamical system, we employ a

Yujie Mo, Zhihe Lu, Runpeng Yu, Xiaofeng Zhu

Self-supervised heterogeneous graph learning (SHGL) has shown promising potential in diverse scenarios. However, while existing SHGL methods share a similar essential with clustering approaches, they encounter two significant limitations: (i) noise in graph structures is often introduced during the message-passing process to weaken node representations, and

Margarita Gapeyenko, Stefano Paris, Markus Isomaki, Boyan Yanakiev

Extended reality (XR) is unlocking numerous possibilities and continues attracting individuals and larger groups across different business sectors. With Virtual reality (VR), Augmented reality (AR), or Mixed reality (MR) it is possible to improve the way we access, deliver and exchange information in education, health care, entertainment, and many other aspe

Jun Wan, He Liu, Yujia Wu, Zhihui Lai

At present, deep neural network methods have played a dominant role in face alignment field. However, they generally use predefined network structures to predict landmarks, which tends to learn general features and leads to mediocre performance, e.g., they perform well on neutral samples but struggle with faces exhibiting large poses or occlusions. Moreover,

Yan-Wei Dai, Yao Heng Su, Sam Young Cho, Huan-Qiang Zhou

Quantum phase transitions (QPTs) are investigated in biquadratic spin-$1$ XY chain with rhombic single-ion anisotropy by using the ground state energy (GE), the bipartite entanglement entropy (BEE), and the mutual information (MI). It turns out that there are three spin nematic phases and two Tomonaga-Luttinger (TL) liquid phases with the central charge $c =

Shubham R. Jathar, Manas Kar, Venkateswaran P. Krishnan, Rahul Raju Pattar

In this article, we establish that any symmetric $m$-tensor field can be recovered pointwise from partial data of the $k$-th weighted divergent ray transform for any $k \in \mathbb{Z}^{+} \cup\{0\}$. Using the unique continuation property of the fractional Laplacian, we further prove the unique continuation of the fractional divergent beam ray transform for

Yuya Koga, Kento Kawaharazuka, Moritaka Onitsuka, Tasuku Makabe

In recent years, some research on musculoskeletal humanoids is in progress. However, there are some challenges such as unmeasurable transformation of body structure and muscle path, and difficulty in measuring own motion because of lack of joint angle sensor. In this study, we suggest two motion acquisition methods. One is a method to acquire antagonistic re

Julian Berberich, Robert L. Kosut, Thomas Schulte-Herbrüggen

Quantum computing comes with the potential to push computational boundaries in various domains including, e.g., cryptography, simulation, optimization, and machine learning. Exploiting the principles of quantum mechanics, new algorithms can be developed with capabilities that are unprecedented by classical computers. However, the experimental realization of

Jinrong Wang, Xiaoqing Yue

In this paper, firstly we construct two classes of Lie conformal superalgebras denoted by $\mathcal{HVS}(\alpha)$ and $\mathcal{HVS}(\beta,\gamma,\tau)$, respectively, where $\alpha$ is an nonzero complex number and $\beta,\gamma,\tau$ are complex numbers. They are both of rank $(2+1)$ and the even part $\mathcal{HVS}(\alpha)_{\bar{0}}=\mathcal{HVS}(\beta,\g

Hanlin Chen, Fangyin Wei, Gim Hee Lee

Humans naturally interact with their 3D surroundings using language, and modeling 3D language fields for scene understanding and interaction has gained growing interest. This paper introduces ChatSplat, a system that constructs a 3D language field, enabling rich chat-based interaction within 3D space. Unlike existing methods that primarily use CLIP-derived l

Jiahao Cui, Hui Li, Yun Zhan, Hanlin Shang

Existing methodologies for animating portrait images face significant challenges, particularly in handling non-frontal perspectives, rendering dynamic objects around the portrait, and generating immersive, realistic backgrounds. In this paper, we introduce the first application of a pretrained transformer-based video generative model that demonstrates strong

Kazuhiro Miyama, Shun Hasegawa, Kento Kawaharazuka, Naoya Yamaguchi

The purpose of this study is to construct a contact point estimation system for the both side of a finger, and to realize a motion of bending the finger after inserting the finger into a tool (hereinafter referred to as the bending after insertion motion). In order to know the contact points of the full finger including the joints, we propose to fabricate a

Ajith Balakrishnan, Sreeja S, Linu Shine

Generating 3D models from multi-view 2D RGB images has gained significant attention, extending the capabilities of technologies like Virtual Reality, Robotic Vision, and human-machine interaction. In this paper, we introduce a hybrid strategy combining CNNs and transformers, featuring a visual auto-encoder with self-attention mechanisms and a 3D refiner netw

Marius Kästingschäfer, Théo Gieruc, Sebastian Bernhard, Dylan Campbell

Models for egocentric 3D and 4D reconstruction, including few-shot interpolation and extrapolation settings, can benefit from having images from exocentric viewpoints as supervision signals. No existing dataset provides the necessary mixture of complex, dynamic, and multi-view data. To facilitate the development of 3D and 4D reconstruction methods in the aut

Qipeng Wang, Rui Sheng, Shaolun Ruan, Xiaofu Jin

Designing synthetic routes for novel molecules is pivotal in various fields like medicine and chemistry. In this process, researchers need to explore a set of synthetic reactions to transform starting molecules into intermediates step by step until the target novel molecule is obtained. However, designing synthetic routes presents challenges for researchers.

Lixin Ge, Zenghai Li, Cho-Kuen Ng, Liling Xiao

A simulation workflow has been developed to study dark current (DC) radiation effects using ACE3P and Geant4. The integrated workflow interfaces particle data transfer and geometry between the electromagnetic (EM) cavity simulation code ACE3P and the radiation code Geant4, targeting large-scale problems using high-performance computing. The process begins by

Naman Deep Singh, Francesco Croce, Matthias Hein

Vision-Language models like CLIP have been shown to be highly effective at linking visual perception and natural language understanding, enabling sophisticated image-text capabilities, including strong retrieval and zero-shot classification performance. Their widespread use, as well as the fact that CLIP models are trained on image-text pairs from the web, m

Sayak Chakrabarty, Souradip Pal

Automated documentation of programming source code is a challenging task with significant practical and scientific implications for the developer community. We present a large language model (LLM)-based application that developers can use as a support tool to generate basic documentation for any publicly available repository. Over the last decade, several pa

Ke Yan

This work analyses the disparity in performance between Decision Transformer (DT) and Decision Mamba (DM) in sequence modelling reinforcement learning tasks for different Atari games. The study first observed that DM generally outperformed DT in the games Breakout and Qbert, while DT performed better in more complicated games, such as Hero and Kung Fu Master

Yuzhan Wang, Sicong Liu, Bin Guo, Boqi Zhang

Deep learning is reshaping mobile applications, with a growing trend of deploying deep neural networks (DNNs) directly to mobile and embedded devices to address real-time performance and privacy. To accommodate local resource limitations, techniques like weight compression, convolution decomposition, and specialized layer architectures have been developed. H

Mohammad Reza Yousefi, Amin Dehghani, Ali Asghar Amini, S. M. Mehdi Mirtalaei

Magnetic Induction Tomography (MIT) is a promising modality for noninvasive imaging due to its contactless and nonionizing technology. In this imaging method, a primary magnetic field is applied by excitation coils to induce eddy currents in the material to be studied, and a secondary magnetic field is detected from these eddy currents using sensing coils. T

Yuan Qiu, Alexander B. Kalmynin

The research in the subfield of analytic number theory around error term of summation of sigma functions possesses a history which can be dated back to the mid-19th century when Dirichlet provided an $O(\sqrt{n})$ estimation of error term of summation of $d(n)$. Later, G. Voronoi, G. Kolesnik, and M.N. Huxley (to name just a few) contributed more on the uppe

Suchithra Kunhoth, Somaya Al- Maadeed, Younes Akbari, Rafif Al Saady

Precision medicine has become a central focus in breast cancer management, advancing beyond conventional methods to deliver more precise and individualized therapies. Traditionally, histopathology images have been used primarily for diagnostic purposes; however, they are now recognized for their potential in molecular profiling, which provides deeper insight

Ziyang Huang, Jun Zhao, Kang Liu

Language Agent could be endowed with different mechanisms for autonomous task accomplishment. Current agents typically rely on fixed mechanisms or a set of mechanisms activated in a predefined order, limiting their adaptation to varied potential task solution structures. To this end, this paper proposes \textbf{A}daptive \textbf{L}anguage \textbf{A}gent \tex

Zheshu Song, Ziyang Ma, Yifan Yang, Jianheng Zhuo

Large Language Models (LLMs) have showcased exceptional performance across diverse NLP tasks, and their integration with speech encoder is rapidly emerging as a dominant trend in the Automatic Speech Recognition (ASR) field. Previous works mainly concentrated on leveraging LLMs for speech recognition in English and Chinese. However, their potential for addre

Ruifan Huang, Haixia Liu

We explore fairness from a statistical perspective by selectively utilizing either conditional distance covariance or distance covariance statistics as measures to assess the independence between predictions and sensitive attributes. We boost fairness with independence by adding a distance covariance-based penalty to the model's training. Additionally, we pr

Shuling Zhao, Fa-Ting Hong, Xiaoshui Huang, Dan Xu

Talking head video generation aims to generate a realistic talking head video that preserves the person's identity from a source image and the motion from a driving video. Despite the promising progress made in the field, it remains a challenging and critical problem to generate videos with accurate poses and fine-grained facial details simultaneously. Essen

Abdulrahman Al-Fakih, A. Koeshidayatullah, Tapan Mukerji, Sadam Al-Azani

Well log analysis is crucial for hydrocarbon exploration, providing detailed insights into subsurface geological formations. However, gaps and inaccuracies in well log data, often due to equipment limitations, operational challenges, and harsh subsurface conditions, can introduce significant uncertainties in reservoir evaluation. Addressing these challenges

Marina Drygala, Silvio Lattanzi, Andreas Maggiori, Miltiadis Stouras

In this paper, we consider a new problem of portfolio optimization using stochastic information. In a setting where there is some uncertainty, we ask how to best select $k$ potential solutions, with the goal of optimizing the value of the best solution. More formally, given a combinatorial problem $\Pi$, a set of value functions $V$ over the solutions of $\P

Hau Mike Ma, Jiazhen Huo, Yongrui Duan

The bullwhip study has received a lot of attention in the literature, but with conflicting results, especially in the context of data aggregation. In this paper, we investigate three widely studied factors in bullwhip measurement: time aggregation, product aggregation, and seasonality. In time aggregation, we decompose the variance into two components: the e

Xiaoxiang Han, Yiman Liu, Jiang Shang, Qingli Li

Segmenting internal structure from echocardiography is essential for the diagnosis and treatment of various heart diseases. Semi-supervised learning shows its ability in alleviating annotations scarcity. While existing semi-supervised methods have been successful in image segmentation across various medical imaging modalities, few have attempted to design me

Wei Guo, Hao Wang, Luankang Zhang, Jin Yao Chin

Recommendation systems are essential for filtering data and retrieving relevant information across various applications. Recent advancements have seen these systems incorporate increasingly large embedding tables, scaling up to tens of terabytes for industrial use. However, the expansion of network parameters in traditional recommendation models has plateaue

Guoao Yang, Jianhui Zhou, Tao Qin

The multipole moments are fundamental properties of insulators, and have attracted lots of attention with emerging of the higher-order topological insulators. A couple of ways, including generalization of the formula for the polarization and the Wilson loop, have been proposed to calculate it in real materials. However, a practical method to explore it in co

Eugene Kuznetsov

In this work, the author gives a character-free proof of the Frobenius theorem. The new proof is based on some notions and results from the theory of ternary operations, the theory of orthogonal binary operations, the theory of transversals in groups and the theory of quasigroups and loops.

Carson Kohlbrenner, Caleb Escobedo, S. Sandra Bae, Alexander Dickhans

Developing whole-body tactile skins for robots remains a challenging task, as existing solutions often prioritize modular, one-size-fits-all designs, which, while versatile, fail to account for the robot's specific shape and the unique demands of its operational context. In this work, we introduce GenTact Toolbox, a computational pipeline for creating versat

Rik Ghosh, Arka Datta, Vidhi Aggarwal, Sudipan Sinha

Decentralized Finance (DeFi), a financial ecosystem without centralized controlling organization, has introduced a new paradigm for lending and borrowing. However, its capital efficiency remains constrained by the inability to effectively assess the risk associated with each user/wallet. This paper introduces the 'On-Chain Credit Risk Score (OCCR Score) in D

Weronika Buczyńska, Jarosław Buczyński, Łucja Farnik

For a fixed projective scheme X, a property P of line bundles is satisfied by sufficiently ample line bundles if there exists a line bundle L_0 on X such that P(L) holds for any L with (L - L_0) ample. As an example, sufficiently ample line bundles are very ample, moreover, for a normal variety X, the embedding corresponding to sufficiently ample line bundle

Tadahisa Funaki

We propose a new type of SPDEs, singular or with regularized noises, motivated by a study of the fluctuation of the density field in a microscopic interacting particle system. They include a large scaling parameter $N$, which is the ratio of macroscopic to microscopic size, and another scaling parameter $K=K(N)$, which controls the formation of the interface

Yue Liu, Chakkrit Tantithamthavorn, Li Li

Recent years have witnessed the emerging trend of extensions in modern Integrated Development Environments (IDEs) like Visual Studio Code (VSCode) that significantly enhance developer productivity. Especially, popular AI coding assistants like GitHub Copilot and Tabnine provide conveniences like automated code completion and debugging. While these extensions

Annika Wilde, Tim Niklas Gruel, Claudio Soriente, Ghassan Karame

An increasing number of distributed platforms combine Trusted Execution Environments (TEEs) with blockchains. Indeed, many hail the combination of TEEs and blockchains a good "marriage": TEEs bring confidential computing to the blockchain while the consensus layer could help defend TEEs from forking attacks. In this paper, we systemize how current blockchain

Yu Zhang, Shuang Li, Yibing Wang, Yu Sun

Photoacoustic imaging (PAI) suffers from inherent limitations that can degrade the quality of reconstructed results, such as noise, artifacts and incomplete data acquisition caused by sparse sampling or partial array detection. In this study, we proposed a new optimization method for both two-dimensional (2D) and three-dimensional (3D) PAI reconstruction res

Guang Wu, Xinbiao Gan, Zhengbin Pang, Bo Huang

Finding the maximum matching in bipartite graphs is a fundamental graph operation widely used in various fields. To expedite the acquisition of the maximum matching, Karp and Sipser introduced two data reduction rules aimed at decreasing the input size. However, the KaSi algorithm, which implements the two data reduction rules, has several drawbacks: a high

Tianchen Hao

A Mesoscale Convective System (MCS) is a collection of thunderstorms operating as a unified system, showcasing nature's untamed power. They represent a phenomenon widely referenced in both the natural sciences and the visual effects (VFX) industries.However, in computer graphics, visually accurate simulation of MCS dynamics remains a significant challenge du

Jun Ye

We propose a method to improve the generalization of skin lesion classification models by combining self-supervised learning (SSL) and active domain adaptation (ADA). The main steps of the approach include selection of an SSL pre-trained model on natural image datasets, subsequent SSL retraining on all available skin-lesion datasets, fine-tuning of the model

Kaixuan Fan, Ze Hua, Siyao Gu, Peng Zhu

Understanding the physical and chemical processes at the interface of metals and topological insulators is crucial for developing the next generation of topological quantum devices. Here we report the discovery of robust superconductivity in Pd/Bi$_2$Se$_3$ bilayers fabricated by sputtering Pd on the surface of Bi$_2$Se$_3$. Through transmission electron mic

Jiancheng Wu, Sizhong Zhou, Hongxia Liu

Let $G$ be a graph and $T$ be a spanning tree of $G$. We use $Q(G)=D(G)+A(G)$ to denote the signless Laplacian matrix of $G$, where $D(G)$ is the diagonal degree matrix of $G$ and $A(G)$ is the adjacency matrix of $G$. The signless Laplacian spectral radius of $G$ is denoted by $q(G)$. A necessary and sufficient condition for a connected bipartite graph $G$

Ashwin Baluja

While Large Language Models (LLMs) have demonstrated impressive natural language understanding capabilities across various text-based tasks, understanding humor has remained a persistent challenge. Humor is frequently multimodal, relying on phonetic ambiguity, rhythm and timing to convey meaning. In this study, we explore a simple multimodal prompting approa

Dongxue Liu, Tao Tao, Jun Li, Qing Jia

A one-dimensional mixing model, incorporating the effects of laser ablation and initial perturbations, is developed to study the influence of ablative Rayleigh-Taylor instability on compression dynamics. The length of the mixing region is determined with the buoyancy-drag model[arXiv:2411.12392v2 (2024)]. The mixing effect on laser ablation is mainly describ

Xiaona Ye, Guangfeng Wang, Xiaoyang Duan, Ziwei Wang

Densely arranged optical vortices are natural solutions of high-symmetry Bloch modes in photonic crystals. However, strict symmetry constraints limit the potential spatial configurations of nearfield vortices, restricting the control over light-matter interaction. Here, we demonstrate a nearfield vortex dynamic within a supercell photonic crystal. By introdu

Tan Le, Van Le

We propose the joint dynamic power allocation and multi-relay selection for the cohabitation of high-priority military radar and low-priority commercial 5G communication. To improve the 5G network performance, we design the full-duplex underlay cognitive radio network for the low-priority commercial 5G network, where multiple relays are selected for concurre

Tao Huang, Qingyu Huang, Jiayang Meng

The increasing reliance on deep computer vision models that process sensitive data has raised significant privacy concerns, particularly regarding the exposure of intermediate results in hidden layers. While traditional privacy risk assessment techniques focus on protecting overall model outputs, they often overlook vulnerabilities within these intermediate

S. Cobzaş

We present some results related to Hahn-Banach extension theorem for linear operators on asymmetric normed spaces. L. Nachbin, Trans. Amer. Math. Soc. 68 (1950), proved that a Banach space has the extension property for linear operators (a property also called injectivity) if and only if it has the Binary Intersection Property (BIP), meaning that every famil

Parvaneh Nikrou, Sajjad Pirboudaghi

Simulating complex hydraulic conditions, particularly two-phase flows over spillway chutes, can be achieved with high accuracy using three-dimensional numerical models. This study investigates the potential for vacuum generation and cavitation phenomena on the Aghchai Dam service spillway through numerical simulations conducted in Flow-3D. The analysis focus

Yunjie Zhu, Liang-yi Huang, chunbo Cheng

The study of Lipschitz equivalence of fractals is a very active topic in recent years. In 2023, Huang \emph{et al.} (\textit{Topology automaton of self-similar sets and its applications to metrical classifications}, Nonlinearity \textbf{36} (2023), 2541-2566.) studied the H\"older and Lipschitz equivalence of a class of p.c.f. self-similar sets which are not

Debadri Bhattacharjee, Koushik Ballav Goswami, Pradip Kumar Chattopadhyay

Perturbative Quantum Chromodynamics corrections and the colour superconductivity indicate that strongly interacting matter can manifest unique physical behaviours under extreme conditions. Motivated by this notion, we investigate the interior structure and properties of quark stars composed of interacting quark matter, which provides a comprehensive avenue t

Yizhou Wang, Tim Meinhardt, Orcun Cetintas, Cheng-Yen Yang

Object perception from multi-view cameras is crucial for intelligent systems, particularly in indoor environments, e.g., warehouses, retail stores, and hospitals. Most traditional multi-target multi-camera (MTMC) detection and tracking methods rely on 2D object detection, single-view multi-object tracking (MOT), and cross-view re-identification (ReID) techni

Yuang Wei, Yuan-Hao Jiang, Jiayi Liu, Changyong Qi

The rapid development of Generative AI (GAI) has sparked revolutionary changes across various aspects of education. Personalized learning, a focal point and challenge in educational research, has also been influenced by the development of GAI. To explore GAI's extensive impact on personalized learning, this study investigates its potential to enhance various

Rizwanul Haque, SM Tareq Aziz, Tahrim Hossain, Faisal Haque Bappy

Proof-of-Work (PoW) systems face critical challenges, including excessive energy consumption and the centralization of mining power among entities with expensive hardware. Static mining pools exacerbate these issues by reducing competition and undermining the decentralized nature of blockchain networks, leading to economic inequality and inefficiencies in re

Carson Kohlbrenner, Mitchell Murray, Yutong Zhang, Caleb Escobedo

Estimating the location of contact is a primary function of artificial tactile sensing apparatuses that perceive the environment through touch. Existing contact localization methods use flat geometry and uniform sensor distributions as a simplifying assumption, limiting their ability to be used on 3D surfaces with variable density sensing arrays. This paper

Tuoping Du

This research provides a formal definition of the arithmetic theta lift for cusp forms of weight $3/2$ and establishes the arithmetic inner product formula, thereby completing the Kudla program on modular curves. This formula is demonstrated to be equivalent to the Gross-Zagier formula, for which we provide a new proof. Additionally, the authors introduce a

Mohamad Haj Fares, Ahmed Mohamed Saad Emam Saad

With increasing concerns over privacy in healthcare, especially for sensitive medical data, this research introduces a federated learning framework that combines local differential privacy and secure aggregation using Secure Multi-Party Computation for medical image classification. Further, we propose DPResNet, a modified ResNet architecture optimized for di

Muhammad Fetrat Qharabagh, Mohammadreza Ghofrani, Kimon Fountoulakis

Counting is a fundamental operation for various real-world visual tasks, requiring both object recognition and robust counting capabilities. Despite their advanced visual perception, large vision-language models (LVLMs) are known to struggle with counting tasks. In this work, we evaluate the performance of several LVLMs on visual counting tasks across multip