December 2024 arXiv papers — page 192

Xingchen Zhou, Nan Li, Hu Zou, Yan Gong

We present a catalogue of photometric redshifts for galaxies from DESI Legacy Imaging Surveys, which includes $\sim0.18$ billion sources covering 14,000 ${\rm deg}^2$. The photometric redshifts, along with their uncertainties, are estimated through galaxy images in three optical bands ($g$, $r$ and $z$) from DESI and two near-infrared bands ($W1$ and $W2$) f

Won Dong Shin, Hoang-Vu Phan, Monica A. Daley, Auke J. Ijspeert

Most birds can navigate seamlessly between aerial and terrestrial environments. Whereas the forelimbs evolved into wings primarily for flight, the hindlimbs serve diverse functions such as walking, hopping, and leaping, and jumping take-off for transitions into flight. These capabilities have inspired engineers to aim for similar multi-modality in aerial rob

Christian Johnson-Richards, Alex Yakovlev, Victor Pacheco-Peña

Topological boundary modes utilizing valley mode waveguides have opened opportunities in, for instance, the design of high transmission waveguides with tolerance to geometrical defects and sharp bends. Applications of these waveguides include linear computational processes and the emulation of logic gates using linear structures, among other scenarios. Here

Marcel Aach, Rakesh Sarma, Helmut Neukirchen, Morris Riedel

On High-Performance Computing (HPC) systems, several hyperparameter configurations can be evaluated in parallel to speed up the Hyperparameter Optimization (HPO) process. State-of-the-art HPO methods follow a bandit-based approach and build on top of successive halving, where the final performance of a combination is estimated based on a lower than fully tra

Zihe Chen, Shilv Yu, Jinlong Ma, Bin Xie

Radiative cooling has showcased great potential for passive refrigeration without extra energy consumption, while its cooling power and efficiency is confined by Kirchhoff's law, that is, the emissivity is equal to the absorptivity. The recent development of thermal nonreciprocity that breaks the limitations of Kirchhoff's law, especially in broadband manner

Blanca Lasheras-Hernandez, Klaus H. Strobl, Sergio Izquierdo, Tim Bodenmüller

Metric depth estimation from visual sensors is crucial for robots to perceive, navigate, and interact with their environment. Traditional range imaging setups, such as stereo or structured light cameras, face hassles including calibration, occlusions, and hardware demands, with accuracy limited by the baseline between cameras. Single- and multi-view monocula

Aniruddha Roy, Puduru Viswanadha Reddy

In this paper, we study infinite-horizon linear-quadratic uncertain differential games with an output feedback information structure. We assume linear time-invariant nominal dynamics influenced by deterministic external disturbances, and players' risk preferences are expressed by a soft-constrained quadratic cost criterion over an infinite horizon. We demons

Jorge Pérez, Jessica Díaz, Ángel González-Prieto, Sergio Gil-Borrás

Context: This work is part of a research project whose ultimate goal is to systematize theory building in qualitative research in the field of software engineering. The proposed methodology involves four phases: conceptualization, operationalization, testing, and application. In previous work, we performed the conceptualization of a theory that investigates

Wahid Ullah

We investigate the multiplicity of solutions for a Hamiltonian system coupling two systems associated with mixed boundary conditions. Corresponding to the first system, we impose periodic boundary conditions and assume the twist assumption commonly used for the Poincar\'e--Birkhoff theorem, while for the second one, we consider a two-point boundary condition

Kangkang Deng, Jiang Hu

This paper studies decentralized optimization over a compact submanifold within a communication network of $n$ nodes, where each node possesses a smooth non-convex local cost function, and the goal is to jointly minimize the sum of these local costs. We focus particularly on the online setting, where local data is processed in real-time as it streams in, wit

Lisa Blum Moyse, Ahmed El Hady

Foraging is a widespread behavior, and being part of a group may bring several benefits compared to solitary foraging, such as collective pooling of information and reducing environmental uncertainty. Often theoretical models of collective behavior use coarse-grained representations, or are too complex for analytical treatment, and generally do not take into

Lorna Wheaton, Sylwia Bujkiewicz

Objectives: Surrogate endpoints, used to substitute for and predict final clinical outcomes, are increasingly being used to support submissions to health technology assessment agencies. The increase in use of surrogate endpoints has been accompanied by literature describing frameworks and statistical methods to ensure their robust validation. The aim of this

Magnus Goffeng, Bram Mesland, Mehmet Haluk Sengun

In analogy with the construction of representations of adelic groups as restricted products of representations of local groups, we study restricted tensor products of Hilbert C*-modules and of C*-correspondences. The construction produces global C*-correspondences from compatible collections of local C*-correspondences. When applied to the collection of C*-c

Uwe R. Fischer

The possibility of an atomic detection of gravitational waves on earth is considered. The combination of extremely high lifetimes and resulting small radiative transition probabilities with rapidly growing interaction strength for Rydberg atoms having principal quantum numbers in a region $10^4\ldots 10^5$ might result in transition probabilities which are h

Stefan Yoshi Buhmann, Andreas Hans, Janine C. Franz, Philipp V. Demekhin

We show that a nonchiral atom can act as an antenna to induce a photoelectron circular dichroism in a nearby chiral molecule in a three-step process: The donor atom (antenna) is initially resonantly excited by circularly polarized radiation. It then transfers its excess energy to the acceptor molecule by means of resonant interatomic Coulombic decay. The lat

Zhiguo Ding, Robert Schober, H. Vincent Poor

Flexible-antenna systems have recently received significant research interest due to their capability to reconfigure wireless channels intelligently. This paper focuses on a new type of flexible-antenna technology, termed pinching antennas, which can be realized by applying small dielectric particles on a waveguide. Analytical results are first developed for

Roldao da Rocha, Pedro H. O. Silva

The mass spectra of light-flavor mesons are analyzed in a deformed AdS/QCD soft-wall model, driven by four distinct anomalous 5-dimensional mass corrections of the scalar field, coupled to Einstein--Hilbert gravity, from the QCD running coupling. Using the differential configurational entropy (DCE) underlying the families of pseudoscalar, axial-vector, scala

Chi An Dong-Páez, Marta Volonteri, Yohan Dubois, Ricarda S. Beckmann

After a merger of two massive black holes (MBHs), the remnant receives a gravitational wave (GW) recoil kick that can have a strong effect on its future evolution. The magnitude of the kick ($v_\mathrm{recoil}$) depends on the mass ratio and the alignment of the spins and orbital angular momenta, therefore on the previous evolution of the MBHs. We investigat

Xichen Ye, Yifan Wu, Yiqi Wang, Xiaoqiang Li

Deep supervised learning has achieved remarkable success across a wide range of tasks, yet it remains susceptible to overfitting when confronted with noisy labels. To address this issue, noise-robust loss functions offer an effective solution for enhancing learning in the presence of label noise. In this work, we systematically investigate the limitation of

Sergey Zinchenko, Sergey Iazov

We propose a novel model for learned query optimization which provides query hints leading to better execution plans. The model addresses the three key challenges in learned hint-based query optimization: reliable hint recommendation (ensuring non-degradation of query latency), efficient hint exploration, and fast inference. We provide an in-depth analysis o

Xi Cao, Quzong Gesang, Yuan Sun, Nuo Qun

Language models based on deep neural networks are vulnerable to textual adversarial attacks. While rich-resource languages like English are receiving focused attention, Tibetan, a cross-border language, is gradually being studied due to its abundant ancient literature and critical language strategy. Currently, there are several Tibetan adversarial text gener

Eerik Alamikkotervo, Henrik Toikka, Kari Tammi, Risto Ojala

Robust road segmentation in all road conditions is required for safe autonomous driving and advanced driver assistance systems. Supervised deep learning methods provide accurate road segmentation in the domain of their training data but cannot be trusted in out-of-distribution scenarios. Including the whole distribution in the trainset is challenging as each

Cedric Waterschoot, Ernst van den Hemel, Antal van den Bosch

A widespread moderation strategy by online news platforms is to feature what the platform deems high quality comments, usually called editor picks or featured comments. In this paper, we compare online discussions of news articles in which certain comments are featured, versus discussions in which no comments are featured. We measure the impact of featuring

Quang Hoang Trung, Nguyen Van Hoang Phuc, Le Trung Hoang, Quang Huu Hieu

Text Retrieval (TR) involves finding and retrieving text-based content relevant to a user's query from a large repository, with applications in real-world scenarios such as legal document retrieval. While most existing studies focus on English, limited work addresses Japanese contexts. In this paper, we introduce a new dataset specifically designed for Japan

Leixin Zhang, Steffen Eger, Yinjie Cheng, Weihe Zhai

Multimodal large language models (LLMs) have demonstrated impressive capabilities in generating high-quality images from textual instructions. However, their performance in generating scientific images--a critical application for accelerating scientific progress--remains underexplored. In this work, we address this gap by introducing ScImage, a benchmark des

Hanti Lin

Otsuka (2023) argues for a correspondence between data science and traditional epistemology: Bayesian statistics is internalist; classical (frequentist) statistics is externalist, owing to its reliabilist nature; model selection is pragmatist; and machine learning is a version of virtue epistemology. Where he sees diversity, I see an opportunity for unity. I

Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood, Karthik Nandakumar

Data augmentation is widely used to enhance generalization in visual classification tasks. However, traditional methods struggle when source and target domains differ, as in domain adaptation, due to their inability to address domain gaps. This paper introduces GenMix, a generalizable prompt-guided generative data augmentation approach that enhances both in-

Cai Heng Li, Luyi Liu, Hanyue Yi, Yan Zhou Zhu

This is one of a series of papers which aims towards a classification of imprimitive affine groups of rank $3$. In this paper, a complete classification is given of such groups of characteristic $p$ such that the point stabilizer is not $p$-local, which shows that such groups are very rare, namely, the two non-isomorphic groups of the form $2^4{:}\mathrm{GL}

Bhera Ram, Bibhas Ranjan Majhi

Following the recently proposed stable and causal first-order relativistic hydrodynamics by Bemfica, Disconzi, and Noronha, we find the heat flow equation in the presence of gravity for a non-viscous fluid, which suffers heat dissipation. The derivation is confined to static and stationary backgrounds. We find that in the presence of gravity, the heat flux t

Dimitri Markushevich, Alexander Tikhomirov

The unirationality of the moduli space of mathematical instantons on the projective 3-space is proved for charges less than or equal to 7.

Thierry Chanelière, Alexei D. Chepelianskii

Controlling polarisation directly at low temperature is crucial for development of optical spectroscopy techniques at sub-Kelvin temperatures, for example, in a hybrid scheme where light is fed into and collected in the cryostat by fibres that are as easy to install as electrical wiring, but where distortions in the fibre need to be compensated for by discre

Matteo Baggioli, Yanyan Bu, Xiyang Sun

Chiral Anomalous Magnetohydrodynamics (CAMHD) provides a low-energy effective framework for describing chiral fluids in the presence of dynamical electromagnetic fields and axial anomaly. This theory finds applications across diverse physical systems, including heavy-ion collisions, the early universe, and Weyl/Dirac semimetals. Along with Schwinger-Keldysh

Carina Kanitz, Jakob Bühler, Vladimír Zobač, Joseph J. Robinson

Diffraction of atoms from surfaces provides detailed insights into structures, interactions, and dynamical processes. However, currently the method is limited to measurements in reflection - diffraction through materials has only been demonstrated for subatomic particles and is an outstanding challenge for atoms. We diffract helium and hydrogen atoms at kilo

Kailing Wang, Chen Yang, Keyang Zhao, Xiaokang Yang

This paper tackles the challenge of automatically performing realistic surgical simulations from readily available surgical videos. Recent efforts have successfully integrated physically grounded dynamics within 3D Gaussians to perform high-fidelity simulations in well-reconstructed simulation environments from static scenes. However, they struggle with the

Ainara Saiz-Pérez, Christian M. Fromm, Manel Perucho, Oliver Porth

Context. Radio galaxies with visible two-sided jet structures, such as NGC 1052, are sources of particular interest to study the collimation and shock structure of active galactic nuclei jets. High-resolution very-long-baseline interferometry observations of such sources can resolve and study the jet collimation profile and probe different physical mechanism

Ian Drosos, Jack Williams, Advait Sarkar, Nicholas Wilson

Effective prompting of generative AI is challenging for many users, particularly in expressing context for comprehension tasks such as explaining spreadsheet formulas, Python code, and text passages. Prompt middleware aims to address this barrier by assisting in prompt construction, but barriers remain for users in expressing adequate control so that they ca

Mingkuan Yang, Hailong Yuan, Zhongrui Bai, Zhenwei Li

We present an analysis of LAMOST J171013.211+532646.04 (hereafter J1710), a binary system comprising a hot subdwarf B star (sdB) and a white dwarf (WD) companion. Multi-epoch spectroscopy reveals an orbital period of 109.20279 minutes, consistent with TESS and ZTF photometric data, marking it as the sixth detached system known to harbor a WD companion with a

Lukas Andreas Widmer, Sebastian Weber, Yunnan Xu, Hans-Jochen Weber

Treatment of cancer has rapidly evolved over time in quite dramatic ways, for example from chemotherapies, targeted therapies to immunotherapies and chimeric antigen receptor T-cells. Nonetheless, the basic design of early phase I trials in oncology still follows pre-dominantly a dose-escalation design. These trials monitor safety over the first treatment cy

Evgueni Doubtsov, Anton Tselishchev, Ioann Vasilyev

Let $X$ be a quasi-Banach space of analytic functions in the unit disc and let $q>0$. A finite positive Borel measure $\mu$ in the closed unit disc $\overline{\mathbb{D}}$ is called a $q$-reverse Carleson measure for $X$ if and only if there exists a constant $C>0$ such that $$\|f\|_{X}\leq C \|f\|_{L^q(\overline{\mathbb D},d\mu)} $$ for all $f\in X\cap C(\o

Václav Janiš

Electrons at the Fermi energy may lose their ability to propagate to long distances in certain random media. We use Green functions and solve parquet equations for the non-local electron-hole vertex in high spatial dimensions to describe the vanishing of diffusion in Anderson localization. It is caused by forming a quantum bound state between the diffusing p

Ethan Smith, Rami Seid, Alberto Hojel, Paramita Mishra

Low-Rank Adaptation (LoRA) and other parameter-efficient fine-tuning (PEFT) methods provide low-memory, storage-efficient solutions for personalizing text-to-image models. However, these methods offer little to no improvement in wall-clock training time or the number of steps needed for convergence compared to full model fine-tuning. While PEFT methods assum

Juhyung Choi, Jinnyeong Kim, Seokjun Choi, Jinwoo Lee

Achieving robust stereo 3D imaging under diverse illumination conditions is an important however challenging task, due to the limited dynamic ranges (DRs) of cameras, which are significantly smaller than real world DR. As a result, the accuracy of existing stereo depth estimation methods is often compromised by under- or over-exposed images. Here, we introdu

Lucrezia Bottegoni, Fabio Renda, Andrea Sciandra

Given a bialgebra $H$ such that the associated trivial topological bialgebra $H[[\hbar]]$ admits a quasitriangular structure $\tilde{\mathcal{R}}=\mathcal{R}(1\otimes 1+\hbar\chi+\mathcal{O}(\hbar^2))$, one gets a distinguished element $\chi \in H \otimes H$ which is an infinitesimal $\mathcal{R}$-matrix, according to the definition given in [1]. In this pap

Marco Casagrande, Daniele Antonioli

FIDO2 is the standard technology for single-factor and second-factor authentication. It is specified in an open standard, including the WebAuthn and CTAP application layer protocols. We focus on CTAP, which allows FIDO2 clients and hardware authenticators to communicate. No prior work has explored the CTAP Authenticator API, a critical protocol-level attack

Claudio Bonanno, Margarita García Pérez, Antonio González-Arroyo, Ken-Ichi Ishikawa

We provide a first-principles non-perturbative determination of the mass of the lightest gluino-gluon bound state (gluino-glue) in large-$N$ $\mathcal{N}=1$ Supersymmetric Yang--Mills theory by means of numerical Monte Carlo simulations of the lattice-discretized theory, and exploiting large-$N$ twisted volume reduction. Our large-$N$ determination is consis

Emanuele Maggio

An exact analytical expression is derived for Bloch states in three dimensions, based on the only assumption that the electronic wavefunction can be expanded in terms of Gaussian type orbitals. The resulting expression features multidimensional $\vartheta -$functions (and their derivatives) on which the action of discrete space group symmetries is evaluated

Fabienne Chouraqui

The Tracy-Singh product of matrices permits to construct a new gate $c \boxtimes c'$ from two $2$-qudit gates $c$ and $c'$. If $c$ and $c'$ are both Yang-Baxter gates, then $c \boxtimes c'$ is also a Yang-Baxter gate, and if at least one of them is entangling, then $c \boxtimes c'$ is also entangling. A natural question arises about the realisation of these

Seul-Ki Yeom, Tae-Ho Kim

Transformer-based architectures have demonstrated remarkable success across various domains, but their deployment on edge devices remains challenging due to high memory and computational demands. In this paper, we introduce a novel Reuse Attention mechanism, tailored for efficient memory access and computational optimization, enabling seamless operation on r

Xi Cao, Nuo Qun, Quzong Gesang, Yulei Zhu

In social media, neural network models have been applied to hate speech detection, sentiment analysis, etc., but neural network models are susceptible to adversarial attacks. For instance, in a text classification task, the attacker elaborately introduces perturbations to the original texts that hardly alter the original semantics in order to trick the model

Kenza Bousedra, Pierre Pelletier

This paper proposes an original methodology based on Named Entity Recognition (NER) to identify companies involved in downstream space activities, i.e., companies that provide services or products exploiting data and technology from space. Using a rule-based approach, the method leverages a corpus of texts from digitized French press articles to extract comp

Jérôme Poineau, Andrea Pulita

In our previous works we proved a finiteness property of the radii of convergence functions associated with a vector bundle with connection on $p$-adic analytic curves. We showed that the radii are locally constant functions outside a locally finite graph in the curve, called controlling graph. In this paper we refine that finiteness results by giving a boun

Harish Srinivas, Graham Cormode, Mehrdad Honarkhah, Samuel Lurye

Cross-device Federated Analytics (FA) is a distributed computation paradigm designed to answer analytics queries about and derive insights from data held locally on users' devices. On-device computations combined with other privacy and security measures ensure that only minimal data is transmitted off-device, achieving a high standard of data protection. Des

Matteo Tarocchi

This dissertation is about rearrangement groups: a class of groups of homeomorphisms of fractal topological spaces. Introduced in 2019 by J. Belk and B. Forrest, this class generalizes the famous trio of Thompson groups $F$, $T$ and $V$ and includes some of their relatives and generalizations. After an introduction to this topic, this dissertation branches i

Nitesh Kumar Singh, Ion Necoara

In this work, we consider convex optimization problems with smooth objective function and nonsmooth functional constraints. We propose a new stochastic gradient algorithm, called Stochastic Halfspace Approximation Method (SHAM), to solve this problem, where at each iteration we first take a gradient step for the objective function and then we perform a proje

Yuya Kanado, Kota Saito

A real number is called simply normal to base $b$ if every digit $0,1,\ldots ,b-1$ should appear in its $b$-adic expansion with the same frequency $1/b$. A real number is called normal to base $b$ if it is simply normal to every base $b, b^2, \ldots$. In this article, we discover a relation between the normality of algebraic numbers and a mean of the Riemann

Zhenyu Li, Mykola Lavreniuk, Jian Shi, Shariq Farooq Bhat

Amodal depth estimation aims to predict the depth of occluded (invisible) parts of objects in a scene. This task addresses the question of whether models can effectively perceive the geometry of occluded regions based on visible cues. Prior methods primarily rely on synthetic datasets and focus on metric depth estimation, limiting their generalization to rea

Ziyang Cheng, Xiangyu Tian, Ruomin Sui, Tiemin Li

Accurate grasp force control is one of the key skills for ensuring successful and damage-free robotic grasping of objects. Although existing methods have conducted in-depth research on slip detection and grasping force planning, they often overlook the issue of adaptive tracking of the actual force to the target force when handling objects with different mat

Jeongwoo Jae, Jeonghoon Hong, Jinho Choo, Yeong-Dae Kwon

Learning quantum states is a crucial task for realizing quantum information technology. Recently, neural approaches have emerged as promising methods for learning quantum states. We propose a meta-learning model that utilizes reinforcement learning (RL) to optimize the process of learning quantum states. To improve the data efficiency of the RL, we introduce

Giulia Bertagnolli, Luca Greco, Claudio Agostinelli

The occurrence of atypical circular observations on the torus can badly affect parameter estimation of the multivariate von Mises distribution. This paper addresses the problem of robust fitting of the multivariate von Mises model using the weighted likelihood methodology. The key ingredients are non-parametric density estimation for multivariate circular da

Zijie Tao

Let $F$ be a real quadratic field in which a fixed prime $p$ is inert, and $E_0$ be an imaginary quadratic field in which $p$ splits; put $E=E_0 F$. Let ${{\rm Sh}}_{1,n-1}$ be the special fiber over $\mathbb{F}_{p^2}$ of the Shimura variety for $G(U(1,n-1)\times U(n-1,1))$ with hyperspecial level structure at $p$ for some integer $n\geq 2$. Let ${{\rm Sh}}_

Sabina Martyniak, Joanna Kaleta, Diego Dall'Alba, Michał Naskręt

Computer-assisted surgical (CAS) systems enhance surgical execution and outcomes by providing advanced support to surgeons. These systems often rely on deep learning models trained on complex, challenging-to-annotate data. While synthetic data generation can address these challenges, enhancing the realism of such data is crucial. This work introduces a multi

Mehmet Arda Eren, Erhan Oztop

In self-supervised robotic learning, agents acquire data through active interaction with their environment, incurring costs such as energy use, human oversight, and experimental time. To mitigate these, sample-efficient exploration is essential. While intrinsic motivation (IM) methods like learning progress (LP) are widely used in robotics, and active learni

Stach E. J. Kuijpers, Panagiotis Kalaitzis, Evangelia Sakkoula, Sebastiaan Y. T. van de Meerakker

In molecular beam scattering experiments, an important technique for measuring product energy and angular distributions is velocity map imaging following photoionization. For studies with cold molecular beams, the resolution is often limited by the product detection process. When state-selective ionization detection is used, excess photon energy can transfer

Ziqing Wu, Zhu Sun, Dongxia Wang, Lu Zhang

Large language models (LLMs) have shown promising potential for next Point-of-Interest (POI) recommendation. However, existing methods only perform direct zero-shot prompting, leading to ineffective extraction of user preferences, insufficient injection of collaborative signals, and a lack of user privacy protection. As such, we propose a novel Multitask Ref

Sofiane Azogagh, Zelma Aubin Birba, Josée Desharnais, Sébastien Gambs

Cryptographic approaches, such as secure multiparty computation, can be used to compute in a secure manner the function of a distributed graph without centralizing the data of each participant. However, the output of the protocol itself can leak sensitive information about the structure of the original graph. In particular, in this work we propose an approac

Jamie McGowan, Wei Sheng Lai, Weibin Chen, Henry Aldridge

In many domains, the most successful AI models tend to be the largest, indeed often too large to be handled by AI players with limited computational resources. To mitigate this, a number of compression methods have been developed, including methods that prune the network down to high sparsity whilst retaining performance. The best-performing pruning techniqu

Yi Zeng, Jinwei Li, Hui Zhu, Shukuan Lu

Passive acoustic mapping (PAM) is a promising tool for monitoring acoustic cavitation activities in the applications of ultrasound therapy. Data-adaptive beamformers for PAM have better image quality compared to the time exposure acoustics (TEA) algorithms. However, the computational cost of data-adaptive beamformers is considerably expensive. In this work,

Felix L. Schwenninger, Jens de Vries

Let $A$ be a $\rho$-contraction and $f$ a rational function mapping the closed unit disk into itself. With a new characterization of $\rho$-contractions we prove that \begin{align*} \big\|f(A)\big\|\leq \frac{\rho}{2}\big(1-|f(0)|^{2}\big)+\sqrt{\frac{\rho^{2}}{4}\big(1-|f(0)|^{2}\big){}^{2}+|f(0)|^{2}}. \end{align*} We further show that this bound is sharp.

Alejandro Tolcachier

It is known that there exist complex solvmanifolds $(\Gamma\backslash G,J)$ whose canonical bundle is trivialized by a holomorphic section which is not invariant under the action of $G$. The main goal of this article is to classify the six-dimensional Lie algebras corresponding to such complex solvmanifolds, thus extending the previous work of Fino, Otal and

Subhadip Pradhan, Kartik Samanta, Ashis K. Nandy

The intricate interplay between magnetism and the topology of electronic structures provides a rich avenue for tailoring materials with unique and potent anomalous transport properties. In this paper, we present a strategy for inducing robust Berry curvature and anomalous transverse conductivity in noncollinear antiferromagnets through an unconventional appr

Xi Cao, Dolma Dawa, Nuo Qun, Trashi Nyima

The textual adversarial attack refers to an attack method in which the attacker adds imperceptible perturbations to the original texts by elaborate design so that the NLP (natural language processing) model produces false judgments. This method is also used to evaluate the robustness of NLP models. Currently, most of the research in this field focuses on Eng

Xinjie Li, Yang Zhao, Dong Wang, Yuan Chen

Large-scale generative models have achieved remarkable advancements in various visual tasks, yet their application to shadow removal in images remains challenging. These models often generate diverse, realistic details without adequate focus on fidelity, failing to meet the crucial requirements of shadow removal, which necessitates precise preservation of im

Yi Yang, Songyang Pu, Yayun Hu, Zi-Xiang Hu

The Kohn-Sham density functional method for the fractional quantum Hall (FQH) effect has recently been developed by mapping the strongly interacting electrons into an auxiliary system of weakly interacting composite fermions (CFs) that experience a density-dependent effective magnetic field. This approach has been successfully applied to explore the edge res

Clemens Brüser, Mario Kummer

We prove that every real nonnegative ternary quartic whose complex zero set is smooth can be represented as the determinant of a symmetric matrix with quadratic entries which is everywhere positive semidefinite. We show that the corresponding statement fails for the Robinson polynomial, answering a question by Buckley and \v{S}ivic.

Chintan Jansari, Stéphane P. A. Bordas, Marco Montemurro, Elena Atroshchenko

The thermal conductivity of Functionally Graded Materials (FGMs) can be efficiently designed through topology optimization to obtain thermal meta-structures that actively steer the heat flow. Compared to conventional analytical design methods, topology optimization allows handling arbitrary geometries, boundary conditions and design requirements; and produci

Zedong Chu, Feng Xiong, Meiduo Liu, Jinzhi Zhang

With the rapid evolution of 3D generation algorithms, the cost of producing 3D humanoid character models has plummeted, yet the field is impeded by the lack of a comprehensive dataset for automatic rigging, which is a pivotal step in character animation. Addressing this gap, we present HumanRig, the first large-scale dataset specifically designed for 3D huma

Alejandro Mendoza Barrionuevo, Samuel Yanes Luis, Daniel Gutiérrez Reina, Sergio L. Toral Marín

This paper presents a model-free deep reinforcement learning framework for informative path planning with heterogeneous fleets of autonomous surface vehicles to locate and collect plastic waste. The system employs two teams of vehicles: scouts and cleaners. Coordination between these teams is achieved through a deep reinforcement approach, allowing agents to

Shivanagouda Biradar, Deepak U Patil

A problem of reconstruction of the topology and the respective edge resistance values of an unknown circular planar passive resistive network using limitedly available resistance distance measurements is considered. We develop a multistage topology reconstruction method, assuming that the number of boundary and interior nodes, the maximum and minimum edge co

Lingcong Cai, Yun Li, Xiaomao Fan, Kaixuan Song

The segmentation of endoscopic images plays a vital role in computer-aided diagnosis and treatment. The advancements in deep learning have led to the employment of numerous models for endoscopic tumor segmentation, achieving promising segmentation performance. Despite recent advancements, precise segmentation remains challenging due to limited annotations an

Jiamian Hu, Yuanyuan Hong, Yihua Chen, He Wang

We present the Noisy Ostracods, a noisy dataset for genus and species classification of crustacean ostracods with specialists' annotations. Over the 71466 specimens collected, 5.58% of them are estimated to be noisy (possibly problematic) at genus level. The dataset is created to addressing a real-world challenge: creating a clean fine-grained taxonomy datas

Anthony R. Yeates, Luca Bertello, Alexander A. Pevtsov, Alexei A. Pevtsov

We compare two candidate nonlinearities for regulating the solar cycle within the Babcock-Leighton paradigm: tilt quenching (whereby the tilt of active regions is reduced in stronger cycles) and latitude quenching (whereby flux emerges at higher latitudes in stronger solar cycles). Digitized historical observations are used to build a database of individual

Jed Homer, Oliver Friedrich, Daniel Gruen

Making inferences about physical properties of the Universe requires knowledge of the data likelihood. A Gaussian distribution is commonly assumed for the uncertainties with a covariance matrix estimated from a set of simulations. The noise in such covariance estimates causes two problems: it distorts the width of the parameter contours, and it adds scatter

Leah Bar, Boaz Lerner, Nir Darshan, Rami Ben-Ari

Active Learning (AL) is a user-interactive approach aimed at reducing annotation costs by selecting the most crucial examples to label. Although AL has been extensively studied for image classification tasks, the specific scenario of interactive image retrieval has received relatively little attention. This scenario presents unique characteristics, including

Njomza Pacolli, Ahmad Awad, Jannick Kehls, Bjorn Sauren

This contribution presents an improved low-order 3D finite element formulation with hourglass stabilization using automatic differentiation (AD). Here, the former Q1STc formulation is enhanced by an approximation-free computation of the inverse Jacobian. To this end, AD tools automate the computation and allow a direct evaluation of the inverse Jacobian, byp

Torine Reed Herstad, Jalal Kazempour, Lesia Mitridati, Bert Zwart

To enable the participation of stochastic distributed energy resources in ancillary service markets, the Danish transmission system operator, Energinet, mandates that flexibility providers satisfy a minimum 90% reliability requirement for reserve bids. This paper examines the bidding strategy of an electric vehicle aggregator under this regulation and develo

Benoît Cerutti, Enzo Figueiredo, Guillaume Dubus

Rotation-powered pulsars represent the main class of identified gamma-ray sources in the Galaxy. The wealth of observational data collected by the AGILE and Fermi gamma-ray space telescopes in the GeV range, and by ground-based Cherenkov telescopes in the TeV band provide invaluable insights into how relativistic plasmas dissipate and accelerate particles. D

Thomas Besnier, Emery Pierson, Sylvain Arguillere, Maks Ovsjanikov

Non-rigid shape deformations pose significant challenges, and most existing methods struggle to handle partial deformations effectively. We propose to learn deformations at the point level, which allows for localized control of 3D surface meshes, enabling Partial Non-rigid Deformations and interpolations of Surfaces (PaNDaS). Unlike previous approaches, our

Han Luo, Weike Yu, Xi Zhang

In this paper, we investigate $V$-harmonic heat flows from complete Riemannian manifolds with nonnegative Bakry-Emery Ricci curvature to complete Riemannian manifolds with sectional curvature bounded above. We give a gradient estimate of ancient solutions to this flow and establish a Liouville type theorem.

David Levin, José M. Ramón, Juan Ruiz-Alvarez, Dionisio F. Yáñez

In this paper, we address a data dependent modification of the moving least squares (MLS) problem. We propose a novel approach by replacing the traditional weight functions with new functions that assign smaller weights to nodes that are close to discontinuities, while still assigning smaller weights to nodes that are far from the point of approximation. Thr

Matteo Longo, Maria Rosaria Pati, Stefano Vigni

Our main result in this article is a proof (under mild technical assumptions) of an analogue for $p$-adic Galois representations attached to a newform $f$ of even weight $k\geq4$ of Kolyvagin's conjecture on the $p$-indivisibility of derived Heegner points on elliptic curves, where $p$ is a prime number that is ordinary for $f$. Our strategy, which is inspir

Guang Wu, Yun Wang, Qian Zhou, Ziyang Zhang

Accurate photovoltaic (PV) power forecasting is critical for integrating renewable energy sources into the grid, optimizing real-time energy management, and ensuring energy reliability amidst increasing demand. However, existing models often struggle with effectively capturing the complex relationships between target variables and covariates, as well as the

Fouad Trad, Ali Chehab

With the rise of sophisticated phishing attacks, there is a growing need for effective and economical detection solutions. This paper explores the use of large multimodal agents, specifically Gemini 1.5 Flash and GPT-4o mini, to analyze both URLs and webpage screenshots via APIs, thus avoiding the complexities of training and maintaining AI systems. Our find

Claude Duhr, Franziska Porkert, Sven F. Stawinski

We discuss for the first time canonical differential equations for hyperelliptic Feynman integrals. We study hyperelliptic Lauricella functions that include in particular the maximal cut of the two-loop non-planar double box, which is known to involve a hyperlliptic curve of genus two. We consider specifically three- and four-parameter Lauricella functions,

Zhenhui Li, Hongwei Zhang, Kan Wu

Cities play a pivotal role in human development and sustainability, yet studying them presents significant challenges due to the vast scale and complexity of spatial-temporal data. One such challenge is the need to uncover universal urban patterns, such as the urban scaling law, across thousands of cities worldwide. In this study, we propose a novel large-sc

Ying-Hsuan Lin, Mayuko Yamashita

We construct {\it Topological Elliptic Genera}, homotopy-theoretic refinements of the elliptic genera for $SU$-manifolds and variants including the Witten-Landweber-Ochanine genus. The codomains are genuinely $G$-equivariant Topological Modular Forms developed by Gepner-Meier, twisted by $G$-representations. As the first installment of a series of articles o

Yan-Ke Chen, Liang-Zhen Wen, Lu Meng, Shi-Lin Zhu

We calculate the electromagnetic polarizabilities of the spin-$\frac{1}{2}$ singly heavy baryons in the heavy baryon chiral perturbation theory up to $\mathcal{O}(p^3)$. We estimate the low-energy constants using the magnetic moments of singly charmed baryons from lattice QCD simulations and the experimental decay widths of $\Sigma_c$ and $\Sigma_c^*$. Our r

Qiuye Jia, Junyong Zhang

We prove Strichartz estimates for the Schr\"odinger equation with scaling-critical electromagnetic potentials in dimensions $n\geq3$. The decay assumption on the magnetic potentials is critical, including the case of the Coulomb potential. Our approach introduces novel techniques, notably the construction of Schwartz kernels for the localized Schr\"odinger p

Yasser Khalafaoui, Martino Lovisetto, Basarab Matei, Nistor Grozavu

The increasing availability and diversity of multimodal data in recommender systems offer new avenues for enhancing recommendation accuracy and user satisfaction. However, these systems must contend with high-dimensional, sparse user-item rating matrices, where reconstructing the matrix with only small subsets of preferred items for each user poses a signifi

Maximilian E. Tschuchnig, Philipp Steininger, Michael Gadermayr

Computer-Assisted Interventions enable clinicians to perform precise, minimally invasive procedures, often relying on advanced imaging methods. Cone-beam computed tomography (CBCT) can be used to facilitate computer-assisted interventions, despite often suffering from artifacts that pose challenges for accurate interpretation. While the degraded image qualit

Nouhaila Innan, Alberto Marchisio, Muhammad Shafique

We present Federated Learning-Quantum Dynamic Spiking Neural Networks (FL-QDSNNs), a privacy-preserving framework that maintains high predictive accuracy on non-IID client data. Its key innovation is a dynamic-threshold spiking mechanism that triggers quantum gates only when local data drift requires added expressiveness, limiting circuit depth and counterin