October 2024 arXiv papers — page 50

Wanja Timm Schulze, Sebastian Schwalbe, Kai Trepte, Stefanie Gräfe

In current electronic structure research endeavors such as warm dense matter or machine learning applications, efficient development necessitates non-monolithic software, providing an extendable and flexible interface. The open-source idea offers the advantage of having a source code base that can be reviewed and modified by the community. However, practical

Francesc Net, Marc Folia, Pep Casals, Lluis Gomez

This paper presents a comparative study of near-duplicate image detection techniques in a real-world use case scenario, where a document management company is commissioned to manually annotate a collection of scanned photographs. Detecting duplicate and near-duplicate photographs can reduce the time spent on manual annotation by archivists. This real use cas

Sai Prasanth Kotturi, Anil Kumar Yerrapragada, Sai Prasad, Radha Krishna Ganti

Accurate localization in indoor environments is a challenge due to the Non Line of Sight (NLoS) nature of the signaling. In this paper, we explore the use of AI/ML techniques for positioning accuracy enhancement in Indoor Factory (InF) scenarios. The proposed neural network, which we term LocNet, is trained on measurements such as Channel Impulse Response (C

Alexandru Căliman, Jérôme Daquin, Anne-Sophie Libert

Lagrangian descriptors (LDs) based on the arc length of orbits previously demonstrated their utility in delineating structures governing the dynamics. Recently, a chaos indicator based on the second derivatives of the LDs, referred to as $\vert\vert \Delta \rm{LD} \vert\vert$, has been introduced to distinguish regular and chaotic trajectories. Thus far, the

Isabelle Hoxha, Leo Sperber, Stefano Palminteri

The tendency of repeating past choices more often than expected from the history of outcomes has been repeatedly empirically observed in reinforcement learning experiments. It can be explained by at least two computational processes: asymmetric update and (gradual) choice perseveration. A recent meta-analysis showed that both mechanisms are detectable in hum

Navinder Singh

The famous Two-Temperature Model (TTM) used extensively in the investigations of energy relaxation in photo-excited systems originated in the seminal work of M. I. Kaganov, I. M. Lifshitz, and L. V. Tanatarov (KLT) in 1957. The idea that with an ultra short laser pulse a temporal (transient) state of electrons in a metal can be created in which electrons aft

Zizhe Zhang, Yuan Yang, Wenqiang Zuo, Guangming Song

The cooperation of a pair of robot manipulators is required to manipulate a target object without any fixtures. The conventional control methods coordinate the end-effector pose of each manipulator with that of the other using their kinematics and joint coordinate measurements. Yet, the manipulators' inaccurate kinematics and joint coordinate measurements ca

Vladimir Hinich

We study localizations of infinity categories that remain localizations after any base change.

Marina Evers, David Hägele, Sören Döring, Daniel Weiskopf

Progressive dimensionality reduction algorithms allow for visually investigating intermediate results, especially for large data sets. While different algorithms exist that progressively increase the number of data points, we propose an algorithm that allows for increasing the number of dimensions. Especially in spatio-temporal data, where each spatial locat

Sharare Zolghadr, Ole Winther, Paul Jeha

Generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have shown promise in sequential recommendation tasks. However, they face challenges, including posterior collapse and limited representation capacity. The work by Li et al. (2023) introduces a novel approach that leverages diffusion models to address these c

Lukas Pflug, Michael Stingl, Andrian Uihlein

Topology optimization under uncertainty or reliability-based topology optimization is usually numerically very expensive. This is mainly due to the fact that an accurate evaluation of the probabilistic model requires the system to be simulated for a large number of varying parameters. Traditional gradient-based optimization schemes thus face the difficulty t

Petra Sohlman, Risto Louhi, Janne Salonen

Using unique research data, we investigate disability retirement risk under the statutory public sector pension scheme in Finland. The statistical analysis yields two indicators: risk for upcoming permanent disability pension and critical duration of sickness absence days for public sector occupations. Statistical analysis is based on logistic regression mod

Yige Li, Hanxun Huang, Jiaming Zhang, Xingjun Ma

Backdoor attacks covertly implant triggers into deep neural networks (DNNs) by poisoning a small portion of the training data with pre-designed backdoor triggers. This vulnerability is exacerbated in the era of large models, where extensive (pre-)training on web-crawled datasets is susceptible to compromise. In this paper, we introduce a novel two-step defen

Daniel Galperin, Ullrich Köthe

Good generative models should not only synthesize high quality data, but also utilize interpretable representations that aid human understanding of their behavior. However, it is difficult to measure objectively if and to what degree desirable properties of disentangled representations have been achieved. Inspired by the principle of independent mechanisms,

Okuto Morikawa, Mizuki Tanaka, Masakiyo Kitazawa, Hiroshi Suzuki

We present the lattice simulation of the renormalization group flow in the $3$-dimensional $O(N)$ linear sigma model. This model possesses a nontrivial infrared fixed point, called Wilson--Fisher fixed point. Arguing that the parameter space of running coupling constants can be spanned by expectation values of operators evolved by the gradient flow, we exemp

Yuekun Dai, Qinyue Li, Shangchen Zhou, Yihang Luo

Line art colorization plays a crucial role in hand-drawn animation production, where digital artists manually colorize segments using a paint bucket tool, guided by RGB values from character color design sheets. This process, often called paint bucket colorization, involves two main tasks: keyframe colorization, where colors are applied according to the char

A. Kh. Khachatryan, Kh. A. Khachatryan, H. S. Petrosyan

A system of singular integral equations with monotone and concave nonlinearity in the subcritical case is investigated. The specified system and its scalar analog have direct applications in various areas of physics and biology. In particular, scalar and vector equations of this nature are encountered in the dynamic theory of p-adic strings, in the theory of

Jianbing Lu, Yu Zhuang

In this paper, we show that for a non-trivial quasi-symmetric $2$-design $\mathcal{D}$ with two block intersection numbers $x=0$ and $2\leq y\leq10$, if $G\leq \mathrm{Aut}(\mathcal{D})$ is flag-transitive and point-primitive, then $G$ is either of affine type or almost simple type. Moreover, we prove that the socle of $G$ cannot be an alternating group. If

Jinghui Wei, Qikun Tian, XinTing Xu, Guangzhao Qin

The Rashba effect in Janus structures, accompanied by nontrivial topology, plays an important role in spintronics and even photovoltaic applications. Herein, through first-principles calculations, we systematically investigate the geometric stability and electronic structures of 135 kinds of Janus MAA'ZxZ'(4-x) family derived from two-dimensional MA2Z4 (M=Mg

Matteo Di Giovanni, Paola Leaci, Pia Astone, Stefano Dal Pra

We present an improved method for vetoing candidates of continuous gravitational-wave sources during all-sky searches utilizing the Frequency Hough pipeline. This approach leverages linear correlations between source parameters induced by the Earth Doppler effect, which can be effectively identified through the Hough Transform. Candidates that do not align w

Hamna, Deepthi Sudharsan, Agrima Seth, Ritvik Budhiraja

Large Language Models (LLMs) and Text-To-Image (T2I) models have demonstrated the ability to generate compelling text and visual stories. However, their outputs are predominantly aligned with the sensibilities of the Global North, often resulting in an outsider's gaze on other cultures. As a result, non-Western communities have to put extra effort into gener

Koji Harada, Shuichiro Tao, Qiang Yin

We calculate the false-vacuum decay rate in one-dimensional quantum mechanics on the basis of the saddle-point approximation in the Euclidean path integral at finite temperature. The saddle points are the finite-T and shifted bounce solutions, which are finite-period analogs of the (zero-temperature) bounce solution, and the shot solutions. We re-examined th

Fabian Müller, Emre Köse, Alfred J. Meixner, Erik Schäffer

We present an innovative optical imaging system for measuring parameters of a small particle such as a macromolecule or nanoparticle at the quantum limit of sensitivity. In comparison to the conventional confocal interferometric scattering (iSCAT) approach, our setup adds a second arm to form a Michelson interferometer that allows us to tune a relative phase

Samuele Cattaruzzi

Fragmentation functions are typically parametrised exploiting measurements performed in $\mathrm{e^+e^-}$ and $\mathrm{ep}$ collisions, under the assumption of universality across collision systems. Measurements of charm-hadron yields in proton--proton (pp) collisions at LHC have proved that the hadronisation of heavy quarks differs between hadronic and lept

Zhenming Yu, Liming Cheng, Hongyu Huang, Wei Zhang

Communication enables the expansion of human visual perception beyond the limitations of time and distance, while computational imaging overcomes the constraints of depth and breadth. Although impressive achievements have been witnessed with the two types of technologies, the occlusive information flow between the two domains is a bottleneck hindering their

Joséphine Monzac, Slava Smartsev, Julius Huijts, Lucas Rovige

Laser-Wakefield Accelerators (LWFA) running at kHz repetition rates hold great potential for applications. They typically operate with low-energy, highly compressed laser pulses focused in high-pressure gas targets. Experiments have shown that the best-quality electron beams are achieved using Hydrogen gas targets. However, continuous operation with Hydrogen

Liding Zhang, Kuanqi Cai, Zewei Sun, Zhenshan Bing

Recent advancements in robotics have transformed industries such as manufacturing, logistics, surgery, and planetary exploration. A key challenge is developing efficient motion planning algorithms that allow robots to navigate complex environments while avoiding collisions and optimizing metrics like path length, sweep area, execution time, and energy consum

Sebastian Pineda Arango, Maciej Janowski, Lennart Purucker, Arber Zela

Finetuning is a common practice widespread across different communities to adapt pretrained models to particular tasks. Text classification is one of these tasks for which many pretrained models are available. On the other hand, ensembles of neural networks are typically used to boost performance and provide reliable uncertainty estimates. However, ensemblin

Valeri P. Frolov

We study spinoptics equations in the Schwarzschild spacetime. We demonstrate that using the explicit and hidden symmetries of this metric one can explicitly solve the equations for complex null tetrad associated with null rays representing photon's and graviton's motion. This allows one to integrate the spinoptics equations both for the electromagnetic and g

Gene Yu, Ce Guo, Wayne Luk

Agent-Based Model (ABM) validation is crucial as it helps ensuring the reliability of simulations, and causal discovery has become a powerful tool in this context. However, current causal discovery methods often face accuracy and robustness challenges when applied to complex and noisy time series data, which is typical in ABM scenarios. This study addresses

Phanindra Dewan, Sumantra Sarkar

Molecular communication (MC) is a model of information transmission where the signal is transmitted by information-carrying molecules through their physical transport from a transmitter to a receiver through a communication channel. Prior efforts have identified suitable "information molecules" whose efficacy for signal transmission has been studied extensiv

Halley Fritze, Sushovan Majhi, Marissa Masden, Atish Mitra

An important problem in topological data analysis (TDA)$\unicode{x2014}$of both theoretical and practical interest$\unicode{x2014}$is to reconstruct the topology and geometry of an underlying (usually unknown) metric graph from possibly noisy data sampled around it. Reeb graphs have recently been successfully employed in abstract metric graph reconstruction

Gerd Kortemeyer, Julian Nöhl

This study explores the use of artificial intelligence in grading high-stakes physics exams, emphasizing the application of psychometric methods, particularly Item Response Theory (IRT), to evaluate the reliability of AI-assisted grading. We examine how grading rubrics can be iteratively refined and how threshold parameters can determine when AI-generated gr

Anca Preda, Goran Senjanović, Michael Zantedeschi

The minimal $SO(10)$ grand unified theory, augmented by higher-dimensional operators, is based on the following Higgs representations: adjoint $45_{\rm H}$, spinor $16_{\rm H}$ and complex vector $10_{\rm H}$. It was recently realized that, as opposed to the conventional wisdom, any intermediate mass scale has to lie close to the grand unification one. This

Daniele Girolimetto, Tommaso Di Fonzo

Cross-temporal forecast reconciliation aims to ensure consistency across forecasts made at different temporal and cross-sectional levels. We explore the relationships between sequential, iterative, and optimal combination approaches, and discuss the conditions under which a sequential reconciliation approach (either first-cross-sectional-then-temporal, or fi

Leo Richter, Xuanli He, Pasquale Minervini, Matt J. Kusner

As language models (LMs) approach human-level performance, a comprehensive understanding of their behavior becomes crucial. This includes evaluating capabilities, biases, task performance, and alignment with societal values. Extensive initial evaluations, including red teaming and diverse benchmarking, can establish a model's behavioral profile. However, sub

Frits Vaandrager, Ivo Melse

A fault domain reflects a tester's assumptions about faults that may occur in an implementation and that need to be detected during testing. A fault domain that has been widely studied in the literature on black-box conformance testing is the class of finite state machines (FSMs) with at most $m$ states. Numerous strategies for generating test suites have be

Antti Käenmäki, Alex Rutar

We study the fine scaling properties of planar self-affine carpets. For Gatzouras--Lalley carpets, we give a precise formula for maximal Hausdorff dimension of a tangent in terms of the Hausdorff dimension of the projection and the Assouad dimension of the corresponding vertical slice. Using regularity properties for the Assouad dimension of non-autonomous s

Tommaso Boccato, Dmitrii Zendrikov, Nicola Toschi, Giacomo Indiveri

Mixed-signal implementations of SNNs offer a promising solution to edge computing applications that require low-power and compact embedded processing systems. However, device mismatch in the analog circuits of these neuromorphic processors poses a significant challenge to the deployment of robust processing in these systems. Here we introduce a novel archite

Zhijian Luo, Yicheng Li, Junhao Lu, Zhu Chen

Accurate estimation of photometric redshifts (photo-$z$s) is crucial for cosmological surveys. Various methods have been developed for this purpose, such as template fitting methods and machine learning techniques, each with its own applications, advantages, and limitations. In this study, we propose a new approach that utilizes a deep learning model based o

Hong-Yu Chen, Han Wang, En-Kun Li, Yi-Ming Hu

Binary black holes are one of the important sources for the TianQin gravitational wave project. Our research has revealed that, for TianQin, the signal-to-noise ratio of inspiral binary black holes can be computed analytically. This finding is expected to greatly simplify the estimation of detection capabilities for binary black holes. In this paper, we demo

Yixiu Mao, Qi Wang, Chen Chen, Yun Qu

In offline reinforcement learning (RL), addressing the out-of-distribution (OOD) action issue has been a focus, but we argue that there exists an OOD state issue that also impairs performance yet has been underexplored. Such an issue describes the scenario when the agent encounters states out of the offline dataset during the test phase, leading to uncontrol

Yun-Dong Wu, Wei Hong, Tong-Jie Zhang

The Hubble tension refers to the significant discrepancy in the Hubble constant $H_{0}$ obtained from two different measurement methods in cosmology. One method derives data from the Cosmic Microwave Background (CMB) observations by the Planck satellite, yielding a value of $67.4\pm{0.5} \ \mathrm{km\ s^{-1}} \mathrm{Mpc^{-1}} $, while the other method relie

I. M. Leibo

The coincidence of the $\Ind$ and $\dim$ dimensions for first countable paracompact $\sigma$-spaces is proved. As a corollary, the equality $\Ind X= \dim X$ for every Nagata (that is, first countable stratifiable) space $X$ is obtained. This gives a positive answer to A.~V.~Ar\-khan\-gel'\-skii's question of whether the dimensions $\ind$, $\Ind$, and $\dim$

Orazbek Narbek, Samat A. Kassabek, Targyn Nauryz

The inverse one-phase Stefan problem in one dimension, aimed at identifying the unknown time-dependent heat flux P(t) with a known moving boundary position s(t), is investigated. A previous study [16] attempted to reconstruct the unknown heat flux P(t) using the Variational Heat Polynomials Method (VHPM). In this paper, we develop the Collocation Heat Polyno

Xing-Jian Lv, Xiao-Jun Bi, Kun Fang, Peng-Fei Yin

Superluminal propagation is an intrinsic problem in the diffusion equation and has not been effectively addressed for a long time. In this work, a rigorous solution to this issue is obtained under the assumption that particles undergo a random flight process, where they move isotropically at a constant speed while experiencing random scatterings. We validate

Min Ru, Julie Tzu-Yueh Wang

We prove Vojta's abc conjecture for projective space ${\Bbb P}^n({\Bbb C})$, assuming that the entire curves in ${\Bbb P}^n({\Bbb C})$ are highly ramified over the coordinate hyperplanes. This extends the results of Guo Ji and the second-named author for the case $n=2$ (see \cite{GW22}). We also explore the corresponding results for projective toric varietie

Haowei Yang, Zhan Cheng, Zhaoyang Zhang, Yuanshuai Luo

As the complexity and dynamism of financial markets continue to grow, traditional financial risk prediction methods increasingly struggle to handle large datasets and intricate behavior patterns. This paper explores the feasibility and effectiveness of using deep learning and big data algorithms for financial risk behavior prediction. First, the application

Giovanni S. Alberti, Douglas Barnes, Aditya Jambhale, Richard Nickl

We remove the dependence on the `hot-spots' conjecture in two of the main theorems of the recent paper of Nickl (2024, Annals of Statistics). Specifically, we characterise the minimax convergence rates for estimation of the transition operator $P_{f}$ arising from the Neumann Laplacian with diffusion coefficient $f$ on arbitrary convex domains with smooth bo

Hassan A. Qureshi, Michael A. Papachatzakis, Ahmed Gaber Abdelmagid, Mikko Salomäki

Planar microcavity polaritons have recently emerged as a promising technology for improving several performance characteristics of organic light-emitting diodes, photodiodes and photovoltaics. To form polaritons and achieve enhanced performance, traditional microcavities with high reflectivity mirrors are fabricated by energy-intensive physical vapor deposit

Min Ru, Julie Tzu-Yueh Wang

This paper studies the defect relation through the GCD method. In particular, among other results, we extend the defect relation result of Chen, Huynh, Sun and Xie to moving targets. The truncated defect relation is also studied. Furthermore, we obtain the degeneracy locus, which can be determined effectively and is independent of the maps under the consider

Qiufan Lin, Hengxin Ruan, Dominique Fouchez, Shupei Chen

Obtaining well-calibrated photometric redshift probability densities for galaxies without a spectroscopic measurement remains a challenge. Deep learning discriminative models, typically fed with multi-band galaxy images, can produce outputs that mimic probability densities and achieve state-of-the-art accuracy. However, such models may be affected by miscali

Joseph C. Cooper, Adam Kirrander

The ground and excited state electronic structure of the molecular photoswitches quadricyclane and norbornadiene is examined qualitatively and quantitatively. A new custom basis set is introduced, optimised for efficient yet accurate calculations. A number of advanced multi-configurational and multi-reference electronic structure methods are evaluated, ident

Qi Xiong, Yan Gong, Xingchen Zhou, Hengjie Lin

We explore the joint weak lensing and galaxy clustering analysis from the photometric survey operated by the China Space Station Telescope (CSST), and study the strength of the cosmological constraints. We employ a high-resolution JiuTian-1G simulation to construct a partial-sky light cone to $z=3$ covering 100 deg$^2$, and obtain the CSST galaxy mock sample

Masashi Wakaiki

We investigate immediately differentiable $C_0$-semigroups $(e^{-tA})_{t \geq 0}$ satisfying $\sup_{0 < t <1} t^{1/\beta}\|Ae^{-tA}\| < \infty$ for some $0 < \beta \leq 1$. Such $C_0$-semigroups are referred to as the Crandall--Pazy class of $C_0$-semigroups. In the Hilbert space setting, we present two characterizations of the Crandall--Pazy class. We then

Javier Esparza, Peter Rossmanith, Stefan Schwoon

In a paper published in Information Processing Letters in 2000, Bouajjani et al. presented an automata-based approach to a number of elementary problems on context-free grammars. This approach is of pedagogical interest since it provides a uniform solution to decision procedures usually solved by independent algorithms in textbooks. This paper improves upon

Liam Barkley, Brink van der Merwe

Large Language Models (LLMs) are powerful computational models trained on extensive corpora of human-readable text, enabling them to perform general-purpose language understanding and generation. LLMs have garnered significant attention in both industry and academia due to their exceptional performance across various natural language processing (NLP) tasks.

Ryota Maruo, Koh Takeuchi, Hisashi Kashima

Designing two-sided matching mechanisms is challenging when practical demands for matching outcomes are difficult to formalize and the designed mechanism must satisfy theoretical conditions. To address this, prior work has proposed a framework that learns a matching mechanism from examples, using a parameterized family that satisfies properties such as stabi

Jiang Zhu, Junnan Ma, Zhenlong Liu, Fengzhong Qu

Analog-to-digital converters (ADCs) play a vital important role in any devices via manipulating analog signals in a digital manner. Given that the amplitude of the signal exceeds the dynamic range of the ADCs, clipping occurs and the quality of the digitized signal degrades significantly. In this paper, we design a joint modulo sampling hardware and processi

Jemma Daniel, Ruan de Kock, Louay Ben Nessir, Sasha Abramowitz

The Transformer model has demonstrated success across a wide range of domains, including in Multi-Agent Reinforcement Learning (MARL) where the Multi-Agent Transformer (MAT) has emerged as a leading algorithm in the field. However, a significant drawback of Transformer models is their quadratic computational complexity relative to input size, making them com

F. Nail, M. MacLeod, A. Oklopčić, M. Gully-Santiago

Recent observations of planetary atmospheres in HAT-P-32 b and HAT-P-67 b reveal extensive outflows reaching up to hundreds of planetary radii. The helium 1083 nm light curves for these planets, captured across their full orbits, show notable asymmetries: both planets display more pronounced pre-transit than post-transit absorptions, with HAT-P-67 b being th

Paul Dobson, Jesus María Sanz-Serna, Konstantinos C. Zygalakis

We study the connections between ordinary differential equations and optimization algorithms in a non-Euclidean setting. We propose a novel accelerated algorithm for minimising convex functions over a convex constrained set. This algorithm is a natural generalization of Nesterov's accelerated gradient descent method to the non-Euclidean setting and can be in

Abdullah Mustafa, Ryo Hanai, Ixchel Ramirez, Floris Erich

Unlike quasi-static robotic manipulation tasks like pick-and-place, dynamic tasks such as non-prehensile manipulation pose greater challenges, especially for vision-based control. Successful control requires the extraction of features relevant to the target task. In visual imitation learning settings, these features can be learnt by backpropagating the polic

Reuben Dorent, Nazim Haouchine, Alexandra Golby, Sarah Frisken

We propose a deep mixture of multimodal hierarchical variational auto-encoders called MMHVAE that synthesizes missing images from observed images in different modalities. MMHVAE's design focuses on tackling four challenges: (i) creating a complex latent representation of multimodal data to generate high-resolution images; (ii) encouraging the variational dis

S. Ota, P. Capel, G. Christian, V. Durant

The ratio is a new reaction observable suggested to extract accurately structure information on halo nuclei. It corresponds to the ratio of differential cross sections for scattering and breakup, which is predicted to remove the uncertainty related to the reaction dynamics. We present here the first experimental test of the method for the 11Be + 12C collisio

Claude-Alain Faure

In a famous paper, R. A. Gordon proved a dozen theorems using tagged partitions and Cousin's theorem. The purpose of this paper is to present several classical results using the key-lemma underlying Cousin's theorem.

Vukan Ninkovic, Dejan Vukobratovic, Dragisa Miskovic, Marco Zennaro

The significance of distributed learning and inference algorithms in Internet of Things (IoT) network is growing since they flexibly distribute computation load between IoT devices and the infrastructure, enhance data privacy, and minimize latency. However, a notable challenge stems from the influence of communication channel conditions on their performance.

Maria Lombardi, Elisa Maiettini, Agnieszka Wykowska, Lorenzo Natale

Gaze is a crucial social cue in any interacting scenario and drives many mechanisms of social cognition (joint and shared attention, predicting human intention, coordination tasks). Gaze direction is an indication of social and emotional functions affecting the way the emotions are perceived. Evidence shows that embodied humanoid robots endowing social abili

Gengyuan Cai, Luosong Guo, Xiangmao Chang

The autonomous exploration of environments by multi-robot systems is a critical task with broad applications in rescue missions, exploration endeavors, and beyond. Current approaches often rely on either greedy frontier selection or end-to-end deep reinforcement learning (DRL) methods, yet these methods are frequently hampered by limitations such as short-si

Bang Giang Le, Viet Cuong Ta

In this work, we study the problem of finding Pareto optimal policies in multi-agent reinforcement learning problems with cooperative reward structures. We show that any algorithm where each agent only optimizes their reward is subject to suboptimal convergence. Therefore, to achieve Pareto optimality, agents have to act altruistically by considering the rew

Spencer Becker-Kahn

An exposition of the mathematics underpinning the neural network architecture of a GPT-3-style LLM.

Chen Liu, Shengdan Tao, Guanyong Wang, Hongyuan Chen

We have investigated the structures and electronic properties of ultra-thin Bi(110) films grown on an s-wave superconductor substrate using low-temperature scanning tunneling microscopy and spectroscopy. Remarkably, our experimental results validate the theoretical predictions that the manipulation of Bi(110) surface atom buckling can control the topological

Qiang Yang, Weilin Xie, Congfan Wang, Bowen Li

In distributed fiber-optic sensing based on optical frequency domain reflectometry (OFDR), Doppler frequency shifts due to the changes of disturbances during one sweep period introduce demodulation errors that accumulate along both the distance and time, impairing the sensing performance. Here, we report distributed dynamic strain sensing using Doppler-shift

Carla Alejandre, Adrián Aguirre-Tamaral, Carlos Briones, Jacobo Aguirre

In the study of life's origins, a key challenge is understanding how RNA could have polymerized and subsequently replicated in early Earth. We present a theoretical and computational framework to model the non-enzymatic polymerization of ribonucleotides and the template-dependent replication of primordial RNA molecules, at the interfaces between the aqueous

Houming Wu, Ling Chen, Wenjie Yu

With the increasing scale of models, the need for efficient distributed training has become increasingly urgent. Recently, many synchronous pipeline parallelism approaches have been proposed to improve training throughput. However, these approaches still suffer from two major issues, i.e., pipeline bubbles caused by periodic flushing and extra communication

Motohiro Sobajima

In this paper, we mainly discuss asymptotic profiles of solutions to a class of abstract second-order evolution equations of the form $u''+Au+u'=0$ in real Hilbert spaces, where $A$ is a nonnegative selfadjoint operator. The main result is the asymptotic expansion for all initial data belonging to the energy space, which is naturally expected. This is an imp

YingHua Zhang, Jundan Nie, Hao Tian, Chao Liu

The potential association of the globular cluster (GC) NGC 4147 with the Sagittarius (Sgr) dwarf spheroidal galaxy has been proposed due to their comparable locations and radial velocities. However, there are still debates about this connection. In this study, we use data from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys to assess their as

Guojun Liu, Doina Caragea, Xinming Ou, Sankardas Roy

When machine learning is used for Android malware detection, an app needs to be represented in a numerical format for training and testing. We identify a widespread occurrence of distinct Android apps that have identical or nearly identical app representations. In particular, among app samples in the testing dataset, there can be a significant percentage of

Lakshmi Srinivas Panchananam, Praveen Kumar Chandaliya, Kishor Upla, Kiran Raja

Abnormalities in the gastrointestinal tract significantly influence the patient's health and require a timely diagnosis for effective treatment. With such consideration, an effective automatic classification of these abnormalities from a video capsule endoscopy (VCE) frame is crucial for improvement in diagnostic workflows. The work presents the process of d

Adimurthi, Prosenjit Roy, Vivek Sahu

We establish Trudinger-type inequality in the context of fractional boundary Hardy-type inequality for the case $sp=d$, where $p>1, ~ s \in (0,1)$ on a bounded Lipschitz domain $\Omega \subset \mathbb{R}^d$. In particular, we establish fractional version of Trudinger-type inequality with an extra singular function, namely $d$-th power of the distance functio

Manuel Weber, Philipp Bogdain, Sophia Viktoria Weißenberger, Diana Marjanovic

Research towards energy optimization in buildings heavily relies on building-related data such as measured indoor climate factors. While data collection is a labor- and cost-intensive task, simulations are a cheap alternative to generate datasets of arbitrary sizes, particularly useful for data-intensive deep learning methods. In this paper, we present the t

Diana Pfau, Alexander Jung

AI systems increasingly shape critical decisions across personal and societal domains. While empirical risk minimization (ERM) drives much of the AI success, it typically prioritizes accuracy over trustworthiness, often resulting in biases, opacity, and other adverse effects. This paper discusses how key requirements for trustworthy AI can be translated into

Zhijie Chen, Xinglin Zhang

This paper proposes a novel approach for designing Single-Parameterized Kolmogorov-Arnold Networks (SKAN) by utilizing a Single-Parameterized Function (SFunc) constructed from trigonometric functions. Three new SKAN variants are developed: LSin-SKAN, LCos-SKAN, and LArctan-SKAN. Experimental validation on the MNIST dataset demonstrates that LArctan-SKAN exce

Yangjing Wang, Xiao Li, Xinping Yi, Shi Jin

With the increasing demand for spectrum efficiency and energy efficiency, reconfigurable intelligent surfaces (RISs) have attracted massive attention due to its low-cost and capability of controlling wireless environment. However, there is still a lack of treatments to deal with the growth of the number of users and RIS elements, which may incur performance

Jiajing Li, Binghong Liu, Mugen Peng

Relying on the powerful communication capabilities and rapidly changing geometric configuration, the Low Earth Orbit (LEO) satellites have the potential to offer integrated communication and navigation (ICAN) services. However, the isolated resource utilization in the traditional satellite communication and navigation systems has led to a compromised system

Niall Byrnes, Matthew R. Foreman

Perturbing resonant systems causes shifts in their associated scattering poles in the complex plane. In a previous study [arXiv: 2408.11360], we demonstrated that these shifts can be calculated numerically by analyzing the residue of a generalized Wigner-Smith operator associated with the perturbation parameter. In this work, we extend this approach by conne

Chao Li, Zhicheng Xu, Bo Wen, Ruibin Mao

In scenarios with limited training data or where explainability is crucial, conventional neural network-based machine learning models often face challenges. In contrast, Bayesian inference-based algorithms excel in providing interpretable predictions and reliable uncertainty estimation in these scenarios. While many state-of-the-art in-memory computing (IMC)

Zhengyao Lv, Chenyang Si, Junhao Song, Zhenyu Yang

In this paper, we present \textbf{\textit{FasterCache}}, a novel training-free strategy designed to accelerate the inference of video diffusion models with high-quality generation. By analyzing existing cache-based methods, we observe that \textit{directly reusing adjacent-step features degrades video quality due to the loss of subtle variations}. We further

Chenyue Feng, Shoumin Liu, Xumin Wang

In this paper, we will compute the characteristic polynomials for finite dimensional representations of classical complex Lie algebras and the exceptional Lie algebra of type G2, which can be obtained through the orbits of integral weights under the action of their corresponding Weyl groups and the invariant polynomial theory of the Weyl groups. We show that

Marvin Alberts, Gianmarco Gabrieli, Irina Espejo Morales

Integrating text and numbers effectively is a crucial step towards enhancing Large Language Models (LLMs) capabilities in assisting in scientific tasks. While most current approaches rely on discrete tokenization of numbers, for instance, conversion to scientific notation or base 10-decomposition, a recent approach proposed a continuous numerical encoding as

Alan Oursland

This paper introduces a theoretical framework that connects neural network linear layers with the Mahalanobis distance, offering a new perspective on neural network interpretability. While previous studies have explored activation functions primarily for performance optimization, our work interprets these functions through statistical distance measures, a le

Artur Bromboszcz

We provide a complete classification, in the language of weak-combinatorics, of minimal plus-one generated line arrangements in the complex projective plane with double and triple intersection points.

Han Zhang, Yunjiang Jiang, Mingming Li, Haowei Yuan

Embedding-based retrieval aims to learn a shared semantic representation space for both queries and items, enabling efficient and effective item retrieval through approximate nearest neighbor (ANN) algorithms. In current industrial practice, retrieval systems typically retrieve a fixed number of items for each query. However, this fixed-size retrieval often

Shishe Wang, Haiping Huang

High dimensional dynamics play a vital role in brain function, ecological systems, and neuro-inspired machine learning. Where and how these dynamics are confined in the phase space remains challenging to solve. Here, we provide an analytic argument that the confinement region is an M-shape when the neural dynamics show a diversity, with two sharp boundaries

Leyla Kabuli, Oliver Cossairt, Florian Schiffers, Nathan Matsuda

Holographic displays are a promising technology for immersive visual experiences, and their potential for compact form factor makes them a strong candidate for head-mounted displays. However, at the short propagation distances needed for a compact, head-mounted architecture, image contrast is low when using a traditional phase-only spatial light modulator (S

Xinyi Mou, Jingcong Liang, Jiayu Lin, Xinnong Zhang

Large language models (LLMs) are increasingly leveraged to empower autonomous agents to simulate human beings in various fields of behavioral research. However, evaluating their capacity to navigate complex social interactions remains a challenge. Previous studies face limitations due to insufficient scenario diversity, complexity, and a single-perspective f

Tatsuya Wada, Masakiyo Kitazawa, Kazuyuki Kanaya

We propose a method to numerically determine the location of a critical point in general systems using the finite-size scaling of Lee-Yang zeros. This method makes use of the fact that the ratios of Lee-Yang zeros on various spatial volumes intersect at the critical point. While the method is similar to the Binder-cumulant analysis, it is advantageous in sup

Veljko Maksimovic, Milos Simic, Milan Stojkov, Miroslav Zaric

With the rising number of distributed computer systems, from microservice web applications to IoT platforms, the question of reliable communication between different parts of the aforementioned systems is becoming increasingly important. As part of this paper, a task queue, which facilitates reliable asynchronous communication between different services, wil

Kadek Hendrawan Palgunadi, Andreas Bergmeister, Andrea Bosisio, Laura Ermert

Accurate prediction and synthesis of seismic waveforms are crucial for seismic-hazard assessment and earthquake-resistant infrastructure design. Existing prediction methods, such as ground-motion models and physics-based wave-field simulations, often fail to capture the full complexity of seismic wavefields, particularly at higher frequencies. This study int

Alexei Kuzmin, Vitalijs Dimitrijevs, Inga Pudza, Aleksandr Kalinko

Extended X-ray absorption fine structure (EXAFS) spectra are sensitive to thermal disorder and are often used to probe local lattice dynamics. Variations in interatomic distances induced by atomic vibrations are described by the temperature-dependent mean-square relative displacement (MSRD), also known as the Debye-Waller factor. In this study, we evaluated