March 2025 arXiv papers — page 209

Ziyi Zhou, Qian Meng, Hadas Kress-Gazit, Ye Zhao

We propose an integrated planning framework for quadrupedal locomotion over dynamically changing, unforeseen terrains. Existing approaches either rely on heuristics for instantaneous foothold selection--compromising safety and versatility--or solve expensive trajectory optimization problems with complex terrain features and long time horizons. In contrast, o

Austin O'Quinn, Conor Snedeker, Siyuan Zhang, Jenna Kline

IoT and edge-based inference systems require unique solutions to overcome resource limitations and unpredictable environments. In this paper, we propose an environment-aware dynamic pruning system that handles the unpredictability of edge inference pipelines. While traditional pruning approaches can reduce model footprint and compute requirements, they are o

Richard Huber

Tomography is a central tool in medical applications, allowing doctors to investigate patients' interior features. The Radon transform (in two dimensions) is commonly used to model the measurement process in parallel-beam CT. Suitable discretization of the Radon transform and its adjoint (called the backprojection) is crucial. The most commonly used discreti

Xusheng Du, Ruihan Gui, Zhengyang Wang, Ye Zhang

In the early stages of architectural design, shoebox models are typically used as a simplified representation of building structures but require extensive operations to transform them into detailed designs. Generative artificial intelligence (AI) provides a promising solution to automate this transformation, but ensuring multi-view consistency remains a sign

Shuning Zhang, Shixuan Li

This paper explores the acceptance of human-AI love among young adults, particularly focusing on Chinese women in romantic or intimate relationships with AI companions. Through qualitative research, including 14 semi-structured interviews, the study investigates how these individuals establish and maintain relationships with AI, their perceptions and attitud

Sara L. Ellison, Qifeng Huang, Dong Yang, Jing Wang

Studying the gas content of post-starburst (PSB) galaxies can provide valuable clues regarding the process of fast quenching. Although previous works have studied the molecular gas content of PSBs, only a handful of HI measurements exist. Here, we present new Five hundred metre Aperture Spherical Telescope (FAST) 21cm observations of 44 PSBs, leading to 43 d

Sean McGrath, Cheng-Han Yang, Jonathan Kimmelman, Omer Ozturk

We consider the problem of meta-analyzing outcome measures based on median survival times. Primary studies with time-to-event outcomes often report estimates of median survival times and confidence intervals based on the Kaplan-Meier estimator. However, outcome measures based on median survival are rarely meta-analyzed, as standard inverse-variance weighted

Boxun Yan, Runzhou Chen, Mau-Chung Frank Chang

In this paper, we present an on-chip antenna at 290 GHz that achieves a maximum efficiency of 42\% on a low-resistivity silicon substrate for sub-terahertz integrated transceivers. The proposed antenna is based on a dual-slot structure to accommodate a limited ground plane and maintain desired radiation and impedance characteristics across the target frequen

Barbora Batíková, Tomáš J. Kepka, Petr C. Němec

In our effort to find an arithmetically pure proof of the Bertrand postulate, we investigate and solve (using only elementary arithmetical methods) another less usual inequality in positive integers inspired by the classical proof of the postulate given by P. Erdős.

Zachary A. H. Goodwin, Daniel M. Markiewitz, Qisheng Wu, Yue Qi

The solvation environments of Li$^+$ in conventional non-aqueous battery electrolytes, such as LiPF$_6$ in mixtures of ethylene carbaronate (EC) and ethyl methyl carbonate (EMC), are often used to rationalize the transport properties of electrolytes and solid electrolyte interphase (SEI) formation. In the SEI, the solvation environments in the compact electr

Sarath Sankar, Matan Lotem, Joshua Folk, Eran Sela

Charge detection offers a powerful probe of mesoscopic structures based on quantum dots, but it also invariably results in measurement back-action (MBA). If strong, MBA can be detrimental to the physical properties being probed. In this work, we focus on the effects of MBA on an Anderson impurity model in which the impurity is coupled electrostatically to a

Timo Jakobs, Marco Garofalo, Tobias Hartung, Karl Jansen

In this paper, we investigate a digitised SU$(2)$ lattice gauge theory in the Hamiltonian formalism. We use partitionings to digitise the gauge degrees of freedom and show how to define a penalty term based on finite element methods to project onto physical states of the system. Moreover, we show for a single plaquette system that in this framework the limit

Jialei He, Zhihao Zhan, Zhituo Tu, Xiang Zhu

Rapid generation of large-scale orthoimages from Unmanned Aerial Vehicles (UAVs) has been a long-standing focus of research in the field of aerial mapping. A multi-sensor UAV system, integrating the Global Positioning System (GPS), Inertial Measurement Unit (IMU), 4D millimeter-wave radar and camera, can provide an effective solution to this problem. In this

Melvyn B. Nathanson

Let $K = \mathbb{R}$ or $\mathbb{C}$. An $n$-element subset $A$ of $K$ is a $B_h$-set if every element of $K$ has at most one representation as the sum of $h$ not necessarily distinct elements of $A$. Associated to the $B_h$ set $A = \{a_1,\ldots, a_n\}$ are the $B_h$-vectors $\mathbf{a} = (a_1,\ldots, a_n)$ in $K^n$. This paper proves that ``almost all'

Haruto Oshiro, Misako Tatsuuma, Satoshi Okuzumi, Hidekazu Tanaka

The collision outcomes of dust aggregates in protoplanetary disks dictate how planetesimals form. Experimental and numerical studies have suggested that bouncing collisions occurring at low impact velocities may limit aggregate growth in the disks, but the conditions under which bouncing occurs have yet to be fully understood. In this study, we perform a sui

Milena T. Bagdasarian, Paul Knoll, Yi-Hsin Li, Florian Barthel

3D Gaussian Splatting (3DGS) has emerged as a cutting-edge technique for real-time radiance field rendering, offering state-of-the-art performance in terms of both quality and speed. 3DGS models a scene as a collection of three-dimensional Gaussians, with additional attributes optimized to conform to the scene's geometric and visual properties. Despite i

Steffen Becker, René Walendy, Markus Weber, Carina Wiesen

Hardware Reverse Engineering (HRE) is a technique for analyzing integrated circuits. Experts employ HRE for security-critical tasks, like detecting Trojans or intellectual property violations, relying not only on their experience and customized tools but also on their cognitive abilities. In this work, we introduce ReverSim, a software environment that model

Manuel Hoff

We study the arithmetic geometry of the reduction modulo $p$ of the Siegel modular variety with parahoric level structure. We realize the EKOR-stratification on this variety as the fibers of a smooth morphism into an algebraic stack parametrizing homogeneously polarized chains of certain truncated displays.

Victor Wang, Michael J. Q. Zhang, Eunsol Choi

Using language models to scalably approximate human preferences on text quality (LLM-as-a-judge) has become a standard practice applicable to many tasks. A judgment is often extracted from the judge's textual output alone, typically with greedy decoding. However, LLM judges naturally provide distributions over judgment tokens, inviting a breadth of inference

Nikhil Pandit

We show that for a rational homology 3-sphere $Y$ equipped with a self-conjugate spin$^c$-structure $\mathfrak s$, the $\operatorname{Pin}(2)$-equivariant monopole Floer homology of $(Y,\mathfrak s)$, as defined by Lin, is isomorphic to the $\operatorname{Pin}(2)$-equivariant Seiberg-Witten Floer homology of $(Y,\mathfrak s)$ defined by Manolescu.

Zhengyao Gu, Henry Peng Zou, Yankai Chen, Aiwei Liu

The high cost of obtaining high-quality annotated data for in-context learning (ICL) has motivated the development of methods that use self-generated annotations in place of ground-truth labels. While these approaches have shown promising results in few-shot settings, they generally do not scale to many-shot scenarios. In this work, we study ICL with self-ge

Victory Idowu

We develop an approach to generate random graphs to a target level of assortativity by using copula structures in graphons. Unlike existing random graph generators, we do not use rewiring or binning approaches to generate the desired random graph. Instead, we connect Archimedean bivariate copulas to graphons in order to produce flexible models that can gener

Ilya Chevyrev, Hao Shen

Local solutions to the 3D stochastic quantisation equations of Yang-Mills-Higgs were constructed in (arXiv:2201.03487), and it was shown that, in the limit of smooth mollifications, there exists a mass renormalisation of the Yang-Mills field such that the solution is gauge covariant. In this paper we prove uniqueness of the mass renormalisation that leads to

T. Koide, F. Nicacio

We present a systematic procedure to derive a quantum master equation for thermal relaxation in real scalar field theory, expanding on the method proposed in [Koide and Nicacio, Phys. Lett. A494, 129277 (2024)]. We begin by introducing a generalized model for a classical scalar field interacting with a Brownian thermostat, consistent with stochastic thermody

Ville Salo, Scott Schmieding

For the action of a group $G$ by homeomorphisms on a space $X$, the automorphism group $\mathrm{Aut}(X,G)$ consists of all self-homeomorphisms of $X$ which commute with $x \mapsto g \cdot x$ for every $g \in G$. A theorem of Ryan shows that for an irreducible $\mathbb{Z}$-shift of finite type $(X,\sigma_{X})$, the center of $\mathrm{Aut}(X,\sigma_{X})$ is ge

Rajesh Kumar, Joaquin Grefa, Konstantin Maslov, Yuhan Wang

We study the equation of state of hot and dense hadronic matter using an extended Chiral Mean Field (CMF) model framework where the addition is the inclusion of interactions of thermally excited mesons. This is implemented by calculating the in-medium masses of pseudoscalar and vector mesons, obtained through the explicit chiral symmetry-breaking and vector

Ikechukwu Uchendu, Jason Jabbour, Korneel Van den Berghe, Joel Runevic

Autonomous agents and systems cover a number of application areas, from robotics and digital assistants to combinatorial optimization, all sharing common, unresolved research challenges. It is not sufficient for agents to merely solve a given task; they must generalize to out-of-distribution tasks, perform reliably, and use hardware resources efficiently dur

Sanju Vaidya, Aihua Li

Vulnerability measures and topological indices are crucial in solving various problems such as the stability of the communication networks and development of mathematical models for chemical compounds. In 1947, Harry Wiener introduced a topological index related to molecular branching. Since then, more than 100 topological indices for graphs were introduced.

Sangjun Park, Hyowoon Seo

Federated learning (FL) is an emerging machine learning paradigm with immense potential to support advanced services and applications in future industries. However, when deployed over wireless communication systems, FL suffers from significant communication overhead, which can be alleviated by integrating over-the-air computation (AirComp). Despite its advan

Rui Li, Kentaro Kubo, Yinghao Ho, Zhiguang Yan

A high-fidelity CZ gate utilizing a double-transmon coupler (DTC) has recently been demonstrated as a building block for superconducting quantum processors. Like many other kinds of tunable couplers, however, the DTC requires a finite DC current for flux-biasing the coupler at the idling point to turn off the coupling, necessitating extra care for wiring and

O. Goktug Poyrazoglu, Rahul Moorthy, Yukang Cao, William Chastek

Sampling-based model predictive controllers generate trajectories by sampling control inputs from a fixed, simple distribution such as the normal or uniform distributions. This sampling method yields trajectory samples that are tightly clustered around a mean trajectory. This clustering behavior in turn, limits the exploration capability of the controller an

Ursula Wurstbauer, Michael J. Manfra, Ken W. West, Loren N. Pfeiffer

Ultra-clean low-dimensional interacting charge carrier systems are the basis to explore correlated states and phases. We report the observation of very narrow collective intersubband excitations (ISBE) of 2D electron systems (2DESs) with ultra-high mobilities in high quality GaAs quantum well structures. These findings from resonant inelastic light scatterin

Nadhir Ben Rached, Shyam Mohan Subbiah Pillai, Raúl Tempone

Given the increasing global emphasis on sustainable energy usage and the rising energy demands of cellular wireless networks, this work seeks an optimal short-term, continuous-time power procurement schedule to minimize operating expenditure and the carbon footprint of cellular wireless networks equipped with energy storage capacity, and hybrid energy system

Shun Liao, Paolo Di Achille, Jiang Wu, Silviu Borac

Resting heart rate (RHR) is an important biomarker of cardiovascular health and mortality, but tracking it longitudinally generally requires a wearable device, limiting its availability. We present PHRM, a deep learning system for passive heart rate (HR) and RHR measurements during everyday smartphone use, using facial video-based photoplethysmography. Our s

Yuki Okoda, Yao-Lun Yang, Neal J. Evans, Jaeyeong Kim

While molecular outflows have been studied in details with radio interferometry, observations of the hotter gas in protostellar outflows at a comparable physical scale is often challenging. Combined with ALMA, JWST allows us to investigate the cold and hot gas with unprecedented spatial resolution and sensitivity. We present a detailed comparison between the

Sizhu Lu, Yanyao Yi, Yongming Qu, Huayu Karen Liu

The analysis of randomized controlled trials is often complicated by intercurrent events (IEs) -- events that occur after treatment initiation and affect either the interpretation or existence of outcome measurements. Examples include treatment discontinuation or the use of additional medications. In two recent clinical trials for systemic lupus erythematosu

Jichen Feng, Ethan Abraham, Joseph Subotnik, Abraham Nitzan

The development of quantitative methods for characterizing molecular chirality can provide an important tool for studying chirality induced phenomena in molecular systems. Significant progress has been made in recent years toward understanding the chirality of molecular normal vibrational modes, mostly focusing on vibrations of helical molecular structures.

Byron Chin, Elchanan Mossel, Youngtak Sohn, Alexander S. Wein

We study the inference of communities in stochastic block models with a growing number of communities. For block models with $n$ vertices and a fixed number of communities $q$, it was predicted in Decelle et al. (2011) that there are computationally efficient algorithms for recovering the communities above the Kesten--Stigum (KS) bound and that efficient rec

Xihan Qin, Li Liao

Comorbidity, the co-occurrence of multiple medical conditions in a single patient, profoundly impacts disease management and outcomes. Understanding these complex interconnections is crucial, especially in contexts where comorbidities exacerbate outcomes. Leveraging insights from the human interactome (HI) and advancements in graph-based methodologies, this

Youssef Shoeb, Azarm Nowzad, Hanno Gottschalk

In this paper, we review the state of the art in Out-of-Distribution (OoD) segmentation, with a focus on road obstacle detection in automated driving as a real-world application. We analyse the performance of existing methods on two widely used benchmarks, SegmentMeIfYouCan Obstacle Track and LostAndFound-NoKnown, highlighting their strengths, limitations, a

Yufei Wang, Ziyu Wang, Mino Nakura, Pratik Bhowal

This paper presents ArticuBot, in which a single learned policy enables a robotics system to open diverse categories of unseen articulated objects in the real world. This task has long been challenging for robotics due to the large variations in the geometry, size, and articulation types of such objects. Our system, Articubot, consists of three parts: genera

Radu Berdan, Beril Besbinar, Christoph Reinders, Junji Otsuka

Edge-based computer vision models running on compact, resource-limited devices benefit greatly from using unprocessed, detail-rich RAW sensor data instead of processed RGB images. Training these models, however, necessitates large labeled RAW datasets, which are costly and often impractical to obtain. Thus, converting existing labeled RGB datasets into senso

Gabriele Sarti, Vilém Zouhar, Grzegorz Chrupała, Ana Guerberof-Arenas

Word-level quality estimation (QE) methods aim to detect erroneous spans in machine translations, which can direct and facilitate human post-editing. While the accuracy of word-level QE systems has been assessed extensively, their usability and downstream influence on the speed, quality and editing choices of human post-editing remain understudied. In this s

Andy Dong, Wei-Ning Chen, Ayfer Ozgur

We study how inherent randomness in the training process -- where each sample (or client in federated learning) contributes only to a randomly selected portion of training -- can be leveraged for privacy amplification. This includes (1) data partitioning, where a sample participates in only a subset of training iterations, and (2) model partitioning, where a

Yan Han, Soumava Kumar Roy, Mehrtash Harandi, Lars Petersson

Deep learning with noisy labels is an interesting challenge in weakly supervised learning. Despite their significant learning capacity, CNNs have a tendency to overfit in the presence of samples with noisy labels. Alleviating this issue, the well known Co-Training framework is used as a fundamental basis for our work. In this paper, we introduce a Contrastiv

Xiangzhi Cao

In this paper, we obtained Eells-Sampson type result of Symphonic map.

Yizhe Zhang, Navdeep Jaitly

Recent advances in large language models have demonstrated impressive capabilities in task-oriented applications, yet building emotionally intelligent chatbots that can engage in natural, strategic conversations remains a challenge. We present a novel approach called SAGE that uses latent variables to control long-horizon behavior in dialogue generation. At

Antonin Schrab

This article provides a practical introduction to kernel discrepancies, focusing on the Maximum Mean Discrepancy (MMD), the Hilbert-Schmidt Independence Criterion (HSIC), and the Kernel Stein Discrepancy (KSD). Various estimators for these discrepancies are presented, including the commonly-used V-statistics and U-statistics, as well as several forms of the

Erfan Entezami, Ali Naseh

Reinforcement learning has shown remarkable performance in aligning language models with human preferences, leading to the rise of attention towards developing RLHF platforms. These platforms enable users to fine-tune models without requiring any expertise in developing complex machine learning algorithms. While these platforms offer useful features such as

Ismael Abdulrahman

This study introduces simple yet effective continuous- and discrete-variable quantum neural network (QNN) models as a transfer-learning approach for forecasting tasks. The CV-QNN features a single quantum layer with two qubits to establish entanglement and utilizes a minimal set of quantum gates, including displacement, rotation, beam splitter, squeezing, an

Shangshang Yang, Congyi Nai, Xinyan Liu, Weidong Li

Machine learning models have shown great success in predicting weather up to two weeks ahead, outperforming process-based benchmarks. However, existing approaches mostly focus on the prediction task, and do not incorporate the necessary data assimilation. Moreover, these models suffer from error accumulation in long roll-outs, limiting their applicability to

Ghazal Ghajari, Elaheh Ghajari, Hossein Mohammadi, Fathi Amsaad

The rapid expansion of Internet of Things (IoT) networks has introduced new security challenges, necessitating efficient and reliable methods for intrusion detection. In this study, a detection framework based on hyperdimensional computing (HDC) is proposed to identify and classify network intrusions using the NSL-KDD dataset, a standard benchmark for intrus

Alexander Strack, Dirk Pflüger

Due to increasing core counts in modern processors, several task-based runtimes emerged, including the C++ Standard Library for Concurrency and Parallelism (HPX). Although the asynchronous many-task runtime HPX allows implicit communication via an Active Global Address Space, it also supports explicit collective operations. Collectives are an efficient way t

Andrew Engel, Thomas Braine, Christian Boutan

The axion is a compelling hypothetical particle that could account for the dark matter in our universe, while simultaneously explaining why quark interactions within the neutron do not appear to give rise to an electric dipole moment. The most sensitive axion detection technique in the 1 to 10 GHz frequency range makes use of the axion-photon coupling and is

Matthew J. Turner, Mike Carenzo, Jackie Lasky, James Morris-King

Cyber threat hunting is the practice of proactively searching for latent threats in a network. Engaging in threat hunting can be difficult due to the volume of network traffic, variety of adversary techniques, and constantly evolving vulnerabilities. To aid analysts in identifying techniques which may be co-occurring as part of a campaign, we present the Tec

Parv Khurana, Alexandra Liosi, Spencer Sherwin, Julien Hoessler

This study applies the high-fidelity spectral/hp element method using the open-source Nektar++ framework to simulate the unsteady, transitional flow around complex 3D geometries representative of the Formula 1 industry. This study extends the work on a previously investigated industrial benchmark, the Imperial Front Wing (IFW), derived from the McLaren MP4-1

F. M. Maier, G. Bollen, B. A. Brown, S. E. Campbell

We present a high-precision mass measurement of the proton-rich nucleus 23Si, performed with the LEBIT Penning trap at the Facility for Rare Isotope Beams (FRIB) utilizing the time-of-flight ion cyclotron resonance (TOF-ICR) technique. We determined a mass excess of 23362.9(5.8) keV, which agrees with a recent storage-ring measurement from CSRe but has a fac

Marcelo Laca, Tyler Schulz

We study the high-temperature equilibrium for the C*-algebra $\mathcal T (\mathbb N^\times \ltimes \mathbb N)$ recently considered by an Huef, Laca and Raeburn. We show that the simplex of KMS$_\beta$ states at each inverse temperature $\beta$ in the critical interval $(0,1]$ is a Bauer simplex whose space of extreme points is homeomorphic to $\mathbb N \sqc

Samir Abdaljalil, Filippo Pallucchini, Andrea Seveso, Hasan Kurban

Despite the state-of-the-art performance of Large Language Models (LLMs), these models often suffer from hallucinations, which can undermine their performance in critical applications. In this work, we propose SAFE, a novel method for detecting and mitigating hallucinations by leveraging Sparse Autoencoders (SAEs). While hallucination detection techniques an

Ghazal Ghajari, Ashutosh Ghimire, Elaheh Ghajari, Fathi Amsaad

With the rapid growth of IoT devices, ensuring robust network security has become a critical challenge. Traditional intrusion detection systems (IDSs) often face limitations in detecting sophisticated attacks within high-dimensional and complex data environments. This paper presents a novel approach to network anomaly detection using hyperdimensional computi

Saleh Baqer, Theodoros P. Horikis, Dimitrios J. Frantzeskakis

In this work, we investigate non-classical wavetrain formations, and particularly dispersive shock waves (DSWs), or undular bores, in systems exhibiting non-convex dispersion. Our prototypical model, which arises in shallow water wave theory, is the extended Korteweg-de Vries (eKdV) equation. The higher-order dispersive and nonlinear terms of the latter, lea

Samir Rosas, Shovasis Kumar Biswas, Wihan Adi, Furkan Kuruoglu

Mid-infrared spectroscopy offers powerful label-free molecular analysis capabilities but faces significant challenges when analyzing complex biological samples. Here, we present a transformative surface-enhanced infrared absorption spectroscopy (SEIRAS) platform that overcomes fundamental limitations through key innovations. First, we demonstrate high-throug

Vincent Astier

We show that the theories of some (ordered) central simple algebras with involution over real closed fields are model-complete or admit quantifier elimination, and characterize positive cones in terms of morphisms into models of some of these theories.

Masoumeh Shafieinejad, Xi He, Bailey Kacsmar

Privacy is an instance of a social norm formed through legal, technical, and cultural dimensions. Institutions such as regulators, industry, and researchers act as societal agents that both influence and respond to evolving norms. Attempts to promote privacy are often ineffective unless they account for this complexity and the dynamic interactions among thes

Lorenzo Laneve

Quantum signal processing (QSP) and quantum singular value transformation (QSVT) are powerful techniques for the development of quantum procedures. They allow to derive circuits preparing desired polynomial transformations. Recent research [Alexis et al. 2024] showed that Non-Linear Fourier Analysis (NLFA) can be employed to numerically compute a QSP protoco

Peter Halmos, Julian Gold, Xinhao Liu, Benjamin J. Raphael

Optimal transport (OT) has enjoyed great success in machine learning as a principled way to align datasets via a least-cost correspondence, driven in large part by the runtime efficiency of the Sinkhorn algorithm (Cuturi, 2013). However, Sinkhorn has quadratic space and time complexity in the number of points, limiting scalability to larger datasets. Low-ran

Lucy Yang

In this paper, we introduce a notion of derived involutive algebras in $ C_2 $-Mackey functors which simultaneously generalize commutative rings with involution and the (non-equivariant) derived algebras of Bhatt--Mathew and Raksit. We show that the $ \infty $-category of derived involutive algebras admits involutive enhancements of the cotangent complexes,

Zakaria Shams Siam, Chaowen Guan, Chong Liu

We study non-linear bandit optimization where the learner maximizes a black-box function with zeroth order function oracle, which has been successfully applied in many critical applications such as drug discovery and materials design. Existing works have showed that with the aid of quantum computing, it is possible to break the classical $\Omega(\sqrt{T})$ r

Ragini Gupta, Shinan Liu, Ruixiao Zhang, Xinyue Hu

Machine learning has shown promise in network intrusion detection systems, yet its performance often degrades due to concept drift and imbalanced data. These challenges are compounded by the labor-intensive process of labeling network traffic, especially when dealing with evolving and rare attack types, which makes preparing the right data for adaptation dif

Saori Yoshino, Honoka Shiratori, Tomoki Yamagami, Ryoichi Horisaki

We propose a model that interpolates between quantum walks and persistent (correlated) random walks using one parameter on the one-dimensional lattice. We show that the limit distribution is described by the normal variance mixture with the arcsine law.

Housen Li, Zhi Liu, Axel Munk

Modern large-scale data analysis increasingly faces the challenge of achieving computational efficiency as well as statistical accuracy, as classical statistically efficient methods often fall short in the first regard. In the context of testing monotonicity of a regression function, we propose FOMT (Fast and Optimal Monotonicity Test), a novel methodology t

Ziyao Xu, Zheng Sun, Yong-Tao Zhang

In this paper, we investigate the stability and time-step constraints for solving advection-diffusion equations using exponential time differencing (ETD) Runge-Kutta (RK) methods in time and discontinuous Galerkin (DG) methods in space. We demonstrate that the resulting fully discrete scheme is stable when the time-step size is upper bounded by a constant. M

Hayden McAlister, Anthony Robins, Lech Szymanski

We extend the existing work on Hopfield network state classification, employing more complex models that remain interpretable, such as densely-connected feed-forward deep neural networks and support vector machines. The states of the Hopfield network can be grouped into several classes, including learned (those presented during training), spurious (stable st

Camillo Brena

We study Perelman's W-entropy functional on finite-dimensional RCD spaces, a synthetic generalization of spaces with Bakry-\'{E}mery Ricci curvature bounded from below. We rigorously justify the formula for the time derivative of the W-entropy and derive its monotonicity and rigidity properties. Additionally, we establish bounds for solutions of the heat equ

Sophia Keip, Daan Camps, Roel Van Beeumen

We introduce QCLAB, an object-oriented MATLAB toolbox for constructing, representing, and simulating quantum circuits. Designed with an emphasis on numerical stability, efficiency, and performance, QCLAB provides a reliable platform for prototyping and testing quantum algorithms. For advanced performance needs, QCLAB++ serves as a complementary C++ package o

Aryan Sharma, Andrei Smolyakov, Raymond J. Spiteri

We present results of one-dimensional collisionless simulations of plasma turbulence and related anomalous electron current of the Electron Cyclotron Drift Instability (ECDI). Our highly resolved, long-term simulations of xenon plasma in the magnetic field performed with the WarpX particle-in-cell (PIC) code show several intermediate non-linear stages before

Pablo Calvo-Barlés, Sergio G. Rodrigo, Luis Martín-Moreno

In this work, we introduce the Equivariance Seeker Model (ESM), a data-driven method for discovering the underlying finite equivariant symmetry group of an arbitrary function. ESM achieves this by optimizing a loss function that balances equivariance preservation with the penalization of redundant solutions, ensuring the complete and accurate identification

Georgios Mylonopoulos, Luca Venturino, Emanuele Grossi, Stefano Buzzi

This paper investigates an integrated sensing and communication system where the base station serves multiple downlink users, while employing a passive reconfigurable intelligent surface to detect small, noncooperative airborne targets. We propose a method to design the two-way beampattern of the RIS-assisted monostatic radar, which allows controlling the si

H. Fribert, L. Fabbietti, P. Gasik, B. Ulukutlu

In this study, we investigate the influence of humidity on the performance of various non-resistive Micro Pattern Gaseous Detectors, such as GEM, Thick-GEM, and Micromegas, operated with \ArCOtwo gas mixture. The water content is introduced in a range of \SIrange[range-units=single]{0}{5000}{\ppmV}. It is observed that the presence of increased humidity does

Keisuke Okamura

Understanding the statistical laws governing citation dynamics remains a fundamental challenge in network theory and the science of science. Citation networks typically exhibit in-degree distributions well approximated by log-normal distributions yet also display power-law behaviour in the high-citation regime -- an apparent contradiction lacking a unified e

Yapeng Meng, Taoyi Wang, Yihan Lin

This technical report presents a novel DMD-based characterization method for vision sensors, particularly neuromorphic sensors such as event-based vision sensors (EVS) and Tianmouc, a complementary vision sensor. Traditional image sensor characterization standards, such as EMVA1288, are unsuitable for BVS due to their dynamic response characteristics. To add

Lennart Meincke, Ethan Mollick, Lilach Mollick, Dan Shapiro

This is the first of a series of short reports that seek to help business, education, and policy leaders understand the technical details of working with AI through rigorous testing. In this report, we demonstrate two things: - There is no single standard for measuring whether a Large Language Model (LLM) passes a benchmark, and that choosing a standard has

Jiarong Wu, Stéphane Popinet, Bertrand Chapron, J. Thomas Farrar

Wave breaking is a critical process in the upper ocean: an energy sink for the surface wave field and a source for turbulence in the ocean surface boundary layer. We apply a novel multi-layer numerical solver resolving upper-ocean dynamics over scales from O(50cm) to O(1km), including a broad-banded wave field and wave breaking. The present numerical study i

Andrea Gurioli, Federico Pennino, João Monteiro, Maurizio Gabbrielli

Deploying language models often requires navigating accuracy vs. performance trade-offs to meet latency constraints while preserving utility. Traditional model distillation reduces size but incurs substantial costs through training separate models. We introduce ModularStarEncoder (MoSE), a 1-billion-parameter multi-exit encoder for code retrieval and classif

Evan Scope Crafts, Umberto Villa

In recent years, the ascendance of diffusion modeling as a state-of-the-art generative modeling approach has spurred significant interest in their use as priors in Bayesian inverse problems. However, it is unclear how to optimally integrate a diffusion model trained on the prior distribution with a given likelihood function to obtain posterior samples. While

Jan Cichowlas, Paweł Dłotko, Marek Kuś, Jan Spaliński

The Pythagorean school attributed consonance in music to simplicity of frequency ratios between musical tones. In the last two centuries, the consonance curves developed by Helmholtz, Plompt and Levelt shifted focus to psycho-acoustic considerations in perceiving consonances. The appearance of peaks of these curves at the ratios considered by the Pythagorean

Rachid El Aitouni, Ahmed Jellal, Pablo Díaz, David Laroze

We study the tunneling effect of Dirac fermions in a graphene sheet by introducing a potential barrier in a region of width $D$ exposed to laser field. This sheet is placed on a boron nitride/ferromagnetic substrate such as cobalt or nickel. By using the Floquet theory, we determine the solutions of the energy spectrum. We calculate the transmission and refl

Raneem Alharthi, Rajwa Alharthi, Ravi Shekhar, Aiqi Jiang

Despite the growing body of research tackling offensive language in social media, this research is predominantly reactive, determining if content already posted in social media is abusive. There is a gap in predictive approaches, which we address in our study by enabling to predict the volume of abusive replies a tweet will receive after being posted. We for

Keyou Zeng

In this paper, we propose a new construction of vertex algebras using the Deligne category. This approach provides a rigorous framework for defining the so-called large $N$ vertex algebra, which has appeared in recent physics literatures. We first define the notion of a vertex algebra in a symmetric monoidal category and extend familiar constructions in ordi

Robert Chang, Pradeep Dogga, Andy Fingerhut, Victor Rios

Wide-area scaling trends require new approaches to Internet Protocol (IP) lookup, enabled by modern networking chips such as Intel Tofino, AMD Pensando, and Nvidia BlueField, which provide substantial ternary content-addressable memory (TCAM) and static random-access memory (SRAM). However, designing and evaluating scalable algorithms for these chips is chal

Mohammad Abtahi, Mahdis Rabbani, Armin Abdolmohammadi, Shima Nazari

The highly nonlinear dynamics of vehicles present a major challenge for the practical implementation of optimal and Model Predictive Control (MPC) approaches in path planning and following. Koopman operator theory offers a global linear representation of nonlinear dynamical systems, making it a promising framework for optimization-based vehicle control. This

Devina Puri, Kyle R. Allison

Escherichia coli has long been a trusty companion, maintaining health in our guts and advancing biological knowledge in the laboratory. In light of recent findings, we discuss multicellular self-organization in E. coli and develop general ideas for multicellularity, including the necessity for multicellular dynamics and interpretation by dynamic graphs, appl

Lin Xi, Xingming Wu, Weihai Chen, Jianhua Wang

Camera-based vital signs monitoring in recent years has attracted more and more researchers and the results are promising. However, a few research works focus on heart rate extraction under extremely low illumination environments. In this paper, we propose a novel framework for remote heart rate estimation under low-light conditions. This method uses singula

Chi Zhang, Peijun Sang, Yingli Qin

Dimension reduction is often necessary in functional data analysis, with functional principal component analysis being one of the most widely used techniques. A key challenge in applying these methods is determining the number of eigen-pairs to retain, a problem known as order determination. When a covariance function admits a finite representation, the chal

Alex Liu, Lief Esbenshade, Min Sun, Shawon Sarkar

As educational settings increasingly integrate artificial intelligence (AI), understanding how AI tools identify -- and adapt their responses to -- varied educational contexts becomes paramount. This study examines conversational AI's effectiveness in supporting K-12 mathematics education across various educational contexts. Through qualitative content analy

Yuxuan Duan, Jia Guo, Chenyang Yang

Transformers have been designed for channel acquisition tasks such as channel prediction and other tasks such as precoding, while graph neural networks (GNNs) have been demonstrated to be efficient for learning a multitude of communication tasks. Nonetheless, whether or not Transformers are efficient for the tasks other than channel acquisition and how to re

Can Firtina

The advent of high-throughput sequencing technologies has revolutionized genome analysis by enabling the rapid and cost-effective sequencing of large genomes. Despite these advancements, the increasing complexity and volume of genomic data present significant challenges related to accuracy, scalability, and computational efficiency. These challenges are main

Alexander P. Antonov, Marco Musacchio, Hartmut Löwen, Lorenzo Caprini

Cooling processes in nature are typically generated by external contact with a cold reservoir or bath. According to the laws of thermodynamics, the final temperature of a system is determined by the temperature of the environment. Here, we report a spontaneous internal cooling phenomenon for active particles, occurring without external contact. This effect,

Pallav Kumar Bera, Samita Rani Pani, Rajesh Kumar

This paper presents a systematic approach to detecting High Impedance Faults (HIFs) in medium voltage distribution networks using recurrence plots and machine learning. We first simulate 1150 internal faults, including 300 HIFs, 1000 external faults, and 40 normal conditions using the PSCAD/EMTDC software. Key features are extracted from the 3-phase differen

Ahmed Jellal, Rachid El Aitouni, Pablo Díaz, David Laroze

We study the transmission and group delay time for fermions in graphene under a proximity exchange field scattered by double barriers. Solving the Dirac equation over five regions, we calculate transmission and reflection coefficients using the transfer matrix method, and analyze group delay time using a Gaussian wave packet and the stationary phase method.