March 2025 arXiv papers — page 81

Ananta R. Bhattarai, Xingzhe He, Alla Sheffer, Helge Rhodin

DreamFusion established a new paradigm for unsupervised 3D reconstruction from virtual views by combining advances in generative models and differentiable rendering. However, the underlying multi-view rendering, along with supervision from large-scale generative models, is computationally expensive and under-constrained. We propose DreamTexture, a novel Shap

Shamisa Shoja, Daniel Arnström, Daniel Axehill

When implementing model predictive control (MPC) for hybrid systems with a linear or a quadratic performance measure, a mixed-integer linear program (MILP) or a mixed-integer quadratic program (MIQP) needs to be solved, respectively, at each sampling instant. Recent work has introduced the possibility to certify the computational complexity of branch-and-bou

Jeong Min Park, Shuwen Sun, Kenji Watanabe, Takashi Taniguchi

Understanding the nature of superconductivity in magic-angle graphene remains challenging. A key difficulty lies in discerning the different energy scales in this strongly interacting system, particularly the superconducting gap. Here, we report the first simultaneous tunneling spectroscopy and transport measurements of magic-angle graphene, providing a nove

Yiran Qin, Li Kang, Xiufeng Song, Zhenfei Yin

Designing effective embodied multi-agent systems is critical for solving complex real-world tasks across domains. Due to the complexity of multi-agent embodied systems, existing methods fail to automatically generate safe and efficient training data for such systems. To this end, we propose the concept of compositional constraints for embodied multi-agent sy

Xiaotao Zheng, Xingye Yue, Jiyang Shi

Deep Feynman-Kac method was first introduced to solve parabolic partial differential equations(PDE) by Beck et al. (SISC, V.43, 2021), named Deep Splitting method since they trained the Neural Networks step by step in the time direction. In this paper, we propose a new training approach with two different features. Firstly, neural networks are trained at all

Li Fan, Wei Shen, Jing Yang, Cong Shen

Pre-trained Transformers, through in-context learning (ICL), have demonstrated exceptional capabilities to adapt to new tasks using example prompts without model update. Transformer-based wireless receivers, where prompts consist of the pilot data in the form of transmitted and received signal pairs, have shown high detection accuracy when pilot data are abu

SeungJu Cha, Kwanyoung Lee, Ye-Chan Kim, Hyunwoo Oh

Recent large-scale text-to-image diffusion models generate photorealistic images but often struggle to accurately depict interactions between humans and objects due to their limited ability to differentiate various interaction words. In this work, we propose VerbDiff to address the challenge of capturing nuanced interactions within text-to-image diffusion mo

Adam Bzdak, Volker Koch, Volodymyr Vovchenko

We analyze joint factorial cumulants of protons and antiprotons in relativistic heavy-ion collisions and point out that they obey the scaling $\hat{C}_{nm}^{p,\bar{p}} \propto \langle N_p \rangle^n \langle N_{\bar{p}} \rangle^m$ as a function of acceptance when only long-range correlations are present in the system, such as global baryon conservation and vol

Daniel G. Zhu

Several recent works have identified patterns that must exist in dense subsets of either the vertices or the edges of a large hypercube. We introduce a framework, based on the concept of series-parallel graphs, that unifies and generalizes these results.

Yifan Sun, Han Wang, Dongbai Li, Gang Wang

Benchmark Data Contamination (BDC)-the inclusion of benchmark testing samples in the training set-has raised increasing concerns in Large Language Model (LLM) evaluation, leading to falsely inflated performance estimates and undermining evaluation reliability. To address this, researchers have proposed various mitigation strategies to update existing benchma

Guanyu Chen, Peiyang Wang, Yizhou Jiang, Yuqian Liu

Large language models (LLMs) have been able to perform various forms of reasoning tasks in a wide range of scenarios, but are they truly engaging in task abstraction and rule-based reasoning beyond mere memorization? To answer this question, we propose a novel experimental approach, Misleading Fine-Tuning (MisFT), to examine whether LLMs perform abstract rea

Haolin Yang, Feilong Tang, Ming Hu, Qingyu Yin

Video diffusion models (VDMs) facilitate the generation of high-quality videos, with current research predominantly concentrated on scaling efforts during training through improvements in data quality, computational resources, and model complexity. However, inference-time scaling has received less attention, with most approaches restricting models to a singl

Chen Chen, Zhirui Wang, Taowei Sheng, Yi Jiang

Existing vision-based 3D occupancy prediction methods are inherently limited in accuracy due to their exclusive reliance on street-view imagery, neglecting the potential benefits of incorporating satellite views. We propose SA-Occ, the first Satellite-Assisted 3D occupancy prediction model, which leverages GPS & IMU to integrate historical yet readily availa

Waïss Azizian, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos

In this paper, we examine the time it takes for stochastic gradient descent (SGD) to reach the global minimum of a general, non-convex loss function. We approach this question through the lens of randomly perturbed dynamical systems and large deviations theory, and we provide a tight characterization of the global convergence time of SGD via matching upper a

Nikita Starodubcev, Ilya Drobyshevskiy, Denis Kuznedelev, Artem Babenko

Recent diffusion distillation methods have achieved remarkable progress, enabling high-quality ${\sim}4$-step sampling for large-scale text-conditional image and video diffusion models. However, further reducing the number of sampling steps becomes more and more challenging, suggesting that efficiency gains may be better mined along other model axes. Motivat

Chun-Han Yao, Yiming Xie, Vikram Voleti, Huaizu Jiang

We present Stable Video 4D 2.0 (SV4D 2.0), a multi-view video diffusion model for dynamic 3D asset generation. Compared to its predecessor SV4D, SV4D 2.0 is more robust to occlusions and large motion, generalizes better to real-world videos, and produces higher-quality outputs in terms of detail sharpness and spatio-temporal consistency. We achieve this by i

Anurag Singh, Siu Lun Chau, Krikamol Muandet

The quality of probabilistic forecasts is crucial for decision-making under uncertainty. While proper scoring rules incentivize truthful reporting of precise forecasts, they fall short when forecasters face epistemic uncertainty about their beliefs, limiting their use in safety-critical domains where decision-makers (DMs) prioritize proper uncertainty manage

Akhil Perincherry, Jacob Krantz, Stefan Lee

Vision-and-Language Navigation (VLN) agents are tasked with navigating an unseen environment using natural language instructions. In this work, we study if visual representations of sub-goals implied by the instructions can serve as navigational cues and lead to increased navigation performance. To synthesize these visual representations or imaginations, we

Tài Huy Hà, Thai Thanh Nguyen, Vinh Anh Pham

We develop the notions of Newton non-degenerate (NND) ideals and Newton polyhedra for regular local rings. These concepts were first defined in the context of complex analysis. We show that the characterization of NND ideals via their integral closures known in the analytical setting extends to regular local rings. We use the limiting body $\mathcal{C}(\math

Anket Mehra, Andreas Aßmuth, Malte Prieß

With AI-based software becoming widely available, the risk of exploiting its capabilities, such as high automation and complex pattern recognition, could significantly increase. An AI used offensively to attack non-AI assets is referred to as offensive AI. Current research explores how offensive AI can be utilized and how its usage can be classified. Additio

Yinqi Chen, Mathias S. Scheurer, Constantin Schrade

Recent experiments have revealed that superconductivity in rhombohedral tetralayer graphene can emerge from a valley-polarized and, hence, chiral normal state. The interplay of pairing and the reduced normal-state symmetries sparked widespread interest. In this work, we demonstrate within a microscopic theoretical formalism that this stabilizes a non-recipro

Junkai Dong, Ophelia Evelyn Sommer, Tomohiro Soejima, Daniel E. Parker

Recent advances in 2D materials featuring nonzero Berry curvature have inspired extensions of the Wigner crystallization paradigm. This paper derives a low-energy effective theory for such quantum crystals, including the anomalous Hall crystal (AHC) with nonzero Chern number. First we show that the low frequency dispersion of phonons in AHC, despite the pres

Luigi Piccinelli, Christos Sakaridis, Mattia Segu, Yung-Hsu Yang

Monocular 3D estimation is crucial for visual perception. However, current methods fall short by relying on oversimplified assumptions, such as pinhole camera models or rectified images. These limitations severely restrict their general applicability, causing poor performance in real-world scenarios with fisheye or panoramic images and resulting in substanti

Kristin Qi, Xinhan Di

Retinal Optical Coherence Tomography (OCT) segmentation is essential for diagnosing pathology. Traditional methods focus on either spatial or spectral domains, overlooking their combined dependencies. We propose a triple-encoder network that integrates CNNs for spatial features, Fast Fourier Convolution (FFC) for spectral features, and attention mechanisms t

Luis A. Mora, Kirsten Morris

In this manuscript, we present a mixed finite element discretization for a class of boundary-damped anisotropic port-Hamiltonian systems. Using a multiplier method, we demonstrate that the resulting approximation model uniformly preserves the exponential stability of the uncontrolled system, establishing a lower bound for the exponential decay rate that is i

Rudin Petrossian-Byrne, Giovanni Villadoro

Localized charged fields are a general feature of many realistic string compactifications. In four dimensions they can lead to a multitude of perturbatively-exact global symmetries. If spontaneously broken, they generate a new axiverse compatible with post-inflationary evolutions.

Xiongzhi Chen

We consider estimating the proportion of random variables for two types of composite null hypotheses: (i) the means or medians of the random variables belonging to a non-empty, bounded interval; (ii) the means or medians of the random variables belonging to an unbounded interval that is not the whole real line. For each type of composite null hypotheses, uni

Federica Gavazzi

A group is called decomposable if it can be expressed as a direct product of two proper subgroups, and indecomposable otherwise. This paper explores the decomposability of virtual Artin groups, which were introduced by Bellingeri, Paris, and Thiel as a generalization of classical Artin groups within the framework of virtual braid theory. We establish that fo

Yijia Luo, Yulin Song, Xingyao Zhang, Jiaheng Liu

Recent advancements in large language models (LLMs) have demonstrated remarkable reasoning capabilities through long chain-of-thought (CoT) reasoning. The R1 distillation scheme has emerged as a promising approach for training cost-effective models with enhanced reasoning abilities. However, the underlying mechanisms driving its effectiveness remain unclear.

Filippo Gaggioli, Daniele Guerci, Liang Fu

The discovery of superconducting states in multilayer rhombohedral graphene with spin and valley polarization has raised an interesting question: how does superconductivity cope with time-reversal symmetry breaking? In this work, using Ginzburg-Landau theory and microscopic calculation, we predict the existence of a new superconducting state at low electron

Robin Blume-Kohout, Timothy Proctor, Kevin Young

Quantum characterization, verification, and validation (QCVV) is a set of techniques to probe, describe, and assess the behavior of quantum bits (qubits), quantum information-processing registers, and quantum computers. QCVV protocols probe and describe the effects of unwanted decoherence so that it can be eliminated or mitigated. They can be usefully divide

Hamish Flynn, Julia Olkhovskaya, Paul Rognon-Vael

We study the benefits of sparsity in nonparametric contextual bandit problems, in which the set of candidate features is countably or uncountably infinite. Our contribution is two-fold. First, using a novel reduction to sequences of multi-armed bandit problems, we provide lower bounds on the minimax regret, which show that polynomial dependence on the number

Ze-Tong Zhuang, Dario Rosenstock, Bao-Jie Liu, Aaron Somoroff

Recent studies have shown that parasitic two-level systems (TLS) in superconducting qubits, which are a leading source of decoherence, can have relaxation times longer than the qubits themselves. However, the standard techniques used to characterize qubit relaxation is only valid for measuring $T_1$ under Markovian assumptions and could mask such non-Markovi

Yunlong Xiao, Xiangjing Liu, Zhenhuan Liu

Quantum information cannot be broadcast -- an intrinsic limitation imposed by quantum mechanics. However, recent advances in virtual operations offer new insights into the no-broadcasting theorem. Here, we focus on the practical utility and introduce sample efficiency as a fundamental constraint, requiring any practical broadcasting protocol perform no worse

P. S. Corasaniti, T. R. G. Richardson, S. Ettori, M. De Petris

Measurements of the sparsity of galaxy clusters can be used to probe the cosmological information encoded in the host dark matter halo profile, and infer constraints on the cosmological model parameters. Key to the success of these analyses is the control of potential sources of systematic uncertainty. As an example, the presence of baryons can alter the clu

Tzu-Yun Tseng, Alexey Nekrasov, Malcolm Burdorf, Bastian Leibe

Existing autonomous driving datasets are predominantly oriented towards well-structured urban settings and favourable weather conditions, leaving the complexities of rural environments and adverse weather conditions largely unaddressed. Although some datasets encompass variations in weather and lighting, bad weather scenarios do not appear often. Rainfall ca

Maciej Kowalczyk, Guillermo A. Mena Marugán, Tomasz Pawłowski

In Loop Quantum Cosmology, the quantization of the Hamiltonian constraint involves a regularization procedure which is affected by certain ambiguities. Moreover, different regularizations lead to distinct mathematical formulations and, consequently, to different physical predictions. In this work, we explore the impact of this regularization on the primordia

Leyang Wang, Joice Lin

The success of modern machine learning, particularly in facial translation networks, is highly dependent on the availability of high-quality, paired, large-scale datasets. However, acquiring sufficient data is often challenging and costly. Inspired by the recent success of diffusion models in high-quality image synthesis and advancements in Large Language Mo

Han-Hung Lee, Qinghong Han, Angel X. Chang

In this paper, we explore the task of generating expansive outdoor scenes, ranging from castles to high-rises. Unlike indoor scene generation, which has been a primary focus of prior work, outdoor scene generation presents unique challenges, including wide variations in scene heights and the need for a method capable of rapidly producing large landscapes. To

Sophie Kahlen, Jan Reineke

In this work we unify two existing lines of work towards cache analysis for non-LRU policies. To this end, we extend the notion of competitiveness to block competitiveness and systematically analyze the competitiveness and block competitiveness of FIFO and MRU relative to LRU for arbitrary associativities. We show how competitiveness and block competitivenes

Karim I. Elghazawy, Chris H. Greene

Three-body loss resonances associated with heavy-heavy-light Efimov states have been observed for over a decade in ultracold mixtures tuned near interspecies Feshbach resonances. For light-light-heavy systems, observing such resonances has been far more challenging due to the substantially large Efimov spacing. In these Efimov-unfavored systems, the intraspe

Simon Shindler, Rong Yang

Studies of iCVD kinetics often make assumptions about the iCVD vapor phase that have been challenging to validate experimentally. We address this gap by investigating heat transfer and mixing in the iCVD reactor using in-situ gas composition sensing. Our work allows practitioners of iCVD to estimate the degree of mixing and temperature profile in the vapor p

Morgan Williamson, Aditya Rao, Evan Segura, Bryson Wylie

We investigate the potential of enhancing small (<20 kg) drone endurance by exploiting the high energy density of hydrocarbons using a prototype generator based on commercial-off-the-shelf (COTS) thermoelectric energy conversion technology. A proof-of-concept prototype was developed to vet design and engineering challenges and to bolster validity of resultan

Simone Salvatore Li Muli, Tor R. Djärv, Christian Forssén, Daniel R. Phillips

A century ago, Wigner&#39;s SU(4) symmetry was introduced to explain the properties of atomic nuclei. Despite recent revived interest, its impact on nuclear structure, transitions, and reactions has not been fully explored. Here, we show that a variety of high-fidelity nuclear interactions predict nuclear states that have $\geq 90$\% probability of being in

Minori Narita, Ryo Kuroiwa, J. Christopher Beck

Domain-Independent Dynamic Programming (DIDP) is a state-space search paradigm based on dynamic programming for combinatorial optimization. In its current implementation, DIDP guides the search using user-defined dual bounds. Reinforcement learning (RL) is increasingly being applied to combinatorial optimization problems and shares several key structures wit

Juan Muñoz-Echániz

We study the $SL(2, \mathbb{C})$ character variety of a Seifert-fibered homology $3$-sphere from the point of view of gauge theory. Namely, we introduce a class of perturbations of the $SL(2,\mathbb{C})$ Chern--Simons functional and prove a localisation result: the perturbed critical points either approach a compact subset of the $SL(2, \mathbb{C})$ characte

Raphael Chetrite, Stefano Marcantoni

We introduce a framework to identify Fluctuation Relations for vector-valued observables in physical systems evolving through a stochastic dynamics. These relations arise from the particular structure of a suitable entropic functional and are induced by transformations in trajectory space that are invertible but are not involutions, typical examples being sp

Mihail Mintchev, Diego Pontello, Erik Tonni

We study the quantum transport generated by the bipartite entanglement in two-dimensional conformal field theory at finite density with the $U(1) \times U(1)$ symmetry associated to the conservation of the electric charge and of the helicity. The bipartition given by an interval is considered, either on the line or on the circle. The continuity equations and

T. Saifollahi, A. Lançon, Michele Cantiello, J. -C. Cuillandre

We present an analysis of globular clusters (GCs) of dwarf galaxies in the Perseus galaxy cluster to explore the relationship between dwarf galaxy properties and their GCs. Our focus is on GC numbers ($N_{\rm GC}$) and GC half-number radii ($R_{\rm GC}$) around dwarf galaxies, and their relations with host galaxy stellar masses ($M_*$), central surface brigh

Samuel Kim, Tim Sleasman, Avrami Rakovsky, Ra'id Awadallah

Backscatter communications is attractive for its low power requirements due to the lack of actively radiating components; however, commonly used devices are typically limited in range and functionality. Here, we design and demonstrate a backscatter device consisting of a flattened Luneburg lens combined with a spatially-tunable dynamic metasurface. Using qua

Muyao Li, Zihao Wang, Kaichen He, Xiaojian Ma

Recently, action-based decision-making in open-world environments has gained significant attention. Visual Language Action (VLA) models, pretrained on large-scale web datasets, have shown promise in decision-making tasks. However, previous work has primarily focused on action post-training, often neglecting enhancements to the foundational model itself. In r

Z. Zarezadeh, N. Zarezadeh

In this paper, we explore the algebra of quantum idempotents and the quantization of fermions which gives rise to a Hilbert space equal to the Grassmann algebra associated with the Lie algebra. Since idempotents carry representations of the algebra under consideration, they form algebraic varieties and smooth manifolds in the natural topology. In addition to

Haoqi He, Yan Xiao

Quantum computing holds significant potential to accelerate machine learning algorithms, especially in solving optimization problems like those encountered in Support Vector Machine (SVM) training. However, current QUBO-based Quantum SVM (QSVM) methods rely solely on binary optimal solutions, limiting their ability to identify fuzzy boundaries in data. Addit

Leithold L. Aurazo-Alvarez, Wladimir Neves

In this work, we proved the existence of a unique global mild solution of the d-dimensional incompressible Navier-Stokes equations, for small initial data in Besov type spaces based on mixed-Lebesgue spaces; namely, mixed-norm Besov-Lebesgue spaces and also mixed-norm Fourier-Besov-Lebesgue spaces. The main tools are the Bernstein's type inequalities, Bony's

Rahul Bhowmick, Harsh Wadhwa, Avinash Singh, Tania Sidana

Quantum computers offer a promising route to tackling problems that are classically intractable such as in prime-factorization, solving large-scale linear algebra and simulating complex quantum systems, but potentially require fault-tolerant quantum hardware. On the other hand, variational quantum algorithms (VQAs) are a promising approach for leveraging nea

Catherine Eva Pfaff, Chi Cheuk Tsang

We show that the axis bundle of a nongeometric fully irreducible outer automorphism admits a canonical "cubist" decomposition into branched cubes that fit together with special combinatorics. From this structure, we locate a canonical finite collection of periodic fold lines in each axis bundle. This can be considered as an analogue of results of Hamenst\"ad

M. McCann, C. P. Ballance, F. McNeill, S. A. Sim

Recent observation and analysis of kilonovae (KNe) spectra as a result of neutron star mergers require accurate and complete atomic structure and collisional data for interpretation. Ideally, the atomic datasets for elements predicted to be abundant in the ejecta should be experimentally calibrated. For near-neutral ion stages of Zirconium in particular, the

Tao Feng, Yifan Xie, Xun Guan, Jiyuan Song

Precise audio-visual synchronization in speech videos is crucial for content quality and viewer comprehension. Existing methods have made significant strides in addressing this challenge through rule-based approaches and end-to-end learning techniques. However, these methods often rely on limited audio-visual representations and suboptimal learning strategie

Yunzhi Yao, Jizhan Fang, Jia-Chen Gu, Ningyu Zhang

Knowledge Editing (KE) enables the modification of outdated or incorrect information in large language models (LLMs). While existing KE methods can update isolated facts, they often fail to generalize these updates to multi-hop reasoning tasks that rely on the modified knowledge. Through an analysis of reasoning circuits -- the neural pathways LLMs use for k

Bibhash Kumar Das, Aneesh Mundayadan

We study the continuity, and dynamical properties (hypercyclicity, periodic vectors, and chaos) for a weighted backward shift $B_w$ on a weighted Bergman space $A^p_{\phi}$ based on the norm estimates of coefficient functionals on $A^p_{\phi}$. Here, the weight function $\phi(z)$ is mostly radial, but our work will also involve a (non-radial) subharmonic wei

Andrea Maffei, Valerio Melani, Gabriele Vezzosi

For a smooth affine algebraic group $G$ over an algebraically closed field, we consider several two-variables generalizations of the affine Grassmannian $G(\!(t)\!)/G[\![t]\!]$, given by quotients of the double loop group $G(\!(x)\!)(\!(y)\!)$. We prove that they are representable by ind-schemes if $G$ is solvable. Given a smooth surface $X$ and a flag of su

Matthew Gillespie, Lucas Hall, Benjamin Jones, Mariusz Tobolski

We study topological quivers $Q$ admitting a free and proper action by a locally compact group $G$ together with their associated $C^*$-algebras. On the topological side, we provide a complete classification of topological quivers which admit such actions in terms of $G$-bundles over the vertex orbit space and an appropriate isomorphism of bundles over the e

Krithik Ramesh, Sameed M. Siddiqui, Albert Gu, Michael D. Mitzenmacher

Deep learning architectures such as convolutional neural networks and Transformers have revolutionized biological sequence modeling, with recent advances driven by scaling up foundation and task-specific models. The computational resources and large datasets required, however, limit their applicability in biological contexts. We introduce Lyra, a subquadrati

Ali Yassin, Abbas Haidar, Hocine Cherifi, Hamida Seba

Networks are essential for analyzing complex systems. However, their growing size necessitates backbone extraction techniques aimed at reducing their size while retaining critical features. In practice, selecting, implementing, and evaluating the most suitable backbone extraction method may be challenging. This paper introduces netbone, a Python package desi

Jiaheng Liu, Dawei Zhu, Zhiqi Bai, Yancheng He

Efficient processing of long contexts has been a persistent pursuit in Natural Language Processing. With the growing number of long documents, dialogues, and other textual data, it is important to develop Long Context Language Models (LCLMs) that can process and analyze extensive inputs in an effective and efficient way. In this paper, we present a comprehen

A. Kostenkov, S. Fabrika, A. Kaldybekova, S. Fedorchenko

In the current paper, we present a study of the spatial distribution of luminous blue variables (LBVs) and various LBV candidates (cLBVs) with respect to OB associations in the M33 galaxy. The identification of blue star groups was based on the LGGS data and was carried out by two clustering algorithms with initial parameters determined during simulations of

Murray Shanahan

Is it possible to articulate a conception of consciousness that is compatible with the exotic characteristics of contemporary, disembodied AI systems, and that can stand up to philosophical scrutiny? How would subjective time and selfhood show up for an entity that conformed to such a conception? Trying to answer these questions, even metaphorically, stretch

Angus Hawkey, Aditya Dash, Xabier Rodríguez-Martínez, Zhiyong Zhao

Semiconducting single-walled carbon nanotubes (SWCNTs) are a promising thermoelectric material with high power factors after chemical p- or n-doping. Understanding the impact of dopant counterions on charge transport and thermoelectric properties of nanotube networks is essential to further optimize doping methods and to develop better dopants. Here, we util

Xiangyu Ren, Yuexun Huang, Zhiding Liang, Antonio Barbalace

Quantum graph states are critical resources for various quantum algorithms, and also determine essential interconnections in distributed quantum computing. There are two schemes for generating graph states probabilistic scheme and deterministic scheme. While the all-photonic probabilistic scheme has garnered significant attention, the emitter-photonic determ

Jonáš Dujava, Petr Vaško

We determine the scaling dimensions in the boundary $\mathsf{CFT}_{d}$ corresponding to the $\mathsf{O}(N)$ model in $\mathsf{EAdS}_{d+1}$. The $\mathsf{CFT}$ data accessible to the 4-point boundary correlator of fundamental fields are extracted in $d=2$ and $d=4$, at a finite coupling, and to the leading nontrivial order in the $1/N$ expansion. We focus on

Yu He, Alexander Magunia, Harijyoti Mandal, Muwaffaq Ali Mourtada

The goal to control short-wavelength radiation for the investigation and manipulation of ultrafast dynamics in quantum systems coevolves with the growing availability of extreme-ultraviolet (XUV) and x-ray sources from high-harmonic generation and free-electron lasers. Here, we present an XUV spatio-spectral phase modulator based on an intense XUV laser beam

Paloma Bengoechea, Sebastián Herrero, Özlem Imamoglu

To each weakly holomorphic modular function $f\not \equiv 0$ for $\mathrm{SL}(2,\mathbb{Z})$, which is non-negative on the geodesic arc $\{e^{it} : \pi/3\leq t\leq 2\pi/3\}$, we attach a $\mathrm{GL}(2,\mathbb{Z})$-invariant map $\Lambda_f:\mathbb{P}^1(\mathbb{R})\to \mathbb{R}$ that generalizes the Lyapunov exponent function introduced by Spalding and Vesel

Haoqi He, Yan Xiao, Wenzhi Xu, Ruoying Liu

Estimating the global Lipschitz constant of neural networks is crucial for understanding and improving their robustness and generalization capabilities. However, precise calculations are NP-hard, and current semidefinite programming (SDP) methods face challenges such as high memory usage and slow processing speeds. In this paper, we propose HiQ-Lip, a hybrid

Lei Li, Siyu Liu, Antonio M. Peralta

We study when an additive mapping preserving orthogonality between two complex inner product spaces is automatically complex-linear or conjugate-linear. Concretely, let $H$ and $K$ be complex inner product spaces with dim$(H)\geq 2$, and let $A: H\to K$ be an additive map preserving orthogonality. We obtain that $A$ is zero or a positive scalar multiple of a

Wei-Chen Wang, Antoine De Comite, Alexandra Voloshina, Monica Daley

Everyday locomotion is a complex sensorimotor process that can unfold over multiple timescales, from long-term path planning to rapid, reactive adjustments. However, we lack an understanding of how factors such as environmental demands, or the available sensory information simultaneously influence these control timescales. To address this, we present a unifi

Nicolas Ladda, Fabian Westmeier, Sagnik Das, Wilfried Dreher

Velocity map imaging spectroscopy is a powerful technique for detecting the momentum distribution of photoelectrons resulting from an ionization experiment on atoms or molecules. However, when used with ultraviolet light sources, scattered photons can lead to the emission of photoelectrons from the spectrometer's electrodes, giving rise to severe noise distu

Shengjun Zhang, Xin Fei, Fangfu Liu, Haixu Song

3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis performance. While conventional methods require per-scene optimization, more recently several feed-forward methods have been proposed to generate pixel-aligned Gaussian representations with a learnable network, which are generalizable to different scenes. However, these methods sim

Qiankun Shi, Jie Peng, Kun Yuan, Xiao Wang

In this paper, we establish tight lower bounds for Byzantine-robust distributed first-order stochastic optimization methods in both strongly convex and non-convex stochastic optimization. We reveal that when the distributed nodes have heterogeneous data, the convergence error comprises two components: a non-vanishing Byzantine error and a vanishing optimizat

Srijan Chakraborty, Samir Datta

The shortest Disjoint Path problem (SDPP) requires us to find pairwise vertex disjoint paths between k designated pairs of terminal vertices such that the sum of the path lengths is minimum. The focus here is on SDPP restricted to planar graphs where all terminals are arbitrarily partitioned over two distinct faces with the additional restriction that each f

Seshu Babu Barma, Mohanakrishnan Hariharan, Satish Arvapalli

An AI-powered quality engineering platform uses artificial intelligence to boost software quality assessments through automated defect prediction and optimized performance alongside improved feature extraction. Existing models result in difficulties addressing noisy data types together with imbalances, pattern recognition complexities, ineffective feature ex

Ying Shen, Lifu Huang

Recent findings reveal that much of the knowledge in a Transformer-based Large Language Model (LLM) is encoded in its feed-forward (FFN) layers, where each FNN layer can be interpreted as the summation of sub-updates, each corresponding to a weighted column vector from the FFN's value parameter matrix that often encodes human-interpretable concepts. In light

Angus Hawkey, Xabier Rodríguez-Martínez, Sebastian Lindenthal, Moritz C. F. Jansen

Networks of semiconducting single-walled carbon nanotubes (SWNTs) are a promising material for thermoelectric energy harvesting due to their mechanical flexibility, solution processability, high Seebeck coefficients and high electrical conductivities after chemical p- or n-doping. Here, we demonstrate that proton-coupled electron transfer (PCET) with benzoqu

Mohamed Bilel Besbes, Diego Elias Costa, Suhaib Mujahid, Gregory Mierzwinski

Performance regressions in software systems can lead to significant financial losses and degraded user satisfaction, making their early detection and mitigation critical. Despite the importance of practices that capture performance regressions early, there is a lack of publicly available datasets that comprehensively capture real-world performance measuremen

Shuai Sun, Xu Wang

The Ho-Kalman algorithm has been widely employed for the identification of discrete-time linear time-invariant (LTI) systems. In this paper, we investigate the pole estimation error for the Ho-Kalman algorithm based on finite input/output sample data. Building upon prior works, we derive finite sample error bounds for system pole estimation in both single-tr

Xiao Chen, Thomas Lettau, Ulf Peschel, Nicolas Tancogne-Dejean

We investigate non-equilibrium electron dynamics in crystalline ZnO induced by ultrashort, relatively intense, infrared laser pulses. Our focus is on understanding the mechanism that facilitates efficient conduction band population in ZnO to enable optically pumped lasing. We consider two different pulse frequencies (in the near-infrared and mid-infrared) fo

Xiaoyu Wang, Yijia Xu, Jingyi Huang, Zhengwei Yang

Remote sensing (RS) technique, enabling the non-contact acquisition of extensive ground observations, is a valuable tool for crop yield predictions. Traditional process-based models struggle to incorporate large volumes of RS data, and most users lack understanding of crop growth mechanisms. In contrast, machine learning (ML) models are often criticized as "

Andrew N. Ivanov, Olexei I. Motrunich

In this work, we present new, highly non-trivial area-law exact zero-energy eigenstates of the one-dimensional (1D) PXP and related models. We formulate sufficient conditions for a matrix product state to represent an exact zero-energy eigenstate of a given 1D kinetically constrained model and use them to prove our new states. We also demonstrate that all pr

Long Yuan, Fengran Mo, Kaiyu Huang, Wenjie Wang

The rapid advancement of multimodal large language models (LLMs) has opened new frontiers in artificial intelligence, enabling the integration of diverse large-scale data types such as text, images, and spatial information. In this paper, we explore the potential of multimodal LLMs (MLLM) for geospatial artificial intelligence (GeoAI), a field that leverages

Mohamad Ali-Dib, Craig Walton

Chondrules are small spherical objects that formed at high temperatures early in the history of the Solar System. The key compositional characteristics of chondrules may be well explained by high gas pressures in their formation environment (Galy et al. 2000; Alexander et al. 2008). However, such high gas pressures are widely considered astrophysically unrea

N. Bostan, R. H. Dejrah, C. Dioguardi, A. Racioppi

In the context of Palatini gravity, $F(R+X)$ models, with X the inflaton kinetic term, are characterized by the appealing property of generating asymptotically flat inflaton potentials, exactly like the more commonly studied Palatini $F(R)$ models, but without the complication of non-canonical inflaton kinetic terms in the Einstein frame. In this paper, we s

Peter Sharpe, R. John Hansman

NeuralFoil is an open-source Python-based tool for rapid aerodynamics analysis of airfoils, similar in purpose to XFoil. Speedups ranging from 8x to 1,000x over XFoil are demonstrated, after controlling for equivalent accuracy. NeuralFoil computes both global and local quantities (lift, drag, velocity distribution, etc.) over a broad input space, including:

Ruonan Yu, Songhua Liu, Zhenxiong Tan, Xinchao Wang

Text-to-image diffusion models have achieved remarkable progress in recent years. However, training models for high-resolution image generation remains challenging, particularly when training data and computational resources are limited. In this paper, we explore this practical problem from two key perspectives: data and parameter efficiency, and propose a s

Jiapeng Xu, Dehua Bi, Shenghua Kelly Fan, Bee Leng Lee

The primary goal of dose allocation in phase I trials is to minimize patient exposure to subtherapeutic or excessively toxic doses, while accurately recommending a phase II dose that is as close as possible to the maximum tolerated dose (MTD). Fan et al. (2012) introduced a curve-free Bayesian decision-theoretic design (CFBD), which leverages the assumption

Noor Nashid, Islem Bouzenia, Michael Pradel, Ali Mesbah

Automated tools for solving GitHub issues are receiving significant attention by both researchers and practitioners, e.g., in the form of foundation models and LLM-based agents prompted with issues. A crucial step toward successfully solving an issue is creating a test case that accurately reproduces the issue. Such a test case can guide the search for an ap

A. Pezo, N. Sebe, A. Manchon, V. Cros

In the context of orbitronics, the rising of the orbital angular momentum generated at light metal interfaces from orbital textures via orbital Rashba-Edelstein effects nowadays represent extraordinary alternatives to the usual heavy-metal spin-based materials. In the light of very recent experimental results [\textcolor{blue}{S. Krishnia \textit{et al.}, Na

Edgar Sucar, Zihang Lai, Eldar Insafutdinov, Andrea Vedaldi

DUSt3R has recently shown that one can reduce many tasks in multi-view geometry, including estimating camera intrinsics and extrinsics, reconstructing the scene in 3D, and establishing image correspondences, to the prediction of a pair of viewpoint-invariant point maps, i.e., pixel-aligned point clouds defined in a common reference frame. This formulation is

Dimitra Tseneklidou, Raimon Luna, Pablo Cerdá-Durán, Alejandro Torres-Forné

The gravitational wave signature from core-collapse supernovae (CCSNe) is dominated by quadrupolar oscillation modes of the newly born proto-neutron star (PNS), and could be detectable at galactic distances. We have developed a framework for computing the normal oscillation modes of a PNS in general relativity, including, for the first time, the presence of

Zhanpeng Zhou, Yongyi Yang, Jie Ren, Mahito Sugiyama

Understanding the learning dynamics of neural networks is a central topic in the deep learning community. In this paper, we take an empirical perspective to study the learning dynamics of neural networks in real-world settings. Specifically, we investigate the evolution process of the empirical Neural Tangent Kernel (eNTK) during training. Our key findings r

Michael Potter, Beyza Kalkanlı, Deniz Erdoğmuş, Michael Everett

Identifying the optimal diagnostic test and hardware system instance to infer reliability characteristics using field data is challenging, especially when constrained by fixed budgets and minimal maintenance cycles. Active Learning (AL) has shown promise for parameter inference with limited data and budget constraints in machine learning/deep learning tasks.

Linda Sansoni, Eleonora Stefanutti, Andrea Chiuri

Understanding the impact of disturbances in quantum channels is of paramount importance for the implementation of many quantum technologies, as noise can be detrimental to quantum correlations. Among the various types of disturbances, we explore the effects of white and colored noise and experimentally test the resilience of a quantum ghost spectrometer agai