May 2024 arXiv papers — page 23

Lorenzo Maggi, Matthew Andrews, Ryo Koblitz

Several Radio Resource Management (RRM) use cases can be framed as sequential decision planning problems, where an agent (the base station, typically) makes decisions that influence the network utility and state. While Reinforcement Learning (RL) in its general form can address this scenario, it is known to be sample inefficient. Following the principle of O

Yun Zeng, Deren Han, Yansheng Su, Jiaxin Xie

In this paper, we propose a novel adaptive stochastic extended iterative method, which can be viewed as an improved extension of the randomized extended Kaczmarz (REK) method, for finding the unique minimum Euclidean norm least-squares solution of a given linear system. In particular, we introduce three equivalent stochastic reformulations of the linear leas

Vyacheslav Ivanovskiy, Shota Komatsu, Victor Mishnyakov, Nikolay Terziev

We study correlation functions on the Coulomb branch of planar $\mathcal{N} = 4$ super-Yang- Mills theory (SYM), and their relationship with integrability, the operator product expansion (OPE), the sum rule, the large charge expansion, and holography. First, we compute one-point functions of arbitrary scalar operators at weak coupling and derive a compact sp

Chen Wang, Minpeng Liao, Zhongqiang Huang, Jiajun Zhang

Recent end-to-end approaches have shown promise in extending large language models (LLMs) to speech inputs, but face limitations in directly assessing and optimizing alignment quality and fail to achieve fine-grained alignment due to speech-text length mismatch. We introduce BLSP-KD, a novel approach for Bootstrapping Language-Speech Pretraining via Knowledg

Chris Martens, Robert J. Simmons, Michael Arntzenius

Logic programming, as exemplified by datalog, defines the meaning of a program as its unique smallest model: the deductive closure of its inference rules. However, many problems call for an enumeration of models that vary along some set of choices while maintaining structural and logical constraints -- there is no single canonical model. The notion of stable

Xin-Zhen Weng, Wei-Zhen Deng, Shi-Lin Zhu

Following the work of Capstick and Isgur [\href{https://doi.org/10.1103/PhysRevD.34.2809}{Phys.~Rev.~D~34,~2809~(1986)}], we systematically study the mass spectrum of the heavy baryons in the relativized quark potential model with chromodynamics. Besides the original Godfrey-Isgur (GI) model, we also adopt a modified GI model which replaces the linear confin

T. Barros, L. Garrote, P. Conde, M. J. Coombes

3D LiDAR-based place recognition remains largely underexplored in horticultural environments, which present unique challenges due to their semi-permeable nature to laser beams. This characteristic often results in highly similar LiDAR scans from adjacent rows, leading to descriptor ambiguity and, consequently, compromised retrieval performance. In this work,

C. A. Middelburg

An earlier paper gives an account of a quest for a satisfactory formalization of the classical informal notion of an algorithm. That notion only covers algorithms that are deterministic and non-interactive. In this paper, an attempt is made to generalize the results of that quest first to a notion of an algorithm that covers both deterministic and non-determ

Naoki Nishikawa, Taiji Suzuki

Deep neural networks based on state space models (SSMs) are attracting significant attention in sequence modeling since their computational cost is much smaller than that of Transformers. While the capabilities of SSMs have been demonstrated through experiments in various tasks, theoretical understanding of SSMs is still limited. In particular, most theoreti

Niclas Vödisch, Kürsat Petek, Markus Käppeler, Abhinav Valada

A key challenge for the widespread application of learning-based models for robotic perception is to significantly reduce the required amount of annotated training data while achieving accurate predictions. This is essential not only to decrease operating costs but also to speed up deployment time. In this work, we address this challenge for PAnoptic SegmenT

Zimo Hao, Michael Röckner, Xicheng Zhang

Motivated by the probabilistic representation for solutions of the Navier-Stokes equations, we introduce a novel class of stochastic differential equations that depend on the entire flow of its time marginals. We establish the existence and uniqueness of both strong and weak solutions under one-sided Lipschitz conditions and for singular drifts. These newly

Yuxi Han, Jihe Wang, Danghui Wang

Graph Neural Networks (GNNs) are computationally demanding and inefficient when applied to graph classification tasks in resource-constrained edge scenarios due to their inherent process, involving multiple rounds of forward and backward propagation. As a lightweight alternative, Hyper-Dimensional Computing (HDC), which leverages high-dimensional vectors for

Balázs Szalontai, Gergő Szalay, Tamás Márton, Anna Sike

Recently, there has been increasing activity in using deep learning for software engineering, including tasks like code generation and summarization. In particular, the most recent coding Large Language Models seem to perform well on these problems. In this technical report, we aim to review how these models perform in code explanation/summarization, while a

Mert Burabak, Tevfik Aytekin

This article presents a novel approach to multimodal recommendation systems, focusing on integrating and purifying multimodal data. Our methodology starts by developing a filter to remove noise from various types of data, making the recommendations more reliable. We studied the impact of top-K sparsification on different datasets, finding optimal values that

Catalin-Mihai Halati, Thierry Giamarchi

We investigate the Hall response of hardcore bosonic atoms on a triangular ladder in a magnetic field. To access the dynamical properties of this many-body quantum system, we employ matrix product states numerical methods. We show that the behavior of the Hall polarization, both in its saturation value and in the short-time dynamics, correlates with the feat

Ross Drummond, Chris Guiver, Matthew C. Turner

With neural networks being used to control safety-critical systems, they increasingly have to be both accurate (in the sense of matching inputs to outputs) and robust. However, these two properties are often at odds with each other and a trade-off has to be navigated. To address this issue, this paper proposes a method to generate an approximation of a neura

Orlin Koop, Teresa Antoja, Amina Helmi, Thomas M. Callingham

Several works have recently applied Jeans modelling to Gaia-based datasets to infer the circular velocity curve for the Milky Way. Such works have consistently found evidence for a continuous decline in the rotation curve beyond $\sim$15kpc possibly indicative of a light dark matter halo. We used Gaia DR3 RVS data, supplemented with Bayesian distances to det

Andrew Zhao, Quentin Xu, Matthieu Lin, Shenzhi Wang

Recent advances in large language model assistants have made them indispensable, raising significant concerns over managing their safety. Automated red teaming offers a promising alternative to the labor-intensive and error-prone manual probing for vulnerabilities, providing more consistent and scalable safety evaluations. However, existing approaches often

Karsten Matthies, Theodora Syntaka

The fractional diffusion equation is rigorously derived as a scaling limit from a deterministic Rayleigh gas, where particles interact via short range potentials with support of size $\varepsilon$ and the background is distributed in space $\mathbb{R}^3$ according to a Poisson process with intensity $N$ and in velocity according to some fat-tailed distributi

Mustafa Mert Çelikok, Frans A. Oliehoek, Jan-Willem van de Meent

We consider inverse reinforcement learning problems with concave utilities. Concave Utility Reinforcement Learning (CURL) is a generalisation of the standard RL objective, which employs a concave function of the state occupancy measure, rather than a linear function. CURL has garnered recent attention for its ability to represent instances of many important

Kevin Schlegel

For the module category of an Artin algebra, we generalize the notion of torsion pairs to ideal torsion pairs. Instead of full subcategories of modules, ideals of morphisms of the ambient category are considered. We characterize the functorially finite ideal torsion pairs, which are those fulfilling some nice approximation conditions, first through correspon

Emmanouil Krasanakis, Symeon Papadopoulos

Creating fair AI systems is a complex problem that involves the assessment of context-dependent bias concerns. Existing research and programming libraries express specific concerns as measures of bias that they aim to constrain or mitigate. In practice, one should explore a wide variety of (sometimes incompatible) measures before deciding which ones warrant

Paolo Haffoud, François Poulet, Mathieu Vincendon, Gianrico Filacchione

The Moons And Jupiter Imaging Spectrometer (MAJIS) is the visible and near-infrared imaging spectrometer onboard ESA s Jupiter Icy Moons Explorer (JUICE) mission. Before its integration into the spacecraft, the instrument undergoes an extensive ground calibration to establish its baseline performances. This process prepares the imaging spectrometer for fligh

Emmanuel Gnandi

The Fisher information metric or the Fisher-Rao metric corresponds to a natural Riemannian metric defined on a parameterized family of probability density functions. As in the case of Riemannian geometry, we can define a distance in terms of the Fisher information metric, called the Fisher-Rao distance. The Fisher information metric has a wide range of appli

Matthaios Chatzopoulos, Phaedon-Stelios Koutsourelakis

We propose Physics-Aware Neural Implicit Solvers (PANIS), a novel, data-driven framework for learning surrogates for parametrized Partial Differential Equations (PDEs). It consists of a probabilistic, learning objective in which weighted residuals are used to probe the PDE and provide a source of {\em virtual} data i.e. the actual PDE never needs to be solve

Phan Nhut Huan

I investigate the effect of Coulomb core-polarization in $^{208}\text{Pb}$, highlighting its impact on uncertainties in neutron distribution measurements due to the discrepancy between neutron excess and neutron skin regions at the surface. This effect is validated using the $(^{3}\text{He}, t)$ isobaric analog state (IAS) reaction at 420 MeV. My findings re

Danil Provodin, Maurits Kaptein, Mykola Pechenizkiy

We present a new algorithm based on posterior sampling for learning in Constrained Markov Decision Processes (CMDP) in the infinite-horizon undiscounted setting. The algorithm achieves near-optimal regret bounds while being advantageous empirically compared to the existing algorithms. Our main theoretical result is a Bayesian regret bound for each cost compo

The Tien Mai

This paper investigates sparse high-dimensional linear regression, particularly examining the properties of the posterior under conditions of random design and unknown error variance. We provide consistency results for the posterior and analyze its concentration rates, demonstrating adaptiveness to the unknown sparsity level of the regression coefficient vec

Xiaotong Cheng, Ioannis Tsetis, Setareh Maghsudi

Modern power systems integrate renewable distributed energy resources (DERs) as an environment-friendly enhancement to meet the ever-increasing demands. However, the inherent unreliability of renewable energy renders developing DER management algorithms imperative. We study the energy-sharing problem in a system consisting of several DERs. Each agent harvest

Bernd Frauenknecht, Artur Eisele, Devdutt Subhasish, Friedrich Solowjow

Dyna-style model-based reinforcement learning (MBRL) combines model-free agents with predictive transition models through model-based rollouts. This combination raises a critical question: 'When to trust your model?'; i.e., which rollout length results in the model providing useful data? Janner et al. (2019) address this question by gradually increasing roll

Jens Püttschneider, Timm Faulwasser

The training of ResNets and neural ODEs can be formulated and analyzed from the perspective of optimal control. This paper proposes a dissipative formulation of the training of ResNets and neural ODEs for classification problems by including a variant of the cross-entropy as a regularization in the stage cost. Based on the dissipative formulation of the trai

Gaole Dai, Cheng-Ching Tseng, Qingpo Wuwu, Rongyu Zhang

The rapid pace of innovation in biological microscopy imaging has led to large images, putting pressure on data storage and impeding efficient sharing, management, and visualization. This necessitates the development of efficient compression solutions. Traditional CODEC methods struggle to adapt to the diverse bioimaging data and often suffer from sub-optima

Maria Symeonaki, Giorgos Stamou, Aggeliki Kazani, Eva Tsouparopoulou

For nearly a century, social researchers and psychologists have debated the efficacy of psychometric scales for attitude measurement, focusing on Thurstone's equal appearing interval scales and Likert's summated rating scales. Thurstone scales fell out of favour due to the labour intensive process of gathering judges' opinions on the initial items. However,

Hao Zhang, Yuyang Zhang, Xiaoguang Li, Wenxuan Shi

Integrating external knowledge into large language models (LLMs) presents a promising solution to overcome the limitations imposed by their antiquated and static parametric memory. Prior studies, however, have tended to over-reliance on external knowledge, underestimating the valuable contributions of an LLMs' intrinsic parametric knowledge. The efficacy of

Jihao Liu, Jinliang Zheng, Boxiao Liu, Yu Liu

Contrastive pre-training on image-text pairs, exemplified by CLIP, becomes a standard technique for learning multi-modal visual-language representations. Although CLIP has demonstrated remarkable performance, training it from scratch on noisy web-scale datasets is computationally demanding. On the other hand, mask-then-predict pre-training approaches, like M

Alexander Gorfer, Rainer Abart, Christoph Dellago

Alkali diffusion is a first-order control for microstructure and compositional evolution of feldspar during cooling from high temperatures of primary magmatic or metamorphic crystallization, and knowledge of the respective diffusion coefficients is crucial for reconstructing thermal histories. Our understanding of alkali diffusion in feldspar is, however, hi

Qiutong Wang, Ci Li, Qingjun Tong

Electron-hole exchange interaction in two-dimensional transition metal dichalcogenides is extremely strong due to the dimension reduction, which promises valley-superposed excitonic states with linearly polarized optical emissions. However, strong circular polarization reflecting valley-polarized excitonic states is commonly observed in helicity-resolved opt

Guglielmo Faggioli, Maarten van de Meent, Alessandra Buonanno, Aldo Gamboa

In this work, we test an effective-one-body radiation-reaction force for eccentric planar orbits of a test mass in a Kerr background, which contains third-order post-Newtonian (PN) non-spinning and second-order PN spin contributions. We compare the analytical fluxes connected to two different resummations of this force, truncated at different PN orders in th

Xuelin Qian, Ruiqi Wu, Gong Cheng, Junwei Han

Lifelong Person Re-Identification (LReID) extends traditional ReID by requiring systems to continually learn from non-overlapping datasets across different times and locations, adapting to new identities while preserving knowledge of previous ones. Existing approaches, either rehearsal-free or rehearsal-based, still suffer from the problem of catastrophic fo

Cu Cui, Paul Grosse-Bley, Guido Kanschat, Robert Strzodka

We present a GPU implementation of vertex-patch smoothers for higher order finite element methods in two and three dimensions. Analysis shows that they are not memory bound with respect to GPU DRAM, but with respect to on-chip scratchpad memory. Multigrid operations are optimized through localization and reorganized local operations in on-chip memory, achiev

Tan Zhang, Zhongjian Wang, Jack Xin, Zhiwen Zhang

This paper aims to investigate the diffusion behavior of particles moving in stochastic flows under a structure-preserving scheme. We compute the effective diffusivity for normal diffusive random flows and establish the power law between spatial and temporal variables for cases with anomalous diffusion phenomena. From a Lagrangian approach, we separate the c

Tobias Boorman, Bernd Braunecker

The massive, real scalar field described by the Klein-Gordon equation in one spatial dimension is the most elementary example of a bosonic quantum field theory. It has been investigated for many decades either as a simple academic theory or as a realistic emergent many-body theory in low-dimensional systems. Despite this, the space and time behavior of its p

Hanqing Dai, Wenqing Dai, Yuanyuan Chen, Wanlu Zhang

Manufacturing hydrogels with identical electrochemical properties are typically riddled with unresolved inquiries and challenges. Here, we utilized ultra-light graphene flakes to trace the influence of convection phenomena during reactions on hydrogels' formation and structural non-uniformity, elucidating its mechanisms. Furthermore, we confirmed that an ext

Lennart Werner, Fang Nan, Pol Eyschen, Filippo A. Spinelli

Automation of hydraulic material handling machinery is currently limited to semi-static pick-and-place cycles. Dynamic throwing motions which utilize the passive joints, can greatly improve time efficiency as well as increase the dumping workspace. In this work, we use Reinforcement Learning (RL) to design dynamic controllers for material handlers with under

Fan Zhang, Daniel Kreuter, Carlos Esteve-Yagüe, Sören Dittmer

Federated learning (FL) promises to enable collaborative machine learning across healthcare sites whilst preserving data privacy. Practical deployment remains limited by statistical heterogeneity arising from differences in patient demographics, treatments, and outcomes, and infrastructure constraints. We introduce FedMAP, a personalised FL (PFL) framework t

Michael Potter, Shuo Tang, Paul Ghanem, Milica Stojanovic

Continuously optimizing sensor placement is essential for precise target localization in various military and civilian applications. While information theory has shown promise in optimizing sensor placement, many studies oversimplify sensor measurement models or neglect dynamic constraints of mobile sensors. To address these challenges, we employ a range mea

Tushant Mittal, Sourya Roy

We give a randomness-efficient homomorphism test in the low soundness regime for functions, $f: G\to \mathbb{U}_t$, from an arbitrary finite group $G$ to $t\times t$ unitary matrices. We show that if such a function passes a derandomized Blum--Luby--Rubinfeld (BLR) test (using small-bias sets), then (i) it correlates with a function arising from a genuine ho

Ziheng Cheng, Longlin Yu, Tianyu Xie, Shiyue Zhang

Semi-implicit variational inference (SIVI) extends traditional variational families with semi-implicit distributions defined in a hierarchical manner. Due to the intractable densities of semi-implicit distributions, classical SIVI often resorts to surrogates of evidence lower bound (ELBO) that would introduce biases for training. A recent advancement in SIVI

Ferruh Ozbudak, Paolo Santonastaso, Ferdinando Zullo

We present a new application of multi-orbit cyclic subspace codes to construct large optical orthogonal codes, with the aid of the multiplicative structure of finite fields extensions. This approach is different from earlier approaches using combinatorial and additive (character sum) structures of finite fields. Consequently, we immediately obtain new classe

Naci Saldi

In this paper, we address the problem of finding the best ergodic or Birkhoff averages in the mean ergodic theorem to ensure rapid convergence to a desired value, using graph filters. Our approach begins by representing a function on the state space as a graph signal, where the (directed) graph is formed by the transition probabilities of a reversible Markov

Christian Greff, Paolo Moretti, Michael Zaiser

We investigate interface failure of model materials representing architected thin films in contact with heterogeneous substrates. We find that, while systems with statistically isotropic distributions of impurities derive their fracture strength from the ability to develop rough detachment fronts, materials with hierarchical microstructures confine failure n

Stefan Tatschner, Sebastian N. Peters, Michael P. Heinl, Tobias Specht

X.509 certificates play a crucial role in establishing secure communication over the internet by enabling authentication and data integrity. Equipped with a rich feature set, the X.509 standard is defined by multiple, comprehensive ISO/IEC documents. Due to its internet-wide usage, there are different implementations in multiple programming languages leading

Eduardo Ortega, Agustín Martínez, Antonio Oliva, Fernando Sanz

The burgeoning interest within the space community in digital beamforming is largely attributable to the superior flexibility that satellites with active antenna systems offer for a wide range of applications, notably in communication services. This paper delves into the analysis and practical implementation of a Digital Beamforming and Digital Down Conversi

Jiaqi Xu, Kunzhe Huang, Xinyi Zou, Yunkuo Chen

This paper introduces EasyAnimate, an efficient and high quality video generation framework that leverages diffusion transformers to achieve high-quality video production, encompassing data processing, model training, and end-to-end inference. Despite substantial advancements achieved by video diffusion models, existing video generation models still struggle

A. G. Akeroyd, S. Alanazi, S. Moretti

While searching at the Large Hadron Collider (LHC) for the production and decay of the CP-odd scalar ($A^0$) in the 2-Higgs-Doublet Model (2HDM) with Natural Flavour Conservation (NFC) via the channels $gg\to A^0$ (through one-loop triangle diagrams) and $A^0\to h^0 Z^*$ (with $m_{h^0} =125$ GeV or $m_{h^0} < 125$ GeV, with $Z$ off-shell), respectively, a fa

Kurudi V Vedavyasa, Ashok Kumar

Quantum machine learning (QML) leverages the potential from machine learning to explore the subtle patterns in huge datasets of complex nature with quantum advantages. This exponentially reduces the time and resources necessary for computations. QML accelerates materials research with active screening of chemical space, identifying novel materials for practi

Adel Alameh

The motion of rockets is part of the study devoted to the motion of variable mass systems. Notably those in which the mass leaves permanently the considered system. Rockets are propelled forward by the reaction force produced by the hot exhausted gases ejected from their tales in the rearward direction. Thus their motion should not violate Newton's third pri

Sujit Kumar Nath, Sanjib Sabhapandit

We study the late time exponential decay of the survival probability $S_\pm(t,a|x_0)\sim e^{-\theta(a)t}$, of a one-dimensional run and tumble particle starting from $x_0<a$ with an initial orientation $\sigma(0)=\pm 1$, under a confining potential $U(x)=\alpha|x|$ with an absorbing boundary at $x=a>0$. We find that the decay rate $\theta(a)$ of the survival

Enrico Wegner, Lenard Lieb, Stephan Smeekes, Ines Wilms

We propose a framework for analysing transmission channels in a large class of dynamic models. We formulate our approach both using graph theory and potential outcomes, which we show to be equivalent. Our method, labelled Transmission Channel Analysis (TCA), allows for the decomposition of total effects captured by impulse response functions into the effects

Minji Lee, Luiz Felipe Vecchietti, Hyunkyu Jung, Hyun Joo Ro

Proteins are complex molecules responsible for different functions in nature. Enhancing the functionality of proteins and cellular fitness can significantly impact various industries. However, protein optimization using computational methods remains challenging, especially when starting from low-fitness sequences. We propose LatProtRL, an optimization method

Bruce D. Scott

The issue of finite magnetic compressibility in low-beta magnetised plasmas is considered within the gyrokinetic description. The gauge transformation method of Littlejohn is used to obtain a Lagrangian which contains this effect additionally. The field theory version obtains a system model which guarantees exact energetic consistency. Gyrocenter drifts unde

Zijiang Yan, Ramsundar Tanikella, Hina Tabassum

In vehicular networks (VNets), ensuring both road safety and dependable network connectivity is of utmost importance. Achieving this necessitates the creation of resilient and efficient decision-making policies that prioritize multiple objectives. In this paper, we develop a Variational Quantum Circuit (VQC)-based multi-objective reinforcement learning (MORL

Ziqing Fan, Jiangchao Yao, Ruipeng Zhang, Lingjuan Lyu

Statistical heterogeneity severely limits the performance of federated learning (FL), motivating several explorations e.g., FedProx, MOON and FedDyn, to alleviate this problem. Despite effectiveness, their considered scenario generally requires samples from almost all classes during the local training of each client, although some covariate shifts may exist

Cu Cui, Guido Kanschat

We present a matrix-free multigrid method for high-order discontinuous Galerkin (DG) finite element methods with GPU acceleration. A performance analysis is conducted, comparing various data and compute layouts. Smoother implementations are optimized through localization and fast diagonalization techniques. Leveraging conflict-free access patterns in shared

Bruno Buongiorno Nardelli, Daniele Iudicone

Revealing the ongoing changes in ocean dynamics and their impact on marine ecosystems requires the joint analysis of multiple variables. Yet, global observational records only cover a few decades, posing a challenge in the separation of climatic trends from internal dynamical modes. Here, we apply an empirical stochastic model to identify the emergent patter

Renchunzi Xie, Ambroise Odonnat, Vasilii Feofanov, Weijian Deng

Leveraging the models' outputs, specifically the logits, is a common approach to estimating the test accuracy of a pre-trained neural network on out-of-distribution (OOD) samples without requiring access to the corresponding ground truth labels. Despite their ease of implementation and computational efficiency, current logit-based methods are vulnerable to o

Md Rashid, Nirupam Roy, J. D. Pandian, Prasun Dutta

Low radio frequency spectral index measurements are a powerful tool to distinguish between different emission mechanisms and, in turn, to understand the nature of the sources. Besides the standard method of estimating the ``broadband" spectral index of sources from observations in two different frequency ``bands", if the observations were made with large ins

Stefan Engels, Robert Wille

Railroad transportation plays a vital role in the future of sustainable mobility. Besides building new infrastructure, capacity can be improved by modern train control systems, e.g., based on moving blocks. At the same time, there is only limited work on how to optimally route trains using the potential gained by these systems. Recently, an initial approach

Clement Lezane, Sophie Langer, Wouter M Koolen

Acceleration for non-convex functions is a fundamental challenge in optimisation. We revisit star-convex functions, which are strictly unimodal on all lines through a minimizer. [1] accelerate unconstrained star-convex minimization of functions that are smooth with respect to the Euclidean norm. To do so, they add a certain binary search step to gradient des

Yanru Sun, Zongxia Xie, Dongyue Chen, Emadeldeen Eldele

Deep learning has significantly advanced time series forecasting through its powerful capacity to capture sequence relationships. However, training these models with the Mean Square Error (MSE) loss often results in over-smooth predictions, making it challenging to handle the complexity and learn high-entropy features from time series data with high variabil

Songtao Liu, Bang Wang, Wei Xiang, Han Xu

Multifaceted ideology detection (MID) aims to detect the ideological leanings of texts towards multiple facets. Previous studies on ideology detection mainly focus on one generic facet and ignore label semantics and explanatory descriptions of ideologies, which are a kind of instructive information and reveal the specific concepts of ideologies. In this pape

Shang Gao, Chris Pasco, Otkur Omar, Qiang Zhang

A codimension-two spiral spin-liquid is a correlated paramagnetic state with one-dimensional ground state degeneracy hosted within a three-dimensional lattice. Here, via neutron scattering experiments and numerical simulations, we establish the existence of a codimension-two spiral spin-liquid in the effective honeycomb-lattice compound Cs$_3$Fe$_2$Cl$_9$, w

Ziqing Fan, Ruipeng Zhang, Jiangchao Yao, Bo Han

Partially class-disjoint data (PCDD), a common yet under-explored data formation where each client contributes a part of classes (instead of all classes) of samples, severely challenges the performance of federated algorithms. Without full classes, the local objective will contradict the global objective, yielding the angle collapse problem for locally missi

Yao Zhao, Zhining Liu, Tianchi Cai, Haipeng Zhang

Position bias, i.e., users' preference of an item is affected by its placing position, is well studied in the recommender system literature. However, most existing methods ignore the widely coupled ranking bias, which is also related to the placing position of the item. Using both synthetic and industrial datasets, we first show how this widely coexisted ran

Lorenzo Iannetti, Sonia Cambiaso, Fabio Rasera, Alberto Giacomello

The Martini model, a coarse-grained forcefield for biomolecular simulations, has experienced a vast increase in popularity in the past decade. Its building-block approach balances computational efficiency with high chemical specificity, enabling the simulation of various organic and inorganic molecules. The modeling of coarse-grained beads as Lennard-Jones p

Chencheng Zhang, Hao Yang, Shaoxuan Cui, Bin Jiang

This paper studies observability for non-uniform hypergraphs with inputs and outputs. To capture higher-order interactions, we define a canonical non-homogeneous dynamical system with nonlinear outputs on hypergraphs. We then construct algebraic necessary and sufficient conditions based on polynomial ideals and varieties for global observability at an initia

Zhangyang Gao, Jue Wang, Cheng Tan, Lirong Wu

Molecule inverse folding has been a long-standing challenge in chemistry and biology, with the potential to revolutionize drug discovery and material science. Despite specified models have been proposed for different small- or macro-molecules, few have attempted to unify the learning process, resulting in redundant efforts. Complementary to recent advancemen

Andrey Rybakov, Carla Boix-Constant, Diego Alba Venero, Herre S. J. van der Zant

The layered metamagnet CrSBr offers a rich interplay between magnetic, optical and electrical properties that can be extended down to the two-dimensional (2D) limit. Despite the extensive research regarding the long-range magnetic order in magnetic van der Waals materials, short-range correlations have been loosely investigated. By using Small-Angle Neutron

Xu Feng, Wenjian Yu, Yuyang Xie

This article presents svds-C, an open-source and high-performance C program for accurately and robustly computing truncated SVD, e.g. computing several largest singular values and corresponding singular vectors. We have re-implemented the algorithm of svds in Matlab in C based on MKL or OpenBLAS and multi-thread computing to obtain the parallel program named

Lan Wu

The success of intelligent robotic missions relies on integrating various research tasks, each demanding distinct representations. Designing task-specific representations for each task is costly and impractical. Unified representations suitable for multiple tasks remain unexplored. My outline introduces a series of research outcomes of GP-based probabilistic

Bernhard Heinzelreiter, John W. Pearson

We derive a new parallel-in-time approach for solving large-scale optimization problems constrained by time-dependent partial differential equations arising from fluid dynamics. The solver involves the use of a block circulant approximation of the original matrices, enabling parallelization-in-time via the use of fast Fourier transforms, and we devise bespok

Shin-ichiro Iai

The aim of this paper is to elucidate the relationship between the Gorenstein Rees algebra $\R(I):=\bigoplus_{i\ge 0}I^i$ of an ideal $I$ in a complete Noetherian local ring $A$ and the graded canonical module of the extended Rees algebra $\R'(I):=\bigoplus_{i\in\Z}I^i$. It is known that the Gorensteinness of $\R(I)$ is closely related to the property of the

Kanat Camlibel, Paolo Rapisarda

We state necessary and sufficient conditions to uniquely identify (modulo state isomorphism) a linear time-invariant minimal input-state-output system from finite input-output data and upper- and lower bounds on lag and state space dimension.

Aswathi Mannathanath Chakkingal, Anton A. Kulbakov, Justus Grumbach, Nikolai S. Pavlovskii

Spinon-magnon mixing was recently reported in botallackite Cu$_2$(OH)$_3$Br with a uniaxially compressed triangular lattice of Cu$^{2+}$ quantum spins [Zhang et al., Phys. Rev. Lett. 125, 037204 (2020)]. Its nitrate counterpart rouaite, Cu$_2$(OH)$_3$NO$_3$, has a highly analogous structure and might be expected to exhibit similar physics. To lay a foundatio

Jose Borreguero, Fernando Galve, José Miguel Algarín, Joseba Alonso

Zero echo time (ZTE) sequences have proven a powerful tool for Magnetic Resonance Imaging (MRI) of ultrashort T2 tissues, but they fail to produce useful images in the presence of strong field inhomogeneities. Here we present a method to correct artifacts induced by strong B0 inhomogeneities in non-Cartesian sequences, based on magnetic field maps obtained f

Rui Yang, Shuang Wang, Yingping Han, Yuanheng Li

Remote Sensing Image-Text Retrieval (RSITR) is pivotal for knowledge services and data mining in the remote sensing (RS) domain. Considering the multi-scale representations in image content and text vocabulary can enable the models to learn richer representations and enhance retrieval. Current multi-scale RSITR approaches typically align multi-scale fused im

Léonard Brice, Jean-François Raskin, Mathieu Sassolas, Guillaume Scerri

Fairness is a desirable and crucial property of many protocols that handle, for instance, exchanges of message. It states that if at least one agent engaging in the protocol is honest, then either the protocol will unfold correctly and fulfill its intended goal for all participants, or it will fail for everyone. In this work, we present a game-based framewor

Jeongmin Kim, Benjamin Rotenberg

Ion partitioning between different compartments (\emph{e.g.} a porous material and a bulk solution reservoir), known as Donnan equilibrium, plays a fundamental role in various contexts such as energy, environment, or water treatment. The linearized Poisson-Boltzmann (PB) equation, capturing the thermal motion of the ions with mean-field electrostatic interac

Kaustav Giri, V. V. Sreedhar

The celebrated Aharonov-Bohm effect is perhaps the first example in which the the interplay between classical topology and quantum theory was explored. This connection has continued to shed light on diverse areas of physics like quantum statistics, anomalies, condensed matter physics, and gauge theories. Several attempts were made to generalize the Aharonov-

Jinzhong Wang, Xuetao Tian, Shun Dai, Tao Zhuo

Multispectral object detection, utilizing both visible (RGB) and thermal infrared (T) modals, has garnered significant attention for its robust performance across diverse weather and lighting conditions. However, effectively exploiting the complementarity between RGB-T modals while maintaining efficiency remains a critical challenge. In this paper, a very si

Hongyu Liu, Catharine W. K. Lo

In this paper, we are concerned with the inverse problem of determining anomalies in the state space associated with the stationary mean field game (MFG) system. We establish novel unique identifiability results for the intrinsic structure of these anomalies in mean field games systems, including their topological structure and parameter configurations, in s

Yihang She, Andrew Blake, Clement Atzberger, Adriano Gualandi

Physically meaningful representations are essential for Earth Observation (EO), yet existing physical models are often simplified and incomplete. This leads to discrepancies between simulation and observations that hinder reliable forward model inversion. Common approaches to EO inversion either ignored this incompleteness or relied on case-specific preproce

Louis Ledoux, Marc Casas

We present a versatile open-source framework designed to facilitate efficient, numerically-tailored Matrix-Matrix Multiplications (MMMs). The framework offers two primary contributions: first, a fine-tuned, automated pipeline for arithmetic datapath generation, enabling highly customizable systolic MMM kernels; second, seamless integration of the generated k

Peter Devine

Training Large Language Models (LLMs) with Reinforcement Learning from AI Feedback (RLAIF) aligns model outputs more closely with human preferences. This involves an evaluator model ranking multiple candidate responses to user prompts. However, the rankings from popular evaluator models such as GPT-4 can be inconsistent. We propose the Repeat Ranking method

Xinyan Yu, Marius Hoggenmueller, Martin Tomitsch

Robots in urban environments will inevitably encounter situations beyond their capabilities (e.g., delivery robots unable to press traffic light buttons), necessitating bystander assistance. These spontaneous collaborations possess challenges distinct from traditional human-robot collaboration, requiring design investigation and tailored interaction strategi

M. Montalto, N. Greco, K. Biazzo, S. Desidera

Aims. We report the confirmation of a new transiting exoplanet orbiting the star TOI-5076. Methods. We present our vetting procedure and follow-up observations which led to the confirmation of the exoplanet TOI-5076b. In particular, we employed high-precision {\it TESS} photometry, high-angular-resolution imaging from several telescopes, and high-precision r

Yasmine Kalboussi, Sarah Dadouch, Baptiste Delatte, Frédéric Miserques

This study investigates the use of Atomic Layer deposition (ALD) to mitigate multipacting phenomena inside superconducting radio frequency (SRF) cavities used in particle accelerators. The unique ALD capability to control the film thickness down to the atomic level on arbitrary complex shape objects enable the fine tuning of TiN film resistivity and total el

Namasivayam Kalithasan, Arnav Tuli, Vishal Bindal, Himanshu Gaurav Singh

Automatically detecting and recovering from failures is an important but challenging problem for autonomous robots. Most of the recent work on learning to plan from demonstrations lacks the ability to detect and recover from errors in the absence of an explicit state representation and/or a (sub-) goal check function. We propose an approach (blending learnin

Alessio Barbieri, Klaus-Jochen Engel

In this note we generalize perturbation results for positive $C_0$-semigroups on AM- and AL-spaces and give a Weiss--Staffans type perturbation result for generators of positive semigroups on Banach lattices. The abstract results are applied to domain perturbations of generators, a heat equation with boundary feedback and perturbations of the first derivativ

Igor Sokolović, Eduardo B. Guedes, Thomas P. van Waas, Samuel Poncé

Two-dimensional electron liquids (2DELs) have increasing technological relevance for ultrafast electronics and spintronics, yet significant gaps in their fundamental understanding are exemplified on the prototypical SrTiO$_3$. We correlate the exact SrTiO$_3$(001) surface structure with distinct 2DELs through combined microscopic angle-resolved photoemission