November 2024 arXiv papers — page 124

Xiao Shen, YiXu Wang, Zigan Xu, Bowen Zou

The present work investigates the atomic interactions among Cu, Al, and Ni elements in bcc-iron matrix, focusing on the formation mechanism of nano-sized core-shell Cu precipitates. Using a combination of atom probe tomography (APT), density functional theory (DFT) cal-culations, and molecular dynamics (MD) simulations, the study provides insights into the a

Taha Bouhsine

We introduce a yat-product-powered neural network, the Neural Matter Network (NMN), a breakthrough in deep learning that achieves non-linear pattern recognition without activation functions. Our key innovation relies on the yat-product and yat-product, which naturally induces non-linearity by projecting inputs into a pseudo-metric space, eliminating the need

J. Wayne Mullinax, Panagiotis G. Anastasiou, Jeffrey Larson, Sophia E. Economou

The ADAPT-VQE algorithm is a promising method for generating a compact ansatz based on derivatives of the underlying cost function, and it yields accurate predictions of electronic energies for molecules. In this work we report the implementation and performance of ADAPT-VQE with our recently developed sparse wavefunction circuit solver (SWCS) in terms of ac

Steven van Leeuwen, Ilya Kolmanovsky

The paper considers a computational governor strategy to facilitate the implementation of Model Predictive Control (MPC) based on inexact optimization when the time available to compute the solution may be insufficient. In the setting of linear-quadratic MPC and a class of optimizers that includes Alternating Direction Method of Multipliers (ADMM), we derive

Christopher Hahne, Omar Rodriguez-Nunez, Éléa Gros, Théotim Lucas

Mueller matrix polarimetry captures essential information about polarized light interactions with a sample, presenting unique challenges for data augmentation in deep learning due to its distinct structure. While augmentations are an effective and affordable way to enhance dataset diversity and reduce overfitting, standard transformations like rotations and

Aniket Deroy, Subhankar Maity

The rapid growth of social media has resulted in an large volume of user-generated content, particularly in niche domains such as cryptocurrency. This task focuses on developing robust classification models to accurately categorize cryptocurrency-related social media posts into predefined classes, including but not limited to objective, positive, negative, e

Philip Döbler, Jannik Pflieger, Fengping Jin, Hans De Raedt

In quantum computing, error mitigation is a method to improve the results of an error-prone quantum processor by post-processing them on a classical computer. In this work, we improve the General Error Mitigation (GEM) method for scalability. GEM relies on the use of a matrix to represent the device error, which requires the execution of $2^{n+1}$ calibratio

J. E. Gough

We give a simple argument to derive the transformation of quantum stochastic calculus formalism between inertial observers, and derive the quantum open system dynamics for a system moving in a vacuum (more generally coherent) quantum field under the usual Markov approximation. We argue that for uniformly accelerated open systems, however, the formalism must

Sen Guo, Yu-Xiang Huang, En-Wei Liang, Yu Liang

The image of a Kerr-Newman (KN) black hole (BH) surrounded by a thin accretion disk is derived. By employing elliptic integrals and ray-tracing methods, we analyze photon trajectories around the KN BH. At low observation inclination angles, the secondary image of particles is embedded within the primary image. However, as the inclination increases, the prima

Adam F. Kowalski, Rachel A. Osten, Yuta Notsu, Isaiah I. Tristan

Flares from M-dwarf stars can attain energies up to $10^4$ times larger than solar flares but are generally thought to result from similar processes of magnetic energy release and particle acceleration. Larger heating rates in the low atmosphere are needed to reproduce the shape and strength of the observed continua in stellar flares, which are often simplif

Ali Elokl, Corey Jones

The prospect of realizing highly entangled states on quantum processors with fundamentally different hardware geometries raises the question: to what extent does a state of a quantum spin system have an intrinsic geometry? In this paper, we propose that both states and dynamics of a spin system have a canonically associated coarse geometry, in the sense of R

Yang Li

Puzzles are still preventing people from further understanding and manipulating the Casimir interaction in spherical systems. Here we investigate the behaviors of Casimir stresses in the system consisting of a ball immersed in the background, emphasising the roles of spherical geometry and inhomogeneity. Spherical modes are employed to evaluate the Green's d

Mercedes López Lora, Elena Chamizo, Mats Eriksson

The assessment of the origin of the anthropogenic contamination in marine regions impacted by other sources than global fallout is a challenge. This is the case of the west coast of Sweden, influenced by the liquid effluents released by the European Nuclear Reprocessing Plants through North Sea currents and by Baltic Sea local and regional sources, among oth

Viktor Bekkert, John William MacQuarrie, Júlio Marques

We give a practical, algorithmic method to calculate minimal projective resolutions of simple modules for a finite dimensional incidence $k$-algebra $\Lambda$, where $k$ is a field. We apply the method to the calculation of Ext groups between simple $\Lambda$-modules, Hochschild cohomology groups $\HH^i(\Lambda, \Lambda)$, and singular cohomology groups of f

Paul M. Voutier

We provide some corrections and clarifications of statements in \cite{Ab} and \cite{BHV} regarding elements in Lehmer sequences that have no primitive divisors for small $n$.

Miao Liu, Chong Shangguan, Chenyang Zhang

An $r$-graph is called $t$-cancellative if for arbitrary $t+2$ distinct edges $A_1,\ldots,A_t,B,C$, it holds that $(\cup_{i=1}^t A_i)\cup B\neq (\cup_{i=1}^t A_i)\cup C$; it is called $t$-union-free if for arbitrary two distinct subsets $\mathcal{A},\mathcal{B}$, each consisting of at most $t$ edges, it holds that $\cup_{A\in\mathcal{A}} A\neq \cup_{B\in\mat

Allison Wan, Christoph Riedl, David Lazer

How does social network structure amplify or stifle behavior diffusion? Existing theory suggests that when social reinforcement makes the adoption of behavior more likely, it should spread more -- both farther and faster -- on clustered networks with redundant ties. Conversely, if adoption does not benefit from social reinforcement, it should spread more on

Hongyun Zhang, Qian Li, Michael G. Scheer, Renqi Wang

Flat bands and nontrivial topological physics are two important topics of condensed matter physics. With a unique stacking configuration analogous to the Su-Schrieffer-Heeger (SSH) model, rhombohedral graphite (RG) is a potential candidate for realizing both flat bands and nontrivial topological physics. Here we report experimental evidence of topological fl

Dmitry M. Korotin, Dmitry Y. Novoselov, Yaroslav M. Plotnikov, Vladimir I. Anisimov

The silver fluoride Ag$_3$F$_5$ consists structurally of square-planar units formed by four fluoride ions coordinated to a central silver ion, which possesses a partially filled $d$-subshell and the formal valence of +5/3. In this study, we demonstrate that the previously published crystal structure of Ag$_3$F$_5$ is unstable due to the Jahn-Teller effect, a

Pietro Anzini, Zeno Filiberti, Alberto Parola

The motion of a fluid induced by thermal gradients in the absence of driving forces is known as thermo-osmosis. The physical explanation of this phenomenon stems from the emergence of gradients in the tangential pressure due to the presence of a confining surface. The microscopic origin of the effect was recently elucidated in the framework of linear respons

R. A. Hyndman, S. Dalla, T. Laitinen, A. Hutchinson

Context: Parameters of solar energetic particle (SEP) event profiles such as the onset time and peak time have been researched extensively to obtain information on acceleration and transport of SEPs. Corotation of particle-filled magnetic flux tubes with the Sun is generally thought to play a minor role in determining intensity profiles. However recent simul

Prabodh Katti, Clement Ruah, Osvaldo Simeone, Bashir M. Al-Hashimi

Bayesian Neural Networks (BNNs) provide superior estimates of uncertainty by generating an ensemble of predictive distributions. However, inference via ensembling is resource-intensive, requiring additional entropy sources to generate stochasticity which increases resource consumption. We introduce Bayes2IMC, an in-memory computing (IMC) architecture designe

Ishaan Gakhar, Aryesh Guha, Aryaman Gupta, Amit Agarwal

Traffic light detection under adverse weather conditions remains largely unexplored in ADAS systems, with existing approaches relying on complex deep learning methods that introduce significant computational overheads during training and deployment. This paper proposes Fourier Domain Adaptation (FDA), which requires only training data modifications without a

Kamalendu Ghosh, Bhavesh Shrimali

Dielectric elastomers are increasingly studied for their potential in soft robotics, actuators, and haptic devices. Under time-dependent loading, they dissipate energy via viscous deformation and friction in electric polarization. However, most constitutive models and finite element (FE) implementations consider only mechanical dissipation because mechanical

Xiao Huo, Junhui Hou, Shuai Wan, Fuzheng Yang

The evolution of 3D visualization techniques has fundamentally transformed how we interact with digital content. At the forefront of this change is point cloud technology, offering an immersive experience that surpasses traditional 2D representations. However, the massive data size of point clouds presents significant challenges in data compression. Current

Jose Beltrán Jiménez, Luis J. Garay, María Pérez Garrote

We compute the evolution of linear perturbations on top of a background solution of a general nonlinear electromagnetic theory. This evolution can be described in terms of two effective metrics, and we analyse under what conditions they are conformally related, so that they can be regarded as analogue models of non-trivial gravitational fields in the eikonal

L. Herrera

The Landauer principle establishes a lower bound in the amount of energy that should be dissipated in the erasure of one bit of information. The specific value of this dissipated energy is tightly related to the definition of entropy. In this article, we present a generalization of the Landauer principle based on the Tsallis entropy. Some consequences result

Adrien Morin

Let $X$ be a variety over a finite field. Given an order $R$ in a semi-simple algebra over the rationals and a constructible \'etale sheaf $F$ of $R$-modules over $X$, one can consider a natural non-commutative $L$-function associated with $F$. We prove a special value formula at negative integers for this $L$-function, expressed in terms of Weil-\'etale coh

Ismael Sierra, Nathalie Wahl

We use algebraic arc complexes to prove a homological stability result for symplectic groups with slope 2/3 for rings with finite unitary stable rank. Symplectic groups are here interpreted as the automorphism groups of formed spaces with boundary, which are algebraic analogues of surfaces with boundary, that we also study in the present paper. Our stabiliza

Sebastian Haney

Mirror symmetry gives predictions for the genus zero Gromov-Witten invariants of a closed Calabi--Yau variety in terms of period integrals on a mirror family of Calabi-Yau varieties. We deduce an analogous mirror theorem for the open Gromov-Witten invariants of certain Lagrangian submanifolds of the quintic threefold from homological mirror symmetry, assumin

Huan Zhang, Xu Zhang, Nian Cai, Jianglei Di

Outdoor images often suffer from severe degradation due to rain, haze, and noise, impairing image quality and challenging high-level tasks. Current image restoration methods struggle to handle complex degradation while maintaining efficiency. This paper introduces a novel image restoration architecture that combines multi-dimensional dynamic attention and se

Benjamin Litterer, David Jurgens, Dallas Card

Podcasts provide highly diverse content to a massive listener base through a unique on-demand modality. However, limited data has prevented large-scale computational analysis of the podcast ecosystem. To fill this gap, we introduce a massive dataset of over 1.1M podcast transcripts that is largely comprehensive of all English language podcasts available thro

Saad Raza, Silvia C. Hirata, Enrico Calzavarini

The effects of a dispersed particulate phase on the onset of Rayleigh-B\'enard convection in a fluid layer is studied theoretically by means of a two-fluid Eulerian modelization. The particles are non-Brownian, spherical, with inertia and heat capacity, and they interact with the surrounding fluid mechanically and thermally. We study both the cases of partic

Yanzhou Wang, Chang Chang, Junling Mei, Simon Leonard

We present a novel approach for minimally invasive flexible needle manipulations by pairing a real-time finite element simulator with the cross-entropy method. Additionally, we demonstrate how a kinematic-driven bang-bang controller can complement the control framework for better tracking performance. We show how electromagnetic (EM) tracking can be readily

Devansh Gupta, A. S. Poornash, Andrew Lowy, Meisam Razaviyayn

Machine learning models are often trained on sensitive data (e.g., medical records and race/gender) that is distributed across different "silos" (e.g., hospitals). These federated learning models may then be used to make consequential decisions, such as allocating healthcare resources. Two key challenges emerge in this setting: (i) maintaining the privacy of

Asha Vincent, Jürgen Müller, Christian Lisdat, Dennis Philipp

Chronometric levelling is a novel technique for the realisation of the International Height Reference System (IHRS). A detailed study of this technique is carried out through closed-loop simulations, aiming to unify regional/local height systems (LHS) in Europe and Brazil. Focusing on a unification accuracy of 1 cm, realistic scenarios with various error par

Maico H. W. Engelaar, Micha P. P. Swaanen, Mircea Lazar, Sofie Haesaert

This paper presents a stochastic model predictive control (SMPC) algorithm for linear systems subject to additive Gaussian mixture disturbances, with the goal of satisfying chance constraints. We focus on a special case where each Gaussian mixture component has a similar variance. To solve the SMPC problem, we formulate a branch model predictive control (BMP

Weillei Zeng, Jiaji Zhang, Lipeng Chen, Carlos L. Benavides-Riveros

Preparing quantum many-body states on classical or quantum devices is a very challenging task that requires accounting for exponentially large Hilbert spaces. Although this complexity can be managed with exponential ans\"atze (such as in the coupled-cluster method), these approaches are often tailored to specific systems, which limits their universality. Rec

Constantin Ulrich, Tassilo Wald, Emily Tempus, Maximilian Rokuss

Effortless and precise segmentation with minimal clinician effort could greatly streamline clinical workflows. Recent interactive segmentation models, inspired by METAs Segment Anything, have made significant progress but face critical limitations in 3D radiology. These include impractical human interaction requirements such as slice-by-slice operations for

Noemi Tagliavacche, Massimo Borghi, Giulia Guarda, Domenico Ribezzo

Entanglement is an essential ingredient in many quantum communication protocols. In particular, entanglement can be exploited in quantum key distribution (QKD) to generate two correlated random bit strings whose randomness is guaranteed by the nonlocal property of quantum mechanics. Most of QKD protocols tested to date rely on polarization and/or time-bin en

Valentin Stegmaier, Walter Schaaf, Nasser Jazdi, Michael Weyrich

Behavior models form an integral component of Digital Twins. The specific characteristics of these models may vary depending on the use case. One of these key characteristics is the modeling depth. Behavior models with a lower modeling depth depict the behavior of the asset in an abstract way, while those with a higher modeling depth depict the behavior in d

Raquel Mallavibarrena, Ragni Piene

We define higher order fundamental forms and osculating spaces of projective algebraic varieties, using sheaves of principal parts. We show that the $m$th fundamental form can be viewed as the differential of the $(m-1)$th Gauss map, and explain why the vanishing of the $m$th fundamental form implies that the variety is contained in a general $(m-1)$th oscul

Y. Damizia, S. Elmore, K. Verhaegh, P. Ryan

This study presents ion temperature (\(T_i\)) measurements in the MAST-U divertor, using a Retarding Field Energy Analyzer (RFEA). Steady state measurements were made during an L-Mode plasma with the strike point on the RFEA. ELM measurements were made with the strike point swept over the RFEA. The scenarios are characterized by a plasma current (\(I_p\)) of

Shreya Dhar, River Newman, Grayson Plumpton, Chenglu Wang

Let $p$ be a prime and let $\mathbb{Q}_p$ be the field of $p$-adic numbers. It is known that the finite extensions of $\mathbb{Q}_p$ of a given degree are finite up to isomorphism. Given a cubic field extension $L$ of $\mathbb{Q}_p$ generated by the root of an irreducible polynomial $h$, we present a practical (closed-form) method to determine the isomorphis

Zhen-Xiang Hao, Ruo-Xi Wu, Hong-Bo Jin, Ya-Zheng Tao

A unified definition for the rotation angle and rotation angular speed of general beams, including those with orbital angular momentum (OAM), has been lacking until now. The rotation of a general beam is characterized by observing the rotational behavior of the directions of the extreme spot sizes during propagation. We introduce the beam quality $M^2(\psi)$

Alexey Kroshnin, Alexandra Suvorikova

We derive explicit Bernstein-type and Bennett-type concentration inequalities for matrix-valued martingale processes with unbounded observations from the Hermitian space $\mathbb{H}(d)$. Specifically, we assume that the $\psi_{\alpha}$-Orlicz (quasi-)norms of their difference process are bounded for some $\alpha > 0$. Further, we generalize the obtained resu

Adélie André, Christophe Hoarau, Yannick Boursier, Afef Cherni

In the context of particle therapy monitoring, we are developing a gamma-ray detector to determine the ion range in vivo from the measurement of particle time-of-flight. For this application, a beam monitor capable to tag in time the incident ion with a time resolution below 235 ps FWHM (100 ps rms) is required to provide a start signal for the acquisition.

Yajie Chen, Sudip Mandal, Hardi Peter, Lakshmi Pradeep Chitta

Arch filament systems (AFSs) are chromospheric and coronal manifestations of emerging magnetic flux. Using high spatial resolution observations taken at a high cadence by the Extreme Ultraviolet Imager (EUI) on board Solar Orbiter, we identified small-scale elongated brightenings within the AFSs. These brightenings appear as bidirectional flows along the thr

Colton Feathers, Mihir Kulkarni, Eli Visbal

A key obstacle to accurate models of the first stars and galaxies is the vast range of distance scales that must be considered. While star formation occurs on sub-parsec scales within dark matter (DM) minihalos, it is influenced by large-scale baryon-dark matter streaming velocities ($v_{\rm bc}$) and Lyman-Werner (LW) radiative feedback which vary significa

Chengde Qian, Guanghui Wang, Zhaojun Wang, Changliang Zou

Changepoint detection is commonly formulated by minimizing the sum of in-sample losses to quantify the model's overall fit. However, for flexible modeling procedures -- especially those involving high-dimensional parameter spaces or hyperparameter tuning -- this strategy can lead to inaccurate changepoint estimation due to over-adaptivity biases. To mitigate

Binxu Wang, Jiaqi Shang, Haim Sompolinsky

Humans excel at discovering regular structures from limited samples and applying inferred rules to novel settings. We investigate whether modern generative models can similarly learn underlying rules from finite samples and perform reasoning through conditional sampling. Inspired by Raven's Progressive Matrices task, we designed GenRAVEN dataset, where each

Kartik Anand

We motivate an intuitive way to think about quantum circuit optimization problem inspired by Feynman's path formalism. While the use of path integrals in quantum circuits remains largely underdeveloped due to the lack of definition of the action functional for such systems. However this feynman's path perspective leads us to consider about how entanglement e

Neelima Agarwal, Sourav Pal, Aditya Srivastav, Anurag Tripathi

Infrared singularities in perturbative Quantum Chromodynamics (QCD) are captured by the Soft function, which can be calculated efficiently using Feynman diagrams known as webs. The starting point for calculating Soft function using webs is to compute the web mixing matrices using a well known replica trick algorithm. We present a package implemented in Mathe

Francesco Chiumento, Mingming Liu

The rapid advancements in Large Language Models (LLMs) and Vision-Language Models (VLMs) have shown great potential in medical diagnostics, particularly in radiology, where datasets such as X-rays are paired with human-generated diagnostic reports. However, a significant research gap exists in the neuroimaging field, especially for conditions such as Alzheim

Xiaofeng Zhu, Jaya Krishna Mandivarapu

Although people are impressed by the content generation skills of large language models, the use of LLMs, such as ChatGPT, is limited by the domain grounding of the content. The correctness and groundedness of the generated content need to be based on a verified context, such as results from Retrieval-Augmented Generation (RAG). One important issue when adap

Fei Shan, Kurt Luther

Genealogy, the study of family history and lineage, has seen tremendous growth over the past decade, fueled by technological advances such as home DNA testing and mass digitization of historical records. However, HCI research on genealogy practices is nascent, with the most recent major studies predating this transformation. In this paper, we present a quali

Luca Barbieri, Lapo Casetti, Andrea Verdini, Simone Landi

All but the most massive main-sequence stars are expected to have a rarefied and hot (million-Kelvin) corona like the Sun. How such a hot corona is formed and supported has not been completely understood yet, even in the case of the Sun. Recently, Barbieri et al. (A&A 2024, J. Plasma Phys. 2024) introduced a new model of a confined plasma atmosphere and appl

Gareth E. Roberts

We study kite central configurations in the Newtonian four-body problem. We present a new proof that there exists a unique convex kite central configuration for a given choice of positive masses and a particular ordering of the bodies. Our proof uses tools from differential topology (e.g., the Poincar\'{e}-Hopf Index Theorem) and computational algebraic geom

Suntao Wang, Vardan Khachatryan

In an accelerator, the nonlinear behavior near a horizontal resonance line ($n\nu_x$) usually involves the appearance of stable fixed points (SFPs) in the horizontal phase space, also referred to as transverse resonance island ``buckets" (TRIBs). Specific conditions are required for TRIBs formation. At the Cornell Electron Storage Ring, a new method is devel

Martin Schnell, Melanie King, Sam Buercklin, Paulo Sarriugarte

We demonstrate numerical refocusing in coherent confocal laser scanning microscopy based on synthetic optical holography. This physics-based approach implements a computational propagation on the complex signal recovered in synthetic holography consistent with the wave physics and the parameters of the microscope. An experimental demonstration is shown to re

Thibaut Delcroix

We prove that, for a spherical Fano threefold not in the Mori-Mukai family 2-29, and a weight function associated with the action of the connected center of a Levi subgroup of its automorphism group, weighted K-polystability is equivalent to vanishing of the weighted Futaki invariant. This is surprising since unlike the case of toric Fano manifold, there exi

Zhenkai Wu, Xiaowen Ma, Rongrong Lian, Kai Zheng

In complex scenes and varied conditions, effectively integrating spatial-temporal context is crucial for accurately identifying changes. However, current RS-CD methods lack a balanced consideration of performance and efficiency. CNNs lack global context, Transformers are computationally expensive, and Mambas face CUDA dependence and local correlation loss. I

Mingkun Wu, Alisa Rupenyan, Burkhard Corves

Unwanted vibrations stemming from the energy-optimized design of Delta robots pose a challenge in their operation, especially with respect to precise reference tracking. To improve tracking accuracy, this paper proposes an adaptive mismatch-compensated iterative learning controller based on input shaping techniques. We establish a dynamic model considering t

Fabian Grübel, Karan Molaverdikhani, Barbara Ercolano, Christian Rab

Polycyclic Aromatic Hydrocarbons (PAHs) have been detected throughout the universe where they play essential roles in the evolution of their environments. For example, they are believed to affect atmospheric loss rates of close-in planets and might contribute to the pre-biotic chemistry and emergence of life. Despite their importance, the study of PAHs in ex

Xiang Meng

This paper presents innovative approaches to optimization problems, focusing on both Single-Objective Multi-Modal Optimization (SOMMOP) and Multi-Objective Optimization (MOO). In SOMMOP, we integrate chaotic evolution with niching techniques, as well as Persistence-Based Clustering combined with Gaussian mutation. The proposed algorithms, Chaotic Evolution w

Loïc Tran, Benjamin Askenazi, Kevin Vynck

Random-walk Monte Carlo simulations are widely used to predict the optical properties of complex, disordered materials. In presence of large heterogeneities (e.g., spatially-extended nonscattering regions in a turbid environment), an explicit description of the micro and macrostructures and of the light propagation therein is generally required, in addition

Yusen Zhang, Sarkar Snigdha Sarathi Das, Rui Zhang

Although Large Language Models (LLMs) have demonstrated their strong capabilities in various tasks, recent work has revealed LLMs also exhibit undesirable behaviors, such as hallucination and toxicity, limiting their reliability and broader adoption. In this paper, we discover an understudied type of undesirable behavior of LLMs, which we term Verbosity Comp

Ana Alonso-Serrano, Marek Liška

Thermodynamics of local causal horizons have been shown to encode the information necessary to derive the equations governing the gravitational dynamics. We have previously shown that, in the presence of matter, this derivation further implies quantum phenomenological corrections to gravitational dynamics. Herein, we study whether similar corrections also oc

Yanyan Shi, Christian Lubich

This paper introduces filtered finite difference methods for numerically solving a dispersive evolution equation with solutions that are highly oscillatory in both space and time. We consider a semiclassically scaled nonlinear Schr\"odinger equation with highly oscillatory initial data in the form of a modulated plane wave. The proposed methods do not need t

Nicholas Kluge Corrêa, Aniket Sen, Sophia Falk, Shiza Fatimah

Significant advances have been made in natural language processing in recent years. However, our current deep learning approach to language modeling requires substantial resources in terms of data and computation. One of the side effects of this data-hungry paradigm is the current schism between languages, separating those considered high-resource, where mos

Ling Huang, Yucheng Xing, Swapnil Mishra, Thierry Denoeux

Time-to-event analysis provides insights into clinical prognosis and treatment recommendations. However, this task is more challenging than standard regression problems due to the presence of censored observations. Additionally, the lack of confidence assessment, model robustness, and prediction calibration raises concerns about the reliability of prediction

A. J. Nurmagambetov

We construct series of solutions for the Kerr-type rotating black hole with non-trivial matter in flat and (A)dS backgrounds. Symmetry arguments and singularity analysis in the proposed black hole models fix the free parameters of the solutions, and the study of popular energy conditions makes it possible to impose constraints on configuration and field cont

Adriana Amieva, Agustín G. Bonifacio, Pablo Neme

We examine the problem of assigning teachers to public schools over time when teachers have tenured positions and can work simultaneously in multiple schools. To do this, we investigate a dynamic many-to-many school choice problem where public schools have priorities over teachers and teachers hold path-independent choice functions selecting subsets of schoo

Xiaoyin Yi, Jiacheng Huang

Adversarial examples, which are inputs deliberately perturbed with imperceptible changes to induce model errors, have raised serious concerns for the reliability and security of deep neural networks (DNNs). While adversarial attacks have been extensively studied in continuous data domains such as images, the discrete nature of text presents unique challenges

R. Kumar, R. P. Malik

We demonstrate the discrete duality symmetry between the Abelian 1-form and 2-form basic gauge fields in the context of a three $(2 + 1)$-dimensional ($3D$) combined system of the field-theoretic model of the free Abelian 1-from and 2-form gauge theories within the framework of Becchi-Rouet-Stora-Tyutin (BRST) formalism. The classical gauge-fixed Lagrangian

Sonia Raychaudhuri, Duy Ta, Katrina Ashton, Angel X. Chang

Large scale scenes such as multifloor homes can be robustly and efficiently mapped with a 3D graph of landmarks estimated jointly with robot poses in a factor graph, a technique commonly used in commercial robots such as drones and robot vacuums. In this work, we propose Language-Inferred Factor Graph for Instruction Following (LIFGIF), a zero-shot method to

Catalina Sobrino Figaredo, Doron Chelouche, Martin Haas, Michael Ramolla

We present the results of a nearly decade-long photometric reverberation mapping (PRM) survey of the H$\alpha$ emission line in nearby ($0.01\lesssim z \lesssim0.05$) Seyfert-Galaxies using small ($15\,\mathrm{cm}-40\,\mathrm{cm}$) telescopes. Broad-band filters were used to trace the continuum emission, while narrow-band filters tracked the H$\alpha$-line s

Piotr P. Goldstein

A detailed description of the asymptotic behaviour in the Belinski-Khalatnikov-Lifshitz (BKL) scenario is presented through a simple geometric picture illustrating the geometry of their ordinary differential equations (ODE), which describe a neighbourhood of the cosmic singularity. The Lagrangian version of the dynamics governed by these equations is describ

Antonia Karamolegkou, Sandrine Schiller Hansen, Ariadni Christopoulou, Filippos Stamatiou

What ethical concerns, if any, do LLM researchers have? We introduce EthiCon, a corpus of 1,580 ethical concern statements extracted from scientific papers published in the ACL Anthology. We extract ethical concern keywords from the statements and show promising results in automating the concern identification process. Through a survey, we compare the ethica

Yuncheng Mao, Claudio Attaccalite, Diego García Ovalle

In this work we analyze the shift current conductivity in helical twisted trilayer graphene. Without loss of generality, we show that the density of states and the twist angle set an upper bound for this response, which is inversely proportional to the square of the twist angle. For the case of ABA stacking and at the magic angle, the shift photoconductivity

Jiacheng Huang, Long Chen

Association as a gift enables people do not have to mention something in completely straightforward words and allows others to understand what they intend to refer to. In this paper, we propose a chain association-based adversarial attack against natural language processing systems, utilizing the comprehension gap between humans and machines. We first genera

Prabodh Katti, Bashir M. Al-Hashimi, Bipin Rajendran

Bayesian Neural Networks (BNNs) provide principled estimates of model and data uncertainty by encoding parameters as distributions. This makes them key enablers for reliable AI that can be deployed on safety critical edge systems. These systems can be made resource efficient by restricting synapses to two synaptic states $\{-1,+1\}$ and using a memristive in

Navpreet Kaur, Juntao Chen, Yingdong Lu

Optimal transport is a powerful framework for the efficient allocation of resources between sources and targets. However, traditional models often struggle to scale effectively in the presence of large and heterogeneous populations. In this work, we introduce a discrete optimal transport framework designed to handle large-scale, heterogeneous target populati

Kihoon Seong, Philippe Sosoe

We study the fluctuations of the focusing $\Phi^4$-measure on the one-dimensional torus in the infinite volume limit. This measure is an invariant Gibbs measure for the nonlinear Schr\"odinger equation. It had previously been shown by B. Rider that the measure is strongly concentrated around a family of minimizers of the Hamiltonian associated with the measu

Wan Dong, Liu-Qin Song, Yi-Fan Zhang, Li Wang

The electron dynamics and SiO2 etching profile evolution in capacitively coupled Ar/CHF3 plasmas driven by sawtooth-waveforms are investigated based on a one-dimensional fluid/Monte-Carlo (MC) model coupled with an etching profile evolution model. The effects of the sawtooth-waveforms synthesized from different numbers of consecutive harmonics, N, of a funda

Elisa Davoli, Leon Happ

In this paper we prove a strong two-scale approximation result for sphere-valued maps in $L^2(\Omega;W^{1,2}_0(Q_0;\mathbb{S}^2))$, where $\Omega\subset \mathbb{R}^3$ is an open domain and $Q_0\subset Q$ an open subset of the unit cube $Q=(0,1)^3$. The proof relies on a generalization of the seminal argument by F. Bethuel and X.M. Zheng to the two-scale sett

Philip Zmushko, Aleksandr Beznosikov, Martin Takáč, Samuel Horváth

With the increase in the number of parameters in large language models, the process of pre-training and fine-tuning increasingly demands larger volumes of GPU memory. A significant portion of this memory is typically consumed by the optimizer state. To overcome this challenge, recent approaches such as low-rank adaptation (LoRA (Hu et al., 2021)), low-rank g

Taisanul Haque

In this work, we explore quantum energy teleportation (QET) protocols, focusing on their behavior at finite temperatures , in ground and excited states. We analyze the role of entanglement as a resource for QET, particularly in thermal states, and compare the performance of QET across these initial states. We then introduce a method to extract ground-state e

J. Eduardo Vera-Valdés, Charisios Grivas

This paper discusses the effect of measurement errors in the estimation of the carbon dioxide (CO$_2$) airborne fraction. We are the first to present regression-based estimates and standard errors that are robust to measurement errors for the extended model, the preferred specification to estimate the CO$_2$ airborne fraction. To achieve this goal, we add to

Shaun McKnight, Vedran Tunukovic, Amine Hifi, Gareth Pierce

This study introduces a novel self-supervised learning approach for volumetric segmentation of defect indications captured by phased array ultrasonic testing data from Carbon Fiber Reinforced Polymers (CFRPs). By employing this self-supervised method, defect segmentation is achieved automatically without the need for labelled training data or examples of def

Emmanuel Azuh Mensah, Anderson Lee, Haoran Zhang, Yitong Shan

The explosion of IoT sensors in industrial, consumer and remote sensing use cases has come with unprecedented demand for computing infrastructure to transmit and to analyze petabytes of data. Concurrently, the world is slowly shifting its focus towards more sustainable computing. For these reasons, there has been a recent effort to reduce the footprint of re

Yitaek Kim, Jeeseop Kim, Albert H. Li, Aaron D. Ames

Robotic grasping requires safe force interaction to prevent a grasped object from being damaged or slipping out of the hand. In this vein, this paper proposes an integrated framework for grasping with formal safety guarantees based on Control Barrier Functions. We first design contact force and force closure constraints, which are enforced by a safety filter

Chao Han, Debabrota Basu, Michael Mangan, Eleni Vasilaki

Learning representations of underlying environmental dynamics from partial observations is a critical challenge in machine learning. In the context of Partially Observable Markov Decision Processes (POMDPs), state representations are often inferred from the history of past observations and actions. We demonstrate that incorporating future information is esse

Csaba Fábri, Gábor J. Halász, Jaroslav Hofierka, Lorenz S. Cederbaum

The coupling of matter to the quantized electromagnetic field of a plasmonic or optical cavity can be harnessed to modify and control chemical and physical properties of molecules. In optical cavities, a term known as the dipole self-energy (DSE) appears in the Hamiltonian to assure gauge invariance. The aim of this work is twofold. First, we introduce a met

Ji-Cai Liu, Huan Liu

Based on a bijection due to Fu and Tang, we provide combinatorial proofs of several partition identities of Andrews and Merca. We also introduce two weights for partitions to extend one of these identities.

Mingkun Wu, Alisa Rupenyan, Burkhard Corves

Singularities, manifesting as special configuration states, deteriorate robot performance and may even lead to a loss of control over the system. This paper addresses the kinematic singularity concerns in robotic systems with model mismatch and actuator constraints through control barrier functions (CBFs). We propose a learning-based control strategy to prev

Danqi Lang, Lorenzo Costigliola, Jeppe C. Dyre

When energy polydispersity is introduced into the Lennard-Jones (LJ) system, there is little effect on structure and dynamics [Ingebrigtsen and Dyre, J. Phys. Chem. B 127, 2837 (2023)]. For instance, at a given state point both the radial distribution function and the mean-square displacement as a function of time are virtually unaffected by even large energ

Lan Sun, Songpengcheng Xia, Junyuan Deng, Jiarui Yang

With the rapid development of wearable technology, devices like smartphones, smartwatches, and headphones equipped with IMUs have become essential for applications such as pedestrian positioning. However, traditional pedestrian dead reckoning (PDR) methods struggle with diverse motion patterns, while recent data-driven approaches, though improving accuracy,

Rong-Qing Chen, Neng-Hui Liao, Xiong Jiang, Yi-Zhong Fan

Although connections between flaring blazars and some IceCube neutrinos have been established, the dominant sources for the bulk extragalactic neutrino emissions are still unclear and one widely suggested candidate is a population of radio galaxies. Because of their relatively low $\gamma$-ray radiation luminosities ($L_\gamma$), it is rather challenging to

Kilian Pfeiffer, Mohamed Aboelenien Ahmed, Ramin Khalili, Jörg Henkel

In recent years, Large Language Models (LLMs) through Transformer structures have dominated many machine learning tasks, especially text processing. However, these models require massive amounts of data for training and induce high resource requirements, particularly in terms of the large number of Floating Point Operations (FLOPs) and the high amounts of me