February 2024 arXiv papers — page 150

Franziska Zeuner, Tanausú del Pino Alemán, Javier Trujillo Bueno, Sami K. Solanki

Solar magnetic fields alter scattering polarization in spectral lines like Sr I at 4607 {\AA} via the Hanle effect, making it a potential diagnostic for small-scale mixed-polarity photospheric magnetic fields. Recently, observational evidence for scattering polarization in the Sr I 4607 {\AA} at the solar disk center was found. Here, we investigate the relia

Mak Gracic, Guy Gubnisky, Roee Diamant

The detection of echolocation clicks is key in understanding the intricate behaviors of cetaceans and monitoring their populations. Cetacean species relying on clicks for navigation, foraging and even communications are sperm whales (Physeter macrocephalus) and a variety of dolphin groups. Echolocation clicks are wideband signals of short duration that are o

Benjamin Schwager, Lars Meschede, Jamal Berakdar

Elastic scattering of a wave can be quantified by a shift in the phase with respect to the incoming wave phase. A qualitative measure of the time during which the effect occurs is given by the Wigner time delay. The tunneling time in turn is known to saturate with increasing tunneling barrier width (Hartman effect). Here, we analyze the elastic quantum mecha

Silvia Manconi, Francesca Calore, Fiorenza Donato

The nature of the GeV gamma-ray Galactic center excess (GCE) in the data of Fermi-Large Area Telescope (LAT) is still to be unveiled. We present a new analysis of the inner Galaxy Fermi-LAT data at energies above 10 GeV, based on an innovative method which combines the skyFACT adaptive template fitting with and the 1pPDF pixel-count statistics. We find a str

Chakib Fettal, Lazhar Labiod, Mohamed Nadif

A common way of partitioning graphs is through minimum cuts. One drawback of classical minimum cut methods is that they tend to produce small groups, which is why more balanced variants such as normalized and ratio cuts have seen more success. However, we believe that with these variants, the balance constraints can be too restrictive for some applications l

Lennart Sinjorgo, Renata Sotirov, Miguel F. Anjos

We consider the complex cut polytope: the convex hull of Hermitian rank 1 matrices $xx^{\mathrm{H}}$, where the elements of $x \in \mathbb{C}^n$ are $m$th unit roots. These polytopes have applications in ${\text{MAX-3-CUT}}$, digital communication technology, angular synchronization and more generally, complex quadratic programming. For ${m=2}$, the complex

Florian Beck, Minh Nhat Vu, Christian Hartl-Nesic, Andreas Kugi

Systematically including dynamically changing waypoints as desired discrete actions, for instance, resulting from superordinate task planning, has been challenging for online model predictive trajectory optimization with short planning horizons. This paper presents a novel waypoint model predictive control (wMPC) concept for online replanning tasks. The main

César Díaz, Julián Cabrera, Fernando Jaureguizar, Narciso García

A video-aware unequal loss protection (ULP) system for protecting RTP video streaming in bursty packet loss networks is proposed. Considering the relevance of the frame, the state of the channel, and the bitrate constraints of the protection bitstream, our algorithm selects in real time the most suitable frames to be protected through forward error protectio

Christian Forsch, Peter Zillmann, Osama Alrabadi, Stefan Brueck

In this work, we consider Terahertz (THz) communications with low-resolution uniform quantization and spatial oversampling at the receiver side, corresponding to a single-input multiple-output (SIMO) transmission. We fairly compare different analog-to-digital converter (ADC) parametrizations by keeping the ADC power consumption constant. Here, 1-, 2-, and 3-

Kévin Colin, Håkan Hjalmarsson, Véronique Chotteau

In this paper, we consider the well known problem of non-linear identification of the rates of the reactions involved in cells with Monod functions. In bioprocesses, generating data is very expensive and long and so it is important to incorporate prior knowledge on the Monod kinetic parameters. Bayesian estimation is an elegant estimation technique which dea

Nikola Koutná, Lars Hultman, Paul H. Mayrhofer, Davide G. Sangiovanni

MAB phases (MABs) are atomically-thin laminates of ceramic/metallic-like layers, having made a breakthrough in the development of 2D materials. Though theoretically offering a vast chemical and phase space, relatively few MABs have yet been synthesised. To guide experiments, we perform a systematic high-throughput {\it{ab initio}} screening of MABs that comb

Joseph P. Conlon

New light hidden sector degrees of freedom represent one of the most approaches to going beyond the Standard Model. I give a short account of how such WISP candidates naturally appear in string compactifications and some descriptions of ways that they can affect early universe cosmology.

Xiao Liu, Mengmeng Wu, Renfei Wang, Xinghao Wang

Surface Acoustic Wave (SAW) is a powerful technique for investigating quantum phases appearing in two-dimensional electron systems. The electrons respond to the piezoelectric field of SAWthrough screening, attenuating its amplitude and shifting its velocity, which is described by the relaxation model. In this work, we systematically study this interaction us

Kenneth H. Karlsen, Yan Rybalko

This study focuses on the Cauchy problem associated with the two-component peakon system featuring a cubic nonlinearity, constrained to the class $(m,n)\in C^{k}(\mathbb{R}) \cap W^{k,1}(\mathbb{R})$ with $k\in\mathbb{N}\cup\{0\}$.This system extends the celebrated Fokas-Olver-Rosenau-Qiao equation, and the following nonlocal (two-place) counterpart proposed

Kit Gallagher, Richard Creswell, Ben Lambert, Martin Robinson

Computational methods and associated software implementations are central to every field of scientific investigation. Modern biological research, particularly within systems biology, has relied heavily on the development of software tools to process and organize increasingly large datasets, simulate complex mechanistic models, provide tools for the analysis

Zhongshi Sun, Guangyan Jia

In this article, we study a continuous-time stochastic $H_\infty$ control problem based on reinforcement learning (RL) techniques that can be viewed as solving a stochastic linear-quadratic two-person zero-sum differential game (LQZSG). First, we propose an RL algorithm that can iteratively solve stochastic game algebraic Riccati equation based on collected

Marc Kaufeld, Rainer Trauth, Johannes Betz

Current validation methods often rely on recorded data and basic functional checks, which may not be sufficient to encompass the scenarios an autonomous vehicle might encounter. In addition, there is a growing need for complex scenarios with changing vehicle interactions for comprehensive validation. This work introduces a novel synchronous multi-agent simul

Hans Gløckner Giil, Arne Brataas

Antiferromagnets are promising candidates as active components in spintronic applications. They share features with ferrimagnets in that opposing spin orientations exist in two or more sublattices. Spin transfer torque and spin pumping are essential ingredients in antiferromagnetic and ferrimagnet spintronics. This paper develops an out-of-equilibrium quantu

Soobin Jeon, Hancheol Cho, Sang-Young Park

This paper presents a nonsingular fast terminal sliding mode-based adaptive smooth control methodology for a distributed space telescope demonstration mission. The distributed space telescope has a flexible focal length that corresponds to the relative position in the formation flying concept. The limited specification of a CubeSat generally restricts the pe

Chenguo Lin, Yadong Mu

Comprehending natural language instructions is a charming property for 3D indoor scene synthesis systems. Existing methods directly model object joint distributions and express object relations implicitly within a scene, thereby hindering the controllability of generation. We introduce InstructScene, a novel generative framework that integrates a semantic gr

Xinrong Xie, Gan Liang, Fei Ma, Yulin Du

Wave localization is a fundamental phenomenon that appears universally in both natural materials and artificial structures and plays a crucial role in understanding the various physical properties of a system. Usually, a localized state has an exponential profile with a localization length independent of the system size. Here, we experimentally demonstrate a

Vadym Iurchuk, Sven Stienen, Jürgen Lindner, Attila Kákay

We present a systematic micromagnetic study of standing spin-wave modes in infinitely long Permalloy strips with rectangular cross-section. Using a finite-element dynamic-matrix method, we first calculate the eigenfrequencies and the corresponding eigenvectors (mode profiles), as a function of the in-plane magnetic field applied across the strip. The ferroma

A. P. Balachandran

In $~^3$He-B, two atoms pair in an orbital angular momentum $1$ spin triplet state above the phase transition temperature with $SO(3) \times SO(3)$ symmetry. Below the transition temperature, this symmetry is spontaneously broken to the diagonal $SO(3)$ due to spin-orbit coupling. Considerations based on effective potentials and solitons show that $SO(3)$'s

Yutaro Oguri, Yusuke Matsui

We present a theoretical and empirical analysis of the adaptive entry point selection for graph-based approximate nearest neighbor search (ANNS). We introduce novel concepts: $b\textit{-monotonic path}$ and $B\textit{-MSNET}$, which better capture an actual graph in practical algorithms than existing concepts like MSNET. We prove that adaptive entry point se

N. Mohammedi

A scale invariant theory of gravity, containing at most two derivatives, requires, in addition to the Riemannian metric, a scalar field and (or) a gauge field. The gauge field is usualy used to construct the affine connection of Weyl geometry. In this note, we incorporate both the gauge field and the scalar field to build a generalised scale invariant Weyl a

Paul Saves

The objective of this Philosophiae Doctor (Ph.D) thesis is to propose an efficient approach for optimizing a multidisciplinary black-box model when the optimization problem is constrained and involves a large number of mixed integer design variables (typically 100 variables). The targeted optimization approach, called EGO, is based on a sequential enrichment

Jiahua Rao, Jiancong Xie, Hanjing Lin, Shuangjia Zheng

Graph Neural Networks (GNNs) have gained considerable traction for their capability to effectively process topological data, yet their interpretability remains a critical concern. Current interpretation methods are dominated by post-hoc explanations to provide a transparent and intuitive understanding of GNNs. However, they have limited performance in interp

Qi-Lin Jia, An-Ping Chen, Yuan-Guo Xu

We study the single parton fragmentation functions (FFs) at the input factorization scale $\mu_0\gtrsim 2m_Q$, with heavy quark mass $m_Q$, in the soft gluon factorization (SGF) approach. We express the FFs in terms of perturbatively calculable short distance hard parts for producing a heavy quark-antiquark pair in all possible states, convoluted with corres

Thomas J. Elliott, Mile Gu

By exploiting the complexity intrinsic to quantum dynamics, quantum technologies promise a whole host of computational advantages. One such advantage lies in the field of stochastic modelling, where it has been shown that quantum stochastic simulators can operate with a lower memory overhead than their best classical counterparts. This advantage is particula

Paola Rossi, Gianluigi Bodo, Silvano Massaglia, Alessandro Capetti

We perform three-dimensional numerical simulations of magnetized relativistic jets propagating in a uniform density environment in order to study the effect of the entrainment and the consequent deceleration, extending a previous work in which magnetic effects were not present. As in previous papers, our aim is to understand the connection between the jet pr

Diego A. Mejía

We present a detailed general framework to describe the forcing $\tilde{\mathbb{E}}$, defined by Kellner, Shelah and Tan\u{a}sie to prove the consistency with ZFC of an alternative order of Cicho\'n's maximum. Our presentation is close to the framework of tree-creature forcing notions from Horowitz and Shelah. We show that the posets in this class have stron

Yifeng Yang, Zhenyu Xu, Adolfo del Campo

Open quantum systems undergo decoherence, which is responsible for the transition from quantum to classical behavior. The time scale in which decoherence takes place can be analyzed using upper limits to its rate. We examine the dynamics of open chaotic quantum systems governed by random Lindblad operators sourced from Gaussian and Ginibre ensembles with Wig

Yinchuan Li, Yuancheng Zhan, Le Zheng, Xiaodong Wang

Millimeter-Wave Massive MIMO is important for beyond 5G or 6G wireless communication networks. The goal of this paper is to establish successful communication between the cellular base stations and devices, focusing on the problem of joint user activity detection and channel estimation. Different from traditional compressed sensing (CS) methods that only use

Xiaoran Ye, Tao Yu, Zhaoxin Liang

Equation of state (EOS) for a pure two-dimensional (2D) Bose gas exhibits a logarithmic dependence on the s-wave scattering length [L. Salasnich, Phys. Rev. Lett. 118, 130402 (2017)]. The pronounced disparity between the EOS of a 2D Bose gas and its 3D counterpart underscores the significance of exploring the dimensional crossover between these two distinct

Zheyuan Zhang, Xiangbao Bu, Daiki Okazaki, Wenqing Song

A narrow-bandwidth actively mode-locked laser using a Cr:ZnS gain medium has been successfully demonstrated. A free-space electro-optic phase modulator is employed in the solid-state laser resonator to achieve frequency-modulation (FM) mode-locking, which achieves a narrow spectral width of ~1 GHz and a pulse duration of ~500 ps over a wide tuning range of 1

Vincent Henkel, Maximilian Kilthau, Felix Gehlhoff, Lukas Wagner

In response to the global shift towards renewable energy resources, the production of green hydrogen through electrolysis is emerging as a promising solution. Modular electrolysis plants, designed for flexibility and scalability, offer a dynamic response to the increasing demand for hydrogen while accommodating the fluctuations inherent in renewable energy s

Rubén Ruiz-Torrubiano, Sebastian Knopp, Lukas Matthias Wolf, Andreas Krystallidis

In modular educational systems, students are allowed to choose a part of their own curriculum themselves. This is typically done in the final class levels which lead to maturity for university access. The rationale behind letting students choose their courses themselves is to enhance self-responsibility, improve student motivation, and allow a focus on speci

Pamela Zoghby, Bogdan Marinescu, Antoine Rosse, Gregoire Prime

Due to the energy transition, today's electrical networks include synchronous machines and inverter-based resources interfacing renewable energies such as wind turbines, solar panels, and Battery Energy Storage Systems to the grid. In such systems, interactions known as coupling modes or dynamic interactions, between synchronous machines and inverter-based r

Lukasz Baran, Luis G. MacDowell

The ice surface is known for presenting a very small kinetic friction coefficient, but the origin of this property remains highly controversial to date. In this work, we revisit recent computer simulations of ice sliding on atomically smooth substrates, using newly calculated bulk viscosities for the TIP4P/Ice water model. The results show that spontaneously

Shashank Kotyan, Po-Yuan Mao, Pin-Yu Chen, Danilo Vasconcellos Vargas

Deep neural networks can be exploited using natural adversarial samples, which do not impact human perception. Current approaches often rely on deep neural networks' white-box nature to generate these adversarial samples or synthetically alter the distribution of adversarial samples compared to the training distribution. In contrast, we propose EvoSeed, a no

Rob Fender, Assaf Horesh, Phil Charles, Patrick Woudt

In this short paper we outline the case for a small radio telescope array in the southern hemisphere with operations dedicated to rapid follow-up and monitoring of astrophysical transients. We argue that the science harvest from such a facility would be very large, using AMI-LA as an outstanding example of how such a programme is already being operated in th

Gordei Anchutkin, Viktor Holubec, Frank Cichos

A hallmark of bacteria is their so-called "run-and-tumble" motion, consisting of a sequence of linear directed "runs" and random rotations that constantly alternate due to biochemical feedback. It plays a crucial role in the ability of bacteria to move through chemical gradients and inspired a fundamental active particle model. Nevertheless, synthetic active

Baohao Liao, Christian Herold, Shahram Khadivi, Christof Monz

Memory-efficient finetuning of large language models (LLMs) has recently attracted huge attention with the increasing size of LLMs, primarily due to the constraints posed by GPU memory limitations and the effectiveness of these methods compared to full finetuning. Despite the advancements, current strategies for memory-efficient finetuning, such as QLoRA, ex

Simeon Pawlowski, Vincent Froese

We study Voronoi games on temporal graphs as introduced by Boehmer et al. (IJCAI 2021) where two players each select a vertex in a temporal graph with the goal of reaching the other vertices earlier than the other player. In this work, we consider the reverse temporal Voronoi game, that is, a player wants to maximize the number of vertices reaching her earli

Didier Bresch, Mitia Duerinckx, Pierre-Emmanuel Jabin

We introduce a new approach to derive mean-field limits for first- and second-order particle systems with singular interactions. It is based on a duality approach combined with the analysis of linearized dual correlations, and it allows to cover for the first time arbitrary square-integrable interaction forces at possibly vanishing temperature. In case of fi

Hongjie Zhang, Daniel Verscharen, Georgios Nicolaou

In order to protect society from space weather impacts, we must monitor space weather and obtain early warnings for extreme events if possible. For this purpose, the European Space Agency is currently preparing to launch the Vigil mission towards the end of this decade as a space-weather monitor at the fifth Lagrange point of the Sun--Earth system. Vigil wil

J. M. Campillo, F. Plazaola, N. de Diego

The effect of the lattice relaxation at vacancy clusters and interstitial-type dislocation loops on the lifetime of positrons in Zn has been studied. Defective relaxed structures have been generated for the lifetime calculations by using a many-body potential for Zn. From the results, it is inferred that the effect of the atomic relaxation is mainly signific

Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan, Aurélie Lozano

Protein language models are a powerful tool for learning protein representations through pre-training on vast protein sequence datasets. However, traditional protein language models lack explicit structural supervision, despite its relevance to protein function. To address this issue, we introduce the integration of remote homology detection to distill struc

Marie Chavent, Guy Chavent

Block Principal Component Analysis (Block PCA) of a data matrix A, where loadings Z are determined by maximization of AZ 2 over unit norm orthogonal loadings, is difficult to use for the design of sparse PCA by 1 regularization, due to the difficulty of taking care of both the orthogonality constraint on loadings and the non differentiable 1 penalty. Our obj

Lei Shi, Jia-Qi Yang

This study investigates leveraging stochastic gradient descent (SGD) to learn operators between general Hilbert spaces. We propose weak and strong regularity conditions for the target operator to depict its intrinsic structure and complexity. Under these conditions, we establish upper bounds for convergence rates of the SGD algorithm and conduct a minimax lo

Orr Avni, Eran Sher, Yuval Dagan

This paper presents a new analytical model for the early stages of vapor bubble growth in superheated liquids. The model bridges a gap in current knowledge by focusing on the surface tension-controlled, near-equilibrium growth regime and its transition to an inertia-controlled regime. A unified analytical model is derived by combining a perturbation method f

Gaëtan Serré, Argyris Kalogeratos, Nicolas Vayatis

In this paper, we present a flow-based method for global optimization of continuous Sobolev functions, called Stein Boltzmann Sampling (SBS). SBS initializes uniformly a number of particles representing candidate solutions, then uses the Stein Variational Gradient Descent (SVGD) algorithm to sequentially and deterministically move those particles in order to

Daniele Locci, Giambattista Aresu, Antonino Petralia, Giuseppina Micela

High-energy radiation from stars impacts planetary atmospheres deeply affecting their chemistry, providing departures from chemical equilibrium. While the upper atmospheric layers are dominated by ionizations induced by extreme ultraviolet radiation, deeper into the atmosphere molecular abundances are controlled by a characteristic X-ray dominated chemistry,

A. V. Podobryaev

We consider a left-invariant (sub-)Lorentzian structure on a Lie group. We assume that this structure is defined by a closed convex salient cone in the corresponding Lie algebra and a continuous antinorm associated with this cone. We derive the Hamiltonian system for (sub-)Lorentzian extremals and give conditions under that normal extremal trajectories keep

Carlos Ricolfe-Viala, Alicia Esparza

Camera calibration is a crucial step in robotics and computer vision. Accurate camera parameters are necessary to achieve robust applications. Nowadays, camera calibration process consists of adjusting a set of data to a pin-hole model, assuming that with a reprojection error close to cero, camera parameters are correct. Since all camera parameters are unkno

Yafei Wang, Xinping Yi, Hongwei Hou, Wenjin Wang

This paper investigates the robust design of symbol-level precoding (SLP) for multiuser multiple-input multiple-output (MIMO) downlink transmission with imperfect channel state information (CSI) caused by channel aging. By utilizing the a posteriori channel model based on the widely adopted jointly correlated channel model, the imperfect CSI is modeled as th

Shaoshi Chen, Ruyong Feng, Manuel Kauers, Xiuyun Li

We propose investigating a summation analog of the paradigm for parallel integration. We make some first steps towards an indefinite summation method applicable to summands that rationally depend on the summation index and a P-recursive sequence and its shifts. There is a distinction between so-called normal and so-called special polynomials. Under the assum

Feliks Rączka

We study the category of modules of minimal dimension over completed Weyl algebras in equal characteristic zero. In particular we prove finiteness of de Rham cohomology of such modules.

Raquel Ramos López, Ana Lucila Sandoval Orozco, Luis Javier García Villalba

The continuous growth of technologies like 4G or 5G has led to a massive use of mobile devices such as smartphones and tablets. This phenomenon, combined with the fact that people use mobile phones for a longer period of time, results in mobile phones becoming the main source of creation of visual information. However, its reliability as a true representatio

R. Casadio, C. Noberto Souza, R. da Rocha

A connection is established between transonic sound waves propagating along a de Laval nozzle and quasinormal modes emitted from hairy black holes obtained with the gravitational decoupling method applied to the Reissner-Nordstr\"om geometry. Aerodynamical features provide an analogue setup to experimentally test fluid flow perturbations in a de Laval nozzle

Tom van Eijk, Indika Kumara, Dario Di Nucci, Damian Andrew Tamburri

Data mesh is an emerging decentralized approach to managing and generating value from analytical enterprise data at scale. It shifts the ownership of the data to the business domains closest to the data, promotes sharing and managing data as autonomous products, and uses a federated and automated data governance model. The data mesh relies on a managed data

Rasool Hafezi, Hideto Asashiba, Mohammad Hossein Keshavarz

Throughout this paper $G$ is a fixed group, and $k$ is a fixed field. All categories are assumed to be $k$-linear. First we give a systematic way to induce $G$-precoverings by adjoint functors using a 2-categorical machinery, which unifies many similar constructions of $G$-precoverings. Now let $\mathcal{C}$ be a skeletally small category with a $G$-action,

C. Wetterich

Field transformations for the quantum effective action lead to different pictures of a given physical situation, as describing a given evolution of the universe by different geometries. Field transformations for functional flow equations can reveal important physical features, as the appearance of bound states. They also allow for technical simplifications.

Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Ruixiang Tang

Large language models (LLMs) increasingly produce natural language explanations, yet these explanations often lack faithfulness, and they do not reliably reflect the evidence the model uses to decide. We introduce FaithLM, a model-agnostic framework that evaluates and improves the faithfulness of LLM explanations without token masking or task-specific heuris

Yoshi Suhara, Dimitris Alikaniotis

Neural abstractive summarization models make summaries in an end-to-end manner, and little is known about how the source information is actually converted into summaries. In this paper, we define input sentences that contain essential information in the generated summary as $\textit{source sentences}$ and study how abstractive summaries are made by analyzing

Patrick Egenlauf, Patric Rommel, Jörg Main

Resonances in open quantum systems depending on at least two controllable parameters can show the phenomenon of exceptional points (EPs), where not only the eigenvalues but also the eigenvectors of two or more resonances coalesce. Their exact localization in the parameter space is challenging, in particular in systems, where the computation of the quantum sp

Saeed Vahidian, Mingyu Wang, Jianyang Gu, Vyacheslav Kungurtsev

Dataset distillation (DD) has emerged as a widely adopted technique for crafting a synthetic dataset that captures the essential information of a training dataset, facilitating the training of accurate neural models. Its applications span various domains, including transfer learning, federated learning, and neural architecture search. The most popular method

Giulio Pascale, Marco Pozzetta

We consider capillarity functionals which measure the perimeter of sets contained in a Euclidean half-space assigning a constant weight $\lambda \in (-1,1)$ to the portion of the boundary that touches the boundary of the half-space. Depending on $\lambda$, sets that minimize this capillarity perimeter among those with fixed volume are known to be suitable tr

Philipp Bach, Oliver Schacht, Victor Chernozhukov, Sven Klaassen

Proper hyperparameter tuning is essential for achieving optimal performance of modern machine learning (ML) methods in predictive tasks. While there is an extensive literature on tuning ML learners for prediction, there is only little guidance available on tuning ML learners for causal machine learning and how to select among different ML learners. In this p

Wensheng Su, Zhenni Li, Minrui Xu, Jiawen Kang

Deep reinforcement learning (DRL) has shown remarkable success in complex autonomous driving scenarios. However, DRL models inevitably bring high memory consumption and computation, which hinders their wide deployment in resource-limited autonomous driving devices. Structured Pruning has been recognized as a useful method to compress and accelerate DRL model

Karim El Khoury, Tiffanie Godelaine, Simon Delvaux, Sebastien Lugan

Emergency response missions depend on the fast relay of visual information, a task to which unmanned aerial vehicles are well adapted. However, the effective use of unmanned aerial vehicles is often compromised by bandwidth limitations that impede fast data transmission, thereby delaying the quick decision-making necessary in emergency situations. To address

Fan Wu, Jinling Gao, Lanqing Hong, Xinbing Wang

In this paper, we focus on a realistic yet challenging task, Single Domain Generalization Object Detection (S-DGOD), where only one source domain's data can be used for training object detectors, but have to generalize multiple distinct target domains. In S-DGOD, both high-capacity fitting and generalization abilities are needed due to the task's complexity.

Yuanfang Zhang, Junxuan Li, Kaiqing Luo, Yiying Yang

Semantic scene completion (SSC) has recently gained popularity because it can provide both semantic and geometric information that can be used directly for autonomous vehicle navigation. However, there are still challenges to overcome. SSC is often hampered by occlusion and short-range perception due to sensor limitations, which can pose safety risks. This p

Giuseppe Orlando, Paolo Francesco Barbante, Luca Bonaventura

We perform a quantitative assessment of different strategies to compute the contribution due to surface tension in incompressible two-phase flows using a conservative level set (CLS) method. More specifically, we compare classical approaches, such as the direct computation of the curvature from the level set or the Laplace-Beltrami operator, with an evolutio

Jie Chen, Fan Gu, Boling Guo

In this paper, we consider the well-posedness of stochastic S-KdV driven by multiplicative noises in $H_x^1\times H_x^1$. To get the local well-posedness, we first develop the bilinear and trilinear Bourgain norm estimates of the nonlinear terms with $b\in\left(0,1/2\right)$. Then, to overcome regularity problems, we introduce a series of approximation equat

Ghadeer O. Ghosheh, Moritz Gögl, Tingting Zhu

The burden of diseases is rising worldwide, with unequal treatment efficacy for patient populations that are underrepresented in clinical trials. Healthcare, however, is driven by the average population effect of medical treatments and, therefore, operates in a "one-size-fits-all" approach, not necessarily what best fits each patient. These facts suggest a p

Anders Hilmar Damm Andersen, John Bagterp Jørgensen

In this paper, we compare the impact of iterated and direct approaches to sensitivity computation in fixed-step explicit singly diagonally-implicit Runge-Kutta (ESDIRK) methods when applied to optimal control problems (OCPs). We use the principle of internal numerical differentiation (IND) strictly for the iterated approach, i.e., reusing the iteration matri

M. A. Escobedo-Sánchez, M. Laurati, H. Löwen, W. C. K. Poon

This is a tribute to honor Stefan U. Egelhaaf (1963-2023), a pioneer in experimental soft condensed matter physics.

Tobias M. Wolff, Victor G. Lopez, Matthias A. Müller

In this paper, we propose a novel Gaussian process-based moving horizon estimation (MHE) framework for unknown nonlinear systems. On the one hand, we approximate the system dynamics by the posterior means of the learned Gaussian processes (GPs). On the other hand, we exploit the posterior variances of the Gaussian processes to design the weighting matrices i

Shin-ichiro Tanaka, Shigemasa Suga, Keiji Ueno, Keisuke Fukutani

The remarkable properties of (1T-)TiSe$_2$ among the transition metal dichalcogenides have attracted the attention of many researchers due to its peculiar behavior during the charge density wave (CDW) transition. Therefore, it is highly desirable to study its electronic structure down to the atomic orbitals. In the present research, we applied momentum-resol

Yulong Huang, Xiaopeng Lin, Hongwei Ren, Haotian Fu

Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Compared to conventional deep Artificial Neural Networks (ANNs), SNNs exhibit superior efficiency and capability to process temporal information. However, it remains a challenge to train SNNs due to their undifferentiable spiking mechanism. The surrogate gradients method is

Guangye Cao

Why would a blockchain-based startup and its venture capital investors choose to finance by issuing tokens instead of equity? What would be their rates of return for each asset? This paper focuses on the liquidity difference between the two fundraising methods. I build a three-period model of an entrepreneur, two types of investors, and users. Some investors

Josu Amorebieta, Angel Ortega-Gomez, Gaizka Durana, Rubén Fernández

We propose and demonstrate a compact and simple vector bending sensor capable of distinguishing any direction and amplitude with high accuracy. The sensor consists of a short segment of asymmetric multicore fiber (MCF) fusion spliced to a standard single mode fiber. The reflection spectrum of such a structure shifts and shrinks in specific manners depending

Tsufit Shua, Liron David, Mahmood Sharif

Traffic-Sign Recognition (TSR) is a critical safety component for autonomous driving. Unfortunately, however, past work has highlighted the vulnerability of TSR models to physical-world attacks, through low-cost, easily deployable adversarial patches leading to misclassification. To mitigate these threats, most defenses focus on altering the training process

Meng-Jie Zhao, Xiao-Jun Bi, Kun Fang, Peng-Fei Yin

The Be isotopic measurements preliminarily reported by the AMS-02 Collaboration have reached an unprecedented energy of 12 GeV/$n$. As secondary cosmic rays (CRs), the Be isotopes include both stable and unstable species, which are crucial for constraining the propagation parameters of Galactic CRs. However, uncertainties in their production cross sections c

Zhu-Ling Deng, Xiang-Dong Li, Yong Shao, Kun Xu

The detection of gravitational wave events has stimulated theoretical modeling of the formation and evolution of double compact objects (DCOs). However, even for the most studied isolated binary evolution channel, there exist large uncertainties in the input parameters and treatments of the binary evolution process. So far, double neutron stars (DNSs) are th

Shuta Ishigaki, Masataka Matsumoto, Ryosuke Yoshii

We investigate spatially inhomogeneous solutions in a top-down holographic model: the D3/D7 model which provides a holographic description of the chiral phase transition for a finite external magnetic field, chemical potential, and temperature. We numerically find a domain wall (or kink) solution in the three dimensional space, which incorporates between the

O. Février, S. Gorno, C. Theiler, M. Carpita

Studying the process of divertor detachment and the associated complex interplay of plasma dynamics and atomic physics processes is of utmost importance for future fusion reactors. Whilst simplified analytical models exist to interpret the general features of detachment, they are limited in their predictive power, and complex 2D or even 3D codes are generall

Shuoyuan Wang, Jindong Wang, Guoqing Wang, Bob Zhang

Vision-language models (VLMs) have emerged as formidable tools, showing their strong capability in handling various open-vocabulary tasks in image recognition, text-driven visual content generation, and visual chatbots, to name a few. In recent years, considerable efforts and resources have been devoted to adaptation methods for improving downstream performa

Alma L. Albujer, Fábio R. dos Santos

Some classification results for closed surfaces in Berger spheres are presented. On the one hand, a Willmore functional for isometrically immersed surfaces into an homogeneous space $\mathbb{E}^{3}(\kappa,\tau)$ with isometry group of dimension $4$ is defined and its first variational formula is computed. Then, we characterize Clifford and Hopf tori as the o

Moshe Eliasof, Eldad Haber, Eran Treister

Obtaining meaningful solutions for inverse problems has been a major challenge with many applications in science and engineering. Recent machine learning techniques based on proximal and diffusion-based methods have shown promising results. However, as we show in this work, they can also face challenges when applied to some exemplary problems. We show that s

Zinuo Cai, Changliang Ren, Tianfeng Feng, Xiaoqi Zhou

This review offers a comprehensive exploration and synthesis of recent advancements in the domain of quantum correlation sharing facilitated through sequential measurements. We initiate our inquiry by delving into the interpretation of the joint probability, laying the foundation for an examination of quantum correlations within the context of specific measu

Martin Hanke

In 1996 Seo proved that two appropriate pairs of current and voltage data measured on the surface of a planar homogeneous object are sufficient to determine a conductive polygonal inclusion with known deviating conductivity. Here we show that the corresponding linearized forward map is injective, and from this we deduce Lipschitz stability of the solution of

Stanislas Strasman, Antonio Ocello, Claire Boyer, Sylvain Le Corff

Score-based generative models (SGMs) aim at estimating a target data distribution by learning score functions using only noise-perturbed samples from the target.Recent literature has focused extensively on assessing the error between the target and estimated distributions, gauging the generative quality through the Kullback-Leibler (KL) divergence and Wasser

Max Fathi, Matthieu Fradelizi, Nathael Gozlan, Simon Zugmeyer

Caffarelli's contraction theorem states that probability measures with uniformly logconcave densities on R d can be realized as the image of a standard Gaussian measure by a globally Lipschitz transport map. We discuss some counterexamples and obstructions that prevent a similar result from holding on the half-sphere endowed with a uniform measure, answering

Guibiao Liao, Kaichen Zhou, Zhenyu Bao, Kanglin Liu

The development of Neural Radiance Fields (NeRFs) has provided a potent representation for encapsulating the geometric and appearance characteristics of 3D scenes. Enhancing the capabilities of NeRFs in open-vocabulary 3D semantic perception tasks has been a recent focus. However, current methods that extract semantics directly from Contrastive Language-Imag

Deqian Kong, Dehong Xu, Minglu Zhao, Bo Pang

In tasks aiming for long-term returns, planning becomes essential. We study generative modeling for planning with datasets repurposed from offline reinforcement learning. Specifically, we identify temporal consistency in the absence of step-wise rewards as one key technical challenge. We introduce the Latent Plan Transformer (LPT), a novel model that leverag

Yanhao Zhang, Zhihan Zhu, Yong Xia

This paper introduces a novel prior called Diversified Block Sparse Prior to characterize the widespread block sparsity phenomenon in real-world data. By allowing diversification on intra-block variance and inter-block correlation matrices, we effectively address the sensitivity issue of existing block sparse learning methods to pre-defined block information

Salil Gokhale, Shivika Narang, Samarth Singla, Rohit Vaish

We study the problem of capacity modification in the many-to-one stable matching of workers and firms. Our goal is to systematically study how the set of stable matchings changes when some seats are added to or removed from the firms. We make three main contributions: First, we examine whether firms and workers can improve or worsen upon changing the capacit

Yuji Roh, Qingyun Liu, Huan Gui, Zhe Yuan

Fine-tuning is becoming widely used for leveraging the power of pre-trained foundation models in new downstream tasks. While there are many successes of fine-tuning on various tasks, recent studies have observed challenges in the generalization of fine-tuned models to unseen distributions (i.e., out-of-distribution; OOD). To improve OOD generalization, some