November 2024 arXiv papers — page 119

Hanna Fiegenbaum

Carbon credits are a key component of most national and organizational climate strategies. Financing and delivering carbon credits from forest-related activities faces multiple risks at the project and asset levels. Financial mechanisms are employed to mitigate risks for investors and project developers, complemented by non-financial measures such as environ

Hao Guo, Wei Fan, Baichun Wei, Jianfei Zhu

Embodied reference understanding is crucial for intelligent agents to predict referents based on human intention through gesture signals and language descriptions. This paper introduces the Attention-Dynamic DINO, a novel framework designed to mitigate misinterpretations of pointing gestures across various interaction contexts. Our approach integrates visual

Suhyeok Jang, Seojin Kim, Jinwoo Shin, Jongheon Jeong

The remarkable advances in deep learning have led to the emergence of many off-the-shelf classifiers, e.g., large pre-trained models. However, since they are typically trained on clean data, they remain vulnerable to adversarial attacks. Despite this vulnerability, their superior performance and transferability make off-the-shelf classifiers still valuable i

Johannes Gruendler, Darya Melnyk, Arash Pourdamghani, Stefan Schmid

Peer review, as a widely used practice to ensure the quality and integrity of publications, lacks a well-defined and common mechanism to self-incentivize virtuous behavior across all the conferences and journals. This is because information about reviewer efforts and author feedback typically remains local to a single venue, while the same group of authors a

Siraj Munir, Alessandro Aldini

Large Language Models (LLMs) are revolutionizing the landscape of Generative Artificial Intelligence (GenAI), with innovative LLM-backed solutions emerging rapidly. However, when applied to database technologies, specifically query generation for graph databases and Knowledge Graphs (KGs), LLMs still face significant challenges. While research on LLM-driven

Yuxuan Zhao, Weikang Weng, Rob van Nieuwpoort, Alexandru Uta

In Function-as-a-Service (FaaS) serverless, large applications are split into short-lived stateless functions. Deploying functions is mutually profitable: users need not be concerned with resource management, while providers can keep their servers at high utilization rates running thousands of functions concurrently on a single machine. It is exactly this hi

Daria de Tinguy, Tim Verbelen, Bart Dhoedt

Inspired by animal navigation strategies, we introduce a novel computational model to navigate and map a space rooted in biologically inspired principles. Animals exhibit extraordinary navigation prowess, harnessing memory, imagination, and strategic decision-making to traverse complex and aliased environments adeptly. Our model aims to replicate these capab

Xiaonan Nie, Qibin Liu, Fangcheng Fu, Shenhan Zhu

Larger transformer models always perform better on various tasks but require more costs to scale up the model size. To efficiently enlarge models, the mixture-of-experts (MoE) architecture is widely adopted, which consists of a gate network and a series of experts and keep the training cost constant by routing the input data to a fixed number of experts inst

Anil Kumar Karn

In this paper, we show that every pair of absolutely compatible Hilbert space effects are coexistent and exhibit a partial orthogonality property. We introduce the notion of partially ortho-coexistence. We generalize absolute compatibility to obtain more examples of partially ortho-coexistent pairs and introduce the notion of generalized compatibility. In th

Thiranat Bumnedpan, Jan Steinheimer, Tom Reichert, Christoph Herold

The UrQMD model with a density dependent equation of state, including a first-order phase transition, is used to study the time dependence of baryon number and proton number susceptibilities up to third order in heavy ion reactions of $E_{\mathrm{lab}}=2-3 A$ GeV. A significant deviation from the Gaussian fluctuations of the baryon number fluctuation in coor

Laiqiao Qin, Tianqing Zhu, Linlin Wang, Wanlei Zhou

Machine unlearning is an emerging technology that removes a subset of the training data from a trained model without significantly affecting the model performance on the remaining data. This topic is becoming increasingly important in protecting user privacy and eliminating harmful or outdated data. The key challenge lies in effectively and efficiently unlea

Abdurrahman Javid Shaikh, Othman Sidek

Despite its excellent performance in microelectronic industry, silicon was not able to perform well in photonic devices arena. This is because the silicon has never been a good optical source mainly due to its indirect band gap structure. Many of the device functionalities in silicon have been reported, with an exception of, until recently, a reliable optica

Jinfang Zhang, Yi Li, Mengyu Zhao, Dongmei Han

Randomness is an essential resource and plays important roles in various applications ranging from cryptography to simulation of complex systems. Certified randomness from quantum process is ensured to have the element of privacy but usually relies on the device's behavior. To certify randomness without the characterization for device, it is crucial to reali

Debanjan Guha Roy, Anagh Venneti, Tuhin Malik, Swastik Bhattacharya

We investigate the role of hybrid and nucleonic equations of state (EOSs) within neutron star (NS) interiors using Bayesian inference to evaluate their alignment with recent observational data from NICER and LIGO-Virgo (LV) collaborations. We find that smooth hybrid EOSs are slightly favoured in explaining NS mass-radius relations, particularly for pulsars s

Anum Afzal, Juraj Vladika, Gentrit Fazlija, Andrei Staradubets

Given the growing trend of many organizations integrating Retrieval Augmented Generation (RAG) into their operations, we assess RAG on domain-specific data and test state-of-the-art models across various optimization techniques. We incorporate four optimizations; Multi-Query, Child-Parent-Retriever, Ensemble Retriever, and In-Context-Learning, to enhance the

Weixiong Huang, Rui Wang, Tao Zhang, Sheng Qi

Solving constrained multi-objective optimization problems (CMOPs) is a challenging task. While many practical algorithms have been developed to tackle CMOPs, real-world scenarios often present cases where the constraint functions are unknown or unquantifiable, resulting in only binary outcomes (feasible or infeasible). This limitation reduces the effectivene

Liwei Ni, Xinquan Li, Biwei Xie, Huawei Li

Boolean circuit is a computational graph that consists of the dynamic directed graph structure and static functionality. The commonly used logic optimization and Boolean matching-based transformation can change the behavior of the Boolean circuit for its graph structure and functionality in logic synthesis. The graph structure-based Boolean circuit classific

Rui Li

Quasi-one-dimensional hole gas is achievable in a semiconductor Ge nanowire. The lowest two subband dispersions of the hole gas are just two shifted parabolic curves with an anticrossing at $k_{z}=0$. This peculiar low-energy subband structure manifests the existence of a strong `spin' (pseudo spin)-orbit coupling. Based on the Luttinger-Kohn Hamiltonian in

Simon Lang, Marc Seidel, Frank Allgöwer

Many cyber-physical systems can naturally be formulated as switched systems with constrained switching. This includes systems where one of the signals in the feedback loop may be lost. Possible sources for losses are shared or unreliable communication media in networked control systems, or signals which are discarded, e.g., when using a shared computation de

Julien Grand-Clément, Nian Si, Shengbo Wang

In this paper we investigate the tractability of robust Markov Decision Processes (RMDPs) under various structural assumptions on the uncertainty set. Surprisingly, we show that in all generality (i.e. without any assumption on the instantaneous rewards), s-rectangular and sa-rectangular uncertainty sets are the only models of uncertainty that are tractable.

Leszek Gąsieniec, Łukasz Kuszner, Ehsan Latif, Ramviyas Parasuraman

In the distributed localization problem (DLP), $n$ anonymous robots (agents) $a_0, a_1, ..., a_{n-1}$ begin at arbitrary positions $p_0, ..., p_{n-1}$ in $S$, where $S$ is an Euclidean space. The primary goal in DLP is for agents to reach a consensus on a unified coordinate system that accurately reflects the relative positions of all points, $p_0, ..., p_{n

Xiaoxiang Wang, Jiaxin Liu, Miaojie Feng, Zhaoxing Zhang

3D Multi-Object Tracking (MOT), a fundamental component of environmental perception, is essential for intelligent systems like autonomous driving and robotic sensing. Although Tracking-by-Detection frameworks have demonstrated excellent performance in recent years, their application in real-world scenarios faces significant challenges. Object movement in com

Minh Nguyen, Ehsan Shareghi

Language agents have shown promising adaptability in dynamic environments to perform complex tasks. However, despite the versatile knowledge embedded in large language models, these agents still fall short when it comes to tasks that require planning. We introduce STEP, a novel framework designed to efficiently learn from previous experiences to enhance the

Wararat Treesukrat, Kem Pumsa-ard, Nopmanee Supanam, Patipan Uttayarat

We study the upper limit on dark matter mass in the context of the inert double model. We derive analytic expression for the upper bound as a function of the mass squared differences between dark matter and other new particles. We find that the upper limit varies between 20$-$80 TeV depending on the mass squared splitting.

Yiming Chen, Guozheng Dai, Kaiti Ding

In this paper, we prove the restricted isometry property of block diagonal random matrices with elements from $\varphi$-sub-Gaussian variables, which extends the previously known results for the sub-Gaussian case. A crucial ingredient of our proof is an improved uniform Hanson-Wright deviation inequality, which should be of independent interest.

Michal Praszalowicz

$\Theta^+$ is a putative light pentaquark state of positive parity with minimal quark content $(uudd\bar{s})$. It naturally emerges in chiral models for baryons, but experimental evidence is uncertain. We review the theoretical foundations of chiral models and their phenomenological applications to exotic states. In particular, we discuss in detail the penta

Sabrina Caputo, Giusi Vaira

In this paper we consider the existence of standing waves for a coupled system of $k$ equations with Lotka-Volterra type interaction. We prove the existence of a standing wave solution with all nontrivial components satisfying a prescribed asymptotic profile. In particular, the $k-1$-last components of such solution exhibits a concentrating behavior, while t

Alexandros Alexakis, Raffaele Marino, Pablo D. Mininni, Adrian van Kan

How turbulent convective fluctuations organise to form large-scale structures in planetary atmospheres remains a question that eludes quantitative answers. The assumption that this process is the result of an inverse cascade was suggested half a century ago in two-dimensional fluids, but its applicability to atmospheric and oceanic flows remains heavily deba

Giulio Cerbai, Anders Claesson

Eulerian polynomials record the distribution of descents over permutations. Caylerian polynomials likewise record the distribution of descents over Cayley permutations, where a Cayley permutation is a word of positive integers such that if a number appears in the word then all positive integers less than that number also appear in the word. Using combinatori

Dariusz Brzezinski, Julia Stachowiak, Jerzy Stefanowski, Izabela Szczech

Society is increasingly relying on predictive models in fields like criminal justice, credit risk management, or hiring. To prevent such automated systems from discriminating against people belonging to certain groups, fairness measures have become a crucial component in socially relevant applications of machine learning. However, existing fairness measures

Feiyu Yin, Yu Lei, Siyuan Dai, Wenwen Zeng

Brain connectivity alternations associated with brain disorders have been widely reported in resting-state functional imaging (rs-fMRI) and diffusion tensor imaging (DTI). While many dual-modal fusion methods based on graph neural networks (GNNs) have been proposed, they generally follow homogenous fusion ways ignoring rich heterogeneity of dual-modal inform

Julianna Winnik, Piotr Zdankowski, Marzena Stefaniuk, Azeem Ahmad

Optical diffraction tomography (ODT) enables non-invasive information-rich 3D refractive index (RI) reconstruction of unimpaired transparent biological and technical samples, crucial in biomedical research, optical metrology, materials sciences, and other fields. ODT bypasses the inherent limitations of 2D integrated quantitative phase imaging methods. To in

B. Krasch, F. Abusaif, T. Arndt, N. Glamann

Undulators are X-ray sources that are widely utilised in advanced synchrotron radiation sources and freeelectron laser facilities. Due to sustainability and energy efficiency, the development focuses on small-scale, high-field, and especially compact undulators with short period lengths ($\leq$ 10 mm) and narrow magnetic gaps ($\leq$ 4 mm). Therefore, highte

Rahul Chhabra

The aim of this article is to give an expository account of the equivalence between modest sets and partial equivalence relations. Our proof is entirely self-contained in that we do not assume any knowledge of categorical realizability. At the heart of the equivalence lies the subquotient construction on a partial equivalence relation. The subquotient constr

M. L. M. François

The structured deformation theory is used within the thermodynamics of irreversible processes framework in order to build a damage model relevant for quasi-brittle materials. The cracks are supposed smeared in the body and their shape is assumed to be sinusoidal. The convex of elasticity supposes a limitation of the thermodynamic force associated to the rela

Qiang Fu, Zenan Wu, Yuxuan Zhu

This paper examines the optimal organizational rules that govern the process of dividing a fixed surplus. The process is modeled as a sequential multilateral bargaining game with costly recognition. The designer sets the voting rule -- i.e., the minimum number of votes required to approve a proposal -- and the mechanism for proposer recognition, which is mod

Yun Long, Yu Zhang

Classroom dialogue plays a crucial role in fostering student engagement and deeper learning. However, analysing dialogue sequences has traditionally relied on either theoretical frameworks or empirical descriptions of practice, with limited integration between the two. This study addresses this gap by developing a comprehensive rule base of dialogue sequence

S Tolila, G Labeyrie, R Kaiser, J. -P Rivet

We present a preliminary laboratory test of a setup designed to measure Hanbury Brown and Twiss-type intensity correlations from a chaotic light source using five spectral channels simultaneously. After averaging the zero-delay correlation peaks from all channels, we obtain an improvement of the signalto-noise ratio fairly consistent with theory. The goal is

Hartmut Führ, Jordy Timo van Velthoven, Felix Voigtlaender

This paper develops methods based on coarse geometry for the comparison of wavelet coorbit spaces defined by different dilation groups, with emphasis on establishing a unified approach to both irreducible and reducible quasi-regular representations. We show that the use of reducible representations is essential to include a variety of examples, such as aniso

Hugo Defienne, Warwick P. Bowen, Maria Chekhova, Gabriela Barreto Lemos

Modern imaging technologies are widely based on classical principles of light or electromagnetic wave propagation. They can be remarkably sophisticated, with recent successes ranging from single molecule microscopy to imaging far-distant galaxies. However, new imaging technologies based on quantum principles are gradually emerging. They can either surpass cl

Chao Huang, Chunyan Chen, Ling Shi, Chen Chen

Machine learning has become a crucial tool for predicting the properties of crystalline materials. However, existing methods primarily represent material information by constructing multi-edge graphs of crystal structures, often overlooking the chemical and physical properties of elements (such as atomic radius, electronegativity, melting point, and ionizati

Rongxin Ouyang, Kokil Jaidka, Subhayan Mukerjee, Guangyu Cui

The prevalence of multi-modal content on social media complicates automated moderation strategies. This calls for an enhancement in multi-modal classification and a deeper understanding of understated meanings in images and memes. Although previous efforts have aimed at improving model performance through fine-tuning, few have explored an end-to-end optimiza

Qinqin Xiong, Jie Cao, Xu Zhu, Yufei Jiang

We consider a real-time state reconstruction system for industrial metaverse. The time-varying physical process states in real space are captured by multiple sensors via wireless links, and then reconstructed in virtual space. In this paper, we use the spatial-temporal correlation of the sensor data of interest to infer the real-time data of the target senso

R. E. Gershberg, N. I. Kleeorin, L. A. Pustilnik, V. S. Airapetian

In the monograph, the authors systematize and generalize the results of studying solar-type activity that is characteristic of a significant part of mid- and low-mass stars of the Galaxy, outline the characteristics of such stars in the quiescent state, during the sporadic flares and variations of magnetic activity over the course of stellar evolution. The o

Quentin François

We prove a formula which gives the number of occurrences of certain labels and local configurations inside two-step puzzles introduced by Buch, Kresch, Purbhoo and Tamvakis from the work of Knutson. Puzzles are tilings of the triangular lattice by edge labeled tiles and are known to compute the Schubert structure constants of the cohomology of two-step flag

Yue-Liang Wu

We investigate the essential properties of gravitational quantum field theory (GQFT) based on spin gauge symmetry, using the general theory of quantum electrodynamics as an example. A constraint equation for the field strength of the gravigauge field is derived, serving as a gravitization equation within the spin-related gravigauge spacetime. This equation r

Qingjie Wu, Beixiong Zheng, Tiantian Ma, Rui Zhang

Fluid antenna system (FAS)/movable antenna (MA) has emerged as a promising technology to fully exploit the spatial degrees of freedom (DoFs). In this paper, we propose a new rotatable antenna (RA) model, as a simplified implementation of six-dimensional movable antenna (6DMA), to improve the performance of wireless communication systems. Different from conve

Yangyang Guo, Fangkai Jiao, Liqiang Nie, Mohan Kankanhalli

The vulnerability of Vision Large Language Models (VLLMs) to jailbreak attacks appears as no surprise. However, recent defense mechanisms against these attacks have reached near-saturation performance on benchmark evaluations, often with minimal effort. This \emph{dual high performance} in both attack and defense raises a fundamental and perplexing paradox.

Franz Franco Gallo, Hui-Yin Wu, Lucile Sassatelli

Virtual environments provide a rich and controlled setting for collecting detailed data on human behavior, offering unique opportunities for predicting human trajectories in dynamic scenes. However, most existing approaches have overlooked the potential of these environments, focusing instead on static contexts without considering userspecific factors. Emplo

Jianjun Wang, Zhaohui Bu, Zhao Wang, Jincheng Xu

This paper proposes a high-precision time measurement method based on digital frequency-domain phase-fitting (DFPF) by using the digitized nuclear pulses. The averaging effect inherent in the frequency-domain cross-correlation and phase-fitting processes effectively minimizes measurement errors, thereby ensuring high precision and resolution in time interval

Seiya Sasaoka, Yusuke Sakai, Diego Dominguez, Kentaro Somiya

Core-collapse supernovae (CCSNe) are potential multimessenger events detectable by current and future gravitational wave (GW) detectors. The GW signals emitted during these events are expected to provide insights into the explosion mechanism and the internal structures of neutron stars. In recent years, several studies have empirically derived the relationsh

Dražen Adamović, Ana Kontrec

We classify all possible occurrences of Kazama-Suzuki duality between the ${N=2}$ superconformal algebra $L^{N=2}_c$ and the subregular $\mathcal{W}$-algebra $\mathcal{W}_{k}(\mathfrak{sl}_4, f_{\rm sub})$. We establish a new Kazama-Suzuki duality between the subregular $\mathcal{W}$-algebra $\mathcal{W}_k(\mathfrak{sl}_4, f_{\rm sub})$ and the $N = 2$ super

Yudai Suzuki, Shiori Aoki, Fabian Key, Katsuhiro Endo

Topology optimization is an essential tool in computational engineering, for example, to improve the design and efficiency of flow channels. At the same time, Ising machines, including digital or quantum annealers, have been used as efficient solvers for combinatorial optimization problems. Beyond combinatorial optimization, recent works have demonstrated ap

Hoyoung Lee, Youngsoo Choi, Yuhee Kwon

Recent advancements in Large Language Models (LLMs) have the potential to transform financial analytics by integrating numerical and textual data. However, challenges such as insufficient context when fusing multimodal information and the difficulty in measuring the utility of qualitative outputs, which LLMs generate as text, have limited their effectiveness

Xun Huang, Jinlong Wang, Qiming Xia, Siheng Chen

Current Vehicle-to-Everything (V2X) systems have significantly enhanced 3D object detection using LiDAR and camera data. However, these methods suffer from performance degradation in adverse weather conditions. The weather-robust 4D radar provides Doppler and additional geometric information, raising the possibility of addressing this challenge. To this end,

Ahmet Kaplan, Diana P. M. Osorio, Erik G. Larsson

Considering the exponential growth of Internet-of-Things devices and the goals toward sustainable networks, the complexity should be focused on the infrastructure side. For a massive number of passive devices, backscatter communication (BC) is a promising technology that reduces cost and increases energy efficiency by enabling transmitting information by bac

Geetansh Kalra, Amit Patel, Atul Chaudhari, Divye Singh

Autonomous robots collaboratively exploring an unknown environment is still an open problem. The problem has its roots in coordination among non-stationary agents, each with only a partial view of information. The problem is compounded when the multiple robots must completely explore the environment. In this paper, we introduce Backtrack Assisted Multi-Agent

Jia-Yong Xie, Jun-ichi Nakashima, Yong Zhang

Circumstellar OH maser lines are useful for studying the dynamics of the circumstellar envelope (CSE) around evolved stars. This study aims to identify CSEs around cold stars, which exhibit deviations from the spherical expansion, by comparing the velocity ranges of the OH main lines (1665/1667 MHz) with those of the satellite line (1612 MHz), using a databa

Jack Anderson, Amy Woodall, Alexandru Zaharescu

We introduce and study arithmetic polygons. We show that these arithmetic polygons are connected to triples of square pyramidal numbers. For every odd $N\geq3$, we prove that there is at least one arithmetic polygon with $N$ sides. We also show that there are infinitely many arithmetic polygons with an even number of sides.

Aoi Ito, Kota Dohi, Yohei Kawaguchi

This paper presents CLaSP, a novel model for retrieving time-series signals using natural language queries that describe signal characteristics. The ability to search time-series signals based on descriptive queries is essential in domains such as industrial diagnostics, where data scientists often need to find signals with specific characteristics. However,

Shihabul Haque, Sourov Roy, Soumitra SenGupta

In this article, we look at the current bounds on the coupling strength of axion-like particles (ALPs) with two photons in the context of the Randall-Sundrum (RS) model. We relate the coupling strength to the compactification radius that governs the size of the extra dimension in the RS warped geometry model and show how the current bounds on the ALP can be

Yuelin Zhang, Long Lei, Wanquan Yan, Tianyi Zhang

Ultrasound (US)-guided needle insertion is widely employed in percutaneous interventions. However, providing feedback on the needle tip position via US imaging presents challenges due to noise, artifacts, and the thin imaging plane of US, which degrades needle features and leads to intermittent tip visibility. In this paper, a Mamba-based US needle tracker M

Levente Rózsa, Dennis Wuhrer, Sebastián A. Díaz, Ulrich Nowak

Frustrated spin models may lead to the formation of both classical non-collinear spin structures and unique quantum phases including highly entangled quantum spin liquids. Here, we study the entanglement and spatial quantum correlations in linear spin-wave theory around a classical spin-spiral ground state. We find that the entanglement between pairs of site

Gabor Balassa

In this paper, the nonlinear Volterra series expansion is extended and used to describe certain types of nonautonomous differential equations related to the inverse scattering problem in nuclear physics. The nonautonomous Volterra series expansion lets us determine a dynamic, polynomial approximation of the variable phase approximation (VPA), which is used t

Geetansh Kalra, Divye Singh, Justin Jose

Reinforcement Learning (RL) is a rapidly growing area of machine learning that finds its application in a broad range of domains, from finance and healthcare to robotics and gaming. Compared to other machine learning techniques, RL agents learn from their own experiences using trial and error, and improve their performance over time. However, assessing RL mo

Mi-Ra Hwang, Eylee Jung, MuSeong Kim, DaeKil Park

Superconductors at temperatures below the critical temperature $T_c$ can be modeled as a mixture of Fermi and Bose gases, where the Fermi gas consists of conduction electrons and the Bose gas comprises Cooper pairs. This simple model enables the computation of the temperature dependence of $2 r(T) / N$, where $N$ is the total number of conduction electrons a

Fengyi Li, Ricardo Baptista, Youssef Marzouk

Computing expected information gain (EIG) from prior to posterior (equivalently, mutual information between candidate observations and model parameters or other quantities of interest) is a fundamental challenge in Bayesian optimal experimental design. We formulate flexible transport-based schemes for EIG estimation in general nonlinear/non-Gaussian settings

Yukina Iwata, Shun Hasegawa, Kento Kawaharazuka, Kei Okada

Flexible object manipulation of paper and cloth is a major research challenge in robot manipulation. Although there have been efforts to develop hardware that enables specific actions and to realize a single action of paper folding using sim-to-real and learning, there have been few proposals for humanoid robots and systems that enable continuous, multi-step

Tsvi Cherny-Shahar, Amiram Yehudai

As part of a research on a novel in-process multiprogramming-language interoperability system, this study investigates the interoperability and usage of multiple programming languages within a large dataset of GitHub projects and Stack Overflow Q\&A. It addresses existing multi-lingual development practices and interactions between programming languages, foc

Konstantinos Alexiou, Daniel B. Cooney

Evolutionary competition often occurs simultaneously at multiple levels of organization, in which traits or behaviors that are costly for an individual can provide collective benefits to groups to which the individual belongs. Building off of recent work that has used ideas from game theory to study evolutionary competition within and among groups, we study

Lifeng Mai, Junteng Yao, Jie Tang, Tuo Wu

This letter proposes a secure beamforming design for downlink non-orthogonal multiple access (NOMA) systems utilizing fluid antenna systems (FAS). We consider a setup where a base station (BS) with $M$ fluid antennas (FAs) communicates to a cell-center user (CU) and a cell-edge user (CEU), each with a FA. The CU is the intended recipient while the CEU is reg

Karen A. Mamian, Vladimir V. Popov, Aleksandr Yu. Frolov, Andrey A. Fedyanin

Enhancement and tailoring of the transverse magneto-optical Kerr effect (TMOKE) in hybrid metasurfaces comprising rectangular silicon nanowires coupled with a nickel substrate are demonstrated. The excitation of Mie modes of different orders in nanowires causes the enhancement. The in-plane magnetic dipole mode leads to the largest TMOKE enhancement compared

Biraj Silwal

The distributed representations currently used are dense and uninterpretable, leading to interpretations that themselves are relative, overcomplete, and hard to interpret. We propose a method that transforms these word vectors into reduced syntactic representations. The resulting representations are compact and interpretable allowing better visualization and

Junteng Yao, Ming Jin, Tuo Wu, Maged Elkashlan

Cognitive radio (CR) networks face significant challenges in spectrum sensing, especially under spectrum scarcity. Fluid antenna systems (FAS) can offer an unorthodox solution due to their ability to dynamically adjust antenna positions for improved channel gain. In this letter, we study a FAS-driven CR setup where a secondary user (SU) adjusts the positions

Abdul Rahman, Neelesh Upadhye

In high frequency trading, accurate prediction of Order Flow Imbalance (OFI) is crucial for understanding market dynamics and maintaining liquidity. This paper introduces a hybrid predictive model that combines Vector Auto Regression (VAR) with a simple feedforward neural network (FNN) to forecast OFI and assess trading intensity. The VAR component captures

Lilia S. Xie, Shannon S. Fender, Cameron Mollazadeh, Wuzhang Fang

Superlattice formation dictates the physical properties of many materials, including the nature of the ground state in magnetic materials. Chemical composition is commonly considered to be the primary determinant of superlattice identity, especially in intercalation compounds. Here, we find that, contrary to this conventional wisdom, kinetic control of super

Xiaofeng Wang, Kang Zhao, Feng Liu, Jiayu Wang

Video generation has emerged as a promising tool for world simulation, leveraging visual data to replicate real-world environments. Within this context, egocentric video generation, which centers on the human perspective, holds significant potential for enhancing applications in virtual reality, augmented reality, and gaming. However, the generation of egoce

Gabor Balassa, Gyorgy Wolf

In this paper, the usual momentum- and coordinate-space distance criteria for creating nuclear clusters in transport simulations are addressed by using a dynamical, covariant description in an off-shell Boltzmann-Uehling-Uhlenbeck transport approach. The free parameter of this clustering scheme is the cluster formation time, which is fitted through the FOPI

Joshua Tian Jin Tee, Kang Zhang, Hee Suk Yoon, Dhananjaya Nagaraja Gowda

Diffusion models have recently emerged as a potent tool in generative modeling. However, their inherent iterative nature often results in sluggish image generation due to the requirement for multiple model evaluations. Recent progress has unveiled the intrinsic link between diffusion models and Probability Flow Ordinary Differential Equations (ODEs), thus en

Tong Wei, Weiyang Ding, Yimin Wei

Dual continuation, an innovative insight into extending the real-valued functions of real matrices to the dual-valued functions of dual matrices with a foundation of the G\^ateaux derivative, is proposed. Theoretically, the general forms of dual-valued vector and matrix norms, the remaining properties in the real field, are provided. In particular, we focus

Zelin Ji, Shuo Wang, Kuojun Yang, Qinchuan Zhang

Automatic modulation classification (AMC) has emerged as a key technique in cognitive radio networks in sixth-generation (6G) communications. AMC enables effective data transmission without requiring prior knowledge of modulation schemes. However, the low classification accuracy under the condition of low signal-to-noise ratio (SNR) limits the implementation

Rawad Melhem, Assef Jafar, Oumayma Al Dakkak

This paper addresses the challenge of speaker separation, which remains an active research topic despite the promising results achieved in recent years. These results, however, often degrade in real recording conditions due to the presence of noise, echo, and other interferences. This is because neural models are typically trained on synthetic datasets consi

Xingbo Fu, Song Wang, Yushun Dong, Binchi Zhang

Federated Graph Learning (FGL) is tasked with training machine learning models, such as Graph Neural Networks (GNNs), for multiple clients, each with its own graph data. Existing methods usually assume that each client has both node features and graph structure of its graph data. In real-world scenarios, however, there exist federated systems where only a pa

Yueming Xu, Haochen Jiang, Zhongyang Xiao, Jianfeng Feng

Achieving robust and precise pose estimation in dynamic scenes is a significant research challenge in Visual Simultaneous Localization and Mapping (SLAM). Recent advancements integrating Gaussian Splatting into SLAM systems have proven effective in creating high-quality renderings using explicit 3D Gaussian models, significantly improving environmental recon

H. A. Kierstead, Alexandr Kostochka, Zimu Xiang

A proper vertex coloring of a graph is equitable if the sizes of all color classes differ by at most $1$. For a list assignment $L$ of $k$ colors to each vertex of an $n$-vertex graph $G$, an equitable $L$-coloring of $G$ is a proper coloring of vertices of $G$ from their lists such that no color is used more than $\lceil n/k\rceil$ times. Call a graph equit

Reina Kaneko, Hayate Kojima, Kenta Yanagiya, Junya Hara

This paper presents a multiscale graph construction method using both graph and signal features. Multiscale graph is a hierarchical representation of the graph, where a node at each level indicates a cluster in a finer resolution. To obtain the hierarchical clusters, existing methods often use graph clustering; however, they may ignore signal variations. As

Siwei Li, Jiayan Fang, Yichun Wua, Wei Wang

Early fault detection and timely maintenance scheduling can significantly mitigate operational risks in NPPs and enhance the reliability of operator decision-making. Therefore, it is necessary to develop an efficient Prognostics and Health Management (PHM) multi-step prediction model for predicting of system health status and prompt execution of maintenance

Gonzalo Flores, Mingu Jung, Gilles Lancien, Colin Petitjean

We show that several operator ideals coincide when intersected with the class of linearizations of Lipschitz maps. In particular, we show that the linearization $\widehat{f}$ of a Lipschitz map $f:M\to N$ is Dunford-Pettis if and only if it is Radon-Nikod\'ym if and only if it does not fix any copy of $L_1$. We also identify and study the corresponding metri

Dong-Ping Xuan, Zhong-Xi Shen, Wen Zhou, Hua Nan

Quantum mechanics gives a new breakthrough to the field of parameter estimation. In the realm of quantum metrology, the precision of parameter estimation is limited by the quantum Fisher information. We introduce the measures of partial coherence based on (quantum) Fisher information by taking into account the post-selective non-unitary parametrization proce

Hadi Hosseini, Debmalya Mandal, Amrit Puhan

An important problem on social information sites is the recovery of ground truth from individual reports when the experts are in the minority. The wisdom of the crowd, i.e. the collective opinion of a group of individuals fails in such a scenario. However, the surprisingly popular (SP) algorithm~\cite{prelec2017solution} can recover the ground truth even whe

Qi Huang, Yanjun Li, Bo Yin, Yaoguo Wang

The Northwest China Real-World and Population-based cohort is an ongoing prospective cohort with more than 25 million population, covering almost all residents across approximately 1.66 million square kilometers in northwest China; The cohort integrates data from various sources, including health profiles, examination records, electronic health records, mort

Ning Tang

We establish the asymptotic stability of the catenoid, as a nonflat stationary solution to the hyperbolic vanishing mean curvature (HVMC) equation in Minkowski space $\mathbb{R}^{1 + (n + 1)}$ for $n = 4$. Our main result is under a ``codimension-$1$'' assumption on initial perturbation, modulo suitable translation and boost (i.e. modulation), without any sy

Bin Jiang, Yi-Yang Li, Junjie Liu, Chen Wang

The Dicke model, which describes the collective interaction between an ensemble of atoms and a single-mode photon field, serves as a fundamental framework for studying light-matter interactions and quantum electrodynamic phenomena. In this work, we investigate the manifestation of non-Hermitian effects in a generalized Dicke model, where two dissipative atom

Arindam Roy, Kevin You

Consider the approximation $\tilde{Z}_N(s) = \sum_{n=1}^N n^{-s} + \chi(s) \sum_{n=1}^N n^{1-s}$ of the Riemann zeta function $\zeta(s)$, where $\chi(s)$ is the ratio of the gamma functions. This arise from the approximate functional equation of $\zeta(s)$. Gonek and Montgomery have shown that $\tilde{Z}_N(s)$ has 100\% of its zeros lie on the critical line.

Jukka Ruohonen

The short paper discusses algorithmic fairness by focusing on non-discrimination and a few important laws in the European Union (EU). In addition to the EU laws addressing discrimination explicitly, the discussion is based on the EU's recently enacted regulation for artificial intelligence (AI) and the older General Data Protection Regulation (GDPR). Through

Pengxiu Yu, Yiping Zhang

In this paper, for a family of second-order parabolic system or equation with rapidly oscillating and time-dependent periodic coefficients over rough boundaries, we obtain the large-scale boundary estimates, by a quantitative approach. The quantitative approach relies on approximating twice: we first approximate the original parabolic problem over rough boun

Skye Mceowen, Daniel J. Calderone, Aman Tiwary, Jason S. K. Zhou

This paper presents auto-tuned primal-dual successive convexification (Auto-SCvx), an algorithm designed to reliably achieve dynamically-feasible trajectory solutions for constrained hypersonic reentry optimal control problems across a large mission parameter space. In Auto-SCvx, we solve a sequence of convex subproblems until convergence to a solution of th

Talha Bozkus, Tara Javidi, Urbashi Mitra

Q-learning is widely employed for optimizing various large-dimensional networks with unknown system dynamics. Recent advancements include multi-environment mixed Q-learning (MEMQ) algorithms, which utilize multiple independent Q-learning algorithms across multiple, structurally related but distinct environments and outperform several state-of-the-art Q-learn

Jian Wang, Tiantian Zhu, Chunlin Xiong, Yan Chen

The construction of attack technique knowledge graphs aims to transform various types of attack knowledge into structured representations for more effective attack procedure modeling. Existing methods typically rely on textual data, such as Cyber Threat Intelligence (CTI) reports, which are often coarse-grained and unstructured, resulting in incomplete and i

Zexu Wang, Huaxing Xu, Ju Li, Jinquan Huang

In practical satellite-based quantum key distribution (QKD) systems, the preparation and transmission of polarization-encoding photons suffer from complex environmental effects and high channel-loss. Consequently, the hinge to enhancing the secure key rate (SKR) lies in achieving robust, low-error and high-speed polarization modulation. Although the schemes