March 2025 arXiv papers — page 138

David Couto, Arash Samani, Alec Yonika

Emitting phased array RF systems have to contend with an ever-increasing number of eavesdroppers as technological advancements provide lower cost and/or more capable radios. Often, eavesdroppers can accumulate sufficient information transmitted in sidelobes by integrating over long enough periods. Directional modulation (DM) disrupts this capability by induc

Zdeněk Stuchlík, Jan Novotný, Jan Hladík

We study dark matter halos modeled by general relativistic polytropic spheres in spacetimes with the repulsive cosmological constant representing vacuum energy density, governed by a polytropic index $n$ and a relativistic (cosmological) parameter $\sigma$ ($\lambda$) determining the ratio of central pressure (vacuum energy density) and central energy densit

Nathaniel Lubin, Yuning Liu, Amanda Yarnell, S. Bryn Austin

Social media platforms have been accused of causing a range of harms, resulting in dozens of lawsuits across jurisdictions. These lawsuits are situated within the context of a long history of American product safety litigation, suggesting opportunities for remediation outside of financial compensation. Anticipating that at least some of these cases may be su

Bousselham El Haddaoui, Raddouane Chiheb, Rdouan Faizi, Abdellatif El Afia

Social media platforms are becoming the foundations of social interactions including messaging and opinion expression. In this regard, Sentiment Analysis techniques focus on providing solutions to ensure the retrieval and analysis of generated data including sentiments, emotions, and discussed topics. International competitions such as the International Work

E. Wiehr, H. Balthasar, G. Stellmacher, M. Bianda

Aims. In the low-collisional, partially ionized plasma (PIP) of solar prominences, uncharged emitters might show different signatures of magnetic line broadening than charged emitters. We investigate if the widths of weak metall emissions in prominences exceed the thermal line broadening by a different amount for charged and for uncharged emitters. Methods.

Bishwas L. Shrestha, David J. McComas, Eric J. Zirnstein, George Livadiotis

This study provides a detailed analysis of fourteen distant interplanetary shocks observed by the Solar Wind Around Pluto (SWAP) instrument onboard New Horizons. These shocks were observed with a pickup ion data cadence of approximately 30 minutes, covering a heliocentric distance range of ~52-60 au. All the shocks observed within this distance range are fas

Leyla Ogurol, Bayram Tekin

As in other partial differential equations, one ends up with some arbitrary constants or arbitrary functions when one integrates Einstein's equations, or more generally field equations of any other gravity. Interpretation of these arbitrary constants and functions as some physical quantities that can, in principle, be measured is a non-trivial matter. Concen

Laurent Gizon

Proceedings of 16th International Conference on Mathematical and Numerical Aspects of Wave Propagation held at the Harnack House, Berlin, Germany, 30 June - 5 July, 2024.

Wenhao Hu, Jinhao Duan, Chunchen Wei, Li Zhang

The rapid advancement of large language models (LLMs) has significantly improved their performance in code generation tasks. However, existing code benchmarks remain static, consisting of fixed datasets with predefined problems. This makes them vulnerable to memorization during training, where LLMs recall specific test cases instead of generalizing to new pr

Martin Roelfs

Kingdon is an open-source Python package designed to seamlessly integrate Geometric Algebra (GA) into existing workflows. Unlike previous GA libraries, kingdon is input-type-agnostic, and hence supports GA's over e.g. PyTorch tensors, NumPy arrays, or SymPy symbolic expressions, to name but a few. Despite this refusal to specialize, it delivers high performa

Maarten Perneel, Ines Adriaens, Ben Aernouts, Jan Verwaeren

Over the past decade, studying animal behaviour with the help of computer vision has become more popular. Replacing human observers by computer vision lowers the cost of data collection and therefore allows to collect more extensive datasets. However, the majority of available computer vision algorithms to study animal behaviour is highly tailored towards a

Rolf Schneider

For the solution of the Gauss image problem for pseudo-cones, which can be considered as a measure transport problem for certain measures on the sphere, we give a new proof, using a special case of Kantorovich duality.

Tian Bai

In the Feedback Arc Set in Tournaments (Subset-FAST) problem, we are given a tournament $D$ and a positive integer $k$, and the objective is to determine whether there exists an arc set $S \subseteq A(D)$ of size at most $k$ whose removal makes the graph acyclic. This problem is well-known to be equivalent to a natural tournament ranking problem, whose task

Jakaria Islam Emon, Md Abu Salek, Kazi Tamanna Alam

Speaker identification in multilingual settings presents unique challenges, particularly when conventional models are predominantly trained on English data. In this paper, we propose WSI (Whisper Speaker Identification), a framework that repurposes the encoder of the Whisper automatic speech recognition model pre trained on extensive multilingual data to gen

Huiyun Tang, Björn Rohles, Yuwei Chuai, Gabriele Lenzini

Reducing the spread of misinformation is challenging. AI-based fact verification systems offer a promising solution by addressing the high costs and slow pace of traditional fact-checking. However, the problem of how to effectively communicate the results to users remains unsolved. Warning labels may seem an easy solution, but they fail to account for fuzzy

Aliaksei Kachanovich

The ATLAS and CMS Collaborations reported that the observed number of Higgs boson decays into a $Z$ boson and a photon is $\mu = 2.2 \pm 0.7$ times higher than predicted by the Standard Model. Initially, this discrepancy was attributed to a modification of the $HZ\gamma$ vertex. In the $H \to Z\gamma$ process, this decay is reconstructed from $H \to \ell\ell

Natalia Garcia-Fritz, Hector Pasten

We prove a completely explicit and effective upper bound for the N\'eron--Tate height of rational points of curves of genus at least $2$ over number fields, provided that they have enough automorphisms with respect to the Mordell--Weil rank of their jacobian. Our arguments build on Arakelov theory for arithmetic surfaces. Our bounds are practical, and we ill

Lars Malmqvist

Post-merger integration (PMI) planning presents significant challenges due to the complex interdependencies between integration initiatives and their associated synergies. While dependency-based planning approaches offer valuable frameworks, practitioners often become anchored to specific integration paths without systematically exploring alternative solutio

Azra Redzovic, Adnan Tahirovic

This paper introduces a unified approach for state estimation and control of nonlinear dynamic systems, employing the State-Dependent Riccati Equation (SDRE) framework. The proposed approach naturally extends classical linear quadratic Gaussian (LQG) methods into nonlinear scenarios, avoiding linearization by using state-dependent coefficient (SDC) matrices.

Kaviya Parthasarathy, Hsin-Min Liu, Ing-Guey Jiang, Li-Chin Yeh

We present Transit Timing Variations (TTVs) of HAT-P-12b, a low-density sub-Saturn mass planet orbiting a metal-poor K4 dwarf star. Using 14 years of observational data (2009-2022), our study incorporates 7 new ground-based photometric transit observations, three sectors of Transiting Exoplanet Survey Satellite (TESS) data, and 23 previously published light

Gustav Schmidt, Holger Heidrich, Philipp Berens, Sarah Müller

Learning from noisy ordinal labels is a key challenge in medical imaging. In this work, we ask whether ordinal disease progression labels (better, worse, or stable) can be used to learn a representation allowing to classify disease state. For neovascular age-related macular degeneration (nAMD), we cast the problem of modeling disease progression between medi

Simone Magistri, Tomaso Trinci, Albin Soutif-Cormerais, Joost van de Weijer

Exemplar-free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this paper, we consider the challenging Cold Start scenario in which insufficient data is available in the first task to learn a high-quality backbone. This is especially challenging for EFCIL since it requires high plastici

Avinash CK, Y. Divakara Mayya, Alessandro Bressan, Jairo A. Alzate Trujillo

We use archival JWST/NIRCam images in the F115W, F150W, and F200W filters to measure the Tip of the Red Giant Branch (TRGB) magnitudes across the disk of the late-type spiral galaxy NGC 628. In this exploratory study, we demonstrate how the metallicity-dependence of TRGB magnitudes in the near-infrared (NIR) filters can be exploited by making use of the theo

Wanhua Li, Renping Zhou, Jiawei Zhou, Yingwei Song

Learning 4D language fields to enable time-sensitive, open-ended language queries in dynamic scenes is essential for many real-world applications. While LangSplat successfully grounds CLIP features into 3D Gaussian representations, achieving precision and efficiency in 3D static scenes, it lacks the ability to handle dynamic 4D fields as CLIP, designed for s

Tanjung Krisnanda, Fernando Valadares, Kyle Timothy Ng Chu, Pengtao Song

Quantum harmonic oscillators serve as fundamental building blocks for quantum information processing, particularly in the context of the bosonic circuit quantum electrodynamics (cQED) platform. Conventional methods for extracting oscillator properties rely on predefined analytical gate sequences to access a restricted set of observables or resource-intensive

Kevin Wilkinghoff, Takuya Fujimura, Keisuke Imoto, Jonathan Le Roux

When detecting anomalous sounds in complex environments, one of the main difficulties is that trained models must be sensitive to subtle differences in monitored target signals, while many practical applications also require them to be insensitive to changes in acoustic domains. Examples of such domain shifts include changing the type of microphone or the lo

Derun Li, Changye Li, Yue Wang, Jianwei Ren

Generating human-like and adaptive trajectories is essential for autonomous driving in dynamic environments. While generative models have shown promise in synthesizing feasible trajectories, they often fail to capture the nuanced variability of personalized driving styles due to dataset biases and distributional shifts. To address this, we introduce TrajHF,

Guy Nason, Daniel Salnikov, Mario Cortina-Borja

Longitudinal networks are becoming increasingly relevant in the study of dynamic processes characterised by known or inferred community structure. Generalised Network Autoregressive (GNAR) models provide a parsimonious framework for exploiting the underlying network and multivariate time series. We introduce the community-$\alpha$ GNAR model with interaction

Can Zheng, Jiguang He, Guofa Cai, Zitong Yu

In this paper, we propose BeamLLM, a vision-aided millimeter-wave (mmWave) beam prediction framework leveraging large language models (LLMs) to address the challenges of high training overhead and latency in mmWave communication systems. By combining computer vision (CV) with LLMs' cross-modal reasoning capabilities, the framework extracts user equipment (UE

Artem Chernyshov, John Nyberg, Vegard Holmstrøm, Md Abulkalam Azad

Deep learning methods for point tracking are applicable in 2D echocardiography, but do not yet take advantage of domain specifics that enable extremely fast and efficient configurations. We developed MyoTracker, a low-complexity architecture (0.3M parameters) for point tracking in echocardiography. It builds on the CoTracker2 architecture by simplifying its

Christoph Bandt

On the one hand, the dynamical interior of a self-similar set with open set condition is the complement of the dynamical boundary. On the other hand, the dynamical interior is the recurrent set of the magnification flow. For a finite type self-similar set, both boundary and interior are described by finite automata. The neighbor graph defines the boundary. T

Paul David, Fabrice Catoire, Luc Bergé

Electromagnetic emissions, known as Brunel radiations, are produced in plasmas through the coupling between the free electron density and ultrafast ionizing laser pulses. The radiation spectrum generated in laser-gas interactions is here investigated from a local current model for laser drivers with two frequency components - or "colors" - being not necessar

Dibyakanti Kumar, Samyak Jha, Anirbit Mukherjee

In this work, we will establish that the Langevin Monte-Carlo algorithm can learn depth-2 neural nets of any size and for any data and we give non-asymptotic convergence rates for it. We achieve this via showing that in q-Renyi divergence, the iterates of Langevin Monte Carlo converge to the Gibbs distribution of Frobenius norm regularized losses for any of

Zhi Rui Tam, Ya-Ting Pai, Yen-Wei Lee, Yun-Nung Chen

In this paper, we propose a comprehensive evaluation benchmark for Visual Language Models (VLM) in Traditional Chinese. Our evaluation suite, the first of its kind, contains two complementary components: (1) VisTW-MCQ, a collection of manually curated exam multi-choice questions from 21 academic subjects designed to test the broad knowledge and reasoning cap

Bennet van den Broek, Javad Pourmostafa Roshan Sharami

The improper disposal and mismanagement of medical waste pose severe environmental and public health risks, contributing to greenhouse gas emissions and the spread of infectious diseases. Efficient and accurate medical waste classification is crucial for mitigating these risks. We explore the integration of capsule networks with a pretrained DenseNet model t

Gunter Malle

We gather evidence on a new local-global conjecture of Moret\'o and Rizo on values of irreducible characters of finite groups. For this we study subnormalisers and picky elements in finite groups of Lie type and determine them in many cases, for unipotent elements as well as for semisimple elements of prime power order. We also discuss subnormalisers of unip

Chunsheng Zuo, Yu Zhao, Juntao Ye

Photoplethysmography (PPG) sensors have been widely used in consumer wearable devices to monitor heart rates (HR) and heart rate variability (HRV). Despite the prevalence, PPG signals can be contaminated by motion artifacts induced from daily activities. Existing approaches mainly use the amplitude information to perform PPG peak detection. However, these ap

Norbert A'Campo, Pablo Portilla Cuadrado

Generic relative immersions of compact one-manifolds in the closed unit disk, i.e. divides, provide a powerful combinatorial framework, and allow a topological construction of fibered classical links, for which the monodromy diffeomorphism is explicitly given as a product of Dehn twists. Complex isolated plane curve singularities provide a classical fibered

Lu Feng, Tian-Nuo Li, Guo-Hong Du, Jing-Fei Zhang

Sterile neutrinos can influence the evolution of the universe, and thus cosmological observations can be used to search for sterile neutrinos. In this study, we utilized the latest baryon acoustic oscillations data from DESI, combined with the cosmic microwave background data from Planck and the five-year supernova data from DES, to constrain the interacting

Ye Zhang, Zijie Fang, Yifeng Wang, Lingbo Zhang

Nuclei segmentation and classification provide an essential basis for tumor immune microenvironment analysis. The previous nuclei segmentation and classification models require splitting large images into smaller patches for training, leading to two significant issues. First, nuclei at the borders of adjacent patches often misalign during inference. Second,

Zhenwei Wang, Tiehua Zhang, Jing Liu, Heng Yu

The application of learning based methods to vehicle routing problems has emerged as a pivotal area of research in combinatorial optimization. These problems are characterized by vast solution spaces and intricate constraints, making traditional approaches such as exact mathematical models or heuristic methods prone to high computational overhead or reliant

Angel Rodríguez-Fernández, Jan-Etienne Pudell, Roman Shayduk, Alejandro Fraile-Gimeno

Material processing with femtosecond lasers has attracted enormous attention because of its potential for technology and industrial applications. In parallel, time-resolved x-ray diffraction has been successfully used to study ultrafast structural distortion dynamics in semiconductor thin films or surface layers. However, real-world processing applications m

Hendrik Scheidel, Camilo Gonzalez, Houshyar Asadi, Tobias Bellmann

In motion simulation, motion cueing algorithms are used for the trajectory planning of the motion simulator platform, where workspace limitations prevent direct reproduction of reference trajectories. Strategies such as motion washout, which return the platform to its center, are crucial in these settings. For serial robotic MSPs with highly nonlinear worksp

Nan Wu, Mathieu BA Freville, Zhiyi Man, Adérito Fins Carreira

This article is a comment of N. Chapuis and L. Bocquet, Sustainable Energy Fuels, 2025, DOI: 10.1039/D4SE01366B which title is "Boosting large scale capacitive harvesting of osmotic power by dynamical matching of ion exchange kinetics". In this work, the authors present an experimental process that shows how it is possible to set up a reverse electrodialysis

Yue Dong, David Yang, Jialun Liu, Aly Abdeldaim

We report the results of synchrotron Bragg Coherent X-ray Diffraction Imaging (BCDI) experiments to investigate domain formation in a micron-sized magnetite crystal undergoing the Verwey transition at low temperature. A strong splitting of the measured 311 Bragg reflection was observed in the low-temperature phase, indicating the formation of domains. BCDI r

Thom Fruehwirth

Runtime repeated recursion unfolding was recently introduced as a just-in-time program transformation strategy that can achieve super-linear speedup. So far, the method was restricted to single linear direct recursive rules in the programming language Constraint Handling Rules (CHR). In this companion paper, we generalize the technique to multiple recursion

Luca Moscadelli, Ciriaco Goddi, Tomoya Hirota, Alberto Sanna

This article focuses on NGC7538 IRS1, one of the most luminous and studied HC HII regions in the northern hemisphere. Our aim is to identify the young stellar objects (YSOs) embedded within the ionized gas and study their kinematic structures. This work expands on a recent survey called "Protostellar Outflows at the EarliesT Stages" (POETS), which has been d

Eeswar Kumar Yalavarthi, Wei Cui, Aswin Vishnuradhan, Nicolas Couture

The ever-increasing demand for high-speed data transmission continues to motivate research and development efforts towards the sixth generation (6G) of wireless communication technologies and beyond. The use of terahertz (THz) carrier frequencies is considered to achieve faster data transmission rates, with the potential to reach terabits per second. However

Xiaoyu Guo, Wenhao Liu, Bing Lv, Liuyan Zhao

Two-dimensional (2D) magnetism realized in van der Waals (vdW) materials has expanded to include a great variety of magnetic phases, over a short decade since its first discovery in 2016-2017. However, most of the investigated vdW magnets so far have highly symmetric crystal fields and isotropic in-plane lattice structures, making their 2D magnetism robust b

Luyuan Xie, Tianyu Luan, Wenyuan Cai, Guochen Yan

Federated learning has wide applications in the medical field. It enables knowledge sharing among different healthcare institutes while protecting patients' privacy. However, existing federated learning systems are typically centralized, requiring clients to upload client-specific knowledge to a central server for aggregation. This centralized approach would

Pierpaolo Della Monica, Ivan Visconti, Andrea Vitaletti, Marco Zecchini

Before a fair exchange takes place, there is typically an advertisement phase with the goal of increasing the appeal of possessing a digital asset while keeping it sufficiently hidden. Advertisement phases are implicit in mainstream definitions, and therefore are not explicitly integrated within fair-exchange protocols. In this work we give an explicit defin

Yuwen Du, Anning Hu, Zichen Chao, Yifan Lu

Roadside Collaborative Perception refers to a system where multiple roadside units collaborate to pool their perceptual data, assisting vehicles in enhancing their environmental awareness. Existing roadside perception methods concentrate on model design but overlook data issues like calibration errors, sparse information, and multi-view consistency, leading

Jicai Huang, Renato Huzak, Otavio Henrique Perez, Jinhui Yao

In this paper we study the cyclicity of sliding cycles for regularized piecewise smooth visible-invisible two-folds, in the presence of singularities of the Filippov sliding vector field located away from two-folds. We obtain a slow-fast system after cylindrical blow-up and use a well-known connection between the divergence integral along orbits and transiti

Jonathan Shaki, Emanuele La Malfa, Michael Wooldridge, Sarit Kraus

We study how large language models (LLMs) reason about memorized knowledge through simple binary relations such as equality ($=$), inequality ($<$), and inclusion ($\subset$). Unlike in-context reasoning, the axioms (e.g., $a < b, b < c$) are only seen during training and not provided in the task prompt (e.g., evaluating $a < c$). The tasks require one or mo

Floriment Klinaku, Sarah Sophie Stieß, Alireza Hakamian, Steffen Becker

The cloud computing model enables the on-demand provisioning of computing resources, reducing manual management, increasing efficiency, and improving environmental impact. Software architects now play a strategic role in designing and deploying elasticity policies for automated resource management. However, creating policies that meet performance and cost ob

Yijing Lin, Mengqi Huang, Shuhan Zhuang, Zhendong Mao

Unifying diverse image generation tasks within a single framework remains a fundamental challenge in visual generation. While large language models (LLMs) achieve unification through task-agnostic data and generation, existing visual generation models fail to meet these principles. Current approaches either rely on per-task datasets and large-scale training

Péter Dobrovoczki, Tamás Kis

In this paper we aim to construct piecewise-linear (PWL) approximations for functions of multiple variables and to build compact mixed-integer linear programming (MILP) formulations to represent the resulting PWL function. On the one hand, we describe a simple heuristic to iteratively construct a triangulation with a small number of triangles, while decreasi

Matteo Gambella, Fabrizio Pittorino, Manuel Roveri

Neural Architecture Search (NAS) has become an essential tool for designing effective and efficient neural networks. In this paper, we investigate the geometric properties of neural architecture spaces commonly used in differentiable NAS methods, specifically NAS-Bench-201 and DARTS. By defining flatness metrics such as neighborhoods and loss barriers along

Jingyu Guo, Sensen Gao, Jia-Wang Bian, Wanhu Sun

Recent 3D content generation pipelines often leverage Variational Autoencoders (VAEs) to encode shapes into compact latent representations, facilitating diffusion-based generation. Efficiently compressing 3D shapes while preserving intricate geometric details remains a key challenge. Existing 3D shape VAEs often employ uniform point sampling and 1D/2D latent

Alexandre Wagemakers, Vipul Periwal

We explore a family of numerical methods, based on the Steffensen divided difference iterative algorithm, that do not evaluate the derivative of the objective functions. The family of methods achieves second-order convergence with two function evaluations per iteration with marginal additional computational cost. An important side benefit of the method is th

Nimesh Khandelwal, Amritanshu Manu, Shakti S. Gupta, Mangal Kothari

This paper presents a novel method for assistive load carrying using quadruped robots. The controller uses proprioceptive sensor data to estimate external base wrench, that is used for precise control of the robot's acceleration during payload transport. The acceleration is controlled using a combination of admittance control and Control Barrier Function (CB

Akira Sone, Akram Touil, Kenji Maeda, Paola Cappellaro

We elucidate the requirements for quantum operations that achieve environment-assisted invariance (envariance), a symmetry of entanglement. While envariance has traditionally been studied within the framework of local unitary operations, we extend the analysis to consider non-unitary local operations. First, we investigate the conditions imposed on operators

Piotr Kicki

Model Predictive Path Integral (MPPI) control is a widely used sampling-based approach for real-time control, valued for its flexibility in handling arbitrary dynamics and cost functions. However, it often suffers from high-frequency noise in the sampled control trajectories, which hinders the search for optimal controls and transfers to the applied controls

Andrey V. Savchenko

This article presents our results for the eighth Affective Behavior Analysis in-the-Wild (ABAW) competition. We combine facial emotional descriptors extracted by pre-trained models, namely, our EmotiEffLib library, with acoustic features and embeddings of texts recognized from speech. The frame-level features are aggregated and fed into simple classifiers, e

Niklas Houba

The Laser Interferometer Space Antenna (LISA) will observe gravitational waves in the millihertz frequency band, detecting signals from a vast number of astrophysical sources embedded in instrumental noise. Extracting individual signals from these overlapping contributions is a fundamental challenge in LISA data analysis and is traditionally addressed using

Gernot Eichmann

We give a pedagogical introduction to hadron spectroscopy and structure studies using functional methods. We explain the basic features of Dyson-Schwinger, Bethe-Salpeter and Faddeev equations, which are employed to calculate the spectra of mesons, baryons and four-quark states. We discuss dynamical mass generation as a consequence of the spontaneous breakin

Brian W. Pogue, Alexander P. Niver

Medical Physics education is delivered through accredited programs with admissions and funding for students determined by individual institutions providing the educational experiences. Public data from accredited graduate programs, along with funding data, were used to analyze institutional trends in this educational market. Temporal trends from 2017 to 2023

A. Reutov, S. Vorobey, A. Katanskiy, V. Balakirev

One of the main issues of the satellite-to-ground optical communication, including free-space satellite quantum key distribution (QKD), is an achievement of the reasonable accuracy of positioning, navigation and optical stabilization. Proportional-integral-derivative (PID) controllers can handle with various control tasks in optical systems. Recent research

Sanu Bera, Snehashis Mukherjee

This article investigates the two-parameter quantum matrix algebra at roots of unity. In the roots of unity setting, this algebra becomes a Polynomial Identity (PI) algebra and it is known that simple modules over such algebra are finite-dimensional with dimension at most the PI degree. We determine the center, compute the PI degree, and classify simple modu

Takuma Takahata, Norito Minamikawa, Takayuki Okuno

Oredango puzzle, one of the pencil puzzles, was originally created by Kanaiboshi and published in the popular puzzle magazine Nikoli. In this paper, we show NP- and ASP-completeness of Oredango by constructing a reduction from the 1-in-3SAT problem. Next, we formulate Oredango as an 0-1 integer-programming problem, and present numerical results obtained by s

Fengxiang Wang, Yulin Wang, Mingshuo Chen, Haiyan Zhao

Recent advances in self-supervised learning for Vision Transformers (ViTs) have fueled breakthroughs in remote sensing (RS) foundation models. However, the quadratic complexity of self-attention poses a significant barrier to scalability, particularly for large models and high-resolution images. While the linear-complexity Mamba architecture offers a promisi

Yufan Deng, Xun Guo, Yizhi Wang, Jacob Zhiyuan Fang

Video generation has witnessed remarkable progress with the advent of deep generative models, particularly diffusion models. While existing methods excel in generating high-quality videos from text prompts or single images, personalized multi-subject video generation remains a largely unexplored challenge. This task involves synthesizing videos that incorpor

Zhiyang He, Alexander Cowtan, Dominic J. Williamson, Theodore J. Yoder

In pursuit of large-scale fault-tolerant quantum computation, quantum low-density parity-check (LDPC) codes have been established as promising candidates for low-overhead memory when compared to conventional approaches based on surface codes. Performing fault-tolerant logical computation on QLDPC memory, however, has been a long standing challenge in theory

J. Wehbeh, E. C. Kerrigan

Generalized semi-infinite programs (generalized SIPs) are problems featuring a finite number of decision variables but an infinite number of constraints. They differ from standard SIPs in that their constraint set itself depends on the choice of the decision variable. Generalized SIPs can be used to model robust optimal control problems where the uncertainty

Ittai Sidilkover, Yun Yen, Sunil Wilfred D'Souza, Jakub Schusser

The orbital angular momentum (OAM) of electron states is an essential ingredient for topological and quantum geometric quantities in solids. For example, Dirac surface states with helical spin- and orbital-angular momenta are a hallmark of a 3D topological insulator. Angle-resolved photoemission spectroscopy (ARPES) with variable circular light polarization,

Oskar von Seeler, Elena C. Offenberg, Carlo Michaelis, Jannik Luboeinski

Progress in neuromorphic computing requires efficient implementation of standard computational problems, like adding numbers. Here we implement a variety of sequential and parallel binary adders in the Lava software framework, and deploy them to the neuromorphic chip Loihi 2. To the best of our knowledge, up to now, a neuromorphic implementation of such para

Xuanke Jiang, Sherief Hashima, Kohei Hatano, Eiji Takimoto

We consider a good arm identification problem in a stochastic bandit setting with multi-objectives, where each arm $i \in [K]$ is associated with a distribution $D_i$ defined over $R^M$. For each round $t$, the player pulls an arm $i_t$ and receives an $M$-dimensional reward vector sampled according to $D_{i_t}$. The goal is to find, with high probability, a

Sitt Min Oo, Olaf Hartig

Although they exist since more than ten years already, have attracted diverse implementations, and have been used successfully in a significant number of applications, declarative mapping languages for constructing knowledge graphs from heterogeneous types of data sources still lack a solid formal foundation. This makes it impossible to introduce implementat

Naum Dimitrieski, Jing Cao, Christian Ebenbauer

This paper presents a novel stochastic gradient descent algorithm for constrained optimization. The proposed algorithm randomly samples constraints and components of the finite sum objective function and relies on a relaxed logarithmic barrier function that is appropriately adapted in each optimization iteration. For a strongly convex objective function and

The DEAP Collaboration, P. Adhikari, R. Ajaj, M. Alpízar-Venegas

In the DEAP-3600 dark matter search experiment, precise reconstruction of the positions of scattering events in liquid argon is key for background rejection and defining a fiducial volume that enhances dark matter candidate events identification. This paper describes three distinct position reconstruction algorithms employed by DEAP-3600, leveraging the spat

Qian Xiao

In one-dimensional Diophantine approximation, the Diophantine properties of a real number are characterized by its partial quotients, especially the growth of its large partial quotients. Notably, Kleinbock and Wadleigh [Proc. Amer. Math. Soc. 2018] made a seminal contribution by linking the improvability of Dirichlet's theorem to the growth of the product o

Karl Pierce

We investigate a novel approach to approximate tensor-network contraction via the exact, matrix-free decomposition of full tensor-networks. We study this method as a means to eliminate the propagation of error in the approximation of tensor-networks. Importantly, this decomposition-based approach is generic, i.e. it does not depend on a specific tensor-netwo

Douglas Z. Plummer, Emily D'Alessandro, Aidan Burrowes, Joshua Fleischer

The demand for computing power has been growing exponentially with the rise of artificial intelligence (AI), machine learning, and the Internet of Things (IoT). This growth requires unconventional computing primitives that prioritize energy efficiency, while also addressing the critical need for scalability. Neuromorphic computing, inspired by the biological

Ayanda Zungu, Ilya Sinayskiy, Francesco Petruccione

Open Quantum Brownian Motion (OQBM) is a new class of quantum Brownian motion in which the dynamics of the Brownian particle depend not only on interactions with a thermal environment but also on the state of its internal degrees of freedom. For an Ohmic bath spectral density with a Lorentz--Drude cutoff frequency at a high-temperature limit, we derive the B

Cis Lagae, Anish M. Amarsi, Karin Lind

The Ca II resonance doublet (HK) and the near-infrared triplet (CaT) are among the strongest features in stellar spectra of FGK-type stars. These spectral lines remain prominent down to extremely low metallicities and are thus useful for providing stellar parameters via ionisation balance and as radial velocity diagnostics. However, the majority of studies t

Qiaoling Chen, Shenggui Li, Wei Gao, Peng Sun

In recent years, Large Language Models (LLMs) have exhibited remarkable capabilities, driving advancements in real-world applications. However, training LLMs on increasingly long input sequences imposes significant challenges due to high GPU memory and computational demands. Existing solutions face two key limitations: (1) memory reduction techniques, such a

MIT CompGeom Group, Hugo A. Akitaya, Erik D. Demaine, Adam Hesterberg

A quasigeodesic is a curve on the surface of a convex polyhedron that has $\le \pi$ surface to each side at every point. In contrast, a geodesic has exactly $\pi$ to each side and so can never pass through a vertex, whereas quasigeodesics can. Although it is known that every convex polyhedron has at least three simple closed quasigeodesics, little else is kn

Ahmed ElGazzar, Marcel van Gerven

In this work, we propose FlowTime, a generative model for probabilistic forecasting of multivariate timeseries data. Given historical measurements and optional future covariates, we formulate forecasting as sampling from a learned conditional distribution over future trajectories. Specifically, we decompose the joint distribution of future observations into

Paul C. Canfield, Tyler J. Slade

The recent availability of step-edge, frit-disc crucible sets (generally sold as Canfield Crucible Sets or CCS) has led to multiple innovations associated with our group's use of solution growth. Use of CCS allows for the clean separation of liquid from solid phases during the growth process. This clean separation enables the reuse of the decanted liquid, ei

Feng Jin, Shiyu Fan, Mingqiang Gu, Qiming Lv

Topotactic reduction of perovskite oxides offers a powerful approach for discovering novel phenomena, such as superconducting infinite-layer nickelates and polar metallicity, and is commonly accompanied by the emergence of multiple valence states and/or complex crystal fields of transition metals. However, understanding the complex interplay between crystal

Minwoo Park, Suk Bum Chung

Bulk n-type SrTiO3 (STO) has long been known to possess a superconducting ground state at an exceptionally dilute carrier density. This has raised questions about the applicability of the BCS-Eliashberg paradigm with its underlying adiabatic assumption. However, recent experimental reports have set the pairing gap to the critical temperature (Tc) ratio at th

Heng Yim Nicole Oo, Min Hun Lee, Jeong Hoon Lim

Algorithmic detection of facial palsy offers the potential to improve current practices, which usually involve labor-intensive and subjective assessments by clinicians. In this paper, we present a multimodal fusion-based deep learning model that utilizes an MLP mixer-based model to process unstructured data (i.e. RGB images or images with facial line segment

Iman Nematollahi, Branton DeMoss, Akshay L Chandra, Nick Hawes

We introduce LUMOS, a language-conditioned multi-task imitation learning framework for robotics. LUMOS learns skills by practicing them over many long-horizon rollouts in the latent space of a learned world model and transfers these skills zero-shot to a real robot. By learning on-policy in the latent space of the learned world model, our algorithm mitigates

Lihui Han, Lujia Tian, Bing-Xin Liu, Zong-liang Li

Ferromagnetism in the two-dimensional materials is of great significance and has become an emerging topic. The ferromagnetic VS$_2$ and VSe$_2$ monolayers have been experimentally synthesized, and O element belongs to the same group as S and Se elements. Thus, whether there exists the ferromagnetic VO$_2$ monolayer is a necessary and urgent question. Using f

Piyush Jangid, Maria Antonietta Vincenti, Luca Carletti, Anton Rudenko

High-harmonic generation (HHG) provides the only source of attosecond pulses -- currently the shortest accessible time intervals, and it is employed as the only table-top source of light in extreme UV and soft X-ray spectral regions. Chiral HHG can be employed as an efficient tool for studying the ultrafast response of chiral properties of matter, as well as

Partha Kumar Paul, Narendra Sahu, Shashwat Sharma

It is a common lore that in thermal leptogenesis within the type-I seesaw framework and a hierarchical spectrum of heavy right-handed neutrinos (RHNs), the CP-violating, out-of-equilibrium decay of the lightest RHN ($N_1$) is the only relevant source of the final $B-L$ asymmetry, since any asymmetry produced by the heavier RHNs is expected to be erased by su

Elad Richardson, Kfir Goldberg, Yuval Alaluf, Daniel Cohen-Or

Advanced generative models excel at synthesizing images but often rely on text-based conditioning. Visual designers, however, often work beyond language, directly drawing inspiration from existing visual elements. In many cases, these elements represent only fragments of a potential concept-such as an uniquely structured wing, or a specific hairstyle-serving

Patrick Tscheppe, Marcel Klett, Henri Menke, Sabine Andergassen

We formulate a quantum embedding algorithm in real-space for the simultaneous theoretical treatment of nonlocal electronic correlations and disorder, the coherent cellular dynamical mean-field theory (C-CDMFT). This algorithm combines the molecular coherent potential approximation with the cellular dynamical mean-field theory. After a pedagogical review of q

Nicolas Zorn Morales, Daniel Steffen Rühl, Sergey Sadofev, Emil List-Kratochvil

We present a tunable plasmonic platform that allows room temperature hybridization of dissimilar excitons, namely of Wannier-Mott excitons in monolayer (1L) WS2 and Frenkel excitons in molecular J-aggregates via simultaneous strong coupling to surface plasmon polaritons. It is based on a simple layered design consisting of a thin planar silver film and a die

Shima Shabani, Mohammadsadegh Khoshghiaferezaee, Michael Breuß

In this paper we study the sparse coding problem in the context of sparse dictionary learning for image recovery. To this end, we consider and compare several state-of-the-art sparse optimization methods constructed using the shrinkage operation. As the mathematical setting of these methods, we consider an online approach as algorithmical basis together with