February 2024 arXiv papers — page 168

Carlos Lozano, Jorge Ponsin

This paper considers the formulation of the adjoint problem in two dimensions when there are shocks in the flow solution. For typical cost functions, the adjoint variables are continuous at shocks, where they have to obey an internal boundary condition, but their derivatives may be discontinuous. The derivation of the adjoint shock equations is reviewed and

Xusheng Zhu, Qingqing Wu, Wen Chen

In this study, we explore the performance of a reconfigurable reflecting surface (RIS)-assisted transmit spatial modulation (SM) system for downlink transmission, wherein the deployment of RIS serves the purpose of blind area coverage within the channel. At the receiving end, we present three detectors, i.e., maximum likelihood (ML) detector, two-stage ML de

Pengfei Han, Fuhua Zhang, Bin Zhao, Xuelong Li

Video frame interpolation methodologies endeavor to create novel frames betwixt extant ones, with the intent of augmenting the video's frame frequency. However, current methods are prone to image blurring and spurious artifacts in challenging scenarios involving occlusions and discontinuous motion. Moreover, they typically rely on optical flow estimation, wh

A. A. Sheykin

It is shown that target space diffeomorphism invariance of a generic Lagrangian for a set of scalar fields leads to an analog of Einstein equations for the geometry of a level set of these fields.

Shuang Zhang, Wanming Hao, Gangcan Sun, Zhengyu Zhu

In this paper, the sensing beam pattern gain under simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS)-enabled integrated sensing and communications (ISAC) systems is investigated, in which multiple targets and multiple users exist. However, multiple targets detection introduces new challenges, since the STAR-RIS cannot

Gleb Ryzhakov, Andrei Chertkov, Artem Basharin, Ivan Oseledets

We develop a new method HTBB for the multidimensional black-box approximation and gradient-free optimization, which is based on the low-rank hierarchical Tucker decomposition with the use of the MaxVol indices selection procedure. Numerical experiments for 14 complex model problems demonstrate the robustness of the proposed method for dimensions up to 1000,

Yasar Abbas Ur Rehman, Kin Wai Lau, Yuyang Xie, Lan Ma

The integration of Federated Learning (FL) and Self-supervised Learning (SSL) offers a unique and synergetic combination to exploit the audio data for general-purpose audio understanding, without compromising user data privacy. However, rare efforts have been made to investigate the SSL models in the FL regime for general-purpose audio understanding, especia

Baptiste Cerclé, Nathan Huguenin

Toda Conformal Field Theories (CFTs hereafter) are generalizations of Liouville CFT where the underlying field is no longer scalar but takes values in a finite-dimensional vector space induced by a complex simple Lie algebra. The goal of this document is to provide a probabilistic construction of such models on all compact hyperbolic Riemann surfaces with or

Otniel-Bogdan Mercea, Alexey Gritsenko, Cordelia Schmid, Anurag Arnab

As foundation models become more popular, there is a growing need to efficiently finetune them for downstream tasks. Although numerous adaptation methods have been proposed, they are designed to be efficient only in terms of how many parameters are trained. They, however, typically still require backpropagating gradients throughout the model, meaning that th

Gorka Abad, Stjepan Picek, Aitor Urbieta

This paper investigates the vulnerability of spiking neural networks (SNNs) and federated learning (FL) to backdoor attacks using neuromorphic data. Despite the efficiency of SNNs and the privacy advantages of FL, particularly in low-powered devices, we demonstrate that these systems are susceptible to such attacks. We first assess the viability of using FL

Kenta Yanagiya, Junya Hara, Hiroshi Higashi, Yuichi Tanaka

This paper proposes a compression framework for adjacency matrices of weighted graphs based on graph filter banks. Adjacency matrices are widely used mathematical representations of graphs and are used in various applications in signal processing, machine learning, and data mining. In many problems of interest, these adjacency matrices can be large, so effic

Lucas Möller, Dmitry Nikolaev, Sebastian Padó

Siamese encoders such as sentence transformers are among the least understood deep models. Established attribution methods cannot tackle this model class since it compares two inputs rather than processing a single one. To address this gap, we have recently proposed an attribution method specifically for Siamese encoders (M\"oller et al., 2023). However, it

Thibault Caillet, Filippo Santambrogio

We prove an existence result for a large class of PDEs with a nonlinear Wasserstein gradient flow structure. We use the classical theory of Wasserstein gradient flow to derive an EDI formulation of our PDE and prove that under some integrability assumptions on the initial condition the PDE is satisfied in the sense of distributions.

Rohan Ramasamy, Ksenia Aleynikova, Nikita Nikulsin, Florian Hindenlang

An important question for the outlook of stellarator reactors is their robustness against pressure driven modes, and the underlying mechanism behind experimentally observed soft $\beta$ limits. Towards building a robust answer to these questions, simulation studies are presented using a recently derived reduced nonlinear MHD model. First, the initial model i

Han-Xiao Tao, Jiaqi Hu, Re-Bing Wu

Quantum machine learning (QML) based on Noisy Intermediate-Scale Quantum (NISQ) devices hinges on the optimal utilization of limited quantum resources. While gate-based QML models are user-friendly for software engineers, their expressivity is restricted by the permissible circuit depth within a finite coherence time. In contrast, pulse-based models enable t

Marta Pérez Rodríguez

Deformation theory is treated for locally notherian formal schemes (non necessarily smooth). The cotangent complex is defined in the derived category through the homology localization functor. The basic properties and results of a deformation theory are proved. And the complex is described for regular closed immersions and complete intersection morphisms of

E. Caffau, D. Katz, A. Gómez, P. Bonifacio

The Gaia satellite has already provided the astronomical community with three data releases, and the Radial Velocity Spectrometer (RVS) on board Gaia has provided the radial velocity for 33 million stars. When deriving the radial velocity from the RVS spectra, several stars are measured to have large values. To verify the credibility of these measurements, w

Cristiana Santos, Nataliia Bielova, Vincent Roca, Mathieu Cunche

We very much welcome the EDPB's Guidelines. Please find hereunder our feedback to the Guidelines 2/2023 on Technical Scope of Art. 5(3) of ePrivacy Directive. Our comments are presented after a quotation from the proposed text by the EDPB in a box.

P. Bonifacio, E. Caffau, L. Monaco, L. Sbordone

The data from the Gaia satellite led us to revise our conception of the Galaxy structure and history. Hitherto unknown components have been discovered and a deep re-thinking of what the Galactic halo is is in progress. We selected from the Gaia catalogue stars with extreme transverse velocities with respect to the Sun ($|V_T| > 500 $ and observed them with F

Ben Sharp

We classify low-energy $\alpha$-harmonic maps from a closed non-spherical Riemannian surface $\Sigma$ of constant curvature to the round sphere via their bubble scales and centres. In particular we show that as $1<\alpha\downarrow 1$ and assuming $E_\alpha$ is close to $| \Sigma|+4\pi$ then degree-one $\alpha$-harmonic maps blow a bubble based at a critical

Gilles Bertrand, Laurent Najman

In the context of discrete Morse theory, we introduce Morse frames, which are maps that associate a set of critical simplexes to all simplexes. The main example of Morse frames are the Morse references. In particular, these Morse references allow computing Morse complexes, an important tool for homology. We highlight the link between Morse references and gra

Anna T. Bui, Stephen J. Cox

A central aim of multiscale modeling is to use results from the Schr\"odinger Equation to predict phenomenology on length scales that far exceed those of typical molecular correlations. In this work, we present a new approach rooted in classical density functional theory (cDFT) that allows us to accurately describe the solvation of apolar solutes across leng

Zeping Yu, Sophia Ananiadou

We investigate the mechanism of in-context learning (ICL) on sentence classification tasks with semantically-unrelated labels ("foo"/"bar"). We find intervening in only 1\% heads (named "in-context heads") significantly affects ICL accuracy from 87.6\% to 24.4\%. To understand this phenomenon, we analyze the value-output vectors in these heads and discover t

Neehar Verma, Camilla Hollanti

Private information retrieval from a single server is considered, utilizing random linear codes. Presented is a modified version of the first code-based single-server computational PIR scheme proposed by Holzbaur, Hollanti, and Wachter-Zeh in [Holzbaur et al., "Computational Code-Based Single-Server Private Information Retrieval", 2020 IEEE ISIT]. The origin

Sebastian Bordt, Eric Raidl, Ulrike von Luxburg

In the rapidly growing literature on explanation algorithms, it often remains unclear what precisely these algorithms are for and how they should be used. In this position paper, we argue for a novel and pragmatic perspective: Explainable machine learning needs to recognize its parallels with applied statistics. Concretely, explanations are statistics of hig

Patrick Erik Bradley

A self-adjoint operator is constructed on the $L_2$-functions on the $K$-rational points $X(K)$ of a Mumford curve $X$ defined over a non-archimedean local field $K$. It generates a Feller semi-group, and the corresponding heat equation describes a Markov process on $X(K)$. Its spectrum is non-positive, contains zero and has finitely many limit points which

Maciej Wołczyk, Bartłomiej Cupiał, Mateusz Ostaszewski, Michał Bortkiewicz

Fine-tuning is a widespread technique that allows practitioners to transfer pre-trained capabilities, as recently showcased by the successful applications of foundation models. However, fine-tuning reinforcement learning (RL) models remains a challenge. This work conceptualizes one specific cause of poor transfer, accentuated in the RL setting by the interpl

Elena Castilla

This paper presents a robust alternative to the Maximum Likelihood Estimator (MLE) for the Polytomous Logistic Regression Model (PLRM), known as the family of minimum R\`enyi Pseudodistance (RP) estimators. The proposed minimum RP estimators are parametrized by a tuning parameter $\alpha\geq0$, and include the MLE as a special case when $\alpha=0$. These est

Bodo Rosenhahn, Christoph Hirche

A Normalizing Flow computes a bijective mapping from an arbitrary distribution to a predefined (e.g. normal) distribution. Such a flow can be used to address different tasks, e.g. anomaly detection, once such a mapping has been learned. In this work we introduce Normalizing Flows for Quantum architectures, describe how to model and optimize such a flow and e

Ascensión Gallardo-Antolín, Juan M. Montero

Speech intelligibility can be affected by multiple factors, such as noisy environments, channel distortions or physiological issues. In this work, we deal with the problem of automatic prediction of the speech intelligibility level in this latter case. Starting from our previous work, a non-intrusive system based on LSTM networks with attention mechanism des

Axel Sorensen, Siyao Peng, Barbara Plank, Rob van der Goot

Annotation tools are the starting point for creating Natural Language Processing (NLP) datasets. There is a wide variety of tools available; setting up these tools is however a hindrance. We propose EEVEE, an annotation tool focused on simplicity, efficiency, and ease of use. It can run directly in the browser (no setup required) and uses tab-separated files

Sintsov S. V., Vodopyanov A. V., Mansfeld D. A., Fokin A. P.

This paper presents the results of an experimental study of a new hybrid plasmatron scheme, which was used to realize a gas discharge at atmospheric pressure supported by continuous focused submillimeter radiation with a frequency of 263 GHz. The implemented design allowed organizing a self-consistent interaction between submillimeter radiation and the super

Giannis Nikolentzos, Siyun Wang, Johannes Lutzeyer, Michalis Vazirgiannis

In recent years, there has been a growing interest in mapping data from different domains to graph structures. Among others, neural network models such as the multi-layer perceptron (MLP) can be modeled as graphs. In fact, MLPs can be represented as directed acyclic graphs. Graph neural networks (GNNs) have recently become the standard tool for performing ma

Emmanouil M Athanasakos, Samir M Perlaza

This paper studies an instance of zero-sum games in which one player (the leader) commits to its opponent (the follower) to choose its actions by sampling a given probability measure (strategy). The actions of the leader are observed by the follower as the output of an arbitrary channel. In response to that, the follower chooses its action based on its curre

Wen-Chen Chang, Jen-Chieh Peng, Stephane Platchkov, Takahiro Sawada

The kaon parton distribution functions (PDFs) are poorly known due to paucity of kaon-induced Drell-Yan data. Nevertheless, these Drell-Yan data suggest a softer valence $u$ quark distribution of kaon than that of pion. We discuss the opportunity to constrain kaon PDFs utilizing existing kaon-induced $J/\psi$ production data. We compare the $K^- / \pi^-$ and

Mathis Chagneux, Pierre Gloaguen, Sylvain Le Corff, Jimmy Olsson

This article addresses online variational estimation in state-space models. We focus on learning the smoothing distribution, i.e. the joint distribution of the latent states given the observations, using a variational approach together with Monte Carlo importance sampling. We propose an efficient algorithm for computing the gradient of the evidence lower bou

Abdelhakim Benechehab, Albert Thomas, Balázs Kégl

We consider the problem of offline reinforcement learning where only a set of system transitions is made available for policy optimization. Following recent advances in the field, we consider a model-based reinforcement learning algorithm that infers the system dynamics from the available data and performs policy optimization on imaginary model rollouts. Thi

Sobihan Surendran, Antoine Godichon-Baggioni, Adeline Fermanian, Sylvain Le Corff

Stochastic Gradient Descent (SGD) with adaptive steps is widely used to train deep neural networks and generative models. Most theoretical results assume that it is possible to obtain unbiased gradient estimators, which is not the case in several recent deep learning and reinforcement learning applications that use Monte Carlo methods. This paper provides a

Pierre Houzelstein, Peter J. Thomas, Benjamin Lindner, Boris S. Gutkin

Phase reduction is an important tool for studying coupled and driven oscillators. The question of how to generalize phase reduction to stochastic oscillators remains actively debated. In this work, we propose a method to derive a self-contained stochastic phase equation of the form $\mathop{}\!\mathrm{d} \phi = a(\phi)\mathop{}\!\mathrm{d} t + \sqrt{2D(\phi)

Shuyao Wang, Yongduo Sui, Jiancan Wu, Zhi Zheng

In the realm of deep learning-based recommendation systems, the increasing computational demands, driven by the growing number of users and items, pose a significant challenge to practical deployment. This challenge is primarily twofold: reducing the model size while effectively learning user and item representations for efficient recommendations. Despite co

Young-Pil Choi, Simone Fagioli, Valeria Iorio

We investigate various versions of multi-dimensional systems involving many species, modeling aggregation phenomena through nonlocal interaction terms. We establish a rigorous connection between kinetic and macroscopic descriptions by considering the small-inertia limit at the kinetic level. The results are proven either under smoothness assumptions on all i

Hao Chen, Conghui Xie, Cunsheng Ding

Cyclic codes are the most studied subclass of linear codes and widely used in data storage and communication systems. Many cyclic codes have optimal parameters or the best parameters known. They are divided into simple-root cyclic codes and repeated-root cyclic codes. Although there are a huge number of references on cyclic codes, few of them are on repeated

Sergi Julià-Farré, Joseph Vovrosh, Alexandre Dauphin

Amorphous solids, i.e., systems which feature well-defined short-range properties but lack long-range order, constitute an important research topic in condensed matter. While their microscopic structure is known to differ from their crystalline counterpart, there are still many open questions concerning the emergent collective behavior in amorphous materials

Haoxiang Wang, Haozhe Si, Huajie Shao, Han Zhao

Real-world applications of machine learning models often confront data distribution shifts, wherein discrepancies exist between the training and test data distributions. In the common multi-domain multi-class setup, as the number of classes and domains scales up, it becomes infeasible to gather training data for every domain-class combination. This challenge

Nikhil Kumar Singh, Indranil Saha

Efficient utilization of the replay buffer plays a significant role in the off-policy actor-critic reinforcement learning (RL) algorithms used for model-free control policy synthesis for complex dynamical systems. We propose a method for achieving sample efficiency, which focuses on selecting unique samples and adding them to the replay buffer during the exp

Miguel Fernández-Díaz, Ascensión Gallardo-Antolín

Speech intelligibility can be degraded due to multiple factors, such as noisy environments, technical difficulties or biological conditions. This work is focused on the development of an automatic non-intrusive system for predicting the speech intelligibility level in this latter case. The main contribution of our research on this topic is the use of Long Sh

Jiwei Zhang, Zhimin Zhang, Chengchao Zhao

Solutions exhibiting weak initial singularities arise in various equations, including diffusion and subdiffusion equations. When employing the well-known L1 scheme to solve subdiffusion equations with weak singularities, numerical simulations reveal that this scheme exhibits varying convergence rates for different choices of model parameters (i.e., domain si

Sk Siddik, Golam Ali Sekh

We consider Shannon entropy, Fisher information, R\'enyi entropy, and Tsallis entropy to study the quantum droplet phase in Bose-Einstein condensates. In the beyond mean-field description, the Gross-Pitaevskii equation with Lee-Huang-Yang correction gives a family of quantum droplets with different chemical potentials. At a larger value of chemical potential

Luca Aceto, Ignacio Fábregas, Álvaro García-Pérez, Anna Ingólfsdóttir

We present a unified rule format for structural operational semantics with terms as labels that guarantees that the associated labelled transition system has some bounded-nondeterminism property. The properties we consider include finite branching, initials finiteness and image finiteness.

Velat Kilic, Neil Macfarlane, Jasper Stround, Samuel Metais

We investigate usage of nonlinear wave chaotic amorphous silicon (a-Si) cavities as physically unclonable functions (PUF). Machine learning attacks on integrated electronic PUFs have been demonstrated to be very effective at modeling PUF behavior. Such attacks on integrated a-Si photonic PUFs are investigated through application of algorithms including linea

Armand Foucault, Franck Mamalet, François Malgouyres

In recent years, Orthogonal Recurrent Neural Networks (ORNNs) have gained popularity due to their ability to manage tasks involving long-term dependencies, such as the copy-task, and their linear complexity. However, existing ORNNs utilize full precision weights and activations, which prevents their deployment on compact devices.In this paper, we explore the

Rolando Magnanini, Riccardo Molinarolo, Giorgio Poggesi

We prove a new general differential identity and an associated integral identity, which entails a pair of solutions of the Poisson equation with constant source term. This generalizes a formula that the first and third authors previously proved and used to obtain quantitative estimates of spherical symmetry for the Serrin overdetermined boundary value proble

Juraj Vladika, Florian Matthes

The increasing rate at which scientific knowledge is discovered and health claims shared online has highlighted the importance of developing efficient fact-checking systems for scientific claims. The usual setting for this task in the literature assumes that the documents containing the evidence for claims are already provided and annotated or contained in a

Milo Bechtloff Weising

The double Dyck path algebra $\mathbb{A}_{q,t}$ was introduced by Carlsson-Mellit in their proof of the Shuffle Theorem. A variant of this algebra, $\mathbb{B}_{q,t}$, was introduced by Carlsson-Gorsky-Mellit in their study of the parabolic flag Hilbert schemes of points in $\mathbb{C}^2$ showing that $\mathbb{B}_{q,t}$ acts naturally on the equivariant $K$-

Jing Yan, Liu Jiang, Jianfei Cui, Zhichen Zhao

Interest modeling in recommender system has been a constant topic for improving user experience, and typical interest modeling tasks (e.g. multi-interest, long-tail interest and long-term interest) have been investigated in many existing works. However, most of them only consider one interest in isolation, while neglecting their interrelationships. In this p

Emmanuel Trélat, Xingwu Zeng, Can Zhang

In this paper, we establish an exponential periodic turnpike property for linear quadratic optimal control problems governed by periodic systems in infinite dimension. We show that the optimal trajectory converges exponentially to a periodic orbit when the time horizon tends to infinity. Similar results are obtained for the optimal control and adjoint state.

M Hassain

Let $\mathfrak{o}$ be a compact discrete valuation ring with maximal ideal $\mathfrak{p}$ such that the finite residue field $\mathfrak{o}/\mathfrak{p}$ has characteristic $p.$ For $r\geq2$ and $p=2,$ we obtain the branching rules for the restriction of a regular representation of $\mathrm{GL}_2(\mathfrak{o}/\mathfrak{p}^r)$ to $\mathrm{SL}_2(\mathfrak{o}/\m

Pei-Rong Han, Wen Ning, Xin-Jie Huang, Ri-Hua Zheng

Owing to the presence of exceptional points (EPs), non-Hermitian (NH) systems can display intriguing topological phenomena without Hermitian analogs. However, experimental characterizations of exceptional topological invariants have been restricted to second-order EPs (EP2s) in classical or semiclassical systems. We here propose an NH multi-mode system with

Prabath Jayathissa, Roshan Hewapathirana

This review underscores the vital role of interoperability in digital health, advocating for a standardized framework. It focuses on implementing a Fast Healthcare Interoperability Resources (FHIR) server, addressing technical, semantic, and process challenges. FHIR's adaptability ensures uniformity within Primary Care Health Information Systems, fostering i

Amandine Decker, Maxime Amblard

Topics play an important role in the global organisation of a conversation as what is currently discussed constrains the possible contributions of the participant. Understanding the way topics are organised in interaction would provide insight on the structure of dialogue beyond the sequence of utterances. However, studying this high-level structure is a com

Farhad Pakdaman, Sanaz Nami, Moncef Gabbouj

Emerging Learned image Compression (LC) achieves significant improvements in coding efficiency by end-to-end training of neural networks for compression. An important benefit of this approach over traditional codecs is that any optimization criteria can be directly applied to the encoder-decoder networks during training. Perceptual optimization of LC to comp

Mingjian He, Shouyin Liu

Continuous variable quantum teleportation provides a path to the long-distance transmission of quantum states. Photon-varying non-Gaussian operations have been shown to improve the fidelity of quantum teleportation when integrated into the protocol. However, given a fixed non-Gaussian operation, the achievable fidelity varies with different input states. An

Bo-Kyeong Kim, Geonmin Kim, Tae-Ho Kim, Thibault Castells

Structured pruning of modern large language models (LLMs) has emerged as a way of decreasing their high computational needs. Width pruning reduces the size of projection weight matrices (e.g., by removing attention heads) while maintaining the number of layers. Depth pruning, in contrast, removes entire layers or blocks, while keeping the size of the remaini

Georg Heppner, David Oberacker, Arne Roennau, Rüdiger Dillmann

While individual robots are becoming increasingly capable, with new sensors and actuators, the complexity of expected missions increased exponentially in comparison. To cope with this complexity, heterogeneous teams of robots have become a significant research interest in recent years. Making effective use of the robots and their unique skills in a team is c

Thomas Hall

We study a subclass of K\"ahler-Einstein Fano polygons and how they behave under mutation. The polygons of interest are K\"ahler-Einstein Fano triangles and symmetric Fano polygons. In particular, we find an explicit bound for the number of these polygons in an arbitrary mutation-equivalence class. An important mutation-invariant of a Fano polygon is its sin

Andrei Khrennikov

As the preface to the special issue for the conference ``Quantum Information and Probability: from Foundations to Engineering'' (QIP23), I wrote these notes with recollection about V\"axj\"o conferences. These conferences covered 25 years of my life (2000-24) and played the crucial role in evolution of my own views on the basic problems of quantum foundation

Avigail Ferdman, Emanuele Ratti

In the past few years, calls for integrating ethics modules in engineering curricula have multiplied. Despite this positive trend, a number of issues with these embedded programs remains. First, learning goals are underspecified. A second limitation is the conflation of different dimensions under the same banner, in particular confusion between ethics curric

Adrián Vázquez-Romero, Ascensión Gallardo-Antolín

This paper proposes a speech-based method for automatic depression classification. The system is based on ensemble learning for Convolutional Neural Networks (CNNs) and is evaluated using the data and the experimental protocol provided in the Depression Classification Sub-Challenge (DCC) at the 2016 Audio-Visual Emotion Challenge (AVEC-2016). In the pre-proc

Stefan Hetzl, Raheleh Jalali

Craig interpolation is a fundamental property of classical and non-classic logics with a plethora of applications from philosophical logic to computer-aided verification. The question of which interpolants can be obtained from an interpolation algorithm is of profound importance. Motivated by this question, we initiate the study of completeness properties of

Michel Zamboni-Rached, Jéssyca Nobre-Pereira, João Quaglio

Recognized for their non-diffracting properties, Bessel beams can be conveniently combined to generate the so-called Frozen Waves, which are monochromatic beams endowed with topological charge and whose longitudinal intensity pattern can be shaped according to a previously chosen function. Continuous superposition of Bessel beams is specially suitable for mi

Anna Varbella, Kenza Amara, Blazhe Gjorgiev, Mennatallah El-Assady

Power grids are critical infrastructures of paramount importance to modern society and, therefore, engineered to operate under diverse conditions and failures. The ongoing energy transition poses new challenges for the decision-makers and system operators. Therefore, developing grid analysis algorithms is important for supporting reliable operations. These k

Yudara Kularathne, Prathapa Janitha, Sithira Ambepitiya, Thanveer Ahamed

Rapid development of disease detection computer vision models is vital in response to urgent medical crises like epidemics or events of bioterrorism. However, traditional data gathering methods are too slow for these scenarios necessitating innovative approaches to generate reliable models quickly from minimal data. We demonstrate our new approach by buildin

Tianyu Zhao, Yue Zhou, Ruijun Shi, Zhoujian Cao

Gravitational wave (GW) astronomy has opened new frontiers in understanding the cosmos, while the integration of artificial intelligence (AI) in science promises to revolutionize data analysis methodologies. However, a significant gap exists, as there is currently no dedicated platform that enables scientists to develop, test, and evaluate AI algorithms effi

Raphael Sonabend, Hugo Gruson, Leo Wolansky, Agnes Kiragga

This paper extends the FAIR (Findable, Accessible, Interoperable, Reusable) guidelines to provide criteria for assessing if software conforms to best practices in open source. By adding 'USE' (User-Centered, Sustainable, Equitable), software development can adhere to open source best practice by incorporating user-input early on, ensuring front-end designs a

Jasper Dekoninck, Mark Niklas Müller, Maximilian Baader, Marc Fischer

Large language models are widespread, with their performance on benchmarks frequently guiding user preferences for one model over another. However, the vast amount of data these models are trained on can inadvertently lead to contamination with public benchmarks, thus compromising performance measurements. While recently developed contamination detection met

Yongtae Heo, Dong-Won Jung, Jae Sik Lee

We perform global fits of the Higgs boson couplings to the full Higgs datasets collected at the LHC with the integrated luminosities per experiment of approximately 5/fb at 7 TeV, 20/fb at 8 TeV, and up to 139/fb at 13 TeV. Our combined analysis based on the experimental signal strengths used in this work and the theoretical ones elaborated for our analysis

Ren Matsusaka, Toshihiro Handa, Yusuke Fujimoto, Takeru Murase

To understand physical properties of the interstellar medium (ISM) on various scales, we investigate it at parsec resolution on the kiloparsec scale. Here, we report on the sub-kpc scale Gas Density Histogram (GDH) of the Milky Way. The GDH is a density probability distribution function (PDF) of the gas volume density. Using this method, we are free from an

Zexin Wang, Changhua Pei, Minghua Ma, Xin Wang

Time series Anomaly Detection (AD) plays a crucial role for web systems. Various web systems rely on time series data to monitor and identify anomalies in real time, as well as to initiate diagnosis and remediation procedures. Variational Autoencoders (VAEs) have gained popularity in recent decades due to their superior de-noising capabilities, which are use

Teagan A. Clarke, Maximiliano Isi, Paul D. Lasky, Eric Thrane

The ringdown portion of a binary black hole merger consists of a sum of modes, each containing an infinite number of tones that are exponentially damped sinusoids. In principle, these can be measured as gravitational-waves with observatories like LIGO/Virgo/KAGRA, however in practice it is unclear how many tones can be meaningfully resolved. We investigate t

Franziska Strube, Thomas Wilhelm, Johanna Sygusch, Bradley M. Guy

The demand for lithium, as well as other critical resources, needed for electrochemical energy storage is expected to grow significantly in the future. Slags obtained from pyrometallurgical recycling represent a promising resource of valuable materials, among them lithium and rare earth elements found in artificial minerals particulate phases. This study inv

Xianli Zeng, Kevin Jiang, Guang Cheng, Edgar Dobriban

Machine learning algorithms may have disparate impacts on protected groups. To address this, we develop methods for Bayes-optimal fair classification, aiming to minimize classification error subject to given group fairness constraints. We introduce the notion of \emph{linear disparity measures}, which are linear functions of a probabilistic classifier; and \

Yifan Wang, Peijie Sun, Weizhi Ma, Min Zhang

Fairness of recommender systems (RS) has attracted increasing attention recently. Based on the involved stakeholders, the fairness of RS can be divided into user fairness, item fairness, and two-sided fairness which considers both user and item fairness simultaneously. However, we argue that the intersectional two-sided unfairness may still exist even if the

Debsoumya Chakraborti, Tuan Tran

Fix $\varepsilon >0$ and consider a multipartite graph $G$ with maximum degree at most $(1-\varepsilon)n$, parts $V_1,\ldots,V_k$ of the same size $n$, and where every vertex has at most $o(n)$ neighbors in any part $V_i$. Loh and Sudakov proved that any such $G$ has an independent transversal. They further conjectured that the vertex set of $G$ can be decom

Ryuji Takagi, Xiao Yuan, Bartosz Regula, Mile Gu

We develop the general framework of virtual resource distillation -- an alternative distillation strategy proposed in [Phys. Rev. Lett. 132, 050203 (2024)], which extends conventional quantum resource distillation by integrating the power of classical postprocessing. The framework presented here is applicable not only to quantum states, but also dynamical qu

M. A. Gigosos, D. Gonzalez-Herrero, R. Florido, A. Calisti

Classical molecular dynamics simulations of hydrogen plasmas have been performed with emphasis on the analysis of equilibration process. Theoretical basis of simulation model as well as numerically relevant aspects -- such as the proper choice and definition of simulation units -- are discussed in detail, thus proving a thorough implementation of the compute

Evgeny A. Stepanov, Maria Chatzieleftheriou, Niklas Wagner, Giorgio Sangiovanni

We analyze the role of spatial electronic correlations and, in particular, of the magnetic fluctuations in Mott insulators. A half-filled Hubbard model is solved at large strength of the repulsion U on a two-dimensional square lattice using an advanced diagrammatic non-perturbative approach capable of going beyond Hartree-Fock and single-site dynamical mean-

Alexander Sattler, Maria Daghofer

Both the Haldane spin chain and a topologically dimerized chain feature topologically protected edge states that are expected to be robust against some kind of noise. To elucidate whether it might be feasible to create such edge states in dimerized chains in a controlled manner in solid states environments, e.g. as spin chains on surfaces, as has already bee

Matthieu Dolbeault, Olga Mula, Agustín Somacal

We consider the problem of real-time reconstruction of urban air pollution maps. The task is challenging due to the heterogeneous sources of available data, the scarcity of direct measurements, the presence of noise, and the large surfaces that need to be considered. In this work, we introduce different reconstruction methods based on posing the problem on c

Ammu R., Debanjali Bhattacharya, Ameiy Acharya, Ninad Aithal

In this study, we present a technique that spans multi-scale views (global scale -- meaning brain network-level and local scale -- examining each individual ROI that constitutes the network) applied to resting-state fMRI volumes. Deep learning based classification is utilized in understanding neurodegeneration. The novelty of the proposed approach lies in ut

Qiang Cheng, Qing-Feng Sun

We study the Josephson effect in the spin-singlet superconductor/altermagnet/spin-triplet superconductor junctions using the Green's function method. The current-phase difference relationships in the junctions strongly depend on the orientation of altermagnet and the types of the Cooper pairs. For the orientation angle equal to odd multiples of $\pi/4$, the

Elena Cordero, Gianluca Giacchi, Luigi Rodino, Mario Valenzano

We study the decay properties of Wigner kernels for Fourier integral operators of types I and II. The symbol spaces that allow a nice decay of these kernels are the Shubin classes $\Gamma^m(\mathbb{R^{2d}})$, with negative order $m$. The phases considered are the so-called tame ones, which appear in the Schr\"odinger propagators. The related canonical transf

Gerardo F. Goya, Vittoria Raffa

Magnetic nanoparticles (MNPs) are the foundation of several new strategies for neural repair and neurological therapies. The fact that a remote force can act on MNPs at the cytoplasmic space constitutes the essence of many new neurotherapeutic concepts. MNPs with a predesigned physicochemical characteristic can interact with external magnetic fields to apply

Alina Chertock, Shumo Cui, Alexander Kurganov, Chenxi Wang

Chemotaxis systems play a crucial role in modeling the dynamics of bacterial and cellular behaviors, including propagation, aggregation, and pattern formation, all under the influence of chemical signals. One notable characteristic of these systems is their ability to simulate concentration phenomena, where cell density undergoes rapid growth near specific c

Luise Häuser, Gerhard Jäger, Taraka Rama, Johann-Mattis List

In traditional studies on language evolution, scholars often emphasize the importance of sound laws and sound correspondences for phylogenetic inference of language family trees. However, to date, computational approaches have typically not taken this potential into account. Most computational studies still rely on lexical cognates as major data source for p

Zhenyi Zhang, Shengbo Ma, Zhennan Zhou

We develop an enthalpy-based modeling and computational framework to quantify uncertainty in Stefan problems with an injection boundary. Inspired by airfoil icing studies, we consider a system featuring an injection boundary inducing domain changes and a free boundary separating phases, resulting in two types of moving boundaries. Our proposed enthalpy-based

Fangru Lin, Emanuele La Malfa, Valentin Hofmann, Elle Michelle Yang

Planning is a fundamental property of human intelligence. Reasoning about asynchronous plans is challenging since it requires sequential and parallel planning to optimize time costs. Can large language models (LLMs) succeed at this task? Here, we present the first large-scale study investigating this question. We find that a representative set of closed and

Chuqi Cao, Renjun Duan, Zongguang Li

In this paper, we consider the perturbed solutions with polynomial tail in large velocities for the non-cutoff Boltzmann equation near global Maxwellians in the whole space. The global in time existence is proved in the weighted Sobolev spaces and the almost optimal time decay is obtained in Fourier transform based low-regularity spaces. The result shows a t

Qidong Liu, Xian Wu, Xiangyu Zhao, Yuanshao Zhu

The recommendation of medication is a vital aspect of intelligent healthcare systems, as it involves prescribing the most suitable drugs based on a patient's specific health needs. Unfortunately, many sophisticated models currently in use tend to overlook the nuanced semantics of medical data, while only relying heavily on identities. Furthermore, these mode

Ricardo Martinez, Juan D. Moreno-Ternero

We take an axiomatic approach to study redistribution problems when agents report income and needs. We formalize axioms reflecting ethical and operational principles such as additivity, impartiality and individual rationality. Different combinations of those axioms characterize three focal rules (laissez faire, full redistribution, and need-adjusted full red

Fei Yuan, Chang Ma, Shuai Yuan, Qiushi Sun

The lottery ticket hypothesis posits the existence of ``winning tickets'' within a randomly initialized neural network. Do winning tickets exist for LLMs in fine-tuning scenarios? How can we find such winning tickets? In this paper, we propose KS-Lottery, a method to identify a small subset of LLM parameters highly effective in multilingual fine-tuning. Our