August 2024 arXiv papers — page 5

William J. Wolf, Carlos García-García, Deaglan J. Bartlett, Pedro G. Ferreira

New constraints on the expansion rate of the Universe seem to favor evolving dark energy in the form of thawing quintessence models, i.e., models for which a canonical, minimally coupled scalar field has, at late times, begun to evolve away from potential energy domination. We scrutinize the evidence for thawing quintessence by exploring what it predicts for

J. A. Montañez-Barrera, G. P. Beretta, Kristel Michielsen, Michael R. von Spakovsky

As quantum processing units (QPUs) scale toward hundreds of qubits, diagnosing noise-induced correlations (crosstalk) becomes critical for reliable quantum computation. In this work, we introduce Zero-Entropy Classical Shadows (ZECS), a diagnostic tool that uses information of a rank-one quantum state tomography (QST) reconstruction from classical shadow (CS

José Villamar, Matthias Kelbling, Heather L. More, Michael Denker

Computer simulations are an essential pillar of knowledge generation in science. Exploring, understanding, reproducing, and sharing the results of simulations relies on tracking and organizing the metadata describing the numerical experiments. The models used to understand real-world systems, and the computational machinery required to simulate them, are typ

Xingni Jiang, Jan Harm van der Walt, Marten Wortel

We develop integration theory for integrating functions taking values into a Dedekind complete unital $f$-algebra $\mathbb{L}$ with respect to $\mathbb{L}$-valued measures. We then discuss and prove completeness results of $\mathbb{L}$-valued $L^p$-spaces.

Yuji Tsuno

For any commutative finite flat group scheme, Grothendieck constructed an embedding into some smooth group scheme. This embedding is called the Grothendieck resolution. Let $p$ be a prime number and $n$ a positive integer. In connection with the normal basis problems in the framework of group schemes proposed by Suwa and the author, we consider the Grothendi

Andrea Conti, Niall T. Macpherson

We derive necessary and sufficient conditions for AdS$_3$ solutions of $d=11$ supergravity to preserve ${\cal N}=(1,1)$ supersymmetry in terms of G-structures. Such solutions necessarily support an SU(3)-structure on the internal 8-manifold M$_8$, in terms of which we phrase the conditions for supersymmetry preservation. We use this to derive the local form

Boris Meden, Asma Brazi, Fabrice Mayran de Chamisso, Steve Bourgeois

6D pose estimation aims at determining the object pose that best explains the camera observation. The unique solution for non-ambiguous objects can turn into a multi-modal pose distribution for symmetrical objects or when occlusions of symmetry-breaking elements happen, depending on the viewpoint. Currently, 6D pose estimation methods are benchmarked on data

Muryel Guolo, Andrew Mummery

We implement a standard thin disk model with the outer disk radius ($R_{\rm out}$) as a free parameter, integrating it into standard X-ray fitting package to enable self-consistent and simultaneous fitting of X-ray spectra and UV/optical/NIR photometry. We apply the model to the late-time data ($Δt \approx 350-1300$ days) of the tidal disruption event (TDE)

Lasse H. Wolff, Paula Belzig, Matthias Christandl, Bergfinnur Durhuus

The optimal rate of reliable communication over a quantum channel can be enhanced by pre-shared entanglement. Whereas the enhancement may be unbounded in infinite-dimensional settings even when the input power is constrained, a long-standing conjecture asserts that the ratio between the entanglement-assisted and unassisted classical capacities is bounded in

Xihong Su, Julien Grand-Clément, Marek Petrik

Optimizing risk-averse objectives in discounted MDPs is challenging because most models do not admit direct dynamic programming equations and require complex history-dependent policies. In this paper, we show that the risk-averse {\em total reward criterion}, under the Entropic Risk Measure (ERM) and Entropic Value at Risk (EVaR) risk measures, can be optimi

Rhui Dih Lee, Laura Wynter, Raghu Kiran Ganti

We present a toolkit for creating low-cost Mixture-of-Domain-Experts (MOE) from trained models. The toolkit can be used for creating a mixture from models or from adapters. We perform extensive tests and offer guidance on defining the architecture of the resulting MOE using the toolkit. A public repository is available.

Clara Panchaud, Ruth King, David Borchers, Hannah Worthington

Obtaining reliable and precise estimates of wildlife species abundance and distribution is essential for the conservation and management of animal populations and natural reserves. Spatial capture-recapture (SCR) models provide estimates of population size and spatial density from data collected from remote sensors such as camera traps. Such data contain spa

Qinji Shu, Hang Sheng, Feng Ji, Hui Feng

To accelerate the training of graph convolutional networks (GCNs) on real-world large-scale sparse graphs, downsampling methods are commonly employed as a preprocessing step. However, the effects of graph sparsity and topological structure on the transferability of downsampling methods have not been rigorously analyzed or theoretically guaranteed, particular

Baichuan Zhou, Haote Yang, Dairong Chen, Junyan Ye

Recent evaluations of Large Multimodal Models (LMMs) have explored their capabilities in various domains, with only few benchmarks specifically focusing on urban environments. Moreover, existing urban benchmarks have been limited to evaluating LMMs with basic region-level urban tasks under singular views, leading to incomplete evaluations of LMMs' abilit

Eteri Samsonadze

The sufficient conditions for solvability of a linear Diophantine equation $\sum_{i=1}^{n}a_ix_i=b$ (with $a_1,a_2,...,a_n\in \mathbb{N}$) in non-negative integers $x_1,x_2,...,x_n$ are given. The explicit formulas are given for Frobenius numbers $g(a_1,a_2,...,a_n)$, for some particular cases,. Besides, a new recurrent method of studying the problem of solv

Yu Liu, Martin B. Plenio

Hybrid quantum architectures that integrate matter and photonic degrees of freedom present a promising pathway toward scalable, fault-tolerant quantum computing. This approach needs to combine well-established entangling operations between distant registers using photonic degrees of freedom with direct interactions between matter qubits within a solid-state

Renyong Guan, Yuxi Hu

This paper investigates the time asymptotic stability of composite waves formed by two shock waves within the context of one-dimensional relaxed compressible Navier-Stokes equations. We demonstrate that the composite waves consisting of two viscous shocks achieve asymptotic nonlinear stability under the condition of having two small, independent wave strengt

Jorge Cayao, Masatoshi Sato

We study non-Hermitian Josephson junctions formed by multiple superconductors and discover the emergence of exceptional points entirely determined by the interplay of the distinct superconducting phases and non-Hermiticity due to normal reservoirs. In particular, in Josephson junctions with three and four superconductors, we find stable lines and surfaces of

Tong Nie, Junlin He, Yuewen Mei, Guoyang Qin

The proliferation of e-commerce and urbanization has significantly intensified delivery operations in urban areas, boosting the volume and complexity of delivery demand. Data-driven predictive methods, especially those utilizing machine learning techniques, have emerged to handle these complexities in urban delivery demand management problems. One particular

Fernando Baltazar-Larios, Mogens Bladt, Michael Sørensen

Stochastic differential equations provide a powerful tool for modelling dynamic phenomena affected by random noise. In case of repeated observations of time series for several experimental units, it is often the case that some of the parameters vary between the individual experimental units, which has motivated a considerable interest in stochastic different

Hudson T. Horne, Collin M. Hugo, Brandon C. Reid, Daniel F. Santavicca

We report a systematic comparison of niobium nitride thin films deposited on oxidized silicon substrates by reactive DC magnetron sputtering and reactive high-power impulse magnetron sputtering (HiPIMS). After determining the nitrogen gas concentration that produces the highest superconducting critical temperature for each process, we characterize the depend

Mouxiang Chen, Lefei Shen, Zhuo Li, Xiaoyun Joy Wang

Foundation models have emerged as a promising approach in time series forecasting (TSF). Existing approaches either repurpose large language models (LLMs) or build large-scale time series datasets to develop TSF foundation models for universal forecasting. However, these methods face challenges due to the severe cross-domain gap or in-domain heterogeneity. T

Mingjun Sun, Shaochuan Wu, Haojie Wang, Yuanwei Liu

Deep learning is widely used in wireless communications but struggles with fixed neural network sizes, which limit their adaptability in environments where the number of users and antennas varies. To overcome this, this paper introduced a generalization strategy for precoding and power allocation in scalable wireless networks. Initially, we employ an innovat

Chelsea Zou, Kenneth J. Kurtz

We introduce a cluster-based generative image segmentation framework to encode higher-level representations of visual concepts based on one-shot learning inspired by the Omniglot Challenge. The inferred parameters of each component of a Gaussian Mixture Model (GMM) represent a distinct topological subpart of a visual concept. Sampling new data from these par

Xiao-Ping Chen, Chun-Lei Tang

This paper focuses on remainder estimates of the magnetic $L^p$-Hardy inequalities for $1<p<2$. \emph{Firstly}, we establish a family of remainder terms involving magnetic gradients of the magnetic $L^p$-Hardy inequalities, which are also new even for the classical $L^p$-Hardy inequalities. \emph{Secondly}, we study another family of remainder terms involvin

Matthieu Barreau, Nicola Bastianello

In this paper, we address the problem of discovering maximal Lyapunov functions, as a means of determining the region of attraction of a dynamical system. To this end, we design a novel neural network architecture, which we prove to be a universal approximator of (maximal) Lyapunov functions. The architecture combines a local quadratic approximation with the

Yiwen Gu, Junchuan Gu, Haibin Shen, Kejie Huang

Spike trains serve as the primary medium for information transmission in Spiking Neural Networks, playing a crucial role in determining system efficiency. Existing encoding schemes based on spike counts or timing often face severe limitations under low-timestep constraints, while more expressive alternatives typically involve complex neuronal dynamics or sys

Tai Dinh, Wong Hauchi, Philippe Fournier-Viger, Daniil Lisik

The clustering of categorical data is a common and important task in computer science, offering profound implications across a spectrum of applications. Unlike purely numerical data, categorical data often lack inherent ordering as in nominal data, or have varying levels of order as in ordinal data, thus requiring specialized methodologies for efficient orga

Jianhai Bao, Goncalo Dos Reis, Yue Wu

In this paper, we study well-posedness of random periodic solutions of stochastic differential equations (SDEs) of McKean-Vlasov type driven by a two-sided Brownian motion, where the random periodic behaviour is characterised by the equations&#39; long-time behaviour. Given the well-known connection between McKean-Vlasov SDEs and interacting particle systems

Boqiao Lai

Proteins are the fundamental macromolecules that play diverse and crucial roles in all living matter and have tremendous implications in healthcare, manufacturing, and biotechnology. Their functions are largely determined by the sequences of amino acids that compose them and their unique three-dimensional structures when folded. The recent surge in highly ac

John M. Ball, Christopher L. Horner

The purpose of this paper is to analyze a nonlinear elasticity model introduced by the authors for comparing two images, regarded as bounded open subsets of $\R^n$ together with associated vector-valued intensity maps. Optimal transformations between the images are sought as minimisers of an integral functional among orientation-preserving homeomorphisms. Th

Lorenzo Guerra, Linhan Xu, Paolo Bellavista, Thomas Chapuis

The integration of digital devices in modern vehicles has revolutionized automotive technology, enhancing safety and the overall driving experience. The Controller Area Network (CAN) bus is a central system for managing in-vehicle communication between the electronic control units (ECUs). However, the CAN protocol poses security challenges due to inherent vu

Haoning Dang, Fei Wang, Song Jiang

Randomized neural network (RaNN) methods have been proposed for solving various partial differential equations (PDEs), demonstrating high accuracy and efficiency. However, initializing the fixed parameters remains challenging. Additionally, RaNNs often struggle to approximate PDE solutions with sharp gradients or discontinuities when using smooth activations

Jakub Kořenek, Pavel Sanda, Jaroslav Hlinka

The description of the dynamics of complex systems, in particular the capture of the interaction structure and causal relationships between elements of the system, is one of the central questions of interdisciplinary research. While the characterization of pairwise causal interactions is a relatively ripe field with established theoretical concepts and the c

F. Alemanno, Q. An, P. Azzarello, F. C. T. Barbato

Precise direct cosmic-ray (CR) measurements provide an important probe to study the energetic particle sources in our Galaxy, and the interstellar environment through which these particles propagate. Uncertainties on hadronic models, ion-nucleon cross sections in particular, are currently the limiting factor towards obtaining more accurate CR ion flux measur

Mitchell Lozier, Ivan Marusic, Rahul Deshpande

The amplitude modulation phenomena, defined originally by Mathis et al. (J. Fluid Mech., 628, 311-337; 2009), corresponds to a unique non-linear interaction between Reynolds number ($Re_τ$) dependent large-scale motions and $Re_τ$-invariant inner-scale motions observed in canonical wall-bounded flows. While similar non-linear interactions have been quantifie

G. Marchegiani, G. Catelani

Superconducting qubits hold promise for quantum computing, but their operation is challenged by various sources of noise, including excitations known as quasiparticles. Qubits with gap asymmetry larger than their transition energy are less susceptible to quasiparticle decoherence as the quasiparticles are mostly trapped in the low-gap side of the junction. B

Roi Kugel, Joop Schaye, Matthieu Schaller, Victor J. Forouhar Moreno

Galaxy cluster counts have historically been important for the measurement of cosmological parameters and upcoming surveys will greatly reduce the statistical errors. To exploit the potential of current and future cluster surveys, theoretical uncertainties on the predicted abundance must be smaller than the statistical errors. Models used to predict cluster

M. Antonello, L. Eikelmann, E. Garutti, R. Klanner

Beam tests using tracking telescopes are a standard method for determining the spatial resolution of detectors. This requires the precise knowledge of the position resolution of beam tracks reconstructed at the Device Under Test (DUT). A method is proposed which achieves this using a segmented silicon detector with readout with charge digitization. It is fou

David Ribar, Clifford E. Woodward, Jan Forsman

Recent experimental results by the Surface Force Apparatus (SFA) have identified a dramatic deviation from previously established theories of simple electrolytes. This deviation, referred to as anomalous underscreening, suggests that the range of electrostatic interactions increase upon a further addition of salt, beyond some threshold concentration (usually

Paulo Emílio de Vilhena, Ori Lahav, Viktor Vafeiadis, Azalea Raad

Programs written in C/C++ often include inline assembly: a snippet of architecture-specific assembly code used to access low-level functionalities that are impossible or expensive to simulate in the source language. Although inline assembly is widely used, its semantics has not yet been formally studied. In this paper, we overcome this deficiency by investig

Runwei Guan, Jianan Liu, Liye Jia, Haocheng Zhao

Recently, visual grounding and multi-sensors setting have been incorporated into perception system for terrestrial autonomous driving systems and Unmanned Surface Vehicles (USVs), yet the high complexity of modern learning-based visual grounding model using multi-sensors prevents such model to be deployed on USVs in the real-life. To this end, we design a lo

Reinder Meinsma

This paper explores the relationship between L-equivalence and D-equivalence for K3 surfaces and hyperkähler manifolds. Building on Efimov&#39;s approach using Hodge theory, we prove that very general L-equivalent K3 surfaces are D-equivalent, leveraging the Derived Torelli Theorem for K3 surfaces. Our main technical contribution is that two distinct lattice

YuTung Yau

Generalizing deformation quantizations with separation of variables of a Kähler manifold $M$, we adopt Fedosov&#39;s gluing argument to construct a category $\mathsf{DQ}$, enriched over sheaves of $\mathbb{C}[[\hbar]]$-modules on $M$, as a quantization of the category of Hermitian holomorphic vector bundles over $M$ with morphisms being smooth sections of ho

Celeste Mendes, Gloria M. Buendia, Per Arne Rikvold

We perform a numerical study of the kinetic Blume-Capel (BC) model to find if it exhibits the metamagnetic anomalies previously observed in the kinetic Ising model for supercritical periods. We employ a heat-bath Monte Carlo (MC) algorithm on a square lattice in which spins can take values of $\pm 1, 0$, with a non-zero crystal field, subjected to a sinusoid

Thomas Schnake, Farnoush Rezaei Jafari, Jonas Lederer, Ping Xiong

Explainable Artificial Intelligence (XAI) plays a crucial role in fostering transparency and trust in AI systems, where traditional XAI approaches typically offer one level of abstraction for explanations, often in the form of heatmaps highlighting single or multiple input features. However, we ask whether abstract reasoning or problem-solving strategies of

Zhengwei Liu, Yuze Ruan

We explicitly construct a (unitary) $\mathbb{Z}/2\mathbb{Z}$ permutation gauging of a (unitary) modular category $\mathcal{C}$. In particular, the formula for the modular data of the gauged theory is provided in terms of modular data of $\mathcal{C}$, which provides positive evidence of the reconstruction program. Moreover as a direct consequence, the formul

J. Kluson

We expand bimetric theory of gravity in small speed of light limit. We find electric and magnetic Carrollian form of the action and discuss their properties.

M. Lanza de Cristoforis

The aim of this paper is to prove continuity results for the volume potential corresponding to the fundamental solution of a second order differential operator with constant coefficients in Schauder spaces of negative exponent and to generalize some classical results in Schauder spaces of positive exponents.

Kamal Santra

Let $G=(V, E)$ be a graph where $V$ and $E$ are the vertex and edge sets, respectively. For two disjoint subsets $A$ and $B$ of $V$, we say $A$ \textit{dominates} $B$ if every vertex of $B$ is adjacent to at least one vertex of $A$ in $G$. A vertex partition $π= \{V_1, V_2, \ldots, V_k\}$ of $G$ is called a \emph{transitive partition} of size $k$ if $V_i$ do

Zhi Zhao, Fatih Kızılaslan

We introduce a generalized promotion time cure model motivated by a new biological consideration. The new approach is flexible to model heterogeneous survival data, in particular for addressing intra-sample heterogeneity. We also indicate that the new approach is suited to model a series or parallel system consisting of multiple subsystems in reliability ana

Ayan Roychowdhury, Saptarshi Dasgupta, Madan Rao

Material renewability in active living systems, such as in cells and tissues, can drive the large-scale patterning of forces, with distinctive phenotypic consequences. This is especially significant in the cell cytoskeleton, where multiple species of myosin bound to actin, apply differential contractile stresses and undergo differential turnover, giving rise

Giuseppe Raguní

Two-Time relativistic Bohmian Model (TTBM) is a theory in which the apparently paradoxical aspects of Quantum Mechanics are the effect of the existence of an extra unobservable time dimension. The hypothesis that matter is capable of motion with respect to an additional independent time (thus resulting instantaneous with respect to usual time) is capable of

Luca Giamattei, Antonio Guerriero, Roberto Pietrantuono, Stefano Russo

Context: Software Quality Assurance (SQA) is a fundamental part of software engineering to ensure stakeholders that software products work as expected after release in operation. Machine Learning (ML) has proven to be able to boost SQA activities and contribute to the development of quality software systems. In this context, Causal Reasoning is gaining incre

ST Wang

In this paper, we investigate the optimal strategies in the Werewolf Game-a widely played strategic social deduction game involving two opposing factions-from a game-theoretic perspective. We consider two scenarios: the game without a prophet and the game with a prophet. In the scenario without a prophet, we propose an enhanced strategy called ``random strat

Debmalya Bandyopadhyay, Allan Lo

Let $K_n^{(k)}$ be the complete $k$-graph on $n$ vertices. A $k$-uniform tight cycle is a $k$-graph with its vertices cyclically ordered so that every $k$ consecutive vertices form an edge and any two consecutive edges share exactly $k-1$ vertices. A result of Bustamante, Corsten, Frankl, Pokrovskiy and Skokan shows that all $r$-edge coloured $K_{n}^{(k)}$ c

Zhen Ye, Peiwen Sun, Jiahe Lei, Hongzhan Lin

Recent advancements in audio generation have been significantly propelled by the capabilities of Large Language Models (LLMs). The existing research on audio LLM has primarily focused on enhancing the architecture and scale of audio language models, as well as leveraging larger datasets, and generally, acoustic codecs, such as EnCodec, are used for audio tok

Renu Chaudhary, Simeon Reich, Juan J. Nieto

This paper deals with time-fractional stochastic Navier-Stokes equations, which are characterized by the coexistence of stochastic noise and a fractional power of the Laplacian. We establish sufficient conditions for the existence and approximate controllability of a unique mild solution to time-fractional stochastic Navier-Stokes equations. Using a fixed po

Benedikt Jahnel, Jonas Köppl, Yannic Steenbeck, Alexander Zass

We prove the Gibbs variational principle for the Asakura--Oosawa model in which particles of random size obey a hardcore constraint of non-overlap and are additionally subject to a temperature-dependent area interaction. The particle size is unbounded, leading to infinite-range interactions, and the potential cannot be written as a $k$-body interaction for f

Zhen Fan, Peng Dai, Zhuo Su, Xu Gao

Egocentric human pose estimation (HPE) using wearable sensors is essential for VR/AR applications. Most methods rely solely on either egocentric-view images or sparse Inertial Measurement Unit (IMU) signals, leading to inaccuracies due to self-occlusion in images or the sparseness and drift of inertial sensors. Most importantly, the lack of real-world datase

ATLAS Collaboration

A search is presented for a heavy scalar ($H$) or pseudo-scalar ($A$) predicted by the two-Higgs-doublet models, where the $H/A$ is produced in association with a top-quark pair ($t\bar{t}H/A$), and with the $H/A$ decaying into a $t\bar{t}$ pair. Events are selected requiring exactly one or two opposite-charge electrons or muons. Data-driven corrections are

Amirfarhad Farhadi, Homayoun Safarpour Motealegh Mahalegi, Abolfazl Pourrezaeian Firouzabad, Azadeh Zamanifar

The supply chain is a critical segment of the product manufacturing cycle, continuously influenced by risky, uncertain, and undesirable events. Optimizing flexibility in the supply chain presents a complex, multi-objective, and nonlinear programming challenge. In the poultry supply chain, the development of mass customization capabilities has led manufacturi

Amartya Pal, Arnob Kumar Ghosh

In recent years, there has been a surge of interest in exploring higher-order topology and their semi-metallic counterparts, particularly in the context of Dirac, Weyl, and nodal line semimetals, termed as higher-order Dirac semimetal (HODSM), higher-order Weyl semimetal, and higher-order nodal line semimetal (HONLSM). The HODSM phase exhibits hinge Fermi ar

Chayadon Lumbut, Donlapark Ponnoprat

This study investigates privacy leakage in dimensionality reduction methods through a novel machine learning-based reconstruction attack. Employing an informed adversary threat model, we develop a neural network capable of reconstructing high-dimensional data from low-dimensional embeddings. We evaluate six popular dimensionality reduction techniques: princi

Geetika, Somya Tyagi, Bapi Chatterjee

The classical line search for learning rate (LR) tuning in the stochastic gradient descent (SGD) algorithm can tame the convergence slowdown due to data-sampling noise. In a federated setting, wherein the client heterogeneity introduces a slowdown to the global convergence, line search can be relevantly adapted. In this work, we show that a stochastic varian

Mingyuan Zhang, Zhicheng Zhang, Hao Wu, Yong Wang

We present flow matching for reaction coordinates (FMRC), a novel deep learning algorithm designed to identify optimal reaction coordinates (RC) in biomolecular reversible dynamics. FMRC is based on the mathematical principles of lumpability and decomposability, which we reformulate into a conditional probability framework for efficient data-driven optimizat

Artemis Stefanidou, Jorgen Cani, Thomas Papadopoulos, Panagiotis Radoglou-Grammatikis

Over the recent years, the protection of the so-called `soft-targets&#39;, i.e. locations easily accessible by the general public with relatively low, though, security measures, has emerged as a rather challenging and increasingly important issue. The complexity and seriousness of this security threat growths nowadays exponentially, due to the emergence of n

Yabiao Wang, Shuo Wang, Jiangning Zhang, Ke Fan

Human-human motion generation is essential for understanding humans as social beings. Current methods fall into two main categories: single-person-based methods and separate modeling-based methods. To delve into this field, we abstract the overall generation process into a general framework MetaMotion, which consists of two phases: temporal modeling and inte

Dimitrios Kouzapas, Christos G. Panayiotou, Demetrios G. Eliades

Modern industrial systems require frequent updates to their cyber and physical infrastructures, often demanding considerable reconfiguration effort. This paper introduces the industrial Cyber-Physical Systems Description Language, iCPS-DL, which enables autonomic reconfigurations for industrial Cyber-Physical Systems. The iCPS-DL maps an industrial process u

Xiaodi Li, Jianfeng Gui, Qian Gao, Haoyuan Shi

Graph Neural Networks have been widely applied in critical decision-making areas that demand interpretable predictions, leading to the flourishing development of interpretability algorithms. However, current graph interpretability algorithms tend to emphasize generality and often overlook biological significance, thereby limiting their applicability in predi

S. M. Troshin, N. E. Tyurin

We discuss decreasing energy behavior of the differential cross-section of elastic hadron interactions at fixed large values of transferred momenta (deep--elastic scattering). This observable effect is relevant to the reflective scattering mode.

Gerald Williams

We consider a family of cyclic presentations and show that, subject to certain conditions on the defining parameters, they are spines of closed 3-manifolds. These are new examples where the reduced Whitehead graphs are of the same type as those of the Fractional Fibonacci presentations; here the corresponding manifolds are often (but not always) hyperbolic.

Ngân Lê, Le Ngoc Tram, Agata Karska, Thiem Hoang

(abridged) Together with gravity, turbulence, and stellar feedback, magnetic fields (B-fields) are thought to play a critical role in the evolution of molecular clouds and star formation processes. We aim to map the morphology and measure the strength of B-fields of the nearby molecular cloud, rho Ophiuchus-A ($ρ$ Oph-A), and then to understand the role of B

Yoshitatsu Matsuda, Kazunori Yamaguch

Independent component analysis (ICA) is a widely used method in various applications of signal processing and feature extraction. It extends principal component analysis (PCA) and can extract important and complicated components with small variances. One of the major problems of ICA is that the uniqueness of the solution is not guaranteed, unlike PCA. That i

Jesse J. Osborne, Ian P. McCulloch

Numerical methods based on matrix product states (MPSs) are currently the de facto standard for calculating the ground-state properties of (quasi-)one-dimensional quantum many-body systems. While the properties of the low-lying excitations in such systems are often studied in this MPS framework through _dynamics_ by means of time-evolution simulations, we ca

Jin Hu, Emrah Sever, Omid Babazadeh, Ian Jeffrey

An H-matrix accelerated direct solver employing the high-order Chebyshev-based Boundary Integral Equation (CBIE) method has been formulated, tested, and profiled for performance on high contrast dielectric materials and electrically large perfect electric conductor objects. The matrix fill performance of the CBIE proves to be fast for small to moderately siz

Siyuan Chen, Shihan Zhao, Weizhi Xiong, Ye Tian

The Muonium-to-Antimuonium Conversion Experiment (MACE) is proposed to search for charged lepton flavor violation and increase the sensitivity by more than two orders of magnitude compared to the MACS experiment at PSI in 1999. A clear signature of this conversion is the positron produced from antimuonium decay. This paper presents a near-$4π$-coverage calor

Camila Martinez Parra, Michel de Lara, Jean-Philippe Chancelier, Pierre Carpentier

The penetration of renewable energies requires additional storages to deal with intermittency. Accordingly, there is growing interest in evaluating the opportunity cost (usage value) associated with stored energy in large storages, a cost obtained by solving a multistage stochastic optimization problem. Today, to compute usage values under uncertainties, an

O. Deligny

Dark matter particles could be superheavy (mass $M_X\gtrsim10^9~$GeV) provided that their lifetime $τ_X$ is extremely long, i.e. greater than $\simeq 10^{22}~$yr. Such stringent constraints on $τ_X$ are generally obtained by limiting the prompt emission of ultrahigh energy ($\gtrsim10^9~$GeV) gamma rays and neutrinos from the decay processes to below the cor

Ryoshun Oba

Generalizing the strong Lefschetz property for an $\mathbb{N}$-graded algebra, we introduce the multigraded strong Lefschetz property for an $\mathbb{N}^m$-graded algebra. We show that, for $\mathbf{a} \in \mathbb{N}^m_+$, the generic $\mathbb{N}^m$-graded Artinian reduction of the Stanley-Reisner ring of an $\mathbf{a}$-balanced homology sphere over a field

Yifan Jiang, Jan Obloj

We study the causal distributionally robust optimization (DRO) in both discrete- and continuous- time settings. The framework captures model uncertainty, with potential models penalized in function of their adapted Wasserstein distance to a given reference model. Strength of the penalty is controlled using a real-valued parameter which, in the special case o

Etienne Levecque, Jan Butora, Patrick Bas

Given a JPEG pipeline (compression or decompression), this paper demonstrates how to find the antecedent of an 8x8 block. If it exists, the block is considered compatible with the pipeline. For unaltered images, all blocks remain compatible with the original pipeline; however, for manipulated images, this is not necessarily true. This article provides a firs

Tianqi Yue, Jin Wang

Recently, the thermodynamics and kinetics of black hole phase transitions have garnered attention, particularly with the black hole radius being utilized as an order parameter within a generalized free energy landscape. In this framework, the local minima and maxima of the free energy correspond to stable and unstable states, respectively, while other states

Hajime Ueda, Shun Katakami, Masato Okada

Phase retrieval refers to the problem of recovering a high-dimensional vector $\boldsymbol{x} \in \mathbb{C}^N$ from the magnitude of its linear transform $\boldsymbol{z} = A \boldsymbol{x}$, observed through a noisy channel. To improve the ill-posed nature of the inverse problem, it is a common practice to observe the magnitude of linear measurements $\bold

Quanwei Dai, Kang Duan, Chung-Yee Kwok

Knowledge of the underlying mechanisms of multiphase flow dynamics in porous media is crucial for optimizing subsurface engineering applications like geological carbon sequestration. However, studying the micro-mechanisms of multiphase fluid--grain interactions in the laboratory is challenging due to the difficulty in obtaining mechanical data such as force

Liming Huang, Yulei Wu, Dimitra Simeonidou

The integration of Artificial Intelligence (AI) within 6G networks is poised to revolutionize connectivity, reliability, and intelligent decision-making. However, the performance of AI models in these networks is crucial, as any decline can significantly impact network efficiency and the services it supports. Understanding the root causes of performance degr

Avideep Mukherjee, Soumya Banerjee, Piyush Rai, Vinay P. Namboodiri

Diffusion-based models demonstrate impressive generation capabilities. However, they also have a massive number of parameters, resulting in enormous model sizes, thus making them unsuitable for deployment on resource-constraint devices. Block-wise generation can be a promising alternative for designing compact-sized (parameter-efficient) deep generative mode

Thomas Bergauer

The latest update of the European Strategy for Particle Physics stimulated the preparation of the European Detector Roadmap document in 2021 by the European Committee for Future Accelerators ECFA. This roadmap, defined during a bottom-up process by the community, outlines nine technology domains for HEP instrumentation and pinpoints urgent R&D topics, known

Vladan Jaguzović

Let \(P_+\) be the Riesz&#39;s projection operator and let \(P_-=I-P_+.\) We find the best upper estimates of the expression \(\left\lVert \left( \left\lvert P_+f \right\rvert ^s + \left\lvert P_-f \right\rvert ^s \right) ^{1/s} \right\rVert _p \) in terms of Lebesgue p-norm of the function \(f \in L^p(\mathbf{T})\) for \(p \in (4/3,2)\) and \(0 < s \leq \fr

Kaiwen Zhu, Zain Mehdi, Joseph J. Hope, Simon A. Haine

We develop a new approach for efficient and scalable simulations of measurement and control of quantum systems built upon existing phase-space methods, namely the Truncated Wigner Approximation (TWA). We benchmark against existing particle-filter methods by simulating measurement based feedback cooling in a two-mode system, whose low-dimensional nature permi

Wenbin Feng, Andrea Giusti, Roberto Casadio

The formalism of the horizon quantum mechanics is applied to electrically neutral and spherically symmetric black hole geometries emerging from coherent quantum states of gravity to compute the probability that the matter source is inside the horizon. We find that quantum corrections to the classical horizon radius become significant if the matter core has a

Chen Hu, Hanchi Ren, Jingjing Deng, Xianghua Xie

Federated learning is a machine learning paradigm that enables decentralized clients to collaboratively learn a shared model while keeping all the training data local. While considerable research has focused on federated image generation, particularly Generative Adversarial Networks, Variational Autoencoders have received less attention. In this paper, we ad

Roland Ketzmerick, Florian Lorenz, Jan Robert Schmidt

Resonance states in quantum chaotic scattering systems have a multifractal structure that depends on their decay rate. We show how classical dynamics describes this structure for all decay rates in the semiclassical limit. This result for chaotic scattering systems corresponds to the well-established quantum ergodicity for closed chaotic systems. Specificall

Victor Ryan, Alexis Derumigny

Elliptical distributions are a simple and flexible class of distributions that depend on a one-dimensional function, called the density generator. In this article, we study the non-parametric estimator of this generator that was introduced by Liebscher (2005). This estimator depends on two tuning parameters: a bandwidth $h$ -- as usual in kernel smoothing --

April Lynne D. Say-awen

A tiling is said to have infinite local complexity (ILC) if it contains infinitely many two-tile patches up to rigid motions. In this work, we provide examples of substitution rules that generate tilings with ILC. The proof relies on Danzer&#39;s algorithm, which assumes that the substitution factor is non-Pisot. In addition to ILC, the tiling space of each

Zizheng Huang, Haoxing Chen, Jiaqi Li, Jun Lan

Recent Vision Mamba (Vim) models exhibit nearly linear complexity in sequence length, making them highly attractive for processing visual data. However, the training methodologies and their potential are still not sufficiently explored. In this paper, we investigate strategies for Vim and propose Stochastic Layer-Wise Shuffle (SLWS), a novel regularization m

Bence Gábor, K. V. Adwaith, Dániel Varga, Bálint Sárközi

By considering linear scattering of laser-driven cold atoms inside an undriven high-finesse optical resonator, we experimentally demonstrate effects unique to a strongly coupled vacuum field. Arranging the atoms in an incommensurate lattice with respect to the radiation wavelength, the Bragg scattering into the cavity can be suppressed by destructive interfe

Claudio Durastanti

This paper investigates aliasing effects emerging from the reconstruction from discrete samples of spin spherical random fields defined on the two-dimensional sphere. We determine the location in the frequency domain and the intensity of the aliases of the harmonic coefficients in the Fourier decomposition of the spin random field and evaluate the consequenc

Xiao-Yun Wang, Yuan Gao, Xiang Liu

In this study, applying the effective Lagrangian approach, we investigate the reaction $π^-p\rightarrow K^{*}Σ$ to explore the production of the hidden-strange molecular pentaquarks $P_{s\bar{s}}$. Specifically, we analyze two scenarios, where the $J^P$ quantum numbers of $P_{s\bar{s}}$ are $3/2^-$ and $1/2^-$. Our results show that the contribution from $s$

Lucas Brunel, Mathieu Balesdent, Loïc Brevault, Rodolphe Le Riche

Multi-fidelity surrogate models combining dimensionality reduction and an intermediate surrogate in the reduced space allow a cost-effective emulation of simulators with functional outputs. The surrogate is an input-output mapping learned from a limited number of simulator evaluations. This computational efficiency makes surrogates commonly used for many-que