October 2024 arXiv papers — page 28

Ruoxin Chen, Zhe Wang, Ke-Yue Zhang, Shuang Wu

Recent advancements in image mixing and generative data augmentation have shown promise in enhancing image classification. However, these techniques face the challenge of balancing semantic fidelity with diversity. Specifically, image mixing involves interpolating two images to create a new one, but this pixel-level interpolation can compromise fidelity. Gen

C. Gentner, J. -F. Berret, P. Berto, S. Reichman

Many processes in microfluidics and biology are driven or affected by viscosity. While several methods are able to measure this parameter globally, very few can provide high resolution viscosity images. Optimizing the locality of viscosity measurements demands smaller probes but also shorter lateral diffusion lengths and measurement times. Here, we propose t

R. Yang, Y. -Y. Zhu, M. Steigleder, X. -G. Qiu

We investigated the infrared-active phonons in ferromagnetic Weyl semimetal Co3Sn3S3 using optical spectroscopy. Below the Curie temperature (T~175~K), we observed asymmetric Fano lineshapes of phonons peaks in the optical conductivities, reflecting the presence of electron-phonon coupling (EPC). Additionally, the detected phonon signals by the polar Kerr ro

Mario U. González-Rivas, Solveig S. Aamlid, Megan R. Rutherford, Jessica Freese

The term sample dependence describes the troublesome tendency of nominally equivalent samples to exhibit different physical properties. High entropy oxides (HEOs) are a class of materials where sample dependence has the potential to be particularly profound due to their inherent chemical complexity. In this work, we prepare a spinel HEO of identical nominal

Yijia Xiao, Edward Sun, Yiqiao Jin, Wei Wang

RNAs are essential molecules that carry genetic information vital for life, with profound implications for drug development and biotechnology. Despite this importance, RNA research is often hindered by the vast literature available on the topic. To streamline this process, we introduce RNA-GPT, a multi-modal RNA chat model designed to simplify RNA discovery

Yuchen Zhuang, Qing-Feng Sun

The photon-assisted tunneling of Majorana bound states in a Majorana nanowire driven by the periodic field is studied both theoretically and numerically. We find that Majorana bound states exhibit an anomalous photon-assisted tunneling signal which is different from an ordinary fermionic state : the height of photonic sidebands is related to the degree of Ma

Minju Seo, Jinheon Baek, Sung Ju Hwang

Large Language Models (LLMs) have demonstrated impressive capabilities in understanding and generating codes. Due to these capabilities, many recent methods are proposed to automatically refine the codes with LLMs. However, we should rethink that the refined codes (from LLMs and even humans) are not always more efficient than their original versions. On the

Jakkapat Seeyangnok, Udomsilp Pinsook, Graeme John Ackland

Metallic hydrogen is the most common condensed material in the universe, comprising the centre of gas giant planets. However, experimental studies are extremely challenging, and most of our understanding of this material has been led by theory. Chemistry in this environment has not been probed experimentally, so here we examine hydrocarbon chemistry in metal

Hunter Ng

This study investigates the emerging phenomenon of "ghost hiring" or "ghost jobs", where employers advertise job openings without intending to fill them. Using a novel dataset from Glassdoor and employing a LLM-BERT technique, I find that up to 21% of job ads may be ghost jobs, and this is particularly prevalent in specialized industries and in larger firms.

Brij Nandan Tripathi, Hanumant Singh Shekhawat, Seip Weiland

In general, matrix or tensor-valued functions are approximated using the method developed for vector-valued functions by transforming the matrix-valued function into vector form. This paper proposes a tensor-based interpolation method to approximate a matrix-valued function without transforming it into the vector form. The tensor-based technique has the adva

Xin Zhang, Zhen Xu, Yue Liu, Mengfang Sun

In the current context of accelerated globalization and digitalization, the complexity and uncertainty of financial markets are increasing, and the identification and prevention of economic risks have become a key link in maintaining the stability of the financial system. Traditional risk identification methods often have limitations because they are difficu

Jakkapat Seeyangnok, Udomsilp Pinsook, Graeme John Ackland

Two-dimensional Janus transition-metal chalcogenide -hydrides (2D-JTMCs) feature a three layered structure, with a central layer of transition metal atoms, with chalcogenides below and hydogens above. This asymmetry endows 2D-JTMCs with unique and tunable electronic, optical, and mechanical properties. In this paper, we systematically investigate two-dimensi

Jizhao Zang, Jesse S. Morgan, Andreas Beling, Scott B. Papp

We demonstrate ultra-broadband optoelectronic mixing of frequency combs that provides phase-coherent detection of a repetition frequency up to 500 GHz, using a high-speed modified uni-traveling carrier (MUTC) photodiode. Nonlinear photo-electron effects in the photodiode itself enable harmonic generation and down-mixing process of combs with widely different

Bristi Ghosh, Malay Bandopadhyay, Ashutosh Singh

Low-energy Fermions in semi-Dirac systems depict linear momentum dispersion along one direction while having the features of parabolic dispersion in the other direction. Equilibrium optical responses of such highly anisotropic dispersion are manifested in direction-dependent optical conductivity tensor. Going beyond the equilibrium framework, here we probe t

Sorouralsadat Fatemi, Yuheng Hu

Financial trading has been a challenging task, as it requires the integration of vast amounts of data from various modalities. Traditional deep learning and reinforcement learning methods require large training data and often involve encoding various data types into numerical formats for model input, which limits the explainability of model behavior. Recentl

Ryutoku Fujii, Eiji Shikoh

Ferromagnetic resonance (FMR) excitation of a ferromagnetic metal single-layer film was tried by using a couple of permanent magnets as the source of the uniform static magnetic field and by using a co-planer waveguide connected with a network analyzer as the source of the radiofrequency (RF) magnetic field. A typical FMR spectrum of a ferromagnetic Ni$_{80}

Ken Abe, Daniel Ginsberg, In-Jee Jeong

We consider ($-\alpha$)-homogeneous solutions (stationary self-similar solutions of degree $-\alpha$) to the two-dimensional inviscid Boussinesq equations in a half-plane. We show their non-existence and existence with both regular and singular profile functions.

Meitong Liu, Xiaoyuan Zhang, Chulin Xie, Kate Donahue

Multi-objective learning (MOL) aims to learn under multiple potentially conflicting objectives and strike a proper balance. While recent preference-guided MOL methods often rely on additional optimization objectives or constraints, we consider the classic Tchebycheff scalarization (TCH) that naturally allows for locating solutions with user-specified trade-o

Sang NguyenQuang, Zong-Lin Gao, Kuan-Wei Ho, Xiem HoangVan

Most learned B-frame codecs with hierarchical temporal prediction suffer from the domain shift issue caused by the discrepancy in the Group-of-Pictures (GOP) size used for training and test. As such, the motion estimation network may fail to predict large motion properly. One effective strategy to mitigate this domain shift issue is to downsample video frame

Foster Tom, Aarush Vailaya

We describe a way to decompose the chromatic symmetric function as a positive sum of smaller pieces. We show that these pieces are $e$-positive for cycles. Then we prove that attaching a cycle to a graph preserves the $e$-positivity of these pieces. From this, we prove an $e$-positive formula for graphs of cycles connected at adjacent vertices. We extend the

Archita Gupta, Pooja Singla

Let $R$ be a principal ideal local ring of finite length with a finite residue field of odd characteristic. Denote by $G(R)$ the general linear group of degree two over $R$, and by $B(R)$ the Borel subgroup of $G(R)$ consisting of upper triangular matrices. In this article, we prove that the pair $(G(R), B(R))$ is a strong Gelfand pair. We also investigate t

KiHwan Kim, Hyunsun Chung, Seonghoon Ahn, Junhyeok Park

Log-Structured Merge (LSM) tree-based Key-Value Stores (KVSs) are widely adopted for their high performance in write-intensive environments, but they often face performance degradation due to write stalls during compaction. Prior solutions, such as regulating I/O traffic or using multiple compaction threads, can cause unexpected drops in throughput or increa

Hang Guo, Yawei Li, Tao Dai, Shu-Tao Xia

Fine-tuning pre-trained diffusion models under limited budgets has gained great success. In particular, the recent advances that directly fine-tune the quantized weights using Low-rank Adaptation (LoRA) further reduces training costs. Despite these progress, we point out that existing adaptation recipes are not inference-efficient. Specifically, additional p

Zhen Zhang, Xiangyu Chu, Yunxi Tang, K. W. Samuel Au

This work proposes DOFS, a pilot dataset of 3D deformable objects (DOs) (e.g., elasto-plastic objects) with full spatial information (i.e., top, side, and bottom information) using a novel and low-cost data collection platform with a transparent operating plane. The dataset consists of active manipulation action, multi-view RGB-D images, well-registered poin

Katsuhiro Morita

We investigate the ground state of a spin-1/2 kagome antiferromagnet at the 1/9 magnetization plateau, focusing primarily on six types of valence bond crystal (VBC) distortions. Among six types of VBC distortions, type 1 consistently exhibits the lowest ground-state energy. Analysis of the second derivative of the energy with respect to the distortion streng

Peng-Yi Liu, Qing-Feng Sun

In recent years, counter-intuitive results have shown that the quantum Hall edge states with topological protection can be dissipative. In this paper, we point out that the non-equilibrium nature of edge states in quantum Hall interferometers leads to inevitable dissipation. We consider a graphene interferometer operating in the integer quantum Hall regime a

Xianghua Wu, Hongda Lin, Honglian Zhang

In this paper, we investigate the structure of the quantum affine superalgebra associated with the orthosymplectic Lie superalgebra $\mathfrak{osp}(2m+1|2n)$ for $m\geqslant 1$. The Drinfeld-Jimbo presentation for this algebra, denoted as $U_q[\mathfrak{osp}(2m+1|2n)^{(1)}]$, was originally introduced by H. Yamane. We provide the definition of the Drinfeld p

Peng-Yi Liu, Yue Mao, Qing-Feng Sun

We propose a superconducting phase-controlled thermal switch based on a four-terminal graphene-superconductor system. By the coupling of two superconducting leads on a zigzag graphene nanoribbon, both the normal-transmission coefficient and the crossed-Andreev-reflection coefficient, which dominate the thermal conductivity of electrons in the graphene nanori

Tomoyasu Shimada, Kazuhiko Murasaki, Shogo Sato, Toshihiko Nishimura

Recent advances in deep learning have improved 3D point cloud registration but increased graphics processing unit (GPU) memory usage, often requiring preliminary sampling that reduces accuracy. We propose an overlapping region sampling method to reduce memory usage while maintaining accuracy. Our approach estimates the overlapping region and intensively samp

Qichen Huang, Biwei Jiang, Zehao Zhang, Albert Zijlstra

We have developed a new method of multi-wavelength data combination for the search of late-type radio dwarfs, and have put it into practice using GLEAM-X DR1 data. The initial sample is selected by cross-matching the Gaia/DR3 objects with the probability of being a star no less than 99$\%$, and removing the extragalactic objects assigned by the SIMBAD databa

Junyu Tang, Hantao Zhang, Ran Cheng

Interplay between magnetic ordering and topological electrons not only enables new topological phases but also underpins electrical control of magnetism. Here we extend the Kane-Mele model to include the exchange coupling to a collinear background antiferromagnetic (AFM) order, which can describe transition metal trichalcogenides. Owing to the spin-orbit cou

Chen Sun, Nolan Andrew Miller, Andrey Zhmoginov, Max Vladymyrov

What happens when a new piece of knowledge is introduced into the training data and how long does it last while a large language model (LM) continues to train? We investigate this question by injecting facts into LMs from a new probing dataset, "Outlandish", which is designed to permit the testing of a spectrum of different fact types. When studying how robu

Bo Jiang, Hao Wu, Beibei Wang, Jin Tang

Recently, graph prompt learning has garnered increasing attention in adapting pre-trained GNN models for downstream graph learning tasks. However, existing works generally conduct prompting over all graph elements (e.g., nodes, edges, node attributes, etc.), which is suboptimal and obviously redundant. To address this issue, we propose exploiting sparse repr

Daniel Tamayo, Samuel Hadden

The traditional approach to analyzing mean motion resonances is through canonical perturbation theory. While this is a powerful method, its generality leads to complicated combinations of variables that are challenging to interpret and require looking up numerical coefficients particular to every different resonance. In this paper we develop simpler scaling

Michał Marczenko, Krzysztof Redlich, Chihiro Sasaki

We investigate the fluctuations of the net-baryon number near the critical point of the liquid-gas phase transition. We use the parity doublet model in the mean-field approximation fixed to the zero-temperature properties of nuclear matter to account for critical behavior. We explicitly calculate the fluctuations of the net-proton and net-neutron numbers as

Yang Xiang, Li Fan, Tulika Saha, Xiaoying Pang

Graph clustering is an essential aspect of network analysis that involves grouping nodes into separate clusters. Recent developments in deep learning have resulted in graph clustering, which has proven effective in many applications. Nonetheless, these methods often encounter difficulties when dealing with real-world graphs, particularly in the presence of n

Cheng-Ming Miao, Yu-Hao Wan, Qing-Feng Sun, Ying-Tao Zhang

We investigate the energy band structure and energy levels of a heterojunction composed of two antiferromagnetic graphene nanoflakes with opposite in-plane antiferromagnetic orderings, in which the modified Kane-Mele model is employed. Before forming an antiferromagnetic graphene heterojunction, the energy gap of helical edge states in each isolated graphene

Zhonghua Yi, Hao Shi, Qi Jiang, Kailun Yang

Event cameras, with high temporal resolution and high dynamic range, have limited research on the inter-modality local feature extraction and matching of event-image data. We propose EI-Nexus, an unmediated and flexible framework that integrates two modality-specific keypoint extractors and a feature matcher. To achieve keypoint extraction across viewpoint a

Jin Miyazawa

We proved that the boundary Dehn twist on the Milnor fiber $M_c(2, q, r)$ is an exotic diffeomorphism relative to the boundary if $q, r$ are odd, coprime integers bigger than $3$ and $(q-1)(r-1)/4$ is an odd number. The proof is given by comparing the family relative Bauer--Furuta invariants of the mapping torus.

Sorouralsadat Fatemi, Yuheng Hu

While Large Language Models (LLMs) have shown impressive capabilities in numerous Natural Language Processing (NLP) tasks, they still struggle with financial question answering (QA), particularly when numerical reasoning is required. Recently, LLM-based multi-agent frameworks have demonstrated remarkable effectiveness in multi-step reasoning, which is crucia

Naresh Kumar Gundla

In the contemporary world of dynamic digital solutions and services, the significance of effective and stable cloud solutions cannot be overestimated. The cloud adaptation is becoming more popular due to mobile advantages, including flexibility, cheaper costs and scalability. However, creating a fail-proof architecture that can accommodate scale-up and enabl

Yubin Hu, Kairui Wen, Heng Zhou, Xiaoyang Guo

Reconstructing accurate 3D surfaces for street-view scenarios is crucial for applications such as digital entertainment and autonomous driving simulation. However, existing street-view datasets, including KITTI, Waymo, and nuScenes, only offer noisy LiDAR points as ground-truth data for geometric evaluation of reconstructed surfaces. These geometric ground-t

Anna Liu, Rohit S. Chandramouli, Otto A. Hannuksela, Nicolás Yunes

Tests of general relativity (GR) can be systematically biased when our waveform models are inaccurate. We here study systematic biases in tests of general relativity induced by neglecting lensing effects for millilensed gravitational-wave signals, where the lens mass is typically in the $10^3M_\odot$--$10^5M_\odot$ range. In particular, we use a nested-sampl

Emir Syahreza Fadhilla, Laurenzius Yudha Prasetyatama, Bobby Eka Gunara, Ardian Nata Atmaja

In this work, we propose that all BPS vortex solutions within the generalized Maxwell-Chern-Simons-Higgs (MCSH) model can be found from a single system of equations. This set of equations is derived using the BPS Lagrangian method, which is a more robust generalization of Bogomolnyi's trick. We show that the known spherically symmetric BPS vortices can be re

Kaustav Chakraborty, Aryaman Gupta, Somil Bansal

Autonomous systems, such as self-driving cars and drones, have made significant strides in recent years by leveraging visual inputs and machine learning for decision-making and control. Despite their impressive performance, these vision-based controllers can make erroneous predictions when faced with novel or out-of-distribution inputs. Such errors can casca

David H. Yi, Deepti Jain

Single crystalline materials, different from polycrystalline and twinning structures, are desired for investigating the intrinsic physical properties, as grain and twin boundaries often work as a source of artifacts. Bismuth chalcogenides, which are van der Waals materials notable as topological insulators, have attracted significant interest due to their ri

Madeline Nurcombe

The ghost algebra is a two-boundary extension of the Temperley-Lieb algebra, constructed recently via a diagrammatic presentation. The existing two-boundary Temperley-Lieb algebra has a basis of two-boundary string diagrams, where the number of strings connected to each boundary must be even. The ghost algebra is similar, but allows this number to be odd, us

Teegan Bailey, Yupei Li, Ruth Luo

A Berge cycle of length $\ell$ in a hypergraph is an alternating sequence of $\ell$ distinct vertices and $\ell$ distinct edges $v_1,e_1,v_2, \ldots, v_\ell, e_{\ell}$ such that $\{v_i, v_{i+1}\} \subseteq e_i$ for all $i$, with indices taken modulo $\ell$. We call an $n$-vertex hypergraph pancyclic if it contains Berge cycles of every length from $3$ to $n$

Cheng-Ming Miao, Qing-Feng Sun, Ying-Tao Zhang

We study the energy spectrum and energy levels of the extended Kane-Mele model with magnetic atoms on their zigzag edges. It is demonstrated that the edges of ferromagnetism or antiferromagnetism are enough to break the time-reversal symmetry and host one-dimensional gapped edge states. Thus, a second-order topological phase transition could happen, which le

Suyang Zhong, Manuel Rigger

Database Management System (DBMS) developers have implemented extensive test suites to test their DBMSs. For example, the SQLite test suites contain over 92 million lines of code. Despite these extensive efforts, test suites are not systematically reused across DBMSs, leading to wasted effort. Integration is challenging, as test suites use various test case

Matheus Farias, H. T. Kung

We introduce a novel approach to reduce the number of times required for reprogramming memristors on bit-sliced compute-in-memory crossbars for deep neural networks (DNNs). Our idea addresses the limited non-volatile memory endurance, which restrict the number of times they can be reprogrammed. To reduce reprogramming demands, we employ two techniques: (1) w

Akane Inda, Satoru Hayami

A ferroic alignment of a time-reversal-even axial vector, which is called the ferroaxial ordered state, is identified by the spontaneous rotational distortion of the lattice structure to break the vertical mirror symmetry. We theoretically investigate what happens in the electronic structure once the ferroaxial ordering occurs by analyzing a tetragonal d-p c

Yan Zhang, Ming Li, Chun Li, Zhaoxia Liu

Evidence-based deep learning represents a burgeoning paradigm for uncertainty estimation, offering reliable predictions with negligible extra computational overheads. Existing methods usually adopt Kullback-Leibler divergence to estimate the uncertainty of network predictions, ignoring domain gaps among various modalities. To tackle this issue, this paper in

Kangyang Luo, Zichen Ding, Zhenmin Weng, Lingfeng Qiao

While Chain of Thought (CoT) prompting approaches have significantly consolidated the reasoning capabilities of large language models (LLMs), they still face limitations that require extensive human effort or have performance needs to be improved. Existing endeavors have focused on bridging these gaps; however, these approaches either hinge on external data

Tianle Jiang, Yuhao Zhang

Online matching is a fundamental problem in the study of online algorithms. We study the problem under a very general arrival model: the edge arrival model. Free disposal is an important notion in the online matching literature, which allows the algorithm to dispose of the previously matched edges. Without free disposal, we cannot achieve any bounded ratio,

Yuxiang Liu, Artan Sheshmani, Shing-Tung Yau

In this paper, we study the multi-rigidity problem in rational homogeneous spaces. A Schubert class is called multi-rigid if every multiple of it can only be represented by a union of Schubert varieties. We prove the multi-rigidity of Schubert classes in rational homogeneous spaces. In particular, we characterize the multi-rigid Schubert classes in partial f

Cheng-Ming Miao, Qing-Feng Sun, Ying-Tao Zhang

We study the energy spectrum and transport property of a one-dimensional Kitaev quantum ring in a threading magnetic field. It is demonstrated that the magnetic field can effectively induce topological phase transitions for the ring in the topologically nontrivial phase at the zero magnetic field. However, for the ring in the topologically trivial phase at t

Houston Schuerger, Nathan Warnberg, Michael Young

Motivated by a conjecture from the automated conjecturing program TxGraffiti, in this paper the relationship between the zero forcing number, $Z(G)$, and the vertex independence number, $\alpha(G)$, of cubic and subcubic graphs is explored. TxGraffiti conjectures that for all connected cubic graphs $G$, that are not $K_4$, $Z(G) \leq \alpha(G) + 1$. This wor

Md Abu Sayed, Asif Rahman, Christopher Kiekintveld, Sebastian Garcia

Domain Generation Algorithms (DGAs) are malicious techniques used by malware to dynamically generate seemingly random domain names for communication with Command & Control (C&C) servers. Due to the fast and simple generation of DGA domains, detection methods must be highly efficient and precise to be effective. Large Language Models (LLMs) have demonstrated

Yongseok Jo, Shy Genel, Joel Leja, Benjamin Wandelt

In this study, we investigate the impact of covariance within uncertainties on the inference of cosmological and astrophysical parameters, specifically focusing on galaxy stellar mass functions derived from the CAMELS simulation suite. Utilizing both Fisher analysis and Implicit Likelihood Inference (ILI), we explore how different covariance structures, incl

Sanhita Pathak, Vinay Kaushik, Brejesh Lall

To prevent unauthorized use of text in images, Scene Text Removal (STR) has become a crucial task. It focuses on automatically removing text and replacing it with a natural, text-less background while preserving significant details such as texture, color, and contrast. Despite its importance in privacy protection, STR faces several challenges, including boun

Yu-Chen Zhuang, Qing-Feng Sun

The quantum Hall (QH) effect, the quantum spin Hall (QSH) effect and the quantum valley Hall (QVH) effect are three peculiar topological insulating phases in graphene. They are characterized by three different types of edge states. These three effects are caused by the external magnetic field, the intrinsic spin-orbit coupling (SOC) and the strain-induced ps

Azim Ospanov, Farzan Farnia

Evaluating the diversity of generative models without reference data poses methodological challenges. The reference-free Vendi and RKE scores address this by quantifying the diversity of generated data using matrix-based entropy measures. Among these two, the Vendi score is typically computed via the eigendecomposition of an $n \times n$ kernel matrix constr

Iftekher S. Chowdhury, Binay Prakash Akhouri, Shah Haque, Martin H. Bacci

We present a novel experimental approach to detect dark matter by probing Yukawa interactions, commonly referred to as a fifth force, between dark matter and baryonic matter. Our method involves optically levitating nanoparticles within a Bessel-Gaussian beam to detect minute forces exerted by potential dark matter interaction with test masses. The non-diffr

Dang Nguyen, Sunil Gupta, Kien Do, Thin Nguyen

While most generative models show achievements in image data generation, few are developed for tabular data generation. Recently, due to success of large language models (LLM) in diverse tasks, they have also been used for tabular data generation. However, these methods do not capture the correct correlation between the features and the target variable, hind

Zhengmian Hu, Tong Zheng, Heng Huang

Authorship attribution aims to identify the origin or author of a document. Traditional approaches have heavily relied on manual features and fail to capture long-range correlations, limiting their effectiveness. Recent advancements leverage text embeddings from pre-trained language models, which require significant fine-tuning on labeled data, posing challe

P. X. Gao, J. C. Xu

Based on the data from the Kodaikanal and Mount Wilson observatories, we investigate the relationships of the tilt angle of sunspot group (SG), including the mean tilt angle and the tilt-angle scatter, during the declining phase with the parameters of the next solar cycle (SC). The main findings are summarized in the following three points. (1) During the de

Zhehao Zhang, Ryan A. Rossi, Branislav Kveton, Yijia Shao

Personalization of Large Language Models (LLMs) has recently become increasingly important with a wide range of applications. Despite the importance and recent progress, most existing works on personalized LLMs have focused either entirely on (a) personalized text generation or (b) leveraging LLMs for personalization-related downstream applications, such as

G. H. Sargsyan, G. Potel, K. Kravvaris, J. E. Escher

Optical potentials are a standard tool in the study of nuclear reactions, as they describe the interaction between a target nucleus and a projectile. The use of phenomenological optical potentials built using experimental data on stable isotopes is widespread. Although successful in their dedicated domain, it is unclear whether these phenomenological potenti

Yuancheng Jiang, Chuqi Zhang, Bonan Ruan, Jiahao Liu

PHP, a dominant scripting language in web development, powers a vast range of websites, from personal blogs to major platforms. While existing research primarily focuses on PHP application-level security issues like code injection, memory errors within the PHP interpreter have been largely overlooked. These memory errors, prevalent due to the PHP interpreter

Julien Pallage, Bertrand Scherrer, Salma Naccache, Christophe Bélanger

In this work, we present a new unsupervised anomaly (outlier) detection (AD) method using the sliced-Wasserstein metric. This filtering technique is conceptually interesting for MLOps pipelines deploying machine learning models in critical sectors, e.g., energy, as it offers a conservative data selection. Additionally, we open the first dataset showcasing lo

Xuetong Li, Xiao-Dong Zhang

Multisource data has spurred the development of advanced clustering algorithms, such as multi-view clustering, which critically relies on constructing similarity matrices. Traditional algorithms typically generate these matrices from sample attributes alone. However, real-world networks often include pairwise directed topological structures critical for clus

Yue Mao, Qing-Feng Sun

Without applied bias voltage, a superconducting phase difference can drive a charge Josephson supercurrent in a superconductor junction. In analogy, we here theoretically propose a spin phase that intrinsically generates spin Josephson supercurrent, and this spin Josephson effect is studied in a junction of superconducting nanowire (SNW). We show that spin-o

Yang Zhang, Bichu Li

The evolution equation of two-point correlation function $\xi$ of galaxies can analytically describe the large scale structure of the galaxy distribution, and the solution depends also upon the initial condition. The primeval spectrum of the baryon acoustic oscillations (BAO) contains multi peaks that survived the Silk damping, and, as a relevant portion, tw

Ruihao Xia, Yu Liang, Peng-Tao Jiang, Hao Zhang

Despite their success, unsupervised domain adaptation methods for semantic segmentation primarily focus on adaptation between image domains and do not utilize other abundant visual modalities like depth, infrared and event. This limitation hinders their performance and restricts their application in real-world multimodal scenarios. To address this issue, we

Yongzhang Yang, Kai Huang, Jianguo Yan, Yuqiang Li

High precision ephemerides not only support space missions, but can also be used to study the origin and future of celestial bodies. In this paper, a coupled orbit rotation dynamics model that fully takes into account the rotation of the Martian moons is developed. Phobos and Deimos rotation are firstly described by Eulerian rotational equations, and integra

Elina Spyrou, Robin Hytowitz, Benjamin F. Hobbs, Ibrahim Krad

As the role of variable renewables in electricity markets expands, new market products help system operators manage imbalances caused by uncertainty and variability. Whereas work in the last decade has focused on constructing demand curves for central procurement of those products, little attention has been paid to designing their settlement scheme and under

Yuxun Qu, Yongqiang Tang, Chenyang Zhang, Wensheng Zhang

Different from the traditional semi-supervised learning paradigm that is constrained by the close-world assumption, Generalized Category Discovery (GCD) presumes that the unlabeled dataset contains new categories not appearing in the labeled set, and aims to not only classify old categories but also discover new categories in the unlabeled data. Existing stu

Shaan Ul Haque, Siva Theja Maguluri

Motivated by engineering applications such as resource allocation in networks and inventory systems, we consider average-reward Reinforcement Learning with unbounded state space and reward function. Recent works studied this problem in the actor-critic framework and established finite sample bounds assuming access to a critic with certain error guarantees. W

Yue Mao, Qing-Feng Sun

Zero-bias charge conductance peak (ZBCCP) is a significant symbol of Majorana zero modes (MZMs). The proximity effect of s-wave superconductor is usually demanded in the fabrication of MZMs. So in transport experiments, the system is inevitably coupled to the s-wave superconductor. Here we study how the ZBCCP is affected by coupling of the s-wave superconduc

Pierre Alquier, William Kengne

In a groundbreaking work, Schmidt-Hieber (2020) proved the minimax optimality of deep neural networks with ReLu activation for least-square regression estimation over a large class of functions defined by composition. In this paper, we extend these results in many directions. First, we remove the i.i.d. assumption on the observations, to allow some time depe

Rui-Jie Chen, Guo-Qing Zhang, Zhi Li, Dan-Wei Zhang

Non-Hermiticity and synthetic gauge fields play two fundamental roles in engineering exotic phases and dynamics in artificial quantum systems. Here we explore the mean-field dynamics of interacting bosons in a two-leg ladder with synthetic magnetic flux and nonreciprocal hopping under the open boundary condition. In the Hermitian limit, we showcase the break

Sebastian Reidy, Manuel Meier, Christian Holz

Deviations in respiratory rate often precede abnormalities in other vital signs. However, continuously monitoring respiratory rates outside clinical settings remains challenging due to the obtrusive nature and sensitivity to body motions in existing monitoring approaches. In this study, we propose a single-point-of-contact wearable device that leverages off-

Svetlana Jitomirskaya, Wencai Liu, Lufang Mi

We develop a sharp palindromic argument for general 1D operators, that proves absence of semi-uniform localization in the regime of exponential symmetry-based resonances. This provides the first examples of operators with dynamical localization but no SULE/SUDL, as well as with nearly uniform distribution of centers of localization in absence of SULE. For th

Thanaporn Sichanugrist, Hajime Fukuda, Takeo Moroi, Kazunori Nakayama

Entanglement is a resource to improve the sensitivity of quantum sensors. In an ideal case, using an entangled state as a probe to detect target fields, we can beat the standard quantum limit by which all classical sensors are bounded. However, since entanglement is fragile against decoherence, it is unclear whether entanglement-enhanced metrology is useful

Khashayar Gatmiry, Nikunj Saunshi, Sashank J. Reddi, Stefanie Jegelka

The intriguing in-context learning (ICL) abilities of deep Transformer models have lately garnered significant attention. By studying in-context linear regression on unimodal Gaussian data, recent empirical and theoretical works have argued that ICL emerges from Transformers' abilities to simulate learning algorithms like gradient descent. However, these wor

Ning Li, Lezhi Li

A general method to construct wavelet function on real number ffeld is proposed in this article,which is based on finite length sequence.This finite length sequence is called the seed sequence, and the corresponding wavelet function is called the seed sequence wavelet function.The seed sequence wavelet function is continuous and energy concentrated in both t

Julia Nakhleh, Joseph Shenouda, Robert D. Nowak

This paper studies the properties of solutions to multi-task shallow ReLU neural network learning problems, wherein the network is trained to fit a dataset with minimal sum of squared weights. Remarkably, the solutions learned for each individual task resemble those obtained by solving a kernel regression problem, revealing a novel connection between neural

Zhihao Liu, Chenhui Hu

As large language models (LLMs) rapidly evolve, they bring significant conveniences to our work and daily lives, but also introduce considerable safety risks. These models can generate texts with social biases or unethical content, and under specific adversarial instructions, may even incite illegal activities. Therefore, rigorous safety assessments of LLMs

Daehyun Kim, Ichiro Obara

We introduce an information order on experiments based on weighted garbling, a generalization of the standard notion of garbling. In this order, an experiment is more informative than another if the latter is a weighted garbling of the former. We show that this is equivalent to ordinary garbling conditional on a payoff-irrelevant event. We also characterize

Greg Knese

This article examines three radii associated to bounded analytic functions on the polydisk: the well-known Bohr radius, the Bohr-Agler radius, and the Schur-Agler radius. We prove explicit upper and lower bounds for the Bohr-Agler radius, an explicit lower bound for the Schur-Agler radius, and an asymptotic upper bound for the Schur-Agler radius. The Bohr-Ag

Yi-Xin Dai, Qing-Feng Sun

We investigate the Andreev reflection in a normal metal/charge-4e superconductor junction.Compare with the electron-hole conversion in normal charge-2e superconductors, here four electrons participate simultaneously, enriching the possibility of conversion ways.Using nonequilibrium Green's function method, we obtain a four-particle-type Laudauer-B\"uttiker f

Hang Yin, Yao Su, Liping Liu, Thomas Hartvigsen

Spike train classification has recently become an important topic in the machine learning community, where each spike train is a binary event sequence with \emph{temporal-sparsity of signals of interest} and \emph{temporal-noise} properties. A promising model for it should follow the design principle of performing intensive computation only when signals of i

Qing-Qi Zeng, Xi-Tong Xu, En-Ke Liu, Zhe Qu

Magnetic Weyl semimetals, which couple magnetic order with topological features, have emerged as promising candidates for advanced topological-materials-based applications. The switching of magnetization and the driving of domain wall motion play key roles in developing such applications. In this study, we suggest that a type of hard-magnetic nuclei dominate

Rajarshi Bhattacharjee, Rajesh Jayaram, Cameron Musco, Christopher Musco

We study algorithms for approximating the spectral density of a symmetric matrix $A$ that is accessed through matrix-vector product queries. By combining a previously studied Chebyshev polynomial moment matching method with a deflation step that approximately projects off the largest magnitude eigendirections of $A$ before estimating the spectral density, we

Dai Aoki, Ilya Sheikin, Nils Marquardt, Gerard Lapertot

We report the magnetoresistance of high-quality single crystals of UTe2 with Tc=2.1K in high magnetic fields up to 36T, with the field direction between the b and c-axes. From the angular dependence of the upper critical field Hc2, we found that the field-reentrant superconducting phase near H // b-axis extends up to a field angle (24 deg) from the b to c-ax

Yibo Gao, Thomas Lam, Lei Xue

We define and study the dual mixed volume rational function of a sequence of polytopes, a dual version of the mixed volume polynomial. This concept has direct relations to the adjoint polynomials and the canonical forms of polytopes. We show that dual mixed volume is additive under mixed subdivisions, and is related by a change of variables to the dual volum

Hao-Xuan Gao, Jin-Jun Geng, Yi-Fang Liang, Hui Sun

The Einstein Probe (EP) satellite, dedicated at time-domain high-energy astrophysics and multi-messenger astronomy, was recently launched and successfully put into operation. The wide-field X-ray telescope (WXT, 0.5-4 keV) onboard has identified multiple gamma-ray burst (GRB) events, with an average duration of several hundred seconds. This duration is sever

Yu-Hao Wan, Qing-Feng Sun

We show that in the proximity of s-wave superconductors, the magnetic topological surface states can transform into Majorana surface state, featuring a single gapless Majorana cone with parity anomaly when the superconducting pairing gap matches the surface magnetization gap. The emergence of $N=1/2$ Majorana chiral edge current is observed at the boundaries

Cuong Tran Manh, Hieu Dinh Vo

While smart contracts are foundational elements of blockchain applications, their inherent susceptibility to security vulnerabilities poses a significant challenge. Existing training datasets employed for vulnerability detection tools may be limited, potentially compromising their efficacy. This paper presents a method for improving the quantity and quality