February 2025 arXiv papers — page 15

Sebastian Eibl, Yi Yao, Matthias Scheffler, Markus Rampp

SISSO (sure-independence screening and sparsifying operator) is an artificial intelligence (AI) method based on symbolic regression and compressed sensing widely used in materials science research. SISSO++ is its C++ implementation that employs MPI and OpenMP for parallelization, rendering it well-suited for high-performance computing (HPC) environments. As

Guang Li, Fuxing Chen, Ping Fang

We investigate the spontaneous parity-time (PT )-symmetry breaking and spectral properties of a PT symmetric quantum kicked rotor under resonance conditions. At resonance, the QKR reduces to a finite-dimensional system. In the localized regime, we find that increasing the non-Hermitian parameter always induces a transition from a phase where the states exhib

Bas van den Heuvel, Martin Sulzmann, Peter Thiemann

Deadlocks are a major source of bugs in concurrent programs. They are hard to predict, because they may only occur under specific scheduling conditions. Dynamic analysis attempts to identify potential deadlocks by examining a single execution trace of the program. A standard approach involves monitoring sequences of lock acquisitions in each thread, with the

Jonatan Piasetzky, Khen Cohen, Yehonatan Drori, Amit Rotem

Scalable quantum information processing with integrated photonics requires quantum logic operations with high fidelity and robustness. Directional couplers, the fundamental elements enabling quantum interference and logic operations, are inherently sensitive to fabrication imperfections and environmental fluctuations, leading to reduced gate fidelities. Here

Tianyang Qi, Shibo Chen, Jun Zhang

With the widespread adoption of electric vehicles (EVs), navigating for EV drivers to select a cost-effective charging station has become an important yet challenging issue due to dynamic traffic conditions, fluctuating electricity prices, and potential competition from other EVs. The state-of-the-art deep reinforcement learning (DRL) algorithms for solving

Yuval Filmus

Dokow and Holzman determined which predicates over $\{0, 1\}$ satisfy an analog of Arrow's theorem: all unanimous aggregators are dictatorial. Szegedy and Xu, extending earlier work of Dokow and Holzman, extended this to predicates over arbitrary finite alphabets. Mossel extended Arrow's theorem in an orthogonal direction, determining all aggregators without

Yidi Jiang, Qian Chen, Shengpeng Ji, Yu Xi

The emergence of audio language models is empowered by neural audio codecs, which establish critical mappings between continuous waveforms and discrete tokens compatible with language model paradigms. The evolutionary trends from multi-layer residual vector quantizer to single-layer quantizer are beneficial for language-autoregressive decoding. However, the

Viktor Khinevich, Wataru Mizukami

We introduce QC-CBT-AFQMC, a hybrid algorithm that incorporates computational basis tomography (CBT) into the quantum-classical auxiliary-field quantum Monte Carlo (QC-AFQMC) method proposed by Huggins et al. [Nature 603, 416-420 (2022)], replacing the use of classical shadows. While the original QC-AFQMC showed high accuracy for quantum chemistry calculatio

Ahmet Onur Akman, Anastasia Psarou, Łukasz Gorczyca, Zoltán György Varga

RouteRL is a novel framework that integrates multi-agent reinforcement learning (MARL) with a microscopic traffic simulation, facilitating the testing and development of efficient route choice strategies for autonomous vehicles (AVs). The proposed framework simulates the daily route choices of driver agents in a city, including two types: human drivers, emul

D. Bazeia, M. A. Marques, R. Menezes, M. Paganelly

In this work, we investigate radially symmetric solutions in arbitrary dimensions in scalar field models in the presence of the cuscuton term. We introduce a first-order formalism compatible with the equation of motion which supports field configurations engendering minimum energy and show that the cuscuton term does not induce instabilities in the solutions

Jackie Baek, Hamsa Bastani, Shihan Chen

We study the impact of strategic behavior in labor markets characterized by algorithmic monoculture, where firms compete for a shared pool of applicants using a common algorithmic evaluation. In this setting, "naive" hiring strategies lead to severe congestion, as firms collectively target the same high-scoring candidates. We model this competition as a game

Xiaoyi Tian, Qingxiang Xu, Chunhong Fu

For each adjointable idempotent $Q$ on a Hilbert $C^*$-module $H$, a specific projection $m(Q)$ called the matched projection of $Q$ was introduced recently due to the characterization of the minimum value among all the distances from projections to $Q$. Inspired by the relationship between $m(Q)$ and $Q$, another term called the quasi-projection pair $(P,Q)

Penghui Chen, Yushi Wang, Changsheng Luo, Wenhan Cai

Humanoid robots encounter considerable difficulties in autonomously recovering from falls, especially within dynamic and unstructured environments. Conventional control methodologies are often inadequate in addressing the complexities associated with high-dimensional dynamics and the contact-rich nature of fall recovery. Meanwhile, reinforcement learning tec

G. S. Kalenkov, S. G. Kalenkov, A. E. Shtanko, B. C. Quirk

Fabrication of optical coherence tomography (OCT) fiber probes in cardiology involves sequentially splicing and cleaving multiple fibers to form a lens. During this process, the splice location-normally invisible under standard illumination-must be identified with micron-level accuracy. This paper presents an approach for splice detection using digital lensl

Haina Li, Yiran Xu

In this paper, we investigate the existence of a unique global smooth solution to the three-dimensional incompressible Navier-Stokes equations and provide a concise proof. We establish a new global well-posedness result that allows the initial data to be arbitrarily large within the critical space $\dot{B}^{-1}_{\infty,\infty}$, while still satisfying the no

Saga Westerberg, Melvin Redon, Ann-Kathrin Raab, Gaspard Beaufort

High-order harmonic generation (HHG) in gases has been studied for almost 40 years in many different conditions, varying the laser wavelength, intensity, focusing geometry, target design, gas species, etc. However, no systematic investigation of the effect of the pulse duration has been performed in spite of its expected impact on phase-matching of the high-

Sergey A. Rukolaine

We consider initial-boundary value problems (IBVPs) on a finite interval for the system of the energy balance equation and Guyer-Krumhansl constitutive equation. Boundary conditions comprise various models of behavior of a physical system at the boundaries, including boundary conditions describing Newton's law, which states that the heat flux at the boundary

Kang Liu, Zhuoqi Ma, Xiaolu Kang, Yunan Li

Automated radiology report generation offers an effective solution to alleviate radiologists' workload. However, most existing methods focus primarily on single or fixed-view images to model current disease conditions, which limits diagnostic accuracy and overlooks disease progression. Although some approaches utilize longitudinal data to track disease progr

Kalyan B. Sinha, Ritabrata Sengupta

We study the problem of Quantum Likelihood Operators (LO) and their connection with quantum Fisher information (QFI). It is observed that the present approaches to this problem tacitly assume commutativity of parametrised density matrix $\rho_\theta$ and its derivative, which, in general, need not be true, and this has nontrivial consequences in QFI. As exam

Tianxiao Gao, Mingle Zhao, Chengzhong Xu, Hui Kong

Accurate and robust state estimation at nighttime is essential for autonomous robotic navigation to achieve nocturnal or round-the-clock tasks. An intuitive question arises: Can low-cost standard cameras be exploited for nocturnal state estimation? Regrettably, most existing visual methods may fail under adverse illumination conditions, even with active ligh

Peter Flauger, Claas Abert, Dieter Suess

We present a computationally efficient strategy that allows to simulate magnetization switching driven by spin-transfer torque in magnetic tunnel junctions within a micromagnetic model coupled with a matrix-based non-equilibrium Green's function algorithm. Exemplary simulation for a realistic set of parameters are carried out and show switching times below 4

Tomáš Dacík, Tomáš Vojnar

RacerF is a static analyser for detection of data races in multithreaded C programs implemented as a plugin of the Frama-C platform. The approach behind RacerF is mostly heuristic and relies on analysis of the sequential behaviour of particular threads whose results are generalised using a combination of under- and over-approximating techniques to allow anal

Zhendong Chen, Chunjing Xie

In the paper, the shock formation for the two-dimensional rotating shallow water system is established. We construct a large class of initial data which leads to the finite-time blow-up for the solutions. Moreover, the solutions are allowed to have non-zero large vorticity (in derivative sense), even up to the shock. Our results provide the first complete ge

Dave Lewis, Marta Lasek-Markey, Delaram Golpayegani, Harshvardhan J. Pandit

The EU AI Act represents the world's first transnational AI regulation with concrete enforcement measures. It builds on existing EU mechanisms for regulating health and safety of products but extends them to protect fundamental rights and to address AI as a horizontal technology across multiple application sectors. We argue that this will lead to multiple un

Chao Fang, Yihan He, Xiao Gong, Gengchiau Liang

In the post-Moore era, the need for efficient solutions to non-deterministic polynomial-time (NP) problems is becoming more pressing. In this context, the Ising model implemented by the probabilistic computing systems with probabilistic bits (p-bits) has attracted attention due to the widespread availability of p-bits and support for large-scale simulations.

P. Suffa, S. Kemmler, H. Koestler, U. Ruede

We employ the Partially Saturated Cells Method (PSM) to model the interaction between the fluid flow and solid moving objects as an extension to the conventional lattice Boltzmann method. We introduce an efficient and accurate method for mapping complex moving geometries onto uniform Cartesian grids suitable for massively parallel processing. A validation of

Ziyu Wu, Yufan Xiong, Mengting Niu, Fangting Xie

Long-term in-bed monitoring benefits automatic and real-time health management within healthcare, and the advancement of human shape reconstruction technologies further enhances the representation and visualization of users' activity patterns. However, existing technologies are primarily based on visual cues, facing serious challenges in non-light-of-sight a

Quan Wang, Renzhe Bi, Songhua Zheng, Ahmet T. Erdogan

Diffuse speckle contrast analysis (DSCA), also called speckle contrast optical spectroscopy(SCOS), has emerged as a groundbreaking optical imaging technique for tracking dynamic biological processes, including blood flow and tissue perfusion. Recent advancements in single-photon avalanche diode (SPAD) cameras have unlocked exceptional capabilities in sensiti

Rayyan Merchant, Akhilesh Kakolu Ramarao, Kevin Tang

Despite speaking mutually intelligible varieties of the same language, speakers of Tajik Persian, written in a modified Cyrillic alphabet, cannot read Iranian and Afghan texts written in the Perso-Arabic script. As the vast majority of Persian text on the Internet is written in Perso-Arabic, monolingual Tajik speakers are unable to interface with the Interne

Marta Lango, Borys Naglik, Mateusz Lango, Iwo Naglik

Aspect-Sentiment Triplet Extraction (ASTE) is one of the most challenging and complex tasks in sentiment analysis. It concerns the construction of triplets that contain an aspect, its associated sentiment polarity, and an opinion phrase that serves as a rationale for the assigned polarity. Despite the growing popularity of the task and the many machine learn

Hengyu Meng, Duotun Wang, Zhijing Shao, Ligang Liu

Professional 3D asset creation often requires diverse sculpting brushes to add surface details and geometric structures. Despite recent progress in 3D generation, producing reusable sculpting brushes compatible with artists' workflows remains an open and challenging problem. These sculpting brushes are typically represented as vector displacement maps (VDMs)

Jiwan Poudel, Alessandro Bacchetta, Jian-Ping Chen, Dustin Keller

We propose to analyze CLAS12 RG-C data to study the tensor transverse-momentum-dependent parton distribution functions (TMDs) on deuteron data. The deuteron is the lightest nucleus with spin-1, in essence a weakly bound system of two spin-1/2 nucleons. However, one of the most intriguing characteristics of the deuteron is that the tensor polarized structure

Amir Mafi, Rando Rasul Qadir, Hero Saremi

Let $R=K[x_1,\ldots, x_n]$ be the polynomial ring in $n$ variables over a field $K$ and $I$ be monomial ideal of $R$. In this paper, we show that if $I$ is a generic monomial ideal, then $R/I$ is pretty clean if and only if $R/I$ is sequentially Cohen-Macaulay. Furthermore, we prove that this equivalence remains unchanged for some special monomial ideals. Mo

Ali Mohammad-Djafari

Digital Twins (DTs) are virtual representations of physical systems synchronized in real time through Internet of Things (IoT) sensors and computational models. In industrial applications, DTs enable predictive maintenance, fault diagnosis, and process optimization. This paper explores the mathematical foundations of DTs, hybrid modeling techniques, includin

Jinhuan Wang, Qian Li, Hui Huang

In this paper, we study the stochastic degenerate Keller-Segel system perturbed by linear multiplicative noise in a bounded domain $\mathcal{O}$. We establish the global existence of martingale solutions for this model with any nonnegative initial data in $H_{2}^{-1}(\mathcal{O})$. The main challenge in proving the existence of solutions arises from the dege

Hengshuo Chu, Xiang Deng, Qi Lv, Xiaoyang Chen

3D Affordance detection is a challenging problem with broad applications on various robotic tasks. Existing methods typically formulate the detection paradigm as a label-based semantic segmentation task. This paradigm relies on predefined labels and lacks the ability to comprehend complex natural language, resulting in limited generalization in open-world sc

Nian Shao, Rui Zhou, Pengyu Wang, Xian Li

In this work, we propose CleanMel, a single-channel Mel-spectrogram denoising and dereverberation network for improving both speech quality and automatic speech recognition (ASR) performance. The proposed network takes as input the noisy and reverberant microphone recording and predicts the corresponding clean Mel-spectrogram. The enhanced Mel-spectrogram ca

Kishore Kumar Naraparaju, Shivangi Joshi, Subhashree Mohapatra

This article presents a higher-order spectral element method for the two-dimensional Stokes interface problem involving a piecewise constant viscosity coefficient. The proposed numerical formulation is based on least-squares formulation. The mesh is aligned with the interface, and the interface is completely resolved using blending element functions. The hig

Yanan Zhang, Xiaochun Ma, Hui Liu, Yinjian Zhao

With the significant advancements in parallel computing techniques, the particle-particle (PP) model has been effectively utilized in various plasma-related applications. However, PP has been limited for solving only electrostatic problems under Coulomb's law, by analogy to the particle-in-cell (PIC) model solving Poisson's equation. While electromagnetic PI

Hongyu Deng, Tianfan Xue, He Chen

Transparent objects are prevalent in everyday environments, but their distinct physical properties pose significant challenges for camera-guided robotic arms. Current research is mainly dependent on camera-only approaches, which often falter in suboptimal conditions, such as low-light environments. In response to this challenge, we present FuseGrasp, the fir

Mateusz Idziejczak, Vasyl Korzavatykh, Mateusz Stawicki, Andrii Chmutov

The proliferation of large language models (LLMs) and autonomous AI agents has raised concerns about their potential for automated persuasion and social influence. While existing research has explored isolated instances of LLM-based manipulation, systematic evaluations of persuasion capabilities across different models remain limited. In this paper, we prese

Yejun Zhang, Shuzhe Wang, Juho Kannala

Visual localization involves estimating the 6-degree-of-freedom (6-DoF) camera pose within a known scene. A critical step in this process is identifying pixel-to-point correspondences between 2D query images and 3D models. Most advanced approaches currently rely on extensive visual descriptors to establish these correspondences, facing challenges in storage,

Xuyang Wei, Chunlin Tian, Li Li

Effective instruction fine-tuning on diverse image-text datasets is crucial for developing a versatile Multimodal Large Language Model (MLLM), where dataset composition dictates the model's adaptability across multimodal tasks. However, complex datasets often contain inherent conflicts -- stemming from modality-specific optimization objectives -- and latent

Hongseok Oh, Wonseok Hwang

Recently, Large Vision-Language Models (LVLMs) show remarkable performance across various domains. However, these models suffer from object hallucination. In this work, we study object hallucination primarily in a discriminative, retrieval-style evaluation setting (OHD-Caps), rather than in free-form caption generation. This study revisits the previous claim

Suryanarayana Sankagiri, Jalal Etesami, Matthias Grossglauser

This paper provides a theoretical analysis of a new learning problem for recommender systems where users provide feedback by comparing pairs of items instead of rating them individually. We assume that comparisons stem from latent user and item features, which reduces the task of predicting preferences to learning these features from comparison data. Similar

Guannan Lai, Yujie Li, Xiangkun Wang, Junbo Zhang

Class Incremental Learning (CIL) aims to enable models to learn new classes sequentially while retaining knowledge of previous ones. Although current methods have alleviated catastrophic forgetting (CF), recent studies highlight that the performance of CIL models is highly sensitive to the order of class arrival, particularly when sequentially introduced cla

Abhishek Mallick, Siddhartha Biswas

This article delves into the intricate dynamics of Rayleigh wave propagation within a nonlocal orthotropic medium, where the presence of void and diffusion adds an intriguing layer to the analysis. Grounded in Eringen nonlocal elasticity theory and embracing the three-phase-lag model of hyperbolic thermoelasticity, the study focuses on the interplay between

Moshe Babaioff, Noam Nisan

The ``EIP-1599 algorithm'' is used by the Ethereum blockchain to assemble transactions into blocks. While prior work has studied it under the assumption that bidders are ``impatient'', we analyze it under the assumption that bidders are ``patient'', which better corresponds to the fact that unscheduled transactions remain in the mempool and can be scheduled

Ioannis Dimanidis, Tolga Ok, Peyman Mohajerin Esfahani

Inspired by the recent successes of Inverse Optimization (IO) across various application domains, we propose a novel offline Reinforcement Learning (ORL) algorithm for continuous state and action spaces, leveraging the convex loss function called ``sub-optimality loss'' from the IO literature. To mitigate the distribution shift commonly observed in ORL probl

Zhenhui Xu, Jiayu Chen, Bing-Chang Wang, Yuhu Wu

This paper studies linear quadratic Gaussian robust mean field social control problems in the presence of multiplicative noise. We aim to compute asymptotic decentralized strategies without requiring full prior knowledge of agents' dynamics. The primary challenges lie in solving an indefinite stochastic algebraic Riccati equation for feedback gains, and an i

Dler O. Hasan, Hardi M. Mohammed, Zrar Khalid Abdul

The FOX optimizer, inspired by red fox hunting behavior, is a powerful algorithm for solving real-world and engineering problems. However, despite balancing exploration and exploitation, it can prematurely converge to local optima, as agent positions are updated solely based on the current best-known position, causing all agents to converge on one location.

Mehmet Demirci, M. Fauzi Mustamin

Neutrinos elastically scattered off atomic electrons offer a unique opportunity to probe the Standard Model (SM) and beyond SM physics. In this work, we examine the new physics effects of light mediators through elastic neutrino-electron scattering using solar neutrinos at the low energy range of PandaX-4T. These mediators, with a mass less than $1$ GeV, are

Chirag Bhuvaneshwara, Lara Chehayeb, Alexander Haberl, Julius Siedentopf

Socioemotional and regulation processes in learning are important. We add to the understanding of previous work on co-regulation processes in the learning sciences, extending the caregiver-child paradigm and focusing on the teacher-student relation by presenting an interactive co-regulation model and the methodology for developing empirically grounded system

Klemens Uhlmann, Daniel Balzani

A simple kinematic growth model for muscular arteries is presented which allows the incorporation of residual stresses such that a homeostatic in-vivo stress state under physiological loading is obtained. To this end, new evolution equations for growth are proposed, which avoid issues with instability of final growth states known from other kinematric growth

Suyeon Son, Minjin Kim, Luis C. Ho

The characteristic timescale ($\tau$) of continuum variability of the accretion disk in active galactic nuclei (AGNs) is known to be related to the thermal timescale, which is predicted to scale with AGN luminosity ($L$) and restframe wavelength ($\lambda_{\rm RF}$) as $t_{\rm th} \propto L^{0.5} \lambda_{\rm RF}^2$ in the standard disk model. Using multi-ep

Zhikai Huang, Shuai Lu, Wei Gu, Ruizhi Yu

Ensuring the safe and reliable operation of integrated electricity and gas systems (IEGS) requires dynamic energy flow (DEF) simulation tools that achieve high accuracy and computational efficiency. However, the inherent strong nonlinearity of gas dynamics and its bidirectional coupling with power grids impose significant challenges on conventional numerical

Ana Clara Araújo Gomes da Silva, Gilmar Teixeira Junior, Lívia Mancine C. de Campos, Renato F. Bulcão-Neto

Environmental sustainability in Systems-of-Systems (SoS) is an emerging field that seeks to integrate technological solutions to promote the efficient management of natural resources. While systematic reviews address sustainability in the context of Smart Cities (a category of SoS), a systematic study synthesizing the existing knowledge on environmental sust

Natsumi Ikeno

We evaluate for the first time the $\eta^\prime p$ femtoscopic correlation function to study the $\eta^\prime N$ interaction. We find it extremely sensitive to the value of the $\eta^\prime p$ scattering length, for which at present there exists only very limited information, not even knowing its sign. The measurement of this correlation function would provi

Hoi-Jeong Lim, Su-Hyeon Park

Meta-analysis is a statistical method that combines the results of individual studies on the same topic. This method is becoming popular, due to providing the combined result that individual studies cannot provide and giving a more precise result. Despite meta-analysis having such significance, there are few Korean guides for the use of the Review Manager (R

Fan Yang, Dongsheng Luo, Wenrui Chen, Jiacheng Lin

Functional dexterous grasping requires precise hand-object interaction, going beyond simple gripping. Existing affordance-based methods primarily predict coarse interaction regions and cannot directly constrain the grasping posture, leading to a disconnection between visual perception and manipulation. To address this issue, we propose a multi-keypoint affor

Georges Bouzerar, Maxime Thumin

One of the great challenges for the large-scale development of quantum technologies is to generate and control the entanglement of quantum bits through interactions of sufficiently long range. Two decades ago, spin chains have been proposed for quantum communication. Unfortunately, couplings are of very short range in general which drastically limits the com

Vera M. Titze, Jessica Reichert, Marcel Schubert, Malte C. Gather

Micro- and nanoplastics pose a growing threat to marine organisms, such as reef-building corals. Yet, our understanding of microplastic uptake, interaction with coral tissue, and incorporation into coral skeletons remains limited, mainly due to the invasiveness of existing methods for detecting microplastics. Here, we exploit optical resonances in polymer sp

Emma Vancayseele, Philip Desenfans, Zifeng Gong, Dries Vanoost

This paper identifies the stator currents, torque and unbalanced magnetic pull (UMP) of an unbalanced induction motor by the System Identification of Nonlinear Dynamics with Control (SINDYc) method from time-series data of measurable quantities. The SINDYc model has been trained on data coming from a nonlinear magnetic equivalent circuit model for three roto

Yonatan Sommer, Ivri Hikri, Lotan Amit, Nir Rosenfeld

When learning is used to inform decisions about humans, such as for loans, hiring, or admissions, this can incentivize users to strategically modify their features, at a cost, to obtain positive predictions. The common assumption is that the function governing costs is exogenous, fixed, and predetermined. We challenge this assumption, and assert that costs c

Takuto Iijima, Tomotaka Momozaki, Shuji Ando

In recent years, cancer clinical trials have increasingly encountered non proportional hazards (NPH) scenarios, particularly with the emergence of immunotherapy. In randomized controlled trials comparing immunotherapy with conventional chemotherapy or placebo, late difference and early crossing survivals scenarios are commonly observed. In such cases, window

Alessandro De Vita, Chiara Bigi, Davide Romanin, Matthew D. Watson

Altermagnetism defies conventional classifications of collinear magnetic phases, standing apart from ferromagnetism and antiferromagnetism with its unique combination of spin-dependent symmetries, net-zero magnetization, and anomalous Hall transport. Although altermagnetic states have been realized experimentally, their integration into functional devices ha

Hoi-Jeong Lim

Determination of sample size is critical, however not easy to do. Sample size defined as the number of observations in a sample should be big enough to have a high likelihood of detecting a true difference between groups. Practical procedure for determining sample size, using G*power and previous dental articles, is shown in this study. Examples involving in

Lang Huang, Qiyu Wu, Zhongtao Miao, Toshihiko Yamasaki

Information retrieval is indispensable for today's Internet applications, yet traditional semantic matching techniques often fall short in capturing the fine-grained cross-modal interactions required for complex queries. Although late-fusion two-tower architectures attempt to bridge this gap by independently encoding visual and textual data before merging th

S. Karadağ, M. Deniz, S. Karmakar, M. K. Singh

We investigate new physics effects on coherent elastic neutrino nucleus scattering within the framework of nonstandard interactions and generalized neutrino interactions. Additionally, we examine the possibility of light mediators from a simplified model that includes all possible Lorentz-invariant interactions of vector, axialvector, scalar, pseudoscalar, a

Sanjay Shukla

We study turbulence in self-gravitating superfluids by performing direct numerical simulations of the 3D Gross-Pitaevskii-Poisson (GPP) equation, which is also a model for dark matter haloes around galaxies. In the absence of self-gravity, the spectrally truncated Gross-Pitaevskii (GP) equation shows the emergence of Kolmogorov's $5/3$ scaling in the incompr

T. Amand, D. Paget

The effect of a uniaxial strain on the optical spin orientation of a cubic semiconductor is investigated by calculating the valence wavefunctions, the optical oscillator strengths and the initial electron spin polarization for near resonant light excitation from heavy and light valence levels. A strain orientation along the [001], [111] or [-110] crystal dir

Samrat Ghosh, Soumen Mondal, Somnath Dutta, Rajib Kumbhakar

We present $I$-band time-series photometric variability studies of three known nearby ($\sim$ 140 pc) and young ( $\sim$ 1 Myr) brown dwarfs (BD) in the Taurus star-forming region in the Perseus Molecular Cloud. From 10 nights of observations over a time span of 10 years, with a typical run of 3 to 6 hours each night, we estimated that the BDs show unstable

Matteo Vilucchio, Yatin Dandi, Matéo Pirio Rossignol, Cedric Gerbelot

The analytic characterization of the high-dimensional behavior of optimization for Generalized Linear Models (GLMs) with Gaussian data has been a central focus in statistics and probability in recent years. While convex cases, such as the LASSO, ridge regression, and logistic regression, have been extensively studied using a variety of techniques, the non-co

Francesco Formicola, Grazia Di Bello, Giulio De Filippis, Vittorio Cataudella

Many-body localization is a dynamical phenomenon characteristic of strongly interacting and disordered many-body quantum systems which fail to achieve thermal equilibrium. From a quantum information perspective, the fingerprint of this phenomenon is the logarithmic growth of the entanglement entropy over time. We perform intensive numerical simulations, appl

CY Yan, Steve Keol, Xo Co, Nate Leung

Decentralized exchanges (DEXs) face persistent challenges in liquidity retention and user engagement due to inefficiencies in conventional automated market maker (AMM) designs. This work proposes a dual-mechanism framework to address these limitations: a ``Better Market Maker (BMM)'', which is a liquidity-optimized AMM based on a power-law invariant ($X^nY =

Jia-Jie Li, Yu Tian, Armen Sedrakian

[Background] Bayesian inference frameworks incorporating multi-messenger astrophysical constraints have recently been applied to covariant density functional (CDF) models to constrain their parameters. Among these, frameworks utilizing CDFs with density-dependent meson-nucleon couplings furnishing the equation of state (EoS) of compact star (CS) matter have

Rachid Belfadli, Lahcen Boulanba, Youssef Ouknine

In this paper, we establish Malliavin differentiability and absolute continuity for $\alpha, \beta$-doubly perturbed diffusion process with parameters $\alpha <1$ and $\beta <1$ such that $|\rho| < 1$, where $ \rho : = \frac{\alpha\beta}{(1-\alpha)(1-\beta)}$. Furthermore, under some regularity conditions on the coefficients, we prove that the solution $X_t$

Antonino Ficarra, Somayeh Moradi

In this paper, we investigate the componentwise linearity and the Castelnuovo-Mumford regularity of symbolic powers of polymatroidal ideals. For a polymatroidal ideal $I$, we conjecture that every symbolic power $I^{(k)}$ is componentwise linear and $$ \text{reg}\,I^{(k)}=\text{reg}\,I^k $$ for all $k \ge 1$. We prove that $\text{reg}\,I^{(k)}\ge\text{reg}\,

Chiara Caiazza, Valerio Luconi, Alessio Vecchio

HTTP is frequently used by smartphones and IoT devices to access information and Web services. Nowadays, HTTP is used in three major versions, each introducing significant changes with respect to the previous one. We evaluated the energy consumption of the major versions of the HTTP protocol when used in the communication between energy-constrained devices a

Jincheng Wang, Le Yu, John C. S. Lui, Xiapu Luo

Distributed Denial of Service (DDoS) attacks persist as significant threats to online services and infrastructure, evolving rapidly in sophistication and eluding traditional detection mechanisms. This evolution demands a comprehensive examination of current trends in DDoS attacks and the efficacy of modern detection strategies. This paper offers an comprehen

Remo Garattini, Athanasios G. Tzikas

A Casimir Wormhole is a Traversable Wormhole powered by a Casimir energy source within a static reference frame. A natural extension of this system is the inclusion of rotation. We will explore two basic configurations: one with radially varying Casimir plates and another with parametrically fixed plates. In both cases, we will show that rotations do not alt

Baige Xu, Yusuke Tanaka, Takashi Matsubara, Takaharu Yaguchi

In recent years, deep learning for modeling physical phenomena which can be described by partial differential equations (PDEs) have received significant attention. For example, for learning Hamiltonian mechanics, methods based on deep neural networks such as Hamiltonian Neural Networks (HNNs) and their variants have achieved progress. However, existing metho

Zhenhui Xu, Jiayu Chen, Bing-Chang Wang, Tielong Shen

This paper presents a novel data-driven approach for approximating the $\varepsilon$-Nash equilibrium in continuous-time linear quadratic Gaussian (LQG) games, where multiple agents interact with each other through their dynamics and infinite horizon discounted costs. The core of our method involves solving two algebraic Riccati equations (AREs) and an ordin

Antonino Ficarra, Somayeh Moradi

Let $G$ be a permutation graph. We show that $G$ is Cohen-Macaulay if and only if $G$ is unmixed and vertex decomposable. When this is the case, we obtain a combinatorial description for the $a$-invariant of $G$. Moreover, we characterize the Gorenstein permutation graphs.

Leimin Tian, Shiyu Xu, Kerry He, Rachel Love

Robots are increasingly working alongside people, delivering food to patrons in restaurants or helping workers on assembly lines. These scenarios often involve object handovers between the person and the robot. To achieve safe and efficient human-robot collaboration (HRC), it is important to incorporate human context in a robot's handover strategies. We deve

Laima Kitovienė, Gediminas Gaigalas, Pavel Rynkun, Nanae Domoto

The neutron star merger is a promising site of heavy element production. By producing heavy elements, the neutron star merger gives rise to a thermal transient called a kilonova. Studying kilonova spectra enables us to quantify the heavy element production. Among the heaviest elements, doubly ionized Thorium (Th, Z=90) is one of the important candidates for

Abdelaali Boudjemaa, Lan Xu, Qing-Shou Tan

We investigate the dynamics of quantum information flow in one and two impurity qubits trapped in a double-well potential and interacting with a one-dimensional ultracold Bose-Bose mixture reservoir. For a single qubit immersed in a binary Bose mixture, we show that the system maintains coherence over finite timescales and exhibits non-Markovian dynamics, pa

Hugo Retief, Kayathri, Vigneswaran, Surajit Ghosh

Reliable daily estimates of reservoir storage are pivotal for water allocation and drought response decisions in semiarid regions. Conventional rating curves at Loskop Dam, the primary storage on South Africa's Olifants River, have become increasingly uncertain owing to sedimentation and episodic drawdown. A 40 year Digital Earth Africa (DEA) surface area ar

Hossein Movasati

This is a collection of articles, written as sections, on arithmetic properties of differential equations, holomorphic foliations, Gauss-Manin connections and Hodge loci. Each section is independent from the others and it has its own abstract and introduction and the reader might get an insight to the text by reading the introduction of each section. The mai

Shaojie Hou, Yuandou Wang, Zhiming Zhao

Active Learning (AL) is a machine learning technique where the model selectively queries the most informative data points for labeling by human experts. Integrating AL with crowdsourcing leverages crowd diversity to enhance data labeling but introduces challenges in consensus and privacy. This poster presents CrowdAL, a blockchain-empowered crowd AL system d

Per Pettersson, Sebastian Krumscheid, Sarah Gasda

We propose a novel decision making framework for forming potential collaboration among otherwise competing agents in subsurface systems. The agents can be, e.g., groundwater, CO$_2$, or hydrogen injectors and extractors with conflicting goals on a geophysically connected system. The operations of a given agent affect the other agents by induced pressure buil

Jana Vatter, Mykhaylo Zayats, Marcos Martínez Galindo, Vanessa López

With the ever-growing size of real-world graphs, numerous techniques to overcome resource limitations when training Graph Neural Networks (GNNs) have been developed. One such approach, GNNAutoScale (GAS), uses graph partitioning to enable training under constrained GPU memory. GAS also stores historical embedding vectors, which are retrieved from one-hop nei

Johan Bijnens, Nils Hermansson-Truedsson, Antonio Rodríguez-Sánchez

This talk discusses short-distance contributions to the hadronic light-by-light part of the muon g-2 as in the Standard Model. A short discussion about the theory prediction is followed by the status of our work. The main new results since the previous chiral dynamics workshop is how to calculate the higher order corrections to the Melnikov-Vainshtein region

Burak Ahmet Celebi, Omer Faruk Akyol, Semiha Tedik Basaran, Ibrahim Hokelek

This paper presents an Orthogonal Time Frequency Space (OTFS) waveform application along with a high altitude platform station (HAPS) relaying for remedying severe Doppler effects in non-terrestrial networks (NTNs). Taking practical challenges into consideration, HAPS is exploited as a decode and forward relay node to mitigate the high path loss between a sa

Eyal Yakir, Dor Tsur, Haim Permuter

Time series forecasting is a long-standing problem in statistics and machine learning. One of the key challenges is processing sequences with long-range dependencies. To that end, a recent line of work applied the short-time Fourier transform (STFT), which partitions the sequence into multiple subsequences and applies a Fourier transform to each separately.

Huazheng Wang, Yongcheng Jing, Haifeng Sun, Yingjie Wang

In this paper, we investigate knowledge forgetting in large language models with a focus on its generalisation, ensuring that models forget not only specific training samples but also related implicit knowledge. To this end, we begin by identifying a broader unlearning scope that includes both target data and logically associated samples, including rephrased

Tanja Baeumel, Josef van Genabith, Simon Ostermann

Autoregressive large language models (LLMs) exhibit impressive performance across various tasks but struggle with simple arithmetic, such as addition of two or more operands. We show that this struggle arises from LLMs' use of a simple one-digit lookahead heuristic, which works fairly well (but not perfect) for two-operand addition but fails in multi-operand

Minghui Chen, Ruinan Jin, Wenlong Deng, Yuanyuan Chen

Recent studies highlight the promise of LLM-based prompt optimization, especially with TextGrad, which automates differentiation'' via texts and backpropagates textual feedback. This approach facilitates training in various real-world applications that do not support numerical gradient propagation or loss calculation. In this paper, we systematically explore

Hongbing Zhang, Bing Zheng

The low-rank tensor completion (LRTC) problem aims to reconstruct a tensor from partial sample information, which has attracted significant interest in a wide range of practical applications such as image processing and computer vision. Among the various techniques employed for the LRTC problem, non-convex relaxation methods have been widely studied for thei

Bowen Song, Andrea Iannelli

Policy gradient (PG) methods are the backbone of many reinforcement learning algorithms due to their good performance in policy optimization problems. As a gradient-based approach, PG methods typically rely on knowledge of the system dynamics. If this is not available, trajectory data can be utilized to approximate first-order information. When the data are