March 2025 arXiv papers — page 134

I. A. Ado, M. Titov, Rembert A. Duine, Arne Brataas

Magnetic effects originating from spin-orbit coupling (SOC) have been attracting major attention. However, SOC contributions to the electron magnetic moment operator are conventionally disregarded. In this work, we analyze relativistic contributions to the latter operator, including those of the SOC-type: in vacuum, for the semiconductor 8 band Kane model, a

Sergey Goncharov, Stefan Milius, Lutz Schröder, Stelios Tsampas

Reasoning about program equivalence in imperative languages is notoriously challenging, as the presence of states (in the form of variable stores) fundamentally increases the observational power of program terms. The key desideratum for any notion of equivalence is compositionality, guaranteeing that subprograms can be safely replaced by equivalent subprogra

Mohammed Alyaseen, Nikolay Atanasov, Jorge Cortes

Control barrier functions (CBFs) offer a powerful tool for enforcing safety specifications in control synthesis. This paper deals with the problem of constructing valid CBFs. Given a second-order system and any desired safety set with linear boundaries in the position space, we construct a provably control-invariant subset of this desired safety set. The con

David d'Enterria, Karen Kang

The cross sections for the single exclusive production of (pseudo)scalar and (pseudo)tensor hadrons, as well as of even-spin QED bound states formed by pairs of opposite-charge leptons or hadrons, are estimated for photon-fusion processes in ultraperipheral collisions (UPCs) of proton-proton, proton-nucleus, and nucleus-nucleus at the RHIC, LHC and FCC colli

Jameel-Un Nabi, Asim Ullah, Majid Iqbal

This work presents the microscopic calculation of energy rates ({\gamma} ray heating and (anti)neutrino cooling rates) due to weak decay of selected Fe isotopes. The isotopes have astrophysical significance during the presupernova evolution of massive stars. The energy rates are calculated using the pn QRPA model and compared with the independent particle mo

Xingfei Wei, Qiankun Mo, Chi Chen, Mark Bathe

Artificial intelligence (AI) models remain an emerging strategy to accelerate materials design and development. We demonstrate that convolutional neural network (CNN) models can characterize DNA origami nanostructures employed in programmable self-assembling, which is important in many applications such as in biomedicine. Specifically, we benchmark the perfo

Josip Josifovski, Shangding Gu, Mohammadhossein Malmir, Haoliang Huang

Domain randomization has emerged as a fundamental technique in reinforcement learning (RL) to facilitate the transfer of policies from simulation to real-world robotic applications. Many existing domain randomization approaches have been proposed to improve robustness and sim2real transfer. These approaches rely on wide randomization ranges to compensate for

Anna Aboud, Patricia Alonso Ruiz, Mary Vaughan

We present an analytic construction of nonlocal energies on the unit interval. The energies are defined using a new graph-directed construction of discrete energies on dyadic approximations of the interval. When the discrete jump kernels are comparable to the kernel of the fractional discrete Laplacian, we prove that the discrete energies Mosco converge and

Moo-Keon Jung, Sung-Chul Yoon

While about 20 Type II supernova progenitors have been identified using optical data from the Hubble Space Telescope (HST), direct detection of type Ib/Ic supernova (SN Ib/Ic) progenitors remains challenging due to their faint optical brightness and highly obscured environments. This study aims to investigate the detection limits and advantages of near-infra

Hiroki Sukeno, Tzu-Chieh Wei

Given a known or unknown phase encoded in a higher-dimensional qudit gate, it is possible to send copies of a gate that encodes the phase to multiple receivers based on a generalized quantum teleportation. We extend this quantum gate broadcast protocol to a quantum network on directed acyclic graphs in which agents can add phase gates to be distributed and p

SE Blake

Phishing is a persistent cybersecurity threat in today's digital landscape. This paper introduces Phishsense-1B, a refined version of the Llama-Guard-3-1B model, specifically tailored for phishing detection and reasoning. This adaptation utilizes Low-Rank Adaptation (LoRA) and the GuardReasoner finetuning methodology. We outline our LoRA-based fine-tuning pr

Marnik Bercx, Samuel Poncé, Yiming Zhang, Giovanni Trezza

We perform a high-throughput computational search for novel phonon-mediated superconductors, starting from the Materials Cloud 3-dimensional structure database of experimentally known inorganic stoichiometric compounds. We first compute the Allen-Dynes critical temperature (T$_c$) for 4533 non-magnetic metals using a direct and progressively finer sampling o

Y. Oubaid, V. Balédent, O. Fabelo, C. V. Colin

BaFe$_2$S$_3$ and BaFe$_2$Se$_3$ are the only two quasi-one-dimensional iron-based compounds that become superconductors under pressure. Interestingly, these two compounds exhibit different symmetries and properties. While more detailed and recent studies on BaFe$_2$Se$_3$ using single crystals have advanced the filed towards a more universal description of

Piyush Gupta, Sangjae Bae, David Isele

The adoption of Large Language Models (LLMs) is rapidly expanding across various tasks that involve inherent graphical structures. Graphs are integral to a wide range of applications, including motion planning for autonomous vehicles, social networks, scene understanding, and knowledge graphs. Many problems, even those not initially perceived as graph-based,

Muhammad Waseem, Chung-Hsuan Huang, Muhammad Muzzammil Sajjad, Laraib Haider Naqvi

Tomato maturity plays a pivotal role in optimizing harvest timing and ensuring product quality, but current methods struggle to achieve high accuracy along computational efficiency simultaneously. Existing deep learning approaches, while accurate, are often too computationally demanding for practical use in resource-constrained agricultural settings. In cont

Yaohua Liu, Peter Torres

Pioneer, a next-generation single-crystal neutron diffractometer, is under development for Oak Ridge National Laboratory's Second Target Station (STS). Designed to address a wide range of scientific questions, Pioneer will deliver homogeneous neutron beams with customizable size and divergence, and provide a polarized beam option. This article introduces its

Yaohua Liu, Peter Torres, Scott Dixon, Cameron Hart

Pioneer is a single-crystal neutron diffractometer optimized for small-volume samples and weak signals at the Second Target Station (STS) at Oak Ridge National Laboratory. This paper presents the preliminary optical design progress, focusing on the rationale behind key design choices. It covers the T$_0$ and bandwidth disk choppers, guide and beam control sy

Haoyu Zhang, Raghavendra Ramachandra, Kiran Raja, Christoph Busch

Face Recognition Systems (FRS) are increasingly vulnerable to face-morphing attacks, prompting the development of Morphing Attack Detection (MAD) algorithms. However, a key challenge in MAD lies in its limited generalizability to unseen data and its lack of explainability-critical for practical application environments such as enrolment stations and automate

Duncan Keilbach, Verena Heidrich-Meisner, Lars Berger, Robert F. Wimmer-Schweingruber

Freshly injected interstellar Pickup Ions (PUIs) are expected to exhibit a simple, torus-shaped velocity distribution function. The PUI velocity in the solar wind frame depends on the velocity of the interstellar neutral (ISN) population at the pick-up position. In this study, we compare PUI velocity distributions measured by the PLasma And SupraThermal Ion

Nitish Mehta, James D. Teoh, Taewan Noh, Ankur Agrawal

For useful quantum computation, error-corrected machines are required that can dramatically reduce the inevitable errors experienced by physical qubits. While significant progress has been made in approaching and exceeding the surface-code threshold in superconducting platforms, large gains in the logical error rate with increasing system size remain out of

Zhexian Li, Ketan Savla

This paper designs traffic signal control policies for a network of signalized intersections without knowing the demand and parameters. Within a model predictive control (MPC) framework, control policies consist of an algorithm that estimates parameters and a one-step MPC that computes control inputs using estimated parameters. The algorithm switches between

Doowon Koh, Igor E. Shparlinski

We obtain finite field analogues of a series of recent results on various mean value theorems for Weyl sums. Instead of the Vinogradov Mean Value Theorem, our results rest on the classical argument of Mordell, combined with several other ideas.

Patrik Guggenberger, Jiaqi Huang

Finding numerical approximations to minimax regret treatment rules is of key interest. To do so when potential outcomes are in {0,1} we discretize the action space of nature and apply a variant of Robinson's (1951) algorithm for iterative solutions for finite two-person zero sum games. Our approach avoids the need to evaluate regret of each treatment rule in

Anirudh Nanduri, Siyuan Huang, Rama Chellappa

Biometric recognition becomes increasingly challenging as we move away from the visible spectrum to infrared imagery, where domain discrepancies significantly impact identification performance. In this paper, we show that body embeddings perform better than face embeddings for cross-spectral person identification in medium-wave infrared (MWIR) and long-wave

Ahmad Talafha

Sparse functional data frequently arise in real-world applications, posing significant challenges for accurate classification. To address this, we propose a novel classification method that integrates functional principal component analysis (FPCA) with Bayesian aggregation. Unlike traditional ensemble methods, our approach combines predicted probabilities ac

Moses Stewart

Researchers often use instrumental variables (IV) models to investigate the causal relationship between an endogenous variable and an outcome while controlling for covariates. When an exogenous variable is unavailable to serve as the instrument for an endogenous treatment, a recurring empirical practice is to construct one from a nonlinear transformation of

David Widhalm, Cory Ohnsted, Corey Knutson, Demetrious Kutzke

We present MeCO, the Medium Cost Open-source autonomous underwater vehicle (AUV), a versatile autonomous vehicle designed to support research and development in underwater human-robot interaction (UHRI) and marine robotics in general. An inexpensive platform to build compared to similarly-capable AUVs, the MeCO design and software are released under open-sou

Angela Lopez-Cardona, Sebastian Idesis, Miguel Barreda-Ángeles, Sergi Abadal

While Large Language Models (LLMs) have significantly advanced natural language processing, aligning them with human preferences remains an open challenge. Although current alignment methods rely primarily on explicit feedback, eye-tracking (ET) data offers insights into real-time cognitive processing during reading. In this paper, we present OASST-ETC, a no

Mollie S. Jagoe Brown, Arthemy V. Kiselev

In this series of papers, we established that $Q^{\gamma_3}_{d=4}(P)$ is a coboundary in 4D (paper II arXiv:2409.12555), and we presented a series of experimental results about the (non)trivialisation of Kontsevich graph flows of Nambu--Poisson brackets on $\mathbb{R}^d$ (paper IV). This immediate sequel V. to I.--IV. is a guide to working with the package $

Michael Albada

Data mining and machine learning hold great potential to enable health systems to systematically use data and analytics to identify inefficiencies and best practices that improve care and reduce costs. Waveform data offers particularly detailed information on how patient health evolves over time and has the potential to significantly improve prediction accur

Moslem Uddin, Huadong Mo, Daoyi Dong

The aim of this study is to present an overview of current research on modelling, evaluation, and optimization methods for improving the reliability of Cyber-Physical System (CPS). Three major modelling approaches, namely analytical, simulation, and hybrid models, are discussed. Various evaluation techniques, including fault tree analysis, Markov models, and

Majid E. Abbasov, Anna A. Gorbunova

We consider the problem of finding an optimal 3D road trajectory between two points on a terrain with variable elevation. Unlike common heuristic pathfinding methods, we propose a rigorous framework based on the calculus of variations, introducing an integral cost functional that incorporates material delivery and construction expenses. The existence of a gl

Farshad Dizani, Ali Olfat

In this paper, a cooperative relay network consisting of a single-antenna source, a multi-antenna relay, and a multi-antenna destination is considered. The relay operates in decode-and-forward (DF) mode under frequency-selective fading. To combat intersymbol interference (ISI), single-carrier frequency-domain equalization (SC-FDE) with or without decision fe

Pierre Nazé, Fabricio Q. Potiguar

We investigate the thermodynamics of overdamped systems weakly driven by time-dependent protocols while interacting with viscoelastic heat baths. Using a generalized Langevin equation with memory, we derive the conditions under which the friction kernel ensures thermodynamic consistency, notably requiring the addition of a Dirac delta. Within linear response

Abeda Sultana, Nabin Pakka, Fei Xu, Xu Yuan

Scheduling deep learning (DL) models to train on powerful clusters with accelerators like GPUs and TPUs, presently falls short, either lacking fine-grained heterogeneity awareness or leaving resources substantially under-utilized. To fill this gap, we propose a novel design of a task-level heterogeneity-aware scheduler, Hadar, based on an optimization framew

Shokoufeh Mirzaei, Jesse Arzate, Yukti Vijay

Transcription of aviation communications has several applications, from assisting air traffic controllers in identifying the accuracy of read-back errors to search and rescue operations. Recent advances in artificial intelligence have provided unprecedented opportunities for improving aviation communication transcription tasks. OpenAI's Whisper is one of the

L. Clark, P. D. Nellist

Electron ptychography describes a family of algorithms which are used to enable the reconstruction of complex specimen transmission functions of a sample in order to obtain both phase and amplitude information, as applied within the realms of electron microscopy. Ptychographic methods can be very useful in the imaging of beam sensitive materials, samples wit

Mollie S. Jagoe Brown, Arthemy V. Kiselev

Kontsevich's graphs allow encoding multi-vectors whose coefficients are differential-polynomial in the coefficients of a given Poisson bracket on an affine real manifold. Encoding formulas by directed graphs adapts to the class of Nambu-determinant Poisson brackets, yet the graph topology becomes dimension-specific. To inspect whether a given Kontsevich grap

Viktorija Paneva, Verena Winterhalter, Naga Sai Surya Vamsy Malladi, Marvin Strauss

Extended reality (XR) devices have become ubiquitous. They are equipped with arrays of sensors, collecting extensive user and environmental data, allowing inferences about sensitive user information users may not realize they are sharing. Current VR privacy notices largely replicate mechanisms from 2D interfaces, failing to leverage the unique affordances of

C. Charalambous, N. Cuello, C. Petrovich

Context. Planetary migration models predict multiple planets captured into a chain of mean-motion resonances during the disk phase. Over a dozen systems have been observed in these configurations, nearly all close-in planets, with a lack of resonant chains for planets with orbital periods larger than ~300 days. Aims. Dynamical studies often overlook the fact

Chaikal Amrullah, Daniel Panangian, Ksenia Bittner

The growing demand for detailed building roof data has driven the development of automated extraction methods to overcome the inefficiencies of traditional approaches, particularly in handling complex variations in building geometries. Re:PolyWorld, which integrates point detection with graph neural networks, presents a promising solution for reconstructing

Linfeng Ye

Deep Neural Networks (DNNs) have become an integral part of our daily lives, especially in vision-related applications. However, the conventional lossy image compression algorithms are primarily designed for the Human Vision System (HVS), which can non-trivially compromise the DNNs' validation accuracy after compression, as noted in \cite{liu2018deepn}. Thus

Peter B. Weichman

Radio frequency antennas based on highly excited Rydberg atom vapors can in principle reach sensitivities beyond those of any conventional wire antenna, especially at lower frequencies where very long wires are needed to accommodate the growing wavelength. Conventional Rydberg sensors are based on individual atom response, with increased signal resolution re

Vasilis Gkatzelis, Randolph Preston McAfee, Renato Paes Leme

We study sequential procurement auctions where the sellers are provided with a ``best and final offer'' (BAFO) strategy. This strategy allows each seller $i$ to effectively ``freeze'' their price while remaining active in the auction, and it signals to the buyer, as well as all other sellers, that seller $i$ would reject any price lower than that. This is in

Yi-Hsiang Huang, Haozhi Wang, Zhuo Shen, Austin Thomas

Extraneous high frequency chip modes parasitic to superconducting quantum circuits can result in decoherence when these modes are excited. To suppress these modes, superconducting air bridges (AB) are commonly used to electrically connect ground planes together when interrupted by transmission lines. Here, we demonstrate the use of two-photon photolithograph

Benjamin David Winter, William J. Teahan

When employing an evolutionary algorithm to optimize a neural networks architecture, developers face the added challenge of tuning the evolutionary algorithm's own hyperparameters - population size, mutation rate, cloning rate, and number of generations. This paper introduces Neuvo Ecological Neural Architecture Search (ENAS), a novel method that incorporate

Xueting Luo, Hao Deng, Jihong Yang, Yao Shen

The necessity of achieving an effective balance between minimizing the losses associated with restricting human mobility and ensuring hospital capacity has gained significant attention in the aftermath of COVID-19. Reinforcement learning (RL)-based strategies for human mobility management have recently advanced in addressing the dynamic evolution of cities a

Viorel Barbu, Michael Röckner

Under suitable assumptions on $\beta:\mathbb{R}\!\to\!\mathbb{R}, \,D:\mathbb{R}^d\!\to\!\mathbb{R}^d$ and $b:\mathbb{R}^d\!\to\!\mathbb{R}$, the nonlinear Fokker-Planck equation $u_t-\Delta\beta(u)+{\rm div}(Db(u)u)=0$, in $(0,\infty)\times\mathbb{R}^d$ where $D=-\nabla\Phi$, can be identified as a smooth gradient flow $\frac{d^+}{dt}\,u(t)+\nabla E_{u(t)}=

Zhuoyan Xu, Khoi Duc Nguyen, Preeti Mukherjee, Saurabh Bagchi

Multimodal Large Language Models (MLLMs) have shown impressive capabilities in visual reasoning, yet come with substantial computational cost, limiting their deployment in resource-constrained settings. Despite recent effort on improving the efficiency of MLLMs, prior solutions fall short in responding to varying runtime conditions, in particular changing re

Dibyendu Das, Aditya Patankar, Nilanjan Chakraborty, C. R. Ramakrishnan

Given a demonstration of a complex manipulation task, such as pouring liquid from one container to another, we seek to generate a motion plan for a new task instance involving objects with different geometries. This is nontrivial since we need to simultaneously ensure that the implicit motion constraints are satisfied (glass held upright while moving), that

Michael Renger, Jeroen Verjauw, Nicola Wurz, Amin Hosseinkhani

In this work we introduce a superconducting quantum processor architecture that uses a transmission-line resonator to implement effective all-to-all connectivity between six transmon qubits. This architecture can be used as a test-bed for algorithms that benefit from high connectivity. We show that the central resonator can be used as a computational element

Benjamin Geisler, James J. Hamlin, Gregory R. Stewart, Richard G. Hennig

Motivated by the recent observation of ambient-pressure superconductivity with $T_c \sim 40$ K in La3Ni2O7 on SrLaAlO4(001) (SLAO), we explore the structural and electronic properties as well as the spin-spin correlation function of this bilayer nickelate system by using density functional theory including a Coulomb repulsion term. We find that the compressi

Hassan Zahid Butt, Xingpeng Li

Second-life batteries (SLBs) can reduce storage investment in microgrids, but their lower initial state of health and round-trip efficiency (RTE) can make degradation-naive sizing unreliable over long horizons. This paper develops DAVIR-MG, a degradation-aware validation and investment refinement framework for microgrid planning. A 20-year mixed-integer line

Mahshid Shiri, Alessandro Bruno, Daniele Loiacono

Generative Adversarial Networks (GANs) have many potential medical imaging applications. Due to the limited memory of Graphical Processing Units (GPUs), most current 3D GAN models are trained on low-resolution medical images, these models cannot scale to high-resolution or are susceptible to patchy artifacts. In this work, we propose an end-to-end novel GAN

Yizhou Huang, Yihua Cheng, Kezhi Wang

Motion prediction is crucial for autonomous driving, as it enables accurate forecasting of future vehicle trajectories based on historical inputs. This paper introduces Trajectory Mamba, a novel efficient trajectory prediction framework based on the selective state-space model (SSM). Conventional attention-based models face the challenge of computational cos

Evgeny Ferapontov, Boris Kruglikov

A rational normal scroll structure on an $(n+1)$-dimensional manifold $M$ is defined as a field of rational normal scrolls of degree $n-1$ in the projectivised cotangent bundle $\mathbb{P}T^*M$. We show that geometry of this kind naturally arises on solutions of various 4D dispersionless integrable hierarchies of heavenly type equations. In this context, rat

Matthias S. Keil

Being hit by a ball is usually not a pleasant experience. While a ball may not be fatal, other objects can be. To protect themselves, many organisms, from humans to insects, have developed neuronal mechanisms to signal approaching objects such as predators and obstacles. The study of these neuronal circuits is still ongoing, both experimentally and theoretic

John Byrne, Jacob Johnston, Carl Schildkraut, Michael Tait

The normalized distance Laplacian matrix $\mathcal{D}^{\mathcal{L}}(G)$ of a graph $G$ is a natural generalization of the normalized Laplacian matrix, arising from the matrix of pairwise distances between vertices rather than the adjacency matrix. Following the motif that this matrix behaves quite differently to the normalized Laplacian matrix, we show that

Jiuding Sun, Jing Huang, Sidharth Baskaran, Karel D'Oosterlinck

Mechanistic interpretability has made great strides in identifying neural network features (e.g., directions in hidden activation space) that mediate concepts(e.g., the birth year of a person) and enable predictable manipulation. Distributed alignment search (DAS) leverages supervision from counterfactual data to learn concept features within hidden states,

Mario Caserta, Tommaso Chiarotti, Marco Vanzini, Nicola Marzari

Electronic correlations beyond static mean-field theories are of fundamental importance in describing the properties of complex materials - such as transition-metal oxides - where the low-energy physics is driven by localized d or f electrons. Here, we show that it is possible to capture these correlations with a local and dynamical self energy, extending to

Oen McKinley, Saugat Pandey, Alvitta Ottley

Despite the importance of viewers' trust in data visualization, there is a lack of research on the viewers' own perspective on their trust. In addition, much of the research on trust remains relatively theoretical and inaccessible for designers. This work aims to address this gap by conducting a qualitative study to explore how viewers perceive different dat

Moad Abudia, Joel A. Rosenfeld, Rushikesh Kamalapurkar

This paper builds on the theoretical foundations for dynamic mode decomposition (DMD) of control-affine dynamical systems by leveraging the theory of vector-valued reproducing kernel Hilbert spaces (RKHSs). Specifically, control Liouville operators and control occupation kernels are used to separate the drift dynamics from the input dynamics. A provably conv

Poulami Dutta Roy, Parthapratim Mahapatra, Anuradha Samajdar, K. G. Arun

We show that Laser Interferometer Space Antenna can uniquely identify the sites of intermediate-mass binary black hole (IMBBH) mergers if they occur in Active Galactic Nuclei (AGN) disks with a gas density $\rho\geq10^{-12} \, {\rm g/cc}$ via measurement of dynamical friction effect in the gravitational waveform. We find that even a single observation of a g

George E. Andrews, Mohamed El Bachraoui

We investigate some weighted integer partitions whose generating functions are double-series. We will establish closed formulas for these $q$-double series and deduce that their coefficients are non-negative. This leads to inequalities among integer partitions.

Lucile Savary

We clarify the origin of what is sometimes called the "topological anomalous Hall effect," provide analytical formulas to compute all the contributions to the Hall conductivity in the presence of Kondo-coupled spins and spin orbit coupling. The derivation is technical but we emphasize that the results can be very easily applied.

Nicole M. Lloyd-Ronning, Patrick Kilian, Guangye Chen, Chengkun Huang

We present particle-in-cell simulations of one dimensional relativistic electromagnetic shocks in a uniform magnetic field, for a range of magnetic field strengths, plasma temperatures and numerical initial conditions. We show that the particle energy distributions of these shocks can develop a state of population inversion in the precursor and shock regions

Parmita Mondal, Mohammad Mahdi Shiraz Bhurwani, Swetadri Vasan Setlur Nagesh, Pui Man Rosalind Lai

Bias from contrast injection variability is a significant obstacle to accurate intracranial aneurysm occlusion prediction using quantitative angiography and deep neural networks . This study explores bias removal and explainable AI for outcome prediction. This study used angiograms from 458 patients with flow diverters treated IAs with six month follow up de

Nathaniel Lesperance, Sujeevan Ratnasingham, Graham W. Taylor

In the context of pressing climate change challenges and the significant biodiversity loss among arthropods, automated taxonomic classification from organismal images is a subject of intense research. However, traditional AI pipelines based on deep neural visual architectures such as CNNs or ViTs face limitations such as degraded performance on the long-tail

Hanyang Zhao, Haoxian Chen, Yucheng Guo, Genta Indra Winata

Traditional preference tuning methods for LLMs/Visual Generative Models often rely solely on reward model labeling, which can be opaque, offer limited insights into the rationale behind preferences, and are prone to issues such as reward hacking or overfitting. We introduce Rich Preference Optimization (RPO), a novel pipeline that leverages rich feedback sig

Kavin M. Govindarajan, Devansh R Agrawal, Dimitra Panagou, Chris Vermillion

In this paper, we present the methodology and results for a real-time velocity trajectory optimization for a solar-powered autonomous surface vessel (ASV), where we combine indirect optimal control techniques with iterative learning. The ASV exhibits cyclic operation due to the nature of the solar profile, but weather patterns create inevitable disturbances

Paul Quinlan, Qingguo Li, Xiaodan Zhu

Large language models are being rapidly deployed across many fields such as healthcare, finance, transportation, and energy, where time-series data are fundamental components. The current works are still limited in their ability to perform reasoning that involves both time-series and the corresponding textual content. We address this gap by introducing Chat-

Eric A. McPherson, Kenneth Kroenlein, Ilona Kretzschmar

Magnetic Janus particles allow access to complex, nonlinear assembled structures that may enable interesting new magnetorheological (MR) fluids with uniquely engineered field responses. However, the overwhelming size of the parameter space for Janus and patchy particles makes exploration of such systems by experimental trial and error or through detailed sim

Jiaxin Zhang, Zhuohang Li, Wendi Cui, Kamalika Das

Large language models (LLMs) have demonstrated remarkable performance, yet their diverse strengths and weaknesses prevent any single LLM from achieving dominance across all tasks. Ensembling multiple LLMs is a promising approach to generate reliable responses but conventional ensembling frameworks suffer from high computational overheads. This work introduce

Brandon Klein, Alejandro J. Soto Franco, Md Mainul Hasan Sabbir, Matthew J. Deutsch

Active nematics in two dimensions stir themselves efficiently through internally generated chaotic flows, largely driven by motile $+1/2$ disclinations. We investigate how this tendency toward chaotic fluid stirring can, counterintuitively, produce certain ordered, periodic flows in confinement, characterized by stable periodic orbits of $+1/2$ disclinations

Benjamin David Winter, William John Teahan

Activation functions play a critical role in the performance and behaviour of neural networks, significantly impacting their ability to learn and generalise. Traditional activation functions, such as ReLU, sigmoid, and tanh, have been widely used with considerable success. However, these functions may not always provide optimal performance for all tasks and

A. A. Popov, K. S. Osipenko, V. I. Scherbakov, A. I. Frank

The work is devoted to the development of a conceptual design for a gradient spin flipper - neutron decelerator, which is the main component of a designed UCN source for a pulsed reactor. In close cooperation between the JINR group and SuperOx, a preliminary design of a stationary gradient magnet for the adiabatic spin flipper has been developed. A thorough

Amiao Gao, Zenong Zhang, Simin Wang, Liguo Huang

Understanding software vulnerabilities and their resolutions is crucial for securing modern software systems. This study presents a novel traceability model that links a pair of sentences describing at least one of the three types of semantics (triggers, crash phenomenon and fix action) for a vulnerability in natural language (NL) vulnerability artifacts, to

Duc S. H. Nguyen, Bach G. Truong, Phuong T. Nguyen, Juri Di Rocco

The proliferation of Large Language Models (LLMs) in recent years has realized many applications in various domains. Being trained with a huge of amount of data coming from various sources, LLMs can be deployed to solve different tasks, including those in Software Engineering (SE). Though they have been widely adopted, the potential of using LLMs cooperative

Hai-Vy Nguyen, Fabrice Gamboa, Sixin Zhang, Reda Chhaibi

In this paper, we introduce a novel spatial attention module that can be easily integrated to any convolutional network. This module guides the model to pay attention to the most discriminative part of an image. This enables the model to attain a better performance by an end-to-end training. In conventional approaches, a spatial attention map is typically ge

David Christian Ohnmacht, Valentin Wilhelm, Wolfgang Belzig

Multiterminal Josephson junctions are a promising platform to study non-trivial topology in engineered quantum systems. Yet, experimentally meaningful insight into what exactly makes these systems topologically non-trivial remains elusive. In this work, we show that zero energy reflectionless scattering modes (RSMs) of the normal scattering matrix result in

Pedro Pessoa, Paul Campitelli, Douglas P. Shepherd, S. Banu Ozkan

State space models, such as Mamba, have recently garnered attention in time series forecasting due to their ability to capture sequence patterns. However, in electricity consumption benchmarks, Mamba forecasts exhibit a mean error of approximately 8\%. Similarly, in traffic occupancy benchmarks, the mean error reaches 18\%. This discrepancy leaves us to wond

Xiangyu Yin, Yi Qi, Jinwei Hu, Zhen Chen

Vision Language Models (VLMs) have demonstrated impressive inference capabilities, but remain vulnerable to jailbreak attacks that can induce harmful or unethical responses. Existing defence methods are predominantly white-box approaches that require access to model parameters and extensive modifications, making them costly and impractical for many real-worl

Suwei Liu, Zi Hao Foo, John H. Lienhard, Sinan Keten

Polyamide membranes, such as nanofiltration (NF) and reverse osmosis (RO) membranes, are widely used for water desalination and purification. However, the mechanisms of solute transport and solute rejection due to charge interactions remain unclear at the molecular level. Here we use molecular dynamics (MD) simulations to examine the transport of single-solu

A. Krut, C. R. Argüelles, P. -H. Chavanis

We present a framework for dark matter (DM) halo formation based on a kinetic theory of self-gravitating fermions together with a solid connection to thermodynamics. Based on maximum entropy arguments, this approach predicts a most likely phase-space distribution which takes into account the Pauli exclusion principle, relativistic effects, and particle evapo

Benjamin David Winter, William John Teahan

The choice of neural network features can have a large impact on both the accuracy and speed of the network. Despite the current industry shift towards large transformer models, specialized binary classifiers remain critical for numerous practical applications where computational efficiency and low latency are essential. Neural network features tend to be de

Niyousha Najmaei, Niels van der Weide, Benedikt Ahrens, Paige Randall North

Recent models of intensional type theory have been constructed in algebraic weak factorization systems (AWFSs). AWFSs give rise to comprehension categories that feature non-trivial morphisms between types; these morphisms are not used in the standard interpretation of Martin-L\"of type theory in comprehension categories. We develop a type theory that interna

Ognjen Milatovic

We show that if we start from a symmetric lower semi-bounded Schr\"odinger operator $\mathcal{H}$ on finitely supported functions on a discrete weighted graph (satisfying certain conditions), apply the Friedrichs construction to get a self-adjoint extension $H$, and then perturb $H$ by a non-negative function $W$, then the resulting form-sum $H\widetilde{+}W

Wali Ullah Khan, Chandan Kumar Sheemar, Eva Lagunas, Symeon Chatzinotas

Beyond diagonal reconfigurable intelligent surfaces (BD-RIS) have emerged as a transformative technology for enhancing wireless communication by intelligently manipulating the propagation environment. Its interconnected elements offer enhanced control over signal redirection, making it a promising solution for integrated terrestrial and non-terrestrial netwo

Zhijie Feng, Emmy Blumenthal, Pankaj Mehta, Akshit Goyal

Predicting the outcomes of species invasions is a central goal of ecology, a task made especially challenging due to ecological feedbacks. To address this, we develop a general theory of ecological invasions applicable to a wide variety of ecological models: including Lotka-Volterra models, consumer resource models, and models with cross feeding. Importantly

Marziyeh Karmand, Mohsen Amini, Morteza Soltani, Ebrahim Ghanbari-Adivi

We investigate the propagation of electron waves in a two-dimensional tilted Dirac cone heterostructure where tilt depends on the coordinate $z$ along the junction. The resulting Dirac equation in an emergent curved spacetime for the spinor $\psi(z)$ can be efficiently solved using 4th-order Runge-Kutta numerical method by a transformation to a "suitable" sp

Jia Xu, Tianyi Wei, Bojian Hou, Patryk Orzechowski

We introduce MentalChat16K, an English benchmark dataset combining a synthetic mental health counseling dataset and a dataset of anonymized transcripts from interventions between Behavioral Health Coaches and Caregivers of patients in palliative or hospice care. Covering a diverse range of conditions like depression, anxiety, and grief, this curated dataset

Benedikt Ahrens, Ambroise Lafont, Thomas Lamiaux

Initial semantics aims to model inductive structures and their properties, and to provide them with recursion principles respecting these properties. An ubiquitous example is the fold operator for lists. We are concerned with initial semantics that model languages with variable binding and their substitution structure, and that provide substitution-safe recu

Paul Balmer, Beren Sanders

We explain how the gluing of a closed piece of the tensor-triangular spectrum with its open complement hinges on the support of the Tate ring.

Elizabeth A. Dinkelman, Walter D. Morris

The polytope $ASM_n$, the convex hull of the $n\times n$ alternating sign matrices, was introduced by Striker and by Behrend and Knight. A face of $ASM_n$ corresponds to an elementary flow grid defined by Striker, and each elementary flow grid determines a doubly directed graph defined by Brualdi and Dahl. We show that a face of $ASM_n$ is symmetric if and o

Sihao Liu, Jake Ke, Tony Nowatzki, Jason Cong

With the advent of modern multi-chiplet FPGA architectures, vendors have begun integrating hardened NoC to address the scalability, resource usage, and frequency disadvantages of soft NoCs. However, as this work shows, effectively harnessing these hardened NoC is not trivial. It requires detailed knowledge of the microarchitecture and how it relates to the p

Avinash Paliwal, Xilong Zhou, Wei Ye, Jinhui Xiong

In this paper, we propose RI3D, a novel 3DGS-based approach that harnesses the power of diffusion models to reconstruct high-quality novel views given a sparse set of input images. Our key contribution is separating the view synthesis process into two tasks of reconstructing visible regions and hallucinating missing regions, and introducing two personalized

Jiarui Sun, Chin-Chia Michael Yeh, Yujie Fan, Xin Dai

Time series forecasting at scale presents significant challenges for modern prediction systems, particularly when dealing with large sets of synchronized series, such as in a global payment network. In such systems, three key challenges must be overcome for accurate and scalable predictions: 1) emergence of new entities, 2) disappearance of existing entities

Kai Zhang, Jianwei Yang, Jeevana Priya Inala, Chandan Singh

Despite the promising results of large multimodal models (LMMs) in complex vision-language tasks that require knowledge, reasoning, and perception abilities together, we surprisingly found that these models struggle with simple tasks on infographics that require perception only. As existing benchmarks primarily focus on end tasks that require various abiliti

Rachel B. Fernandes, Galen J. Bergsten, Gijs D. Mulders, Ilaria Pascucci

Comparative studies of young and old exoplanet populations offer a glimpse into how planets may form and evolve with time. We present an occurrence rate study of short-period ($<$12 days) planets between 1.8--10 Rearth around 1374 FGK stars in nearby (200 pc) young clusters ($<$1 Gyr), utilizing data from the Transiting Exoplanet Survey Satellite (TESS) miss

Russel Arbore, Aaron Councilman, Xavier Routh, Ryan Ziegler

Modern computing systems increasingly rely on composing heterogeneous devices to improve performance and efficiency. Programming these systems is often unproductive: algorithm implementations must be coupled to system-specific logic, including device-specific optimizations, partitioning, and inter-device communication and synchronization, which requires deve