October 2023 arXiv papers — page 153

P. San Miguel Claveria, D. Storey, G. J. Cao, A. Di Piazza

The upgraded Facility for Advanced Accelerator Experimental Tests (FACET-II) at SLAC National Accelerator Laboratory has been designed to deliver ultra-relativistic electron and positron beams with unprecedented parameters, especially in terms of high peak current and low emittance. For most of the foreseen experimental campaigns hosted at this facility, the

Itshak Lapidot, Jean-Francois Bonastre

In the context of spoofing attacks in speaker recognition systems, we observed that the waveform probability mass function (PMF) of genuine speech differs significantly from the PMF of speech resulting from the attacks. This is true for synthesized or converted speech as well as replayed speech. We also noticed that this observation seems to have a significa

Holger R Dullin

We show that the Lagrange top with a linearly time-dependent moment of inertia is equivalent to the degenerate fifth Painlev\'e equation. More generally we show that the harmonic Lagrange top (the ordinary Lagrange top with a quadratic term added in the potential) is equivalent to the fifth Painlev\'e equation when the potential is made time-dependent in an

Hui Yu, Dayu Yan, Zhaopeng Guo, Yizhou Zhou

Quantum Spin Hall (QSH) insulators possess distinct helical in-gap states, enabling their edge states to act as one-dimensional conducting channels when backscattering is prohibited by time-reversal symmetry. However, it remains challenging to achieve high-performance combinations of nontrivial topological QSH states with superconductivity for applications a

Jinghu Liu, Shuai Yin, Li Chen

While the zeros of complex partition functions, such as Lee-Yang zeros and Fisher zeros, have been pivotal in characterizing temperature-driven phase transitions, extending this concept to zero temperature remains an open question. In this work, we propose a solution to this issue by calculating the imaginary-temperature zeros (ITZs), which are defined as th

Josef Koumar, Karel Hynek, Jaroslav Pešek, Tomáš Čejka

Network traffic monitoring based on IP Flows is a standard monitoring approach that can be deployed to various network infrastructures, even the large IPS-based networks connecting millions of people. Since flow records traditionally contain only limited information (addresses, transport ports, and amount of exchanged data), they are also commonly extended f

Qi Li, Jianzhe Liu, Bai Cui, Wenzhan Song

A distribution system can flexibly adjust its substation-level power output by aggregating its local distributed energy resources (DERs). Due to DER and network constraints, characterizing the exact feasible power output region is computationally intensive. Hence, existing results usually rely on unpractical assumptions or suffer from conservativeness issues

Adam Kanigowski, Mariusz Lemańczyk, Florian Karl Richter, Joni Teräväinen

We consider vanishing properties of exponential sums of the Liouville function $\lambda$ of the form $$ \lim_{H\to\infty}\limsup_{X\to\infty}\frac{1}{\log X}\sum_{m\leq X}\frac{1}{m}\sup_{\alpha\in C}\bigg|\frac{1}{H}\sum_{h\leq H}\lambda(m+h)e^{2\pi ih\alpha}\bigg|=0, $$ where $C\subset\mathbb{T}$. The case $C=\mathbb{T}$ corresponds to the local $1$-Fourie

Zenan Lu, Wanyue Xu, Zhongzhi Zhang

The diagonal entries of pseudoinverse of the Laplacian matrix of a graph appear in many important practical applications, since they contain much information of the graph and many relevant quantities can be expressed in terms of them, such as Kirchhoff index and current flow centrality. However, a na\"{\i}ve approach for computing the diagonal of a matrix in

Pietro Hiram Guzzi, Annamaria De Filippo, Pierangelo Veltri

Patient triage plays a crucial role in healthcare, ensuring timely and appropriate care based on the urgency of patient conditions. Traditional triage methods heavily rely on human judgment, which can be subjective and prone to errors. Recently, a growing interest has been in leveraging artificial intelligence (AI) to develop algorithms for triaging patients

Andreas Gerhardus, Jonas Wahl, Sofia Faltenbacher, Urmi Ninad

In recent years, a growing number of method and application works have adapted and applied the causal-graphical-model framework to time series data. Many of these works employ time-resolved causal graphs that extend infinitely into the past and future and whose edges are repetitive in time, thereby reflecting the assumption of stationary causal relationships

Shivraj Hanumant Gonde, Christoph Frisch, Svetoslav Duhovnikov, Martin Kubisch

Cellular communication technologies such as 5G are deployed on a large scale around the world. Compared to other communication technologies such as WiFi, Bluetooth, or Ultra Wideband, the 5G communication standard describes support for a large variety of use cases, e.g., Internet of Things, vehicular, industrial, and campus-wide communications. An organizati

Baixin Xu, Jiangbei Hu, Fei Hou, Kwan-Yee Lin

The advancements in neural rendering have increased the need for techniques that enable intuitive editing of 3D objects represented as neural implicit surfaces. This paper introduces a novel neural algorithm for parameterizing neural implicit surfaces to simple parametric domains like spheres and polycubes. Our method allows users to specify the number of cu

Sromona Nandi, Subhadip Pradhan, Ashis K. Nandy, Rudra Sekhar Manna

In double perovskite oxides (A$_2$BB$^\prime$O$_6$), magnetism often arises from diluted magnetic lattices, created by combining a perovskite structure with localized 3$d$ magnetic elements (B) alongside another perovskite lattice containing nearly nonmagnetic delocalized 4$d$/$5d$ elements (B$^\prime$). Alternatively, the magnetic lattice can consist entire

Nicolas Boulanger, Guillaume Lhost, Sylvain Thomée

We revisit the problem of constructing consistent interactions between a massive spin-3/2 field and a partially massless graviton in four-dimensional (A)dS spacetime. We use the Stueckelberg formulation of the action principle for these fields and find two non-trivial cubic vertices with less than two derivatives, when going to the unitary gauge. One of the

Filippo Bracci, Daniela Kraus, Oliver Roth

We prove a boundary version of the strong form of the Ahlfors-Schwarz lemma with optimal error term. This result provides nonlinear extensions of the boundary Schwarz lemma of Burns and Krantz to the class of negatively curved conformal pseudometrics defined on arbitary hyperbolic domains in the complex plane. Based on a new boundary Harnack inequality for s

Jan Baumgärtner, Gajanan Kanagalingam, Alexander Puchtaand Jürgen Fleischer

The task-specific optimization of robotic systems has long been divided into the optimization of the robot and the optimization of the environment. In this letter, we argue that these two problems are interdependent and should be treated as such. To this end, we present a unified problem formulation that enables for the simultaneous optimization of both the

Tobias Metzlaff

The problem of computing the global minimum of a trigonometric polynomial is computationally hard. We address this problem for the case, where the polynomial is invariant under the exponential action of a finite group. The strategy is to follow an established relaxation strategy in order to obtain a converging hierarchy of lower bounds. Those bounds are obta

David Brellmann, Eloïse Berthier, David Filliat, Goran Frehse

Temporal Difference (TD) algorithms are widely used in Deep Reinforcement Learning (RL). Their performance is heavily influenced by the size of the neural network. While in supervised learning, the regime of over-parameterization and its benefits are well understood, the situation in RL is much less clear. In this paper, we present a theoretical analysis of

Binqing Wu, Weiqi Chen, Wengwei Wang, Bingqing Peng

Due to insufficient local area information, numerical weather prediction (NWP) may yield biases for specific areas. Previous studies correct biases mainly by employing handcrafted features or applying data-driven methods intuitively, overlooking the complicated dependencies between weather factors and between areas. To address this issue, we propose WeatherG

Alain Burgisser, Ally Peccia, Terry Plank, Yves Moussallam

The 2050 14C yBP caldera-forming eruption of Okmok volcano, Alaska, had a global atmospheric impact. The associated global climate cooling was driven by the amount of sulfur injected into the stratosphere during the climactic phase of the eruption. This phase was dominated by pyroclastic density currents, which have complex emplacement dynamics precluding di

Omar Fawzi, Paul Fermé

We address the problem of coding for classical broadcast channels, which entails maximizing the success probability that can be achieved by sending a fixed number of messages over a broadcast channel. For point-to-point channels, Barman and Fawzi found in~\cite{BF18} a $(1-e^{-1})$-approximation algorithm running in polynomial time, and showed that it is \te

Bart Folkers, Thies Jansen, Thijs J. Roskamp, Pim Reith

LaMnO$_3$ (LMO) thin films epitaxially grown on SrTiO$_3$ (STO) usually exhibit ferromagnetism above a critical layer thickness. We report the use of scanning SQUID microscopy (SSM) to study the suppression of the ferromagnetism in STO/LMO/metal structures. By partially covering the LMO surface with a metallic layer, both covered and uncovered LMO regions ca

Jiatong Shi, William Chen, Dan Berrebbi, Hsiu-Hsuan Wang

The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge expands upon the acclaimed SUPERB framework, emphasizing self-supervised models in multilingual speech recognition and language identification. The challenge comprises a research track focused on applying ML-SUPERB to specific multilingual subjects, a Challenge Track for model

Hartmut Surmann, Artur Leinweber, Gerhard Senkowski, Julien Meine

In this paper, we present a method for detecting objects of interest, including cars, humans, and fire, in aerial images captured by unmanned aerial vehicles (UAVs) usually during vegetation fires. To achieve this, we use artificial neural networks and create a dataset for supervised learning. We accomplish the assisted labeling of the dataset through the im

Yuan Yin, Yifei Huang, Ryosuke Furuta, Yoichi Sato

Point-level supervised temporal action localization (PTAL) aims at recognizing and localizing actions in untrimmed videos where only a single point (frame) within every action instance is annotated in training data. Without temporal annotations, most previous works adopt the multiple instance learning (MIL) framework, where the input video is segmented into

Yu. V. Tarasov, O. M. Stadnyk

The practical applications of surface plasmon polaritons (SPPs) require the deep understanding of the impact of electrical characteristics variability and geometrical irregularity of the metal-dielectric interface. Traditional methods in the theory of wave scattering at rough interfaces fail to treat simultaneously the interference (Anderson) localization of

M. J. Álvarez, J. L. Bravo, L. A. Calderón

We consider the planar family of rigid systems of the form $x'=-y+xP(x,y), y'=x+yP(x,y)$, where $P$ is any polynomial with monomials of degree one and three. This is the simplest non-trivial family of rigid systems with no rotatory parameters. The family can be compactified to the Poincar\'e sphere such that the vector field along the equator is not identica

Saeid Tafazzol, Nan Li, Ilya Kolmanovsky, Dimitar Filev

Markov chain-based modeling and Koopman operator-based modeling are two popular frameworks for data-driven modeling of dynamical systems. They share notable similarities from a computational and practitioner's perspective, especially for modeling autonomous systems. The first part of this paper aims to elucidate these similarities. For modeling systems with

Chengpeng Li, Zheng Yuan, Hongyi Yuan, Guanting Dong

In math reasoning with large language models (LLMs), fine-tuning data augmentation by query evolution and diverse reasoning paths is empirically verified effective, profoundly narrowing the gap between open-sourced LLMs and cutting-edge proprietary LLMs. In this paper, we conduct an investigation for such data augmentation in math reasoning and are intended

Alexandre Mascarenhas, Yuri Lavinas, Claus Aranha

Dynamic Optimization Problems (DOPs) are characterized by changes in the fitness landscape that can occur at any time and are common in real world applications. The main issues to be considered include detecting the change in the fitness landscape and reacting in accord. Over the years, several evolutionary algorithms have been proposed to take into account

Chunge Bai, Ruijie Fu, Xiang Gao

State-of-the-art techniques for monocular camera reconstruction predominantly rely on the Structure from Motion (SfM) pipeline. However, such methods often yield reconstruction outcomes that lack crucial scale information, and over time, accumulation of images leads to inevitable drift issues. In contrast, mapping methods based on LiDAR scans are popular in

Anatoly Kopylov, Igor Orekhov, Valery Petukhov

In this paper, we report the results obtained in the PHoton ELectron EXperiment (PHELEX) during 208 days of measurements in the search for dark photons using a multi-cathode counter with an iron cathode. We observed a systematic excess of single-electron events in four runs, with a confidence level better than 5 sigma, in the time interval from 8-00 to 12-00

Yun Luo, Zhen Yang, Fandong Meng, Yingjie Li

Active learning (AL), which aims to construct an effective training set by iteratively curating the most formative unlabeled data for annotation, has been widely used in low-resource tasks. Most active learning techniques in classification rely on the model's uncertainty or disagreement to choose unlabeled data, suffering from the problem of over-confidence

Stefania Bellavia, Greta Malaspina, Benedetta Morini

We develop and analyze stochastic inexact Gauss-Newton methods for nonlinear least-squares problems and for nonlinear systems ofequations. Random models are formed using suitable sampling strategies for the matrices involved in the deterministic models. The analysis of the expected number of iterations needed in the worst case to achieve a desired level of a

Nikolas O. Aksamit, Alex P. Encinas-Bartos, George Haller, David E. Rival

As most mathematically justifiable Lagrangian coherent structure detection methods rely on spatial derivatives, their applicability to sparse trajectory data has been limited. For experimental fluid dynamicists and natural scientists working with Lagrangian trajectory data via passive tracers in unsteady flows (e.g. Lagrangian particle tracking or ocean buoy

Florian Azendorf, André Sandmann, Wolfgang Reimer, Michael Eiselt

A coherent optical subassembly (COSA) is evaluated for coherent-correlation optical time domain reflectometry (CC-OTDR) based fibre sensing. Even though the COSA was originally designed for digital communication applications, acoustic signals with frequencies up to 360 Hz can be detected after 50 km of transmission.

Shirui Pan, Yizhen Zheng, Yixin Liu

Large language models (LLMs) such as GPT-4 have emerged as frontrunners, showcasing unparalleled prowess in diverse applications, including answering queries, code generation, and more. Parallelly, graph-structured data, an intrinsic data type, is pervasive in real-world scenarios. Merging the capabilities of LLMs with graph-structured data has been a topic

Nanqing Liu, Xun Xu, Yingjie Gao, Heng-Chao Li

Annotating remote sensing images (RSIs) presents a notable challenge due to its labor-intensive nature. Semi-supervised object detection (SSOD) methods tackle this issue by generating pseudo-labels for the unlabeled data, assuming that all classes found in the unlabeled dataset are also represented in the labeled data. However, real-world situations introduc

E. J. P. Miranda, V. F. Dal Poggetto, N. M. Pugno, J. M. C. Dos Santos

The extended plane wave expansion (EPWE) formulation is derived to obtain the complex band structure of flexural waves in viscoelastic thin phononic crystal plates considering the Kirchhoff-Love plate theory. The presented formulation yields the evanescent behavior of flexural waves in periodic thin plates considering viscoelastic effects. The viscosity is m

Jean-Marie Malherbe, Frédéric Sayède, Pierre Mein

Imaging spectroscopy is intended to be coupled with adaptive optics (AO) on large telescopes, such as EST, in order to produce high spatial and temporal resolution measurements of velocities and magnetic fields upon a 2D FOV. We propose a Multichannel Subtractive Double Pass (MSDP) incorporated to the EST visible and IR spectrographs, using new generation sl

Guinevere Kauffmann, Iker Millán-Irigoyen, Paul Crowther, Claudia Maraston

We have selected galaxies with very high levels of H$\alpha$ emission (EQW(H$\alpha$)$>$700 \AA.) in their central regions from the final data release of the MaNGA survey . Our study focuses on 14 very well-resolved nearby galaxies with stellar masses in the range $9.5 < \log M_*/(M_{\odot}) < 11.5$. We investigate a variety of procedures for selecting galax

Xin Liu, Wei li, Dazhi Zhan, Yu Pan

Federated learning (FL) is a widely employed distributed paradigm for collaboratively training machine learning models from multiple clients without sharing local data. In practice, FL encounters challenges in dealing with partial client participation due to the limited bandwidth, intermittent connection and strict synchronized delay. Simultaneously, there e

Tesshu Hanaka, Yasuaki Kobayashi

In this paper, we study the problem of finding a minimum weight spanning tree that contains each vertex in a given subset $V_{\rm NT}$ of vertices as an internal vertex. This problem, called Minimum Weight Non-Terminal Spanning Tree, includes $s$-$t$ Hamiltonian Path as a special case, and hence it is NP-hard. In this paper, we first observe that Non-Termina

Ken Chen

The monitoring of data generated by a large number of devices in Internet of Things (IoT) systems is an important and complex issue. Several studies have explored the use of generic rule engine, primarily based on the RETE algorithm, for monitoring the flow of device data. In order to solve the performance problem of the RETE algorithm in IoT scenarios, some

Guanting Dong, Hongyi Yuan, Keming Lu, Chengpeng Li

Large language models (LLMs) with enormous pre-training tokens and parameters emerge diverse abilities, including math reasoning, code generation, and instruction following. These abilities are further enhanced by supervised fine-tuning (SFT). While the open-source community has explored ad-hoc SFT for enhancing individual capabilities, proprietary LLMs exhi

Chao-Qiang Geng, Xiao-Gang He, Xiang-Nan Jin, Chia-Wei Liu

The BESIII collaboration has recently reported the first time measurement of the decay asymmetry $\alpha(\Lambda_c^+ \to \Xi^0 K^+) = 0.01 \pm 0.16(stat.) \pm 0.03(syst.)$ and also a sizable phase shift of $\delta_P-\delta_S = -1.55 \pm 0.25$ or $1.59\pm 0.25$ between S- and P-wave amplitudes. This implies significant strong phase shifts in the decay amplitu

Domenico Fiorini, Alessia Simonini, Johan Steelant, David Seveno

This work investigates the wetting dynamics of cryogenic fluids in inertia-dominated conditions. We experimentally characterized an oscillating gas-liquid interface of liquid nitrogen in a partially filled U-shaped quartz tube. The experiments were carried out in controlled cryogenic conditions, with interface oscillations produced by releasing the liquid co

Micheal R A Abdelmalik, Zhenning Cai, Teddy Pichard

The $\phi$-divergence-based moment method was recently introduced Abdelmalik et al. (2023) for the discretization of the radiative transfer equation. At the continuous level, this method is very close to the entropy-based MN methods and possesses its main properties, i.e. entropy dissipation, rotational invariance and energy conservation. However, the $\phi$

Christophe Hugon, Vladimir Kulikovskiy

We give a vacuum description with zero-point density for virtual fluctuations. One of the goals is to explain the origin of the vacuum permittivity and permeability and to calculate their values. In particular, we improve on existing calculations by avoiding assumptions on the volume occupied by virtual fluctuations. We propose testing of the models that ass

Luis Ferroni, Benjamin Schröter

This paper initiates the explicit study of face numbers of matroid polytopes and their computation. We prove that, for the large class of split matroid polytopes, their face numbers depend solely on the number of cyclic flats of each rank and size, together with information on the modular pairs of cyclic flats. We provide a formula which allows us to calcula

Nicolas Vanspranghe, Lucas Brivadis, Lassi Paunonen

A stabilizer based on the forwarding technique is proposed for semilinear infinite-dimensional systems in cascade form. Sufficient conditions for local exponentially stability and global asymptotic stability of the closed-loop are derived. Results for the problem of local set-point output regulation are also obtained. Finally, an application to a system cons

C. Oliver, D. Dalmas, J. Scheibert

The most direct measurement of adhesion is the pull-off force, i.e. the tensile force necessary to separate two solids in contact. For a given interface, it depends on various experimental parameters, including separation speed, contact age and maximum loading force. Here, using smooth contacts between elastomer spheres and rigid plates, we show that the pul

Yixuan Zhang, Quyu Kong, Feng Zhou

In this study, we propose a novel deep spatio-temporal point process model, Deep Kernel Mixture Point Processes (DKMPP), that incorporates multimodal covariate information. DKMPP is an enhanced version of Deep Mixture Point Processes (DMPP), which uses a more flexible deep kernel to model complex relationships between events and covariate data, improving the

Vageesh Saxena, Benjamin Bashpole, Gijs Van Dijck, Gerasimos Spanakis

Human trafficking (HT) is a pervasive global issue affecting vulnerable individuals, violating their fundamental human rights. Investigations reveal that a significant number of HT cases are associated with online advertisements (ads), particularly in escort markets. Consequently, identifying and connecting HT vendors has become increasingly challenging for

Xiaochao Pan, Jiawei Yao, Hongrui Kou, Tong Wu

In the realm of autonomous driving, achieving precise 3D reconstruction of the driving environment is critical for ensuring safety and effective navigation. Neural Radiance Fields (NeRF) have shown promise in creating highly detailed and accurate models of complex environments. However, the application of NeRF in autonomous driving scenarios encounters sever

Yutaka Takeuchi

Reflecting diffusions on continuum percolation clusters are considered. Assuming that the occupied region has a unique unbounded cluster and the cluster satisfies geometrical conditions such as volume regularity, isoperimetric conditions, and a hole size condition, we prove a quenched local central limit theorem for reflecting diffusions on the cluster.

Usashi Chatterjee, Amit Gajbhiye, Steven Schockaert

The theory of Conceptual Spaces is an influential cognitive-linguistic framework for representing the meaning of concepts. Conceptual spaces are constructed from a set of quality dimensions, which essentially correspond to primitive perceptual features (e.g. hue or size). These quality dimensions are usually learned from human judgements, which means that ap

Siddharth Gupta, Guy Sa'ar, Meirav Zehavi

We initiate the study of the parameterized complexity of the {\sc Collective Graph Exploration} ({\sc CGE}) problem. In {\sc CGE}, the input consists of an undirected connected graph $G$ and a collection of $k$ robots, initially placed at the same vertex $r$ of $G$, and each one of them has an energy budget of $B$. The objective is to decide whether $G$ can

Chen Pan, Fan Zhou, Xuanwei Hu, Xinxin Zhu

Deciding the best future execution time is a critical task in many business activities while evolving time series forecasting, and optimal timing strategy provides such a solution, which is driven by observed data. This solution has plenty of valuable applications to reduce the operation costs. In this paper, we propose a mechanism that combines a probabilis

Moritz Leuthner, Oliver Hayden

Pumps are indispensable for analytical applications and ensure controlled fluid movement. Syringe pumps are among today_s most prevalent liquid delivery systems, especially for high-pressure, stable, low-flow-rate microfluidic applications. Due to moving mechanical parts of the assembly, regular maintenance is essential to ensure reliable operation and flow

Wenjing Chen, Zexi Wang

In this paper, we are concerned with the following fractional $N/s$-Laplacian Choquard equation \begin{align*} \begin{cases} (-\Delta)^s_{N/s}u=\lambda |u|^{\frac{N}{s}-2}u +(I_\mu*F(u))f(u),\ \ \mbox{in}\ \mathbb{R}^N, \displaystyle\int_{\mathbb{R}^N}|u|^{N/s} \mathrm{d}x=a^{N/s}, \end{cases} \end{align*} where $s\in(0,1)$, $1<\frac{N}{s}\in \mathbb{N}^+$,

Takaaki Fujita

The domain of Information and Communication Technology (ICT) education has garnered significant consideration in recent times. However, several challenges are inherent to this area of study, including monetary expense, temporal factors, pedagogical environment, teacher training programs, incentive, syllabus design, and health-related concerns. This paper pre

Yiwei Tao, Miaowen Wen, Yao Ge, Jun Li

Affine frequency division multiplexing (AFDM) is a new multicarrier technique based on chirp signals tailored for high-mobility communications, which can achieve full diversity. In this paper, we propose an index modulation (IM) scheme based on the framework of AFDM systems, named AFDM-IM. In the proposed AFDM-IM scheme, the information bits are carried by t

Federica Pompa, Olga Mena

The next-generation water Cherenkov Hyper-Kamiokande detector will be able to detect thousands of neutrino events from a galactic Supernova explosion via Inverse Beta Decay processes followed by neutron capture on Gadolinium. This superb statistics provides a unique window to set bounds on neutrino properties, as its mass and lifetime. We shall explore the c

Yang Bai, Xinxing Xu, Yong Liu, Salman Khan

Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption. Most existing CIR models adopt the late-fusion strategy to combine visual and language features. Besides, several approaches have also been suggested to generate a pseudo-word token from the reference image, w

Zhiyu Fan, Yannic Noller, Ashish Dandekar, Abhik Roychoudhury

The growing number of students enrolling in Computer Science (CS) programmes is pushing CS educators to their limits. This poses significant challenges to computing education, particularly the teaching of introductory programming and advanced software engineering (SE) courses. First-year programming courses often face overwhelming enrollments, including inte

Siddharth Gupta, Guy Sa'ar, Meirav Zehavi

Over the past decade, we witness an increasing amount of interest in the design of exact exponential-time and parameterized algorithms for problems in Graph Drawing. Unfortunately, we still lack knowledge of general methods to develop such algorithms. An even more serious issue is that, here, "standard" parameters very often yield intractability. In particul

Junlong Li, Shichao Sun, Weizhe Yuan, Run-Ze Fan

The rapid development of Large Language Models (LLMs) has substantially expanded the range of tasks they can address. In the field of Natural Language Processing (NLP), researchers have shifted their focus from conventional NLP tasks (e.g., sequence tagging and parsing) towards tasks that revolve around aligning with human needs (e.g., brainstorming and emai

Jan van Delden, Julius Schultz, Christopher Blech, Sabine C. Langer

In mechanical structures like airplanes, cars and houses, noise is generated and transmitted through vibrations. To take measures to reduce this noise, vibrations need to be simulated with expensive numerical computations. Deep learning surrogate models present a promising alternative to classical numerical simulations as they can be evaluated magnitudes fas

Pengcheng Xie

This paper analyzes the sufficient conditions for distance reduction between minimizers of local nonconvex quadratic approximate functions with diagonal Hessian in the (\ell^2)-norm trust regions after two iterations. Some examples illustrate the theoretical results of this study.

Alessio Arcudi, Davide Frizzo, Chiara Masiero, Gian Antonio Susto

Anomaly Detection (AD) focuses on identifying unusual behaviors in complex datasets. Machine Learning (ML) algorithms and Decision Support Systems (DSSs) provide effective solutions for AD, but detecting anomalies alone may not be enough, especially in engineering, where diagnostics and maintenance are crucial. Users need clear explanations to support root c

Junru Zhang, Lang Feng, Yang He, Yuhan Wu

While one-dimensional convolutional neural networks (1D-CNNs) have been empirically proven effective in time series classification tasks, we find that there remain undesirable outcomes that could arise in their application, motivating us to further investigate and understand their underlying mechanisms. In this work, we propose a Temporal Convolutional Explo

Máté L. Telek

We investigate the signed support, that is, the set of the exponent vectors and the signs of the coefficients, of a multivariate polynomial $f$. We describe conditions on the signed support ensuring that the semi-algebraic set, denoted as $\{ f < 0 \}$, containing points in the positive real orthant where $f$ takes negative values, has at most one connected

Aykut Özdönmez, Hüseyin ER, İlham Nasiroglu

We present 36 new mid-eclipse times obtained between 2017 and 2023 using the T100 telescope in Turkey, extending the time span of the $O-C$ diagram to 25 years. Once again, these new observations show significant deviations from previous published models that were able to explain the observed variations of the binary period. We investigate two plausible expl

Ricardo Knauer, Erik Rodner

A key challenge in machine learning is to design interpretable models that can reduce their inputs to the best subset for making transparent predictions, especially in the clinical domain. In this work, we propose a certifiably optimal feature selection procedure for logistic regression from a mixed-integer conic optimization perspective that can take an aux

Francesco Gili, Geurt Jongbloed, Aad van der Vaart

We consider nonparametric estimation in Wicksell's problem which has relevant applications in astronomy for estimating the distribution of the positions of the stars in a galaxy given projected stellar positions and in material sciences to determine the 3D microstructure of a material, using its 2D cross sections. In the classical setting, we study the isoto

Xianming Gu, Lihui Wang, Zeyu Deng, Ying Cao

Multi-modal medical image fusion is essential for the precise clinical diagnosis and surgical navigation since it can merge the complementary information in multi-modalities into a single image. The quality of the fused image depends on the extracted single modality features as well as the fusion rules for multi-modal information. Existing deep learning-base

Francisco Andrade, Gabriel Peyre, Clarice Poon

Optimal Transport is a useful metric to compare probability distributions and to compute a pairing given a ground cost. Its entropic regularization variant (eOT) is crucial to have fast algorithms and reflect fuzzy/noisy matchings. This work focuses on Inverse Optimal Transport (iOT), the problem of inferring the ground cost from samples drawn from a couplin

Masato Nozawa, Takashi Torii

The vacuum Robinson-Trautman solution admits a shear-free and twist-free null geodesic congruence with a nonvanishing expansion. We perform a comprehensive classification of solutions exhibiting this property in Einstein's gravity with a massless scalar field, assuming that the solution belongs at least to Petrov-type II and some of the components of Ricci t

M. Zubair, Muhammad Ali Raza, Eiman Maqsood

This article deals with the study of some properties of the static and rotating black holes in Kalb-Ramond gravity in four dimensional spacetime. First, we discuss the action of the corresponding theory and the static black hole metric. Then we investigate the light sphere for the static black hole by using the Hamiltonian formalism and the corresponding lin

Shinya Okabe, Philip Schrader, Valentina Wheeler, Glen Wheeler

In this article we study the $H^1(du)$-gradient flow for the energy $E[X] = Q[X]/A[X]$ where $Q[X]$ is the Dirichlet energy of $X$, $A[X]$ is the signedenclosed area of $X$, and $X:\mathbb{S}\rightarrow\mathbb{R}^2$ is a $H^1(du)$ map. We prove that solutions with initially positive signed enclosed area exist eternally, and converge as $t\rightarrow\infty$ t

Shiwen Zhang

Let $G$ be an additive finite abelian group with exponent $\exp(G)$. For $L\subseteq \mathbb N$, let $\mathsf{s}_{L}(G)$ be the smallest integer $\ell$ such that every sequence $S$ over $G$ of length $\ell$ has a zero-sum subsequence $T$ of length $|T|\in L$. In this paper, we consider the invariants $\mathsf{s}_{[1,t]}(G)$ and $\mathsf{s}_{\{k\exp(G)\}}(G)$

Marcos Dajczer, Miguel I. Jimenez, Theodoros Vlachos

We investigate the topology of the compact hypersurfaces in round spheres whose Ricci curvature satisfies an appropriate bound that only depends on the mean curvature of the submanifold. In this paper, the use of the Bochner technique allows same stronger results than the ones obtained by us in the case of submanifolds lying in any codimension.

Peiwen Tan

This research introduces a novel methodology for optimizing Bayesian Neural Networks (BNNs) by synergistically integrating them with traditional machine learning algorithms such as Random Forests (RF), Gradient Boosting (GB), and Support Vector Machines (SVM). Feature integration solidifies these results by emphasizing the second-order conditions for optimal

Luting Xu, Fan Yang

Materials with kagome lattice have attracted significant research attention due to their nontrivial features in energy bands. In this work, we theoretically investigate the evolution of electronic band structures of kagome lattice in response to uniaxial strain using both a tight-binding model and an antidot model based on a periodic muffin-tin potential. It

Hajime Kaneko, Wolfgang Steiner

For the Lagrange spectrum and other applications, we determine the smallest accumulation point of binary sequences that are maximal in their shift orbits. This problem is trivial for the lexicographic order, and its solution is the fixed point of a substitution for the alternating lexicographic order. For orders defined by cylinders, we show that the solutio

Yuxiang Lai, Yi Zhou, Xinghong Liu, Tao Zhou

Universal domain adaptation aims to align the classes and reduce the feature gap between the same category of the source and target domains. The target private category is set as the unknown class during the adaptation process, as it is not included in the source domain. However, most existing methods overlook the intra-class structure within a category, esp

Haotong Yang, Fanxu Meng, Zhouchen Lin, Muhan Zhang

The pre-trained large language models (LLMs) have shown their extraordinary capacity to solve reasoning tasks, even on tasks that require a complex process involving multiple sub-steps. However, given the vast possible generation space of all the tasks, how the pretrained model learns the reasoning ability remains an open question. We firstly propose that an

Zahraa Abdallah, Stéphane Gerbi, Chiraz Kassem, Ali Wehbe

We investigate a two-dimensional transmission model consisting of a wave equation and a Kirchhoff plate equation with dynamical boundary controls under geometric conditions. The two equations are coupled through transmission conditions along a steady interface between the domains in which the wave and plate equations evolve, respectively. Our primary concern

Hongqiu Wu, Linfeng Liu, Hai Zhao, Min Zhang

Beyond the great cognitive powers showcased by language models, it is crucial to scrutinize whether their reasoning capabilities stem from strong generalization or merely exposure to relevant data. As opposed to constructing increasingly complex logic, this paper probes into the boolean logic, the root capability of a logical reasoner. We find that any pre-t

Lorenzo Buffoni, Francesco Coghi, Stefano Gherardini

In this work, we introduce a generalization of the Landauer bound for erasure processes that stems from absolutely irreversible dynamics. Assuming that the erasure process is carried out in an absolutely irreversible way so that the probability of observing some trajectories is zero in the forward process but finite in the reverse process, we derive a genera

Christian Brennecke, Jinyeop Lee, Phan Thành Nam

We consider a translation-invariant system of $N$ bosons in $\mathbb{T}^{3}$ that interact through a repulsive two-body potential with scattering length of order $N^{-1}$ in the limit $N\to \infty$. We derive second order expressions for the one- and two-particle reduced density matrix matrices of the Gibbs state at fixed positive temperatures, thus obtainin

Xinzhu Ma, Yongtao Wang, Yinmin Zhang, Zhiyi Xia

In this work, we build a modular-designed codebase, formulate strong training recipes, design an error diagnosis toolbox, and discuss current methods for image-based 3D object detection. In particular, different from other highly mature tasks, e.g., 2D object detection, the community of image-based 3D object detection is still evolving, where methods often a

Khaled ELKarazle, Valliappan Raman, Caslon Chua, Patrick Then

Vision Transformers (ViTs) have revolutionized medical imaging analysis, showcasing superior efficacy compared to conventional Convolutional Neural Networks (CNNs) in vital tasks such as polyp classification, detection, and segmentation. Leveraging attention mechanisms to focus on specific image regions, ViTs exhibit contextual awareness in processing visual

Ruisheng Su, Matthijs van der Sluijs, Sandra Cornelissen, Wim van Zwam

Cerebral X-ray digital subtraction angiography (DSA) is the standard imaging technique for visualizing blood flow and guiding endovascular treatments. The quality of DSA is often negatively impacted by body motion during acquisition, leading to decreased diagnostic value. Time-consuming iterative methods address motion correction based on non-rigid registrat

Zhiqing Wei, Jinghui Piao, Xin Yuan, Huici Wu

Integrated sensing and communication (ISAC) is regarded as the enabling technology in the future 5th-Generation-Advanced (5G-A) and 6th-Generation (6G) mobile communication system. ISAC waveform design is critical in ISAC system. However, the difference of the performance metrics between sensing and communication brings challenges for the ISAC waveform desig

Jorge Dueñas-Pamplona, Sergio Rodríguez-Aparicio, Alejandro Gonzalo, Savannah F. Bifulco

Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, affecting over 1% of the population. It is usually triggered by irregular electrical impulses that cause the atria to contract irregularly and ineffectively. It increases blood stasis and the risk of thrombus formation within the left atrial appendage (LAA) and aggravates a

Robert Litschko, Max Müller-Eberstein, Rob van der Goot, Leon Weber

Language understanding is a multi-faceted cognitive capability, which the Natural Language Processing (NLP) community has striven to model computationally for decades. Traditionally, facets of linguistic intelligence have been compartmentalized into tasks with specialized model architectures and corresponding evaluation protocols. With the advent of large la

Carlos Villarroel, Gustavo Düring

We investigated the yielding phenomenon in the quasistatic limit using numerical simulations of soft particles. Two different deformation scenarios, simple shear (passive) and self-random force (active), and two interaction potentials were used. Our approach reveals that the exponents describing the avalanche distribution are universal within the margin of e