December 2024 arXiv papers — page 11

Marta Emilia Bielińska, Michał Eckstein, Paweł Horodecki

Nonlinear quantum dynamics is often invoked in models trying to bridge the gap between the quantum micro-world and the classical macro-world. Such endeavors, however, encounter challenges at the nexus with relativity. In 1989 Nicolas Gisin proved a powerful no-go theorem, according to which nonlinear quantum dynamics would lead to superluminal signalling, vi

Matheus V. X. Ferreira, Yotam Gafni, Max Resnick

We consider a refinement to the notions of collusion-resistance in transaction fee mechanisms. In particular, we require that the collusion is by itself incentive-compatible and individually rational to all of its participants. We then study the structural properties of these notions, and importantly, characterize the class of collusion-resistant and incenti

Arthur Blanc-Renaudie, Camille Cazaux, Guillaume Conchon-Kerjan, Tanguy Lions

We consider a recent model of random walk that recursively grows the network on which it evolves, namely the Tree Builder Random Walk (TBRW). We introduce a bias $\rho \in (0,\infty)$ towards the root, and exhibit a phase transition for transience/recurrence at a critical threshold $\rho_c =1+2\overline{\nu}$, where $\overline{\nu}$ is the (possibly infinite

Vasiliy A. Es'kin, Alexey O. Malkhanov, Mikhail E. Smorkalov

The article is devoted to the study of neural networks with one hidden layer and a modified activation function for solving physical problems. A rectified sigmoid activation function has been proposed to solve physical problems described by the ODE with neural networks. Algorithms for physics-informed data-driven initialization of a neural network and a neur

Wei Chao, Yu Gao, Ming-jie Jin, Xiao-sheng Liu

In this paper, we investigate constraints of the transmon qubit, an improved version of the charge qubit, on bosonic light dark matters. Phonon excitations induced by the scattering or absorption of dark matter on a superconductor may destroy the Cooper pair, leading to the production of quasiparticles made by the electron. By measuring the production rate o

Rajkamal Nailwal, Aljaž Zalar

Let $\mu$ be a positive Borel measure on the real line and let $L$ be the linear functional on univariate polynomials of bounded degree, defined as integration with respect to $\mu$. In 2020, Blekherman et al., the characterization of all minimal quadrature rules of $\mu$ in terms of the roots of a bivariate polynomial is given and two determinantal represen

Yigit Demirag

As neural computation is revolutionizing the field of Artificial Intelligence (AI), rethinking the ideal neural hardware is becoming the next frontier. Fast and reliable von Neumann architecture has been the hosting platform for neural computation. Although capable, its separation of memory and computation creates the bottleneck for the energy efficiency of

Alif Aqsha, Fayçal Drissi, Leandro Sánchez-Betancourt

We study strategic interactions in a broker-mediated market in which agents learn and exploit each other's private information. A broker provides liquidity to an informed trader and to noise traders while managing inventory in a lit market. The informed trader infers the broker's trading activity in the lit market, while the broker estimates the trader's pri

Xingjian Tao, Yiwei Wang, Yujun Cai, Zhicheng Yang

Large language models (LLMs) have shown promise as parametric knowledge bases, but often underperform on question answering (QA) tasks due to hallucinations and uncertainty. While prior work attributes these failures to knowledge gaps in the model's parameters, we uncover a complementary phenomenon: LLMs frequently retain correct knowledge even when generati

Dongdong Li, Jiuxiang Dong

Policy iteration is one of the classical frameworks of reinforcement learning, which requires a known initial stabilizing control. However, finding the initial stabilizing control depends on the known system model. To relax this requirement and achieve model-free optimal control, in this paper, two different reinforcement learning algorithms based on policy

Daan Kuppens, Sebastiano Barbieri, Daisy van den Berg, Pepijn Schouten

Deep learning has proven to be a suitable alternative to least-squares (LSQ) fitting for parameter estimation in various quantitative MRI (QMRI) models. However, current deep learning implementations are not robust to changes in MR acquisition protocols. In practice, QMRI acquisition protocols differ substantially between different studies and clinical setti

Zhuotao Xie, Yu-Feng Song, Yuan-Yao He

We present a {\it numerically exact} study of the Hubbard model with spin-dependent anisotropic hopping on the square lattice using auxiliary-field quantum Monte Carlo method. At half filling, the system undergoes Ising phase transitions upon cooling, leading to the formation of Ising-type antiferromagnetic order for repulsive interactions and charge-density

Zhihao Ouyang, Hubing Xiao, Marina Manganaro, Shangchun Xie

In this work, we analyzed multi-wavelength data of the BL Lac object S5 0716+714 to investigate its emission mechanisms during a flaring state observed in early 2015. We examined the temporal behavior and broadband spectral energy distributions (SEDs) during the flare. The size of the $\gamma$-ray emission region was estimated based on the variability timesc

Zhangkai Huang

In this paper, we study harmonic RCD$(K,N)$ spaces as the counterpart of harmonic Riemannian manifolds with Ricci curvature bounded from below. We prove that a compact RCD$(K,N)$ space is isometric to a smooth closed Riemannian manifold if it satisfies either of the following harmonicity conditions:(1) the heat kernel $\rho(x,y,t)$ depends only on the variab

Xinqin Feng, Wenjie Hu, Pu Yang, Tingyu Li

Regression discontinuity designs are widely used when treatment assignment is determined by whether a running variable exceeds a predefined threshold. However, most research focuses on estimating local causal effects at the threshold, leaving the challenge of identifying treatment effects away from the cutoff largely unaddressed. The primary difficulty in th

Daniele Iannarelli, Francesco Napoli, Antonella Ingenito, Alessandro Cardinali

The current study, as part of a PhD project on the design of a helicon thruster, aims to provide a rational methodology for the design of the helicon thruster's main component, i.e., the helicon antenna. A helicon thruster is an innovative electrodeless plasma thruster that works by exciting helicon waves in a magnetized plasma, and its antenna is capable of

Shubh Singhal, Raül Pérez-Gonzalo, Andreas Espersen, Antonio Agudo

Accurate segmentation of wind turbine blade (WTB) images is critical for effective assessments, as it directly influences the performance of automated damage detection systems. Despite advancements in large universal vision models, these models often underperform in domain-specific tasks like WTB segmentation. To address this, we extend Intrinsic LoRA for im

Mohammad Azam, Manasa Manasa, Tatiana Zajarniuk, Ryszard Diduszko

High-pressure synthesis can be an effective method for improving the sample quality of materials as well as their superconducting properties. In this paper, the synthesis process of F- doped SmFeAsO has been optimized by preparing a series of bulk SmFeAsO0.8F0.2 (Sm1111) using the high gas pressure and high-temperature synthesis (HP-HTS) method, considering

Miroslav D. Filipović, S. Lazarević, M. Araya, N. Hurley-Walker

We present a re-discovery of G278.94+1.35 as possibly one of the largest known Galactic supernova remnants (SNR) - that we name Diprotodon. While previously established as a Galactic SNR, Diprotodon is visible in our new EMU and GLEAM radio continuum images at an angular size of 3.33x3.23 deg, much larger than previously measured. At the previously suggested

Valery Isaev

In this paper, we present a constructive generalization of metric and uniform spaces by introducing a new class of spaces, called cover spaces. These spaces form a topological concrete category with a full reflective subcategory of complete spaces. This subcategory is closely related to a particular subcategory of locales, offering an alternative approach to

Jianfei Zhang, Jun Bai, Bei Li, Yanmeng Wang

Aligning Large Language Models (LLMs) with general human preferences has been proved crucial in improving the interaction quality between LLMs and human. However, human values are inherently diverse among different individuals, making it insufficient to align LLMs solely with general preferences. To address this, personalizing LLMs according to individual fe

Yi Zhang, Weize Gao, Changtao Miao, Man Luo

In this paper, we present the Global Multimedia Deepfake Detection held concurrently with the Inclusion 2024. Our Multimedia Deepfake Detection aims to detect automatic image and audio-video manipulations including but not limited to editing, synthesis, generation, Photoshop,etc. Our challenge has attracted 1500 teams from all over the world, with about 5000

Himanshu Rewri, Surjeet Kour

In this paper, we study the isotropy group of Lotka-Volterra derivations of $K[x_{1},\cdots,x_{n}]$, i.e., a derivation $d$ of the form $d(x_{i})=x_{i}(x_{i-1}-C_{i}x_{i+1})$. If $n=3$ or $n \geq 5$, we have shown that the isotropy group of $d$ is finite. However, for $n=4$, it is observed that the isotropy group of $d$ need not be finite. Indeed, for $C_{i}

Shambel Sahlu, Amare Abebe

This paper examines the late-time accelerating Universe and the formation of large-scale structures within the modified symmetric teleparallel gravity framework, specifically using the $f(Q)$-gravity model, in light of recent cosmological data. After reviewing the background history of the Universe, and the linear cosmological perturbations, we consider the

Hrishikesh Gupta, Stefan Thalhammer, Jean-Baptiste Weibel, Alexander Haberl

Transparent objects are ubiquitous in daily life, making their perception and robotics manipulation important. However, they present a major challenge due to their distinct refractive and reflective properties when it comes to accurately estimating the 6D pose. To solve this, we present ReFlow6D, a novel method for transparent object 6D pose estimation that

Si-Mao Guo, Wen-Bin Liu, Jiang Long

We construct quantum flux operators with respect to the Poincar\'e symmetry in the massless Dirac theory at future null infinity. An anomalous helicity flux operator emerges from the commutator of the superrotation generators. The helicity flux operator corresponds to the local chiral symmetry which is the analog of superduality in the gauge theories. We als

A. G. Mazko

The problem of a generalized type of $H_\infty$-control is investigated for a class of admissible descriptor systems with a non-zero initial vector. A generalized performance measure is used, which characterizes the weighted damping level of external and initial disturbances. A non-degenerate transformation of the system is proposed, which allows to apply kn

Fanjun Bu, Wendy Ju

Internet-scaled datasets are a luxury for human-robot interaction (HRI) researchers, as collecting natural interaction data in the wild is time-consuming and logistically challenging. The problem is exacerbated by robots' different form factors and interaction modalities. Inspired by recent work on ethnomethodological and conversation analysis (EMCA) in the

Emilio Jorge, Christos Dimitrakakis, Debabrota Basu

Common assumptions, like linear or RKHS models, and Gaussian or log-concave posteriors over the models, do not explain practical success of RL across a wider range of distributions and models. Thus, we study how to design RL algorithms with sublinear regret for isoperimetric distributions, specifically the ones satisfying the Log-Sobolev Inequality (LSI). LS

Olga Rozanova

A class of non-strictly hyperbolic systems of quasilinear equations with oscillatory solutions of the Cauchy problem, globally smooth in time in some open neighborhood of the zero stationary state, is found. For such systems, the period of oscillation of solutions does not depend on the initial point of the Lagrangian trajectory. The question of the possibil

Lukas Förner, Kartikay Tehlan, Thomas Wendler

Unsupervised deep learning is a promising method in brain MRI registration to reduce the reliance on anatomical labels, while still achieving anatomically accurate transformations. For the Learn2Reg2024 LUMIR challenge, we propose fine-tuning of the pre-trained TransMorph model to improve the convergence stability as well as the deformation smoothness. The f

Xuechen Wang, Shiwan Zhao, Haoqin Sun, Hui Wang

Multimodal emotion recognition (MER), leveraging speech and text, has emerged as a pivotal domain within human-computer interaction, demanding sophisticated methods for effective multimodal integration. The challenge of aligning features across these modalities is significant, with most existing approaches adopting a singular alignment strategy. Such a narro

Runtao Ren, Yinyu Wu, Xuhui Zhang, Jinke Ren

The rapid evolution of mobile edge computing (MEC) has introduced significant challenges in optimizing resource allocation in highly dynamic wireless communication systems, in which task offloading decisions should be made in real-time. However, existing resource allocation strategies cannot well adapt to the dynamic and heterogeneous characteristics of MEC

Zhendong Li, Jianle Ba, Zhou Su, Jinyuan Huang

The movable antenna (MA)-enabled integrated sensing and communication (ISAC) system attracts widespread attention as an innovative framework. The ISAC system integrates sensing and communication functions, achieving resource sharing across various domains, significantly enhancing communication and sensing performance, and promoting the intelligent interconne

Zdzislaw Burda, Mario Kieburg

We study the dynamical aspects of the top rank statistics of particles, performing Brownian motions on a half-line, which are ranked by their distance from the origin. For this purpose, we introduce an observable that we call the overlap ratio $\Omega(t)$, whose average is the probability that a particle that is on the top-$n$ list at some time will also be

Ruohan Xu, Tingzhang Shi, H. T. Quan

In previous studies of relativistic thermodynamics, the temperature of a static system, as perceived by a moving observer, has traditionally been treated as a scalar. This assumption has also been extended to the research on the cosmic microwave background. However, the validity of this assumption is a consequence of the massless nature of photons. More gene

Seojeong Park, Jiho Choi, Kyungjune Baek, Hyunjung Shim

Video Moment Retrieval (MR) aims to localize moments within a video based on a given natural language query. Given the prevalent use of platforms like YouTube for information retrieval, the demand for MR techniques is significantly growing. Recent DETR-based models have made notable advances in performance but still struggle with accurately localizing short

Sayantan Ghosh

Neutron stars (NSs), superdense objects with exceptionally strong gravitational fields, provide an ideal laboratory for probing general relativity (GR) in the high-curvature regime. They also present an exciting opportunity to explore new gravitational physics beyond the traditional framework of GR. Thus, investigating alternative theories of gravity in the

Jarah Evslin, Hui Liu, Baiyang Zhang

We calculate the one-loop tension of the domain wall soliton in the $\phi^4$ double-well model. Our result agrees with previous results from Dashen, Hasslacher and Neveu (1974) in 1+1d and Jaimunga, Semenoff and Zarembo (1999) in 2+1d. After an additional 25 year interval, we have obtained a one-loop tension correction of $0.0410959m^3$ in 3+1d. In this case

Govind S. Krishnaswami, Sonakshi Sachdev, Pritish Sinha

We study the linear stability of inviscid steady parallel flow of an ideal gas in a channel of finite width. Compressible isothermal two-dimensional monochromatic perturbations are considered. The eigenvalue problem governing density and velocity perturbations is a compressible version of Rayleigh's equation and involves two parameters: a flow Mach number $M

Jagannath Sutradhar, Jonathan Ruhman, Avraham Klein, Dimitri Gutman

The entanglement spectrum serves as a powerful tool for probing the structure and dynamics of quantum many-body systems, revealing key information about symmetry, topology, and excitations. While the entanglement entropy (EE) of ground states typically follows an area law, highly excited states obey a volume law, leading to a striking contrast in their scali

Chenglong Deng, Xuding Zhu

An AT-orientation of a graph $G$ is an orientation $D$ of $G$ such that the number of even Eulerian sub-digraphs and the number of odd Eulerian sub-digraphs of $D$ are distinct. Given a mapping $f: V(G) \to \mathbb{N}$, we say $G$ is $f$-AT if $G$ has an AT-orientation $D$ with $ < f(v)$ for each vertex $v$. For a positive integer $k$, we say $G$ is $k$-trun

Huanyu Zhang, Chang Xu, Yi-Fan Zhang, Zhang Zhang

Time series forecasting plays a crucial role in data mining, driving rapid advancements across numerous industries. With the emergence of large models, time series foundation models (TSFMs) have exhibited remarkable generalization capabilities, such as zero-shot learning, through large-scale pre-training. Meanwhile, Retrieval-Augmented Generation (RAG) metho

Dmitri I. Panyushev

Let $G$ be a simple algebraic group and $\mathcal O$ a nilpotent orbit in $\mathfrak g$. Let ${\mathbf{CS}}(\mathcal O)$ denote the affine cone over the secant variety of $\overline{\mathbb P\mathcal O}\subset \mathbb P\mathfrak g$. Using the theory of doubled actions of $G$, we describe ${\mathbf{CS}}(\mathcal O)$ for all $\mathcal O$. We compute $\dim{\mat

Sándor Demes, Dariusz Kędziera, Alexandre Faure, François Lique

Chloronium (H$_2$Cl$^+$) is an important intermediate of Cl-chemistry in space. The accurate knowledge of its collisional properties allows a better interpretation of the corresponding observations in interstellar clouds and therefore a better estimation of its abundance in these environments. While the ro-vibrational spectroscopy of H$_2$Cl$^+$ is well know

Hui Zeng, Sanshuai Cui, Biwei Chen, Anjie Peng

With much longer optimization time than that of untargeted attacks notwithstanding, the transferability of targeted attacks is still far from satisfactory. Recent studies reveal that fine-tuning an existing adversarial example (AE) in feature space can efficiently boost its targeted transferability. However, existing fine-tuning schemes only utilize the endp

Boxiao Shen, Yongpeng Wu, Wenjun Zhang, Symeon Chatzinotas

This paper investigates joint device identification, channel estimation, and symbol detection for LEO satellite-enabled grant-free random access systems, specifically targeting scenarios where remote Internet-of-Things (IoT) devices operate without global navigation satellite system (GNSS) assistance. Considering the constrained power consumption of these de

Li Kewei, Zhou Hengshun, Shen Kai, Dai Yusheng

User-defined keyword spotting (KWS) enhances the user experience by allowing individuals to customize keywords. However, in open-vocabulary scenarios, most existing methods commonly suffer from high false alarm rates with confusable words and are limited to either audio-only or text-only enrollment. Therefore, in this paper, we first explore the model's robu

Youshuai Tan, Zhanwei Zhang, Jinfu Chen, Zishuo Ding

Numerical programs form the foundation of modern science and engineering, providing essential solutions to complex mathematical problems. Therefore, errors in numerical results would lead to harmful consequences, especially in safety-critical applications. Since only a few inputs may lead to substantial errors for numerical programs, it is essential to deter

Riling Wei, Hanjie Chen, Kelu Yao, Chuanguang Yang

Photoplethsmography (PPG)-based individual identification aiming at recognizing humans via intrinsic cardiovascular activities has raised extensive attention due to its high security and resistance to mimicry. However, this kind of technology witnesses unpromising results due to the limitation of low information density. To this end, electrocardiogram (ECG)

Xiaotian Si, Linghui Li, Liwei Zhang, Ziduo Guo

A plethora of face forgery detectors exist to tackle facial deepfake risks. However, their practical application is hindered by the challenge of generalizing to forgeries unseen during the training stage. To this end, we introduce an insertable adaptation module that can adapt a trained off-the-shelf detector using only online unlabeled test data, without re

Shaojin Wu, Fei Ding, Mengqi Huang, Wei Liu

While diffusion models show extraordinary talents in text-to-image generation, they may still fail to generate highly aesthetic images. More specifically, there is still a gap between the generated images and the real-world aesthetic images in finer-grained dimensions including color, lighting, composition, etc. In this paper, we propose Cross-Attention Valu

Yuqi Li, Yuanzhong Zheng, Yaoxuan Wang, Jianjun Yin

In the realm of DeepFake detection, the challenge of adapting to various synthesis methodologies such as Faceswap, Deepfakes, Face2Face, and NeuralTextures significantly impacts the performance of traditional machine learning models. These models often suffer from static feature representation, which struggles to perform consistently across diversely generat

Supriya Manna, Niladri Sett

Deep learning's preponderance across scientific domains has reshaped high-stakes decision-making, making it essential to follow rigorous operational frameworks that include both Right-to-Privacy (RTP) and Right-to-Explanation (RTE). This paper examines the complexities of combining these two requirements. For RTP, we focus on `Differential privacy` (DP), whi

Steven V Sam, Andrew Snowden

We compute the coherent cohomology of the structure sheaf of complex periplectic Grassmannians. In particular, we show that it can be decomposed as a tensor product of the singular cohomology ring of a Grassmannian for either the symplectic or orthogonal group together with a semisimple representation of the periplectic Lie supergroup. The restriction of the

Yuanchang Zhou, Siyu Hu, Chen Wang, Lin-Wang Wang

Graph neural network universal interatomic potentials (GNN-UIPs) have demonstrated remarkable generalization and transfer capabilities in material discovery and property prediction. These models can accelerate molecular dynamics (MD) simulation by several orders of magnitude while maintaining \textit{ab initio} accuracy, making them a promising new paradigm

David Herrera

We define a symmetry map $\varphi$ on a unital $C^\ast$-algebra $\mathcal A$ to be an $\mathbb{R}$-linear map on $\mathcal A$ that generalizes transformations on matrices like: transpose, adjoint, complex-conjugation, conjugation by a unitary matrix, and their compositions. We include an overview of such symmetry maps on unital $C^\ast$-algebras. We say that

Maxim N. Chernodub, Vladimir A. Goy, Alexander V. Molochkov, Konstantin R. Pak

In our earlier work on the Casimir effect in (3+1)-dimensional Yang-Mills theory, we identified two novel nonperturbative states arising in QCD with boundaries: the glueton and the quarkiton. The glueton, or "gluon exciton", is a colorless bound state formed by gluons interacting with their negatively colored images in a chromometallic mirror. The quarkiton,

Niklas Houba, Jean-Baptiste Bayle, Michele Vallisneri

The Laser Interferometer Space Antenna (LISA), an ESA L-class mission, is designed to detect gravitational waves in the millihertz frequency band, with operations expected to begin in the next decade. LISA will enable studies of astrophysical phenomena such as massive black hole mergers, extreme mass ratio inspirals, and compact binary systems. A key challen

Maxime Fevrier, Alexandru Nica, Kamil Szpojankowski

We study the problem of free denoising. For free selfadjoint random variables $a,b$, where we interpret $a$ as a signal and $b$ as noise, we find $E(a|a+b)$. To that end, we study a probability measure $\mu^{( \mathrm{ov} )}_{a,a+b}$ on $\mathbb{R}^2$ which we call the overlap measure. We show that $\mu^{( \mathrm{ov} )}_{a,a+b}$ is absolutely continuous wit

Robert Stańczy, Dorota Bors

We consider fairly general class of dynamical systems under the assumptions guaranteeing the existence of Lyapunov function around some nontrivial stationary point. Moreover, the existence of heteroclinic trajectory is proved motivated by integrated densities approach to some astrophysical models of self-gravitating particles both in relativistic and non--re

En Fu, Yanyan Hu

Contrastive learning underpins most current self-supervised time series representation methods. The strategy for constructing positive and negative sample pairs significantly affects the final representation quality. However, due to the continuous nature of time series semantics, the modeling approach of contrastive learning struggles to accommodate the char

Chayan Chatterjee, Abigail Petulante, Karan Jani, Jesse Spencer-Smith

As gravitational wave detectors become more advanced and sensitive, the number of signals recorded by Advanced LIGO and Virgo from merging compact objects is expected to rise dramatically. This surge in detection rates necessitates the development of adaptable, scalable, and efficient tools capable of addressing a wide range of tasks in gravitational wave as

Yifei Sun, Chao Yu, Yan Luo, Tony Xiao Han

Given the prospects of the low-altitude economy (LAE) and the popularity of unmanned aerial vehicles (UAVs), there are increasing demands on monitoring flying objects at low altitude in wide urban areas. In this work, the widely deployed long-term evolution (LTE) base station (BS) is exploited to illuminate UAVs in bistatic trajectory tracking. Specifically,

Pengfei Jing, Mengyun Tang, Xiaorong Shi, Xing Zheng

Evaluating Large Language Models (LLMs) is crucial for understanding their capabilities and limitations across various applications, including natural language processing and code generation. Existing benchmarks like MMLU, C-Eval, and HumanEval assess general LLM performance but lack focus on specific expert domains such as cybersecurity. Previous attempts t

Afsaneh Mahmoudi, Ming Xiao, Emil Björnson

Federated Learning (FL) enables clients to share learning parameters instead of local data, reducing communication overhead. Traditional wireless networks face latency challenges with FL. In contrast, Cell-Free Massive MIMO (CFmMIMO) can serve multiple clients on shared resources, boosting spectral efficiency and reducing latency for large-scale FL. However,

Chengyue Wang, Haicheng Liao, Kaiqun Zhu, Guohui Zhang

Autonomous vehicles (AVs) rely on accurate trajectory prediction of surrounding vehicles to ensure the safety of both passengers and other road users. Trajectory prediction spans both short-term and long-term horizons, each requiring distinct considerations: short-term predictions rely on accurately capturing the vehicle's dynamics, while long-term predictio

Erasmo Caponio, Argam Ohanyan, Shin-ichi Ohta

A timelike splitting theorem for Finsler spacetimes was previously established by the third author, in collaboration with Lu and Minguzzi, under relatively strong hypotheses, including the Berwald condition. This contrasts with the more general results known for positive definite Finsler manifolds. In this article, we employ a recently developed strategy for

Robert Denkert, Idris Kharroubi, Huyên Pham

We study mean-field control (MFC) problems with common noise using the control randomisation framework, where we substitute the control process with an independent Poisson point process, controlling its intensity instead. To address the challenges posed by the mean-field interactions in this randomisation approach, we reformulate the admissible control as L

David Coupier, Benoît Henry, Benedikt Jahnel, Jonas Köppl

The k-neighbor graph is a directed percolation model on the hypercubic lattice Z d in which each vertex independently picks exactly k of its 2d nearest neighbors at random, and we open directed edges towards those. We prove that the 2-neighbor graph percolates on Z 2 , i.e., that the origin is connected to infinity with positive probability. The proof rests

Kyungtak Lim, Paolo Ricci, Leonard Lebrun

The effects of negative triangularity (NT) on boundary plasma turbulence in double-null (DN) configurations are investigated using global, nonlinear, three-dimensional, flux-driven two-fluid simulations. NT plasmas exhibit suppressed interchange-driven instabilities, resulting in enhanced confinement and lower fluctuation levels compared to positive triangul

Antonin Jacquet

In the models of first-passage percolation and directed first-passage percolation on $\mathbb{Z}^d$, we consider a family of i.i.d. random variables indexed by the set of edges of the graph, called passage times. For every vertex $x \in \mathbb{Z}^d$ with nonnegative coordinates, we denote by $t(0,x)$ the shortest passage time to go from $0$ to $x$ and by $\

Alemdar Hasanov, Onur Baysal

In this paper, a novel mathematical model is developed to evaluate the spatiotemporal vehicle loads on long bridges from slope measurements made at the ends of a bridge based on Euler-Bernoulli beam model with internal and external damping. The mathematical modelling of this phenomena leads to the inverse source problem of determining the spatiotemporal vehi

Liangliang Chen, Yufei Chen, Gerry Bauer, Leonard G. Spiegel

In particle physics, it is needed to evaluate the possibility that excesses of events in mass spectra are due to statistical fluctuations as quantified by the standards of local and global significances. Without prior knowledge of a particle's mass, it is especially critical to estimate its global significance. The usual approach is to count the number of ti

Shaoyang Jia, Michael R. Pennington

With the introduction of a spectral representation, the Schwinger--Dyson equation (SDE) for the fermion propagator is formulated in Minkowski space in QED. After imposing the on-shell renormalization conditions, numeric solutions for the fermion propagator spectral functions are obtained in four dimensions with a renormalizable version of the Gauge Technique

P. Bonifacio, E. Caffau, P. François, N. Martin

Context: The C-19 stellar stream is the most metal-poor stream known to date. While its width and velocity dispersion indicate a dwarf galaxy origin, its metallicity spread and abundance patterns are more similar to those of globular clusters (GCs). If it is indeed of GC origin, its extremely low metallicity ([Fe/H]=-3.4, estimated from giant stars) implies

Igal Sason, Noam Krupnik, Suleiman Hamud, Abraham Berman

The study of spectral graph determination is a fascinating area of research in spectral graph theory and algebraic combinatorics. This field focuses on examining the spectral characterization of various classes of graphs, developing methods to construct or distinguish cospectral nonisomorphic graphs, and analyzing the conditions under which a graph's spectru

Wei-Qi Fan, Yi Liao, Xiao-Dong Ma, Hao-Lin Wang

Most studies on baryon number violating (BNV) processes in the literature focus on free or bound nucleons in nuclei, with limited attention given to the decay of bound atoms. Given that hydrogen is the most abundant atom in the universe, it is particularly intriguing to investigate the decay of hydrogen atom as a means to probe BNV interactions. In this stud

Julien Aubert-Béduchaud, Florian Boudin, Béatrice Daille, Richard Dufour

Familiarizing oneself with a new scientific field and its existing literature can be daunting due to the large amount of available articles. Curated lists of academic references, or reading lists, compiled by experts, offer a structured way to gain a comprehensive overview of a domain or a specific scientific challenge. In this work, we introduce ACL-rlg, th

Mickaël Latocca, Vincent Munnier

We prove interpolation results in the spirit of the Marcinkiewicz theorem. The operators considered in this article are defined on M\"untz spaces, which are not dense subspaces of $L^p$, and for which the classical interpolation theory cannot be applied directly. Our proofs crucially rely on strong decoupling of $L^p$ norms, a that was first observed by Gura

Tianyue Zheng, Linglong Dai

The advance of Artificial Intelligence (AI) is continuously reshaping the future 6G wireless communications. Particularly, the development of Large Language Models (LLMs) offers a promising approach to effectively improve the performance and generalization of AI in different physical-layer (PHY) tasks. However, most existing works finetune dedicated LLM netw

Shubhransh Singhvi

Constructing Reed-Solomon (RS) codes that can correct insertion and deletion (ins-del) errors has been the focus of several recent studies. However, efficient decoding algorithms for such codes have received less attention and remain a significant open problem. In this work, we take a first step toward addressing this problem by designing a decoding algorith

Mehdi Benallegue, Guillaume Lorthioir, Antonin Dallard, Rafael Cisneros-Limón

This paper describes RHP Friends, a social humanoid robot developed to enable assistive robotic deployments in human-coexisting environments. As a use-case application, we present its potential use in nursing by extending its capabilities to operate human devices and tools according to the task and by enabling remote assistance operations. To meet a wide var

Ji Hoon Lee, Douglas Stanford

The maximal giant graviton is a D-brane wrapping a maximal $S^3\subset S^5$ within $\text{AdS}_5\times S^5$. It represents an upper bound on the $R$ charge that can be carried by certain bulk states. We study the maximal giant and its half-BPS fluctuations, motivated by a recent proposal \cite{Lee:2023iil} connecting these fluctuations to trace relations in

Jiyang Guan, Jian Liang, Yanbo Wang, Ran He

Face recognition has witnessed remarkable advancements in recent years, thanks to the development of deep learning techniques.However, an off-the-shelf face recognition model as a commercial service could be stolen by model stealing attacks, posing great threats to the rights of the model owner.Model fingerprinting, as a model stealing detection method, aims

Keng-Wei Chang, Zi-Ming Wang, Shang-Hong Lai

Reconstructing high-quality 3D models from sparse 2D images has garnered significant attention in computer vision. Recently, 3D Gaussian Splatting (3DGS) has gained prominence due to its explicit representation with efficient training speed and real-time rendering capabilities. However, existing methods still heavily depend on accurate camera poses for recon

David E. Kaplan, Xuheng Luo, Surjeet Rajendran

We study the consequences of new long-range forces between neutrinos on cosmic scales. If these forces are a few orders of magnitude stronger than gravity, they can induce perturbation instability in the non-relativistic cosmic neutrino background in the late time universe. As a result, the cosmic neutrino background may form nonlinear bound states instead o

Tianqi Hu, Jun Kai Ng, Guan Ming Wong, Cheng Chen

We report on the performance evaluation of a prototype muon trigger detector for the PSI muEDM experiment, conducted as a proof-of-principle test at the $\pi$E1 beamline of the Paul Scherrer Institute (PSI) using \SI{27.5}{MeV/c} muons. The detector is designed to identify muons within the acceptance phase space of a compact storage solenoid and activate a p

Jungsik Hwang, Ahmadreza Ahmadi

The ability to generate and recognize sequential data is fundamental for autonomous systems operating in dynamic environments. Inspired by the key principles of the brain-predictive coding and the Bayesian brain-we propose a novel stochastic Recurrent Neural Network with Parametric Biases (RNNPB). The proposed model incorporates stochasticity into the latent

Kangle Li, Liujun Zou

The interplay between symmetry, entanglement and correlation is an interesting and important topic in quantum many-body physics. Within the framework of matrix product states, in this paper we study the minimal entanglement and correlation enforced by the $SO(3)$ spin rotation symmetry and lattice translation symmetry in a quantum spin-$J$ chain, with $J$ a

Alexander Kalinin

We introduce kernels and resolvents on preordered sets and derive sharp resolvent inequalities that entail Gronwall inequalities for functions of several variables. In this way, we can prove a fixed point result for operators on topological spaces that extends Banach's fixed point theorem and allows for a wide range of applications.

Wei Li, Yi-Lun Du, Nan Su, Konrad Tywoniuk

Community detection, also known as graph partitioning, is a well-known NP-hard combinatorial optimization problem with applications in diverse fields such as complex network theory, transportation, and smart power grids. The problem's solution space grows drastically with the number of vertices and subgroups, making efficient algorithms crucial. In recent ye

Zhiqiang Yu, Qing Zhang

In this paper, we show the existence of a near-group category of type $\mathbb{Z} / 4\mathbb{Z} \times \mathbb{Z} / 4\mathbb{Z}+16$ and compute the modular data of its Drinfeld center. We prove that a modular data of rank $10$ can be obtained through condensation of the Drinfeld center of the near-group category $\mathbb{Z} / 4\mathbb{Z} \times \mathbb{Z} /

Koffka Khan

Federated learning enables collaborative model training across multiple clients without sharing raw data, thereby enhancing privacy. However, the exchange of model updates can still expose sensitive information. Quantum teleportation, a process that transfers quantum states between distant locations without physical transmission of the particles themselves,

Jie Jing, Yongjian Huang, Serena J. -W. Wang, Shuangpeng Han

We introduce intra-class memorability, where certain images within the same class are more memorable than others despite shared category characteristics. To investigate what features make one object instance more memorable than others, we design and conduct human behavior experiments, where participants are shown a series of images, and they must identify wh

Huihan Li, Arnav Goel, Keyu He, Xiang Ren

In open-ended generative tasks like narrative writing or dialogue, large language models often exhibit cultural biases, showing limited knowledge and generating templated outputs for less prevalent cultures. Recent works show that these biases may stem from uneven cultural representation in pretraining corpora. This work investigates how pretraining leads to

Bruno Sebastian Scheihing-Hitschfeld

In this thesis, we make progress in two concrete directions in the vast landscape of hot QCD physics. The first one is quarkonium transport inside quark-gluon plasma (QGP), the high temperature phase of QCD. Over the past two decades it has been realized that a significant fraction of quarkonium suppression in high energy heavy ion collisions comes from dyna

Mayue Shi, Yongqi Zhang, Xiaotong Guo, Eric M. Yeatman

Tactile sensing is critical in advanced interactive systems by emulating the human sense of touch to detect stimuli. Vision-based tactile sensors are promising for providing multimodal capabilities and high robustness, yet existing technologies still have limitations in sensitivity, spatial resolution, and high computational demands of deep learning-based im

Xiaoqing Zhang, Yuhan Liu, Flood Sung, Xiuying Chen

Code generation is crucial in software engineering for automating the coding process efficiently. While test-time computation methods show promise, they suffer from high latency due to multiple computation rounds. To overcome this, we introduce \textbf{ThinkCoder}, a framework that combines thorough exploration with optimal refinement. The exploration phase

Hyeonjae Choi, Donghyun Kim, Seung Jin Lee

Lusztig $q$-weight multiplicities extend the Kostka-Foulkes polynomials to a broader range of Lie types. In this work, we investigate these multiplicities through the framework of Kirillov-Reshetikhin crystals. Specifically, for type $C$ with dominant weights and type $B$ with dominant spin weights, we present a combinatorial formula for Lusztig $q$-weight m