April 2024 arXiv papers — page 25

Rijan Karkee, David A. Strubbe

Transition-metal dichalcogenides (TMDs) show unique physical, optical, and electronic properties. The known phases of TMDs are 2H and 3R in bulk form, 1T and associated reconstructions, and 1H in monolayer form. This paper reports a hypothetical phase, 9R, that may exist in TMDs (Mo, W)(S, Se, Te)$_2$, meeting both dynamical stability and elastic stability c

Kaustubh Agashe, Sagar Airen, Roberto Franceschini, Doojin Kim

We develop the idea that the unprecedented precision in Standard Model (SM) measurements, with further improvement at the HL-LHC, enables new searches for physics Beyond the Standard Model (BSM).As an illustration, we demonstrate that the measured kinematic distributions of the lepton$+MET$ final state not only determine the mass of the $W$ boson, but are al

Johannes Alt, Torben Krüger

We consider $n\times n$ non-Hermitian random matrices with independent entries and a variance profile, as well as an additive deterministic diagonal deformation. We show that their empirical eigenvalue distribution converges to a limiting density as $n$ tends to infinity and that the support of this density in the complex plane exactly coincides with the $\v

Nawaj KC, Andrew J. Soto Levins

We introduce and study a notion of Serre liftable modules; these are modules that are liftable to modules of the maximal possible dimension over a regular local ring. We establish new cases of Serre's positivity conjecture over ramified regular local rings by proving it for Serre liftable modules. Furthermore, we show that the length of a nonzero Serre lifta

Zhengze Xu, Mengting Chen, Zhao Wang, Linyu Xing

Video try-on is a challenging task and has not been well tackled in previous works. The main obstacle lies in preserving the details of the clothing and modeling the coherent motions simultaneously. Faced with those difficulties, we address video try-on by proposing a diffusion-based framework named "Tunnel Try-on." The core idea is excavating a "focus tunne

Koen Alexander, Andrea Bahgat, Avishai Benyamini, Dylan Black

Whilst holding great promise for low noise, ease of operation and networking, useful photonic quantum computing has been precluded by the need for beyond-state-of-the-art components, manufactured by the millions. Here we introduce a manufacturable platform for quantum computing with photons. We benchmark a set of monolithically-integrated silicon photonics-b

Shangzan Zhang, Sida Peng, Tao Xu, Yuanbo Yang

This paper aims to generate materials for 3D meshes from text descriptions. Unlike existing methods that synthesize texture maps, we propose to generate segment-wise procedural material graphs as the appearance representation, which supports high-quality rendering and provides substantial flexibility in editing. Instead of relying on extensive paired data, i

Giuliano Iorio, Stefano Torniamenti, Michela Mapelli, Marco Dall'Amico

Gaia BH3 is the first observed dormant black hole (BH) with a mass of $\approx{30}$ M$_\odot$ and represents the first confirmation that such massive BHs are associated with metal-poor stars. Here, we explore the isolated binary formation channel for Gaia BH3 focusing on the old and metal-poor stellar population of the Milky Way halo. We use the MIST stellar

Rui Shi, John W. Appel, Charles L. Bennett, Ricardo Bustos

Improved polarization measurements at frequencies below 70 GHz with degree-level angular resolution are crucial for advancing our understanding of the Galactic synchrotron radiation and the potential polarized anomalous microwave emission and ultimately benefiting the detection of primordial $B$ modes. In this study, we present sensitivity-improved 40 GHz po

Juan Carlos Hernandez-Bocanegra, Gabriel Villa-Salvador

In this paper we obtain the extended genus field of a finite abelian extension of a global rational function field. We first study the case of a cyclic extension of prime power degree. Next, we use that the extended genus fields of a composite of two cyclotomic extensions of a global rational function field is equal to the composite of their respective exten

Mubashir Noman, Mustansar Fiaz, Hisham Cholakkal

Change detection (CD) is a fundamental task in remote sensing (RS) which aims to detect the semantic changes between the same geographical regions at different time stamps. Existing convolutional neural networks (CNNs) based approaches often struggle to capture long-range dependencies. Whereas recent transformer-based methods are prone to the dominant global

Mohammed Elaroussi, Lhouari Nourine, Simon Vilmin

We study half-space separation in the convexity of chordless paths of a graph, i.e., monophonic convexity. In this problem, one is given a graph and two (disjoint) subsets of vertices and asks whether these two sets can be separated by complementary convex sets, called half-spaces. While it is known this problem is $\mathbf{NP}$-complete for geodesic convexi

Yoonsoo Nam, Nayara Fonseca, Seok Hyeong Lee, Chris Mingard

Deep learning models can exhibit what appears to be a sudden ability to solve a new problem as training time, training data, or model size increases, a phenomenon known as emergence. In this paper, we present a framework where each new ability (a skill) is represented as a basis function. We solve a simple multi-linear model in this skill-basis, finding anal

Junu Lee, Zhimei Ren

The e-BH procedure is an e-value-based multiple testing procedure that provably controls the false discovery rate (FDR) under any dependence structure between the e-values. Despite this appealing theoretical FDR control guarantee, the e-BH procedure often suffers from low power in practice. In this paper, we propose a general framework that boosts the power

Ziyi Liang, Tianmin Xie, Xin Tong, Matteo Sesia

We develop a conformal inference method to construct a joint confidence region for a given group of missing entries within a sparsely observed matrix, focusing primarily on entries from the same column. Our method is model-agnostic and can be combined with any ``black-box'' matrix completion algorithm to provide reliable uncertainty estimation for group-leve

Chenfeng Cao, Yeqing Zhou, Swamit Tannu, Nic Shannon

Variational quantum algorithms (VQAs) represent a promising pathway toward achieving practical quantum advantage on near-term hardware. Despite this promise, for generic, expressive ans\"atze, their scalability is critically hindered by barren plateaus--regimes of exponentially vanishing gradients. We demonstrate that initializing a hardware-efficient, Floqu

ESSnuSB, :, J. Aguilar, M. Anastasopoulos

Neutrino oscillation experiments provide a unique window in exploring several new physics scenarios beyond the standard three flavour. One such scenario is quantum decoherence in neutrino oscillation which tends to destroy the interference pattern of neutrinos reaching the far detector from the source. In this work, we study the decoherence in neutrino oscil

Darren Pereira, Erich J. Mueller

Motivated by discrepancies between recent cold atom experiments and the associated theory, we explore the effect of immobile holes on the quantum dynamics of $x$-$z$ spin helices in the one-dimensional $XX$ model. We calculate the exact spin dynamics by mapping onto a system of non-interacting fermions, averaging over the distribution of holes. At small hole

Emese Tóth, Olivér A. Fekete, Balázs Bánhelyi, Mária Csete

Azimuthal orientation and handedness dependence of the optical responses, accompanied by asymmetric transmission and asymmetric dichroism, were demonstrated on multilayers constructed with subwavelength periodic arrays of Babinet complementary miniarrays, illuminated by linearly and circularly polarized light. In case of single-sided illumination asymmetric

J. van der Duin, R. Loll

Correlation functions are ubiquitous tools in quantum field theory from both a fundamental and a practical point of view. However, up to now their use in theories of quantum gravity beyond perturbative and asymptotically flat regimes has been limited, due to difficulties associated with diffeomorphism invariance and the dynamical nature of geometry. We prese

Martin Lebrat, Anant Kale, Lev Haldar Kendrick, Muqing Xu

Strongly correlated materials feature multiple electronic orbitals which are crucial to accurately understand their many-body properties, from cuprate materials to twisted bilayer graphene. In such multi-band models, quantum interference can lead to dispersionless bands whose large degeneracy gives rise to itinerant magnetism even with weak interactions. Her

Jing Lin, Nina Mylly, Per Olof Hedekvist, Jingchun Shen

This paper proposes the context driven Critical Integrative Levels (CIL), a novel approach to lighting asset management in public libraries that aligns with the transformative vision of human-centric and integrative lighting. This approach encompasses not only the visual aspects of lighting performance but also prioritizes the physiological and psychological

Xunzheng Zhang, Shadi Moazzeni, Juan Marcelo Parra-Ullauri, Reza Nejabati

The increasing concerns of knowledge transfer and data privacy challenge the traditional gather-and-analyse paradigm in networks. Specifically, the intelligent orchestration of Virtual Network Functions (VNFs) requires understanding and profiling the resource consumption. However, profiling all kinds of VNFs is time-consuming. It is important to consider tra

Rémi Uro, David Doukhan, Albert Rilliard, Laëtitia Larcher

This paper presents a semi-automatic approach to create a diachronic corpus of voices balanced for speaker's age, gender, and recording period, according to 32 categories (2 genders, 4 age ranges and 4 recording periods). Corpora were selected at French National Institute of Audiovisual (INA) to obtain at least 30 speakers per category (a total of 960 speake

Salome O. Ighomereho, Ifeoma E. Ezeabasili

Public policy decision making has become more complex and complicated in recent times. Some authors have attributed this to the fact that public policy decision makers now have more variables to consider in every decision more than ever before. Others have argued that the rate of civilization, globalization and information technology has made the public to b

Marc Heinrich, Maximilian Zipfl, Marc Uecker, Sven Ochs

Real world testing is of vital importance to the success of automated driving. While many players in the business design purpose build testing vehicles, we designed and build a modular platform that offers high flexibility for any kind of scenario. CoCar NextGen is equipped with next generation hardware that addresses all future use cases. Its extensive, red

M. D. Afonina, S. B. Popov

At the moment, there are two neutron star X-ray binaries with massive red supergiants as donors. De et al. (2023) proposed that the system SWIFT J0850.8-4219 contains a neutron star at the propeller stage. We study this possibility by applying various models of propeller spin-down. We demonstrate that the duration of the propeller stage is very sensitive to

Artemiy Burov, Oliver Nagl, Clément Javerzac-Galy

While the recent demonstration of accurate computations of classically intractable simulations on noisy quantum processors brings quantum advantage closer, there is still the challenge of demonstrating it for practical problems. Here we investigate the application of noisy intermediate-scale quantum devices for simulating nuclear magnetic resonance (NMR) exp

Salim Janji, Piotr Wawrzyniak, Piotr Formanowicz, Adrian Kliks

The emerging concept of 3D networks, integrating terrestrial, aerial, and space layers, introduces a novel and complex structure characterized by stations relaying backhaul loads through point-to-point wireless links, forming a wireless 3D backhaul mesh. A key challenge is the strategic placement of aerial platform such as drone base stations (DBSs), conside

Stephen Zhao, Rob Brekelmans, Alireza Makhzani, Roger Grosse

Numerous capability and safety techniques of Large Language Models (LLMs), including RLHF, automated red-teaming, prompt engineering, and infilling, can be cast as sampling from an unnormalized target distribution defined by a given reward or potential function over the full sequence. In this work, we leverage the rich toolkit of Sequential Monte Carlo (SMC)

Arianna Crippa, Simone Romiti, Lena Funcke, Karl Jansen

In this paper, we examine a compact $U(1)$ lattice gauge theory in $(2+1)$ dimensions and present a strategy for studying the running coupling and extracting the non-perturbative $\Lambda$-parameter. To this end, we combine Monte Carlo simulations and quantum computing, where the former can be used to determine the numerical value of the lattice spacing $a$,

Christopher Chung, William Jannen, Samuel McCauley, Bertrand Simon

Write-optimized dictionaries are a class of cache-efficient data structures that buffer updates and apply them in batches to optimize the amortized cache misses per update. For example, a B^epsilon tree inserts updates as messages at the root. B^epsilon trees only move ("flush") messages when they have total size close to a cache line, optimizing the amount

Daniel Berg Thomsen, Nikita Doikov

In this work, we study the iteration complexity of gradient methods for minimizing convex quadratic functions regularized by powers of Euclidean norms. We show that, due to the uniform convexity of the objective, gradient methods have improved convergence rates. Thus, for the basic gradient descent with a novel step size, we prove a convergence rate of $O(N^

Takeharu Matsuda, Satoshi Ii

Particle-based methods are a practical tool in computational fluid dynamics, and novel types of methods have been proposed. However, widely developed Lagrangian-type formulations suffer from the nonuniform distribution of particles, which is enhanced over time and result in problems in computational efficiency and parallel computations. To mitigate these pro

Sergio I. Bugosen, Robert B. Parker, Carleton Coffrin

We demonstrate that valid inequalities, or lifted nonlinear cuts (LNC), can be projected to tighten the Second Order Cone (SOC), Convex DistFlow (CDF), and Network Flow (NF) relaxations of the AC Optimal Power Flow (AC-OPF) problem. We conduct experiments on 36 cases from the PGLib-OPF library for two objective functions, (1) power generation maximization an

Ralph M. Kaufmann, Benjamin C. Ward

We prove that the Feynman category encoding Schwarz's variant of modular operads is Koszul. Our proof uses a generalization of the theory of distributive laws to the groupoid colored setting.

Iñigo Robredo, Niels Schröeter, Claudia Felser, Jennifer Cano

The discovery of topological semimetals with multifold band crossings has opened up a new and exciting frontier in the field of topological physics. These materials exhibit large Chern numbers, leading to long double Fermi arcs on their surfaces, which are protected by either crystal symmetries or topological order. The impact of these multifold crossings ex

C. Guevara, J. Stutzki V. Ossenkopf-Okada, U. Graf, Y. Okada

Context. Recent studies of the optical depth comparing [12CII] and [13CII] line profiles in Galactic star-forming regions revealed strong self-absorption in [12CII] by low excitation foreground material, implying a large column density of C+ corresponding to an equivalent AV of a few, up to about 10 mag. Aims. As the nature and origin of such a large column

Ali Shahidikia

In this note, we show that there exist non-unital right artinian rings which are not generalized Rickart. In particular, we provide examples to show that, [16, Corollary 2.31] is not true for non-unital artinian rings.

Anthony W. Hager, Brian Wynne

The property of a vector lattice of sufficiently many projections (SMP) is informed by restricting attention to archimedean $A$ with a distinguished weak order unit $u$ (the class, or category, $\bf{W}$), where the Yosida representation $A \leq D(Y(A,u))$ is available. Here, $A$ SMP is equivalent to $Y(A,u)$ having a $\pi$-base of clopen sets of a certain ty

Camillo De Lellis, Federico Glaudo, Annalisa Massaccesi, Davide Vittone

We consider the following classical conjecture of Besicovitch: a $1$-dimensional Borel set in the plane with finite Hausdorff $1$-dimensional measure $\mathcal{H}^1$ which has lower density strictly larger than $\frac{1}{2}$ almost everywhere must be countably rectifiable. We improve the best known bound, due to Preiss and Ti\v{s}er, showing that the stateme

Imran Nasim, Joaõ Lucas de Sousa Almeida

The recently introduced class of architectures known as Neural Operators has emerged as highly versatile tools applicable to a wide range of tasks in the field of Scientific Machine Learning (SciML), including data representation and forecasting. In this study, we investigate the capabilities of Neural Implicit Flow (NIF), a recently developed mesh-agnostic

Yuhang Huang, Zihan Wu, Chongyang Gao, Jiawei Peng

Large Vision-Language Models (LVLMs) are gaining traction for their remarkable ability to process and integrate visual and textual data. Despite their popularity, the capacity of LVLMs to generate precise, fine-grained textual descriptions has not been fully explored. This study addresses this gap by focusing on \textit{distinctiveness} and \textit{fidelity}

Simone Cecchini, Sven Hirsch, Rudolf Zeidler

We establish new mean curvature rigidity theorems for spin fill-ins with non-negative scalar curvature using two different spinorial techniques. Our results address two questions by Miao and Gromov, respectively. The first technique is based on extending boundary spinors satisfying a generalized eigenvalue equation via the Fredholm alternative for an APS bou

Mohammadsaleh Nikooroo, Juan Estrada-Jimenez, Aurel Machalek, Jerome Harri

New Radio (NR) Vehicle-to-Everything (V2X) Sidelink (SL), an integral part of the 5G NR standard, is expected to revolutionize the automotive and rail industries by enabling direct and low-latency exchange of critical information between traffic participants independently of cellular networks. However, this advancement depends primarily on efficient SL resou

S. Guinchard, W. Sengupta, S. R. Hudson

The Floquet exponents of periodic field lines are studied through the variations of the magnetic action on the magnetic axis, which is assumed to be elliptical. The near-axis formalism developed by Mercier, Solov'ev and Shafranov is combined with a Lagrangian approach. The on-axis Floquet exponent is shown to coincide with the on-axis rotational transform, a

Anahita Jain, Husni R. Idris, John-Paul Clarke

In this paper, we investigate the dynamic emergence of traffic order in a distributed multi-agent system, aiming to minimize inefficiencies that stem from unnecessary structural impositions. We introduce a methodology for developing a dynamically-updating traffic pattern map of the airspace by leveraging information about the consistency and frequency of flo

Nele Köhler, Fabian Neuhaus

In our work, we systematize and analyze implicit ontological commitments in the responses generated by large language models (LLMs), focusing on ChatGPT 3.5 as a case study. We investigate how LLMs, despite having no explicit ontology, exhibit implicit ontological categorizations that are reflected in the texts they generate. The paper proposes an approach t

Coline Emprin

We develop a general obstruction theory to the formality of algebraic structures over any commutative ground ring. It relies on the construction of Kaledin obstruction classes that faithfully detect the formality of differential graded algebras over operads or properads, possibly colored in groupoids. The present treatment generalizes the previous obstructio

Tianqi Liu, Xinyi Ye, Min Shi, Zihao Huang

Generalizable NeRF aims to synthesize novel views for unseen scenes. Common practices involve constructing variance-based cost volumes for geometry reconstruction and encoding 3D descriptors for decoding novel views. However, existing methods show limited generalization ability in challenging conditions due to inaccurate geometry, sub-optimal descriptors, an

Florin Boenkost, Julie Tourniaire

We consider a one-dimensional dyadic branching Brownian motion on $\mathbb{R}$ with positive drift $\beta \in (0,1)$, branching rate $1/2$, reflected at $0$ and killed at a boundary $L > 0$. The killing boundary $L$ is chosen so that the total population size remains approximately constant, proportional to $N \in \mathbb{N}$. This branching process models a

Fatemeh Arbabifar, Nader Morshedian, Leila Ghasemzadeh, Shahin Atashbar Tehrani

This paper provides a thorough examination of the $xF_3$ structure functions in deep-inelastic scattering through a comprehensive QCD analysis. Our approach harnesses sophisticated mathematical techniques, namely the Mellin transform combined with Gegenbauer polynomials. We have employed the Jacobi polynomials approach for analysis, conducting investigations

Yayati Jadhav, Amir Barati Farimani

Conventional mechanical design follows an iterative process in which initial concepts are refined through cycles of expert assessment and resource-intensive Finite Element Method (FEM) analysis to meet performance goals. While machine learning models have been developed to assist in parts of this process, they typically require large datasets, extensive trai

Luis Miguel Vieira da Silva, Aljosha Köcher, Felix Gehlhoff, Alexander Fay

Capability ontologies are increasingly used to model functionalities of systems or machines. The creation of such ontological models with all properties and constraints of capabilities is very complex and can only be done by ontology experts. However, Large Language Models (LLMs) have shown that they can generate machine-interpretable models from natural lan

Jennifer Scora, Diana Valencia, Alessandro Morbidelli, Seth Jacobson

Mercury is notoriously difficult to form in solar system simulations, due to its small mass and iron-rich composition. Smooth particle hydrodynamics simulations of collisions have found that a Mercury-like body could be formed by one or multiple giant impacts, but due to the chaotic nature of collisions it is difficult to create a scenario where such impacts

Shabnam Hassani

This research explores the application of Large Language Models (LLMs) for automating the extraction of requirement-related legal content in the food safety domain and checking legal compliance of regulatory artifacts. With Industry 4.0 revolutionizing the food industry and with the General Data Protection Regulation (GDPR) reshaping privacy policies and dat

Puhao Li, Tengyu Liu, Yuyang Li, Muzhi Han

Autonomous robotic systems capable of learning novel manipulation tasks are poised to transform industries from manufacturing to service automation. However, modern methods (e.g., VIP and R3M) still face significant hurdles, notably the domain gap among robotic embodiments and the sparsity of successful task executions within specific action spaces, resultin

Haicheng Liao, Zhenning Li, Chengyue Wang, Bonan Wang

As autonomous driving technology progresses, the need for precise trajectory prediction models becomes paramount. This paper introduces an innovative model that infuses cognitive insights into trajectory prediction, focusing on perceived safety and dynamic decision-making. Distinct from traditional approaches, our model excels in analyzing interactions and b

Thomas Le Menestrel, Erin Craig, Robert Tibshirani, Trevor Hastie

Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals, underscoring a critical gap in genetic research. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data. We evaluate the performance of Group-

Yingyao Zhou, Natasha Devroye, Gyorgy Turan, Milos Zefran

Deep learning methods have recently been used to construct non-linear codes for the additive white Gaussian noise (AWGN) channel with feedback. However, there is limited understanding of how these black-box-like codes with many learned parameters use feedback. This study aims to uncover the fundamental principles underlying the first deep-learned feedback co

Eckhard Meinrenken, Selim Tawfik

The notion of quasi-Poisson $G$-spaces with $D/G$-valued moment maps was introduced by Alekseev and Kosmann-Schwarzbach in 1999. Our main result is a \emph{Lifting Theorem}, establishing a bijective correspondence between the categories of quasi-Poisson $G$-spaces with $D/G$-valued moment maps and of quasi-Poisson $G\times G$-spaces with $D$-valued moment ma

O. Kochukhov

Global magnetic fields of early-type stars are commonly characterised by the mean longitudinal magnetic field $\langle B_{\rm z} \rangle$ and the mean field modulus $\langle B \rangle$, derived from the circular polarisation and intensity spectra, respectively. Observational studies often report a root mean square (rms) of $\langle B_{\rm z} \rangle$ and an

Su Gao, Ming Xiao

In this paper we first consider hyperfinite Borel equivalence relations with a pair of Borel $\mathbb{Z}$-orderings. We define a notion of compatibility between such pairs, and prove a dichotomy theorem which characterizes exactly when a pair of Borel $\mathbb{Z}$-orderings are compatible with each other. We show that, if a pair of Borel $\mathbb{Z}$-orderin

Tyler Barna, Brandon Reed, Igor Andreoni, Michael W. Coughlin

The identification of extragalactic fast optical transients (eFOTs) as potential multi-messenger sources is one of the main challenges in time-domain astronomy. However, recent developments have allowed for probes of rapidly-evolving transients. With the increasing number of alert streams from optical time-domain surveys, the next paradigm is building techno

Jackson Philbrook, Benjamin Peet

This paper investigates the impossibility of certain $({n^2+n+k}_{n+1})$ configurations. Firstly, for $k=2$, the result of \cite{gropp1992non} that $\frac{n^2+n}{2}$ is even and $n+1$ is a perfect square or $\frac{n^2+n}{2}$ is odd and $n-1$ is a perfect square is reproved using the incidence matrix $N$ and analysing the form of $N^TN$. Then, for all $k$, co

Zhengwei Tao, Zhi Jin, Yifan Zhang, Xiancai Chen

Event reasoning is a fundamental ability that underlies many applications. It requires event schema knowledge to perform global reasoning and needs to deal with the diversity of the inter-event relations and the reasoning paradigms. How well LLMs accomplish event reasoning on various relations and reasoning paradigms remains unknown. To mitigate this dispari

Andrew Campbell, Giorgio Cipolloni, László Erdős, Hong Chang Ji

For general non-Hermitian random matrices $X$ and deterministic deformation matrices $A$, we prove that the local eigenvalue statistics of $A+X$ close to the typical edge points of its spectrum are universal. Furthermore, we show that under natural assumptions on $A$ the spectrum of $A+X$ does not have outliers at a distance larger than the natural fluctuati

Duna Zhan, Dongliang Guo, Pengsheng Ji, Sheng Li

Graph neural networks (GNNs) have emerged as a powerful tool for analyzing and learning from complex data structured as graphs, demonstrating remarkable effectiveness in various applications, such as social network analysis, recommendation systems, and drug discovery. However, despite their impressive performance, the fairness problem has increasingly gained

Qingtian Miao, G. S. Agarwal

Nonlinearity induced nonreciprocity is studied in a system comprising two resonators coupled to a one-dimensional waveguide when the linear system does not exhibit nonreciprocity. The analysis is based on the Hamiltonian of the coupled system and includes the dissipative coupling between the waveguide and resonators, along with the input-output relations. We

Nairen Cao, Vincent Cohen-Addad, Euiwoong Lee, Shi Li

In the classic Correlation Clustering problem introduced by Bansal, Blum, and Chawla (FOCS 2002), the input is a complete graph where edges are labeled either $+$ or $-$, and the goal is to find a partition of the vertices that minimizes the sum of the +edges across parts plus the sum of the -edges within parts. In recent years, Chawla, Makarychev, Schramm a

Dorian Florescu, Matthew England

We present a new methodology for utilising machine learning technology in symbolic computation research. We explain how a well known human-designed heuristic to make the choice of variable ordering in cylindrical algebraic decomposition may be represented as a constrained neural network. This allows us to then use machine learning methods to further optimise

Wonjae Kim, Sanghyuk Chun, Taekyung Kim, Dongyoon Han

In an era where the volume of data drives the effectiveness of self-supervised learning, the specificity and clarity of data semantics play a crucial role in model training. Addressing this, we introduce HYPerbolic Entailment filtering (HYPE), a novel methodology designed to meticulously extract modality-wise meaningful and well-aligned data from extensive,

Pierluigi Colli, Gabriela Marinoschi, Elisabetta Rocca, Alex Viguerie

Partial differential equation (PDE) models for infectious disease have received renewed interest in recent years. Most models of this type extend classical compartmental formulations with additional terms accounting for spatial dynamics, with Fickian diffusion being the most common such term. However, while diffusion may be appropriate for modeling vector-bo

D. Okuyama, M. Bleuel, Q. Ye, J. Krzywon

Lattice formation of swirling textures is ubiquitous in solid-state materials, such as a magnetic skyrmion lattice in chiral magnets. In the magnetic skyrmion lattices, their moving states and dynamics under external perturbations are still unrevealed, although a detailed understanding of the dynamics is crucial to realizing spintronic applications, such as

Abhishek Samlodia, Vamika Longia, Raghav G. Jha, Anosh Joseph

We use the higher-order tensor renormalization group method to study the two-dimensional generalized XY model that admits integer and half-integer vortices. This model is the deformation of the classical XY model and has a rich phase structure consisting of nematic, ferromagnetic, and disordered phases and three transition lines belonging to the Berezinskii-

Libang Chen, Jinyan Lin, Qihang Bian, Yikun Liu

Imaging through dense fog presents unique challenges, with essential visual information crucial for applications like object detection and recognition obscured, thereby hindering conventional image processing methods. Despite improvements through neural network-based approaches, these techniques falter under extremely low visibility conditions exacerbated by

Gabriel Bathie, Panagiotis Charalampopoulos, Tatiana Starikovskaya

In this work, we consider pattern matching variants in small space, that is, in the read-only setting, where we want to bound the space usage on top of storing the strings. Our main contribution is a space-time trade-off for the Internal Pattern Matching (IPM) problem, where the goal is to construct a data structure over a string $S$ of length $n$ that allow

Georg Angehrn, Henry F. Legg, Daniel Loss, Jelena Klinovaja

Nonreciprocal transport effects can occur in the normal state of conductors and in superconductors when both inversion and time-reversal symmetry are broken. Here, we consider systems where magnetochiral anisotropy (MCA) of the energy spectrum due to an externally applied magnetic field results in a rectification effect in the normal state and a superconduct

Eddaly Guerra-Velasco, Boris Percino-Figueroa, Russell-Aarón Quiñones-Estrella

In this article, we show that the $N$-center problem with rational weak and moderate forces is not rationally integrable for all but a finite number of values $\alpha\in(0,2)\cap \mathbb{Q}$, where $\alpha$ is the order of the singularities. We identify the remaining cases and provide the necessary conditions for their integrability.

Theodora-Augustina Drăgan, Akshat Tandon, Carsten Strobel, Jasper Simon Krauser

Quantum machine learning (QML) as combination of quantum computing with machine learning (ML) is a promising direction to explore, in particular due to the advances in realizing quantum computers and the hoped-for quantum advantage. A field within QML that is only little approached is quantum multi-agent reinforcement learning (QMARL), despite having shown t

Lucas Ventura, Cordelia Schmid, Gül Varol

We describe a protocol to study text-to-video retrieval training with unlabeled videos, where we assume (i) no access to labels for any videos, i.e., no access to the set of ground-truth captions, but (ii) access to labeled images in the form of text. Using image expert models is a realistic scenario given that annotating images is cheaper therefore scalable

Atharva Naik

The task of code generation from natural language (NL2Code) has become extremely popular, especially with the advent of Large Language Models (LLMs). However, efforts to quantify and track this progress have suffered due to a lack of reliable metrics for functional correctness. While popular benchmarks like HumanEval have test cases to enable reliable evalua

Adrien Teurtrie, Nathanaël Perraudin, Thomas Holvoet, Hui Chen

We present the development of a new algorithm which combines state-of-the-art energy-dispersive X-ray (EDX) spectroscopy theory and a suitable machine learning formulation for the hyperspectral unmixing of scanning transmission electron microscope EDX spectrum images. The algorithm is based on non-negative matrix factorization (NMF) incorporating a physics-g

Marco Rando, Martin James, Alessandro Verri, Lorenzo Rosasco

We consider the problem of olfactory searches in a turbulent environment. We focus on agents that respond solely to odor stimuli, with no access to spatial perception nor prior information about the odor. We ask whether navigation to a target can be learned robustly within a sequential decision making framework. We develop a reinforcement learning algorithm

Abigail Burrows, Samuel Halverson, Jared C. Siegel, Christian Gilbertson

We revisit the long-studied radial velocity (RV) target HD26965 using recent observations from the NASA-NSF 'NEID' precision Doppler facility. Leveraging a suite of classical activity indicators, combined with line-by-line RV analyses, we demonstrate that the claimed 45-day signal previously identified as a planet candidate is most likely an activity-induced

Fabio Massimo Zennaro, Nicholas Bishop, Joel Dyer, Yorgos Felekis

Multi-armed bandits (MAB) and causal MABs (CMAB) are established frameworks for decision-making problems. The majority of prior work typically studies and solves individual MAB and CMAB in isolation for a given problem and associated data. However, decision-makers are often faced with multiple related problems and multi-scale observations where joint formula

Robert O Shea, Prabodh Katti, Bipin Rajendran

Common artefacts such as baseline drift, rescaling, and noise critically limit the performance of machine learningbased automated ECG analysis and interpretation. This study proposes Derived Peak (DP) encoding, a non-parametric method that generates signed spikes corresponding to zero crossings of the signals first and second-order time derivatives. Notably,

Yuya Uezato

Backreferences and lookaheads are vital features to make classical regular expressions (REGEX) practical. Although these features have been widely used, understanding of the unrestricted combination of them has been limited. Practically, most likely no implementation fully supports them. Theoretically, while some studies have addressed these features separat

Alfredo Alegría, Xavier Emery, Tobia Filosi, Emilio Porcu

This work addresses the problem of simulating Gaussian random fields that are continuously indexed over a class of metric graphs, termed graphs with Euclidean edges, being more general and flexible than linear networks. We introduce three general algorithms that allow to reconstruct a wide spectrum of random fields having a covariance function that depends o

Richard F. Lyon, Rob Schonberger, Malcolm Slaney, Mihajlo Velimirović

The open-source CARFAC (Cascade of Asymmetric Resonators with Fast-Acting Compression) cochlear model is upgraded to version 2, with improvements to the Matlab implementation, and with new Python/NumPy and JAX implementations -- but C++ version changes are still pending. One change addresses the DC (direct current, or zero frequency) quadratic distortion ano

Wei Cui, Rasa Hosseinzadeh, Junwei Ma, Tongzi Wu

Contrastive learning is a model pre-training technique by first creating similar views of the original data, and then encouraging the data and its corresponding views to be close in the embedding space. Contrastive learning has witnessed success in image and natural language data, thanks to the domain-specific augmentation techniques that are both intuitive

Danja Brandt, Martin Tschaikner, Teodor Chiaburu, Henning Schmidt

Preserving the number and diversity of insects is one of our society's most important goals in the area of environmental sustainability. A prerequisite for this is a systematic and up-scaled monitoring in order to detect correlations and identify countermeasures. Therefore, automatized monitoring using live traps is important, but so far there is no system t

Shayan Kiyani, George Pappas, Hamed Hassani

In this paper, we focus on the problem of conformal prediction with conditional guarantees. Prior work has shown that it is impossible to construct nontrivial prediction sets with full conditional coverage guarantees. A wealth of research has considered relaxations of full conditional guarantees, relying on some predefined uncertainty structures. Departing f

Hengfei Wang, Zhongqun Zhang, Yihua Cheng, Hyung Jin Chang

Generating face image with specific gaze information has attracted considerable attention. Existing approaches typically input gaze values directly for face generation, which is unnatural and requires annotated gaze datasets for training, thereby limiting its application. In this paper, we present a novel gaze-controllable face generation task. Our approach

Tianyu Zhang, Chuanyu Xue, Jiachen Wang, Zelin Yun

Industrial Internet of Things (IIoT) technologies have revolutionized industrial processes, enabling smart automation, real-time data analytics, and improved operational efficiency across diverse industry sectors. IIoT testbeds play a critical role in advancing IIoT research and development (R&D) to provide controlled environments for technology evaluation b

Zhenghong Li, Jiaxiang Ren, Wensheng Cheng, Yanzuo Liu

Optical coherence Doppler tomography (ODT) is an emerging blood flow imaging technique. The fundamental unit of ODT is the 1D depth-resolved trace named raw A-scans (or A-line). A 2D ODT image (B-scan) is formed by reconstructing a cross-sectional flow image via Doppler phase-subtraction of raw A-scans along B-line. To obtain a high-fidelity B-scan, densely

Yoichi Chikahara, Kansei Ushiyama

There is a growing interest in estimating heterogeneous treatment effects across individuals using their high-dimensional feature attributes. Achieving high performance in such high-dimensional heterogeneous treatment effect estimation is challenging because in this setup, it is usual that some features induce sample selection bias while others do not but ar

Gilberto P. Alcântara Junior, Gustavo H. A. Pereira

Logistic regression is widely used in many areas of knowledge. Several works compare the performance of lasso and maximum likelihood estimation in logistic regression. However, part of these works do not perform simulation studies and the remaining ones do not consider scenarios in which the ratio of the number of covariates to sample size is high. In this w

Tyler Loakman, Chenghua Lin

This paper presents a partial reproduction of Generating Fact Checking Explanations by Anatanasova et al (2020) as part of the ReproHum element of the ReproNLP shared task to reproduce the findings of NLP research regarding human evaluation. This shared task aims to investigate the extent to which NLP as a field is becoming more or less reproducible over tim

Alessandro Carosi, Alicia López-Oramas

The development of the latest generation of Imaging Atmospheric Cherenkov Telescopes (IACTs) over recent decades has led to the discovery of new extreme astrophysical phenomena in the very-high-energy (VHE, E > 100 GeV) gamma-ray regime. Time-domain and multi-messenger astronomy are inevitably connected to the physics of transient VHE emitters, which show un