March 2025 arXiv papers — page 133

Jesús Lucia-Tamudo, Michelle Menkel-Lantz, Enrico Tapavicza

Second-generation molecular nanomotors are becoming more popular within the biomedical field and intense research is being conducted to increase their efficiency for light-induced ultrafast photoisomerization. A key requirement for designing efficient molecular nanomotors is ensuring unidirectional rotation during isomerization and thermal helix inversion. H

Hasan Iqbal, Nazmul Karim, Umar Khalid, Azib Farooq

Instruction-guided generative models, especially those using text-to-image (T2I) and text-to-video (T2V) diffusion frameworks, have advanced the field of content editing in recent years. To extend these capabilities to 4D scene, we introduce a progressive sampling framework for 4D editing (PSF-4D) that ensures temporal and multi-view consistency by intuitive

Hongkai Zheng, Wenda Chu, Bingliang Zhang, Zihui Wu

Plug-and-play diffusion priors (PnPDP) have emerged as a promising research direction for solving inverse problems. However, current studies primarily focus on natural image restoration, leaving the performance of these algorithms in scientific inverse problems largely unexplored. To address this gap, we introduce \textsc{InverseBench}, a framework that eval

Mihai Fulger, Victor Lozovanu

We introduce two sets of invariants for a line bundle at a point: infinitesimal successive minima and asymptotic partial jet separation. They are inspired by the local analogue of Ambro-Ito, and by the jet-theoretic interpretation of the Seshadri constant respectively. Under mild restrictions the two sets are equal. Moving to convex geometry, we prove that t

Qiyin Huang, Tiemin Li, Yao Jiang

Object reorientation is a critical task for robotic grippers, especially when manipulating objects within constrained environments. The task poses significant challenges for motion planning due to the high-dimensional output actions with the complex input information, including unknown object properties and nonlinear contact forces. Traditional approaches si

Bruno Pinheiro, M. C. Llerena Velasquez, Giovane Faria, A. F. C. Aquino

In this paper, the impact of inverter-based resources (IBRs) on the frequency dynamics of the Brazilian Interconnected Power System (BIPS) is evaluated. A measurement-based framework is proposed to assess the impact of IBR penetration on the system-wide and regional/local frequency dynamic. The analysis leverages data from a low-voltage wide area monitoring

Tatsuya Miki, Hsiao-Yi Chen, Takashi Koretsune, Yusuke Nomura

DiracBilinears.jl is a Julia package for computing Dirac bilinears, which are fundamental physical quantities of electrons in relativistic quantum theory, using first-principles calculations for solids. In relativistic quantum theory, 16 independent bilinears can be defined using the four-component Dirac field. We take the non-relativistic limit for the bili

Mingjie Wei, Xuemei Xie, Guangming Shi

Attributes such as style, fine-grained text, and trajectory are specific conditions for describing motion. However, existing methods often lack precise user control over motion attributes and suffer from limited generalizability to unseen motions. This work introduces an Attribute Controllable Motion generation architecture, to address these challenges via d

Saman Ahmadi, Andrea Raith, Mahdi Jalili

Constrained pathfinding is a well-studied, yet challenging network optimisation problem that can be seen in a broad range of real-world applications. Pathfinding with multiple resource limits, which is known as the Resource Constrained Shortest Path Problem (RCSP), aims to plan a cost-optimum path subject to limited usage of resources. Given the recent advan

Tatsuya Shirato, Ryota Yambe, Satoru Hayami

We theoretically propose a method to generate topological spin textures by irradiating a classical spin system with a linearly polarized AC electric field. To this end, we investigate non-equilibrium steady states in a classical Heisenberg model with frustrated exchange interactions on a two-dimensional triangular lattice by numerically solving the Landau-Li

Yun Wang, KaiFan Ji, Zhenyu Jin, Hui Liu

Image alignment plays a crucial role in solar physics research, primarily involving translation, rotation, and scaling. \G{The different wavelength images of the chromosphere and transition region have structural complexity and differences in similarity, which poses a challenge to their alignment.} Therefore, a novel alignment approach based on dense optical

Andreas Tataris, Tristan van Leeuwen, Alexander V. Mamonov

In this paper we develop a numerical method for solving an inverse scattering problem of estimating the scattering potential in a Schr\"{o}dinger equation from frequency domain measurements based on reduced order models (ROM). The ROM is a projection of Schr\"{o}dinger operator onto a subspace spanned by its solution snapshots at certain wavenumbers. Provide

Takumi Terauchi, Wei Yi, Rikuto Takada Hirofumi Akamatsu Ryo Ota Shuki Torii, Koji Fujita

The strategic design of ferroelectric materials exhibiting robust and reversible spontaneous polarization remains a pivotal challenge in functional materials research. Here, A-site cation disorder engineering is employed in the n = 2 Ruddlesden-Popper layered perovskite La2Ba1-xSrxIn2O7 to achieve room-temperature ferroelectricity. Systematic substitution of

Lilin Zhang, Chengpei Wu, Ning Yang

Existing adversarial training (AT) methods often suffer from incomplete perturbation, meaning that not all non-robust features are perturbed when generating adversarial examples (AEs). This results in residual correlations between non-robust features and labels, leading to suboptimal learning of robust features. However, achieving complete perturbation, i.e.

Anirban Chandra, Marius Koch, Suraj Pawar, Aniruddha Panda

This study aims to develop surrogate models for accelerating decision making processes associated with carbon capture and storage (CCS) technologies. Selection of sub-surface $CO_2$ storage sites often necessitates expensive and involved simulations of $CO_2$ flow fields. Here, we develop a Fourier Neural Operator (FNO) based model for real-time, high-resolu

Ming Deng, Sijin Sun, Zihao Li, Xiaochuan Hu

Camouflaged Object Detection (COD) is challenging due to the strong similarity between camouflaged objects and their surroundings, which complicates identification. Existing methods mainly rely on spatial local features, failing to capture global information, while Transformers increase computational costs. To address this, the Frequency-Assisted Mamba-Like

Weiye Gan, Yicheng Li, Qian Lin, Zuoqiang Shi

Spectral bias is a significant phenomenon in neural network training and can be explained by neural tangent kernel (NTK) theory. In this work, we develop the NTK theory for deep neural networks with physics-informed loss, providing insights into the convergence of NTK during initialization and training, and revealing its explicit structure. We find that, in

Yixuan Zhang, Qing Chang, Yuxi Wang, Guang Chen

Speech-driven 3D facial animation seeks to produce lifelike facial expressions that are synchronized with the speech content and its emotional nuances, finding applications in various multimedia fields. However, previous methods often overlook emotional facial expressions or fail to disentangle them effectively from the speech content. To address these chall

Hejia Chen, Haoxian Zhang, Shoulong Zhang, Xiaoqiang Liu

Speech-driven 3D talking face method should offer both accurate lip synchronization and controllable expressions. Previous methods solely adopt discrete emotion labels to globally control expressions throughout sequences while limiting flexible fine-grained facial control within the spatiotemporal domain. We propose a diffusion-transformer-based 3D talking f

Chongxiao Zhao, Qi Ou, Chenyang Li, Wenjie Dou

An implementation of stochastic resolution of identity (sRI) approximation to CC2 oscillator strengths as well as ground state analytical gradients is presented. The essential 4-index electron repulsion integrals (ERIs) are contracted with a set of stochastic orbitals on the basis of the RI technique and the orbital energy differences in the denominators are

Chun-Ping Su, Zhao-Fan Cai, Tao Liu

Higher-order topological insulators have attracted significant interest in both static single-particle and many-body lattice systems. While periodically driven (Floquet) higher-order topological phases have been explored at the single-particle level, the role of interactions in such systems remains less understood. In this paper, we extend previous studies b

Sungwoo Cho, Jeongsoo Choi, Sungnyun Kim, Se-Young Yun

Despite recent advances in text-to-speech (TTS) models, audio-visual-to-audio-visual (AV2AV) translation still faces a critical challenge: maintaining speaker consistency between the original and translated vocal and facial features. To address this issue, we propose a conditional flow matching (CFM) zero-shot audio-visual renderer that utilizes strong dual

Leonardus B. Putra, H. S. Ramadhan

We investigate the gravitational lensing signatures of vorton configurations, considering the circular vorton, the Kibble-Turok vorton, and a newly proposed class that incorporates simultaneous excitations of the first, second, and third harmonic modes. Working within the weak-field and thin-lens approximations, we demonstrate that circular vortons produce a

Aditya De Saha

About 20 years ago, Bogdan Nica conjectured that the boundary of any word-hyperbolic group admits admits a fixed-point-free involution. In this very short article, we prove a variation of the conjecture, replacing word-hyperbolic groups with Right-angled coxeter groups. This is not a solution to Nica's conjecture, but hopefully the techniques will shed some

Imane Jarni, Ayoub Laayoun, Badr Missaoui

This paper establishes an equilibrium existence result for a class of Mean Field Games involving Reflected Stochastic Differential Equations. The proof relies on the framework of relaxed controls and martingale problems.

Haotian Dong, Jingyan Jiang, Rongwei Lu, Jiajun Luo

The emergence of large language models (LLMs) has revolutionized AI development, yet the resource demands beyond a single cluster or even datacenter, limiting accessibility to well-resourced organizations. Decentralized training has emerged as a promising paradigm to leverage dispersed resources across clusters, datacenters and regions, offering the potentia

Hai Huang, Ziteng Xu, Qi Xin, Zhaoyu Zhang

We present an fully AI-driven design framework for photonic crystals (PhCs), engineered to achieve high efficiency in photonic crystal surface-emitting lasers (PCSELs). By discretizing the PhC structure into a grid, where the edges of the holes are represented by the cross-sections of two-dimensional Gaussian surfaces, we achieve high-degree-of-freedom and f

Dylan Hirsch, Sylvia Herbert

Hamilton-Jacobi reachability (HJR) is an exciting framework used for control of safety-critical systems with nonlinear and possibly uncertain dynamics. However, HJR suffers from the curse of dimensionality, with computation times growing exponentially in the dimension of the system state. Many autonomous and controlled systems involve dynamics that evolve on

Ruochen Hou, Mingzhang Zhu, Hyunwoo Nam, Gabriel I. Fernandez

Accurate robot localization is essential for effective operation. Monte Carlo Localization (MCL) is commonly used with known maps but is computationally expensive due to landmark matching for each particle. Humanoid robots face additional challenges, including sensor noise from locomotion vibrations and a limited field of view (FOV) due to camera placement.

Pengcheng Wang, Xinghao Zhu, Yuxin Chen, Chenfeng Xu

Reinforcement Learning and Imitation Learning have achieved widespread success in many domains but remain constrained during real-world deployment. One of the main issues is the additional requirements that were not considered during training. To address this challenge, policy customization has been introduced, aiming to adapt a prior policy while preserving

Andrew Anderson, David Piorkowski, Margaret Burnett, Justin Weisz

Software engineers use code-fluent large language models (LLMs) to help explain unfamiliar code, yet LLM explanations are not adapted to engineers' diverse problem-solving needs. We prompted an LLM to adapt to five problem-solving style types from an inclusive design method, the Gender Inclusiveness Magnifier (GenderMag). We ran a user study with software en

Jiaqi Jin, Siwei Wang, Zhibin Dong, Xihong Yang

Multi-view clustering leverages complementary representations from diverse sources to enhance performance. However, real-world data often suffer incomplete cases due to factors like privacy concerns and device malfunctions. A key challenge is effectively utilizing available instances to recover missing views. Existing methods frequently overlook the heteroge

Han Wang, Jialin Zhang, Hongwei Yu

We analyze quantum parameter estimation by studying the dynamics of the quantum Fisher information (QFI) for two classes of parameters, acceleration and initial-state weight, in an Unruh-DeWitt detector undergoing four distinct noninertial motions: linear, cusped, catenary, and circular trajectories respectively. We assume that the detector is initialized in

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

We search, for the first time, for an exotic molecular state with quantum numbers $J^{PC}=1^{-+}$, called $X$, via the process $e^{+}e^{-} \to \gamma D^{+}_{s} D_{s1}^{-}(2536) +c.c.$ using data samples corresponding to a luminosity of $5.8~\mathrm{fb^{-1}}$ across center-of-mass energies from 4.612 to 4.951~GeV, collected with the BESIII detector operating

Yuanqing Zhang, Huanshui Zhang

In this paper, we propose an online learning-based predictive control (LPC) approach designed for nonlinear systems that lack explicit system dynamics. Unlike traditional model predictive control (MPC) algorithms that rely on known system models to optimize controller outputs, our proposed algorithm integrates a reinforcement learning component to learn opti

Qian Wang, Jiaxiang Li, Yu Xie

We have developed a phonon calculation software based on the supercell finite displacement method: ARES-Phonon. It can perform phonon and related property calculations using either non-diagonal or diagonal supercell approaches. Particularly for the non-diagonal supercell method, for phonons with wave vectors $(\frac{n_1}{m_1},\frac{n_2}{m_2},\frac{n_3}{m_3})

Ming Yang, Hongyu Cao, Lixuan Zhao, Chenrui Zhang

This paper presents a fully autonomous robotic system that performs sim-to-real transfer in complex long-horizon tasks involving navigation, recognition, grasping, and stacking in an environment with multiple obstacles. The key feature of the system is the ability to overcome typical sensing and actuation discrepancies during sim-to-real transfer and to achi

Abrar Hossain, Abubeker Abdurahman, Mohammad A. Islam, Kishwar Ahmed

This paper introduces TARDIS (Temporal Allocation for Resource Distribution using Intelligent Scheduling), a novel power-aware job scheduler for High-Performance Computing (HPC) systems that minimizes electricity costs through both temporal and spatial optimization. Our approach addresses the growing concerns of energy consumption in HPC centers, where elect

Chung-Ting Ke, Jun-Yi Tsai, Yen-Chun Chen, Zhen-Wei Xu

The superconducting qubit is one of the promising directions in realizing fault-tolerant quantum computing (FTQC), which requires many high-quality qubits. To achieve this, it is desirable to leverage modern semiconductor industry technology to ensure quality, uniformity, and reproducibility. However, conventional Josephson junction fabrication relies mainly

Raoni Arroyo, Lauro de Matos Nunes Filho, Frederik Moreira dos Santos

The processual consciousness interpretation (PCI), as developed in Arroyo (2024, chap. 5) and Arroyo, Nunes Filho, and Moreira dos Santos (2024), proposes a process ontology as a solution to the measurement problem. This article presents the standard interpretation taken to its ultimate ontological consequences; introduces the PCI; and offers reflections bas

Marcel Granetzny, Barret Elward, Oliver Schmitz

The Madison AWAKE Prototype (MAP) is a high-power, high-density helicon plasma experiment. The project's main goal is to develop a scalable plasma source for use in a beam-driven plasma wakefield accelerator as part of the AWAKE project. We measure the plasma density with a new heterodyne microwave interferometer that features several improvements over tradi

Gian Antariksa, Rohit Chakraborty, Shriyank Somvanshi, Subasish Das

Visual object detection utilizing deep learning plays a vital role in computer vision and has extensive applications in transportation engineering. This paper focuses on detecting pavement marking quality during daytime using the You Only Look Once (YOLO) model, leveraging its advanced architectural features to enhance road safety through precise and real-ti

Erfaun Noorani, Zachary Serlin, Ben Price, Alvaro Velasquez

The DARPA Transfer from Imprecise and Abstract Models to Autonomous Technologies (TIAMAT) program aims to address rapid and robust transfer of autonomy technologies across dynamic and complex environments, goals, and platforms. Existing methods for simulation-to-reality (sim-to-real) transfer often rely on high-fidelity simulations and struggle with broad ad

Yaohua Liu, Xinyuan Song, Yunfu Deng, Yifan Xie

Vision-and-Language Navigation (VLN) requires an embodied agent to traverse complex environments by following natural language instructions, demanding accurate alignment between visual observations and linguistic guidance. Despite recent progress, existing methods typically encode visual and directional cues in a coupled manner, and process instructions with

Chuhan Zhang, Chaoyang Zhu, Pingcheng Dong, Long Chen

In pursuit of detecting unstinted objects that extend beyond predefined categories, prior arts of open-vocabulary object detection (OVD) typically resort to pretrained vision-language models (VLMs) for base-to-novel category generalization. However, to mitigate the misalignment between upstream image-text pretraining and downstream region-level perception, a

Jiangning Wei, Lixiong Qin, Bo Yu, Tianjian Zou

Action recognition is a crucial task in artificial intelligence, with significant implications across various domains. We initially perform a comprehensive analysis of seven prominent action recognition methods across five widely-used datasets. This analysis reveals a critical, yet previously overlooked, observation: as the velocity of actions increases, the

Shriyank Somvanshi, Rohit Chakraborty, Subasish Das, Anandi K Dutta

Child bicyclists (14 years and younger) are among the most vulnerable road users, often experiencing severe injuries or fatalities in crashes. This study analyzed 2,394 child bicyclist crashes in Texas from 2017 to 2022 using two deep tabular learning models (ARM-Net and MambaNet). To address the issue of data imbalance, the SMOTEENN technique was applied, r

Hyunmin Cheong, Mehran Ebrahimi, Adrian Butscher, Francesco Iorio

A configuration design problem in mechanical engineering involves finding an optimal assembly of components and joints that realizes some desired performance criteria. Such a problem is a discrete, constrained, and black-box optimization problem. A novel method is developed to solve the problem by applying Bivariate Marginal Distribution Algorithm (BMDA) and

Yingrui Zhuang, Lin Cheng, Can Wan, Rui Xie

Prediction deviations of different uncertainties have varying impacts on downstream decision-making. Improving the prediction accuracy of critical uncertainties with significant impacts on decision-making quality yields better optimization results. Motivated by this observation, this paper proposes a novel weighted predict-and-optimize (WPO) framework for de

Hyunmin Cheong, Mehran Ebrahimi, Timothy Duggan

While multi-joint continuum robots are highly dexterous and flexible, designing an optimal robot can be challenging due to its kinematics involving curvatures. Hence, the current work presents a computational method developed to find optimal designs of continuum robots given reachability constraints. First, we leverage both forward and inverse kinematic comp

Moka Nishigaki, Peter Behroozi, Masami Ouchi, Hong Guo

We present an empirical chemical evolution model that explains the distribution of metals in the interstellar medium (ISM) and the circumgalactic medium (CGM) of galaxies based on the UniverseMachine and NeutralUniverseMachine models in the framework of $\Lambda$CDM structure formation. We parameterize the fractions of outflowing metals returned and mixed in

Hyunmin Cheong, Adrian Butscher

The current work presents an ontology developed for physics-based simulation in engineering design, called Physics-based Simulation Ontology (PSO). The purpose of the ontology is to assist in modelling the physical phenomenon of interest in a veridical manner, while capturing the necessary and reusable information for physics-based simulation solvers. The de

Quentin G. Bailey, Hailey S. Murray, Dario T. Walter-Cardona

In this work, we study a vector model of spontaneous spacetime-symmetry breaking coupled to gravity: the bumblebee model. The primary focus is on static spherically symmetric solutions. Complementing previous work on black hole solutions, we study the effects on the solutions when the vector field does not lie at the minimum of its potential. We first invest

Aashish Anantha Ramakrishnan, Aadarsh Anantha Ramakrishnan, Dongwon Lee

Writing Assistants (e.g., Grammarly, Microsoft Copilot) traditionally generate diverse image captions by employing syntactic and semantic variations to describe image components. However, human-written captions prioritize conveying a central message alongside visual descriptions using pragmatic cues. To enhance caption diversity, it is essential to explore a

Gaotang Li, Yuzhong Chen, Hanghang Tong

Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge. Previous works attribute this conflict to the interplay between "memory heads" and "context heads", attention heads assumed to promote either memory or context exclusively. In this study, we go beyond this fundamental assumption by uncovering a

Nishat Raihan, Marcos Zampieri

The development of Large Language Models (LLMs) remains heavily skewed towards English and a few other high-resource languages. This linguistic disparity is particularly evident for Bangla - the 5th most spoken language. A few initiatives attempted to create open-source Bangla LLMs with performance still behind high-resource languages and limited reproducibi

Jun-Feng Yang, Yan-Quan Feng, Fu-Gang Yin, Jin-Xin Zhou

A Cayley digraph on a group $G$ is called NNN if the Cayley digraph is normal and its automorphism group contains a non-normal regular subgroup isomorphic to $G$. A group is called NNND-group or NNN-group if there is an NNN Cayley digraph or graph on the group, respectively. In this paper, it is shown that there is no cyclic NNND-group, and hence no cyclic N

JuneYoung Park, YuMi Lee, Tae-Joon Kim, Jang-Hwan Choi

Meta-learning, or "learning to learn," aims to enable models to quickly adapt to new tasks with minimal data. While traditional methods like Model-Agnostic Meta-Learning (MAML) optimize parameters in Euclidean space, they often struggle to capture complex learning dynamics, particularly in few-shot learning scenarios. To address this limitation, we propose S

Yanjie Xu, Handing Xu, Tianmu Wang, Yaguan Li

Rotation-invariant recognition of shapes is a common challenge in computer vision. Recent approaches have significantly improved the accuracy of rotation-invariant recognition by encoding the rotational invariance of shapes as hand-crafted image features and introducing deep neural networks. However, the methods based on pixels have too much redundant inform

Juan Michael Sargado, Joachim Mathiesen

The enforcement of global energy conservation in phase-field fracture simulations has been an open problem for the last 25 years. Specifically, the occurrence of unstable fracture is accompanied by a loss in total potential energy, which suggests a violation of the energy conservation law. This phenomenon can occur even with purely quasi-static, displacement

Kaizhao Liu, Qi Long, Zhekun Shi, Weijie J. Su

Aligning large language models (LLMs) with diverse human preferences is critical for ensuring fairness and informed outcomes when deploying these models for decision-making. In this paper, we seek to uncover fundamental statistical limits concerning aligning LLMs with human preferences, with a focus on the probabilistic representation of human preferences an

Heonjoon Park, Weijie Li, Chaowei Hu, Christiano Beach

The fractional quantum anomalous Hall effect has recently been experimentally observed in zero-field fractional Chern insulators (FCI). However, an outstanding challenge is the presence of a substantial longitudinal resistance $R_{xx}$ (a few k$\Omega$), even though the anomalous Hall resistance $R_{xy}$ is quantized. This dissipative behavior is likely link

Laleh Aghababaie Beni, Oscar Higgott, Noah Shutty

Tesseract is a Most-Likely Error decoder designed for low-density-parity-check quantum error-correcting codes. Tesseract conducts a search through a graph on the set of all subsets of errors to find the lowest cost subset of errors consistent with the input syndrome. Although this graph is exponentially large, the search can be made efficient in practice for

Xuanpeng Xiao, Panpan Qi, Gongming Yu, Haitao Yang

In the Coulomb and Proximity Potential Model (CPPM) framework, we have investigated the cluster radioactivity and alpha decay half-lives of superheavy nuclei. We study 22 different versions of proximity potential forms that have been proposed to describe proton radioactivity, two-proton radioactivity, heavy-ion radioactivity, quasi-elastic scattering, fusion

Zhicheng Feng, Xieyuanli Chen, Chenghao Shi, Lun Luo

In this paper, we introduce a novel image-goal navigation approach, named RFSG. Our focus lies in leveraging the fine-grained connections between goals, observations, and the environment within limited image data, all the while keeping the navigation architecture simple and lightweight. To this end, we propose the spatial-channel attention mechanism, enablin

Yuanfang Yue, Junjie Cao, Fei Li, Zehan Li

In the Higgsino dark matter (DM) scenario of the Minimal Supersymmetric Model (MSSM), the mixing of Gaugino and Higgsino influences the mass splitting between neutralinos predominantly composed of Higgsino and introduces coupling between the DM and Higgs bosons. These effects modify the DM-nucleon scattering cross-section, causing conflicts with the latest d

Hanti Lin

The problem of the priors is well known: it concerns the challenge of identifying norms that govern one's prior credences. I argue that a key to addressing this problem lies in considering what I call the problem of the posteriors -- the challenge of identifying norms that directly govern one's posterior credences, which backward induce some norms on the pri

Tobias Fischbach, Pierre Talbot, Pascal Bouvry

Quantum computers allow a near-exponential speed-up for specific applications when compared to classical computers. Despite recent advances in the hardware of quantum computers, their practical usage is still severely limited due to a restricted number of available physical qubits and quantum gates, short coherence time, and high error rates. This paper lays

Reef Alturki, Adrian Hilton, Jean-Yves Guillemaut

Occlusion poses a significant challenge in pedestrian detection from a single view. To address this, multi-view detection systems have been utilized to aggregate information from multiple perspectives. Recent advances in multi-view detection utilized an early-fusion strategy that strategically projects the features onto the ground plane, where detection anal

Fawaz Sammani, Jonas Fischer, Nikos Deligiannis

Concept Bottleneck Models (CBMs) map dense feature representations into human-interpretable concepts which are then combined linearly to make a prediction. However, modern CBMs rely on the CLIP model to obtain image-concept annotations, and it remains unclear how to design CBMs without the CLIP bottleneck. Methods that do not use CLIP instead require manual,

Brendan C. Mulkerin, Olivier Bleu, Cesar R. Cabrera, Meera M. Parish

We explore the paired superfluid phases of a Fermi gas in the presence of a continuous Rabi drive. We focus on the case where two components are strongly coupled by the drive, forming hybrid superpositions, and interacting with an uncoupled third component. Using a generalized Bardeen- Cooper-Schrieffer (BCS) ansatz, we show that there are two coupled superf

Mohammad T. H. Bhuiyan, Qile Li, James Blyth, Ji-Eun Lee

Quantum materials combining magnetism and topological fermions are a key platform for low-energy electronics, spintronics, and quantum phases that break time-reversal symmetry (TRS), such as the quantum anomalous Hall effect (QAHE). Coupling a topological insulator to a magnetic material allows proximity magnetization with the potential to achieve these phas

Ayoub Laayoun, Badr Missaoui

We establish the existence of both optimal relaxed controls and strict optimal controls for systems driven by Reflected Stochastic Differential Equations RSDEs. Our approach is based on weak convergence techniques for the associated RSDEs in the uniform convergence topology, along with an appropriate Skorokhod representation theorem.

Moritz Beller, Amanda Park, Karim Nakad, Akshay Patel

This paper introduces Diff Authoring Time (DAT), a powerful, yet conceptually simple approach to measuring software development productivity that enables rigorous experimentation. DAT is a time based metric, which assess how long engineers take to develop changes, using a privacy-aware telemetry system integrated with version control, the IDE, and the OS. We

Olivier Bleu, Brendan C. Mulkerin, Cesar R. Cabrera, Jesper Levinsen

We investigate the possibility of using a Rabi drive to tune the interactions in an atomic Fermi gas. Specifically, we consider the scenario where two fermion species (spins) are Rabi coupled and interacting with a third uncoupled species. Using an exact calculation within a minimal low-energy model, we derive analytical expressions for the effective scatter

Yi-Hsiang Huang, Haozhi Wang, Yizhou Huang, Sylvie McKnight-Milles

The presence of non-equilibrium quasiparticles in superconducting resonators and qubits operating at millikelvin temperature has been known for decades. One metric for the number of quasiparticles affecting qubits is the rate of single-electron change in charge on the qubit island ($\textit i.e.$ the charge parity rate). Here, we have utilized a Ramsey-like

Shin-Ichiro Ei, Yasuhito Miyamoto, Tatsuki Mori

We show in a rigorous way that a stable internal single-layer stationary solution is destabilized by the Hopf bifurcation as the time constant exceeds a certain critical value. Moreover, the exact critical value and the exact period of oscillatory solutions can be obtained. The exact period indicates that the oscillation is very slow, i.e., the period is of

Shanghua Gao, Richard Zhu, Zhenglun Kong, Ayush Noori

Precision therapeutics require multimodal adaptive models that generate personalized treatment recommendations. We introduce TxAgent, an AI agent that leverages multi-step reasoning and real-time biomedical knowledge retrieval across a toolbox of 211 tools to analyze drug interactions, contraindications, and patient-specific treatment strategies. TxAgent eva

Lu Zhong, Lei Dong, Qi Wang, Chaoming Song

Recent advances in human mobility research have revealed consistent pairwise characteristics in movement behavior, yet existing mobility models often overlook the spatial and topological structure of mobility networks. By analyzing millions of devices' anonymized cell phone trajectories, we uncover a distinct modular organization within these networks, demon

Theodore Andronikos, Constantinos Bitsakos, Konstantinos Nikas, Georgios I. Goumas

This paper introduces a novel quantum algorithm that is able to classify a hierarchy of classes of imbalanced Boolean functions. The fundamental characteristic of imbalanced Boolean functions is that the proportion of elements in their domain that take the value $0$ is not equal to the proportion of elements that take the value $1$. For every positive intege

Camilo Chacón Sartori, Christian Blum

Large Language Models (LLMs) have shown notable potential in code generation for optimization algorithms, unlocking exciting new opportunities. This paper examines how LLMs, rather than creating algorithms from scratch, can improve existing ones without the need for specialized expertise. To explore this potential, we selected 10 baseline optimization algori

M. A. Vasiliev

Nonlinear field equations for the supersymmetric higher-spin gauge theory describing totally symmetric bosonic and fermionic massless fields along with hook-type bosonic fields of all spins in any space-time dimension are presented. One of the novel features of the proposed formalism is that the $osp(1,2)$ invariance and factorisation conditions are formulat

David Snyder, Asher James Hancock, Apurva Badithela, Emma Dixon

Imitation learning has enabled robots to perform complex, long-horizon tasks in challenging dexterous manipulation settings. As new methods are developed, they must be rigorously evaluated and compared against corresponding baselines through repeated evaluation trials. However, policy comparison is fundamentally constrained by a small feasible sample size (e

Samuel Marks, Johannes Treutlein, Trenton Bricken, Jack Lindsey

We study the feasibility of conducting alignment audits: investigations into whether models have undesired objectives. As a testbed, we train a language model with a hidden objective. Our training pipeline first teaches the model about exploitable errors in RLHF reward models (RMs), then trains the model to exploit some of these errors. We verify via out-of-

Will LeVine, Bijan Varjavand

Modern Large Language Model (LLM) systems typically rely on Retrieval Augmented Generation (RAG) which aims to gather context that is useful for response generation. These RAG systems typically optimize strictly towards retrieving context that is maximally relevant to the query. However, conventional theory suggests that retrieval systems which seek to maxim

Yuto Watanabe, Yang Zheng

The Linear Quadratic Regulator (LQR) is a cornerstone of optimal control theory, widely studied in both model-based and model-free approaches. Despite its well-established nature, certain foundational aspects remain subtle. In this paper, we revisit three key properties of policy optimization in LQR: (i) strong duality in the nonconvex policy optimization fo

Francisca Sagredo, María Camarasa-Gómez, Francesco Ricci, Aurélie Champagne

Hybrid functionals have been considered insufficiently reliable for the prediction of band gaps in solids and surfaces. We revisit this issue with a new generation of optimally-tuned range-separated hybrid functionals, focusing on the reconstructed Si(111)-(2x1) and Ge(111)-(2x1) surfaces. We show that certain hybrid functionals can accurately predict the su

Xue Feng, M. Paul Laiu, Thomas Strohmer

Federated learning (FL) is a distributed machine learning approach that enables multiple local clients and a central server to collaboratively train a model while keeping the data on their own devices. First-order methods, particularly those incorporating variance reduction techniques, are the most widely used FL algorithms due to their simple implementation

Masaho Onose, Hidefumi Takahashi, Hajime Sagayama, Yuichi Yamasaki

Perovskite-type iron oxides with Fe4+ ions have attracted much attention for their versatile helimagnetic phases. While the introduction of a layered A-site ordered structure to AFeO3 with Fe4+ ions potentially lead to novel helimagnetic phases, the synthetic pathway spanning high pressure range is apparently difficult to elucidate. Here, we explored new A-s

Eduard Tulchinskii, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev

Topological methods for comparing weighted graphs are valuable in various learning tasks but often suffer from computational inefficiency on large datasets. We introduce RTD-Lite, a scalable algorithm that efficiently compares topological features, specifically connectivity or cluster structures at arbitrary scales, of two weighted graphs with one-to-one cor

Daisuke Miki, Youka Kaku, Yubao Liu, Yiqiu Ma

In order to test the quantum nature of gravity, it is essential to explore the construction of classical gravity theories that are as consistent with experiments as possible. In particular, the classical gravity field must receive input regarding matter distribution. Previously, such input has been constructed by taking expectation values of the matter densi

Nathan S. Babcock, Brandy N. Babcock

This technical monograph provides a comprehensive overview of the field of quantum biology. It approaches quantum biology from a physical perspective with core quantum mechanical concepts presented foremost to provide a theoretical foundation for the field. An extensive body of research is covered to clarify the significance of quantum biology as a scientifi

Evangelos Piliouras, Dennis Lucarelli, Edwin Barnes

The noisy nature of quantum hardware necessitates the implementation of high-fidelity quantum gates in a noise-insensitive manner. While there exist many powerful methods for designing dynamically corrected gates, they typically involve an exploration across a large-dimensional landscape filled with solutions that are only locally optimal, making it challeng

Sedir Mohammed, Felix Naumann, Hazar Harmouch

Data quality is crucial in machine learning (ML) applications, as errors in the data can significantly impact the prediction accuracy of the underlying ML model. Therefore, data cleaning is an integral component of any ML pipeline. However, in practical scenarios, data cleaning incurs significant costs, as it often involves domain experts for configuring and

Max Großmann, Kai Daniel Hanke, Chris Yannic Bohlemann, Agnieszka Paszuk

Reflection anisotropy spectroscopy (RAS) is a powerful method for probing the optical properties of surfaces, used routinely in research and industrial applications, yet the origin of 'bulk-related' features that appear in the spectra of various surfaces has been debated for nearly 40 years. It is often argued that these features are related to surfa

Ghanshyam Bharate, J. C. Mandal

This paper enhances the Diffuse Interface Method (DIM) for simulating compressible multiphase flows across all Mach numbers by addressing the accuracy challenges posed at low Mach regimes. A correction to the Riemann solver is introduced, designed to mitigate excessive numerical diffusion while maintaining simplicity and efficiency. The validity of this corr

Kevin Riehl, Anastasios Kouvelas, Michail Makridis

Traffic engineering aims to control infrastructure and population behaviour to achieve optimal usage of road networks. Fairness is fundamental to stimulate cooperation in large populations, and plays an important role in traffic engineering, as it increases the well-being of users, improves driving safety by rule-adherence, and overcomes public resistance at

Akshat Ramachandran, Mingyu Lee, Huan Xu, Souvik Kundu

We present OuroMamba, the first data-free post-training quantization (DFQ) method for vision Mamba-based models (VMMs). We identify two key challenges in enabling DFQ for VMMs, (1) VMM's recurrent state transitions restricts capturing of long-range interactions and leads to semantically weak synthetic data, (2) VMM activations exhibit dynamic outlier variati

Alister W. Graham, Igor V. Chilingarian, Dieu D. Nguyen, Roberto Soria

The high redshift 'little red dots' (LRDs) detected with the James Webb Space Telescope are considered to be the cores of emerging galaxies that host active galactic nuclei (AGN). For the first time, we compare LRDs with local compact stellar systems and an array of galaxy-morphology-dependent stellar mass-black hole mass scaling relations in the $M_{\rm bh}

Michael Charles Albada, Mojolaoluwa Joshua Sonola

Applying deep learning and computational intelligence to finance has been a popular area of applied research, both within academia and industry, and continues to attract active attention. The inherently high volatility and non-stationary of the data pose substantial challenges to machine learning models, especially so for today's expressive and highly-parame