October 2024 arXiv papers — page 108

Shiao Xie, Hongyi Wang, Ziwei Niu, Hao Sun

Semi-supervised learning (SSL) for medical image segmentation is a challenging yet highly practical task, which reduces reliance on large-scale labeled dataset by leveraging unlabeled samples. Among SSL techniques, the weak-to-strong consistency framework, popularized by FixMatch, has emerged as a state-of-the-art method in classification tasks. Notably, suc

Đorđe Dangić, Giovanni Caldarelli, Raffaello Bianco, Ivana Savić

In crystalline materials, low lattice thermal conductivity is often associated with strong anharmonicity, which can cause significant deviations from the expected Lorentzian lineshape of phonon spectral functions. These deviations, occurring in an overdamped regime, raise questions about the applicability of the Boltzmann transport equation. Furthermore, str

A. M. W. Mitchell, S. Celli

Supernova remnants (SNRs) are considered as the most promising source class to account for the bulk of the Galactic cosmic-ray flux. Yet amongst the population of ultra-high energy (UHE) sources that has recently emerged, due to high-altitude particle detector experiments such as LHAASO and HAWC, remarkably few are associated with known SNRs. These observati

M. E. Cluver, T. H. Jarrett, D. A. Dale, J. -D. T. Smith

In this work we present source-tailored WISE mid-infrared photometry (at 3.4$\mu$m, 4.6$\mu$m, 12$\mu$m, and 23$\mu$m) of 2812 galaxies in the extended Spitzer Survey of Stellar Structure in Galaxies (S$^4$G) sample, and characterise the mid-infrared colors and dust properties of this legacy nearby galaxy data set. Informed by the relative emission between W

Marek Kwiek, Lukasz Szymula

In this research, we quantify an inflow of women into science in the past three decades. Structured Big Data allow us to estimate the contribution of women scientists to the growth of science by disciplines (N = STEMM 14 disciplines) and over time (1990-2023). A monolithic segment of STEMM science emerges from this research as divided between the disciplines

Alexandru Paler

A quantum operating system (QCOS) is a classic software running on classic hardware. The QCOS is preparing, starting, controlling and managing quantum computations. The reliable execution of fault-tolerant quantum computations will require the QCOS to be as reliable and fault-tolerant as the computation itself. In the following, we discuss why a QCOS should

Maria Axenovich, Leon Kießle, Arsenii Sagdeev, Maksim Zhukovskii

What is the maximum length ${\rm f}_{\rm max}(\ell, \Sigma)$ of a facial cycle of an inclusion-maximal graph with girth at least $\ell$ embedded on a given surface $\Sigma$? If $\Sigma=\mathcal{P}$ is a plane, we show that $3\ell-11\leq {\rm f}_{\rm max}(\ell, \mathcal{P})\leq 8\ell-13$. We also prove that ${\rm f}_{\rm max}(\ell, \Sigma)$ is bounded for any

Diomidis Spinellis, Panos Louridas, Maria Kechagia, Tushar Sharma

Is the quality of existing code correlated with the quality of subsequent changes? According to the (controversial) broken windows theory, which inspired this study, disorder sets descriptive norms and signals behavior that further increases it. From a large code corpus, we examine whether code history does indeed affect the evolution of code quality. We exa

Damian Niwiński, Marcin Przybyłko, Michał Skrzypczak

This work addresses the problem of computing measures of recognisable sets of infinite trees. An algorithm is provided to compute the probability measure of a tree language recognisable by a weak alternating automaton, or equivalently definable in weak monadic second-order logic. The measure is the uniform coin-flipping measure or more generally it is genera

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

Using $(27.12\pm 0.14)\times10^{8}$ $\psi(3686)$ events collected with the BESIII detector, the decay $\chi_{c0}\to\Sigma^{+}\bar{\Sigma}^{-}\eta$ is observed for the first time with a statistical significance of $7.0\sigma$, and evidence for $\chi_{c1}\to\Sigma^{+}\bar{\Sigma}^{-}\eta$ and $\chi_{c2}\to\Sigma^{+}\bar{\Sigma}^{-}\eta$ is found with statistic

Sebastian Werner, Sepideh Masoudi, Fernando Castillo, Fabian Piper

The rapid evolution of cloud-native applications, characterized by dynamic, interconnected services, presents significant challenges for maintaining trustworthy and auditable systems, especially in sensitive contexts, such as finance or healthcare. Traditional methods of verification and certification are often inadequate due to the fast-past and dynamic dev

C. L. Dinkova, R. P. Encheva, A. A. Ali

Geometric constructions are widely used in computer graphics and engineering drawing. A right generalized cylinder is a ruled surface whose base curve is a plane curve perpendicular to the rulings. The paper discusses relations between the base curve and the non-planar space curve on the right generalized cylinder. Based on these relations, a method for obta

Shi-Biao Zheng

In this reply, I point out that the comment by Cline (arXiv:2410.05826) on my manuscripts (arXiv:2407.00954 and arXiv:2410.03133) has overlooked the critical fact that the quantum entanglement between the neutrino's mass and other degrees of freedom would destroy the quantum coherence between the mass eigenstates.

Tomasz Kozacki, Juan Martinez-Carranza, Maksymilian Chlipala, Rafal Kukolowicz

Numerical inspection of wide-angle (WA) or ultra-wide-angle (UWA) computer-generated holograms (CGH) is a computationally demanding task. To surpass this limitation, we propose a novel computational approach for fast and accurate WA-CGH reconstruction based on Fast Fourier transform Fresnel diffraction (FrT) and non-uniform frequency hologram magnification.

Chao Wei, Tao Xin

Partial tomography, which focuses on reconstructing reduced density matrices (RDMs), has emerged as a promising approach for characterizing complex quantum systems, particularly when full state tomography is impractical. Recently, overlapping tomography has been proposed as an efficient method for determining all $k$-qubit RDMs using logarithmic polynomial m

Ziyang Chen, Yiwen Ye, Yongsheng Pan, Jingfeng Zhang

Distribution shifts widely exist in medical images acquired from different medical centres, hindering the deployment of semantic segmentation models trained on one centre (source domain) to another (target domain). While unsupervised domain adaptation has shown significant promise in mitigating these shifts, it poses privacy risks due to sharing data between

Bin Wang, Fei Deng, Shuang Wang, Wen Luo

Semantic segmentation of remote sensing (RS) images is a challenging yet essential task with broad applications. While deep learning, particularly supervised learning with large-scale labeled datasets, has significantly advanced this field, the acquisition of high-quality labeled data remains costly and time-intensive. Unsupervised domain adaptation (UDA) pr

Swastik Kopparty, Mrinal Kumar, Harry Sha

The classical Reed-Muller codes over a finite field $\mathbb{F}_q$ are based on evaluations of $m$-variate polynomials of degree at most $d$ over a product set $U^m$, for some $d$ less than $|U|$. Because of their good distance properties, as well as the ubiquity and expressive power of polynomials, these codes have played an influential role in coding theor

Michele Guerra, Simone Scardapane, Filippo Maria Bianchi

Spatiotemporal graph neural networks (STGNNs) have shown promising results in many domains, from forecasting to epidemiology. However, understanding the dynamics learned by these models and explaining their behaviour is significantly more difficult than for models that deal with static data. Inspired by Koopman theory, which allows a simple description of in

Pengcheng Mu

In this paper, we consider the low Mach and Rossby number singular limits and longtime existence of strong solution to the initial value problem of 3D compressible rotating Euler equations with ill-prepared initial data. We establish the Strichartz decay estimates that are uniform to the Mach number, the Rossby number, and the ratio of these two parameters f

H. Neshasteh, I. Shlesinger, M. Ravaro, M. Gély

We present an optomechanical method for locally measuring the rheological properties of complex fluids in the ultra-high frequency range (UHF). A mechanical disk of microscale volume is used as a small-amplitude oscillating probe that monitors the fluid at rest in thermal equilibrium, while the oscillation is detected by optomechanical transduction within a

Sarah J. Gascoigne, Manel Vila-Vidal, Nathan Evans, Christopher Thornton

During clinical treatment for epilepsy, the area of the brain thought to be responsible for pathological activity is identified. This identification is typically performed through visual assessment of EEG recordings; however, this is time consuming and prone to subjective inconsistency. Automated onset localisation algorithms provide objective identification

Varun Burde, Artem Moroz, Vit Zeman, Pavel Burget

Object pose estimation is a prominent task in computer vision. The object pose gives the orientation and translation of the object in real-world space, which allows various applications such as manipulation, augmented reality, etc. Various objects exhibit different properties with light, such as reflections, absorption, etc. This makes it challenging to unde

Jielin Song, Siyu Liu, Bin Zhu, Yanghui Rao

As large language models (LLMs) continue to advance, instruction tuning has become critical for improving their ability to generate accurate and contextually appropriate responses. Although numerous instruction-tuning datasets have been developed to enhance LLM performance, selecting high-quality instruction data from large source datasets typically demands

Riccardo Poiani, Nicole Nobili, Alberto Maria Metelli, Marcello Restelli

Policy evaluation via Monte Carlo (MC) simulation is at the core of many MC Reinforcement Learning (RL) algorithms (e.g., policy gradient methods). In this context, the designer of the learning system specifies an interaction budget that the agent usually spends by collecting trajectories of fixed length within a simulator. However, is this data collection s

Alex Elkjær Vasegaard, Andreas Kühne Larsen

EOSpython is a PyPI published Python package that encompass everything within a centralized earth observation satellite scheduling system in terms of customer database setup, scenario generation, pre-processing, problem setup, scheduling solution approach, decision maker preference integration, and visualization. The package is tailored to easily configure i

Hao Mark Chen, Fuwen Tan, Alexandros Kouris, Royson Lee

In spite of the great potential of large language models (LLMs) across various tasks, their deployment on resource-constrained devices remains challenging due to their excessive computational and memory demands. Quantization has emerged as an effective solution by storing weights in reduced precision. However, utilizing low precisions (i.e.~2/3-bit) to subst

Qisheng Hu, Quanyu Long, Wenya Wang

Fact-checking pipelines increasingly adopt the Decompose-Then-Verify paradigm, where texts are broken down into smaller claims for individual verification and subsequently combined for a veracity decision. While decomposition is widely-adopted in such pipelines, its effects on final fact-checking performance remain underexplored. Some studies have reported i

Ronja Stern, Ken Kawamura, Matthias Stürmer, Ilias Chalkidis

Many court systems are overwhelmed all over the world, leading to huge backlogs of pending cases. Effective triage systems, like those in emergency rooms, could ensure proper prioritization of open cases, optimizing time and resource allocation in the court system. In this work, we introduce the Criticality Prediction dataset, a novel resource for evaluating

Lou-Jean Leila Cobigo

We explore connections between the category of tropical abelian varieties (tav), $\mathbb{T}\mathcal{A}$, and the the category of tropical curves, $\mathbb{T}\mathcal{C}$, first in a broader context and then specifically by studying the phenomenon of tropical split Jacobians. Jacobians of genus $2$ curves are two-dimensional tav and as such more complicated

Wenhan Han, Meng Fang, Zihan Zhang, Yu Yin

The integration of large language model (LLM) techniques in the field of medical analysis has brought about significant advancements, yet the scarcity of large, diverse, and well-annotated datasets remains a major challenge. Medical data and tasks, which vary in format, size, and other parameters, require extensive preprocessing and standardization for effec

Lorenzo Cesario, Tim Lichtenberg, Eleonora Alei, Óscar Carrión-González

The increased brightness temperature of young rocky protoplanets during their magma ocean epoch makes them potentially amenable to atmospheric characterization to distances from the solar system far greater than thermally equilibrated terrestrial exoplanets, offering observational opportunities for unique insights into the origin of secondary atmospheres and

Luca Rolshoven, Vishvaksenan Rasiah, Srinanda Brügger Bose, Sarah Hostettler

Legal research depends on headnotes: concise summaries that help lawyers quickly identify relevant cases. Yet, many court decisions lack them due to the high cost of manual annotation. To address this gap, we introduce the Swiss Landmark Decisions Summarization (SLDS) dataset containing 20K rulings from the Swiss Federal Supreme Court, each with headnotes in

Tobechukwu Okamkpa, Joshua Okechukwu, Divine Mbachu, Chigbo Mgbemene

This study explores the integration of thermoelectric generators (TEGs) and phase change materials (PCMs) to enhance the efficiency of photovoltaic (PV) panels in high-temperature conditions. An AP-PM-20 Polycrystalline PV panel, SP-1848-27145 Bismuth Telluride TEG, and paraffin wax PCM in an aluminum container were used. Four configurations were tested: sta

Chenhang Yan, Liping Yan, Yuezu Lv, Bolei Dong

How to achieve precise distributed optimization despite unknown attacks, especially the Byzantine attacks, is one of the critical challenges for multiagent systems. This paper addresses a distributed resilient optimization for linear heterogeneous multi-agent systems faced with adversarial threats. We establish a framework aimed at realizing resilient optimi

Ant Duru, Alptekin Temizel

Data augmentation is a critical component of deep learning pipelines, enhancing model generalization by increasing dataset diversity. Traditional augmentation strategies rely on manually designed transformations, stochastic sampling, or automated search-based approaches. Although automated methods improve performance, they often require extensive computation

Johannes Dahlke

Defining artificial intelligence (AI) is a persistent challenge, often muddied by technical ambiguity and varying interpretations. Commonly used definitions heavily emphasize technical properties of AI but neglect the human purpose of it. This essay makes a case for a sociotechnical definition of AI, which is essential for researchers who require clarity in

Daoyuan Chen, Simon Meierhans, Maximilian Probst Gutenberg, Thatchaphol Saranurak

Expander decompositions have become one of the central frameworks in the design of fast algorithms. For an undirected graph $G=(V,E)$, a near-optimal $\phi$-expander decomposition is a partition $V_1, V_2, \ldots, V_k$ of the vertex set $V$ where each subgraph $G[V_i]$ is a $\phi$-expander, and only an $\widetilde{O}(\phi)$-fraction of the edges cross betwee

Shang Li

We derive an It\^o-type formula for a measure-valued process that has a decomposition analogous to a classical semimartingale. The derivation begins with a time partitioning approach similar to the classical proof of It\^o's formula. To address the new challenges arising from the measure-valued setting, we employ symmetric polynomials to approximate the seco

Elena Kim, Theresa C. Anderson, Robert J. Lemke Oliver

Quantum cat maps are toy models in quantum chaos associated to hyperbolic symplectic matrices $A\in \operatorname{Sp}(2n,\mathbb{Z})$. The macroscopic limits of sequences of eigenfunctions of a quantum cat map are characterized by semiclassical measures on the torus $\mathbb{R}^{2n}/\mathbb{Z}^{2n}$. We show that if the characteristic polynomial of every pow

Jinyang Liu, Tessa Steensgaard, Marvin N. Wright, Niklas Pfister

Many existing interpretation methods are based on Partial Dependence (PD) functions that, for a pre-trained machine learning model, capture how a subset of the features affects the predictions by averaging over the remaining features. Notable methods include Shapley additive explanations (SHAP) which computes feature contributions based on a game theoretical

Soumik Ghosh, Subir K. Das

While the kinetics of domain growth, even for conserved order-parameter dynamics, is widely studied for short-range inter-particle interactions, systems having long-range interactions are receiving attention only recently. Here we present results, for such dynamics, from a Monte Carlo (MC) study of the two-dimensional long-range Ising model, with critical co

Michail Palaiologos, Mario H. Castãneda García, Tobias Laas, Richard A. Stirling-Gallacher

We consider an integrated sensing and communication (ISAC) system with a single communication user and multiple targets. For the communication functionality, the achievable rate is employed as the performance metric, while for sensing, we focus on minimizing the mean squared error (MSE) between the designed beampattern and a desired one for tracking the targ

Abhishek Gupta, Amruta Parulekar, Sameep Chattopadhyay, Preethi Jyothi

Automatic speech recognition (ASR) for low-resource languages remains a challenge due to the scarcity of labeled training data. Parameter-efficient fine-tuning and text-only adaptation are two popular methods that have been used to address such low-resource settings. In this work, we investigate how these techniques can be effectively combined using a multil

Sumedha, Matteo Marsili

We introduce a spin-1 version of the random energy model with crystal field. Crystal field controls the density of 0 spins in the system. We solve the model in the micro-canonincal ensemble. The model has a spin-glass transition at a finite temperature for all strengths of the crystal field. By introducing the magnetic field we also obtain the de Almeida Tho

Maharaj Brahma, Pramit Sahoo, Maunendra Sankar Desarkar

This paper describes NLIP Lab's multilingual machine translation system for the WAT24 shared task on multilingual Indic MT task for 22 scheduled languages belonging to 4 language families. We explore pre-training for Indic languages using alignment agreement objectives. We utilize bi-lingual dictionaries to substitute words from source sentences. Furthermore

B. Almeida-Zamora, J. J. Cobos-Martínez, A. Bashir, K. Raya

The internal structure of the lowest-lying pseudo-scalar mesons with heavy-light quark content is thoroughly studied using an algebraic model that has been successfully applied to similar physical observables of pseudoscalar and vector mesons with hidden-flavor quark content, ranging from light to heavy quark sectors. This model is based on constructing simp

Hongqiu Wu, Xingyuan Liu, Yan Wang, Hai Zhao

The Instruction-Driven Game Engine (IDGE) project aims to democratize game development by enabling a large language model (LLM) to follow free-form game descriptions and generate game-play processes. The IDGE allows users to create games simply by natural language instructions, which significantly lowers the barrier for game development. We approach the lear

K de Vos, N. A. Hatch, M. R. Merrifield

To investigate how the radio-identified active galactic nuclei (AGN) fraction varies with cluster-centric radius, we present the projected and de-projected distributions of a large sample of LOFAR-identified radio AGN out to $30R_{500}$ around galaxy clusters. The AGN fraction experiences a $\sim 25\%$ increase above the field fraction in the cluster outskir

Chufen Wu, Yonghui Xia, Jianshe Yu

We propose a lattice dynamical system that arises in a discrete diffusive two-group epidemic model with latency in a patchy environment. The model considers the SIS form and latency of the disease in group 1, while the SIR form without latency of the disease in group 2. The system includes double nonlocal effects, one effect is the nonlocal diffusion of indi

Bartosz Malman, Daniel Seco

To every non-extreme point $b$ of the unit ball of $\hil^\infty$ of the unit disk there corresponds a Pythagorean mate, a bounded outer function $a$ satisfying the equation $|a|^2 + |b|^2 = 1$ on the boundary of the disk. We study universal, i.e., simultaneous multipliers for families of de Branges-Rovnyak spaces $\hb$, and develop a general framework for th

Changcheng Xiao, Qiong Cao, Yujie Zhong, Xiang Zhang

Referring multi-object tracking (RMOT) is an emerging cross-modal task that aims to locate an arbitrary number of target objects and maintain their identities referred by a language expression in a video. This intricate task involves the reasoning of linguistic and visual modalities, along with the temporal association of target objects. However, the seminal

Zhihao Lin, Chang Gao, Junkun Yan, Qingfu Zhang

Multi-frame detection algorithms can effectively utilize the correlation between consecutive echoes to improve the detection performance of weak targets. Existing efficient multi-frame detection algorithms are typically based on three sequential steps: plot extraction via a relative low primary threshold, track search and track detection. However, these thre

R. Rodríguez-Guzmán, L. M. Robledo, R. N. Bernard

This paper extends previous studies on the impact of tensor forces in fission dynamics of neutron-deficient Thorium isotopes to other isotopic chains of heavy actinides and low-mass super-heavy nuclei. Calculations are carried out within a mean-field framework based on the Gogny-D1S parametrization supplemented with the D1ST2a perturbative tensor term as dri

Steven H. Simon, Ajit C. Balram

We use several techniques to probe the wave functions proposed to describe the ${\cal A}$ phases by Das, Das, and Mandal [Phys. Rev. Lett. 131, 056202 (2023); Phys. Rev. Lett. 132, 106501 (2024); Phys. Rev. B 110, L121303 (2024).]. As opposed to representing fractional quantum Hall liquids, we find these wave functions to describe states that clearly display

Gopal Datt, Naveen Gupta, Nikhil Khanna, Ritesh Pal

In this paper, we extend a result of Schwick concerning normality and sharing values in one complex variable for families of holomorphic curves taking values in $\mathbb{P}^n$. We consider wandering moving hyperplanes (i.e., depending on the respective holomorphic curve in the family under consideration), and establish a sufficient condition of normality con

Giacomo Lucertini, Stéphane Menozzi, Stefano Pagliarani

We establish strong well-posedness for a class of degenerate SDEs of kinetic type with autonomous diffusion driven by a symmetric $\alpha$-stable process under H\"older regularity conditions for the drift term. We partially recover the thresholds for the H\"older regularity that are optimal for weak uniqueness. In general dimension, we only consider $\alpha

Zhanpeng Wang, Shenghao Li, Jiameng Che, Chen Wang

Diffusion Models (DMs) have achieved remarkable progress in generative modeling. However, the mismatch between the forward terminal distribution and reverse initial distribution introduces prior error, leading to deviations of sampling trajectories from the true distribution and severely limiting model performance. This issue further triggers cascading probl

Archit Agarwal

Ramanujan recorded five $q$-series identities at the end of his second notebook and an unified generalization of these identities obtained by Bhoria, Eyyunni and Maji. Recently, Dixit and Patel gave a finite analogue of the identity of Bhoria et. al. which in turn gives finite analogue of all the aforementioned identities of Ramanujan. In this paper, one of

Miroslav Popovic, Marko Popovic, Miodrag Djukic, Ilija Basicevic

Python Testbed for Federated Learning Algorithms (PTB-FLA) is a simple FL framework targeting smart Internet of Things in edge systems that provides both generic centralized and decentralized FL algorithms, which implement the corresponding FL orchestration protocols that were formally verified using the process algebra CSP. This approach is appropriate for

Guoqing Hu, Zhengyi Yang, Zhibo Cai, An Zhang

Recent advancements in generative recommendation systems, particularly in the realm of sequential recommendation tasks, have shown promise in enhancing generalization to new items. Among these approaches, diffusion-based generative recommendation has emerged as an effective tool, leveraging its ability to capture data distributions and generate high-quality

Kamil Kwarciak, Mateusz Daniol, Daria Hemmerling, Marek Wodzinski

The skull segmentation from CT scans can be seen as an already solved problem. However, in MR this task has a significantly greater complexity due to the presence of soft tissues rather than bones. Capturing the bone structures from MR images of the head, where the main visualization objective is the brain, is very demanding. The attempts that make use of sk

Yuuya Yoshida

Solov'ev (1966), Nielsen (1973), and Blom (1982) independently showed a formula for the expected waiting time until a given finite pattern first occurs in random data. In this paper, we give a simple and combinatorial proof of the formula.

Rolf Hoffmann

The objective is the design of a Cellular Automata rule that can form patterns with 'touching' loops. A loop is defined as a closed path of 1-cells in a 2D grid on a zero background and with a zero border. A path cell is connected with two of its adjacent neighbors. In touching loops a path cell is also allowed to touch another on a diagonal. A CA rule was d

V. A. Beylin, M. Yu. Khlopov, D. O. Sopin

The dark atoms $XHe$ are the composite Thomson like atomic dark matter candidates. We address two cosmological problems of this model. The excess of new superheavy particles with even negative charge $X^{-2n}$ over the corresponding antiparticles is balanced by sphaleron transitions with baryon asymmetry and the mass range of $X$ particles should be specifie

Michael Mandl, Michael W. Hansen, Erhard Seiler, Dénes Sexty

The method of complex Langevin simulations is a tool that can be used to tackle the complex-action problem encountered, for instance, in finite-density lattice quantum chromodynamics or real-time lattice field theories. The method is based on a stochastic evolution of the dynamical degrees of freedom via (complex) Langevin equations, which, however, sometime

Sotiris Papatheodorou, Anthony Tzes

The objective in this article is to develop a control strategy for coverage purposes of a convex region by a fleet of Mobile Aerial Agents (MAAs). Each MAA is equipped with a downward facing camera that senses a convex portion of the area while its altitude flight is constrained. Rather than relying on typical Voronoi-like tessellations of the area to be cov

Jeremy Chopin, Rozenn Dahyot

Data embeddings with CLIP and ImageBind provide powerful features for the analysis of multimedia and/or multimodal data. We assess their performance here for classification using a Gaussian Mixture models (GMMs) based layer as an alternative to the standard Softmax layer. GMMs based classifiers have recently been shown to have interesting performances as par

Lorenzo Tedesco, Francesco Finazzi

The Proportional Hazards (PH) model is one of the most widely used models in survival analysis, typically assuming a log-linear relationship between covariates and the hazard function. However, in the context of spatial survival data, where the time-to-event variable is associated with a spatial location within a given domain, this assumption is often unreal

Yutian Wang, Wanyin Yang, Zhenrong Dai, Yilong Zhang

At present, neural network models show powerful sequence prediction ability and are used in many automatic composition models. In comparison, the way humans compose music is very different from it. Composers usually start by creating musical motifs and then develop them into music through a series of rules. This process ensures that the music has a specific

Cunxi Dai, Xiaohan Liu, Koushil Sreenath, Zhongyu Li

This paper introduces a framework for interactive navigation through adaptive non-prehensile mobile manipulation. A key challenge in this process is handling objects with unknown dynamics, which are difficult to infer from visual observation. To address this, we propose an adaptive dynamics model for common movable indoor objects via learned SE(2) dynamics r

Milan Donvil, Stefaan Vaes

We construct discrete groups $G$ with infinite center that are nevertheless W*-superrigid, meaning that the group von Neumann algebra $L(G)$ fully remembers the group $G$. We obtain these rigidity results both up to isomorphisms and up to virtual isomorphisms of the groups and their von Neumann algebras. Our methods combine rigidity results for the quotient

Dimitrios Kelesis, Dimitris Fotakis, Georgios Paliouras

In this work we investigate an observation made by Kipf \& Welling, who suggested that untrained GCNs can generate meaningful node embeddings. In particular, we investigate the effect of training only a single layer of a GCN, while keeping the rest of the layers frozen. We propose a basis on which the effect of the untrained layers and their contribution to

Mikael Rinkinen, Lauri Koskinen, Olli Silven, Mehdi Safarpour

Reduced voltage operation is an effective technique for substantial energy efficiency improvement in digital circuits. This brief introduces a simple approach for enabling reduced voltage operation of Deep Neural Network (DNN) accelerators by mere software modifications. Conventional approaches for enabling reduced voltage operation e.g., Timing Error Detect

Keiichiro Hattori, Yuki Mizuno, Asato Tsuchiya

We study regularization of matrices in the covariant derivative interpretation of matrix models, a typical example of which is the type IIB matrix model. The covariant derivative interpretation provides a possible way in which curved spacetimes are described by matrices, which are viewed as differential operators. One needs to regularize the operators as mat

Chengyu Du, Jinyi Han, Yizhou Ying, Aili Chen

Recent advancements in large language models (LLMs) have demonstrated that progressive refinement, rather than providing a single answer, results in more accurate and thoughtful outputs. However, existing methods often rely heavily on supervision signals to evaluate previous responses, making it difficult to assess output quality in more open-ended scenarios

Roberto Fontana, Patrizia Semeraro

Any discrete distribution with support on $\{0,\ldots, d\}$ can be constructed as the distribution of sums of Bernoulli variables. We prove that the class of $d$-dimensional Bernoulli variables $\boldsymbol{X}=(X_1,\ldots, X_d)$ whose sums $\sum_{i=1}^dX_i$ have the same distribution $p$ is a convex polytope $\mathcal{P}(p)$ and we analytically find its extr

Fatih Dogangun, Serdar Bahar, Yigit Yildirim, Bora Toprak Temir

This paper introduces Robotic Augmented Reality for Machine Programming by Demonstration (RAMPA), the first ML-integrated, XR-driven end-to-end robotic system, allowing training and deployment of ML models such as ProMPs on the fly, and utilizing the capabilities of state-of-the-art and commercially available AR headsets, e.g., Meta Quest 3, to facilitate th

Anton Mitrofanov, Tatiana Prisyach, Tatiana Timofeeva, Sergei Novoselov

This paper describes the STCON system for the CHiME-8 Challenge Task 1 (DASR) aimed at distant automatic speech transcription and diarization with multiple recording devices. Our main attention was paid to carefully trained and tuned diarization pipeline and speaker counting. This allowed to significantly reduce diarization error rate (DER) and obtain more r

Paul Tod

We consider four-dimensional, Riemannian metrics for which one or other of the self-dual or anti-self-dual Weyl tensors is type-D and which satisfy the Einstein-Maxwell equations with the corresponding Maxwell field aligned with the type-D Weyl spinor, in the sense of sharing the same Principal Null Directions (or PNDs). Such metrics always have a valence-2

Zirui Li, Patrick Wolf, Meng Wang

Accurate arrival time prediction (ATP) of buses and trams plays a crucial role in public transport operations. Current methods focused on modeling one-dimensional temporal information but overlooked the latent periodic information within time series. Moreover, most studies developed algorithms for ATP based on a single or a few routes of public transport, wh

Linyan Yang, Jingwei Cheng, Chuanhao Xu, Xihao Wang

Entity alignment (EA) refers to the task of linking entities in different knowledge graphs (KGs). Existing EA methods rely heavily on structural isomorphism. However, in real-world KGs, aligned entities usually have non-isomorphic neighborhood structures, which paralyses the application of these structure-dependent methods. In this paper, we investigate and

Chuanyu Tang, Yilong Chen, Zhenyu Zhang, Junyuan Shang

Low-Rank Adaptation (LoRA) drives research to align its performance with full fine-tuning. However, significant challenges remain: (1) Simply increasing the rank size of LoRA does not effectively capture high-rank information, which leads to a performance bottleneck.(2) MoE-style LoRA methods substantially increase parameters and inference latency, contradic

Kui Yang, Nieqing Cao, Yan Ding, Chao Chen

Embodied Artificial Intelligence (Embodied AI) emphasizes agents' ability to perceive, understand, and act in physical environments. Simulation platforms play a crucial role in advancing this field by enabling the validation and optimization of algorithms. However, existing platforms face challenges such as multilevel technical integration complexity, insuff

Indu K. Dihingia, Akhil Uniyal, Yosuke Mizuno

Can a naked singularity (NkS) be distinguished from a black hole (BH)? We have investigated it with cutting-edge general relativistic magneto-hydrodynamic (GRMHD) simulations, followed by general relativistic radiation transfer (GRRT) calculation for magnetized accretion flow around NkS and BH. Based on our simulations, the accreting matter close enough to t

Xianglong Deng, Shengyu Fan, Zhicheng Hu, Zhuoyu Tian

In this paper, we present the first multi-modal FHE accelerator based on a unified architecture, which efficiently supports CKKS, TFHE, and their conversion scheme within a single accelerator. To achieve this goal, we first analyze the theoretical foundations of the aforementioned schemes and highlight their composition from a finite number of arithmetic ker

Opeyemi Sheu Alamu, Bismar Jorge Gutierrez Choque, Syed Wajeeh Abbs Rizvi, Samah Badr Hammed

Breast cancer remains a significant global health challenge, with prognosis and treatment decisions largely dependent on clinical characteristics. Accurate prediction of patient outcomes is crucial for personalized treatment strategies. This study employs survival analysis techniques, including Cox proportional hazards and parametric survival models, to enha

Cedric Luger

Let $K$ be a number field, let $X$ be a smooth integral variety over $K$, and assume that there exists a finite set of finite places $S$ of $K$ such that the $S$-integral points on $X$ are dense. Then the combined conjectures of Campana and Corvaja-Zannier predict that, for every closed subscheme $Z$ of $X$ of codimension at least two, there exists a finite

Qize Liu, Xiaofan Pan, Xutao Zheng, Huaizhong Gao

The Gamma-Ray Integrated Detectors (GRID) are a space science mission that employs compact gamma-ray detectors mounted on NanoSats in low Earth orbit (LEO) to monitor the transient gamma-ray sky. Owing to the unpredictability of the time and location of gamma-ray bursts (GRBs), obtaining the photon responses of gamma-ray detectors at various incident angles

Sankaran Ramesh, Yonghong Wang, Pavel Chabera, Rafael Araujo

Lead-free metal halide perovskites are emerging as less-toxic alternatives to their lead-based counterparts. However, their applicability in optoelectronic devices is limited, and the charge transport dynamics remain poorly understood. Understanding photo-induced charge and structural dynamics is critical for unlocking the potential of these novel systems. I

Thanasis Troboukis, Kelly Kiki, Antonis Galanopoulos, Pavlos Sermpezis

This chapter introduces a research project titled "Analyzing the Political Discourse: A Collaboration Between Humans and Artificial Intelligence", which was initiated in preparation for Greece's 2023 general elections. The project focused on the analysis of political leaders' campaign speeches, employing Artificial Intelligence (AI), in conjunction with an i

Masahito Hayashi

We study Heisenberg scaling of quantum metrology in the viewpoint of population coding. Although Fisher information has been used for a figure of merit to characterize Heisenberg scaling in quantum metrology, several studies pointed out it does not work as a figure of merit because it does not reflect the global structure. As an alternative figure of merit,

Alex Bendrick, Daniel Tappe, Rolf Ernst

Future autonomous mobile systems will greatly benefit from cooperation and real-time sensor data exchange using V2X communication. In such applications, wireless communication has to cope with stringent real-time and safety constraints, a huge challenge given the inherently lossy wireless communication with highly dynamic channel and error conditions. To mee

Wusheng Wang, Masahito Hayashi

Digital signatures are a powerful cryptographic tool widely employed across various industries for securely authenticating the identity of a signer during communication between signers and verifiers. While quantum digital signatures have been extensively studied, the security still depends on a trusted third-party. To address this limitation and enhance the

Marcell Fekete, Johannes Bjerva

Understanding how linguistic knowledge is encoded in language models is crucial for improving their generalisation capabilities. In this paper, we investigate the processing of morphosyntactic phenomena, by leveraging a recently proposed method for probing language models via Shapley Head Values (SHVs). Using the English language BLiMP dataset, we test our a

Emeric Battaglia, Anna Ma

When solving linear systems $Ax=b$, $A$ and $b$ are given, but the measurements $b$ often contain corruptions. Inspired by recent work on the quantile-randomized Kaczmarz method, we propose an acceleration of the randomized Kaczmarz method using quantile information. We show that the proposed acceleration converges faster than the randomized Kaczmarz algorit

Sumanth Doddapaneni, Mohammed Safi Ur Rahman Khan, Dilip Venkatesh, Raj Dabre

Evaluating machine-generated text remains a significant challenge in NLP, especially for non-English languages. Current methodologies, including automated metrics, human assessments, and LLM-based evaluations, predominantly focus on English, revealing a significant gap in multilingual evaluation frameworks. We introduce the Cross Lingual Auto Evaluation (CIA

Arthur Ermatov, Melisande Kost, Xin Yin, Paul Butler

While DNA origami is a powerful bottom-up fabrication technique, the physical and chemical stability of DNA nanostructures is generally limited to aqueous buffer conditions. Wet chemical silicification can stabilise these structures but does not add further functionality. Here, we demonstrate a versatile 3D nanofabrication technique to conformally coat micro

Markus Huff, Elanur Ulakçı

Large language models (LLMs) increasingly mimic human cognition in various language-based tasks. However, their capacity for metacognition - particularly in predicting memory performance - remains unexplored. Here, we introduce a cross-agent prediction model to assess whether ChatGPT-based LLMs align with human judgments of learning (JOL), a metacognitive me

H. Isern, T. Brochard, T. Dufrane, P. Brumund

Dark Field X-ray Microscopy (DFXM) is a full-field imaging technique for non-destructive 3D mapping of orientation and strain in crystalline elements. The new DFXM beamline at ID03, developed as part of the ESRF Phase II Upgrade Project (EBSL2), was designed to provide cutting-edge capabilities for studying embedded microstructures. The project relocated and