March 2025 arXiv papers — page 71

Zhen Ma, He Xu, Jielong Dou, Yi Qin

Magnetic adhesion tracked wall-climbing robots face potential risks of overturning during high-altitude operations, making their stability crucial for ensuring safety. This study presents a dynamic feature selection method based on Proximal Policy Optimization (PPO) reinforcement learning, combined with typical machine learning models, aimed at improving the

Woo Seung Jung, Yudai Ichikawa, Byung Min Kang, Jung Keun Ahn

We present a measurement of the production of $\Xi^-$ and $\Lambda\Lambda$ in the $^{12}$C$(K^-, K^+)$ reaction at an incident beam momentum of 1.8 GeV/$\mathit{c}$, based on high-statistics data from J-PARC E42. The cross section for the $^{12}$C$(K^-, K^+\Xi^-)$ reaction, compared to the inclusive $^{12}$C$(K^-, K^+)$ reaction cross section, indicates that

Nicholas H. Tenev

This paper studies a model of technology adoption: a manager tries to induce a group of workers to exert costly effort to vet a new technology before they choose whether to use it. The manager finds it too costly to simultaneously replace large groups of unproductive workers, so they shirk when coordination is possible. Widely applicable technology expands p

Partha Chowdhury, Belur Ravindra, Sanjiv Kumar Tiwari

The dynamics of magnetic fields in the Sun's active regions plays a key role in triggering solar eruptions. Studies have shown that changes in the photosphere's magnetic field can destabilize large-scale structure of the corona, leading to explosive events such as flares and coronal mass ejections (CMEs). This paper delves into the magnetic field evolution a

Jhixon Macías

Let $\mathbb{N}$ be the set of natural numbers. The Mac\'ias space $M(\mathbb{N})$ is the topological space $(\mathbb{N},\tau_M)$ where $\tau_M$ is generated by the collection of sets $\sigma_n := \{ m \in \mathbb{N} : \gcd(n, m) = 1 \}$. In this paper, we characterize the continuity of polynomials over $ M(\mathbb{N})$ and prove that the only continuous pol

Prosper Dellah Allo, A. Aadhi, Amirhossein Mosaddegh Yengejeh, Hazel Bakajsa

The fabrication of silicon photonic components in commercial CMOS-compatible foundries has revolutionized the impact of silicon photonics on advancing communication, quantum computing and artificial intelligence, due to their benefits of mass production, high throughput, low cost, and high performance. The indirect bandgap of silicon introduces a fundamental

Shriyank Somvanshi, Md Monzurul Islam, Mahmuda Sultana Mimi, Sazzad Bin Bashar Polock

Structured State Space Models (SSMs) have become a prominent class of sequence models, developed against two long-standing difficulties: the sequential computation and gradient propagation limits of Recurrent Neural Networks (RNNs), and the quadratic time and memory cost of self-attention in Transformers. By combining structured recurrence with state-space r

Ning Jiang, Zhen Pan

Recent observations have confirmed the direct association between tidal disruption events (TDEs) and quasiperiodic eruptions (QPEs). In addition, TDE hosts and QPE hosts are statistically found to be similar in their morphological properties and in the strong overrepresentation of poststarburst galaxies. Particularly, both of them show an intriguing preferen

Amanda L. Lund, Julien Esseiva, Seth R. Johnson, Elliott Biondo

Celeritas is a GPU-optimized MC particle transport code designed to meet the growing computational demands of next-generation HEP experiments. It provides efficient simulation of EM physics processes in complex geometries with magnetic fields, detector hit scoring, and seamless integration into Geant4-driven applications to offload EM physics to GPUs. Recent

Hadassah Griffin, Andrei Kryjevski

Boltzmann transport equation (BE) is a potent approach to dynamics of a photoexcited (nano)material. BE collision integrals for different relaxation channels can be systematically computed using the Kadanoff-Baym-Keldysh (KBK) formalism (also called NEGF) utilizing the Density Functional Theory (DFT) simulation output. However, accurate description of phonon

Serina Chang, Ashton Anderson, Jake M. Hofman

With the rapid adoption of LLM-based chatbots, there is a pressing need to evaluate what humans and LLMs can achieve together. However, standard benchmarks, such as MMLU, measure LLM capabilities in isolation (i.e., "AI-alone"). Here, we design and conduct a user study to convert MMLU questions into user-AI conversations, by seeding the user with the questio

Zeynab Aghabazaz, Michael J Daniels, Hongyan Ning, Donald M. Lloyd-Jones

We introduce a statistical framework for combining data from multiple large longitudinal cardiovascular cohorts to enable the study of long-term cardiovascular health starting in early adulthood. Using data from seven cohorts belonging to the Lifetime Risk Pooling Project (LRPP), we present a Bayesian hierarchical multivariate approach that jointly models mu

Akhil Joshi, Sai Teja Erukude, Lior Shamir

With the availability of virtually infinite number text documents in digital format, automatic comparison of textual data is essential for extracting meaningful insights that are difficult to identify manually. Many existing tools, including AI and large language models, struggle to provide precise and explainable insights into textual similarities. In many

Vignesh Prabhakar, Md Amirul Islam, Adam Atanas, Yao-Ting Wang

Large Language Models (LLMs) have demonstrated remarkable potential in advancing scientific knowledge and addressing complex challenges. In this work, we introduce OmniScience, a specialized large reasoning model for general science, developed through three key components: (1) domain adaptive pretraining on a carefully curated corpus of scientific literature

Arun Iyengar, Ashish Kundu, Ramana Kompella, Sai Nandan Mamidi

Caching has the potential to be of significant benefit for accessing large language models (LLMs) due to their high latencies which typically range from a small number of seconds to well over a minute. Furthermore, many LLMs charge money for queries; caching thus has a clear monetary benefit. This paper presents a new caching system for improving user experi

Injae Shin, Blaise Tine

Modern day applications have grown in size and require more computational power. The rise of machine learning and AI increased the need for parallel computation, which has increased the need for GPGPUs. With the increasing demand for computational power, GPGPUs' SIMT architecture has solved this with an increase in the number of threads and the number of cor

Ryuichi Shindou, Pengwei Zhao

U(1) non-linear sigma model (NLSM) with a one-dimensional (1D) Berry phase is studied by a renormalization group theory. Order-disorder transition in U(1) NLSMs in $D \!\ (\ge 2)$-dimensional space ($d+1$-dimensional spacetime; $d\ge 1$) is instigated by the proliferation of vortex excitations, where the 1D Berry phase term confers finite phase factors upon

Diluka Galappaththige, Chintha Tellambura

As data traffic grows, wireless systems shift to higher frequency bands (6 GHz and above), where radar systems also operate. This coexistence demands effective interference management and efficient wideband utilization. Cognitive Radio (CR) offers a solution but remains limited to single-node or narrowband systems. This paper introduces a generalized wideban

Natenael B. Semmineh, Indranil Guha, Deborah Healey, Anagha Chandrasekharan

Vascular remodelling is inherent to the pathogenesis of many diseases including cancer, neurodegeneration, fibrosis, hypertension, and diabetes. In this paper, a new susceptibility-contrast based MRI approach is established to analyse intravoxel vessel size distribution (VSD) enabling more comprehensive and quantitative assessment of vascular remodelling tha

Takashi Izumo

In everyday life, we frequently make coarse-grained judgments. When we say that Olivia and Noah excel in mathematics, we disregard the specific differences in their mathematical abilities. Similarly, when we claim that a particular automobile manufacturer produces high-quality cars, we overlook the minor variations among individual vehicles. These coarse-gra

Yunhan Wang, Yang Wu, Xiangyu Ye, Chang-Kui Duan

Topological phases and transitions are of fundamental importance in physics, which provide a deep insight into the understanding of materials. Recently, non-Abelian topological transitions have been investigated in Hermitian systems, revealing important topological features. With non-Hermiticity introduced, non-Hermitian non-Abelian topological transitions b

Tonghuan Jiang, Nikolay A. Bogdanov, Ali Alavi, Ji Chen

It is now widely accepted that the antiferromagnetic coupling within high temperature superconductors strongly exhibits a profound correlation with the upper limit of superconducting transition temperature these materials can reach. Thus, accurately calculating the positive and negative mechanisms that influence magnetic coupling in specific materials is cru

Said Hamoun

Through this paper, we show that $\text{TC}_r(Z)\leq r\cdot \text{cat}(Z)+\chi_{\pi}(Z)$, for any simply-connected elliptic space $Z$ admitting a pure minimal Sullivan model with a differential of constant length. Here $\chi_{\pi}(Z)$ denotes the homotopy characteristic and $r$ is an integer greater or equals than $2$. We also give a lower bound for $\text{T

Jackson Loper, Jeffrey Regier

We construct a new tail bound for the sum of independent random variables for situations in which the expected value of the sum is known and each random variable lies within a specified interval, which may be different for each variable. This new bound can be computed by solving a two-dimensional convex optimization problem. Simulations demonstrate that the

Mehdi Noroozi, Alberto Gil Ramos, Luca Morreale, Ruchika Chavhan

Current sampling mechanisms for conditional diffusion models rely mainly on Classifier Free Guidance (CFG) to generate high-quality images. However, CFG requires several denoising passes in each time step, e.g., up to three passes in image editing tasks, resulting in excessive computational costs. This paper introduces a novel conditioning technique to ease

Alhasan Abdellatif, Hannah P. Menke, Julien Maes, Ahmed H. Elsheikh

Accurately capturing the complex interaction between CO2 and water in porous media at the pore scale is essential for various geoscience applications, including carbon capture and storage (CCS). We introduce a comprehensive dataset generated from high-fidelity numerical simulations to capture the intricate interaction between CO2 and water at the pore scale.

Pedro Linck, Nadja K. Bernardes

Open Quantum Walks (OQW) are a type of quantum walk governed by the system's interaction with its environment. We explore the time evolution and the limit behavior of the OQW framework for Quantum Computation and show how we can represent random unitary quantum channels, such as the dephasing and depolarizing channels, in this model. We also develop a si

Marvin Randig, Max von Renesse

We present a block gradient ascent method for solving the quantum optimal transport problem with entropic regularisation similar to the algorithm proposed in [D. Feliciangeli, A. Gerolin, L. Portinale: J. Funct. Anal. 285 (2023), no. 4, 109963] and [E. Caputo, A. Gerolin, N. Monina, L. Portinale: arXiv:2409.03698]. We prove a linear convergence rate based on

Mayur Sawant, Abdelhamid Tayebi

This paper extends first-order motion planners to robots governed by second-order dynamics. Two control schemes are proposed based on the knowledge of a scalar function whose negative gradient aligns with a given first-order motion planner. When such a function is known, the first-order motion planner is combined with a damping velocity vector with a dynamic

Sai Ritvik Tanksalkar, Siddharth Muralee, Srihari Danduri, Paschal Amusuo

Dynamic analysis, through rehosting, is an important capability for security assessment in embedded systems software. Existing rehosting techniques aim to provide high-fidelity execution by accurately emulating hardware and peripheral interactions. However, these techniques face challenges in adoption due to the increasing number of available peripherals and

Jaeyeon Lee, Guantong Qi, Matthew Brady Neeley, Zhandong Liu

Recent advancements in large language models (LLMs) integrating explicit reasoning, such as OpenAI's o3-mini, DeepSeek-R1, and QWQ-32B, enable smaller models to solve complex tasks by generating intermediate reasoning steps prior to providing answers. However, this approach significantly increases computational costs, both monetarily and environmentally. The

Tatsuya Akimoto, Masato Takei, Keisuke Taniguchi

We study the long time behavior of the elephant random walk with stops, introduced by Kumar, Harbola and Lindenberg (2010), and establish the phase transition of the number of visited points up to time $n$, and the correlation between the position at time $n$ and the number of moves up to time $n$.

Saiyang Zhang, Boyuan Liu, Volker Bromm, Junehyoung Jeon

We investigate the impact of massive primordial black holes (PBHs; $m_{\rm BH}\sim 10^6~M_{\odot}$) on the star formation and first galaxy assembly process using high-resolution hydrodynamical simulations from $z = 1100$ to $z \sim 9$. We find that PBH accretion is self-regulated by feedback, suppressing mass growth unless feedback is weak. PBHs accelerate s

Shunsuke Ichii, Tetsuhiro S. Hatakeyama, Kunihiko Kaneko

Enhanced enzyme diffusion (EED), in which the diffusion coefficient of an enzyme transiently increases during catalysis, has been extensively reported experimentally. We numerically and analytically demonstrate that such enzymes can act as Maxwell's demons. They use their enhanced diffusion as a memory of the previous catalytic reaction, to gain information

Yu-Huai Li, Ting Zeng, Min-Yan Wang, Cong Jiang

Twin-field quantum key distribution (TF-QKD) elevates the secure key rate from a linear to a square-root dependence on channel loss while preserving measurement-device-independent security. This protocol is uniquely positioned to enable global-scale quantum networks, even under extreme channel loss. While fiber-based TF-QKD implementations have advanced rapi

Per Åhag, Rafał Czyż, Håkan Samuelsson Kalm, Aron Persson

We argue for more widespread use of manifold-like polyfolds (M-polyfolds) as differential geometric objects. M-polyfolds possess a distinct advantage over differentiable manifolds, enabling a smooth and local change of dimension. To establish their utility, we introduce tensors and prove the existence of Riemannian metrics, symplectic structures, and almost

Norbert Agbeko

The key characteristic of a true free market economy is that exchanges are entirely voluntary. When there is a monopoly in the creation of currency as we have in today's markets, you no longer have a true free market. Features of the current economic system such as central banking and taxation would be nonexistent in a free market. This paper examines ho

Carlos Granados, Leon A. Valencia

The Allee effect describes a decline in population fitness at low densities, potentially leading to extinction. In predator-prey systems, an emergent Allee effect can arise due to interactions such as density-dependent maturation rates and predation constraints. This work studies a stochastic predator-prey model where the prey population is structured into j

K. E. Gray

Analyses of experimental data in the literature show thresholds that directly imply a coexistent superconductive (SC) and pseudogap (PG) phase that, to our knowledge, has not been previously identified. The data used emphasize the essences of d-wave cuprate SC, i.e., the superfluid density and the momentum dependence. For severe underdoping, these data imply

Sarah Brauner, Patricia Commins, Darij Grinberg, Franco Saliola

We generalize Reiner--Saliola--Welker's well-known but mysterious family of *$k$-random-to-random shuffles* from Markov chains on symmetric groups to Markov chains on the Type-$A$ Iwahori--Hecke algebras. We prove that the family of operators pairwise commutes and has eigenvalues that are polynomials in $q$ with non-negative integer coefficients. Our work ge

Suet-Ying Lam, Qingcheng Zeng, Jingyi Wu, Rob Voigt

Whether large language models (LLMs) process language similarly to humans has been the subject of much theoretical and practical debate. We examine this question through the lens of the production-interpretation distinction found in human sentence processing and evaluate the extent to which instruction-tuned LLMs replicate this distinction. Using an empirica

Sharon Lin, Krishnamurthy, Dvijotham, Jamie Hayes

Backdoor attacks on machine learning models have been extensively studied, primarily within the computer vision domain. Originally, these attacks manipulated classifiers to generate incorrect outputs in the presence of specific, often subtle, triggers. This paper re-examines the concept of backdoor attacks in the context of Large Language Models (LLMs), focu

Zeynab Aghabazaz, Michael J Daniels, Donald M Lloyd-Jones, Juned Siddique

We introduce a novel Bayesian approach for jointly modeling longitudinal cardiovascular disease (CVD) risk factor trajectories, medication use, and time-to-events. Our methodology incorporates longitudinal risk factor trajectories into the time-to-event model, considers the temporal aspect of medication use, incorporates uncertainty due to missing medication

Simona Kocour, Assia Benbihi, Aikaterini Adam, Torsten Sattler

Searching in and editing 3D scenes has become extremely intuitive with trainable scene representations that allow linking human concepts to elements in the scene. These operations are often evaluated on the basis of how accurately the searched element is segmented or extracted from the scene. In this paper, we address the inverse problem, that is, how much o

Yuqing Wang, Xiao Yang

With the rapid expansion of cloud computing applications, optimizing resource allocation has become crucial for improving system performance and cost efficiency. This paper proposes an intelligent resource allocation algorithm that leverages deep learning (LSTM) for demand prediction and reinforcement learning (DQN) for dynamic scheduling. By accurately fore

Victor Ulisses Pugliese, Oséias F. de A. Ferreira, Fabio A. Faria

This paper proposes a novel approach based on deep reinforcement learning (DRL) for the 2D+1 packing problem with spatial constraints. This problem is an extension of the traditional 2D packing problem, incorporating an additional constraint on the height dimension. Therefore, a simulator using the OpenAI Gym framework has been developed to efficiently simul

Niklas Mohrin

In inductive inference, we investigate the learnability of classes of formal languages. We are interested in what classes of languages are learnable in certain learning settings. A class of languages is learnable, if there is a learner that can identify all of its languages and satisfies the constraints of the learning setting. To identify a language, a lear

Alexander Berkovich, Aritram Dhar

In this paper, we count the total number of hooks of length two in all odd partitions of $n$ and all distinct partitions of $n$ with a bound on the largest part of the partitions. We generalize inequalities of Ballantine, Burson, Craig, Folsom and Wen by showing there is a bias in the number of hooks of length two in all odd partitions over all distinct part

Anmol Aggarwal, Ashi Mittal, George M. Seabroke, Nitin K. Puri

We report two anti-reflection (AR) coatings that give better quantum efficiency (QE) than the existing AR coating on the Gaia astrometric field (AF) CCDs. Light being the core of optical astronomy is extremely important for such missions, therefore, the QE of the devices that are used to capture it should be substantially high. To reduce the losses due to th

Khadija Zanna, Akane Sano

Causal discovery (CD) plays a pivotal role in numerous scientific fields by clarifying the causal relationships that underlie phenomena observed in diverse disciplines. Despite significant advancements in CD algorithms that enhance bias and fairness analyses in machine learning, their application faces challenges due to the high computational demands and com

Markel Pardo-Almanza, Yuita Fujisawa, Takatsugu Onishi, Chia Hsiu Hsu

The heterointerface between topological insulators and magnetic materials provides a crucial platform for investigating exotic electronic and magnetic phases, with implications for both fundamental studies and potential applications. A key challenge is determining the spatial extent of magnetic perturbation across the interface. In this study, we grew Bi2Te3

Nguyen Lam, Guozhen Lu, Andrey Russanov

We use the Bakry-\'{E}mery curvature-dimension criterion and $\Gamma$-calculus to establish the Poincar\'{e} inequality with monomial Gaussian measure, and then apply the duality approach to study its improvements and its gradient stability. We also set up the scale-dependent Poincar\'{e} inequality with monomial Gaussian type measure and use it to inspect t

Aseem Subedi, Rahul, Lora Cavuoto, Steven Schwaitzberg

The real-time assessment of complex motor skills presents a challenge in fields such as surgical training and rehabilitation. Recent advancements in neuroimaging, particularly functional near-infrared spectroscopy (fNIRS), have enabled objective assessment of such skills with high accuracy. However, these techniques are hindered by extensive preprocessing re

Akshay Raman, Chad Merrill, Abraham George, Amir Barati Farimani

Additive manufacturing (AM) has transformed the production landscape by enabling the precision creation of complex geometries. However, AM faces limitations when applied to challenging environments, such as elevated surfaces and remote locations. Aerial additive manufacturing, facilitated by drones, presents a solution to these challenges. However, despite a

Simin Keykhosravi, Ebrahim Bedeer

Faster-than-Nyquist (FTN) signaling and superimposed pilot (SP) techniques are effective solutions for significantly enhancing the spectral efficiency (SE) in next-generation wireless communication systems. This paper proposes an innovative SP-aided channel estimation method for FTN signaling enhancing the SE over doubly-selective (i.e., time- and frequency-

Vishwesh Ramanathan, Tony Xu, Pushpak Pati, Faruk Ahmed

Prediction tasks in digital pathology are challenging due to the massive size of whole-slide images (WSIs) and the weak nature of training signals. Advances in computing, data availability, and self-supervised learning (SSL) have paved the way for slide-level foundation models (SLFMs) that can improve prediction tasks in low-data regimes. However, current me

Antoine Durant, Asma Mabrouk, Rafik Zayani

This paper addresses the critical challenge of mitigating hardware impairments (HWIs) in downlink cell-free massive MIMO (CF-mMIMO) networks while ensuring computational scalability. We propose a novel sequential hardware-aware (HW-aware) precoding technique that leverages the serial fronthaul topology to perform over-the-air HWI cancellation. This approach

Adrien Prévost, Vincent Léchappé, Romain Delpoux, Xavier Brun

This article proposes a mechanical sensorless control strategy for the synchronous rectification of small wind turbines equipped with a surface-mounted Permanent Magnet Synchronous Generator (PMSG). By means of Lyapunov theory, the Global Asymptotic Stability (GAS) of the closed loop system is proven. It allows the use of a classical Sliding Mode Observer (S

Nuwan Weeraratne, Lyn Hunt, Jason Kurz

Principal Component Analysis is a key technique for reducing the complexity of high-dimensional data while preserving its fundamental data structure, ensuring models remain stable and interpretable. This is achieved by transforming the original variables into a new set of uncorrelated variables (principal components) based on the covariance structure of the

Chenyu Liu, Yang Xu, Peng Bao, Yangyi Lu

The interaction between excited states of a closed-shell chromophore and a nearby free radical species gives rise to spin-coupled doublet states, namely singdoublet and tripdoublet, as well as a quartet state. This coupling facilitates transitions that are otherwise spin-forbidden, thereby enhancing intersystem crossing and influencing luminescence and non-r

Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti

Recent efforts in neural compression have focused on the rate-distortion-perception (RDP) tradeoff, where the perception constraint ensures the source and reconstruction distributions are close in terms of a statistical divergence. Theoretical work on RDP describes properties of RDP-optimal compressors without providing constructive and low complexity soluti

Jason Hamilton, Luis Chacon, Giannis Keramidas, Xianzhu Tang

The conventional approach for thermal quench mitigation in a tokamak disruption is through a high-Z impurity injection that radiates away the plasma's thermal energy before it reaches the wall. The downside is a robust Ohmic-to-runaway current conversion due to the radiatively clamped low post-thermal-quench electron temperature. An alternative approach is t

Zachary Hamaker, Brendon Rhoades

Beginning with work of Zeilberger on classical pattern counts, there are a variety of structural results for moments of permutation statistics applied to random permutations. Using tools from representation theory, Gaetz and Ryba generalized Zeilberger's results to uniformly random permutations of a given cycle type. We introduce regular statistics and chara

Merlin Füllgraf, Jiaozi Wang, Robin Steinigeweg, Jochen Gemmer

We suggest a method to compute approximations to temporal correlation functions of few-body observables in chaotic many-body systems in the thermodynamic limit based on the respective Lanczos coefficients. Given the knowledge of these Lanczos coefficients, the method is very cheap. Usually accuracy increases with more Lanczos coefficients taken into account,

Mingyu Ma, Giang T. Nguyen

Programmable data planes enable users to design data plane algorithms for network devices, providing extensive flexibility for network customization. Programming Protocol-Independent Packet Processors (P4) has become the most widely adopted abstraction, programming language, and framework for data plane programming. However, existing simulation platforms lac

Humza Nusrat, Bing Luo, Ryan Hall, Joshua Kim

Radiotherapy treatment planning is a complex and time-intensive process, often impacted by inter-planner variability and subjective decision-making. To address these challenges, we introduce Dose Optimization Language Agent (DOLA), an autonomous large language model (LLM)-based agent designed for optimizing radiotherapy treatment plans while rigorously prote

Zachary Hamaker, Brendon Rhoades

Let $I = (i_1, \dots, i_k)$ and $J = (j_1, \dots, j_k)$ be two length $k$ sequences drawn from $\{1, \dots, n \}$. We have the group algebra element $[I,J] := \sum_{w(I) = J} w \in \mathbb{C}[\mathfrak{S}_n]$ where the sum is over permutations $w \in \mathfrak{S}_n$ which satisfy $w(i_p) = j_p$ for $p = 1, \dots, k$. We give an algorithm for evaluating irred

Yu Sun, Yin Li, Ruixiao Sun, Chunhui Liu

Transformer-based multimodal models are widely used in industrial-scale recommendation, search, and advertising systems for content understanding and relevance ranking. Enhancing labeled training data quality and cross-modal fusion significantly improves model performance, influencing key metrics such as quality view rates and ad revenue. High-quality annota

Niranjan Kumar, Farid Seifi, Marisa Conte, Allen Flynn

Clinical calculators are widely used, and large language models (LLMs) make it possible to engage them using natural language. We demonstrate a purpose-built chatbot that leverages software implementations of verifiable clinical calculators via LLM tools and metadata about these calculators via retrieval augmented generation (RAG). We compare the chatbot's r

Andrzej Rostworowski

Validating the results of [A.M. Abrahams and C.R. Evans, Phys. Rev. Lett. 70, 2980] poses a numerical challenge and has been inspiring a lot of research. We join these efforts and present our first steps to achieve this goal: we discuss a formulation of Einstein equations for a vacuum axisymmetric spacetime with vanishing twist in spherical-polar coordinates

Matthew X. Burns, Michael C. Huang

Analog Ising machines (IMs) occupy an increasingly prominent area of computer architecture research, offering high-quality and low latency/energy solutions to intractable computing tasks. However, IMs have a fixed capacity, with little to no utility in out-of-capacity problems. Previous works have proposed parallel, multi-IM architectures to circumvent this

Bart Bussmann, Noa Nabeshima, Adam Karvonen, Neel Nanda

Sparse autoencoders (SAEs) have emerged as a powerful tool for interpreting neural networks by extracting the concepts represented in their activations. However, choosing the size of the SAE dictionary (i.e. number of learned concepts) creates a tension: as dictionary size increases to capture more relevant concepts, sparsity incentivizes features to be spli

Tâm Johan Nguyên, Darrick Lee, Bernadette Jana Stolz

The behavior of multivariate dynamical processes is often governed by underlying structural connections that relate the components of the system. For example, brain activity, which is often measured via time series is determined by an underlying structural graph, where nodes represent neurons or brain regions and edges cortical connectivity. Existing methods

Diana Wallison, Lyric Haylow, Jessica Wert, Jonathan M. Snodgrass

In this paper, we propose a unifying co-simulation framework integrating transportation demand, grid assets, land use, demographics, and emissions to optimally accelerate electric vehicle (EV) development as well as measure the impact of EV integration. 96 urban and long-haul truck charging demand simulations were developed and integrated into a combined tra

Yan Zhang, Yao Feng, Alpár Cseke, Nitin Saini

We formulate the motor system of an interactive avatar as a generative motion model that can drive the body to move through 3D space in a perpetual, realistic, controllable, and responsive manner. Although human motion generation has been extensively studied, many existing methods lack the responsiveness and realism of real human movements. Inspired by recen

Moein Heidari, Afshin Bozorgpour, AmirHossein Zarif-Fakharnia, Wenjin Chen

Objective To develop a robust and computationally efficient deep learning model for automated left ventricular ejection fraction (LVEF) estimation from echocardiography videos that is suitable for real-time point-of-care ultrasound (POCUS) deployment. Methods We propose Echo-E$^3$Net, an endocardial spatio-temporal network that explicitly incorporates cardia

E. M. H. E. B. Ekanayake, N. Shukla

Oscillator Ising machines (OIMs) represent an exemplar case of using physics-inspired non-linear dynamical systems to solve computationally challenging combinatorial optimization problems (COPs). The computational performance of such systems is highly sensitive to the underlying dynamical properties, the topology of the input graph, and their relative compat

G. Gomes, B. Gelli, V. C. Palavéri, R. Sola

Noble liquid detectors rely on wavelength shifter materials, such as p-terphenyl (pTP) and Tetraphenyl-butadiene (TPB), which are widely used in neutrino and dark matter experiments. Given their importance, a thorough understanding and characterization of these compounds are essential for optimizing experimental techniques and enhancing detector performance.

Caitlin M. Davis, Boyana Martinova

We prove that truncations of nonstandard graded polynomial rings are (nonstandard) Koszul modules in the sense of Herzog and Iyengar. This provides an analogue of the fact that such truncations have linear resolutions in the standard graded case.

Bin Xie, Yan Yan, Gady Agam

State Space Models (SSMs) have recently demonstrated outstanding performance in long-sequence modeling, particularly in natural language processing. However, their direct application to medical image segmentation poses several challenges. SSMs, originally designed for 1D sequences, struggle with 3D spatial structures in medical images due to discontinuities

Bhishma Dedhia, David Bourgin, Krishna Kumar Singh, Yuheng Li

Diffusion Transformers (DiTs) can generate short photorealistic videos, yet directly training and sampling longer videos with full attention across the video remains computationally challenging. Alternative methods break long videos down into sequential generation of short video segments, requiring multiple sampling chain iterations and specialized consisten

Licong Lin, Song Mei

Contrastive learning -- a modern approach to extract useful representations from unlabeled data by training models to distinguish similar samples from dissimilar ones -- has driven significant progress in foundation models. In this work, we develop a new theoretical framework for analyzing data augmentation-based contrastive learning, with a focus on SimCLR

Srijita Basu, Miroslaw Staron

The automotive industry has experienced a drastic transformation in the past few years when vehicles got connected to the internet. Nowadays, connected vehicles require complex architecture and interdependent functionalities, facilitating modern lifestyles and their needs. As a result, automotive software has shifted from just embedded system or SoC (System

Nusrat Munia, Abdullah-Al-Zubaer Imran

Skin diseases, such as skin cancer, are a significant public health issue, and early diagnosis is crucial for effective treatment. Artificial intelligence (AI) algorithms have the potential to assist in triaging benign vs malignant skin lesions and improve diagnostic accuracy. However, existing AI models for skin disease diagnosis are often developed and tes

Owen Melia, Daniel Fortunato, Jeremy Hoskins, Rebecca Willett

We provide a flexible, open-source framework for hardware acceleration, namely massively-parallel execution on general-purpose graphics processing units (GPUs), applied to the hierarchical Poincar\'e--Steklov (HPS) family of algorithms for building fast direct solvers for linear elliptic partial differential equations. To take full advantage of the power of

Amin Abbasishahkoo, Mahboubeh Dadkhah, Lionel Briand, Dayi Lin

Deep Neural Networks (DNNs) face challenges during deployment due to covariate shift, i.e., data distribution shifts between development and deployment contexts. Fine-tuning adapts pre-trained models to new contexts requiring smaller labeled sets. However, testing fine-tuned models under constrained labeling budgets remains a critical challenge. This paper i

Leonardo F. Dos Santos, Cícero Zanette, Elisa G. Vergamini, Lucca Maitan

This paper proposes a novel 3D graphical representation for impedance control, called the impedance space, to foster the analysis of the dynamic behavior of robotic compliant controllers. The method overcomes limitations of existing 2D graphical approaches by incorporating mass, stiffness, and damping dynamics, and associates the impedance control parameters

Oleksandr Mokliachuk

In this paper, models that approximate stochastic processes from the space $Sub_\varphi(\Omega)$ with given reliability and accuracy in $L_p(T)$ are considered for some specific functions $\varphi(t)$. For processes that are decomposited in series using orthonormal bases, such models are constructed in the case where elements of such decomposition cannot be

Tianyu Zhang, Fan Wan, Haoran Duan, Kevin W. Tong

Spatial convolution is fundamental in constructing deep Convolutional Neural Networks (CNNs) for visual recognition. While dynamic convolution enhances model accuracy by adaptively combining static kernels, it incurs significant computational overhead, limiting its deployment in resource-constrained environments such as federated edge computing. To address t

Madhab Barman, Nachiketa Mishra

An epidemic Susceptible-Vaccinated-Infected-Removed-Susceptible (SVIRS) model is presented on a weighted-undirected network with graph Laplacian diffusion. Disease-free equilibrium always exists while the existence and uniqueness of endemic equilibrium have been shown. When the basic reproduction number is below unity, the disease-free equilibrium is asympto

Vincent Maillou, Lisa Gaedke-Merzhaeuser, Alexandros Nikolaos Ziogas, Olaf Schenk

The inversion of structured sparse matrices is a key but computationally and memory-intensive operation in many scientific applications. There are cases, however, where only particular entries of the full inverse are required. This has motivated the development of so-called selected-inversion algorithms, capable of computing only specific elements of the ful

Kevin Korner, Brandon Talamini, Julian Andrej, Michael Tupek

We develop a gradient based optimization approach for the equations of compressible, Lagrangian hydrodynamics and demonstrate how it can be employed to automatically uncover strategies to control hydrodynamic instabilities arising from shock acceleration of density interfaces. Strategies for controlling the Richtmyer-Meshkov instability (RMI) are of great be

Sébastien Quetin, Tapotosh Ghosh, Farhad Maleki

Contrastive learning methods in self-supervised settings have primarily focused on pre-training encoders, while decoders are typically introduced and trained separately for downstream dense prediction tasks. However, this conventional approach overlooks the potential benefits of jointly pre-training both encoder and decoder. In this paper, we propose DeCon,

Zachary P. Bradshaw, Margarite L. LaBorde

Neven et al. have explored an unexpected alliance between the mathematical insights of Sir Isaac Newton and Ren\'e Descartes which culminates in the reduction of the Positive Partial Transpose (PPT) criterion to an equivalent hierarchy of entanglement tests based on the moments of the partial transpose. By repurposing these classical results in the context o

Lincoln Curtis, E. Craig Dukes, Ralf Ehrlich, Josh Greaves

The cosmic ray veto detector for the Mu2e experiment consists of scintillation bars embedded with wavelength-shifting fibers and read out by silicon photomultipliers (SiPMs). In this manuscript the calibration procedures of the SiPMs are described including corrections for the temperature dependence of their light yield. These corrections are needed as the S

Linlu Qiu, Fei Sha, Kelsey Allen, Yoon Kim

Large language models (LLMs) are increasingly used as agents that interact with users and with the world. To do so successfully, LLMs must construct representations of the world and form probabilistic beliefs about them. To provide personalized recommendations, for example, the LLM needs to infer a user's preferences from their behavior over multiple interac

Annet Kyomuhangi, Emanuela Marangone, Claudiu Raicu, Ethan Reed

We describe the package "IncidenceCorrespondenceCohomology" for the computer algebra system Macaulay2. The main feature concerns the computation of characters and dimensions for the cohomology groups of line bundles on the incidence correspondence (the partial flag variety parametrizing pairs consisting of a point in projective space and a hyperplane contain

Marina Meilă

The Generalized Mallows Model (GMM) is a well known family of models for ranking data. A GMM is a distribution over $\mathbb{S}_n$, the set of permutations of n objects, characterized by a location parameter $\sigma \in \mathbb{S}_n$, known as central permutation and a set of dispersion parameters $\theta_{1:n-1}\in(0,1]$. The GMM shares many properties, suc

Mewael Isiet, Musanna Galib, Yunhuan Xiao, Jerry I. Dadap

Ceramic materials are widely used in high-strain-rate applications due to their exceptional strength-to-weight ratio. However, under these extreme conditions, spall failure becomes a critical concern, which is driven by a large hydrostatic tensile stress state. This study introduces a novel two-laser setup to generate controlled hydrostatic stress states at

Enikő Pichler, Bendegúz Koncz, Krisztina É. Gabányi, András Péter Joó

Areas of massive star formation are strongly influenced by stellar winds and supernovae, therefore, enhanced turbulent flows are expected. We analyse high-quality Karl G. Jansky Very Large Array observations of the neutral hydrogen gas content of DDO 43, a relatively nearby irregular dwarf galaxy. The line wings of neutral hydrogen spectral lines, which prov