October 2023 arXiv papers — page 181

Mishel Carelli, Sergei O. Ivanov

We give a necessary and sufficient condition for an inverse sequence $S_0 \leftarrow S_1 \leftarrow \dots$ indexed by natural numbers to have ${\rm lim}^1S=0$. This condition can be treated as a transfinite version of the Mittag-Leffler condition. We consider inverse sequences in an arbitrary abelian category having a generator and satisfying Grothendieck ax

Alexander A. Davydov, Stefano Marcugini, Fernanda Pambianco

The length function $\ell_q(r,R)$ is the smallest possible length $n$ of a $ q $-ary linear $[n,n-r]_qR$ code with codimension (redundancy) $r$ and covering radius $R$. Let $s_q(N,\rho)$ be the smallest size of a $\rho$-saturating set in the projective space $\mathrm{PG}(N,q)$. There is a one-to-one correspondence between $[n,n-r]_qR$ codes and $(R-1)$-satur

Xuanmeng Zhang, Jianfeng Zhang, Rohan Chacko, Hongyi Xu

We study the problem of 3D-aware full-body human generation, aiming at creating animatable human avatars with high-quality textures and geometries. Generally, two challenges remain in this field: i) existing methods struggle to generate geometries with rich realistic details such as the wrinkles of garments; ii) they typically utilize volumetric radiance fie

Gyutaek Oh, Baekgyu Choi, Inkyung Jung, Jong Chul Ye

Single-cell RNA sequencing (scRNA-seq) has made significant strides in unraveling the intricate cellular diversity within complex tissues. This is particularly critical in the brain, presenting a greater diversity of cell types than other tissue types, to gain a deeper understanding of brain function within various cellular contexts. However, analyzing scRNA

Jangho Park, Gihyun Kwon, Jong Chul Ye

Recently, there has been a significant advancement in text-to-image diffusion models, leading to groundbreaking performance in 2D image generation. These advancements have been extended to 3D models, enabling the generation of novel 3D objects from textual descriptions. This has evolved into NeRF editing methods, which allow the manipulation of existing 3D o

Kangkan Goswami, Dushmanta Sahu, Jayanta Dey, Raghunath Sahoo

Strong transient magnetic fields are generated in non-central relativistic heavy-ion collisions. These fields induce anisotropy within the strongly interacting medium that, in principle, can affect the thermodynamic properties of the medium. We use the Polyakov loop extended Nambu Jona-Lasinio model to study the quark matter subjected to an external magnetic

Minsu Kim, Taeyoung Yun, Emmanuel Bengio, Dinghuai Zhang

Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their rewards. GFlowNets exhibit a remarkable ability to generate diverse samples, yet occasionally struggle to consistently produce samples with high rewards due to over-exploration on wide sample space. This paper proposes to

Xingyu Gao, Sumukh Vaidya, Saakshi Dikshit, Peng Ju

Optically addressable spin defects in three-dimensional (3D) crystals and two-dimensional (2D) van der Waals (vdW) materials are revolutionizing nanoscale quantum sensing. Spin defects in one-dimensional (1D) vdW nanotubes will provide unique opportunities due to their small sizes in two dimensions and absence of dangling bonds on side walls. However, optica

Hongyu Li, Shanpu Shen, Matteo Nerini, Marco Di Renzo

This work studies the modeling and optimization of beyond diagonal reconfigurable intelligent surface (BD-RIS) aided wireless communication systems in the presence of mutual coupling among the RIS elements. Specifically, we first derive the mutual coupling aware BD-RIS aided communication model using scattering and impedance parameter analysis. Based on the

James Kennedy

These are lecture notes from a course given at the summer school "Heat kernels and spectral geometry: from manifolds to graphs" in Bregenz, Austria, 2022. They are designed to be accessible to doctoral level students, and include background chapters on Laplacians on domains and quantum graphs before moving on to specialised topics involving the dependence an

Niels Benedikter, Sascha Lill

We consider a system of interacting fermions on the three-dimensional torus in a mean-field scaling limit. Our objective is computing the occupation number of the Fourier modes in a trial state obtained through the random phase approximation (in its collective bosonization formulation) for the ground state. We prove that the trial state's momentum distributi

J. Tobias Tsang, Michele Della Morte

We discuss the main issues in dealing with heavy quarks on the lattice and shortly present the different approaches used. We discuss a selection of computations covering first the b-quark mass and the B(s) meson decay constants as the consolidated results (neglecting isospin breaking corrections). In the second part we consider recent calculations of form fa

Terru Stübinger, Lars Hupel

The Isabelle proof assistant includes a small functional language, which allows users to write and reason about programs. So far, these programs could be extracted into a number of functional languages: Standard ML, OCaml, Scala, and Haskell. This work adds support for Go as a fifth target language for the Code Generator. Unlike the previous targets, Go is n

Kislaya Ravi, Tobias Neckel, Hans-Joachim Bungartz

Markov Chain Monte Carlo (MCMC) methods often take many iterations to converge for highly correlated or high-dimensional target density functions. Methods such as Hamiltonian Monte Carlo (HMC) or No-U-Turn Sampling (NUTS) use the first-order derivative of the density function to tackle the aforementioned issues. However, the calculation of the derivative rep

Dun Zeng, Zenglin Xu, Shiyu Liu, Yu Pan

Federated averaging (FedAvg) is the most fundamental algorithm in Federated learning (FL). Previous theoretical results assert that FedAvg convergence and generalization degenerate under heterogeneous clients. However, recent empirical results show that FedAvg can perform well in many real-world heterogeneous tasks. These results reveal an inconsistency betw

James B. Kennedy, João P. Ribeiro

We study partition problems based on two ostensibly different kinds of energy functionals defined on $k$-partitions of metric graphs: Cheeger-type functionals whose minimisers are the $k$-Cheeger cuts of the graph, and the corresponding values are the $k$-Cheeger constants of the graph; and functionals built using the first eigenvalue of the Laplacian with p

Diego Gutierrez-Oribio, Ioannis Stefanou

Deep Geothermal Energy, Carbon Capture, and Storage and Hydrogen Storage have significant potential to meet the large-scale needs of the energy sector and reduce the CO$_2$ emissions. However, the injection of fluids into the earth's crust, upon which these activities rely, can lead to the formation of new seismogenic faults or the reactivation of existing o

Umberto Cappellazzo, Enrico Fini, Muqiao Yang, Daniele Falavigna

Recently, neural networks have shown impressive progress across diverse fields, with speech processing being no exception. However, recent breakthroughs in this area require extensive offline training using large datasets and tremendous computing resources. Unfortunately, these models struggle to retain their previously acquired knowledge when learning new t

Dun Zeng, Zenglin Xu, Yu Pan, Xu Luo

Federated Learning (FL) is a distributed learning paradigm to train a global model across multiple devices without collecting local data. In FL, a server typically selects a subset of clients for each training round to optimize resource usage. Central to this process is the technique of unbiased client sampling, which ensures a representative selection of cl

Stefan Ruschel, Benoit Huard

We investigate the consequences of periodic, on-off glucose infusion on the glucose-insulin regulatory system on the basis of a system-level mathematical model with two explicit time delays. Studying the effects of such infusion protocols is mathematically challenging yet a promising direction for probing the system response to infusion. We pay special atten

Gergo Igneczi, Erno Horvath, Roland Toth, Krisztian Nyilas

Automated driving systems are often used for lane keeping tasks. By these systems, a local path is planned ahead of the vehicle. However, these paths are often found unnatural by human drivers. We propose a linear driver model, which can calculate node points that reflect the preferences of human drivers and based on these node points a human driver preferre

Adam T. Stevenson, Carole A. Haswell, John R. Barnes, Joanna K. Barstow

We have created an up-to-date catalogue of 214 brown dwarfs (BDs) in binaries with $P < 10^4$ d. This allows us to examine the population statistics of the brown dwarf desert. We searched $\textit{Gaia}$ DR3 NSS results for orbital inclinations of BD candidates, deriving 12 new masses. Three remain as desert BDs whereas nine candidates are found to be low-ma

Yafei Sun, Qimin Xu, Cailian Chen, Xinping Guan

Time-Sensitive Networking (TSN) is a promising industrial Internet of Things technology. Clock synchronization provides unified time reference, which is critical to the deterministic communication of TSN. However, changes in internal network status and external work environments of devices both degrade practical synchronization performance. This paper propos

Nokyung Park, Daewon Chae, Jeongyong Shim, Sangpil Kim

Learning domain-invariant visual representations is important to train a model that can generalize well to unseen target task domains. Recent works demonstrate that text descriptions contain high-level class-discriminative information and such auxiliary semantic cues can be used as effective pivot embedding for domain generalization problems. However, they u

Victor Mangeleer, Gilles Louppe

In climate simulations, small-scale processes shape ocean dynamics but remain computationally expensive to resolve directly. For this reason, their contributions are commonly approximated using empirical parameterizations, which lead to significant errors in long-term projections. In this work, we develop parameterizations based on Fourier Neural Operators,

Guoxin Wang, Xuyang Cao, Shan An, Fengmei Fan

Deep learning approaches, together with neuroimaging techniques, play an important role in psychiatric disorders classification. Previous studies on psychiatric disorders diagnosis mainly focus on using functional connectivity matrices of resting-state functional magnetic resonance imaging (rs-fMRI) as input, which still needs to fully utilize the rich tempo

Matthias Runge, Klaus Reimann, Michael Woerner, Thomas Elsaesser

The nonlinear polaronic response of electrons solvated in liquid 2-propanol is studied by two-dimensional terahertz spectroscopy. Solvated electrons with a concentration of c$_e \approx$ 800 $\mu$M are generated by femtosecond photoionization of alcohol molecules. Electron relaxation to a localized ground state impulsively excites coherent polaron oscillatio

Eduardo Fernandez-Saiz, Rutwig Campoamor-Stursberg, Francisco J. Herranz

The theory of Lie-Hamilton systems is used to construct generalized time-dependent SIS epidemic Hamiltonians with a variable infection rate from the 'book' Lie algebra. Although these are characterized by a set of non-autonomous nonlinear and coupled differential equations, their corresponding exact solution is explicitly found. Moreover, the quantum deforma

Moyang Li, Peng Wang, Lingzhe Zhao, Bangyan Liao

Neural Radiance Fields (NeRF) has received much attention recently due to its impressive capability to represent 3D scene and synthesize novel view images. Existing works usually assume that the input images are captured by a global shutter camera. Thus, rolling shutter (RS) images cannot be trivially applied to an off-the-shelf NeRF algorithm for novel view

Alessio Paviglianiti, Xhek Turkeshi, Marco Schirò, Alessandro Silva

Understanding the influence of measurements on the properties of many-body systems is a fundamental problem in quantum mechanics and for quantum technologies. This paper explores how a finite density of stochastic local measurement modifies a given state's entanglement structure. Considering various measurement protocols, we explore the typical quantum corre

Zhiyang Liu, Shijun Mao

Coupling strength effect on the quark matter with finite isospin chemical potential is studied in a Pauli-Villars regularized NJL model. A BCS-BEC crossover occurs along the phase boundary of pion superfluid phase transition, as increasing coupling strength $G$. For strong coupling cases, the critical isospin chemical potential for pion superfluid phase tran

Sangjun Park, JinYeong Bak

Making neural networks remember over the long term has been a longstanding issue. Although several external memory techniques have been introduced, most focus on retaining recent information in the short term. Regardless of its importance, information tends to be fatefully forgotten over time. We present Memoria, a memory system for artificial neural network

Romain Meriot, Benoit Semelin

The Square Kilometer array is expected to measure the 21cm signal from the Epoch of Reionization (EoR) in the coming decade, and its pathfinders may provide a statistical detection even earlier. The currently reported upper limits provide tentative constraints on the astrophysical parameters of the models of the EoR. In order to interpret such data with 3D r

Jeanine Shea, Gerhard Jung, Friederike Schmid

Langevin equations or generalized Langevin equations (GLEs) are popular models for describing the motion of a particle in a fluid medium in an effective manner. Here we examine particles immersed in an inherently nonequilibrium fluid, i.e., an active bath, which are subject to an external force. Specifically, we consider two types of forces that are highly r

Arindam Bhattacharjee, Muktajyoti Saha

We construct a one-dimensional dual theory that effectively describes the sector of the (2+1)D flat gravity phase space near a Flat Space Cosmology (FSC) saddle labeled by definite mass and angular momentum. This Schwarzian type action describes the dynamics of the (Pseudo-) Goldstone Bosons of BMS$_3$ algebra on a circle as the symmetry is spontaneously and

A. Sicilia-Aguilar, J. Campbell-White, V. Roccatagliata, J. Desira

We examine the long-term spectroscopic and photometric variability of EX~Lupi and TW~Hya, studying the presence of stable accretion and the role it plays in the observed variability. Analysing the velocity modulations of the emission lines with STAR-MELT, we obtain information on the structure of the accretion columns and the disk-star connection. The emissi

Tássia L. S. Quaresma, Tristan Hehnen, Lukas Arnold

Predictive flame spread models based on temperature dependent pyrolysis rates require numerous material properties as input parameters. These parameters are typically derived by optimisation and inverse modelling using data from bench scale experiments such as the cone calorimeter. The estimated parameters are then transferred to flame spread simulations, wh

Dinghuai Zhang, Ricky T. Q. Chen, Cheng-Hao Liu, Aaron Courville

We tackle the problem of sampling from intractable high-dimensional density functions, a fundamental task that often appears in machine learning and statistics. We extend recent sampling-based approaches that leverage controlled stochastic processes to model approximate samples from these target densities. The main drawback of these approaches is that the tr

Suryansh Sharma, Tristan Dijkstra, R. Venkatesha Prasad

Testing the aerodynamics of micro- and nano-UAVs without actually flying is highly challenging. To address this issue, we introduce Open Gimbal, a specially designed 3 Degrees of Freedom platform that caters to the unique requirements of micro- and nano-UAVs. This platform allows for unrestricted and free rotational motion, enabling comprehensive experimenta

Mohamed Nedal, Kamen Kozarev, Peijin Zhang, Pietro Zucca

This study aims to investigate the ambiguous source and the underlying physical processes of the solar type III radio bursts that occurred on April 3, 2019, through the utilization of multiwavelength observations from the LOFAR radio telescope and the PSP space mission, as well as incorporating results from a PFSS and MHD models. The primary goal is to ident

Eren Unlu

Position encoding is the primary mechanism which induces notion of sequential order for input tokens in transformer architectures. Even though this formulation in the original transformer paper has yielded plausible performance for general purpose language understanding and generation, several new frameworks such as Rotary Position Embedding (RoPE) are propo

Yujin Tang, Jiaming Zhou, Xiang Pan, Zeying Gong

Accurate precipitation forecasting is a vital challenge of societal importance. Though data-driven approaches have emerged as a widely used solution, solely relying on data-driven approaches has limitations in modeling the underlying physics, making accurate predictions difficult. We focus on the Numerical Weather Prediction (NWP) post-processing based preci

Ying Kit Tsui, C. N. Kuo, C. E. Hsu, Wei Zhang

Despite being a layered, easily-exfoliated compound, copper monotelluride (CuTe) features an unusual quasi-one-dimensional charge density wave below $T_{\rm CDW}\approx335$ K. Within a CuTe layer, the electrical resistivity depends sensitively on the direction of the electrical current. Here, we use magnetotransport to probe the metallic state of CuTe with t

Hongruixuan Chen, Cuiling Lan, Jian Song, Clifford Broni-Bediako

Optical high-resolution imagery and OSM data are two important data sources of change detection (CD). Previous related studies focus on utilizing the information in OSM data to aid the CD on optical high-resolution images. This paper pioneers the direct detection of land-cover changes utilizing paired OSM data and optical imagery, thereby expanding the scope

Sandro Franceschi, Irina Kourkova, Maxence Petit

We consider a transient Brownian motion reflected obliquely in a two-dimensional wedge. A precise asymptotic expansion of Green's functions is found in all directions. To this end, we first determine a kernel functional equation connecting the Laplace transforms of the Green's functions. We then extend the Laplace transforms analytically and study its singul

A. V. Lipatov, G. I. Lykasov, M. A. Malyshev

Using the analytical expression for transverse momentum dependent (TMD) gluon density in a proton, a self-consistent simultaneous description of low $Q^2$ data on proton structure function $F_2(x,Q^2)$, reduced cross section for the electron-proton deep inelastic scattering at HERA and soft hadron production in $pp$ collisions at the LHC is achieved in the f

Sara Klein, Simon Weissmann, Leif Döring

Markov Decision Processes (MDPs) are a formal framework for modeling and solving sequential decision-making problems. In finite-time horizons such problems are relevant for instance for optimal stopping or specific supply chain problems, but also in the training of large language models. In contrast to infinite horizon MDPs optimal policies are not stationar

Itai Boneh, Dvir Fried, Shay Golan, Matan Kraus

We introduce the natural notion of a matching frame in a $2$-dimensional string. A matching frame in a $2$-dimensional $n\times m$ string $M$, is a rectangle such that the strings written on the horizontal sides of the rectangle are identical, and so are the strings written on the vertical sides of the rectangle. Formally, a matching frame in $M$ is a tuple

Theodore Brown, Stephen Marsden, Vignesh Gopakumar, Alexander Terenin

The safety factor profile is a key property in determining the stability of tokamak plasmas. To design the safety factor profile in the United Kingdom's proposed Spherical Tokamak for Energy Production (STEP), we apply multi-objective Bayesian optimisation to design electron-cyclotron heating profiles. Bayesian optimisation is an iterative machine learning t

Ki-Ahm Lee, Taehun Lee

We consider a flow by powers of Gauss curvature under the obstruction that the flow cannot penetrate a prescribed region, so called an obstacle. For all dimensions and positive powers, we prove the optimal curvature bounds of solutions and all time existence with its long time behavior. We also prove the $C^1$ regularity of free boundaries under a uniform th

T. Das, C. A. Ullrich, U. D. Jentschura

The onset of retardation effects in atom-wall interactions is studied. It is shown that the transition range from the 1/z^3 short-range (van der Waals) interaction to the 1/z^4 long-range (Casimir) retarded interaction critically depends on the atomic properties and on the dielectric function of the material. For simple non-alkali atoms (e.g., ground-state h

Biswajit Rath, K. Sanjay Kumar, D. Vamshee Krishna

Let $f$ be analytic in the unit disk $\mathbb{D}= \{z \in \mathbb{C}~:~ |z| < 1\}$, and $\mathcal{S}$ be the subclass of normalized univalent functions given by $f(z)=\sum_{n=1}^{\infty}a_{n}z^{n},~a_{1}:=1$ for $z \in\mathbb{D}$. We present the sharp bounds of the third-order Hankel determinant for inverse functions when it belongs to of the class of Ozaki

Yuri Kifer

We obtain strong invariance principles for normalized multiple iterated sums and integrals of the form $\bbS_N^{(\nu)}(t)=N^{-\nu/2}\sum_{0\leq k_1<...<k_\nu\leq Nt}\xi(k_1)\otimes\cdots\otimes\xi(k_\nu)$, $t\in[0,T]$ and $\bbS_N^{(\nu)}(t)=N^{-\nu/2}\int_{0\leq s_1\leq...\leq s_\nu\leq Nt}\xi(s_1)\otimes\cdots\otimes\xi(s_\nu)ds_1\cdots ds_\nu$, where $\{\x

Xiangming Gu, Chao Du, Tianyu Pang, Chongxuan Li

Due to their capacity to generate novel and high-quality samples, diffusion models have attracted significant research interest in recent years. Notably, the typical training objective of diffusion models, i.e., denoising score matching, has a closed-form optimal solution that can only generate training data replicating samples. This indicates that a memoriz

Xuhang Chen, Chi-Man Pun, Shuqiang Wang

Cross-modal medical image translation is an essential task for synthesizing missing modality data for clinical diagnosis. However, current learning-based techniques have limitations in capturing cross-modal and global features, restricting their suitability to specific pairs of modalities. This lack of versatility undermines their practical usefulness, parti

Shilun Li

Under investigation in this paper is the dynamics and probability of heads in the toss of a coin with symmetric inhomogeneous density. Such coins are assumed to have diagonal inertia matrix. The rotational motion of the coin is determined by the initial angular momentum and initial position of the coin. We described the dynamic behavior of the unit normal ve

Giuseppe Dattoli, Roberto Garra, Silvia Licciardi

The multivariable version of ordinary and generalized Hermite polynomials are the natural solutions of the classical heat equation and of its higher order versions. We derive the associated Burgers equations and show that analogous non-linear partial differential equations can be derived for Laguerre polynomials and for the relevant generalizations.

Chao Qian, Tianheng Ling, Gregor Schiele

To process sensor data in the Internet of Things(IoTs), embedded deep learning for 1-dimensional data is an important technique. In the past, CNNs were frequently used because they are simple to optimise for special embedded hardware such as FPGAs. This work proposes a novel LSTM cell optimisation aimed at energy-efficient inference on end devices. Using the

Jaeyoun You, Bongwon Suh

The adoption of generative AI technologies is swiftly expanding. Services employing both linguistic and mul-timodal models are evolving, offering users increasingly precise responses. Consequently, human reliance on these technologies is expected to grow rapidly. With the premise that people will be impacted by the output of AI, we explored approaches to hel

Meera Nandakumar, Prasun Dutta

The generation mechanism of compressible fluid turbulence at kiloparsec scales in the Interstellar Medium (ISM) is a long-lasting puzzle. In this work, we explore the nature of large-scale turbulence in the external spiral galaxy NGC~6946. We use the Visibility Moment Estimator (VME) to measure the \HI column density and line of sight turbulent velocity powe

Orhan Aygün, Bertan Turhan

This paper studies a many-to-one matching between individuals and institutions where institutions comprise multiple divisions and face cross-divisional constraints. We introduce a parametrized family of choice rules, which we call generalized sequential (GSq), that encompasses many different choice rules encountered in practice and in market design literatur

Alessandro Arsie, Nataliya A. Balabanova

Leveraging on the results of arXiv:2210.13644 , we carry out an investigation of the algebraic three-fold $\Sigma_{C,h}$, the common level set of the Hamiltonian and the Casimir, for the two-body problem for equal masses on $S^2$ subject to a gravitational potential of cotangent type. We determine the topology of its compactification $\overline{\Sigma}_{C,h}

Benjamin Ritz, Alexander Felfernig, Viet-Man Le, Sebastian Lubos

In many scenarios, configurators support the configuration of a solution that satisfies the preferences of a single user. The concept of \emph{multi-configuration} is based on the idea of configuring a set of configurations. Such a functionality is relevant in scenarios such as the configuration of personalized exams, the configuration of project teams, and

Nicholas Perikli, Srimoy Bhattacharya, Blessing Ogbuokiri, Zahra Movahedi Nia

Very large numbers of M-pox cases have, since the start of May 2022, been reported in non-endemic countries leading many to fear that the M-pox Outbreak would rapidly transition into another pandemic, while the COVID-19 pandemic ravages on. Given the similarities of M-pox with COVID-19, we chose to test the performance of COVID-19 models trained on South Afr

Jarosław Adam Miszczak

The emergence of complex structures in the systems governed by a simple set of rules is among the most fascinating aspects of Nature. The particularly powerful and versatile model suitable for investigating this phenomenon is provided by cellular automata, with the Game of Life being one of the most prominent examples. However, this simplified model can be t

D. D. Zhang, B. Li, D. Vretenar, T. Nikšić

The microscopic framework of time-dependent covariant density functional theory is applied to a systematic study of ternary quasifission in collisions of pairs of $^{238}$U nuclei. It is shown that the inclusion of octupole degree of freedom in the case of head-to-head collisions, extends the energy window in which ternary quasifission occurs, and greatly en

Mahdi Esmailoghli, Christoph Schnell, Renée J. Miller, Ziawasch Abedjan

Most research on data discovery has so far focused on improving individual discovery operators such as join, correlation, or union discovery. However, in practice, a combination of these techniques and their corresponding indexes may be necessary to support arbitrary discovery tasks. We propose BLEND, a comprehensive data discovery system that supports exist

Filippo Perrina, Francesco Marchiori, Mauro Conti, Nino Vincenzo Verde

Cyber Threat Intelligence (CTI) reporting is pivotal in contemporary risk management strategies. As the volume of CTI reports continues to surge, the demand for automated tools to streamline report generation becomes increasingly apparent. While Natural Language Processing techniques have shown potential in handling text data, they often struggle to address

Tianheng Ling, Chao Qian, Lukas Einhaus, Gregor Schiele

This study explores the quantisation-aware training (QAT) on time series Transformer models. We propose a novel adaptive quantisation scheme that dynamically selects between symmetric and asymmetric schemes during the QAT phase. Our approach demonstrates that matching the quantisation scheme to the real data distribution can reduce computational overhead whi

Makoto Naoi, Shiro Hirano

A similar waveform search plays a crucial role in seismology for detecting seismic events, such as small earthquakes and low-frequency events. However, the high computational costs associated with waveform cross-correlation calculations represent bottlenecks during the analysis of long, continuous records obtained from numerous stations. In this study, we de

Alessio Porretta, Michele Ricciardi

We study an ergodic mean field game problem with state constraints. In our model the agents are affected by idiosyncratic noise and use a (singular) feedback control to prevent the Brownian motion from exiting the domain. We characterize the equilibrium as the (possibly unique) solution to a second-order MFG system, where the value function blows up at the b

Xavier Tan, Han Yu

The success of Federated Learning (FL) depends on the quantity and quality of the data owners (DOs) as well as their motivation to join FL model training. Reputation-based FL participant selection methods have been proposed. However, they still face the challenges of the cold start problem and potential selection bias towards highly reputable DOs. Such a bia

Matthew Hanlon, Boyang Sun, Marc Pollefeys, Hermann Blum

Rather than having each newly deployed robot create its own map of its surroundings, the growing availability of SLAM-enabled devices provides the option of simply localizing in a map of another robot or device. In cases such as multi-robot or human-robot collaboration, localizing all agents in the same map is even necessary. However, localizing e.g. a groun

Paul Bryan, Mat Langford, Jonathan J. Zhu

We prove that curve shortening flow on the round sphere displays sharp chord-arc improvement, precisely as in the planar setting (Andrews and Bryan, Comm. Anal. Geom., 2011). As in the planar case, the sharp estimate implies control on the curvature, resulting in a direct and efficient proof that simple spherical curves either contract to round points (in fi

Zhiqiang Jian, Songyi Zhang, Lingfeng Sun, Wei Zhan

Local planning for a differential wheeled robot is designed to generate kinodynamic feasible actions that guide the robot to a goal position along the navigation path while avoiding obstacles. Reactive, predictive, and learning-based methods are widely used in local planning. However, few of them can fit static and crowd environments while satisfying kinodyn

Ryuya Hora, Yuhi Kamio

This paper gives a classification of classes of discrete dynamical systems (a set equipped with an endofunction) closed under finite limits and small colimits. The conclusion is simple: they bijectively correspond to the ideals of the product poset $\mathbb{N} \times \mathbb{N}$, where the first $\mathbb{N}$ is ordered by the usual order and the second is by

Yifan Zhang, Qing Wang

The matching problem and the distribution law of Galton boards with interactions are studied in this paper. The general matching problem appeals at many scenarios, such as the reaction rate of molecules and the hailing rate of ride-hailing drivers. The Galton board is often used in the classroom as a demonstration experiment for the probability distribution

Nagwa A. Saeed, Deepak B. Pachpatte

In this paper, we investigate existence and uniqueness of solutions for Darboux type problem for fuzzy fractional order differential equation. We used Caputo-Katogampola fuzzy fractional derivative for proving our results. Schauder's fixed point theorem is used in proving our results. some applications are also provided to give the usefulness of our results.

Lasse Gresista, Ciarán Hickey, Simon Trebst, Yasir Iqbal

Quantum antiferromagnets on geometrically frustrated lattices have long attracted interest for the formation of quantum disordered states and the possible emergence of quantum spin liquid (QSL) ground states. Here we turn to the nearest-neighbor spin-$1/2$ Heisenberg antiferromagnet on the maple-leaf lattice, which is known to relieve frustration by the form

Lorenzo Orsini, Hanan Herzig Sheinfux, Yandong Li, Seojoo Lee

Topological nanophotonics presents the potential for cutting-edge photonic systems, with a core aim revolving around the emergence of topological edge states. These states are primed to propagate robustly while embracing deep subwavelength confinement that defies diffraction limits. Such attributes make them particularly appealing for nanoscale applications,

Tasuku Soma, Kam Chuen Tung, Yuichi Yoshida

We provide the first online algorithm for spectral hypergraph sparsification. In the online setting, hyperedges with positive weights are arriving in a stream, and upon the arrival of each hyperedge, we must irrevocably decide whether or not to include it in the sparsifier. Our algorithm produces an $(\epsilon, \delta)$-spectral sparsifier with multiplicativ

Yansong Peng, Yueyi Zhang, Zhiwei Xiong, Xiaoyan Sun

Event cameras are a type of novel neuromorphic sen-sor that has been gaining increasing attention. Existing event-based backbones mainly rely on image-based designs to extract spatial information within the image transformed from events, overlooking important event properties like time and polarity. To address this issue, we propose a novel Group-based visio

Han Zhang, Qiguang Chen, Lok Ming Lui

Images degraded by geometric distortions pose a significant challenge to imaging and computer vision tasks such as object recognition. Deep learning-based imaging models usually fail to give accurate performance for geometrically distorted images. In this paper, we propose the deformation-invariant neural network (DINN), a framework to address the problem of

Erica Cooper, Wen-Chin Huang, Yu Tsao, Hsin-Min Wang

We present the second edition of the VoiceMOS Challenge, a scientific event that aims to promote the study of automatic prediction of the mean opinion score (MOS) of synthesized and processed speech. This year, we emphasize real-world and challenging zero-shot out-of-domain MOS prediction with three tracks for three different voice evaluation scenarios. Ten

C. -T. Chan, V. Mishnyakov, A. Popolitov, K. Tsybikov

We argue that the recently discovered bilinear superintegrability arXiv:2206.02045 generalizes, in a non-trivial way, to monomial matrix models in pure phase. The structure is much richer: for the trivial core Schur functions required modifications are minor, and the only new ingredient is a certain (contour-dependent) permutation matrix; for non-trivial-cor

Zhihao Zong, Fazhi He, Rubin Fan, Yuxin Liu

Computer Aided Design (CAD), especially the feature-based parametric CAD, plays an important role in modern industry and society. However, the reconstruction of featured CAD model is more challenging than the reconstruction of other CAD models. To this end, this paper proposes an end-to-end network to reconstruct featured CAD model from point cloud (P2CADNet

Sergio Cabello, Siu-Wing Cheng, Otfried Cheong, Christian Knauer

Let $P$ be a set of at most $n$ points and let $R$ be a set of at most $n$ geometric ranges, such as for example disks or rectangles, where each $p \in P$ has an associated supply $s_{p} > 0$, and each $r \in R$ has an associated demand $d_{r} > 0$. A (many-to-many) matching is a set $\mathcal{A}$ of ordered triples $(p,r,a_{pr}) \in P \times R \times \mathb

Luofu Liu, Rui Wang

Modeling liquid crystal elastomers (LCEs) at the molecular level is crucial for the predictable design of energy-conversion and stimuli-responsive materials. Here, we develop a self-consistent field theory for LCEs which captures the coupling between nematic ordering, backbone alignment and network deformation. Molecular features such as density of elastic s

Weirui Ye, Yunsheng Zhang, Haoyang Weng, Xianfan Gu

Reinforcement learning (RL) is a promising approach for solving robotic manipulation tasks. However, it is challenging to apply the RL algorithms directly in the real world. For one thing, RL is data-intensive and typically requires millions of interactions with environments, which are impractical in real scenarios. For another, it is necessary to make heavy

Jean-Noël Mettler, Björn S. Konrad, Sascha P. Quanz, Ravit Helled

In this study, we treat Earth as an exoplanet and investigate our home planet by means of a potential future mid-infrared (MIR) space mission called the Large Interferometer For Exoplanets (LIFE). We combine thermal spectra from an empirical dataset of disk-integrated Earth observations with a noise model for LIFE to create mock observations. We apply a stat

Yuto Omae, Masaya Mori, Yohei Kakimoto

Decision trees offer the benefit of easy interpretation because they allow the classification of input data based on if--then rules. However, as decision trees are constructed by an algorithm that achieves clear classification with minimum necessary rules, the trees possess the drawback of extracting only minimum rules, even when various latent rules exist i

Ivan Lopushenko, Alexander Bykov, Igor Meglinski

We consider the origin of unpolarised light resulting from the backscattering of circularly polarized light by random turbid tissue-like disperse medium. We reveal the dynamics of the backscattered fraction of unpolarized light, disclosing its meticulous decomposition into two rigorously polarized components characterized by opposing helicities, with fully d

Marius Constantin Chirita Mihaila, Gabriel Lukas Szabo, Alexander Redl, Markus Goldberger

We present an efficient method to produce laser-triggered proton pulses well below 500 ps pulse width at keV energies. We use femtosecond photoelectron pulses emitted from a cathode to enable ultrafast electron-stimulated desorption of adsorbates on a stainless steel plate under ultrahigh vacuum conditions. While direct photoionization of atoms to form well-

Tzung Hsuen Khoo, Dharini Pathmanathan, Philipp Otto, Sophie Dabo-Niang

Stock market indices are volatile by nature, and sudden shocks are known to affect volatility patterns. The autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) models neglect structural breaks triggered by sudden shocks that may lead to an overestimation of persistence, causing an upward bias in the estimates. Different regime-s

Peikun Chen, Fan Yu, Yuhao Lian, Hongfei Xue

Mixture-of-experts based models, which use language experts to extract language-specific representations effectively, have been well applied in code-switching automatic speech recognition. However, there is still substantial space to improve as similar pronunciation across languages may result in ineffective multi-language modeling and inaccurate language bo

Serena Dipierro, Kanishka Perera, Caterina Sportelli, Enrico Valdinoci

We establish the existence of multiple solutions for a nonlinear problem of critical type. The problem considered is fractional in nature, since it is obtained by the superposition of $(s,p)$-fractional Laplacians of different orders. The results obtained are new even in the case of the sum of two different fractional $p$-Laplacians, or the sum of a fraction

Diego García-Lucas, Ángel del Río

We continue the analysis of the Modular Isomorphism Problem for $2$-generated $p$-groups with cyclic derived subgroup, $p>2$, started in [D. Garc\'ia-Lucas, \'A. del R\'io, and M. Stanojkovski. On group invariants determined by modular group algebras: even versus odd characteristic. Algebr. Represent. Theory. https://doi.org/10.1007/s10468-022-10182-x, 2022]

Hemanth Manikantan, Manish Kumar, Biswajit Paul, Vikram Rana

We report the orbital decay rate of the high mass X-ray binary GX 301$-$2 from an analysis of its long-term X-ray light curves and pulsed flux histories from CGRO/BATSE, RXTE/ASM, Swift/BAT, Fermi/GBM and MAXI by timing the pre-periastron flares over a span of almost 30 years. The time of arrival of the pre-periastron flares exhibits an energy dependence (ha

Zhiqiang Jian, Songyi Zhang, Lingfeng Sun, Wei Zhan

Trajectory planning is crucial for the safe driving of autonomous vehicles in highway traffic flow. Currently, some advanced trajectory planning methods utilize spatio-temporal voxels to construct feasible regions and then convert trajectory planning into optimization problem solving based on the feasible regions. However, these feasible region construction

Zhongfu Guo, Xinsheng Ji, Wei You, Mingyan Xu

In B5G edge networks, end-to-end low-latency and high-reliability transmissions between edge computing nodes and terminal devices are essential. This paper investigates the queue-aware coding scheduling transmission of randomly arriving data packets, taking into account potential eavesdroppers in edge networks. To address these concerns, we introduce SCLER,