November 2025 arXiv papers — page 20

Lyes Khacef, Philipp Weidel, Susumu Hogyoku, Harry Liu

Fall detection for elderly care using non-invasive vision-based systems remains an important yet unsolved problem. Driven by strict privacy requirements, inference must run at the edge of the vision sensor, demanding robust, real-time, and always-on perception under tight hardware constraints. To address these challenges, we propose a neuromorphic fall detec

Jiawei Zhang, Lei Chu, Jiahao Li, Zhenyu Zang

We present a unified framework for reconstructing animatable 3D human avatars from a single portrait across head, half-body, and full-body inputs. Our method tackles three bottlenecks: pose- and framing-sensitive feature representations, limited scalable data, and unreliable proxy-mesh estimation. We introduce a Dual-UV representation that maps image feature

Ämin Baumeler, Eleftherios-Ermis Tselentis, Stefan Wolf

Bell inequalities limit the possible observations of non-communicating parties. Here, we present analogous inequalities for any number of communicating parties under the causal constraints of static causal order, definite causal order, and bi-causal order. All derived inequalities are remarkably simple. They correspond to upper bounds on the winning chance i

Elham Cheshmikhani, Hamed Farbeh, Hossein Asad

Recent development in memory technologies has introduced Spin-Transfer Torque Magnetic RAM (STT-MRAM) as the most promising replacement for SRAMs in on-chip cache memories. Besides its lower leakage power, higher density, immunity to radiation-induced particles, and non-volatility, an unintentional bit flip during read operation, referred to as read disturba

Mahdi Tavassoli Kejani, Fadi Dornaika, Jean-Michel Loubes

Graph Neural Networks (GNNs) have demonstrated exceptional efficacy in relational learning tasks, including node classification and link prediction. However, their application raises significant fairness concerns, as GNNs can perpetuate and even amplify societal biases against protected groups defined by sensitive attributes such as race or gender. These bia

Yacine Mohamed Idir, Olivier Orfila, Patrice Chatellier, Vincent Judalet

This study addresses the critical challenge of modeling and mapping urban air quality to ascertain pollutant concentrations in unmonitored locations. The advent of low-cost sensors, particularly those deployed in vehicular networks, presents novel datasets that hold the potential to enhance air quality modeling. This research conducts a comprehensive review

Mikołaj Sienicki, Krzysztof Sienicki

This short note comments on \citet{Aerts2024Origin}, which proposes that ranked word frequencies in texts should be read through the lens of Bose--Einstein (BE) statistics and even used to illuminate the origin of quantum statistics in physics. The core message here is modest: the paper offers an interesting analogy and an eye-catching fit, but several key s

Ruoyu Feng, Yunpeng Qi, Jinming Liu, Yixin Gao

Image compression methods are usually optimized isolatedly for human perception or machine analysis tasks. We reveal fundamental commonalities between these objectives: preserving accurate semantic information is paramount, as it directly dictates the integrity of critical information for intelligent tasks and aids human understanding. Concurrently, enhanced

Valerio Lattanzi, Miguel Sanz-Novo, Víctor M. Rivilla, Izaskun Jiménez-Serra

Sulfur-bearing molecules are central to interstellar chemistry, yet their observed abundances in the gas phase remain far below cosmic expectations in dense interstellar regions. Mixed N-S-O species such as thionylimide (HNSO) are particularly relevant, as they incorporate three key biogenic elements. The $cis$ conformer of HNSO has recently been detected in

Jin-Xin Tan, Zhi-Chao Gong, Jun Hua, Xiangdong Ji

The Collins-Soper (CS) kernel governs the rapidity evolution of transverse-momentum-dependent (TMD) parton distributions, a cornerstone for QCD factorization and linking nucleon structure data across scales. Its nonperturbative behavior at large transverse separations ($b_{\perp}$) remains weakly constrained due to phenomenological model dependencies. We pre

Enhao Feng, Sara Mehidi

We prove that smooth non-klt toric orbifolds are separably Campana rationally connected, extending the result in the klt case. We also show that there always exists a positive characteristic in which a singular weighted projective space, viewed as a non-klt Campana orbifold, is not separably Campana rationally connected.

Yacine Mohamed Idir, Olivier Orfila, Vincent Judalet, Benoit Sagot

With the advancement of technology and the arrival of miniaturized environmental sensors that offer greater performance, the idea of building mobile network sensing for air quality has quickly emerged to increase our knowledge of air pollution in urban environments. However, with these new techniques, the difficulty of building mathematical models capable of

Damian Maingi

In this paper we study the cohomological criterion for the splitting of vector bundles on multiprojective spaces $\mathbb{P}^{n_1}\times\ldots\times\mathbb{P}^{n_s}$. We also give a generalization of vanishing cohomological criteria for vector bundles on $\mathbb{P}^{n}\times\ldots\times\mathbb{P}^{n}$.

Pavel Chigansky, Marina Kleptsyna

The mixed fractional Brownian motion - the sum of independent fractional and standard Brownian motions - is known to be a semimartingale if the Hurst exponent $H$ of its fractional component satisfies $H > 3/4$. The question posed in the title is motivated by recent findings in quantitative finance. In this note, we show that the drift in its Doob-Meyer deco

Jinyang Li, Marcello Farina, Luca Mozzarelli, Luca Cattaneo

This paper describes the design and the realization of a prototype of the novel guide robot BUDD-e for visually impaired users. The robot has been tested in a real scenario with the help of visually disabled volunteers at ASST Grande Ospedale Metropolitano Niguarda, in Milan. The results of the experimental campaign are throughly described in the paper, disp

Hannah O. Wood, Fulu Zhou, Jan Dočkal, Martin Lísal

The structure and thickness of the electrical double layer (EDL) at carbon electrodes strongly influence electrochemical performance, yet remain poorly understood in super-concentrated aqueous electrolytes. Here we combine classical and quantum-mechanical molecular dynamics simulations to resolve the interfacial organisation of aqueous LiCl from dilute to wa

Anastasiia Kurmukova, Selim F. Yilmaz, Emre Ozfatura, Deniz Gunduz

Reliable communication over noisy channels requires the design of specialized error-correcting codes (ECCs) tailored to specific system requirements. Recently, neural network-based decoders have emerged as promising tools for enhancing ECC reliability, yet their high computational complexity prevents their potential practical deployment. In this paper, we ta

Hyotae Kim, Athanasios Kottas

The Hawkes process is a versatile stochastic model for point patterns that exhibit self-excitation, that is, the property that an event occurrence increases the rate of occurrence for some period of time in the future. We present a Bayesian nonparametric modeling approach for temporal marked Hawkes processes. Our focus is on point process modeling of earthqu

Kinnari Dave, Louis Lemonnier, Romain Péchoux, Vladimir Zamdzhiev

The two main notions of control in quantum programming languages are often referred to as "quantum" control and "classical" control. With the latter, the control flow is based on classical information, potentially resulting from a quantum measurement, and this paradigm is well-suited to mixed state quantum computation. Whereas with quantum control, we are pr

Fengming Zhu, Yuxin Pan, Xiaomeng Zhu, Fangzhen Lin

Originating in psychology, $\textit{Theory of Mind}$ (ToM) has attracted significant attention across multiple research communities, especially logic, economics, and robotics. Most psychological work does not aim at formalizing those central concepts, namely $\textit{goals}$, $\textit{intentions}$, and $\textit{beliefs}$, to automate a ToM-based computationa

Joris Mulder, Robbie C. M. van Aert

Bayesian hypothesis testing via Bayes factors offers a principled alternative to classical p-value methods in meta-analysis, particularly suited to its cumulative and sequential nature. Unlike commonly reported p-values for standard null hypothesis significance testing, Bayes factors allow for quantifying support both for and against the existence of an effe

C. T. Hao, J. H. Jing, X. L. Han, H. R. Lan

We analyze a sample of\textit{ Swift} gamma-ray bursts (GRBs) with extended emissions in $\gamma$-rays and/or X-ray plateaus that may be driven by magnetars. Multi-wavelength data and multi-standards have been adopted to investigate the issue jointly. First, we find that GRBs with both extended emission and X-ray plateau satisfy a three-parameter relation be

Mengyu Yang, Yanming Yang, Chenyi Xu, Chenxi Song

Diffusion models have achieved impressive generative quality across modalities like 2D images, videos, and 3D shapes, but their inference remains computationally expensive due to the iterative denoising process. While recent caching-based methods effectively reuse redundant computations to speed up 2D and video generation, directly applying these techniques

Zhaohui Wang, Tengbo Yu, Hao Tang

Vision-Language-Action (VLA) models have recently attracted growing attention in end-to-end autonomous driving for their strong reasoning capabilities and rich world knowledge. However, existing VLAs often suffer from limited numerical reasoning ability and overly simplified input-output mappings, which hinder their performance in complex driving scenarios r

Kevin Ivan Piterman, John Shareshian, Volkmar Welker

We propose definitions of the common bases complex, the poset of decompositions, and the poset of partial decompositions for arbitrary spherical buildings. We show that the poset of decompositions is Cohen-Macaulay, and that the poset of partial decompositions is spherical and homotopy equivalent to the common bases complex. To prove these results, we rely o

A. Claret, G. Torres

In this Research Note we present new gravity-darkening exponents ($\beta$) for several stellar evolution models from the ZAMS up to the giant phase. The models were computed using the MESA code (version 7385) for the composition $X = 0.70$ and $Z = 0.02$, adopting A09 opacities and a mixing length parameter of $\alpha_{\rm MLT} = 1.84$. Results were calculat

Florian Faucher, Ha Pham, Damien Fournier, Patrick Amestoy

With increasing quantity and quality of solar observations, it becomes essential to account for three-dimensional heterogeneities in wave modeling for seismic data interpretation. In this context, we present a 3D solver of the time-harmonic adiabatic stellar oscillation equations without background flows on a domain consisting of the Sun and its photosphere.

F. Alessandria, F. B. Armani, S. Coelli, D. Cortis

Among the global efforts to directly detect dark matter, the only positive claim so far relies on NaI(Tl) crystal detectors, making this technology of particular interest. ASTAROTH is a project aimed at developing the next generation of such detectors by reading out their scintillation light with SiPM matrices operated at cryogenic temperatures. This paper d

Edward Moroshko, Weizhe Qin, Desen Kirli, Mohammed Qais

Due to changes in frequency and intensity of extreme weather events, such as heatwaves and storms, power systems around the globe are having to deal with increased imbalance between demand and supply and additional risk of loss of supply, calling for advanced control strategies that strengthen system resilience. This paper develops a Model Predictive Control

Dhruv Muley, León-Alexander Hühn, Haochang Jiang, David Melon Fuksman

High-resolution, near-infrared observations have revealed prominent, two-armed spirals in a multitude of systems, such as MWC~758, SAO~206462, and V1247~Ori. Alongside the classical theory of disk-companion interaction, shadow-based driving has come into vogue as a potential explanation for such large-scale substructures. How might these two mechanisms be di

Todor Antić, Aleksa Džuklevski, Jiří Fiala, Jan Kratochvíl

Let S be a set of distinct points in general position in the Euclidean plane. A plane Hamiltonian path on S is a crossing-free geometric path such that every point of S is a vertex of the path. It is known that, if S is sufficiently large, there exist three edge-disjoint plane Hamiltonian paths on S. In this paper we study an edge-constrained version of the

Ziang Liu, Wonjae Shin, Bruno Clerckx

Low Earth orbit (LEO) satellites are a promising technology for providing low-latency, high-data-rate, and wide-coverage communication services. However, with growing demand for data transmission, future non-terrestrial networks (NTNs) require high spectral efficiency especially with low-gain antennas at the ground devices. This motivates the adoption of in-

Herbod Pourali, Sajjad Hashemian, Ebrahim Ardeshir-Larijani

We introduce an expander-sketching framework for list-decodable linear regression that achieves sample complexity $\tilde{O}((d+\log(1/\delta))/\alpha)$, list size $O(1/\alpha)$, and near input-sparsity running time $\tilde{O}(\mathrm{nnz}(X)+d^{3}/\alpha)$ under standard sub-Gaussian assumptions. Our method uses lossless expanders to synthesize lightly cont

Jérome Ricciardi, Sébastien Bardin, Christophe Chareton, Benoît Valiron

Equivalence checking of hybrid quantum circuits is of primary importance, given that quantum circuit transformations are omnipresent along the quantum compiler chain. While some approaches exist for automating this task, most focus on the simple case of unitary circuits. At the same time, real quantum computing requires hybrid circuits equipped with measurem

Hyun-Sik Jeong, Hanse Kim, Keun-Young Kim, Gaya Yun

We apply physics-informed machine learning (PIML) to solve inverse problems in holography and classical mechanics, focusing on neural ordinary differential equations (Neural ODEs) and physics-informed neural networks (PINNs) for solving non-linear differential equations of motion. First, we introduce holographic inverse problems and demonstrate how PIML can

E. Quintin, N. Khan, N. A. Webb, R. Webbe

After six years of studies following the discovery of GSN069, a link is starting to appear between the elusive Quasi-Periodic Eruptions (QPEs) and other types of nuclear transients, among which are Tidal Disruption Events (TDEs). As such, observing strategies are adapting, with a current trend focusing on late-time X-ray follow-ups of (optical) TDEs. While t

Xin Lu, Wanjia Fu, Hongzi Li, Haoyang Yu

Causal inference, as a major research area in statistics and data science, plays a central role across diverse fields such as medicine, economics, education, and the social sciences. Design-based causal inference begins with randomized experiments and emphasizes conducting statistical inference by leveraging the known randomization mechanism, thereby enablin

L. P. R. Ospedal, R. Turcati, S. B. Duarte

Motivated by ideas from effective field theories, we conduct a twofold investigation in CP-violating non-linear electrodynamics models. On the one hand, features related to field equations and wave propagation in the presence of a magnetic background field are evaluated. Interestingly, we show that the CP-violating term in our framework induces a bi-anisotro

O. Benton, Y. Skourski, D. Gorbunov, A. Miyata

We explore the magnetic properties of Nd$_2$Zr$_2$O$_7$ and Pr$_2$Zr$_2$O$_7$ single crystals subjected to pulsed magnetic fields up to 60 T using magnetization and magnetocaloric-effect (MCE) measurements, with initial temperatures ranging from 2 to 31K. The MCE data exhibit pronounced and unconventional hysteresis loops, in which the sample temperature inc

Silas B. Schack, Kristine M. L. Krighaar, Emma Y. Lenander, Kim Lefmann

A new component for the accurate simulation of neutron scattering from magnetic excitations has been developed for the neutron ray-tracing software McStas. The component SpinWave_BCO simulates inelastic neutron scattering from ferro-, antiferro-, and altermagnetic excitations in a body-centered orthorhombic crystal structure, where the dispersion relation an

Shiva Parsarad, Isabel Wagner

Recommender systems (RSs) output ranked lists of items, such as movies or restaurants, that users may find interesting, based on the user's past ratings and ratings from other users. RSs increasingly incorporate differential privacy (DP) to protect user data, raising questions about how privacy mechanisms affect both recommendation accuracy and fairness. We

Marianne Johnson, António Malheiro

We give an alternative description of the grammic monoid in terms of weakly increasing subsequences. Specifically, we show that words $u,v$ in the generators $\{1,\ldots, n\}$ determine the same element of the grammic monoid of rank $n$ if and only if for all $1 \leq p \leq q$, the maximum length of a weakly increasing subsequence on alphabet $\{p,\ldots, q\

Jérôme Pfeiffer, Nicolai Maisch, Sebastian Friedl, Matthias Milan Strljic

The growing adoption of federated data spaces, such as in the GAIA-X and the International Data Spaces (IDS) initiative, promises secure and sovereign data sharing across organizational boundaries in Industry 4.0. In manufacturing ecosystems, this enables use cases, such as cross-factory process optimization, predictive maintenance, and supplier integration.

Tian-Nuo Li, Guo-Hong Du, Sheng-Han Zhou, Yun-He Li

Recent baryon acoustic oscillation (BAO) measurements released by DESI, when combined with cosmic microwave background (CMB) data and type Ia supernova (SN) data, suggest a significant preference for dynamical dark energy (DDE) that exhibits the phantom-like behavior in the past and has transitioned into quintessence-like behavior today. In this work, we con

N. I. Petrov

Pure states are usually used to observe quantum phenomena. In this study, we show that a quantum superposition of spatially displaced mixed cat states can be generated within an optical waveguide via nonparaxial unitary evolution of the initial low coherence (low purity) light beam. It is shown that highly mixed Schrodinger cat states can be observed at a we

Martin Kjøllesdal Johnsrud, Navdeep Rana

We present stochastic variants of the exponential time differencing schemes for stiff stochastic differential equations. We derive three explicit schemes that offer better stability compared to Euler-Maruyama and Milstein's method, and achieve strong convergence up to order O(h) in the time step h. We combine these schemes with a pseudo-spectral approach to

Zhongyi Yang, Datong Chen, Zihao Li, Huangjun Zhu

Efficient fidelity estimation of multiqubit quantum states is crucial to many applications in quantum information processing. However, to estimate the infidelity $\epsilon$ with multiplicative precision, conventional estimation protocols require (order) $1/\epsilon^2$ different circuits in addition to $1/\epsilon^2$ samples, which is quite resource-intensive

Sabine Cornelsen, Henry Förster, Siddharth Gupta, Stephen Kobourov

A hypergraph consists of a set of vertices and a set of subsets of vertices, called hyperedges. In the metro map metaphor, each hyperedge is represented by a path (the metro line) and the union of all these paths is the support graph (metro network) of the hypergraph. Formally speaking, a path-based support is a graph together with a set of paths. We conside

Rostyslav Kozhan, František Štampach

We study asymptotic behavior of orthogonal polynomials on the unit circle with varying Verblunsky coefficients $\alpha_{n,N}$ when the ratio $n/N$ converges as $n,N\to\infty$. First, we give a streamlined proof of ratio asymptotics for orthogonal and paraorthogonal polynomials in the case of asymptotically constant and asymptotically periodic coefficients $\

Youenn Le Gal, Marco Schirò

In this work we derive the replica field theory for monitored quantum many-body systems evolving under the quantum jumps protocol, corresponding to a non-Hermitian evolution interspersed with random quantum jumps whose distribution is state-dependent. We show that the density matrix of $R$ replicas evolves according to a master equation where the non-Hermiti

Xiujian Liang, Jiacheng Liu, Mingyang Sun, Qichen He

Robot manipulation in the real world is fundamentally constrained by the visual sim2real gap, where depth observations collected in simulation fail to reflect the complex noise patterns inherent to real sensors. In this work, inspired by the denoising capability of diffusion models, we invert the conventional perspective and propose a clean-to-noisy paradigm

Nada Alghamdi, Paolo de Angelis, Pietro Asinari, Eliodoro Chiavazzo

The development of new battery materials, particularly novel cathode chemistries, is essential for enabling next generation energy storage technologies. In this work, we employ a multi-fidelity screening protocol combining the Energy-GNoME confident criteria, foundational MACE machine-learning force fields (MLFF), and physically motivated heuristic filters t

Pablo Krupa, Hasna El Hasnaouy, Mario Zanon, Alberto Bemporad

In Model Predictive Control (MPC), the objective function plays a central role in determining the closed-loop behavior of the system, and must therefore be designed to achieve the desired closed-loop performance. However, in real-world scenarios, its design is often challenging, as it requires balancing complex trade-offs and accurately capturing a performan

José A. C. Nogales, K. Luz-Burgoa, Laysa G. Martins

In this study, we explore the thermodynamic aspects of a modified version of Rastall's gravity theory and its implications for cosmological scenarios. We analyze the role of non-conserved energy-momentum tensor equations and investigate their influence on particle production within an irreversible thermodynamic framework. By introducing a novel Lagrangian, w

Yacine Mohamed Idir, Olivier Orfila, Patrice Chatellier, Vincent Judalet

This paper explores Vecchia likelihood approximation for modeling physical phenomena sensed by mobile and fixed low-cost sensors in urban environments. A three-level hierarchical model is proposed to simultaneously accounts for the physical process of interest and measurement errors inherent in low-cost sensors. Several innovative configurations of Vecchia's

Hyakka Nakada, Marika Kubota

The advent of generative models has dramatically improved the accuracy of image inpainting. In particular, by removing specific text from document images, reconstructing original images is extremely important for industrial applications. However, most existing methods of text removal focus on deleting simple scene text which appears in images captured by a c

Faezeh Labbaf, Tomáš Kolárik, Martin Blicha, Grigory Fedyukovich

We present a novel logic-based concept called Space Explanations for classifying neural networks that gives provable guarantees of the behavior of the network in continuous areas of the input feature space. To automatically generate space explanations, we leverage a range of flexible Craig interpolation algorithms and unsatisfiable core generation. Based on

Christopher B. C. Dean, János Engländer, Emma Horton

We offer a new proof of the classical law of large numbers for a general class of branching Markov processes based on the asymptotic behaviour of the moments developed in \cite{bmoments, gonzalez2022erratum}. Moreover, we show that the law of the limiting random variable, that is the almost sure limit of the classical additive martingale, is completely deter

Kyeongha Rho, Hyeongkeun Lee, Jae Won Cho, Joon Son Chung

In this paper, we propose Mixture of Layer-Wise Tokens (MoLT), a parameter- and memory-efficient adaptation framework for audio-visual learning. The key idea of MoLT is to replace conventional, computationally heavy sequential adaptation at every transformer layer with a parallel, lightweight scheme that extracts and fuses layer-wise tokens only from the lat

Wenxin Wang, Yingzhi Tian

An edge subset \( S \subseteq E(G) \) is called a 3-restricted edge-cut if \( G - S \) is disconnected and each component of \( G - S \) contains at least three vertices. The 3-restricted edge-connectivity of a graph \( G \), denoted by \( \lambda_3(G) \), is defined as the minimum cardinality among all 3-restricted edge-cuts if there are at least one; other

Wesley Fussner, Andrew Tedder

There are exactly two maximal schematic extensions of the relevant logic R with the variable sharing property. We establish that one of them has a strong form of interpolation for deducibility, thereby giving an example of a well-known relevant logic with interpolation.

Daan Frenkel

In his Equilibrium of Heterogeneous Substances Gibbs seems to suggest that the chemical potential of a crystal nucleus need not be equal to that of the coexisting fluid. In the field, Gibbs's statement has been something of a hot potato. I argue that a consistent treatment of point defects in the critical nucleus is essential for clarifying the meaning of th

Zida Liu, Jun Gao, Zhang Ji, Li Zhao

Social bots are increasingly polluting online platforms by spreading misinformation and engaging in coordinated manipulation, posing severe threats to cybersecurity. Graph Neural Networks (GNNs) have become mainstream for social bot detection due to their ability to integrate structural and attribute features, with spectral-based approaches demonstrating par

Juan Palomeque-Gonzalez

This technical note presents a reproducible workflow for converting a legacy archaeological image collection into a structured and segmentation ready dataset. The case study focuses on the Lower Palaeolithic hand axe and biface collection curated by the Archaeology Data Service (ADS), a dataset that provides thousands of standardised photographs but no mecha

Qingnan Zhang, Yingzhi Tian

Given a connected graph $G=(V,E)$ and a $k$-set $S\subseteq V(G)$, the $Steiner$ $distance$ $d_{G}(S)$ of $S$ is defined as the size of a minimum tree including $S$ in $G$. The $Steiner$ $k$-$eccentricity$ of a vertex $v$ in $G$ is the maximum value of $d_G(S)$ over all $S\subseteq V(G)$ with $|S|=k$ and $v\in S$. The minimum Steiner $k$-eccentricity over al

Antonino Nunnari, Valentina D'Orazi, Giuliana Fiorentino, Vittorio F. Braga

Classical Cepheids (CCs) have long been considered excellent tracers of the chemical evolution of the Milky Way's young disk. We present a homogeneous, NLTE spectroscopic analysis of 401 Galactic CCs, based on 1,351 high-resolution optical spectra, spanning Galactocentric distances from 4.6 to 29.3 kpc. Using PySME with MARCS atmospheres and state-of-the-art

Shun Inadumi, Shohei Tanaka, Tosho Hirasawa, Atsushi Hashimoto

As the number of scientific papers continues to grow, there is a demand for approaches that can effectively convey research findings, with posters serving as a key medium for presenting paper contents. Poster layouts determine how effectively research is communicated and understood, highlighting their growing importance. In particular, a gap remains in under

Jinhyun Park

For an arbitrary field $k$, and an arbitrary regular henselian local $k$-scheme $X$ of dimension $1$ with the residue field $k$, we introduce two subcomplexes of the higher Chow complexes of $X$ using certain extended face intersection conditions. We define suitable equivalence relations on them, and prove that their Milnor range cycle class groups offer geo

Baptiste Chopin, Tashvik Dhamija, Pranav Balaji, Yaohui Wang

We propose Dimitra++, a novel framework for audio-driven talking head generation, streamlined to learn lip motion, facial expression, as well as head pose motion. Specifically, we propose a conditional Motion Diffusion Transformer (cMDT) to model facial motion sequences, employing a 3D representation. The cMDT is conditioned on two inputs: a reference facial

Aseel Rawashdeh

Reinforcement learning in mobile health (mHealth) interventions requires balancing intervention efficacy with user burden, particularly when state measurements (for example, user surveys or feedback) are costly yet essential. The Act-Then-Measure (ATM) heuristic addresses this challenge by decoupling control and measurement actions within the Action-Continge

Datong Chen, Huangjun Zhu

Fidelity is the standard measure for quantifying the similarity between two quantum states. It is equal to the square of the minimum Bhattacharyya coefficient between the probability distributions induced by quantum measurements on the two states. Though established for over thirty years, the structure of fidelity-optimal quantum measurements remains unclear

Andreas Vitsos, Leonora Misciattelli Mocenigo Soranzo, You Zhou

The recently developed ``imaging-by-smashing" technique has emerged as a powerful approach to connect final-state collective flow phenomena in ultra-relativistic nuclear collisions with the intrinsic structure of the colliding nuclei. While most efforts have focused on constraining nuclear shape properties such as deformation and triaxiality, less attention

Alexander Zappacosta, Ben Haylock, Paul Fisher, Naoya Morioka

We report electrical spin state readout and coherent control of an ensemble ($\sim$540) of silicon vacancies ($\mathrm{V}_{\mathrm{Si}}^{-}$) in a silicon carbide-on-insulator (SiCOI) platform, with excitation wavelengths from 780 to 990 nm, demonstrating for the first time spin state readout well beyond the zero phonon line of the V2 $\mathrm{V}_{\mathrm{Si

Duc V. Dinh, Xiang Lü, Oliver Brandt, Dilara Sen

We present a comprehensive optical characterization of 200-nm-thick CrN(111) films grown simultaneously on Al$_2$O$_3$(0001) and AlN/Al$_2$O$_3$(0001) using plasma-assisted molecular beam epitaxy. Spectroscopic ellipsometry, spanning the far-infrared to ultraviolet range (0.04 - 5.5 eV), is conducted at room temperature to determine the optical constants $n$

Guanxi Lu, Hao Mark Chen, Zhiqiang Que, Wayne Luk

Large language models (LLMs) have shown promising performance across various tasks. However, their autoregressive decoding process poses significant challenges for efficient deployment on existing AI hardware. Quantization alleviates memory and compute pressure by compressing weights, activations, and KV caches to low precisions while preserving generation q

Isabel Gonçalves, Paulo Cavalin, Claudio Pinhanez

Finetuning pre-trained language models with small amounts of data is a commonly-used method to create translators for ultra-low resource languages such as endangered Indigenous languages. However, previous works have reported substantially different performances with translators created using similar methodology and data. In this work we systematically explo

Amirreza Yousefzadeh

Digital neuromorphic processors are emerging as a promising computing substrate for low-power, always-on EdgeAI applications. In this tutorial paper, we outline the main architectural design principles behind fully digital neuromorphic processors and illustrate them using the SENECA platform as a running example. Starting from a flexible array of tiny RISC-V

A. Sheleg, D. Vovchuk, K. Boiko, P. Ginzburg

We report an experimental demonstration of room-temperature Hong-Ou-Mandel (HOM) interference at a radio-wave frequency of 120 MHz using conditional build-up of quantum states from classical phase-averaged coherent states. This approach enables observation of quantum effects in spectral regimes where conventional single-photon sources and detectors are unava

Yu Zhuge, Dan Guo, Zhan-Wei Liu, Derek B. Leinweber

Over the past few years, Hamiltonian effective field theory has been successfully applied to studies of nucleon and hyperon excited states. By discretizing the Hamiltonian in a finite volume, one can obtain the energy spectrum and compare it with the results calculated from lattice QCD. Through the analysis of experimental data, Hamiltonian effective field t

Wan-Hsuan Lin, Fangchun Liang, Mario Motta, Haimeng Zhang

The unitary cluster Jastrow (UCJ) ansatz and its variant known as local UCJ (LUCJ) are promising choices for variational quantum algorithms for chemistry due to their combination of physical motivation and hardware efficiency. The parameters of these ansatzes can be initialized from the output of a coupled cluster, singles and doubles (CCSD) calculation perf

Shanchuan Lin, Ceyuan Yang, Zhijie Lin, Hao Chen

We present adversarial flow models, a class of generative models that belongs to both the adversarial and flow families. Our method supports native one-step and multi-step generation and is trained with an adversarial objective. Unlike traditional GANs, in which the generator learns an arbitrary transport map between the noise and data distributions, our gen

Yevheniya Nosyk, Malte Tashiro, Qasim Lone, Robert Kisteleki

Network measurement platforms are increasingly popular among researchers and operators alike due to their distributed nature, simplifying measuring the remote parts of the Internet. RIPE Atlas boasts over 12.9K vantage points in 178 countries worldwide and serves as a vital tool for analyzing anycast deployment, network latency, and topology, to name a few.

Ali Al Khansa

Integrated Sensing and Communication (ISAC) with Orthogonal Frequency Division Multiplexing (OFDM) waveforms is a key enabler for next-generation wireless systems. Recent studies show that Convolutional Neural Networks (CNNs) can estimate the number of targets from two-dimensional (2D) range-Doppler periodogram maps, yet accuracy often degrades as scenes bec

Jun Lin, Jing Feng, Zhenhua Ge, Jiang Tian

The Solar Close Observations and Proximity Experiments (SCOPE) mission will send a spacecraft into the solar atmosphere at a low altitude of just 5 R_sun from the solar center. It aims to elucidate the mechanisms behind solar eruptions and coronal heating, and to directly measure the coronal magnetic field. The mission will perform in situ measurements of th

Zhenglin Huang, Jason Li, Haiquan Wen, Tianxiao Li

As generative models become increasingly diverse and powerful, cross-generator detection has emerged as a new challenge. Existing detection methods often memorize artifacts of specific generative models rather than learning transferable cues, leading to substantial failures on unseen generators. Surprisingly, this work finds that frozen visual foundation mod

Tien-Huy Nguyen, Huu-Loc Tran, Huu-Phong Phan-Nguyen, Quang-Vinh Dinh

Text-based person anomaly retrieval has emerged as a challenging task, with most existing approaches relying on complex deep-learning techniques. This raises a research question: How can the model be optimized to achieve greater fine-grained features? To address this, we propose a Local-Global Hybrid Perspective (LHP) module integrated with a Vision-Language

Catherine Drysdale, Matthew Colbrook, Michael T. M. Woodley

We establish a connection between quantum mechanics and computation, revealing fundamental limitations for algorithms computing spectra, especially in non-Hermitian settings. Introducing the concept of locally trivial pseudospectra (LTP), we show such assumptions are necessary for spectral computation. LTP adapts dynamically to system energies, enabling spec

Giuseppe Viterbo, Tobias Buck

We introduce \textsc{Odisseo} (Optimized Differentiable Integrator for Stellar Systems Evolution of Orbits), a differentiable N-body code designed to constrain the gravitational potential of the Milky Way (MW) through dynamical modeling of accreted structures such as stellar streams. \textsc{Odisseo} is implemented in JAX, enabling just-in-time compilation,

François Provost, Faisal Hawlader, Mehdi Testouri, Raphaël Frank

Latency is a key performance factor for the teleoperation of Connected and Autonomous Vehicles (CAVs). It affects how quickly an operator can perceive changes in the driving environment and apply corrective actions. Most existing work focuses on Glass-to-Glass (G2G) latency, which captures delays only in the video pipeline. However, there is no standard meth

Xiyan Liu, Han Wang, Yuhu Wang, Junjie Cai

Understanding mid-level road semantics, which capture the structural and contextual cues that link low-level perception to high-level planning, is essential for reliable autonomous driving and digital map construction. However, existing benchmarks primarily target perception tasks such as detection or segmentation, overlooking the reasoning capabilities requ

Elham Cheshmikhani, Hamed Farbeh

Associative cache memory significantly influences processor performance and energy consumption. Because it occupies over half of the chip area, cache memory is highly susceptible to transient and permanent faults, posing reliability challenges. As the only hardware-managed memory module, the cache tag array is the most active and critical component, dominati

Nathan Herring, Daniel Boyanovsky

Misalignment dynamics, the non-equilibrium evolution of a scalar (or pseudoscalar) condensate in a potential landscape, broadly describes a solution to the strong CP problem, a mechanism for cold dark matter production and (pre) reheating post inflation. Often, radiative corrections are included phenomenologically by replacing the potential by the effective

Xinyi Che, Wenbo Wang, Jian Guan, Qijun Zhao

Multimodal Emotion Recognition in Conversation (MERC) significantly enhances emotion recognition performance by integrating complementary emotional cues from text, audio, and visual modalities. While existing methods commonly utilize techniques such as contrastive learning and cross-attention mechanisms to align cross-modal emotional semantics, they typicall

Dario Baum, Lucas Visscher, Arno Förster

We present a simple linear model to estimate the basis set incompleteness errors (BSIE) of (vertex-corrected) $GW$ QP energies based on the kinetic energy of the corresponding orbital only. We parametrise the model for $G_0W_0$, quasi-particle self-consistent $GW$ (qs$GW$), and vertex-corrected ($\Sigma^{BSE}@L^{BSE}$) QP energies on a large set of molecules

Jean-Philippe Lansberg, Kate Lynch, Ronan McNulty, Charlotte Van Hulse

Measurements of inclusive quarkonium photoproduction provide strong constraints on the quarkonium production mechanism; however, this process has not yet been measured at the LHC. We summarise our previously developed selection strategy for isolating inclusive quarkonium photoproduction in pPb collisions at the LHC, which offer an optimal balance of photon f

Yifan Lei, Jiahua Luo, Tingyu Jiang, Bo Zhang

In large-scale advertising recommendation systems, retrieval serves as a critical component, aiming to efficiently select a subset of candidate ads relevant to user behaviors from a massive ad inventory for subsequent ranking and recommendation. The Embedding-Based Retrieval (EBR) methods modeled by the dual-tower network are widely used in the industry to m

Ali Al Khansa, Youssef Bahannis

In Orthogonal Frequency Division Multiplexing (OFDM) Integrated Sensing and Communication (ISAC) systems, a key challenge is balancing sidelobe attenuation and resolution for multi-target detection scenarios. While windowing functions are typically used to manage this trade-off, state-of-the-art methods rely on a single, fixed window followed by a predefined

Fei Guo, Dmitry Usanov, Eduardo B. Guedes, Arnaud Magrez

Fundamental quantum transition time scales are accessible through the spin polarization of photoelectrons coming from initially spin-degenerate states for solid-state materials . In this work we investigate the modification of this time scale in the vicinity of a Fano resonance in photoemission from a solid. We employ spin- and angle-resolved photoemission s

Zhenglin Zhou, Fan Ma, Xiaobo Xia, Hehe Fan

We explore inference-time scaling in text-guided 3D diffusion models to enhance generative quality without additional training. To this end, we introduce ITS3D, a framework that formulates the task as an optimization problem to identify the most effective Gaussian noise input. The framework is driven by a verifier-guided search algorithm, where the search al

Saurabh Atreya, Nabyl Quignon, Baptiste Chopin, Abhijit Das

The rapid advancement of generative models has led to increasingly realistic deepfake videos, posing significant societal and security risks. While existing detection methods focus on distinguishing real from fake videos, such approaches fail to address a fundamental question: What is the intent behind a manipulated video? Towards addressing this question, w