December 2024 arXiv papers — page 79

Ron Keuth, Maren Balks, Sebastian Tschauner, Ludger Tüshaus

Fractures, particularly in the distal forearm, are among the most common injuries in children and adolescents, with approximately 800 000 cases treated annually in Germany. The AO/OTA system provides a structured fracture type classification, which serves as the foundation for treatment decisions. Although accurately classifying fractures can be challenging,

Jiaming Yu, Le Liang, Chongtao Guo, Ziyang Guo

This paper investigates the use of multi-agent reinforcement learning (MARL) to address distributed channel access in wireless local area networks. In particular, we consider the challenging yet more practical case where the agents heterogeneously adopt value-based or policy-based reinforcement learning algorithms to train the model. We propose a heterogeneo

Bo-Yong Chen, Yuanpu Xiong

In this article, we investigate the connection between certain real variable things and the Bergman theory. We first use Hardy-type inequalities to give an $L^2$ Hartogs-type extension theorem and an $L^p$ integrability theorem for the Bergman kernel $K_\Omega(\cdot,w)$. We then use the Sobolev-Morrey inequality to show the absolute continuity of Bergman ker

Enea Figini, Mario Paolone

Data centers are large electricity consumers due to the high consumption needs of servers and their cooling systems. Given the current crypto-currency and artificial intelligence trends, the data center electricity demand is bound to grow significantly. With the electricity sector being responsible for a large share of global greenhouse gas (GHG) emissions,

Felix Lehner, Pasquale Lombardo, Susana Castillo, Oliver Hupe

In this research work, we present our open-source Geant4-based Monte-Carlo simulation application, called RadField3D, for generating threedimensional radiation field datasets for dosimetry. Accompanying, we introduce a fast, machine-interpretable data format with a Python API for easy integration into neural network research, that we call RadFiled3D. Both de

David Fleckenstein, Robert Klein, Vienna Klein, Claudius Steinhardt

The widespread adoption of digital distribution channels both enables and forces more and more logistical service providers to manage booking processes actively to maintain competitiveness. As a result, their operational planning is no longer limited to solving vehicle routing problems. Instead, demand management decisions and vehicle routing decisions are o

K. Beuermann, K. Reinsch

We report an observed accretion rate of $\dot M_1 = (3.86\pm0.60)\times 10^{-11}$ $M_{\odot}$yr$^{-1}$ for the white dwarf in the short-period, intermediate polar EX Hya. This result is based upon the accretion-induced $4\pi$-averaged energy flux from 2.45 $\mu$m to 100 keV and the corresponding luminosity at the Gaia distance of 56.77 pc. Our result is in p

Can Wang, Feng-Ming Liu, He Chen, Yi-Fei Du

Despite the significant progress in superconducting quantum computation over the past years, quantum state measurement still lags nearly an order of magnitude behind quantum gate operations in speed and fidelity. The main challenge is that the strong coupling and readout signal used to probe the quantum state may also introduce additional channels which may

Jinrui Zhang, Deyu Zhang, Tingting Long, Wenxin Chen

We present MobiFuse, a high-precision depth perception system on mobile devices that combines dual RGB and Time-of-Flight (ToF) cameras. To achieve this, we leverage physical principles from various environmental factors to propose the Depth Error Indication (DEI) modality, characterizing the depth error of ToF and stereo-matching. Furthermore, we employ a p

Yuchong Geng, Ao Tang

Humans possess a remarkable ability to acquire knowledge efficiently and apply it across diverse modalities through a coherent and shared understanding of the world. Inspired by this cognitive capability, we introduce a concept-centric multi-modality learning framework built around a modality-agnostic concept space that captures structured, abstract knowledg

Gianmario Voria, Stefano Lambiase, Maria Concetta Schiavone, Gemma Catolino

As the adoption of machine learning (ML) systems continues to grow across industries, concerns about fairness and bias in these systems have taken center stage. Fairness toolkits, designed to mitigate bias in ML models, serve as critical tools for addressing these ethical concerns. However, their adoption in the context of software development remains undere

Xi Ding, Lei Wang

Large language models (LLMs) have revolutionized video-based computer vision applications, including action recognition, anomaly detection, and video summarization. Videos inherently pose unique challenges, combining spatial complexity with temporal dynamics that are absent in static images or textual data. Current approaches to video understanding with LLMs

Chi Liu, Jiangxia Cao, Rui Huang, Kuo Cai

Recommendation systems (RecSys) are designed to connect users with relevant items from a vast pool of candidates while aligning with the business goals of the platform. A typical industrial RecSys is composed of two main stages, retrieval and ranking: (1) the retrieval stage aims at searching hundreds of item candidates satisfied user interests; (2) based on

Omar Alnaseri, Laith Alzubaidi, Yassine Himeur, Mohammed Alaa Ala'anzy

Traditional mathematical models used in designing next-generation communication systems often fall short due to inherent simplifications, narrow scope, and computational limitations. In recent years, the incorporation of deep learning (DL) methodologies into communication systems has made significant progress in system design and performance optimisation. Au

Rafaela Scaciota, Malith Gallage, Sumudu Samarakoon, Mehdi Bennis

This paper introduces a novel method for predicting blockages in millimeter-wave (mmWave) communication systems towards enabling reliable connectivity. It employs a self-supervised learning approach to label radio frequency (RF) data with the locations of blockage-causing objects extracted from light detection and ranging (LiDAR) data, which is then used to

Shuyin Xia, Xinjun Ma, Zhiyuan Liu, Cheng Liu

Graph Neural Networks (GNNs) have demonstrated significant achievements in processing graph data, yet scalability remains a substantial challenge. To address this, numerous graph coarsening methods have been developed. However, most existing coarsening methods are training-dependent, leading to lower efficiency, and they all require a predefined coarsening r

Yichen Li, Haozhao Wang, Wenchao Xu, Tianzhe Xiao

Non-Centralized Continual Learning (NCCL) has become an emerging paradigm for enabling distributed devices such as vehicles and servers to handle streaming data from a joint non-stationary environment. To achieve high reliability and scalability in deploying this paradigm in distributed systems, it is essential to conquer challenges stemming from both spatia

Mathias Mikkelsen, Yuya O. Nakagawa

Quantum-selected configuration interaction (QSCI) utilizes an input quantum state on a quantum device to select important bases (electron configurations in quantum chemistry) that define a subspace in which to diagonalize a target Hamiltonian, i.e., perform selected configuration interaction, on classical computers. Previous proposals for preparing a good in

Prajwal Pisal, Ondrej Krejci, Patrick Rinke

Transforming CO$_2$ into methanol represents a crucial step towards closing the carbon cycle, with thermoreduction technology nearing industrial application. However, obtaining high methanol yields and ensuring the stability of heterocatalysts remain significant challenges. Herein, we present a sophisticated computational framework to accelerate the discover

Samuele Brunati, Michele Bucelli, Roberto Piersanti, Luca Dede'

We propose a novel partitioned scheme based on Eikonal equations to model the coupled propagation of the electrical signal in the His-Purkinje system and in the myocardium for cardiac electrophysiology. This scheme allows, for the first time in Eikonal-based modeling, to capture all possible signal reentries between the Purkinje network and the cardiac muscl

Betony Adams, Angela Illing, Francesco Petruccione

The origins of life is a question that continues to intrigue scientists across disciplines. One theory - the iron-sulphur theory - suggests that reactions essential to the synthesis of biological materials got their catalytic 'spark' from mineral surfaces such as iron pyrite, commonly known as fool's gold. Additionally, the binding affinity of the ligands sy

Alberto Testoni, Barbara Plank, Raquel Fernández

Ambiguity resolution is key to effective communication. While humans effortlessly address ambiguity through conversational grounding strategies, the extent to which current language models can emulate these strategies remains unclear. In this work, we examine referential ambiguity in image-based question answering by introducing RACQUET, a carefully curated

Z. Wang, S. Li, Dongrui Wu

Epilepsy significantly impacts global health, affecting about 65 million people worldwide, along with various animal species. The diagnostic processes of epilepsy are often hindered by the transient and unpredictable nature of seizures. Here we propose a multi-space alignment approach based on cross-species and cross-modality electroencephalogram (EEG) data

Giacomo Pacini, Fabio Carrara, Nicola Messina, Nicola Tonellotto

Query suggestion, a technique widely adopted in information retrieval, enhances system interactivity and the browsing experience of document collections. In cross-modal retrieval, many works have focused on retrieving relevant items from natural language queries, while few have explored query suggestion solutions. In this work, we address query suggestion in

Kees Wapenaar

According to Huygens' principle, all points on a wave front act as secondary sources emitting spherical waves, and the envelope of these spherical waves forms a new wave front. In the mathematical formulation of Huygens' principle, the waves emitted by the secondary sources are represented by Green's functions. In many present-day applications of Huygens' pr

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using $(2712.4\pm 14.3)\times10^6 \psi(3686)$ events collected by the BESIII detector operating at the BEPCII collider, we present the first observations of the decays $\chi_{cJ}(J=0,1,2)\to p\bar{p}\eta\pi^{0}$. Their decay branching fractions are determined to be ${\cal B}(\chi_{c0}\to p\bar{p}\eta\pi^{0})=({2.41 \pm 0.07 \pm 0.19}) \times 10^{-4}$, ${\cal

Shuting Cui, Fa Chen, Liyang Liao, Jiacheng Lu

Strengthening magnetoacoustic coupling is crucial to the improvement of the surface acoustic wave (SAW)-driven spintronics devices. A key challenge in enhancing magnetoacoustic coupling is minimizing the phonon and magnon dissipation of the device, which usually requires complicated techniques for generating shear-horizontal (SH) or standing waves to suppres

Jiayu Yuan, Liyu Shi, Tiequan Xu, Yue Wang

Recent developments in nonequilibrium and nonlinear terahertz (THz) spectroscopies have significantly advanced our understanding of collective excitations in superconductors. However, there is still debate surrounding the identification of Higgs or Leggett modes, as well as BCS charge fluctuations, in the well-known two-band superconductor MgB$_2$. Here, we

Jakub Rembieliński

In this paper, we propose a specific, nontrivial extension of the Standard Model of weak interactions based on the ${SU(2)}_L\times{U(1)}_Y\times{U(1)}_C$ group. Our motivation follows from the identification of the globally conserved charge $\Omega=\mathrm{B}-\mathrm{L}-\mathrm{Q}$ as a neutrino charge. An intriguing feature of the model is the emergence of

Timothée Crin-Barat, Nicola De Nitti, Stefan Škondrić, Alessandro Violini

We characterize the Leray--Hopf solutions of the 2D inhomogeneous Navier--Stokes system that become strong for positive times. This characterization relies on the strong energy inequality and the regularity properties of the pressure. As an application, we establish a weak-strong uniqueness result and provide a unified framework for several recent advances i

N. A. Rather, Wani Naseer

We investigate the problem of determining the zeros of quaternionic polynomials using matrix method. In a recent paper, Dar et al. \cite{RD} proved that the zeros of a quaternionic polynomial and the left eigenvalues of the corresponding companion matrix are identical. Building on this, we employ various newly developed matrix techniques to establish several

Felix Binkowski, Fridtjof Betz, Martin Hammerschmidt, Lin Zschiedrich

We present a framework for the efficient and accurate computation of resonance modes in photonic waveguides. The framework is based on AAA rational approximation with the application of special light sources. It allows one to calculate only relevant modes, such as the fundamental resonance modes localized in the central core of the waveguides. We demonstrate

Lianghao Xia, Meiyan Xie, Yong Xu, Chao Huang

For modern recommender systems, the use of low-dimensional latent representations to embed users and items based on their observed interactions has become commonplace. However, many existing recommendation models are primarily designed for coarse-grained and homogeneous interactions, which limits their effectiveness in two critical dimensions. Firstly, these

Sungjin Lee

We study uniform Lipschitz regularity estimates for elliptic systems in divergence form with continuous coefficients, based on rapidly oscillating periodic coefficients derived from homogenization theory. We extend a result by Avellaneda and Lin [Comm. Pure Appl. Math. 40 (1987), pp. 803-847] by minimizing all regularity conditions of the given data to integ

Wangyu Wu, Xianglin Qiu, Siqi Song, Xiaowei Huang

Weakly Supervised Semantic Segmentation (WSSS), which leverages image-level labels, has garnered significant attention due to its cost-effectiveness. The previous methods mainly strengthen the inter-class differences to avoid class semantic ambiguity which may lead to erroneous activation. However, they overlook the positive function of some shared informati

Junxian Shi, Linning Peng, Wentao Jing, Lingnan Xie

Radio frequency (RF) fingerprint technology is utilized for wireless device identification, extensively employed in the internet of things (IoT). The operating environment for IoT devices is challenging, with pervasive noise and distortion on the signals which blur the feature space of RF fingerprints. Consequently, the model accuracy obtained through traini

Edward Kembery

This paper argues that existing governance mechanisms for mitigating risks from AI systems are based on the `Big Compute' paradigm -- a set of assumptions about the relationship between AI capabilities and infrastructure -- that may not hold in the future. To address this, the paper introduces the `Proliferation' paradigm, which anticipates the rise of small

Alice Marché, Gianluca Morettini, Leonardo Mazza, Lorenzo Gotta

We study a dephasing many-body open quantum system that hosts, together with the infinite-temperature state, another additional stationary state. The latter is exceptional in many respects, as it is pure and retains memory of the initial condition, whereas any orthogonal state evolves towards the infinite-temperature state erasing any information on the init

Xing-Chen Guo, Mao-Sheng Li

Measurement-device-independent entanglement witnesses (MDI-EWs) enable the detection of entanglement without relying on characterized measurements. However, the entanglement criteria for MDI-EWs typically assume the idealized condition that the lab input states are precisely the desired ones. In this work, we remove this idealization by considering the reali

Haoyang Li, Wei Chen, Xiaojin Zhang

Gradient leakage attacks pose a significant threat to the privacy guarantees of federated learning. While distortion-based protection mechanisms are commonly employed to mitigate this issue, they often lead to notable performance degradation. Existing methods struggle to preserve model performance while ensuring privacy. To address this challenge, we propose

Le Yang, Ziwei Zheng, Boxu Chen, Zhengyu Zhao

Recent studies have shown that large vision-language models (LVLMs) often suffer from the issue of object hallucinations (OH). To mitigate this issue, we introduce an efficient method that edits the model weights based on an unsafe subspace, which we call HalluSpace in this paper. With truthful and hallucinated text prompts accompanying the visual content as

Shen Zhang, Xueyi Shen, Ruida Zhu, Zilu Liang

Providing commons in the risky world is crucial for human survival, however, suffers more from the "free-riding" problem. Here, we proposed a solution that limits the access of the resource to an agent and tested its efficiency with a novel public investment game. On each trial, all group members invest in an agent responsible for a gamble and distribution.

Hao Li, Xiangyuan Yang, Mengzhu Wang, Long Lan

Object detection is a critical task in computer vision, with applications in various domains such as autonomous driving and urban scene monitoring. However, deep learning-based approaches often demand large volumes of annotated data, which are costly and difficult to acquire, particularly in complex and unpredictable real-world environments. This dependency

Yi-jia Yang, Yu-qiang Liu, Zheng Liu, Chang-shui Yu

Non-equilibrium spin-chain systems have been attracting increasing interest in energy transport. This work studies a one-dimensional non-equilibrium Ising chain immersed in a tilted magnetic field, every spin contacts a Boson reservoir with the dissipative system-environment interaction. We analytically investigate the dynamics and the steady-state energy tr

Giulia Bernardini, Philip Bille, Inge Li Gørtz, Teresa Anna Steiner

For databases consisting of many text documents, one of the most fundamental data analysis tasks is counting (i) how often a pattern appears as a substring in the database (substring counting) and (ii) how many documents in the collection contain the pattern as a substring (document counting). If such a database contains sensitive data, it is crucial to prot

Meng Fai Lim, Chao Qin

Coates, Fukaya, Kato, Sujatha and Venjakob come up with a procedure of attaching suitable characteristic element to Selmer groups defined over a non-commutative $p$-adic Lie extension, which is subsequently refined by Burns and Venjakob. By their construction, these characteristic elements are realized as elements in an appropriate localized $K_1$-group. In

Jonas Weidner, Michal Balcerak, Ivan Ezhov, André Datchev

Glioblastoma, the most aggressive primary brain tumor, poses a severe clinical challenge due to its diffuse microscopic infiltration, which remains largely undetected on standard MRI. As a result, current radiotherapy planning employs a uniform 15 mm margin around the resection cavity, failing to capture patient-specific tumor spread. Tumor growth modeling o

Dimitrios Mallis, Ahmet Serdar Karadeniz, Sebastian Cavada, Danila Rukhovich

We propose CAD-Assistant, a general-purpose CAD agent for AI-assisted design. Our approach is based on a powerful Vision and Large Language Model (VLLM) as a planner and a tool-augmentation paradigm using CAD-specific tools. CAD-Assistant addresses multimodal user queries by generating actions that are iteratively executed on a Python interpreter equipped wi

Julien Audiffren, Christophe Broillet, Ljiljana Dolamic, Philippe Cudré-Mauroux

In Extreme Multi Label Completion (XMLCo), the objective is to predict the missing labels of a collection of documents. Together with XML Classification, XMLCo is arguably one of the most challenging document classification tasks, as the very high number of labels (at least ten of thousands) is generally very large compared to the number of available labelle

Beniamin Bogosel

The sensitivity of the areas of Reuleaux polygons and disk polygons is computed with respect to vertex perturbations. Computations are completed for both constrained and Lagrangian formulations and they imply that the only critical Reuleaux polygons for the area functional are the regular ones. As a consequence, new variational proofs for the Blaschke-Lebesg

Mohammad Rohaninejad, Behdin Vahedi-Nouri, Reza Tavakkoli-Moghaddam, Zdeněk Hanzálek

This research investigates a multi-product capacitated lot-sizing and scheduling problem incorporating a novel learning effect, namely the period-based learning effect. This is inspired by a real case in a core analysis laboratory under a job shop setting. Accordingly, a Mixed-Integer Linear Programming (MILP) model is extended based on the big-bucket formul

Elisa A. Tau, Antonela Monachesi, Facundo A. Gomez, Robert J. J. Grand

The stellar halos of dwarf galaxies are becoming an object of interest in the extragalactic community due to their detection in some recent observations. Additionally, new cosmological simulations of very high resolution were performed, allowing their study. These stellar halos could help shed light on our understanding of the assembly of dwarf galaxies and

Ilyes Jalisse, Aditya Jha, Lionel Buisson, Frédéric Nallet

Compliant walls are widespread in biological and engineering systems. Because of their singular nature, adapted tools are required to accurately study their rheological properties as well as the consequences of the latter within a given mechanical setting. Because of their slender nature, membranes can be considered as prototypical examples of highly complia

Tian Li, Xiao-Yue Xu, Chen Ding, Tian-Ci Tian

Quantum computing promises to revolutionize various fields, yet the execution of quantum programs necessitates an effective compilation process. This involves strategically mapping quantum circuits onto the physical qubits of a quantum processor. The qubits' arrangement, or topology, is pivotal to the circuit's performance, a factor that often defies traditi

J. Stasielak, N. Borodai, D. Góra, M. Niechciol

Flares produced by certain classes of astrophysical objects may be sources of some ultra-high-energy particles, which, if they are photons, would group into clusters of events correlated in space and time. Identification of such clustering in cosmic-ray data would provide important evidence for possible existence of ultra-high-energy (UHE) photons and could

Zixuan Chen, Jiaxin Li, Liming Tan, Yejie Guo

Intelligent robots need to interact with diverse objects across various environments. The appearance and state of objects frequently undergo complex transformations depending on the object properties, e.g., phase transitions. However, in the vision community, segmenting dynamic objects with phase transitions is overlooked. In light of this, we introduce the

Huiwen Yang, Yu Zhou, Taolue Chen

Autonomous driving systems (ADS) have achieved remarkable progress in recent years. However, ensuring their safety and reliability remains a critical challenge due to the complexity and uncertainty of driving scenarios. In this paper, we focus on simulation testing for ADS, where generating diverse and effective testing scenarios is a central task. Existing

Beiqi Zhang, Peng Liang, Xin Zhou, Xiyu Zhou

Automated code smell detection faces persistent challenges due to the subjectivity of heuristic rules and the limited performance of traditional ML/DL models. While Large Language Models (LLMs) offer a promising alternative, their adoption is impeded by high fine-tuning costs and a lack of "LM-ready" benchmarks. To bridge these gaps, we present a study with

Álvaro Pastor-Gutiérrez, Jan M. Pawlowski, Manuel Reichert, Giacomo Ruisi

We study the electron-positron to muon--anti-muon cross-section in the asymptotically safe Standard Model. In particular, we include the graviton contributions to the scattering amplitude, which is computed from momentum-dependent timelike one-particle-irreducible correlation functions. Specifically, we employ reconstruction techniques for the graviton spect

Ramona Kühn, Jelena Mitrović, Michael Granitzer

Rhetorical figures play an important role in our communication. They are used to convey subtle, implicit meaning, or to emphasize statements. We notice them in hate speech, fake news, and propaganda. By improving the systems for computational detection of rhetorical figures, we can also improve tasks such as hate speech and fake news detection, sentiment ana

Sjir J. C. Schielen, Jesper Pilmeyer, Albert P. Aldenkamp, Danny Ruijters

Functional magnetic resonance imaging (fMRI) has become instrumental in researching brain function. One application of fMRI is investigating potential neural features that distinguish people with autism spectrum disorder (ASD) from healthy controls. The Autism Brain Imaging Data Exchange (ABIDE) facilitates this research through its extensive data-sharing in

Guido Violano, Savino Dibitonto, Luciano Afferrante

Mushroom-shaped pillars have been extensively studied for their superior adhesive properties, often drawing inspiration from natural attachment systems observed in insects. Typically, pillars are modeled with linear elastic materials in the literature; in reality, the soft materials used for their fabrication exhibit a rate-dependent constitutive behavior. T

Jackson Arndt, Malia Jansen, Payton McBurney, Katherine Vance

In 2003, Ozsv\'ath, Szab\'o, and Rasmussen introduced the $\tau$ invariant for knots, and in 2011, Sarkar published a computational shortcut for the $\tau$ invariant of knots that can be represented by diagonal grid diagrams. Previously, the only knots known to have diagonal grid diagram representations were torus knots. We prove that all such knots are posi

Pengxiang Li, Lu Yin, Shiwei Liu

Large Language Models (LLMs) have achieved remarkable success, yet recent findings reveal that their deeper layers often contribute minimally and can be pruned without affecting overall performance. While some view this as an opportunity for model compression, we identify it as a training shortfall rooted in the widespread use of Pre-Layer Normalization (Pre

Vageesh Saxena, Benjamin Bashpole, Gijs Van Dijck, Gerasimos Spanakis

Human trafficking (HT) remains a critical issue, with traffickers increasingly leveraging online escort advertisements (ads) to advertise victims anonymously. Existing detection methods, including Authorship Attribution (AA), often center on text-based analyses and neglect the multimodal nature of online escort ads, which typically pair text with images. To

Hideaki Takahashi, Raffaele Borrelli

We present a comprehensive theory for a novel method to discretize the spectral density of a bosonic heat bath, as introduced in [H. Takahashi and R. Borrelli, J. Chem. Phys. \textbf{161}, 151101 (2024)]. The approach leverages a low-rank decomposition of the Fourier-transform relation connecting the bath correlation function to its spectral density. By capt

Jing Gao, Xueliang Li

Let $F_k=K_1\vee P_{k-1}$ be the fan graph on $k$ vertices. A graph is said to be $F_k$-free if it does not contain $F_k$ as a subgraph. Yu et al. in [arXiv:2404.03423] conjectured that for $k\geq2$ and $m$ sufficiently large, if $G$ is an $F_{2k+1}$-free or $F_{2k+2}$-free graph, then $\lambda(G)\leq \frac{k-1+\sqrt{4m-k^2+1}}{2}$ and the equality holds if

Xinyu Pang, Ruixin Hong, Zhanke Zhou, Fangrui Lv

Physics problems constitute a significant aspect of reasoning, necessitating complicated reasoning ability and abundant physics knowledge. However, existing large language models (LLMs) frequently fail due to a lack of knowledge or incorrect knowledge application. To mitigate these issues, we propose Physics Reasoner, a knowledge-augmented framework to solve

Chenhao Zhang, Shaofei Shen, Weitong Chen, Miao Xu

Machine unlearning without access to real data distribution is challenging. The existing method based on data-free distillation achieved unlearning by filtering out synthetic samples containing forgetting information but struggled to distill the retaining-related knowledge efficiently. In this work, we analyze that such a problem is due to over-filtering, wh

François Bernard, Antoine Boivin

In this paper, we provide a combinatorial description of seminormal toric varieties. The corresponding combinatorial object is a fan equipped with a collection of groups assigned to each cone. This framework introduces a more general class of toric varieties than classical normal toric varieties, while having simpler combinatorial data compared to general no

Yingzhi Wang, Anas Alhmoud, Muhammad Alqurishi

In recent years, the enhanced capabilities of ASR models and the emergence of multi-dialect datasets have increasingly pushed Arabic ASR model development toward an all-dialect-in-one direction. This trend highlights the need for benchmarking studies that evaluate model performance on multiple dialects, providing the community with insights into models' gene

Jonas Fransson, Yael Kapon, Lilach Brann, Shira Yochelis

Recent experiments suggest that the conditions for ferromagnetic order in, e.g., magnetite, can be modified by adsorption of chiral molecules. Especially, the coercivity of magnetite was increased by nearly 100 \%, or 20 times the earth magnetic flux density, at room temperature. The coercivity was, moreover, demonstrated to increase linearly with temperatur

Chenyu Yang, Shuai Wang, Hangting Chen, Jianwei Yu

The emergence of novel generative modeling paradigms, particularly audio language models, has significantly advanced the field of song generation. Although state-of-the-art models are capable of synthesizing both vocals and accompaniment tracks up to several minutes long concurrently, research about partial adjustments or editing of existing songs is still u

Calliope Ryan-Smith

Under the assumption of small violations of choice with seed $S$ ($\mathsf{SVC}(S)$), the failure of many choice principles reflect to to local properties of $S$, which can be a helpful characterisation for preservation proofs. We demonstrate the reflections of $\mathsf{DC}$, $\mathsf{AC}_\lambda$, $\mathsf{PP}$, and other important forms of choice. As a con

Cheng-Jie Wang, Yuxin Li, Zhe Ding, Pengfei Wang

Frequency multiplication involves generating harmonics from an input frequency, a technique particularly useful for integrating spin-wave devices operating at different frequencies. While topological magnetic textures offer distinct advantages in spin-wave applications, frequency multiplication has not yet been observed in these structures. Here, we study th

Javier López-Pedrares, Marcos Suárez-Vázquez, Juan Pérez-Mercader, Alberto P. Muñuzuri

Pattern formation is ubiquitous in nature and the mechanism widely-accepted to underlay them is based on the Turing instability, predicted by Alan Turing decades ago. This is a non-trivial mechanism that involves nonlinear interaction terms between the different species involved and transport mechanisms. We present here a mathematical analysis aiming to expl

Yifan Lu, Yigeng Zhou, Jing Li, Yequan Wang

Multi-hop question answering (MHQA) poses a significant challenge for large language models (LLMs) due to the extensive knowledge demands involved. Knowledge editing, which aims to precisely modify the LLMs to incorporate specific knowledge without negatively impacting other unrelated knowledge, offers a potential solution for addressing MHQA challenges with

Yaoke Wang, Yun Zhu, Xintong Bao, Wenqiao Zhang

Despite the remarkable capabilities of large language models (LLMs) in natural language understanding and reasoning, they often display undesirable behaviors, such as generating hallucinations and unfaithful reasoning. A prevalent strategy to mitigate these issues is the use of reflection, which refines responses through an iterative process. However, while

C. Beirão da Cruz e Silva, G. Marozzo, G. Da Molin, J. Hollar

Forward proton spectrometers at high-energy proton colliders rely on precision timing to discriminate signal from background. Silicon low gain avalanche diodes (LGADs) are a candidate for future timing detectors in these systems. A major challenge for the use of LGADs is that these detectors must be placed within a few mm of the beams, resulting in a very la

Yichen Li, Yuying Wang, Haozhao Wang, Yining Qi

Continual Federated Learning (CFL) allows distributed devices to collaboratively learn novel concepts from continuously shifting training data while avoiding knowledge forgetting of previously seen tasks. To tackle this challenge, most current CFL approaches rely on extensive rehearsal of previous data. Despite effectiveness, rehearsal comes at a cost to mem

V. Yokar, A. Mehrpooya, Y. Teng, S. Shen

This paper proposes and validates a PTP-synchronized 8.4ns optical switching with a 100ns jitter at the switching edges. This approach is adopted and demonstrated for instant network recovery within 2.7ms and scheduled network recovery.

Alessandro Paghi, Laura Borgongino, Sebastiano Battisti, Simone Tortorella

InAs on Insulator (InAsOI) has been recently demonstrated as a promising platform to develop hybrid semiconducting-superconducting Josephson Junctions (JJs) and Josephson Field Effect Transistors (JoFETs). The InAsOI consists of an InAs epilayer grown onto a cryogenic-electrically-insulating InAlAs metamorphic buffer, which allows the electrical decoupling o

Mikhail A. Baranov

An exact result for the reduced density matrix on a finite interval for a $1+1$ dimensional free real scalar field in the ground state is presented. In the massless case, the Williamson decomposition of the appearing kernels is explicitly performed, which allows to reproduce the known result for the entanglement (modular) Hamiltonian and, for a small mass $M

Shin'ichi Nojiri, S. D. Odintsov

We investigate the radii of the photon sphere and the black hole shadow in the framework of $F(R)$ gravity. For this purpose, we derive the field equation for the corresponding theory when the general spherically symmetric and static configuration is considered. This equation is the third-order differential equation with respect to $F_R(r)\equiv \left. \frac

Jonas Werheid, Oleksandr Melnychuk, Hans Zhou, Meike Huber

Effective decision-making in automation equipment selection is critical for reducing ramp-up time and maintaining production quality, especially in the face of increasing product variation and market demands. However, limited expertise and resource constraints often result in inefficiencies during the ramp-up phase when new products are integrated into produ

Daigo Mugita, Kazuyoshi Souno, Masaharu Isobe

Inherent structures, derived by eliminating thermal fluctuations from complex trajectories, illuminate fundamental mechanisms underlying structural relaxation and dynamic heterogeneity in dense glassy systems. However, determining these structures in hard disk/sphere systems presents unique challenges due to the discontinuous nature of inter-particle potenti

Haiming Zhang, Ying Xue, Xu Yan, Jiacheng Zhang

The field of autonomous driving is experiencing a surge of interest in world models, which aim to predict potential future scenarios based on historical observations. In this paper, we introduce DFIT-OccWorld, an efficient 3D occupancy world model that leverages decoupled dynamic flow and image-assisted training strategy, substantially improving 4D scene for

Guanghan Li, Xun Zhang, Yufei Zhang, Yifan Yin

Large language models (LLMs), endowed with exceptional reasoning capabilities, are adept at discerning profound user interests from historical behaviors, thereby presenting a promising avenue for the advancement of recommendation systems. However, a notable discrepancy persists between the sparse collaborative semantics typically found in recommendation syst

Liang Zhang, Zhanrong Ou, Changhui Hu, Haibin Kan

Data sharing is ubiquitous in the metaverse, which adopts blockchain as its foundation. Blockchain is employed because it enables data transparency, achieves tamper resistance, and supports smart contracts. However, securely sharing data based on blockchain necessitates further consideration. Ciphertext-policy attribute-based encryption (CP-ABE) is a promisi

Edi Sutoyo, Andrea Capiluppi

Self-Admitted Technical Debt (SATD) refers to instances where developers knowingly introduce suboptimal solutions into code and document them, often through textual artifacts. This paper provides a comprehensive state-of-practice report on the development and adoption of SATD detection tools. Through a systematic review of the available literature and tools,

Hari Hara Suthan Chittoor, Paul Robert Griffin, Ariel Neufeld, Jayne Thompson

Long-term time series forecasting (LTSF) involves predicting a large number of future values of a time series based on the past values. This is an essential task in a wide range of domains including weather forecasting, stock market analysis and disease outbreak prediction. Over the decades LTSF algorithms have transitioned from statistical models to deep le

Markus Banagl

We construct an equivariant L-class for orientation preserving actions of a compact Lie group on a Whitney stratified compact oriented pseudomanifold that satisfies the Witt condition, for example on a compact pure-dimensional complex algebraic variety. The class lies in equivariant rational homology and its restriction to the trivial group is the Goresky-Ma

Jonas Nüßlein, Leo Sünkel, Jonas Stein, Tobias Rohe

Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing are prominent approaches for solving combinatorial optimization problems, such as those formulated as Quadratic Unconstrained Binary Optimization (QUBO). These algorithms aim to minimize the objective function $x^T Q x$, where $Q$ is a QUBO matrix. However, the number of two-qubit CNOT g

Taesik Nam, Seungjae Lee, Kiwoong Park, Sunbeom Kwon

Distributed resource allocation algorithms differ from centralized methods by relying on locally collected information for resource selection, leading to a low vehicle-to-everything (V2X) communication quality of service (QoS) in high-traffic congestion. To overcome these challenges, this study proposes a proactive received signal strength indicator (RSSI)-b

Robin Kaiser, Lionel Levine, Ecaterina Sava-Huss

Locally Markov walks are natural generalizations of classical Markov chains, where instead of a particle moving independently of the past, it decides where to move next depending on the last action performed at the current location. We introduce the concept of locally Markov walks and we describe their stationary distribution and recurrent states, and we pro

Ali Hamdi, Ahmed Abdelmoneim Mazrou, Mohamed Shaltout

Current methods for analyzing student engagement in e-learning platforms, including automated systems, often struggle with challenges such as handling fuzzy sentiment in text comments and relying on limited metadata. Traditional approaches, such as surveys and questionnaires, also face issues like small sample sizes and scalability. In this paper, we introdu

Riku Rantanen, Erkki Thuneberg, Vladimir Eltsov

We have performed numerical calculations of the structure of the single-quantum vortex in superfluid $^3$He-A. The GPU-accelerated large-scale numerical simulation is performed in the Ginzburg-Landau model and resolves length scales of both coherence-length-sized hard core and dipolar-length-sized soft core of the vortex. The calculations support previously

Ricardo Zarzuela, Rodrigo Jaeschke-Ubiergo, Olena Gomonay, Libor Šmejkal

We develop a mesoscale transport theory for the charge and spin degrees of freedom of itinerant carriers in a $d$-wave altermagnet. Our effective Lagrangian description is built upon the slave-boson formulation of the microscopic $t-J$ model. We obtain a spin-polarized diffusive contribution to the effective Hamiltonian, with no counterpart in conventional a

Adam Żychowski, Andrew Perrault, Jacek Mańdziuk

Decision trees are widely used in machine learning due to their simplicity and interpretability, but they often lack robustness to adversarial attacks and data perturbations. The paper proposes a novel island-based coevolutionary algorithm (ICoEvoRDF) for constructing robust decision tree ensembles. The algorithm operates on multiple islands, each containing

Jan Magnus Sischka, Ingo Nitschke, Axel Voigt

We consider membranes as fluid deformable surface and allow for higher order geometric terms in the bending energy. The evolution equations are derived and numerically solved using surface finite elements. The higher order geometric terms related to the Gaussian curvature squared have a tendency to stabilize tubes and enhance the evolution towards equilibriu