November 2024 arXiv papers — page 12

Luca Barsellotti, Lorenzo Bianchi, Nicola Messina, Fabio Carrara

Open-Vocabulary Segmentation (OVS) aims at segmenting images from free-form textual concepts without predefined training classes. While existing vision-language models such as CLIP can generate segmentation masks by leveraging coarse spatial information from Vision Transformers, they face challenges in spatial localization due to their global alignment of im

Visweshwar Ram Marthi, Yogesh Maan

We report a direct measurement of the electron density turbulence parameter $C_1$, enabled by 550-750~MHz baseband observations with the upgraded Giant Metrewave Radio Telescope. The parameter $C_1$ depends on the power law index of the wavenumber spectrum of electron density inhomogeneities in the ionized interstellar medium. Radio waves propagating through

G. Munoz-Sanchez, M. Kalitsounaki, S. de Wit, K. Antoniadis

Red Supergiants (RSGs) are cool, evolved massive stars in their final evolutionary stage before exploding as a supernova. However, the evolution and fate of the most luminous RSGs remain uncertain. Observational evidence for luminous warm, post-RSG objects and the apparent lack of luminous RSGs as supernova progenitors suggest a blueward evolution. Since the

Alexis Reboul-Salze, Paul Barrère, Kenta Kiuchi, Jérôme Guilet

In binary neutron star mergers, the remnant can be stabilized by differential rotation before it collapses into a black hole. Therefore, the angular momentum transport mechanisms are crucial for predicting the lifetime of the hypermassive neutron star. One such mechanism is the Tayler-Spruit dynamo, and recent simulations have shown that it could grow in pro

Fernando Arias-Aragón, Maurizio Giannotti, Giovanni Grilli di Cortona, Federico Mescia

We revisit and update the axion-induced pair production process in a nuclear electric field mediated by the axion-electron coupling, $a+{{}^{A}_{Z}X} \rightarrow {{}^{A}_{Z}X} + e^{+} + e^{-}$. This process emerges as one of the most efficient channels for detecting axions with energies above a few MeV in large underground detectors. It is particularly relev

Emmy L. Escott, Leah K. Morabito, Jan Scholtz, Ryan C. Hickox

Some Active Galactic Nuclei (AGN) host outflows which have the potential to alter the host galaxy's evolution (AGN feedback). These outflows have been linked to enhanced radio emission. Here we investigate the connection between low-frequency radio emission using the International LOFAR Telescope and [O III] $\lambda$5007 ionised gas outflows using the Sloan

Muhammad Sohail Danish, Muhammad Akhtar Munir, Syed Roshaan Ali Shah, Kartik Kuckreja

While numerous recent benchmarks focus on evaluating generic Vision-Language Models (VLMs), they do not effectively address the specific challenges of geospatial applications. Generic VLM benchmarks are not designed to handle the complexities of geospatial data, an essential component for applications such as environmental monitoring, urban planning, and dis

Zeqi Xiao, Wenqi Ouyang, Yifan Zhou, Shuai Yang

Recent advancements in video generation have been greatly driven by video diffusion models, with camera motion control emerging as a crucial challenge in creating view-customized visual content. This paper introduces trajectory attention, a novel approach that performs attention along available pixel trajectories for fine-grained camera motion control. Unlik

Marzieh Alireza Mirhoseini

Flood hazard assessment demands fast and accurate predictions. Hydrodynamic models are detailed but computationally intensive, making them impractical for quantifying uncertainty or identifying extremes. In contrast, machine learning surrogates can be rapid, but training on scarce simulated or observed extreme data can also be ineffective. This work demonstr

Michael Fischer, Iliyan Georgiev, Thibault Groueix, Vladimir G. Kim

Decomposing 3D assets into material parts is a common task for artists, yet remains a highly manual process. In this work, we introduce Select Any Material (SAMa), a material selection approach for in-the-wild objects in arbitrary 3D representations. Building on SAM2's video prior, we construct a material-centric video dataset that extends it to the material

Shammi Malhotra, Sarika Goyal, K. Sreenadh

In this article, we study the following quasilinear Schr\"{o}dinger equation involving Hardy potential and Choquard type exponential nonlinearity with a parameter $\alpha$ \begin{equation*} \left\{ \begin{array}{l} - \Delta_N w - \Delta_N(|w|^{2\alpha}) |w|^{2\alpha - 2} w - \lambda \frac{|w|^{2\alpha N-2}w}{\left( |x| \log\left(\frac{R}{|x|} \right) \right)

Chancharik Mitra, Brandon Huang, Tianning Chai, Zhiqiu Lin

Generative Large Multimodal Models (LMMs) like LLaVA and Qwen-VL excel at a wide variety of vision-language (VL) tasks. Despite strong performance, LMMs' generative outputs are not specialized for vision-language classification tasks (i.e., tasks with vision-language inputs and discrete labels) such as image classification and multiple-choice VQA. One key ch

Haotian Zhang, Li Li, Dong Liu

In learned image compression, probabilistic models play an essential role in characterizing the distribution of latent variables. The Gaussian model with mean and scale parameters has been widely used for its simplicity and effectiveness. Probabilistic models with more parameters, such as the Gaussian mixture models, can fit the distribution of latent variab

Riju Bindua, Thomas Brüstle, Luis Scoccola

Given a functor from any category into the category of topological spaces, one obtains a linear representation of the category by post-composing the given functor with a homology functor with field coefficients. This construction is fundamental in persistence theory, where it is known as persistent homology, and where the category is typically a poset. Persi

Yuan Liu

Given a finite abelian group $\Gamma$, we study the distribution of the $p$-part of the class group $\operatorname{Cl}(K)$ as $K$ varies over Galois extensions of $\mathbb{Q}$ or $\mathbb{F}_q(t)$ with Galois group isomorphic to $\Gamma$. We first construct a discrete valuation ring $e\mathbb{Z}_p[\Gamma]$ for each primitive idempotent $e$ of $\mathbb{Q}_p[\

Bo Yuan, Damiano Brigo, Antoine Jacquier, Nicola Pede

Deep learning methods have become a widespread toolbox for pricing and calibration of financial models. While they often provide new directions and research results, their `black box' nature also results in a lack of interpretability. We provide a detailed interpretability analysis of these methods in the context of rough volatility - a new class of volatili

Yakob Kahane, Marni Mishna

Symmetrically self-similar graphs are an important type of fractal graph. Their Green functions satisfy order one iterative functional equations. We show when the branching number of a generating cell is two, either the graph is a star consisting of finitely many one-sided lines meeting at an origin vertex, in which case the Green function is algebraic, or t

Bivas Mallick, Nirman Ganguly, A. S. Majumdar

Transmission of high dimensional entanglement through quantum channels is a significant area of interest in quantum information science. The certification of high dimensional entanglement is usually done through Schmidt numbers, which quantify the entanglement dimensionality of quantum states. States with high Schmidt numbers provide a larger advantage in va

Jonad Pulaj, Kenan Wood, Carl Yerger

Given a configuration of indistinguishable pebbles on the vertices of a graph, a pebbling move consists of removing two pebbles from one vertex and placing one pebble on an adjacent vertex. The pebbling number of a graph is the least integer such that any configuration with that many pebbles and any target vertex, some sequence of pebbling moves can place a

Grzegorz Graff, Wacław Marzantowicz, Łukasz Patryk Michalak

The sequence of Dold coefficients $(a_n(f))$ of a self-map $f\colon X \to X$ forms a dual sequence to the sequence of Lefschetz numbers $(L(f^n))$ of iterations of $f$ under the M\"obius inversion formula. The set ${\mathcal AP}(f) = \{ n \,\colon\, a_n(f) \neq 0 \}$ is called the set of algebraic periods of $f$. Both the set of algebraic periods and sequenc

Andrew Buchanan

A method is presented for computing the R\'enyi entropy of a perturbed massless vacuum on the ball via a comparison with lattice field theory. If the perturbed state is Gaussian with smoothly varying correlation functions and the perturbation parameter has units of energy, I show the coefficients for R\'enyi entropy are analytically computable for all R\'eny

Ruolin Liu, Jerome Quintin, Niayesh Afshordi

We explore the possibility that quadratic gravity, as a renormalizable theory, describes the interior of quantum black holes. We find new exact power-law solutions to pure quadratic gravity under spherical symmetry, which are complex valued. The resulting solutions, dubbed powerballs, are horizonless compact objects that become Schwarzschild-like a small dis

Tamás Vaszary, Animesh Datta, Tom Goffrey, Brian Appelbe

We present a mapping of the nonlinear, electrostatic Vlasov equation with Krook-type collision operators, discretized on a (1+1) dimensional grid, onto a recent Carleman linearization-based quantum algorithm for solving ordinary differential equations (ODEs) with quadratic nonlinearities. We derive upper bounds for the query- and gate complexities of the qua

Zijian Zhang, Kaiyuan Zheng, Zhaorun Chen, Joel Jang

Despite the recent advancements of vision-language-action (VLA) models on a variety of robotics tasks, they suffer from critical issues such as poor generalizability to unseen tasks, due to their reliance on behavior cloning exclusively from successful rollouts. Furthermore, they are typically fine-tuned to replicate demonstrations collected by experts under

Yuchen Zhu, Jinglei Cheng, Boxi Li, Yidong Zhou

In the scaling development of quantum computers, the calibration process emerges as a critical challenge. Existing calibration methods, utilizing the same pulse waveform for two-qubit gates across the device, overlook hardware differences among physical qubits and lack efficient parallel calibration. In this paper, we enlarge the pulse candidates for two-qub

Rui-Yang Gong, Zi-Yu He, Cheng-He Yu, Ge-Fei Zhang

The unique photon-scattering phenomena of giant-atom systems offer a novel paradigm for exploring innovative quantum optics phenomena and applications. Here, we investigate a giant-atom configuration embedded in a dual-rail waveguide, whose scattering behavior is analytically derived based on a four-port model and affected by both waveguide-induced and inter

Carina M. Persson

A key to understand exoplanets is characterisation of their host stars. One of the most powerful tools to characterise stellar properties like effective temperature, surface gravity and metallicity, is spectroscopy based on observations of stellar atmospheres. This chapter describes the stellar parameters that can be derived from a spectrum with examples of

Pengpeng Xiao, Phillip Si, Peng Chen

Data assimilation techniques are crucial for accurately tracking complex dynamical systems by integrating observational data with numerical forecasts. Recently, score-based data assimilation methods emerged as powerful tools for high-dimensional and nonlinear data assimilation. However, these methods still incur substantial computational costs due to the nee

Anamaria Mojica-Hanke, David Nader Palacio, Denys Poshyvanyk, Mario Linares-Vásquez

Context: Machine Learning (ML) significantly impacts Software Engineering (SE), but studies mainly focus on practitioners, neglecting researchers. This overlooks practices and challenges in teaching, researching, or reviewing ML applications in SE. Objective: This study aims to contribute to the knowledge, about the synergy between ML and SE from the perspec

Konstantin Stankevich, Alexander Studenikin, Maksim Vyalkov

A new theoretical framework, based on the quantum field theory of open systems applied to neutrinos, has been developed. This framework aims to describe the neutrino evolution in external environment, taking into account the effect of neutrino quantum decoherence. We have applied this approach to investigate a novel mechanism for neutrino quantum decoherence

Francesco Bigazzi, Aldo L. Cotrone, Andrea Olzi

This work investigates cosmic topological defects in gauge theories, focusing on models with an $SU(N)$ gauge group coupled with a single flavor, explored through a holographic framework. At low energies, the effective theory is described by an axion-like particle resulting from the spontaneous breaking of the axial $U(1)_A$ flavor symmetry. As the Universe

Amir Tavanaei, Kee Kiat Koo, Hayreddin Ceker, Shaobai Jiang

In this paper, we study the problem of generating structured objects that conform to a complex schema, with intricate dependencies between the different components (facets) of the object. The facets of the object (attributes, fields, columns, properties) can be a mix of short, structured, type-constrained facts, or long natural-language descriptions. The obj

Jérémy O'Byrne, Michael E. Cates

In this article, we study the time-reversal properties of a generic Markovian stochastic field dynamics with Gaussian noise. We introduce a convenient functional geometric formalism that allows us to straightforwardly generalize known results from finite dimensional systems to the case of continuous fields. We give, at field level, full reversibility conditi

Trevor Camper, Mishko Mitkovski

We obtain Szeg\H o-type limit theorems for Toeplitz operators on the weighted Bergman spaces $A^{2}_{\alpha}(\mathbb{B}^{n})$, and on $L^{2}(G)$, presenting separate formulations for compact and locally compact Abelian groups. Furthermore, we establish a broad class of abstract Szeg\H{o} limit theorems that unify and extend many classical results.

Son Thai Ly, Hien V. Nguyen

Adapting vision transformer foundation models through parameter-efficient fine-tuning (PEFT) methods has become increasingly popular. These methods optimize a limited subset of parameters, enabling efficient adaptation without the need to fine-tune the entire model while still achieving competitive performance. However, traditional PEFT methods may limit the

Xiao-Wei Bai, Yingsheng Huang, Wen-Long Sang

We compute the fragmentation function of a light quark into S-wave fully-charmed tetraquarks ($T_{4c}$) within the nonrelativistic QCD (NRQCD) framework, at leading order in $\alpha_{s}$ and $v$. We present results for light quark fragmentation into $T_{4c}$ and predict its contribution to $T_{4c}$ production at high transverse momentum ($p_{T}$) at the LHC

Clémence Sebe, Sarah Cohen-Boulakia, Olivier Ferret, Aurélie Névéol

Bioinformatics workflows are essential for complex biological data analyses and are often described in scientific articles with source code in public repositories. Extracting detailed workflow information from articles can improve accessibility and reusability but is hindered by limited annotated corpora. To address this, we framed the problem as a low-resou

A. A. Zaitsev, P. I. Zarubin, S. D. Murashko, N. Marimuthu

The results of the analysis of solid-state track detectors CR39 and nuclear photoemulsion plates irradiated in beams of accelerated xenon ions with energies of 3.2 MeV/n and 3.8 GeV/n at the NICA accelerator complex are presented.

Piotr Miska, Błażej Żmija

For non-negative integer parameters $r,u,m,n$ define \begin{align*} \cal{D}(r,u,m,n) := \big\{\ \sigma\in \cal{S}_{r+n}\ \big|\ \sigma(x)=y \textrm{ for exactly } u \textrm{ pairs } (x,y) \textrm{ such that } 1\leq x,y\leq r \textrm{ and } \sigma(t)=t \textrm{ for exactly } m \textrm{ elements } r+1\leq t\leq r+n\ \big\} \end{align*} and \begin{align*} \cal{

Jaehwan Kim, Sanghoon Lee

In this paper, we construct an infinite-dimensional family of solutions for the Yang-Mills flow on $\mathbb{R}^n \times SO(n)$ for $5 \leq n \leq 9$, which converge to $SO(n)$-equivariant homothetically shrinking solitons, modulo the gauge group. As a corollary, we prove the existence of asymmetric Type-I blowup solutions for the Yang-Mills flow.

Yichong Lu, Yichi Cai, Shangzhan Zhang, Hongyu Zhou

Photorealistic 3D vehicle models with high controllability are essential for autonomous driving simulation and data augmentation. While handcrafted CAD models provide flexible controllability, free CAD libraries often lack the high-quality materials necessary for photorealistic rendering. Conversely, reconstructed 3D models offer high-fidelity rendering but

Madeleine Goertz, Aaron Williams

We investigate solutions to the new "Ziggu" family of exponential puzzles. These puzzles have $p$ pieces that form $m$ mazes. We encode the puzzle state as an quaternary number (base $4$) with $n=m+1$ digits, where each digit gives the horizontal or vertical position in one maze. We show that the number of states on a shortest solution is $6 \cdot 2^n - 3n -

Yun-Jin Li, Mariia Gladkova, Yan Xia, Daniel Cremers

Understanding dynamic 3D scenes is crucial for extended reality (XR) and autonomous driving. Incorporating semantic information into 3D reconstruction enables holistic scene representations, unlocking immersive and interactive applications. To this end, we introduce TRASE, a novel tracking-free 4D segmentation method for dynamic scene understanding. TRASE le

Rui Zhou, Jingbin Liu, Junbin Xie, Jianyu Zhang

Dynamic visual-inertial odometry (VIO) requires reliable suppression of motion-corrupted measurements, yet prior semantic-assisted approaches depend on category-limited segmenters and degrade under partial occlusion. Promptable foundation segmentation models offer category-agnostic dynamic parsing, but their effectiveness in VIO depends critically on the tem

Andreas Bierwage, Philipp Lauber, Noriyoshi Nakajima, Kouji Shinohara

The contribution of a time-independent toroidally-symmetric radial electric field $E_r$ is implemented in VisualStart [Comp. Phys. Comm. 275 (2022) 108305; arXiv:2111.08224], a code whose purposes include the construction of guiding center (GC) drift orbit databases for the study of plasma instabilities in tokamaks. $E_r$ is important for the thermal part of

Chen-Shen Lee, Ken Shiozaki, Chang-Tse Hsieh

We present a framework for investigating the effects of interactions on crystalline symmetry-protected topological (SPT) phases. Within this framework, one can establish a direct connection between the equivalence classes of free-fermion systems and their corresponding interacting classes. A central component of this framework is the Atiyah-Hirzebruch spectr

Peter Athron, Marco Chianese, Satyabrata Datta, Rome Samanta

Blue-tilted Gravitational Waves (BGWs) have been proposed as a potential candidate for the cosmic gravitational waves detected by Pulsar Timing Arrays (PTA). In the standard cosmological framework, BGWs are constrained in their frequency range by the Big Bang Nucleosynthesis (BBN) limit on GW amplitude, which precludes their detection at interferometer scale

Jianming Pan, Zeqi Ye, Xiao Yang, Xu Yang

Data-driven decision-making processes increasingly utilize end-to-end learnable deep neural networks to render final decisions. Sometimes, the output of the forward functions in certain layers is determined by the solutions to mathematical optimization problems, leading to the emergence of differentiable optimization layers that permit gradient back-propagat

Özge Canlı Usta, Erik M. Bollt

Determining causal inference has become popular in physical and engineering applications. While the problem has immense challenges, it provides a way to model the complex networks by observing the time series. In this paper, we present the optimal conditional correlation dimensional geometric information flow principle ($oGeoC$) that can reveal direct and in

Alexander R. Miller

We provide an example of a finite group with a conjugacy class of average size on which fewer than half of the irreducible characters are either zero or a root of unity.

Aniruddha Das, Ashish Pal, Satish Nagarajaiah, Mohamed Sajeer M

Displacement plays a crucial role in structural health monitoring (SHM) and damage detection of structural systems subjected to dynamic loads. However, due to the inconvenience associated with the direct measurement of displacement during dynamic loading and the high cost of displacement sensors, the use of displacement measurements often gets restricted. In

Berta Casas, Xavier Bonet-Monroig, Adrián Pérez-Salinas

Quantum machine learning (QML) has surged as a prominent area of research with the objective to go beyond the capabilities of classical machine learning models. A critical aspect of any learning task is the process of data embedding, which directly impacts model performance. Poorly designed data-embedding strategies can significantly impact the success of a

Saskia A. Putri, Xiaoyu Ge, Javad Khazaei

This study investigates the economic dispatch and optimal power flow (OPF) for microgrids, focusing on two configurations: a single-bus islanded microgrid and a three-bus grid-tied microgrid. The methodologies integrate renewable energy sources (solar PV and wind turbines), battery energy storage systems (BESS), and conventional generators (CHP, diesel, and

Yiming Zuo, Willow Yang, Zeyu Ma, Jia Deng

Depth completion (DC) aims to predict a dense depth map from an RGB image and a sparse depth map. Existing DC methods generalize poorly to new datasets or unseen sparse depth patterns, limiting their real-world applications. We propose OMNI-DC, a highly robust DC model that generalizes well zero-shot to various datasets. The key design is a novel Multi-resol

Laura Serino, Markus Rambach, Benjamin Brecht, Jacquiline Romero

High-dimensional time-frequency encodings have the potential to significantly advance quantum information science; however, practical applications require precise knowledge of the encoded quantum states, which becomes increasingly challenging for larger Hilbert spaces. Self-guided tomography (SGT) has emerged as a practical and scalable technique for this pu

Yihao Luo

In the field of data-driven 3D shape analysis and generation, the estimation of global topological features from localized representations such as point clouds, voxels, and neural implicit fields is a longstanding challenge. This paper introduces a novel, differentiable algorithm tailored to accurately estimate the global topology of 3D shapes, overcoming th

Daniel Basilewitsch, João F. Bravo, Christian Tutschku, Frederick Struckmeier

We compare the performance of randomized classical and quantum neural networks (NNs) as well as classical and quantum-classical hybrid convolutional neural networks (CNNs) for the task of supervised binary image classification. We keep the employed quantum circuits compatible with near-term quantum devices and use two distinct methodologies: applying randomi

Merlijn Sevenhuijsen, Khashayar Etemadi, Mattias Nyberg

Large language models have demonstrated impressive capabilities in generating code, yet they often produce programs with flaws or deviations from intended behavior, limiting their suitability for safety-critical applications. To address this limitation, this paper introduces VECOGEN, a novel tool that combines large language models with formal verification t

Jon Gutiérrez-Zaballa, Koldo Basterretxea, Javier Echanobe, M. Victoria Martínez

Most of current computer vision-based advanced driver assistance systems (ADAS) perform detection and tracking of objects quite successfully under regular conditions. However, under adverse weather and changing lighting conditions, and in complex situations with many overlapping objects, these systems are not completely reliable. The spectral reflectance of

R. E. Mennickent, G. Djurašević, J. A. Rosales, J. Garcés

Our study aims to elucidate both short-term and long-term variations in the light curve of the eclipsing system OGLE-LMC-ECL-14413, with a particular focus on the unusual reversals in eclipse depth. We aim to clarify the role of the accretion disk in these fluctuations, especially in long-cycle changes spanning hundreds of days. Additionally, we seek to dete

Muhammad Huzaifa, Yova Kementchedjhieva

Text-to-image retrieval is a critical task for managing diverse visual content, but common benchmarks for the task rely on small, single-domain datasets that fail to capture real-world complexity. Pre-trained vision-language models tend to perform well with easy negatives but struggle with hard negatives--visually similar yet incorrect images--especially in

Vu Thi Huong, Duong Thi Kim Huyen, Nguyen Dong Yen

The problem of minimizing the difference of two lower semicontinuous, proper, convex functions (a DC function) on a nonempty closed convex set in a locally convex Hausdorff topological vector space is studied in this paper. The focus is made on the situations where either the second component of the objective function is a generalized polyhedral convex funct

Uyen Ly, Quang Nguyen, Dang Nguyen, Tu Thai

Digital pathology in the form of whole-slide-imaging has been used to support diagnostic consultation through telepathology. Previous studies have mostly addressed the technical aspects of telepathology and general pathology consultation. In this study, we focus on our experience at University of Texas Health-McGovern Medical School in Houston, Texas in prov

Xuqian Ren, Matias Turkulainen, Jiepeng Wang, Otto Seiskari

Geometric priors are often used to enhance 3D reconstruction. With many smartphones featuring low-resolution depth sensors and the prevalence of off-the-shelf monocular geometry estimators, incorporating geometric priors as regularization signals has become common in 3D vision tasks. However, the accuracy of depth estimates from mobile devices is typically p

G. R. Krishna Chand Avatar, Vivekanand Dabade

Kirchhoff's kinetic analogy relates the equilibrium solutions of an elastic rod or strip to the motion of a spinning top. In this analogy, time is replaced by the arc length parameter in the phase portrait to determine the equilibrium configurations of the rod. Predicted equilibrium solutions from the phase portrait for specific boundary value problems, as w

Massil Hihat, Adeline Fermanian

We tackle online inventory problems where at each time period the manager makes a replenishment decision based on partial historical information in order to meet demands and minimize costs. To solve such problems, we build upon recent works in online learning and control, use insights from inventory theory and propose a new algorithm called GAPSI. This algor

Chen-Shen Lee, Ken Shiozaki, Chang-Tse Hsieh

We explore one-dimensional fermionic symmetry-protected topological (SPT) phases related by the crystalline equivalence principle. In particular, we study charge-conserving many-body topological phases of fermions protected respectively by chiral and reflection symmetries. While the classifications of the two crystalline-equivalent SPT phases are identical,

Asier Calbet

We say that two vertices are twins if they have the same neighbourhood and that a graph is $K_r$-saturated if it does not contain $K_r$ but adding any new edge to it creates a $K_r$. In 1964, Erd\H{o}s, Hajnal and Moon showed that $sat(n,K_r)=(r-2)n+o(n)$ for $r \geq 3$, where $sat(n,K_r)$ is the minimum number of edges in a $K_r$-saturated graph on $n$ vert

Pierre Bousseyroux, Jean-Philippe Bouchaud, Marc Potters

The study of eigenvalue distributions in random matrix theory is often conducted by analyzing the resolvent matrix $ \mathbf{G}_{\mathbf{M}}^N(z) = (z \mathbf{1} - \mathbf{M})^{-1} $. The normalized trace of the resolvent, known as the Stieltjes transform $ \mathfrak{g}_{\mathbf{M}}^N(z) $, converges to a limit $ \mathfrak{g}_{\mathbf{M}}(z) $ as the matrix

Sihang Li, Siqi Tan, Bowen Chang, Jing Zhang

Visual localization, which estimates a camera's pose within a known scene, is a fundamental capability for autonomous systems. While absolute pose regression (APR) methods have shown promise for efficient inference, they often struggle with generalization. Recent approaches attempt to address this through data augmentation with varied viewpoints, yet they ov

Jianguo Huang, Yuejin Xu

In this paper, in order to improve the spatial accuracy, the exponential integrator Fourier Galerkin method (EIFG) is proposed for solving semilinear parabolic equations in rectangular domains. In this proposed method, the spatial discretization is first carried out by the Fourier-based Galerkin approximation, and then the time integration of the resulting s

Andrey Voloshin, Anat Siddharth, Simone Bianconi, Alaina Attanasio

Ultra-low noise lasers are essential tools in a wide variety of applications, including data communication, light detection and ranging (LiDAR), quantum computing and sensing, and optical metrology. Recent advances in integrated photonics, specifically the development of ultra-low loss silicon nitride (Si$_3$N$_4$) platform, have allowed attaining performanc

Dirk Hundertmark, Marvin R. Schulz, Semjon Vugalter

We study a system of three bosons interacting with short-range potentials which can move along three different lines. Two of these lines are parallel to each other within one plane. The third line is constrained to a plane perpendicular to the first one. Recently it was predicted in physics literature that such a system exhibits the so-called confinement ind

Emma Prevot, Rory Toogood, Filippo Pagani, Paul D. W. Kirk

Cluster analyses of high-dimensional data are often hampered by the presence of large numbers of variables that do not provide relevant information, as well as the perennial issue of choosing an appropriate number of clusters. These challenges are frequently encountered when analysing `omics datasets, such as in molecular precision medicine, where a key goal

Huiguo He, Qiuyue Wang, Yuan Zhou, Yuxuan Cai

Training-free diffusion models have achieved remarkable progress in generating multi-subject consistent images within open-domain scenarios. The key idea of these methods is to incorporate reference subject information within the attention layer. However, existing methods still obtain suboptimal performance when handling numerous subjects. This paper reveals

Patricio Almirón

The aim of this survey is to explore complete intersection monomial curves from a contemporary perspective. The main goal is to help readers understand the intricate connections within the field and its potential applications. The properties of any monomial curve singularity will be first reviewed, highlighting the interaction between combinatorial and algeb

Hirotaka Akatsuka

Ramanujan investigated maximal order for the number of divisors function by introducing some notion such as (superior) highly composite numbers. He also studied maximal order for other arithmetic functions including the sum of powers of divisors function. In this paper we relate zero-free regions for the Riemann zeta-function to maximal order for the sum of

Amon Lahr, Joshua Näf, Kim P. Wabersich, Jonathan Frey

Incorporating learning-based models, such as artificial neural networks or Gaussian processes, into model predictive control (MPC) strategies can significantly improve control performance and online adaptation capabilities for real-world applications. Still, enabling state-of-the-art implementations of learning-based models for MPC is complicated by the chal

Abdul Saboor, Shoaib Khalid, Anderson Janotti

Adding a few atomic percent of Bi to III--V semiconductors leads to significant changes in their electronic structure and optical properties. Bismuth substitution on the pnictogen site leads to a large increase in spin-orbit splitting $\Delta_{\rm SO}$ at the top of the valence band ($\Gamma_{8v}-\Gamma_{7v}$) and a large reduction in the band gap, creating

Xiaoxi Jia, Kai Wang

The proximal gradient method is a standard approach for solving composite minimization problems in which the objective function is the sum of a continuously differentiable function and a lower semicontinuous, extended-valued function. The traditional convergence theory for both monotone and nonmonotone variants replies heavily on the assumption of global Lip

A. Logachov, O. Logachova, A. Yambartsev

In this paper, we expand and generalize the findings presented in our previous work on the law of large numbers and the large deviation principle for Poisson processes with uniform catastrophes. We study three distinct scalings: sublinear (moderate deviations), linear (large deviations), and superlinear (superlarge deviations). Across these scales, we establ

Hong-Wei Li, Yi-Hao Fan, Shu-Ting Shen, Xiao-Jing Yan

Quantum coherence, a fundamental aspect of quantum mechanics, plays a crucial role in various quantum information tasks. However, preserving coherence under extreme conditions, such as relativistic acceleration, poses significant challenges. In this paper, we investigate the influence of Unruh temperature and energy levels on the evolution of maximal steered

Pranav Vaidhyanathan, Florian Marquardt, Mark T. Mitchison, Natalia Ares

Attention-based neural networks such as transformers have revolutionized various fields such as natural language processing, genomics, and vision. Here, we demonstrate the use of transformers for quantum feedback control through both a supervised and reinforcement learning approach. In particular, due to the transformer's ability to capture long-range tempor

Mariana L. S. Dias, Antônio F. B. da Cunha, Carlos A. P. Bengaly, Rodrigo S. Gonçalves

The assumption of a flat Universe that follows the cosmological principle, i.e., that the universe is statistically homogeneous and isotropic at large scales, comprises one of the core foundations of the standard cosmological model -- namely, the $\Lambda$CDM paradigm. Nevertheless, it has been rarely tested in the literature. In this work, we assess the val

Masaharu Kodama, Runhe Huang

Skeleton detection is a technique that can beapplied to a variety of situations. It is especially critical identifying and tracking the movements of the elderly, especially in real-time fall detection. While conventional image processing methods exist, there's a growing preference for utilizing pointclouds data collected by mmWave radars from viewpoint of pr

Viswanathan Palaniappan, S. Ramanan, Michael Urban

The properties of dilute neutron matter are mostly determined by the s-wave two-body (2N) interaction, while three-body (3N) interactions are suppressed by the Pauli principle. In a previous work, we showed that it can be advantageous to use renormalization group based effective interactions with cutoffs scaled with the Fermi momentum, especially at low dens

Daniel Pook-Kolb, Erik Agrell, Bruce Allen

New lattice quantizers with lower normalized second moments than previously reported are constructed in 13 and 14 dimensions and conjectured to be optimal. Our construction combines an initial numerical optimization with a subsequent analytical optimization of families of lattices, whose Voronoi regions are constructed exactly. The new lattices are construct

Pierrick Philippe, Théo Ladune, Gordon Clare, Félix Henry

Neural image compression, based on auto-encoders and overfitted representations, relies on a latent representation of the coded signal. This representation needs to be compact and uses low resolution feature maps. In the decoding process, those latents are upsampled and filtered using stacks of convolution filters and non linear elements to recover the decod

Xiaofan Niu, Minquan Cheng, Kai Wan, Robert Caiming Qiu

Reconfigurable Intelligent Surface (RIS) has emerged as a promising technology to enhance the wireless propagation environment for next-generation wireless communication systems. This paper introduces a new RIS-assisted multiple-antenna coded caching problem. Unlike the existing multi-antenna coded caching models, our considered model incorporates a passive

Samira Elghaayda, Asad Ali, Saif Al-Kuwari, Artur Czerwinski

Finding a quantum battery model that demonstrates a quantum advantage while remaining feasible for experimental production is a considerable challenge. Here, a superconducting quantum battery (SQB) model that exhibits such an advantage is introduced. The model consists of two coupled superconducting qubits that interact during the unitary charging process wh

Xuehao Cui, Guangyang Wu, Zhenghao Gan, Guangtao Zhai

Existing methods to generate aesthetic QR codes, such as image and style transfer techniques, tend to compromise either the visual appeal or the scannability of QR codes when they incorporate human face identity. Addressing these imperfections, we present Face2QR-a novel pipeline specifically designed for generating personalized QR codes that harmoniously bl

Oriol Corcoll Andreu, Athanasios Vlontzos, Michael O'Riordan, Ciaran M. Gilligan-Lee

Estimating causal effects is vital for decision making. In standard causal effect estimation, treatments are usually binary- or continuous-valued. However, in many important real-world settings, treatments can be structured, high-dimensional objects, such as text, video, or audio. This provides a challenge to traditional causal effect estimation. While lever

Jinu Nyachhyon, Mridul Sharma, Prajwal Thapa, Bal Krishna Bal

The Nepali language has distinct linguistic features, especially its complex script (Devanagari script), morphology, and various dialects,which pose a unique challenge for Natural Language Understanding (NLU) tasks. While the Nepali Language Understanding Evaluation (Nep-gLUE) benchmark provides a foundation for evaluating models, it remains limited in scope

Jialin Wang

In the previous work, Lim and the author determined the rank variety of the simple $\mathbb{F}\mathfrak{S}_{kp}$-module $D(p-1)=D^{(kp-p+1,1^{p-1})}$ with respect to some maximal elementary abelian $p$-subgroup $E_k$ and the complexity when $k\not\equiv 1\pmod p$ and $p$ is odd. Their method relied on the dimension of the module, which is dependent on $k$. I

Michael Cummins, Guner Dilsad Er, Michael Muehlebach

We address the problem of client participation in federated learning, where traditional methods typically rely on a random selection of a small subset of clients for each training round. In contrast, we propose FedBack, a deterministic approach that leverages control-theoretic principles to manage client participation in ADMM-based federated learning. FedBac

Marius Lemm, Tom Wessel

Recent works have revealed the intricate effect of long-range interactions on information transport in quantum many-body systems: In $D$ spatial dimensions, interactions decaying as a power-law $r^{-\alpha}$ with $\alpha > 2 D+1$ exhibit a Lieb-Robinson bound (LRB) with a linear light cone and the threshold $2D +1$ is sharp in general. Here, we observe that

Marion Thaler, Abdullatif Köksal, Alina Leidinger, Anna Korhonen

As LLMs are increasingly integrated into user-facing applications, addressing biases that perpetuate societal inequalities is crucial. While much work has gone into measuring or mitigating biases in these models, fewer studies have investigated their origins. Therefore, this study examines the correlation between gender-occupation bias in pre-training data a

Wenda Shi, Yiren Song, Dengming Zhang, Jiaming Liu

Visual text rendering are widespread in various real-world applications, requiring careful font selection and typographic choices. Recent progress in diffusion transformer (DiT)-based text-to-image (T2I) models show promise in automating these processes. However, these methods still encounter challenges like inconsistent fonts, style variation, and limited f

Cipriano Junior Cioffo, Maria Emilia Maietti, Samuele Maschio

We describe the fibrational structure of sets within the predicative variant $\mathbf{pEff}$ of Hyland's Effective Topos $\mathbf{Eff}$ previously introduced in Feferman's predicative theory of non-iterative fixpoints $\widehat{ID_1}$. Our structural analysis can be carried out in constructive and predicative variants of $\mathbf{Eff}$ within extensions of A

Marino Gran, Andrea Sciandra

Hopf braces have been introduced as a Hopf-theoretic generalization of skew braces. Under the assumption of cocommutativity, these algebraic structures are equivalent to matched pairs of actions on Hopf algebras, that can be used to produce solutions of the quantum Yang-Baxter equation. We prove that the category of cocommutative Hopf braces is semi-abelian