March 2025 arXiv papers — page 184

Paul Hacking, Ailsa Keating

We prove the homological mirror symmetry conjecture of Kontsevich for K3 surfaces in the following form: The Fukaya category of a projective K3 surface is equivalent to the derived category of coherent sheaves on the mirror, which is a K3 surface of Picard rank $19$ over the field $\mathbb{C}((q))$ of formal Laurent series. This builds on prior work of Seide

J. Aalbers, D. S. Akerib, A. K. Al Musalhi, F. Alder

Electron-capture decays of $^{125}$Xe and $^{127}$Xe, and double-electron-capture decays of $^{124}$Xe, are backgrounds in searches for weakly interacting massive particles (WIMPs) conducted by dual-phase xenon time projection chambers such as LUX-ZEPLIN (LZ). These decays produce signals with more light and less charge than equivalent-energy $\beta$ decays,

Darmindra Arumugam

Mode competition in nonequilibrium Rydberg gases enables the exploration of emergent many-body phases. This work leverages this emergent phase for electric field detection at room temperature. Sensitive frequency-resolved electric field measurements at very low-frequencies (VLF) are of central importance in a wide range of applications where deep-penetration

Joseph Lam

Multidimensional poverty measurement is crucial for capturing deprivation beyond income-based metrics. This study compares the Alkire-Foster (AF) method and a Markov Random Field (MRF) approach for classifying multidimensional poverty using a simulation-based analysis. The AF method applies a deterministic threshold-based classification, while the MRF approa

Ted Shaowang, Shinan Liu, Jonatas Marques, Nick Feamster

Machine learning can analyze vast amounts of data generated by IoT devices to identify patterns, make predictions, and enable real-time decision-making. By processing sensor data, machine learning models can optimize processes, improve efficiency, and enhance personalized user experiences in smart systems. However, IoT systems are often deployed in sensitive

K. Pentland, N. C. Amorisco, P. E. Farrell, C. J. Ham

The Grad-Shafranov (GS) equation is a nonlinear elliptic partial differential equation that governs the ideal magnetohydrodynamic equilibrium of a tokamak plasma. Previous studies have demonstrated the existence of multiple solutions to the GS equation when solved in idealistic geometries with simplified plasma current density profiles and boundary condition

Saronath Halder, Remigiusz Augusiak

We consider collections of mixed states supported on mutually orthogonal subspaces whose rank add up to the total dimension of the underlying Hilbert space. We then ask whether it is possible to find such collections in which no state from the set can be unambiguously identified by local operations and classical communication (LOCC) with non-zero success pro

Jørgen S. Dokken, Patrick E. Farrell, Brendan Keith, Ioannis P. A. Papadopoulos

The latent variable proximal point (LVPP) algorithm is a framework for solving infinite-dimensional variational problems with pointwise inequality constraints. The algorithm is a saddle point reformulation of the Bregman proximal point algorithm. At the continuous level, the two formulations are equivalent, but the saddle point formulation is more amenable t

Misha Schmalian

We describe an algorithm that, given a 3-manifold M, outputs a finite set containing all minimal volume k-component hyperbolic link complements in M. A key step, that might be of independent interest, is an algorithm that, given two 3-manifolds N and M, decides whether they are related by Dehn filling. In fact, we show that the set of boundary slopes giving

Miho Fuyama, Andrei Khrennikov, Masanao Ozawa

We characterize the class of quantum measurements that matches the applications of quantum theory to cognition (and decision making) - quantum-like modeling. Projective measurements describe the canonical measurements of the basic observables of quantum physics. However, the combinations of the basic cognitive effects, such as the question order and response

Arun Mambra, Ravi Pant, Joy Mitra

Epsilon-near-zero (ENZ) systems exhibit unconventional electromagnetic response close to their zero permittivity regime. Here, we explore the ability of ultrathin ENZ films to modulate the transmission of radiation from an underlying quantum emitter through active control of the carrier density of the ENZ film. The achievable on/off switching ratio is shown

JB Manchak, Thomas Barrett, Hans Halvorson, James Owen Weatherall

This paper concerns the question of which collections of general relativistic spacetimes are deterministic relative to which definitions. We begin by considering a series of three definitions of increasing strength due to Belot (1995). The strongest of these definitions is particularly interesting for spacetime theories because it involves an asymmetry condi

Benoit Assi, Christian Bierlich, Philip Ilten, Tony Menzo

We update the HOMER method, a technique to solve a restricted version of the inverse problem of hadronization -- extracting the Lund string fragmentation function $f(z)$ from data using only observable information. Here, we demonstrate its utility by extracting $f(z)$ from synthetic Pythia simulations using high-level observables constructed on an event-by-e

Yulun Zhuang, Yichen Wang, Yanran Ding

In this paper, we introduce a kinodynamic model predictive control (MPC) framework that exploits unidirectional parallel springs (UPS) to improve the energy efficiency of dynamic legged robots. The proposed method employs a hierarchical control structure, where the solution of MPC with simplified dynamic models is used to warm-start the kinodynamic MPC, whic

Zengqun Zhao, Ziquan Liu, Yu Cao, Shaogang Gong

Recent advances in generative models have sparked research on improving model fairness with AI-generated data. However, existing methods often face limitations in the diversity and quality of synthetic data, leading to compromised fairness and overall model accuracy. Moreover, many approaches rely on the availability of demographic group labels, which are of

Xinchao Zhou, Deepak A. Suresh, F. Robicheaux, Chen-Lung Hung

We experimentally and theoretically study collective emission of a dense atomic ensemble coupled to a single mode in a nanophotonic microring resonator. Because many cold atoms are localized in a small volume, these trapped atoms collectively couple not only to the guided resonator mode but also to the nonguided modes in free space. Through tuning the atom-p

A. Coletta, S. Molinari, E. Schisano, A. Traficante

The mechanisms behind the fragmentation of high-mass dense clumps into compact star-forming cores are fundamental topics in current astrophysical research. The ALMAGAL survey provides the opportunity to study this process at an unprecedented level of detail and statistical significance, featuring high-angular resolution $1.38$ mm ALMA observations of $1013$

Matthew Faw, Constantine Caramanis, Jessica Hoffmann

Unconscious bias has been shown to influence how we assess our peers, with consequences for hiring, promotions and admissions. In this work, we focus on affinity bias, the component of unconscious bias which leads us to prefer people who are similar to us, despite no deliberate intention of favoritism. In a world where the people hired today become part of t

Feodor F. Dragan, Ekkehard Köhler

Two graph parameters are said to be coarsely equivalent if they are within constant factors from each other for every graph $G$. Recently, several graph parameters were shown to be coarsely equivalent to tree-length. Recall that the length of a tree-decomposition ${\cal T}(G)$ of a graph $G$ is the largest diameter of a bag in ${\cal T}(G)$, and the tree-len

Jonathan T. Weber, András Kovács, Michalis Charilaou, Deli Kong

The all-optical control of magnetization at room temperature broadens the scope of applications of spin degrees-of-freedom in data storage, spintronics, and quantum computing. Topological magnetic spin structures, such as skyrmions, are of particular interest due to their particle-like properties, small size and inherent stability. Controlling skyrmion state

Yu Zhang, Shutong Qiao, Jiaqi Zhang, Tzu-Heng Lin

Information technology has profoundly altered the way humans interact with information. The vast amount of content created, shared, and disseminated online has made it increasingly difficult to access relevant information. Over the past two decades, recommender systems and search (collectively referred to as information retrieval systems) have evolved signif

Manuel Morales-Alvarado, Fernando Quevedo, Mario Ramos-Hamud, Diego Restrepo

We perform a comprehensive statistical analysis of key scientific metrics to evaluate the productivity and impact of research conducted in Latin American countries within the fields of High Energy Physics, Cosmology and Astroparticle Physics (HECAP). Using data from the widely used open-access digital library INSPIRE-HEP, we provide a detailed assessment of

Yasser H. Khalil, Leo Brunswic, Soufiane Lamghari, Xu Li

Federated unlearning (FU) aims to remove a participant's data contributions from a trained federated learning (FL) model, ensuring privacy and regulatory compliance. Traditional FU methods often depend on auxiliary storage on either the client or server side or require direct access to the data targeted for removal-a dependency that may not be feasible if th

Jianye Xu, Johannes Betz, Armin Mokhtarian, Archak Mittal

This article proposes a roadmap to address the current challenges in small-scale testbeds for Connected and Automated Vehicles (CAVs) and robot swarms. The roadmap is a joint effort of participants in the workshop "1st Workshop on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms," held on June 2 at the IEEE Intelligent Vehicles Symp

Simone Ausilio, Fausto Borgonovi, Giuseppe Luca Celardo, Jorge Yago Malo

Complex quantum networks are powerful tools in the modeling of transport phenomena, particularly for biological systems, and enable the study of emergent phenomena in many-body quantum systems. High connectivity and long-range interactions induce strong constraints on the system dynamics. Here, we study the transport properties of a quantum network described

William T. Emond, Laura Engelbrecht, Nathan Moynihan, Chris D. White

The double copy connects scattering amplitudes and other objects in gauge and gravity theories. Open conceptual issues include whether non-local information in gravity theories can be generated from the double copy, and how the double copy should be practically implemented in unseen cases. In this paper, we consider topological theories (with and without mas

Yunfan Jiang, Ruohan Zhang, Josiah Wong, Chen Wang

Real-world household tasks present significant challenges for mobile manipulation robots. An analysis of existing robotics benchmarks reveals that successful task performance hinges on three key whole-body control capabilities: bimanual coordination, stable and precise navigation, and extensive end-effector reachability. Achieving these capabilities requires

Hajime Kobayashi, Shinji Mukohyama, Johannes Noller, Sergi Sirera

Black hole solutions in general scalar-tensor theories are known to permit hair, i.e. non-trivial scalar profiles and/or metric solutions different from the ones of General Relativity (GR). Imposing that some such solutions$\unicode{x2013}$e.g. Schwarzschild or de Sitter solutions motivated in the context of black hole physics or cosmology$\unicode{x2013}$sh

Susanna Bertelli, Fabio Bossi, Riccardo De Sangro, Claudio Di Giulio

The PADME experiment at the Frascati DA$\Phi$NE LINAC has performed a search for the hypothetical X17 particle, with a mass of around 17 MeV, by scanning the energy of a positron beam striking a fixed target. The X17 should be produced from the resulting $e^+e^-$ annihilation. Since the expected mass of this particle is only roughly known, data sidebands can

Leon Pietschmann, Michel Schimpf, Zhu-Tian Chen, Hanspeter Pfister

This study investigates the influence of Visual Guidance (VG) on user performance and human factors within Augmented Reality (AR) via a between-subjects experiment. VG is a crucial component in AR applications, serving as a bridge between digital information and real-world interactions. Unlike prior research, which often produced inconsistent outcomes, our s

Harish Panneer Selvam, Bharat Jayaprakash, Yan Li, Shashi Shekhar

This work presents a physics-based machine learning framework to predict and analyze oxides of nitrogen (NOx) emissions from compression-ignition engine-powered vehicles using on-board diagnostics (OBD) data as input. Accurate NOx prediction from OBD datasets is difficult because NOx formation inside an engine combustion chamber is governed by complex proces

Yihao Liu, Yu-Chun Ku, Jiaming Zhang, Hao Ding

Data scarcity has long been an issue in the robot learning community. Particularly, in safety-critical domains like surgical applications, obtaining high-quality data can be especially difficult. It poses challenges to researchers seeking to exploit recent advancements in reinforcement learning and imitation learning, which have greatly improved generalizabi

Tatsuya Daikoku, Usha Goswami

Infant-directed speech (IDS) is highly rhythmic, and in European languages IDS is dominated by patterns of amplitude modulation (AM) at ~2Hz (reflecting prosody) and ~5Hz (reflecting individual syllables). The rhythm structure of spoken Japanese is thought to differ from European stress-timed and syllable-timed languages, depending on moraic units. Morae com

Jiang-Hua Lu, Mykola Matviichuk

For a complex algebraic torus $\mathbb{T}$, we study $\mathbb{T}$-invariant Poisson deformations of a $\mathbb{T}$-log-symplectic log-canonical Poisson structure $\pi_0$ on $\mathbb{C}^n$. We show that every $\mathbb{T}$-invariant first-order deformation of $\pi_0$ with linearly independent $(\mathbb{C}^\times)^n$-weights is unobstructed. For a special class

Ivon Dorado, Gonzalo Medina

We present a new solution to the classification problem for the category of representations of a quiver of type $\widetilde{A}_{3}$. Our approach uses linear algebra techniques which lead us to a reduction that allows to use induction. As an application, the solution to the classical Kronecker problem and its contragredient version are obtained in an element

Yilin Xie, Shiqiang Zhang, Jixiang Qing, Ruth Misener

Graph-structured data are central to many scientific and industrial applications where the goal is to optimize expensive black-box objectives defined over graph structures or node configurations -- as seen in molecular design, supply chains, and sensor placement. Bayesian optimization offers a principled approach for such settings, but existing methods large

Han Lin Mai, Xiang-Yuan Cui, Tilmann Hickel, Jörg Neugebauer

Segregation of alloying elements and impurities at grain boundaries (GBs) critically influences material behavior by affecting cohesion. In this study, we present an ab initio high-throughput evaluation of segregation energies and cohesive effects for all elements in the periodic table (Z: 1 to 92, H to U) across six model ferritic iron GBs using density fun

Yuxuan Bian, Zhaoyang Zhang, Xuan Ju, Mingdeng Cao

Video inpainting, which aims to restore corrupted video content, has experienced substantial progress. Despite these advances, existing methods, whether propagating unmasked region pixels through optical flow and receptive field priors, or extending image-inpainting models temporally, face challenges in generating fully masked objects or balancing the compet

Mark YU, Wenbo Hu, Jinbo Xing, Ying Shan

We present TrajectoryCrafter, a novel approach to redirect camera trajectories for monocular videos. By disentangling deterministic view transformations from stochastic content generation, our method achieves precise control over user-specified camera trajectories. We propose a novel dual-stream conditional video diffusion model that concurrently integrates

Lee Kendall, Dawn Ford, Giovanni Zangari, Stephen McDonnell

We developed a facile and scalable 3-step hydrothermal, electrodeposition, and annealing technique to synthesize a variety of nanowire heterostructures. The heterojunction catalysts of CP-ZnO-MoS2 and CP-ZnO-ZnS-MoS2 both saw an increase in catalytic activity in the acidic regime, with overpotentials to reach 10 mA/cm2 of 181 mV and 154 mV respectively, over

Sarah T. Stewart, Simon J. Lock, Philip J. Carter, Erik J. Davies

The origin of chondrules, and the chondritic sedimentary rocks that dominate the meteoritic record, is a long-standing problem in planetary science. Here, we develop a physical model for the formation of chondritic mixtures as an outcome of vaporizing collisions between planetesimals that were dynamically excited by the growth and migration of planets. We pr

Metin Gürses, Aslı Pekcan

Frobenius companion matrices arise when we write an $n$-th order linear ordinary differential equation as a system of first order differential equations. These matrices and their transpose have very nice properties. By using the powers of these matrices we form a closed algebra under the matrix multiplication. Structure constants of this commuting algebra ar

Tat-Thang Vo, Tran Trong Khoi Le, Sivem Afach, Stijn Vansteelandt

Obtaining causally interpretable meta-analysis results is challenging when there are differences in the distribution of effect modifiers between eligible trials. To overcome this, recent work on transportability methods has considered standardizing results of individual studies over the case-mix of a target population, prior to pooling them as in a classical

Bernard Akwei, Luke Rogers, Alexander Teplyaev

Motivated by the problem of understanding theoretical bounds for the performance of the Belkin-Niyogi Laplacian eigencoordinate approach to dimension reduction in machine learning problems, we consider the convergence of random graph Laplacian operators to a Laplacian-type operator on a manifold. For $\{X_j\}$ i.i.d.\ random variables taking values in $\math

Aaditya K. Singh, Ted Moskovitz, Sara Dragutinovic, Felix Hill

In-context learning (ICL) is a powerful ability that emerges in transformer models, enabling them to learn from context without weight updates. Recent work has established emergent ICL as a transient phenomenon that can sometimes disappear after long training times. In this work, we sought a mechanistic understanding of these transient dynamics. Firstly, we

Tian Qiu, Ruiming Du, Nikolai Spine, Lailiang Cheng

Modern orchards are planted in structured rows with distinct panel divisions to improve management. Accurate and efficient joint segmentation of point cloud from Panel to Tree and Branch (P2TB) is essential for robotic operations. However, most current segmentation methods focus on single instance segmentation and depend on a sequence of deep networks to per

Ali Samimi Fard, Mohammadreza Mashhadigholamali, Samaneh Zolfaghari, Hajar Abedi

Human Activity Recognition has gained significant attention due to its diverse applications, including ambient assisted living and remote sensing. Wearable sensor-based solutions often suffer from user discomfort and reliability issues, while video-based methods raise privacy concerns and perform poorly in low-light conditions or long ranges. This study intr

Dan Hendrycks, Eric Schmidt, Alexandr Wang

Rapid advances in AI are beginning to reshape national security. Destabilizing AI developments could rupture the balance of power and raise the odds of great-power conflict, while widespread proliferation of capable AI hackers and virologists would lower barriers for rogue actors to cause catastrophe. Superintelligence -- AI vastly better than humans at near

Jingyu Xu, Yang Wang

Artificial intelligence has shown the potential to improve diagnostic accuracy through medical image analysis for pneumonia diagnosis. However, traditional multimodal approaches often fail to address real-world challenges such as incomplete data and modality loss. In this study, a Flexible Multimodal Transformer (FMT) was proposed, which uses ResNet-50 and B

Jose Gonzalez, Tobias Stauber

We propose a new route to induce flat bands with a strong superconducting instability in graphene bilayers with heteroshear, where the 1D character of the moir\'e leads to stronger correlations than in twisted bilayer graphene. We adopt an exact diagonalization approach, on top of a real-space self-consistent Hartree-Fock approximation, to show how the valle

Basak Sakcak, Dylan A. Shell, Jason M. O'Kane

There is now a large body of techniques, many based on formal methods, for describing and realizing complex robotics tasks, including those involving a variety of rich goals and time-extended behavior. This paper explores the limits of what sorts of tasks are specifiable, examining how the precise grounding of specifications, that is, whether the specificati

Kyle Heuton, F. Samuel Muench, Shikhar Shrestha, Thomas J. Stopka

Optimal allocation of scarce resources is a common problem for decision makers faced with choosing a limited number of locations for intervention. Spatiotemporal prediction models could make such decisions data-driven. A recent performance metric called fraction of best possible reach (BPR) measures the impact of using a model's recommended size K subset of

Richard C. Brower, George T. Fleming, Jin-Yun Lin, Nobuyuki Matsumoto

We review the recent construction \cite{brower2024isingmodelmathbbs2} of the 2d Ising model on a triangulated sphere $\mathbb{S}^2$. Surprisingly, this led to a precise map of the lattice couplings to the target geometry in order to reach the conform field theory (CFT) in the continuum limit. For the integrable 2d Ising CFT, the map was found analytically \c

Xuanqing Liu, Luyang Kong, Wei Niu, Afshin Khashei

Large language models (LLMs) have demonstrated remarkable capabilities in handling complex dialogue tasks without requiring use case-specific fine-tuning. However, analyzing live dialogues in real-time necessitates low-latency processing systems, making it impractical to deploy models with billions of parameters due to latency constraints. As a result, pract

Qian Ying Lee, Suhas Raghavendra Kulkarni, Kenzhi Iskandar Wong, Lin Yang

Generalizing robot trajectories from human demonstrations to new contexts remains a key challenge in Learning from Demonstration (LfD), particularly when only single-context demonstrations are available. We present a novel Gaussian Mixture Model (GMM)-based approach that enables systematic generalization from single-context demonstrations to a wide range of

Luca Mossina, Corentin Friedrich

Image segmentation is a challenging task influenced by multiple sources of uncertainty, such as the data labeling process or the sampling of training data. In this paper we focus on binary segmentation and address these challenges using conformal prediction, a family of model- and data-agnostic methods for uncertainty quantification that provide finite-sampl

Prakash Thakolkaran, Yaqi Guo, Shivam Saini, Mathias Peirlinck

Traditional constitutive models rely on hand-crafted parametric forms with limited expressivity and generalizability, while neural network-based models can capture complex material behavior but often lack interpretability. To balance these trade-offs, we present monotonic Input-Convex Kolmogorov-Arnold Networks (ICKANs) for learning polyconvex hyperelastic c

Alberto Raiola, Emanuele Locatelli, Davide Marenduzzo, and Enzo Orlandini

Magnetic polymers are examples of composite soft materials in which the competition between the large configurational entropy of the soft substrate (polymer) and the magnetic interaction may give rise to rich equilibrium phase diagrams as well as non-standard critical phenomena. Here, we study a self-avoiding walk model decorated by Ising spins of value $0$

Giorgia Proto

The standard gas mixture for the Resistive Plate Chambers (RPC), composed of C2H2F4/i-C4H10/SF6, allows the detector operation in avalanche mode, as required by the high-luminosity collider experiments. The gas density, the low current and the comfortable avalanche-streamer separation guarantee high detection efficiency, rate capability and slow detector age

Dane Wachs

We propose a novel derived cohomological framework for the Birch and Swinnerton-Dyer (BSD) conjecture for elliptic curves. In our approach, local arithmetic data are encoded in derived sheaves which, when glued via a mapping cone construction, yield an adelic complex. A natural Postnikov filtration on this complex gives rise to a spectral sequence whose firs

Dong Shu, Xuansheng Wu, Haiyan Zhao, Daking Rai

Large Language Models (LLMs) have transformed natural language processing, yet their internal mechanisms remain largely opaque. Recently, mechanistic interpretability has attracted significant attention from the research community as a means to understand the inner workings of LLMs. Among various mechanistic interpretability approaches, Sparse Autoencoders (

Courtney Klein, Jenny D. Wang, Luke Xia, James S. Bullock

We study the intrinsic and observable shapes of approximately 700 star-forming galaxies with stellar masses of $10^8 - 10^{11}$ M$_\odot$ from the FIREbox simulation at $z=0$. We calculate intrinsic axis ratios using inertia tensors weighted by three morphology types: "All Stars," "Young Stars," and "Luminosity-weighted Stars." Young Stars shows mass-depende

W. Oxley, R. R. Kerswell

Transient growth mechanisms operating on streaky shear flows are believed important for sustaining near-wall turbulence. Of the three individual mechanisms present - Orr, lift-up and 'push over' - Lozano-Duran et. al. (J. Fluid Mech. 914, A8, 2021) have recently observed that both Orr and push over need to be present to sustain turbulent fluctuations given s

Bernard Akwei

On the unit interval (I), and the Sierpinski Gasket ($\mathcal{SG}$), the spectral decimation function of the Laplacian has similar properties that result in positive minimum spacing of eigenvalues. Other fractals, for example the level-3 Sierpinski Gasket, $\mathcal{SG}_3$, may not necessarily enjoy these properties. Our goal is to obtain an easy and suffic

Pushkar Mishra, Charvi Rastogi, Stephen R. Pfohl, Alicia Parrish

Ensuring the safety of Generative AI requires a nuanced understanding of pluralistic viewpoints. In this paper, we introduce a novel data-driven approach for analyzing ordinal safety ratings in pluralistic settings. Specifically, we address the challenge of interpreting nuanced differences in safety feedback from a diverse population expressed via ordinal sc

Austin A. Morris, Tianhao Wei, Zhi Wang, Ying Xia

We use experimental and simulated excitation functions to estimate the yield of deuteron activations on a [$^{176}$Yb]Yb$_2$O$_3$ target enriched to 99%. Subsequent calculations are used to determine the production of radiotherapeutic $^{177}$Lu according to a 10 mA, 18 MeV $D^+$ compact linear accelerator. The design comprises a single radio-frequency quadr

Cyprien Tamekue, ShiNung Ching

We study controllability and constructive synthesis for control-affine systems. We introduce trajectory-dependent Gramian maps that extend the linear time-varying Gramian and yield explicit fixed-point synthesis maps. On feasible coercivity classes (uniform eigenvalue lower bounds), the Gramian map is Lipschitz, and under a comparison estimate criterion, syn

Hanae Elmekki, Ahmed Alagha, Hani Sami, Amanda Spilkin

Cardiac ultrasound (US) scanning is a commonly used techniques in cardiology to diagnose the health of the heart and its proper functioning. Therefore, it is necessary to consider ways to automate these tasks and assist medical professionals in classifying and assessing cardiac US images. Machine learning (ML) techniques are regarded as a prominent solution

Jia-Chen Shang

The patterned design of flexible sensors enables customized performance to meet diverse application demands. However, when multiple geometric parameters and sensing metrics are involved, experimental approaches to establish structure-performance relationships become costly and inefficient. Here, a novel universal piezoresistive model--overcoming limitations

Roberto Flórez-Ablan, Marco Roth, Jan Schnabel

Quantum kernels (QK) are widely used in quantum machine learning applications; yet, their potential to surpass classical machine learning methods on classical datasets remains uncertain. This limitation can be attributed to the exponential concentration phenomenon, which can impair generalization. A common strategy to alleviate this is bandwidth tuning, whic

Mufan Liu, Qi Yang, Miaoran Zhao, He Huang

Implicit neural representations (INRs) enable fast video compression and effective video processing, but a single model rarely offers scalable decoding across rates and resolutions. In practice, multi-resolution typically relies on retraining or multi-branch designs, and structured pruning failed to provide a permutation-invariant progressive transmission or

Yajun Zhou

We prove and generalize some recent conjectures of Z.-W. Sun on infinite series whose summands involve products of harmonic numbers and several binomial coefficients. We evaluate various classes of infinite sums in closed form by interpreting them as automorphic objects on the moduli spaces for Legendre curves $Y^{ g+1}=(1-X)^{ g}X(1-t X)$ of positive genera

Giorgia Proto

A new generation of Resistive Plate Chambers have been developed for the ATLAS phase-2 upgrade in sight of the High-Luminosity phase of the Large Hadron Collider. These RPCs consist of three independent 1 mm gas gaps(singlets) equipped with a newly low-threshold Front-End electronics, assembled in the same mechanical structure (triplet). During 2024 the prod

Simone Faro, Arianna Pavone, Caterina Viola

String matching is a fundamental problem in computer science, with critical applications in text retrieval, bioinformatics, and data analysis. Among the numerous solutions that have emerged for this problem in recent decades, bit-parallelism has significantly enhanced their practical efficiency, leading to the development of several optimized approaches for

Zheng Li, Liangbin Xie, Jiantao Zhou, Xintao Wang

Although diffusion-based techniques have shown remarkable success in image generation and editing tasks, their abuse can lead to severe negative social impacts. Recently, some works have been proposed to provide defense against the abuse of diffusion-based methods. However, their protection may be limited in specific scenarios by manually defined prompts or

Julia Ackermann, Thomas Kruse, Mikhail Urusov

We analyze a continuous-time optimal trade execution problem in multiple assets where the price impact and the resilience can be matrix-valued stochastic processes that incorporate cross-impact effects. In addition, we allow for stochastic terminal and running targets. Initially, we formulate the optimal trade execution task as a stochastic control problem w

Bimin Cai, Maarten L. Van de Put, Massimo V. Fischetti

Electronic transport in low-dimensional structures, such as thin bodies, nanosheets, nanoribbons and nanowires, is strongly affected by electron and phonon confinement, in addition to interface roughness. Here we use a quantum-transport formulation based on empirical pseudopotentials and the Master equation to study the effect of the phonon boundary conditio

Huatong Song, Jinhao Jiang, Yingqian Min, Jie Chen

Existing Large Reasoning Models (LRMs) have shown the potential of reinforcement learning (RL) to enhance the complex reasoning capabilities of Large Language Models~(LLMs). While they achieve remarkable performance on challenging tasks such as mathematics and coding, they often rely on their internal knowledge to solve problems, which can be inadequate for

Mariya L. Ivanova, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic

This paper introduces several proof-of-concept (PoC) computational methods intended to offer biochemical researchers straightforward, time- and cost-effective strategies to accelerate their work. While Machine Learning (ML) models were developed, the study's central purpose was to explore approaches for the identification of desirable functional groups/fragm

Fabian Frei, Dennis Komm, Moritz Stocker, Philip Whittington

The time-optimal $k$-server problem minimizes the time spent serving all requests instead of the distances traveled. We give a lower bound of $2k-1$ on the competitive ratio of any deterministic online algorithm for this problem, which coincides with the best known upper bound on the competitive ratio achieved by the work-function algorithm for the classical

Shiping Yang, Jie Wu, Wenbiao Ding, Ning Wu

Robustness has become a critical attribute for the deployment of RAG systems in real-world applications. Existing research focuses on robustness to explicit noise (e.g., document semantics) but overlooks implicit noise (spurious features). Moreover, previous studies on spurious features in LLMs are limited to specific types (e.g., formats) and narrow scenari

Peter Ludwig Rodríguez-Kessler

Herein, the structure and stability of double icosahedron Ag$_{17}$M$_2$ (M = Ni, Cu, Zn) clusters are investigated using density functional theory (DFT) computations. The results indicate that the clusters favor endohedral configurations in the doublet state, as confirmed with four different functionals: BP86, PBE0, B3PW91, and TPSSh. Additionally, the dope

Libo Zhu, Haotong Qin, Kaicheng Yang, Wenbo Li

One-step diffusion-based image super-resolution (OSDSR) models are showing increasingly superior performance nowadays. However, although their denoising steps are reduced to one and they can be quantized to 8-bit to reduce the costs further, there is still significant potential for OSDSR to quantize to lower bits. To explore more possibilities of quantized O

Simone Rossi, Valentina Caprotti, Andrea Filippi, Emiliano Bonera

Relativistic effects influence the motion of charged particles in solids by intertwining spin and momentum. The resulting phenomena exhibit rich and intriguing properties that can unveil radically new quantum devices. In this context, the two-dimensional hole gas formed in group IV heterostructures is a particularly promising platform, owning to a notable sp

Yang Mu, Muhammad Shahzad, Xiao Xiang Zhu

Multivariate Time Series Classification (MTSC) is crucial in extensive practical applications, such as environmental monitoring, medical EEG analysis, and action recognition. Real-world time series datasets typically exhibit complex dynamics. To capture this complexity, RNN-based, CNN-based, Transformer-based, and hybrid models have been proposed. Unfortunat

Michael Radica, Jake Taylor, Hannah R. Wakeford, David Lafrenière

A planet's albedo is a fundamental property that sets its energy budget by dictating the fraction of incident radiation absorbed versus reflected back to space. Generally, optical eclipse observations have revealed the majority of hot, giant planets to have low albedos, indicating dayside atmospheres dominated by absorption instead of reflection. However, th

Sameer Sethi, Donald Martin, Emmanuel Klu

This paper presents SYMBIOSIS, an AI-powered framework and platform designed to make Systems Thinking accessible for addressing societal challenges and unlock paths for leveraging systems thinking frameworks to improve AI systems. The platform establishes a centralized, open-source repository of systems thinking/system dynamics models categorized by Sustaina

Jens Braun, Andreas Geißel, Jan M. Pawlowski, Franz R. Sattler

Systematic expansion schemes in functional approaches require the inclusion of higher order vertices. These vertices are expanded in independent tensor bases with a rapidly increasing number of basis elements. Amongst the related tasks are the construction of bases and projection operators, the importance ordering of their elements, and the optimisation of s

Shea D. Burns, Dennis Davenport, Shakuan Frankson, Conner Griffin

We characterize relative notions of syndetic and thick sets using, what we call, "derived" sets along ultrafilters. Manipulations of derived sets is a characteristic feature of algebra in the Stone-\v{C}ech compactification and its applications. Combined with the existence of idempotents and structure of the smallest ideal in closed subsemigroups of the Ston

Jian Liu, Wei Sun, Kai Zeng, Jin Zheng

Pose estimation-guided unseen object 6-DoF robotic manipulation is a key task in robotics. However, the scalability of current pose estimation methods to unseen objects remains a fundamental challenge, as they generally rely on CAD models or dense reference views of unseen objects, which are difficult to acquire, ultimately limit their scalability. In this p

Daniel Hollarek, Henrik Schopmans, Jona Östreicher, Jonas Teufel

Powder X-ray diffraction (pXRD) experiments are a cornerstone for materials structure characterization. Despite their widespread application, analyzing pXRD diffractograms still presents a significant challenge to automation and a bottleneck in high-throughput discovery in self-driving labs. Machine learning promises to resolve this bottleneck by enabling au

F. G. Mertens, M. Mevius, L. V. E. Koopmans, A. R. Offringa

We present new upper limits on the 21-cm signal power spectrum from the Epoch of Reionization (EoR), at redshifts $z \approx 10.1, 9.1, \text{ and } 8.3$, based on reprocessed observations from the Low-Frequency Array (LOFAR). The analysis incorporates significant enhancements in calibration methods, sky model subtraction, radio-frequency interference (RFI)

Toby Boyne, Jose Pablo Folch, Robert M Lee, Behrang Shafei

We perform Bayesian optimization using a Gaussian process perspective on Bayesian Additive Regression Trees (BART). Our BART Kernel (BARK) uses tree agreement to define a posterior over piecewise-constant functions, and we explore the space of tree kernels using a Markov chain Monte Carlo approach. Where BART only samples functions, the resulting BARK model

Feeza Khan Khanzada, Jaerock Kwon

Model-based Reinforcement Learning (MBRL) has emerged as a promising paradigm for autonomous driving, where data efficiency and robustness are critical. Yet, existing solutions often rely on carefully crafted, task specific extrinsic rewards, limiting generalization to new tasks or environments. In this paper, we propose InDRiVE (Intrinsic Disagreement based

Ville Salo

We study groups of reversible cellular automata, or CA groups, on groups. More generally, we consider automorphism groups of subshifts of finite type on groups. It is known that word problems of CA groups on virtually nilpotent groups are in co-NP, and can be co-NP-hard. We show that under the Gap Conjecture of Grigorchuk, their word problems are PSPACE-hard

Julius Schöning, Niklas Kruse

The increasing integration of artificial intelligence (AI) systems in various fields requires solid concepts to ensure compliance with upcoming legislation. This paper systematically examines the compliance of AI systems with relevant legislation, focusing on the EU's AI Act and the compliance of data sets. The analysis highlighted many challenges associated

Yernar Zhetpissov, Xihan Ma, Kehan Yang, Haichong K. Zhang

Conventional freehand ultrasound (US) imaging is highly dependent on the skill of the operator, often leading to inconsistent results and increased physical demand on sonographers. Robotic Ultrasound Systems (RUSS) aim to address these limitations by providing standardized and automated imaging solutions, especially in environments with limited access to ski

Xiaobei Zhao, Xiangrong Zeng, Yihang Ma, Pengjin Tang

In tomato greenhouse, phenotypic measurement is meaningful for researchers and farmers to monitor crop growth, thereby precisely control environmental conditions in time, leading to better quality and higher yield. Traditional phenotyping mainly relies on manual measurement, which is accurate but inefficient, more importantly, endangering the health and safe

S. Tchuiaga, C. Dor Kewir

We introduce a systematic theory of Weil bundles over \( p \)-adic analytic manifolds, forging new connections between differential calculus over non-archimedean fields and arithmetic geometry. By developing a framework for infinitesimal structures in the \( p \)-adic setting, we establish that Weil bundles \( M^A \) associated with a \( p \)-adic manifold \

Lin Deng, Chengguan Fang, Hang Yu, Yisen Wang

On-site potentials are ubiquitous in physical systems and strongly influence their heat transport and energy localization. These potentials will inevitably affect the dynamical properties of $q$-breathers (QBs), defined as periodic orbits exponentially localized in normal mode space. By integrating on-site terms into the Fermi-Pasta-Ulam-Tsingou-$\beta$ syst