October 2024 arXiv papers — page 63

Aimilios Hadjiliasi, Louis Nisiotis, Irene Polycarpou

As educational technology evolves, the potential of Pedagogical Agents (PAs) in supporting education is extensively explored. Typically, research on PAs has primarily focused on computer-based learning environments, but their use in VR-based environments and integration into education is still in its infancy. To address this gap, this paper presents a mixed

Christian Klein, Christof Sparber

We consider nonlinear dispersive equations of Schr\"odinger-type involving fractional powers $0<s\le 1$ of the Laplacian and a defocusing power-law nonlinearity. We conduct numerical simulations in the case of small, energy supercritical $s$ and provide evidence for a novel type of highly oscillatory singularity within the solution.

Fenton Clawson, Edward B. Flagg

A new solver, named FLiMESolve, is exhibited which is well suited for time-periodic (Floquet-type) Hamiltonians. This solver maintains the benefits of other solvers presently considered, while superceding their usefulness within its own regime. FLiMESolve is able to efficiently and accurately simulate time-periodic systems while being faster and more accurat

Ziemowit Domański

The existence of a minimum measurable length scale was suggested by various theories of quantum gravity, string theory and black hole physics. Motivated by this, we examine a quantum theory exhibiting a minimum measurable time scale. We use the Page-Wootters formalism to describe time evolution of a quantum system with the modified commutation relations betw

Konstantin V. Pinigin

Lipid membranes, fundamental to cellular function, undergo various mechanical deformations. Accurate modeling of these processes necessitates a thorough understanding of membrane elasticity. The lateral shear modulus, a critical parameter describing membrane resistance to lateral stresses, remains elusive due to the membrane's fluid nature. Two contrasting h

Son Ho, Guillaume Boisseau, Lucas Franceschino, Yoann Prak

With the explosion in popularity of the Rust programming language, a wealth of tools have recently been developed to analyze, verify, and test Rust programs. Alas, the Rust ecosystem remains relatively young, meaning that every one of these tools has had to re-implement difficult, time-consuming machinery to interface with the Rust compiler and its cargo bui

Min Liu, Yen Joe Tan

Machine-learning-based phase pickers have been successfully leveraged to build high-resolution earthquake catalogs using seismic data on land. However, their performance when applied to ocean bottom seismic (OBS) data remains to be evaluated. In this study, we first adopt three machine-learning-based phase pickers - EQTransformer, Pickblue, and OBSTansformer

Anna Glazkova, Dmitry Morozov, Timur Garipov

Keyphrase selection is a challenging task in natural language processing that has a wide range of applications. Adapting existing supervised and unsupervised solutions for the Russian language faces several limitations due to the rich morphology of Russian and the limited number of training datasets available. Recent studies conducted on English texts show t

James M. Sullivan, Shi-Fan Chen

Local primordial non-Gaussianity (LPNG) couples long-wavelength cosmological fluctuations to the short-wavelength behavior of galaxies. This coupling is encoded in bias parameters including $b_{\phi}$ and $b_{\delta\phi}$ at linear and quadratic order in the large-scale biasing framework. We perform the first field-level measurement of $b_{\phi}$ and $b_{\de

Aditya K Kamath, Ramya Prabhu, Jayashree Mohan, Simon Peter

Each request in LLM inference goes through two phases: compute-bound prefill and memory-bandwidth-bound decode. To improve GPU utilization, recent systems use hybrid batching that combines the prefill and decode phases of different requests into the same batch. This approach optimizes linear operations but remains inefficient for attention computation becaus

Hilel Hagai Diamandi, Yizhi Luo, David Mason, Tevfik Bulent Kanmaz

High-fidelity quantum optomechanical control of a mechanical oscillator requires the ability to perform efficient, low-noise operations on long-lived phononic excitations. Microfabricated high-overtone bulk acoustic wave resonators ($\mathrm{\mu}$HBARs) have been shown to support high-frequency (> 10 GHz) mechanical modes with exceptionally long coherence ti

Mattia Trama, Irene Gaiardoni, Claudio Guarcello, Jorge I. Facio

In altermagnets, time-reversal symmetry breaking spin-polarizes electronic states, while total magnetization remains zero. In addition, at altermagnetic surfaces Rashba-spin orbit coupling is activated due to broken inversion symmetry, introducing a competing spin-momentum locking interaction. Here we show that their interplay leads to the formation of compl

Shashwat Gupta, L. Gokulnath, Akshan Aggarwal, Mahim Naz

Differentiating between Intestinal Tuberculosis (ITB) and Crohn's Disease (CD) poses a significant clinical challenge due to their similar symptoms, clinical presentations, and imaging features. This study leverages Computed Tomography Enterography (CTE) scans, deep learning, and traditional computer vision to address this diagnostic dilemma. A consensus amo

Jingfan Zhang, Yi Zhao, Dan Chen, Xing Tian

Low-rank adaptation (LoRA) and its mixture-of-experts (MOE) variants are highly effective parameter-efficient fine-tuning (PEFT) methods. However, they introduce significant latency in multi-tenant settings due to the LoRA modules and MOE routers added to multiple linear modules in the Transformer layer. To address this issue, we propose Mixture of Low-Rank

Baptiste Rognerud

There are three classical lattices on the Catalan numbers: the Tamari lattice, the lattice of noncrossing partitions and the lattice of Dyck paths. The first is known to be isomorphic to the lattice of torsion classes of the path algebra of an equioriented quiver of type $A$ and the second is known to be isomorphic to its lattice of wide subcategories. Inspi

Ilaria Pascucci, Tracy L. Beck, Sylvie Cabrit, Naman S. Bajaj

Radially extended disk winds could be the key to unlocking how protoplanetary disks accrete and how planets form and migrate. A distinctive characteristic is their nested morphology of velocity and chemistry. Here we report JWST/NIRSpec spectro-imaging of four young stars with edge-on disks in the Taurus star-forming region that demonstrate the ubiquity of t

Xin Li, Qizhi Chu, Yubin Chen, Yang Liu

Graphs are widely used for modeling relational data in real-world scenarios, such as social networks and urban computing. Existing LLM-based graph analysis approaches either integrate graph neural networks (GNNs) for specific machine learning tasks, limiting their transferability, or rely solely on LLMs' internal reasoning ability, resulting in suboptimal pe

Rachid Nouicer

First results from the sPHENIX experiment on the $\pi^0$ ${\rm v_{_{2}}}$ and $dE_T/d\eta$ in Au+Au collisions at $\sqrt{s_{_{NN}}}$ = 200 GeV using detector commissioning data during the RHIC 2023 Run are presented. These results are shown across a large centrality range, and compared to previous PHENIX and STAR results. These measurements demonstrate the s

Joseph M. Monti, Ishan Srivastava, Leonardo E. Silbert, A. P. Santos

We perform a structural analysis of large scale jammed packings of monodisperse, frictionless and frictional spheres to elucidate structural signatures of the static structure factor in the low-to-intermediate wavenumber ($q$) region. We employ discrete element method simulations containing up to eighty million particles, in which the particle friction coeff

Jean-François Lafont, Nicholas Miller, Lorenzo Ruffoni

We show that there are infinitely many homeomorphism types of atoroidal surface bundles over surfaces which have signature zero.

Rovan F. Lopes, Milton A. Tumelero, Clodoaldo I. L. de Araujo, Antonio M. H. de Andrade

The magnetic textures generated by a perpendicularly applied magnetic field at the ferromagnetic layer of $Co/Al_{2}O_{3}/Nb$ thin film heterostructures are investigated using magneto-optical imaging and micromagnetic simulations. It is observed that the stray field caused by flux avalanches in the superconducting layer prints out a non-trivial in-plane text

Jiwoo Hong, Noah Lee, Rodrigo Martínez-Castaño, César Rodríguez

Reinforcement learning with human feedback (RLHF) is shown to largely benefit from precise reward models (RMs). However, recent studies in reward modeling schemes are skewed towards English, limiting the applicability of RLHF in multilingual alignments. In this work, we investigate the cross-lingual transfer of RMs trained in diverse languages, primarily fro

Jamie Portsmouth, Peter Kutz, Stephen Hill

We introduce the "Energy-preserving Oren--Nayar" (EON) model for reflection from rough surfaces. Unlike the popular qualitative Oren--Nayar model (QON) and its variants, our model is energy-preserving via analytical energy compensation. We include self-contained GLSL source code for efficient evaluation of the new model and importance sampling based on a nov

Lorenzo Cavicchi, Koen J. A. Reijnders, Mikhail I. Katsnelson, Marco Polini

In the long-wavelength limit, Bloch-band Berry curvature has no effect on the bulk plasmons of a two-dimensional electron system. In this Letter we show instead that bulk plasmons are a probe of real-space topology. In particular, we focus on orbital Skyrme textures in twisted transition metal dichalcogenides, presenting detailed semiclassical and quantum me

Adittya Chaudhuri, Ralf Köhl, Olaf Wolkenhauer

The complexity of molecular and cellular processes forces experimental studies to focus on subsystems. To study the functioning of biological systems across levels of structural and functional organisation, we require tools to compose and organise networks with different levels of detail and abstraction. Systems Biology Graphical Notation (SBGN) is a standar

Jayasmita Behera, Salil Bedkihal, Bijay Kumar Agarwalla, Malay Bandyopadhyay

We investigate the thermoelectric performance of minimally nonlinear irreversible heat engines with broken time-reversal symmetry (TRS), realized through voltage and voltage-temperature probe configurations. Our framework extends the Onsager relations by incorporating a nonlinear power dissipation term into the heat current. We derive and analyze analytical

Radosław Grabarczyk

For particles decaying without parity violation, it is impossible to reconstruct the full spin-density matrix from the velocities of their decay products. In this work, we consider Bell inequalities based on squares of spin operators. The corresponding Bell operators probe only the part of the density matrix that can be reconstructed from any gauge boson dec

Wen Su, Kin-Yat Liu, Guosheng Yin, Jian Huang

We propose a novel deep learning approach to nonparametric statistical inference for the conditional hazard function of survival time with right-censored data. We use a deep neural network (DNN) to approximate the logarithm of a conditional hazard function given covariates and obtain a DNN likelihood-based estimator of the conditional hazard function. Such a

Manuel Núñez-Regueiro, Thibaut Devillers, Eric Beaugnon, Armand de Marles

It has been claimed that graphite hosts superconductivity at room temperature, although all efforts to isolate it have been vain. Here we report a separation method that uses magnetic field gradients to sort the superconducting from normal grains out of industrial graphite powders. We have obtained a concentrate of above room temperature superconducting part

Enrico Trizio, Andrea Rizzi, Pablo M. Piaggi, Michele Invernizzi

Many biological processes occur on time scales longer than those accessible to molecular dynamics simulations. Identifying collective variables (CVs) and introducing an external potential to accelerate them is a popular approach to address this problem. In particular, $\texttt{PLUMED}$ is a community-developed library that implements several methods for CV-b

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

This paper presents the first measurement of $\psi{(2S)}$ and $\chi_{c1}(3872)$ meson production within fully reconstructed jets. Each quarkonium state (tag) is reconstructed via its decay to the $J/\psi$($\rightarrow\mu^+\mu^-$)$\pi^+\pi^-$ final state in the forward region using proton-proton collision data collected by the LHCb experiment at the center-of

Yu-Chen Chao, Shreyas Gokhale, Lisa Lin, Alasdair Hastewell

Emergent nonreciprocity in active matter drives the formation of self-organized states that transcend the behaviors of equilibrium systems. Integrating experiments, theory and simulations, we demonstrate that active solids composed of living starfish embryos spontaneously transition between stable fluctuating and oscillatory steady states. The nonequilibrium

Xun-Jiang Luo, Jia-Zheng Li, Meng Xiao, Fengcheng Wu

We construct a family of chiral symmetry-protected third-order topological insulators by stacking Su-Schrieffer-Heeger (SSH) chains and provide a unified topological characterization by a series of Bott indices. Our approach is informed by the analytical solution of corner states for the model Hamiltonians written as a summation of the extended SSH model alo

Xun-Jiang Luo, Fengcheng Wu

In a class of systems, there are gapped boundary-localized states described by a boundary Hamiltonian. The topological classification of gapped boundary Hamiltonians, same as the standard tenfold way for gapped bulk states, can lead to the emergence of boundary topological insulators (TIs) and superconductors (TSCs). In this work, we present a theoretical st

Leonardo de la Cruz, David A. Kosower, Pavel P. Novichkov

We investigate a geometric approach to determining the complete set of numerators giving rise to finite Feynman integrals. Our approach proceeds graph by graph, and makes use of the Newton polytope associated to the integral's Symanzik polynomials. It relies on a theorem by Berkesch, Forsg{\aa}rd, and Passare on the convergence of Euler--Mellin integrals, wh

Shyamgopal Karthik, Huseyin Coskun, Zeynep Akata, Sergey Tulyakov

Direct Preference Optimization (DPO) has emerged as a powerful approach to align text-to-image (T2I) models with human feedback. Unfortunately, successful application of DPO to T2I models requires a huge amount of resources to collect and label large-scale datasets, e.g., millions of generated paired images annotated with human preferences. In addition, thes

Sungil Seok, Shuide Wen, Qiyuan Yang, Juan Feng

The Federal Funds rate in the United States plays a significant role in both domestic and international financial markets. However, research has predominantly focused on the effects of adjustments to the Federal Funds rate rather than on the decision-making process itself. Recent advancements in large language models(LLMs) offer a potential method for recons

Kevin P. O'Keeffe

The global stability of oscillator networks has attracted much recent attention. Ordinarily, the oscillators in such studies are motionless; their spatial degrees of freedom are either ignored (e.g. mean field models) or inactive (e.g geometrically embedded networks like lattices). Yet many real-world oscillators are mobile, moving around in space as they sy

Alessandro Pacco, Alberto Rosso, Valentina Ros

We compute the distribution of triplets of stationary points in the energy landscape of the spherical p-spin model, by evaluating the quenched three-point complexity by means of the Kac-Rice formalism. We show the occurrence of transitions in the organization of stationary points in the landscape, identifying regions where local minima and saddles accumulate

Rajni Chahal, Luke D Gibson, Santanu Roy, Vyacheslav S Bryantsev

Molten salts are promising candidates in numerous clean energy applications, where challenges in experimental methods limit knowledge of their safety-critical temperature-properties correlations. Herein, we developed and employed machine learning interatomic potentials (MLIP) to study AlCl3 molten salt across varied thermodynamic conditions. The MLIP accurat

Maria Chiara Brambilla, Olivia Dumitrescu, Elisa Postinghel, Luis José Santana Sánchez

We study the birational geometry of $X^n_s$, the blow-up of $\mathbb{P}^n_\mathbb{C}$ at $s$ points in general position. We identify a set of subvarieties, which we call Weyl $r$-planes, that belong to an orbit for the action of the Weyl group on $r$-cycles. They satisfy the following properties: they appear as stable base locus of divisors; each Weyl $r$-pl

Zihan Wang, Mengran Li, Ronghui Zhang, Jing Zhao

With the development of intelligent connected vehicle technology, human-machine shared control has gained popularity in vehicle following due to its effectiveness in driver assistance. However, traditional vehicle following systems struggle to maintain stability when driver reaction time fluctuates, as these variations require different levels of system inte

Martha Kalina, Tom Schneider, Haim Waisman, Markus Kästner

Fatigue fracture is one of the main causes of failure in structures. However, the simulation of fatigue crack growth is computationally demanding due to the large number of load cycles involved. Metals in the low cycle fatigue range often show significant plastic zones at the crack tip, calling for elastic-plastic material models, which increase the computat

Gabriel Rioux, Ziv Goldfeld, Kengo Kato

The Gromov-Wasserstein (GW) distance enables comparing metric measure spaces based solely on their internal structure, making it invariant to isomorphic transformations. This property is particularly useful for comparing datasets that naturally admit isomorphic representations, such as unlabelled graphs or objects embedded in space. However, apart from the r

Longxiu Huang, Dongyang Li, Sui Tang, Qing Yao

In this work, we investigate the sampling and reconstruction of spectrally $s$-sparse bandlimited graph signals governed by heat diffusion processes. We propose a random space-time sampling regime, referred to as {randomized} dynamical sampling, where a small subset of space-time nodes is randomly selected at each time step based on a probability distributio

Giovanni Mauro, Nicola Pedreschi, Renaud Lambiotte, Luca Pappalardo

The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups -- low, middle, and high -- driven by living costs. The m

Elise Özalp, Luca Magri

The data-driven learning of solutions of partial differential equations can be based on a divide-and-conquer strategy. First, the high dimensional data is compressed to a latent space with an autoencoder; and, second, the temporal dynamics are inferred on the latent space with a form of recurrent neural network. In chaotic systems and turbulence, convolution

Zifan Zhang, Zhiyuan Peng, Hanzhi Yu, Mingzhe Chen

Digital network twins (DNTs), by representing a physical network using a virtual model, offer significant benefits such as streamlined network development, enhanced productivity, and cost reduction for next-generation (nextG) communication infrastructure. Existing works mainly describe the deployment of DNT technologies in various service sections.The full l

Suho Kang, Jungyang Park, Joonseo Ha, SoMin Kim

Foundation models (FMs) have achieved significant success across various tasks, leading to research on benchmarks for reasoning abilities. However, there is a lack of studies on FMs performance in exceptional scenarios, which we define as out-of-distribution (OOD) reasoning tasks. This paper is the first to address these cases, developing a novel dataset for

Vincent Chambouleyron, J. Kent Wallace, Rebecca Jensen-Clem, Bruce Macintosh

A crucial component of the high-contrast instrumental chain in astronomy is the wavefront sensor (WFS). A key property of this component is its sensitivities, which reflect its ability to efficiently use incoming photons to encode the phase aberrations. This paper introduces a new class of highly sensitive wavefront sensors that approach the fundamental sens

Xuan Mao, He-Yang Liu, Song Wang, Zhixing Ling

The Lobster Eye Imager for Astronomy (LEIA) detected a new X-ray transient on 2022 November 7, identified as a superflare event occurring on a nearby K-type giant star HD 251108. The flux increase was also detected in follow-up observations at X-ray, UV and optical wavelengths. The flare lasted for about 40 days in soft X-ray observations, reaching a peak lu

Yanshi Li, Shaopan Xiong, Gengru Chen, Xiaoyang Li

Reinforcement Learning (RL) has proven highly effective in aligning Large Language Models (LLMs) with human preferences. Typical RL methods optimize under an overall sequence reward, which can lead to a suboptimal learning process. This reflects a key credit assignment problem: identifying which tokens to reinforce or suppress. To rectify these shortcomings,

Chanwoo Chun, SueYeon Chung, Daniel D. Lee

Analyzing the structure of sampled features from an input data distribution is challenging when constrained by limited measurements in both the number of inputs and features. Traditional approaches often rely on the eigenvalue spectrum of the sample covariance matrix derived from finite measurement matrices; however, these spectra are sensitive to the size o

Alexander R. Pruss

We consider the problem of recovering the position and orientation of a camera equipped with an accelerometer from sensor images of two labeled landmarks whose positions in a coordinate system aligned in a known way with gravity are known. This a variant on the much studied P$n$P problem of recovering camera position and orientation from $n$ points without a

Pranav Verma, Viney Kumar, Samit Bhattacharyya

The global impact of the COVID-19 pandemic on the livelihoods of people worldwide prompted the implementation of a range of preventive measures at local, national, and international levels. Early in the outbreak, before the vaccine became accessible, voluntary quarantine and social isolation emerged as crucial strategies to curb the spread of infection. In t

L. Nies, D. Atanasov, M. Athanasakis-Kaklamanakis, M. Au

Mass measurements with the ISOLTRAP mass spectrometer at CERN-ISOLDE improve mass uncertainties of neutron-deficient tin isotopes towards doubly-magic $^{100}$Sn. The mass uncertainty of $^{103}$Sn was reduced by a factor of 4, and the new value for the mass excess of -67104(18) keV is compared with nuclear \textit{ab initio} and density functional theory ca

Daniel Baumann, Harry Goodhew, Hayden Lee

Recently, an interesting pattern was found in the differential equations satisfied by the Feynman integrals describing tree-level correlators of conformally coupled scalars in a power-law FRW cosmology [1,2]. It was proven that simple and universal graphical rules predict the equations for arbitrary graphs as a flow in kinematic space. In this note, we show

Mengli Hu, Oleg Janson, Claudia Felser, Paul McClarty

Altermagnets are a newly discovered class of magnetic phases that combine the spin polarization behavior of ferromagnetic band structures with the vanishing net magnetization characteristic of antiferromagnets. Initially proposed for collinear magnets, the concept has since been extended to include certain non-collinear structures. A recent development in La

Kwok Ho Wan

We present numerical simulation results for the 7-to-1 and 15-to-1 state distillation circuits, constructed using transversal CNOTs acting on multiple surface code patches. The distillation circuits are decoded iteratively using the method outlined in [arXiv:2407.20976]. We show that, with a re-configurable qubit architecture, we can perform fast magic state

K. Vignesh, B. Pranavi, Ch. Sreenidhi

As AI emerged as highest valued technology, We used that to create a web application that makes a patient work easier .It detects the disease name based on the symptoms given by the patient and recommends medication for respective disease, precautions to take, diet to follow and workouts to do, so the disease can be minimized. The web application is made wit

Pratyasha Rudra, Swastik Mondal

Chemiresistive materials, which alter their electrical resistance in response to interactions with surrounding chemicals, are valued for their robustness, rapid detection ability and high sensitivity. Recent research has revealed that the sensing performance of these materials can be enhanced by applying an external magnetic field. In this study, we report a

Farshad Jafari, Claire Arthur

Initiating a quest to unravel the complexities of musical aesthetics through the lens of information dynamics, our study delves into the realm of musical sequence modeling, drawing a parallel between the sequential structured nature of music and natural language. Despite the prevalence of neural network models in MIR, the modeling of symbolic music events as

Orestis Loukas, Ho-Ryun Chung

Statistical hypothesis testing is the central method to demarcate scientific theories in both exploratory and inferential analyses. However, whether this method befits such purpose remains a matter of debate. Established approaches to hypothesis testing make several assumptions on the data generation process beyond the scientific theory. Most of these assump

Zhiwu Zheng, Lauren Mentzer, Berk Iskender, Michael Price

Robots operating in unstructured environments require a comprehensive understanding of their surroundings, necessitating geometric and semantic information from sensor data. Traditional RGB-D processing pipelines focus primarily on geometric reconstruction, limiting their ability to support advanced robotic perception, planning, and interaction. A key challe

Diego Cruces, Cristiano Germani, Amin Nassiri-Rad, Masahide Yamaguchi

By introducing the small noise expansion techniques, we show that the fully nonlinear (non-Markovian) stochastic inflationary system, may be re-cast in terms of an infinite set of Wiener processes (stochastic equations with white noises). As a byproduct, we show that the Starobinsky test field approximation might only provide information about the linear reg

Zhaomin Wu, Junyi Hou, Yiqun Diao, Bingsheng He

Federated Learning (FL) is an evolving paradigm that enables multiple parties to collaboratively train models without sharing raw data. Among its variants, Vertical Federated Learning (VFL) is particularly relevant in real-world, cross-organizational collaborations, where distinct features of a shared instance group are contributed by different parties. In t

Arianna Cortesi, Claudia Mignone, Alan Alves Brito, Gracy Moreira

The way we look at the sky is connected to the cosmological paradigm embraced by the society we live in. On the other hand, several astronomical concepts reinforce the idea of a common humanity. Yet, scientific outreach is frequenty reaching out only to a specific part of the world population, often excluding people living in extreme social vulnerability, vi

Andrey Gogolev, Levi Keck, Kevin Lewis

We introduce a new class of billiard-like system, ``bouncing outer billiards" which are 3-dimensional cousins of outer billiards of Neumann and Moser. We prove that bouncing outer billiard on a smooth convex body has at least four 1-parameter families of fixed points. We also fully describe dynamics of bouncing outer billiard on a line segment. Finally we ca

V. Adibekyan, M. Deal, C. Dorn, I. Dittrich

The composition of rocky planets is strongly driven by the primordial materials in the protoplanetary disk, which can be inferred from the abundances of the host star. Understanding this compositional link is crucial for characterizing exoplanets. We aim to investigate the relationship between the compositions of low-mass planets and their host stars. We det

Vladislav Pyatov, Iaroslav Koshelev, Stamatis Lefkimmiatis

We present a novel two-view geometry estimation framework which is based on a differentiable robust loss function fitting. We propose to treat the robust fundamental matrix estimation as an implicit layer, which allows us to avoid backpropagation through time and significantly improves the numerical stability. To take full advantage of the information from t

Chi Zhang, Yingpu Deng

In 2021, the $p$-adic signature scheme and public-key encryption cryptosystem were introduced. These schemes have good efficiency but are shown to be not secure. The attack succeeds because the extension fields used in these schemes are totally ramified. In order to avoid this attack, the extension field should have a large residue degree. In this paper, we

Christopher T. Howard, William D. Hunt, Kenneth W. Allen

The dielectric layers surrounding a metasurface have a large impact on its frequency and angular response. The notion of effective permittivity captures this dependence by suggesting that a layered dielectric environment will perturb metasurface resonances in the same manner as an infinite environment of that effective permittivity. A model for effective per

Shawn Tan, Songlin Yang, Aaron Courville, Rameswar Panda

The self-attention mechanism traditionally relies on the softmax operator, necessitating positional embeddings like RoPE, or position biases to account for token order. But current methods using still face length generalisation challenges. We investigate an alternative attention mechanism based on the stick-breaking process in larger scale settings. The meth

N. Uday Kiran

Ramanujan sums have attracted significant attention in both mathematical and engineering disciplines due to their diverse applications. In this paper, we introduce an algebraic generalization of Ramanujan sums, derived through polynomial remaindering. This generalization is motivated by its applications in Restricted Partition Theory and Coding Theory. Our i

Claire Hilaire, Martin Milanič, Nicolas Trotignon, Djordje Vasić

We prove that a hereditary class of graphs is $(\mathsf{tw}, \omega)$-bounded if and only if the induced minors of the graphs from the class form a $(\mathsf{tw}, \omega)$-bounded class.

Mikaela Iacobelli, Stefano Rossi, Klaus Widmayer

We study the asymptotic behavior of small data solutions to the screened Vlasov-Poisson equation on $\mathbb{R}^d\times\mathbb{R}^d$ near vacuum. We show that for dimensions $d\geq 2$, under mild assumptions on localization (in terms of spatial moments) and regularity (in terms of at most three Sobolev derivatives) solutions scatter freely. In dimension $d=1

Nestor Colin

For an odd prime $p$, we determine the $p$-primary component of the Farrell cohomology of the pure mapping class groups of a non orientable surface of genus $p$ with $k\geqslant 1$ marked points. To do this, we classify conjugacy classes of subgroups of order $p$ of the pure mapping class group of a non orientable surface of any genus with marked points. Thi

Prashanth S Velayudhan, Xiaoqiao Xu, Prajkta Kallurkar, Ana Patricia Balbon

metasnf is an R package that enables users to apply meta clustering, a method for efficiently searching a broad space of cluster solutions by clustering the solutions themselves, to clustering workflows based on similarity network fusion (SNF). SNF is a multi-modal data integration algorithm commonly used for biomedical subtype discovery. The package also co

Karel Peetermans, Jonah Richards, Max Kellermeier, Klaus Floettmann

In order to exploit the complete scientific potential of user-oriented accelerator facilities, it is necessary to provide adequate pump sources to enable pump-probe science. The users of the European XFEL have requested a THz pump source matching the X-ray repetition rate (10 Hz burst mode with up to 2700 bunches per burst) with a wide range of properties. T

G. Martinelli, S. Simula, L. Vittorio

We extend the Standard Model (SM) analysis of Ref. [1], which was limited to light leptons in the final state, to the semileptonic $B \to D^* \tau \nu_\tau$ decay. By using quantities that can be analised without the knowledge of $\vert V_{cb}\vert$, we derive important information about the helicity amplitudes and the hadronic form factors that can be compa

Sourabh Deoghare, Diptesh Kanojia, Pushpak Bhattacharyya

This exploratory study investigates the potential of multilingual Automatic Post-Editing (APE) systems to enhance the quality of machine translations for low-resource Indo-Aryan languages. Focusing on two closely related language pairs, English-Marathi and English-Hindi, we exploit the linguistic similarities to develop a robust multilingual APE model. To fa

Ana Ezquerro, David Vilares, Carlos Gómez-Rodríguez

Various linearizations have been proposed to cast syntactic dependency parsing as sequence labeling. However, these approaches do not support more complex graph-based representations, such as semantic dependencies or enhanced universal dependencies, as they cannot handle reentrancy or cycles. By extending them, we define a range of unbounded and bounded line

Jared D. Willard, Fabio Ciulla, Helen Weierbach, Vipin Kumar

The prediction of streamflows and other environmental variables in unmonitored basins is a grand challenge in hydrology. Recent machine learning (ML) models can harness vast datasets for accurate predictions at large spatial scales. However, there are open questions regarding model design and data needed for inputs and training to improve performance. This s

Nguyen Van Huynh, Bolun Zhang, Dinh-Hieu Tran, Dinh Thai Hoang

Spectrum access is an essential problem in device-to-device (D2D) communications. However, with the recent growth in the number of mobile devices, the wireless spectrum is becoming scarce, resulting in low spectral efficiency for D2D communications. To address this problem, this paper aims to integrate the ambient backscatter communication technology into D2

Shiqi Chen, Yuhang Li, Hanlong Chen, Aydogan Ozcan

Generative models cover various application areas, including image, video and music synthesis, natural language processing, and molecular design, among many others. As digital generative models become larger, scalable inference in a fast and energy-efficient manner becomes a challenge. Here, we present optical generative models inspired by diffusion models,

June Kayath, Connor Lane, Ben Neifeld, Tianyu Ni

In this paper, we define a version of the arithmetic-geometric mean (AGM) function for arbitrary finite fields $\mathbb{F}_q$, and study the resulting AGM graph with points $(a,b) \in \mathbb{F}_q \times \mathbb{F}_q$ and directed edges between points $(a,b)$, $(\frac{a+b}{2},\sqrt{ab})$ and $(a,b)$, $(\frac{a+b}{2},-\sqrt{ab})$. The points in this graph are

Bhaskar Dutta, Bai-Shan Hu, Wei-Chih Huang, Richard G. Van de Water

ATOMKI nuclear anomaly has suggested a new BSM (Beyond the Standard Model) boson with mass $\sim17$ MeV emitted from excited nuclei and quickly decays into a pair of $e^+e^-$. In order to search for the new particle, we propose a new approach that utilizes the ongoing Coherent CAPTAIN-Mills (CCM) 10-ton LAr (liquid argon) detectors. The neutrons from the Luj

Imad Bouhou, Stefano Fortunati, Leila Gharsalli, Alexandre Renaux

The joint detection and tracking of a moving target embedded in an unknown disturbance represents a key feature that motivates the development of the cognitive radar paradigm. Building upon recent advancements in robust target detection with multiple-input multiple-output (MIMO) radars, this work explores the application of a Partially Observable Markov Deci

Lorenzo Aloisi, Luigi Sigillo, Aurelio Uncini, Danilo Comminiello

In recent years, diffusion models have emerged as a superior alternative to generative adversarial networks (GANs) for high-fidelity image generation, with wide applications in text-to-image generation, image-to-image translation, and super-resolution. However, their real-time feasibility is hindered by slow training and inference speeds. This study addresse

Naoya Kitajima, Shota Nakagawa, Fuminobu Takahashi, Wen Yin

We derive a bound on dark photon dark matter scenarios where the dark photon mass is generated through the Higgs mechanism, based on the requirement that symmetry breaking must occur sufficiently early in the universe. We emphasize that dark photon production occurs successfully when the dark Higgs field remains in the symmetric phase due to non-thermal trap

Sayantani Bhattacharyya, Sukanya Mitra, Shuvayu Roy, Rajeev Singh

In this letter, we investigate how field redefinition influences the spectrum of linearized perturbations in relativistic fluid dynamics. We show that the hydrodynamic modes do not get affected under local field redefinition, whereas the non-hydrodynamic modes do. These non-hydrodynamic modes can be removed through a suitable all-order field redefinition. Th

Horacio Thompson, Marcelo Errecalde

The eRisk laboratory aims to address issues related to early risk detection on the Web. In this year's edition, three tasks were proposed, where Task 2 was about early detection of signs of anorexia. Early risk detection is a problem where precision and speed are two crucial objectives. Our research group solved Task 2 by defining a CPI+DMC approach, address

Ian Ball, Teemu Pekkarinen

We comment on the regularity assumptions in the multi-agent sequential screening model of Eso and Szentes (2007). First, we observe that the regularity assumptions are not invariant to relabeling each agent's signal realizations. Second, we show that the regularity assumptions rule out valuation distributions with common bounded support. Third, we show that

Riccardo Salami, Pietro Buzzega, Matteo Mosconi, Jacopo Bonato

Model merging has emerged as a crucial technique in Deep Learning, enabling the integration of multiple models into a unified system while preserving perfor-mance and scalability. In this respect, the compositional properties of low-rank adaptation techniques (e.g., LoRA) have proven beneficial, as simple averaging LoRA modules yields a single model that mos

Kai-Robin Lange, Jonas Rieger, Niklas Benner, Carsten Jentsch

From a monarchy to a democracy, to a dictatorship and back to a democracy -- the German political landscape has been constantly changing ever since the first German national state was formed in 1871. After World War II, the Federal Republic of Germany was formed in 1949. Since then every plenary session of the German Bundestag was logged and even has been di

William Cagas, Chan Ko, Blake Hsiao, Shryuk Grandhi

The proliferation of machine learning models in diverse clinical applications has led to a growing need for high-fidelity, medical image training data. Such data is often scarce due to cost constraints and privacy concerns. Alleviating this burden, medical image synthesis via generative adversarial networks (GANs) emerged as a powerful method for synthetical

Xi Chen, Anindya De, Shivam Nadimpalli, Rocco A. Servedio

We consider the problem of testing whether an unknown and arbitrary set $S \subseteq \mathbb{R}^n$ (given as a black-box membership oracle) is convex, versus $\varepsilon$-far from every convex set, under the standard Gaussian distribution. The current state-of-the-art testing algorithms for this problem make $2^{\tilde{O}(\sqrt{n})\cdot \mathrm{poly}(1/\var

Zebin Yang, Renze Chen, Taiqiang Wu, Ngai Wong

In this paper, we propose MCUBERT to enable language models like BERT on tiny microcontroller units (MCUs) through network and scheduling co-optimization. We observe the embedding table contributes to the major storage bottleneck for tiny BERT models. Hence, at the network level, we propose an MCU-aware two-stage neural architecture search algorithm based on

Lucia Gebauer-Werner, Oscar Macias, Christoph Weniger

Observations with the Fermi Gamma-Ray Space Telescope reveal an excess of extended gamma-ray emission likely caused by an undiscovered population of millisecond pulsars (MSPs) in the core of the Sagittarius dwarf spheroidal galaxy (Sgr dSph). However, additional evidence, such as multi-wavelength searches, is necessary to confirm this theory. A significant d

Kunpeng Zhu, Xiaoyan Zhou, Yinxin Zhang, Zhanhua Huang

We derive new exceptional point (EP) conditions of the coupled microring resonators using coupled mode theory in space, a more accurate approach than the commonly used coupled mode theory in time. Transmission spectra around EPs obtained from the two models have been compared on two material platforms, revealing non-negligible deviations. Our analysis provid