March 2026 arXiv papers — page 74

Jian Ding, Cheng Wang, Hongju Li, Cheng Shu

In Shamir's secret sharing scheme, all participants possess equal privileges. However, in many practical scenarios, it is often necessary to assign different levels of authority to different participants. To address this requirement, Hierarchical Secret Sharing (HSS) schemes were developed, which partitioned all participants into multiple subsets and assigne

Alexey Gordeev

In 1992, Bollob\'as and Meir showed that for every $k \geq 1$ there exists a constant $c_k$ such that, for any $n$ points in the $k$-dimensional unit cube $[0, 1]^k$, one can find a tour $x_1, \dots, x_n$ through these $n$ points with $\sum_{i = 1}^n |x_i - x_{i + 1}|^k \leq c_k$, where $x_{n + 1} = x_1$ and $|x - y|$ is the Euclidean distance between $x$ an

Giacomo Cristinelli, José A. Iglesias

The Wasserstein-Fisher-Rao (WFR) distance on $S^{2}$ has recently been shown to coincide with a classical elastic distance between $S^{2}$-immersions in the theory of Riemannian shape analysis. While this correspondence holds in dimension $2$, the analogous statement fails in general on $S^{1}$ and, in the case of convex curves, it cannot be derived from exi

Simon Lupart, Maxime Louis, Thibault Formal, Hervé Déjean

Retrieval over large codebases is a key component of modern LLM-based software engineering systems. Existing approaches predominantly rely on dense embedding models, while learned sparse retrieval (LSR) remains largely unexplored for code. However, applying sparse retrieval to code is challenging due to subword fragmentation, semantic gaps between natural-la

Yao Fu, Carl Schmidt, C. --P. Yuan

We present an extended version of the \texttt{ePump} framework that enables the simultaneous profiling of proton parton distribution functions (PDFs) and the strong coupling $\alpha_s$ using new experimental data. By promoting $\alpha_s$ to a fit parameter within the Hessian updating formalism, the method performs coherent updates of $\{\text{PDFs},\alpha_s\

Zixuan Wang, Yuxin Chen, Yuqi Liu, Jinhui Ye

Vision-Language-Action (VLA) models typically map visual observations and linguistic instructions directly to control signals. This "black-box" mapping forces a single forward pass to simultaneously handle instruction interpretation, spatial grounding, and low-level control, often leading to poor spatial precision and limited robustness in out-of-distributio

Duy D. Nguyen, Phat T. Tran-Truong

The advent of Transformer and Mamba-based architectures has significantly advanced 3D medical image segmentation by enabling global contextual modeling, a capability traditionally limited in Convolutional Neural Networks (CNNs). However, state-of-the-art Transformer models often entail substantial computational complexity and parameter counts, which is parti

Jian Ding, Cheng Wang, Hongju Li, Cheng Shu

Conjunctive Hierarchical Secret Sharing (CHSS) is a type of secret sharing that divides participants into multiple distinct hierarchical levels, with each level having a specific threshold. An authorized subset must simultaneously meet the threshold of all levels. Existing Chinese Remainder Theorem (CRT)-based CHSS schemes either have security vulnerabilitie

Paolo Toccaceli

We propose a method for non-parametric conditional distribution estimation based on partitioning covariate-sorted observations into contiguous bins and using the within-bin empirical CDF as the predictive distribution. Bin boundaries are chosen to minimise the total leave-one-out Continuous Ranked Probability Score (LOO-CRPS), which admits a closed-form cost

Jianlin Chen, Gongyang Li, Zhijiang Zhang, Liang Chang

Transformer-based methods for RGB-D Salient Object Detection (SOD) have gained significant interest, owing to the transformer's exceptional capacity to capture long-range pixel dependencies. Nevertheless, current RGB-D SOD methods face challenges, such as the quadratic complexity of the attention mechanism and the limited local detail extraction. To overcome

Prajal Chettri, Shailesh Srivastava

Femtosecond laser micromachining (FLM) fabricated waveguides inherently form elliptical cores due to differences in focal spot size and the Rayleigh range of the microscope objective. Consequently, it is essential to study their propagation characteristics, which differ from those of conventional circular-core waveguides. In this work, we present the results

M. Fazri Nizar

Segmenting fetal head structures from prenatal ultrasound remains a practical bottleneck in obstetric imaging. The current state-of-the-art baseline, proposed alongside the published dataset, adapts the Segment Anything Model with per-class Dice and Lov\'{a}sz losses but still depends on bounding-box prompts at test time. We build a prompt-free pipeline on t

H. Lenske, F. Cappuzzello, A. Spatafora

Lepton number violating (LNV) $A(e^-,e^+)X$ double charge exchange (LDCE) reactions on nuclei at accelerator facilities with multi-GeV beams are proposed as a probe for physics beyond the Standard Model (BSM). A second order formalism is presented including LNV dynamics by the left-right symmetric model (LRSM). For practical studies a phenomenological model

Christian Durán-Romero, Luis J. Garay, Mercedes Martín-Benito, Rita B. Neves

We investigate the behavior of scalar quantum fields in cosmological backgrounds under modified dispersion relations, specifically focusing on how ultraviolet asymptotics influence field quantization. We establish the conditions for both the validity of the adiabatic approximation and the unitary equivalence between quantizations defined via different time v

Sauvik Poddar, Angsuman Das

A finite group $G$ is said to be Cayley integral if every undirected Cayley graph $\operatorname{Cay}(G,S)$ on $G$ is integral. In this paper, we introduce three natural extensions of this concept; namely as: Cayley colour integral, $\mathfrak{F}$-Cayley colour integral and normal Cayley integral groups. We characterize the first two families in its entirety

Seth D. Temple, Jonathan Terhorst

Stochastic epidemic models can estimate infection and removal rates, and derived quantities such as the basic reproductive number ($R_0$), when both infection and removal times are observed. In practice, however, removal times are often available while infection times are not, and existing methods that rely only on removal times can become unstable or biased

Cristian Pérez-Corral, Alberto Fernández-Hernández, Jose I. Mestre, Manuel F. Dolz

Gaussian Error Linear Unit (GELU) is a widely used smooth alternative to Rectifier Linear Unit (ReLU), yet many deployment, compression, and analysis toolchains are most naturally expressed for piecewise-linear (ReLU-type) networks. We study a hardness-parameterized formulation of GELU, f(x;{\lambda})=x{\Phi}({\lambda} x), where {\Phi} is the Gaussian CDF an

Yi-Tong Chen, En-Kai Chang, Nanyi Bi, Nitesh Goyal

After Taiwan's legalization of same-sex marriage in 2019, LGBTQ+ communities continue to face hostility on social media. Using the lens of hermeneutical injustice and autonomy, we examine how technological conditions affect LGBTQ+ individuals' identity exploration, narrative seeking, and community resilience. We conducted a multi-stage study with Taiwanese L

Dilina Rajapakse, Juan C. Rosero, Ivana Dusparic

Reinforcement Learning (RL) has demonstrated its ability to solve complex decision-making problems in a variety of domains, by optimizing reward signals obtained through interaction with an environment. However, many real-world scenarios involve multiple, potentially conflicting objectives that cannot be easily represented by a single scalar reward. Multi-Ob

Xingyu Chen, Junxiu An, Jun Guo, Yuqian Zhou

Data-driven discovery of partial differential equations (PDEs) offers a promising paradigm for uncovering governing physical laws from observational data. However, in practical scenarios, measurements are often contaminated by noise and limited by sparse sampling, which poses significant challenges to existing approaches based on numerical differentiation or

Nour Alhuda Albashir, Lars Pernickel, Danial Hamoud, Idriss Gouigah

Autonomous vehicles face major perception and navigation challenges in adverse weather such as rain, fog, and snow, which degrade the performance of LiDAR, RADAR, and RGB camera sensors. While each sensor type offers unique strengths, such as RADAR robustness in poor visibility and LiDAR precision in clear conditions, they also suffer distinct limitations wh

SII-GAIR, Sand. ai, :, Ethan Chern

We present daVinci-MagiHuman, an open-source audio-video generative foundation model for human-centric generation. daVinci-MagiHuman jointly generates synchronized video and audio using a single-stream Transformer that processes text, video, and audio within a unified token sequence via self-attention only. This single-stream design avoids the complexity of

Nabeel Salim, Mehak Narula, P. Arumugam

A systematic investigation of the odd-mass neutron-rich bromine isotopes $^{87-91}$Br has been carried out within the configuration-constrained cranked shell model (CSM) framework. The calculated kinematic moments of inertia and angular-momentum alignments reproduce the experimental trends with excellent agreement for the proposed quasiparticle configuration

Natalie Behague, Francesco Di Braccio, Bertille Granet, Allan Lo

The renowned theorem of Dirac states that if $G$ is a graph with minimum degree at least $n/2$ then $G$ has a Hamilton cycle. A natural generalisation asks what properties of an edge-colouring of $G$ guarantee the existence of a properly edge-coloured Hamilton cycle in $G$. This concept can be further generalised as follows: an \emph{incompatibility system}

Sławomir Kolasiński, Mario Santilli

Suppose $ F $ is an integrand associated with a uniformly convex $ \mathscr{C}^{3} $-norm, and $ V $ is a $ n $-dimensional varifold in an open subset of $ \mathbf{R}^{n+1} $ such that $ \mathscr{H}^n \llcorner \operatorname{spt} \| V \| $ is absolutely continuous with respect to $ \| V \| $ and the mean $ F $-curvature $ \mathbf{h}_{F}(V, \cdot) $ is bounde

Stephan Grebien, Julian Gurs, Roman Schnabel, Mikhail Korobko

Quantum-correlated networks distribute quantum resources such as squeezed and entangled states. These states are central to modern quantum technology, including photonic quantum computing, quantum communications, non-destructive biological sensing and gravitational-wave detection. Even for squeezed states of light - the most robust quantum-correlated resourc

Zhong-Chen Gao, Tianyi Zhang, Feifei Wang, Jingguo Hu

Elasticity has long been regarded as a property exclusive to material media. Here we uncover its hidden existence in the spin degree of freedom. We introduce spin elasticity-an intrinsic mechanism that governs recoverable deformation of spin morphology. This discovery reveals a previously unrecognized universality: elasticity operates in both matter and spin

M. L. Terpstra, C. A. T. van den Berg

Motivation: Quickly obtaining high-quality MRI from accelerated acquisitions is important to mitigate motion artifacts, maintain patient comfort, and improve clinical efficiency. Goals: To obtain high-quality dynamic MRI using efficient, personalized models. Approach: We propose a novel explicit representation learning approach using Gaussian splatting. Mult

Elliot Bes, Guillaume Balarac, Juan Ignacio Polanco

We investigate the kinetic energy cascade in zero-temperature quantum turbulence. Using simple theoretical arguments and unprecedented numerical simulations, we unveil an universal mechanism transferring energy directly from large to very small scales, thus bypassing the Kolmogorov-like local energy cascade and resulting in nonclassical energy spectra. This

Xiaolei Zhou, Chuangjie Fang, Jie Wu, Jingyi Yang

Parametric Computer-Aided Design (CAD) is fundamental to modern 3D modeling, yet existing methods struggle to generate long command sequences, especially under complex geometric and topological dependencies. Transformer-based architectures dominate CAD sequence generation due to their strong dependency modeling, but their quadratic attention cost and limited

Ehimare Okoyomon, Christoph Goebel

Accurate power flow analysis is critical for modern distribution systems, yet classical solvers face scalability issues, and current machine learning models often struggle with generalization. We introduce BOOST-RPF, a novel method that reformulates voltage prediction from a global graph regression task into a sequential path-based learning problem. By decom

Eduardo Banados

These notes are from three lectures given at the 54th Saas-Fee Advanced Course of the Swiss Society of Astrophysics and Astronomy in January 2025. This chapter reviews the dramatic evolution in our understanding of supermassive black holes in the first billion years, from ground-based discoveries to recent space-based infrared observations with JWST. Section

Marco Arazzi, Vignesh Kumar Kembu, Antonino Nocera

Large language models are becoming pervasive core components in many real-world applications. As a consequence, security alignment represents a critical requirement for their safe deployment. Although previous related works focused primarily on model architectures and alignment methodologies, these approaches alone cannot ensure the complete elimination of h

Fatemeh Kalantarifard, Parviz Elahi

High power end pumped solid state lasers often operate in regimes where pump induced heating creates a strong refractive index gradient (thermal lensing) that governs resonator stability and mode quality. When the pump is absorbed according to the Beer Lambert law, the thermal load, and hence the GRIN strength, vary along the crystal length, so the standard

Matan Bocarsly, Indranil Roy, Weifeng Zhi, Li-Qiao Xia

In moir\'e graphene systems, electronic interactions lift spin and valley degeneracies, leading to symmetry-broken ground states. In helical trilayer graphene (HTG), we uncover a distinct interaction-driven mechanism in which the roles of sublattice-polarized valence and conduction bands are cyclically reversed. Using scanning nano-SQUID magnetometry, we det

Xixi Wu, Qianguo Sun, Ruiyang Zhang, Chao Song

Reinforcement Learning (RL) is essential for evolving Large Language Models (LLMs) into autonomous agents capable of long-horizon planning, yet a practical recipe for scaling RL in complex, multi-turn environments remains elusive. This paper presents a systematic empirical study using TravelPlanner, a challenging testbed requiring tool orchestration to satis

Ming Chen, Wenxu Lu, Yun Wang, Zhiwei Zhang

Given $a,b,c\in\mathbb N$, let $D_{a,b,c}$ be the tournament on $a+b+c$ vertices obtained by replacing the vertices of the directed triangle $C_3$ with transitive tournaments $TT_a$, $TT_b$, and $TT_c$, respectively. Keevash and Sudakov (2009) showed that every sufficiently large oriented graph $G$ on $n$ vertices with $\delta^{0}(G)\geqslant (1/2-o(1))n$ co

David Brundage

No publicly available, ML ready datasets exist for wildlife health conditions in camera trap imagery, creating a fundamental barrier to automated health screening. We present a pipeline for generating synthetic training images depicting alopecia and body condition deterioration in wildlife from real camera trap photographs. Our pipeline constructs a curated

Ulugbek Shernazarov, Rostislav Svitsov, Bin Shi

Fine-tuning large language models for domain-specific tasks such as medical text summarization demands substantial computational resources. Parameter-efficient fine-tuning (PEFT) methods offer promising alternatives by updating only a small fraction of parameters. This paper compares three adaptation approaches-Low-Rank Adaptation (LoRA), Prompt Tuning, and

Bikashkali Midya

Theoretical analysis of a prototypical two-qubit effective non-Hermitian system characterized by asymmetric Heisenberg $XY$ interactions in the absence of external magnetic fields demonstrates that maximal bipartite entanglement and quantum phase transitions can be induced exclusively through non-Hermiticity. At thermal equilibrium as $T\rightarrow 0$, the s

Marcel Wolbers, Miriam Pedrera Gómez, Alex Ocampo, Isaac Gravestock

Evaluating treatment effect heterogeneity across patient subgroups is a fundamental aspect of clinical trial analysis. These analyses have inherent limitations due to small sample sizes and the substantial number of subgroups investigated. There is a tendency to focus on extreme estimates, which may reflect random variation rather than true effects, potentia

Swan Htet Aung, Hein Htet, Htoo Say Wah Khaing, Thuya Myo Nyunt

We introduce the Burmese Handwritten Digit Dataset (BHDD), a collection of 87,561 grayscale images of handwritten Burmese digits in ten classes. Each image is 28x28 pixels, following the MNIST format. The training set has 60,000 samples split evenly across classes; the test set has 27,561 samples with class frequencies as they arose during collection. Over 1

CMS Collaboration

A search for resonances with masses from 50 to 300 GeV decaying into a quark-antiquark pair is presented. The search uses proton-proton collision data at $\sqrt{s}$ = 13 TeV collected by the CMS experiment at the CERN LHC in 2016$-$2018, corresponding to an integrated luminosity of 13 fb$^{-1}$. Two coupling scenarios are considered, with the resonances coup

Lizhou Liu, Peng-Yi Liu, Tian-Yi Zhang, Qing-Feng Sun

Chiral-induced spin selectivity (CISS) is a striking phenomenon in which spin-unpolarized electrons become spin-polarized after traversing a chiral medium. Theoretical studies have shown that spin-orbit coupling, geometric chirality, and dephasing act cooperatively for this effect to emerge. Inspired by this, we demonstrate a solid-state realization of CISS

Edilberto O. Silva

We investigate finite-width optical-beam propagation in a medium with uniform torsion described by the geometric theory of a continuous distribution of screw dislocations. Starting from the Riemann--Cartan framework that yields torsion-induced circular birefringence for local plane waves, we construct a minimal paraxial beam model in which the same contortio

Dorothee Frey, Siliang Weng

We consider magnetic Schr\"odinger equations with sublinear magnetic potentials and subquadratic electric potentials on $\mathbb{R}^{d}$, as well as generalizations thereof. We obtain new results on the global well-posedness of the Cauchy problem with initial data in magnetic modulation spaces $M^{p}_{A}(\mathbb{R}^{d})$. Our results are achieved by approxim

Damien Gobin, Benoît Grébert, Bernard Helffer, François Nicoleau

We study an inverse spectral problem for singular AKNS operators based on spectral data associated with two distinct values of the effective angular momentum parameter $\kappa\,$. Our main focus is the local inverse problem near the zero potential. For the pairs $(\kappa_1,\kappa_2)=(0,1)$, $(1,2)$ and $(0,3)\,$, we establish local uniqueness. For $(0,2)\,$,

Guanyang He, Yuxuan Lei, Tianheng Wei, Yanzhao Liu

Superconductivity in the iron-chalcogenide series FeSe-Fe(Te, Se)-FeTe has been restricted to the near neighbor of iron selenide (FeSe), with a general consensus that iron telluride (FeTe) is not superconducting. In this study, we report the method to grow FeTe islands with atomically flat surface and hexagonal lattice on SrTiO3 (001) substrates, in which a

Ying Shuai Quan, Paolo Falcone, Jonas Sjöberg

Safety-critical motion planning in mixed traffic remains challenging for autonomous vehicles, especially when it involves interactions between the ego vehicle (EV) and surrounding vehicles (SVs). In dense traffic, the feasibility of a lane change depends strongly on how SVs respond to the EV motion. This paper presents an interaction-aware safety framework t

Junhao Du, Jialong Xue, Anqi Li, Jincheng Dai

Video large language models (Video-LLMs) face high computational costs due to large volumes of visual tokens. Existing token compression methods typically adopt a two-stage spatiotemporal compression strategy, relying on stage-specific metrics and an implicit assumption of spatiotemporal separability. Under extremely low retention ratios, however, such appro

Harsh Prasad

We prove that local weak solutions to nonlocal parabolic $p$-Laplace equations are locally Lipschitz continuous in space, uniformly in time for every $1<p<\infty$ and $s \in (0,1)$ whenever $sp > p-1$. Our results hold for symmetric, translation-invariant kernels satisfying standard ellipticity bounds, including kernels that may be discontinuous and require

Koustav Roy, Latu Kalita, B. Tanatar, Saurabh Basu

Higher order topology, in the form of the emergence of corner modes, is observed in two dimensions when crystalline symmetries are superposed on the Altland-Zirnbauer classification of topological insulators. It occurs in Benalcazar-Bernevig-Hughes (BBH) model on a 2D square lattice, which owing to an embedded $\mathbb{Z}_2$ gauge field, features a bulk quad

Brahmani Nutakki, Manon Lilott Kempermann, Ingmar Weber

In March 2025, Meta announced a new AI system to rank the order of the comments shown to Instagram users. With existing research showing how feed personalization systems can lead to increased polarization, the introduction of this new system raises similar questions. This paper presents a small-scale exploratory study examining whether the ranking system pro

Anant Mathur, Benoit Liquet, Samuel Muller, Sarat Moka

High-dimensional biomedical studies require models that are simultaneously accurate, sparse, and interpretable, yet exact best subset selection for generalized linear models is computationally intractable. We develop a scalable method that combines a continuous Boolean relaxation of the subset problem with a Frank--Wolfe algorithm driven by envelope gradient

Javier De Miguel, Enrique Joven, Elvio Hernández-Suárez, Juan F. Hernández-Cabrera

We report a pilot dark-matter search with a cryogenic, magnetized, scaled-down DALI prototype. An analysis of 36 hours of data reveals no statistically significant excess attributable to axionlike particles. We therefore set new exclusion limits in the 6.883--6.920 GHz band, reaching an axion-photon coupling sensitivity of $g_{a\gamma\gamma}\lesssim 1.27\tim

Miquel Saucedo, Sergey Tikhonov

For a function $F$ represented as $F(x)=\sum_{n=0}^\infty{f_n (x) e^{2 \pi i \lambda_n x}},$ where each $f_n$ satisfies $\operatorname{spec}(f_n) \subset [0, 1]$ and $(\lambda_n)_{n\geq 0}\subset \mathbb{R}_+$ is a lacunary sequence, we obtain $$ \|F\|_{L^2(\mathbb{R})}\lesssim \|F\chi_{E}\|_{L^2(\mathbb{R})} $$ provided that $E$ is a thick subset of $\mathb

Małgorzata Biernacka, Witold Charatonik, Tomasz Drab

Strong call-by-need combines full normalization with the sharing discipline of lazy evaluation, yet no prior implementation achieved both simplicity and efficiency. We introduce RKNL, an abstract machine that realizes strong call-by-need with bilinear overhead. The machine has been derived automatically from a higher-order evaluator that uses the technique o

Chengzhi Li, Heyan Huang, Ping Jian, Yanghao Zhou

Audio-Visual Semantic Segmentation (AVSS) aligns audio and video at the pixel level but requires costly per-frame annotations. We introduce Weakly Supervised Audio-Visual Semantic Segmentation (WSAVSS), which uses only video-level labels to generate per-frame semantic masks of sounding objects. We decompose WSAVSS into looking, listening, and segmentation, a

Hohto Bekki, Ryotaro Sakamoto

In this paper, we study hyperbolic cycles in the first homology group with local coefficients of congruence subgroups of $\mathrm{SL}_2(\mathbb{Z})$. We prove that, for any prime number $p$, the $p$-ordinary part of the first homology group is generated by hyperbolic cycles.

Youbin Kim, Jinho Park, Hogun Park, Eunbyung Park

Open-vocabulary 3D object detection aims to localize and recognize objects beyond a fixed training taxonomy. In multi-view RGB settings, recent approaches often decouple geometry-based instance construction from semantic labeling, generating class-agnostic fragments and assigning open-vocabulary categories post hoc. While flexible, such decoupling leaves ins

Ayesh Abu Lehyeh, Xiaohan Zhang, Ahmad Arrabi, Waqas Sultani

Accurate and fast localization is vital for safe autonomous navigation in GPS-denied areas. Fine-Grained Cross-View Geolocalization (FG-CVG) aims to estimate the precise 2-Degree-of-Freedom (2-DoF) location of a ground image relative to a satellite image. However, current methods force a difficult trade-off, with high-accuracy models being slow for real-time

Junyi An, Xinyu Lu, Yun-Fei Shi, Li-Cheng Xu

We introduce Suiren-1.0, a family of molecular foundation models for the accurate modeling of diverse organic systems. Suiren-1.0 comprising three specialized variants (Suiren-Base, Suiren-Dimer, and Suiren-ConfAvg) is integrated within an algorithmic framework that bridges the gap between 3D conformational geometry and 2D statistical ensemble spaces. We fir

Ludovic Varrin

In the presence of spacetime boundaries, diffeomorphisms in gravitational theories can become physical and acquire non-vanishing Noether charges. These charges obey an algebra which, within the extended phase-space formalism, faithfully realizes diffeomorphism algebra. The corner proposal takes this algebra of physical corner symmetries as a fundamental ingr

Haroun Elleuch, Salima Mdhaffar, Yannick Estève, Fethi Bougares

Spoken Language Understanding (SLU) aims to extract the semantic information from the speech utterance of user queries. It is a core component in a task-oriented dialogue system. With the spectacular progress of deep neural network models and the evolution of pre-trained language models, SLU has obtained significant breakthroughs. However, only a few high-re

Zhilin Tu, Kemou Li, Fengpeng Li, Jianwei Fei

The rapid iteration and widespread dissemination of deepfake technology have posed severe challenges to information security, making robust and generalizable detection of AI-generated forged images increasingly important. In this paper, we propose FeatDistill, an AI-generated image detection framework that integrates feature distillation with a multi-expert

Joseph Smith

The Schwinger model is perhaps the simplest non-trivial exactly-solvable QFT. In this note we examine the perturbative structure of the theory on the sphere and provide evidence that its quantum corrections match those predicted by the expansion of the exact solution.

Wenqing Tian, Hanyi Mao, Zhaocheng Liu, Lihua Zhang

Subject-driven image generation is increasingly expected to support fine-grained control over multiple entities within a single image. In multi-reference workflows, users may provide several subject images, a background reference, and long, entity-indexed prompts to control multiple people within one scene. In this setting, a key failure mode is cross-subjec

Roy Amoyal, Oren Freifeld, Chaim Baskin

We present Gaussian Splatting Alignment (GSA), a novel method for aligning two independent 3D Gaussian Splatting (3DGS) models via a similarity transformation (rotation, translation, and scale), even when they are of different objects in the same category (e.g., different cars). In contrast, existing methods can only align 3DGS models of the same object (e.g

Clemens Watzenböck, Daniel Aletaha, Michaël Deman, Thomas Deimel

Quantitative disease severity scoring in medical imaging is costly, time-consuming, and subject to inter-reader variability. At the same time, clinical archives contain far more longitudinal imaging data than expert-annotated severity scores. Existing self-supervised methods typically ignore this chronological structure. We introduce ChronoCon, a contrastive

Joon-Hwi Kim

We investigate the tension between symplecticity and gauge covariance in classical Hamiltonian mechanics. The pursuit of manifest covariance over manifest symplecticity results in a unique geometric formulation. Firstly, covariant yet non-canonical coordinates are employed by adopting Souriau's approach to minimal coupling. Secondly, covariant yet non-coordi

Peter Fasogbon, Ugurcan Budak, Patrice Rondao Alface, Hamed Rezazadegan Tavakoli

The pruning of 3D Gaussian splats is essential for reducing their complexity to enable efficient storage, transmission, and downstream processing. However, most of the existing pruning strategies depend on camera parameters, rendered images, or view-dependent measures. This dependency becomes a hindrance in emerging camera-agnostic exchange settings, where s

Matteo Bizzarri

This paper models firm-to-firm trade in a production network as a set of double auctions. Firms have multilateral market power, namely, can affect prices in both input and output markets. The size and division of surplus are endogenous and depend only on technology, network position, and consumer preferences. The standard simplifying assumption of price-taki

Valentin Wagner, Sebastian Bullinger, Michael Arens, Rainer Stiefelhagen

We present SatGeo-NeRF, a geometrically regularized NeRF for satellite imagery that mitigates overfitting-induced geometric artifacts observed in current state-of-the-art models using three model-agnostic regularizers. Gravity-Aligned Planarity Regularization aligns depth-inferred, approximated surface normals with the gravity axis to promote local planarity

P. D. Alvarez, K. Krasnov

Inspired by the MacDowell-Mansouri formulation of four-dimensional General Relativity, we study a class of four-dimensional gauge-theoretic functionals obtained from the Pontryagin density of a G-connection by inserting, under the trace, a matrix that breaks the gauge group G to a subgroup H. Concretely, we study the model with the pair (G,H) given by (SU(3)

Steffen Schmidt

We present a novel classification of unitarizable supermodules over special linear Lie superalgebras using an algebraic quadratic Dirac operator introduced by Huang and Pand\v{z}i\'c and a corresponding Dirac inequality.

Guannan Lai, Da-Wei Zhou, Zhenguo Li, Han-Jia Ye

Continual Test-Time Adaptation (CTTA) aims to enable models to adapt online to unlabeled data streams under distribution shift without accessing source data. Existing CTTA methods face an efficiency-generalization trade-off: updating more parameters improves adaptation but severely reduces online inference efficiency. An ideal solution is to achieve comparab

Junyi Zou

Adapters are often selected and deployed based on nominal labels (e.g., instruction-tuned), which implicitly suggest what capability improves after adaptation. We test whether nominal training objectives reliably align with realized cross-task capability gains by evaluating the same LoRA adapter across tasks. Our strongest evidence is tied to strict, automat

Moritz Janning, Roman Kramer, Michael Turaev, Sayak Ray

We investigate the nonequilibrium dynamics of an open photon Bose-Einstein condensate in a dye-filled microcavity using a Lindblad master-equation approach, treating the condensate and the noncondensed fluctuations on the same footing. The driven-dissipative condensate exhibits a long-lived, metastable plateau stabilized by a ghost attractor, a fixed point t

Marvin Seegert, Christian Oefinger, Korbinian Moller, Christoph Bank

Proprietary Autonomous Driving Systems are typically evaluated through disengagements, unplanned manual interventions to alter vehicle behavior, as annually reported by the California Department of Motor Vehicles. However, the real-world capabilities of prototypical open-source Level 4 vehicles over substantial distances remain largely unexplored. This study

Shuying Chen, Sen Cui, Zhong Cao

In this work, we propose Oph-Guid-RAG, a multimodal visual RAG system for ophthalmology clinical question answering and decision support. We treat each guideline page as an independent evidence unit and directly retrieve page images, preserving tables, flowcharts, and layout information. We further design a controllable retrieval framework with routing and f

Lei Wang, Linxuan Song, Elbert E. M. Chia, Peijie Sun

State-of-the-art metallic terahertz (THz) emitters rely predominantly on spintronic heterostructures, where heavy metals serve as passive spin-to-charge converters. Here, we demonstrate efficient THz radiation from standalone Pt nanofilms at cryogenic temperatures and under external magnetic fields. The governing mechanism is identified as the ultrafast phot

Xiqi Cheng, Rui Meng, Xiaodong Xu, Haixiao Gao

With the rapid advancement of 6G, identity authentication has become increasingly critical for ensuring wireless security. The lightweight and keyless Physical Layer Authentication (PLA) is regarded as an instrumental security measure in addition to traditional cryptography-based authentication methods. However, existing PLA schemes often struggle to adapt t

E. Riordan, E. Lhotel, N. -R. Camara, C. Marin

The magnetocaloric effect in the quantum dipolar magnet Yb$_3$Ga$_5$O$_{12}$ is studied both for pure material and with non-magnetic substitution: (Yb$_{1-x}$Y$_x$)$_3$Ga$_5$O$_{12}$. Magnetization measurements have been performed on a single crystal, $x=0$, and on powder samples with $x = 0.2$ and 0.4 in the temperature range between 70 mK to 300 K and in m

Juan Sebastian Rojas, Chi-Guhn Lee

The temporal difference (TD) error was first formalized in Sutton (1988), where it was first characterized as the difference between temporally successive predictions, and later, in that same work, formulated as the difference between a bootstrapped target and a prediction. Since then, these two interpretations of the TD error have been used interchangeably

German Svistunov, Matteo Bernabe, David Lopez-Perez

The increasing demand for ubiquitous, highcapacity mobile connectivity has driven cellular systems to explore beyond-terrestrial deployments. In this paper, we present a system-level performance evaluation of fifth-generation (5G) non-terrestrial network (NTN) enabled by high-altitude platform station (HAPS)-based base stations (BSs) equipped with tri-sector

Kristiana Mihali, Dennis Wörthmüller, Pierre Sens

Actin flow in the cortical cytoskeleton underneath the cell membrane generates mechanical stresses that shape the cell surface. We study this mechanism using a hydrodynamic model of a compressible active gel polymerizing at the membrane and undergoing turnover. We determine how actin flow, density relaxation and friction of actin with the membrane generate s

L. Riso, M. G. Zoia

This paper develops a unified framework for measuring concentration in weighted systems embedded in networks of interactions. While traditional indices such as the Herfindahl-Hirschman Index capture dispersion in weights, they neglect the topology of relationships among the elements receiving those weights. To address this limitation, we introduce a family o

Niklas Bengtsson, Per Engström

Governments routinely adjust capacity in rationed programs such as university fields, medical training and public housing, where admitting one individual displaces others and triggers chains of reallocation. We show that in such settings, the standard multi-treatment two-stage least squares (2SLS) coefficient identifies exactly the total societal effect of a

Simon Weissmann

This paper investigates ensemble Kalman inversion (EKI) for variational inverse problems with convex, potentially non-smooth regularization. While deterministic EKI and its Tikhonov-regularized variants have primarily been analyzed for smooth objectives, a corresponding framework accommodating subgradient dynamics has not yet been established. To address thi

Xiyun Luo, Weirong Luo, Kening Zhu, Taizhou Chen

Virtual Reality (VR) emphasizes immersive experiences, while text entry often requires hands or visual attention, which may disrupt the interaction flows in VR. We present AnkleType, a hand- and eye-free text-entry technique that leverages ankle-based gestures for both standing and sitting situations. We began with two preliminary studies: one investigated t

Hien Duy Nguyen, Mayetri Gupta

We study identifiability of finite mixtures of Dirichlet distributions on the interior of the simplex. We first prove a shift identity showing that every Dirichlet density can be written as a mixture of $J$ shifted Dirichlet densities, where $J-1$ is the dimension of the simplex support, which yields non-identifiability on the full parameter space. We then s

Seungyeop Lee, Jong-Han Kim

In structured multi-agent transportation systems, agents often must follow predefined routes, making spatial rerouting undesirable or impossible. This paper addresses route-constrained multi-agent coordination by optimizing waypoint passage times while preserving each agent's assigned waypoint order and nominal route assignment. A differentiable surrogate tr

H. E. S. S. Collaboration, A. Acharyya, F. Aharonian, H. Ashkar

Context: Composite galaxies that contain both Seyfert and starburst components may produce very high-energy (VHE; >100 GeV) gamma-ray emission at a wide range of spatial scales, from a few Schwarzschild radii of a supermassive black hole to dimensions of kiloparsec-size jet-driven outflows. In addition to supernova remnants, various sources have been suggest

Jonathan Crespo, Ramón Barber, O. M. Mozos, Daniel Beßler

Semantic navigation is the navigation paradigm in which environmental semantic concepts and their relationships are taken into account to plan the route of a mobile robot. This paradigm facilitates the interaction with humans and the understanding of human environments in terms of navigation goals and tasks. At the high level, a semantic navigation system re

Sufia Shahin, Mahdi Benkhelifa, Yogesh Singh Chauhan, Hussam Amrouch

In this article, we study the impact of self-heating effects (SHEs) and middle of line (MOL) and back-end of line (BEOL) induced parasitics on multi-tier CFET design, where multiple nanosheet devices are vertically stacked. We analyze and compare the 4-tier CFET design with the conventional 2-tier CFET, using TCAD models calibrated to experimental measuremen

Suchuan Dong, Yuchuan Zhang

We present a systematic method for exactly enforcing Dirichlet, Neumann, and Robin type conditions on general quadrilateral domains with arbitrary curved boundaries. Our method is built upon exact mappings between general quadrilateral domains and the standard domain, and employs a combination of TFC (theory of functional connections) constrained expressions

Ziyang Zhang, Zheshun Wu, Jie Liu, Luca Mottola

Deploying deep neural networks (DNNs) on power-sensitive edge devices presents a formidable challenge. While Dynamic Voltage and Frequency Scaling (DVFS) is widely employed for energy optimization, traditional model-level scaling is often too coarse to capture intra-inference variations, whereas fine-grained operator-level scaling suffers from prohibitive pe

Carmelo Civello, Luca Maffioli, Edward Smith, James Ewen

The transient time correlation function method (TTCF) has emerged as a powerful methodology for accurately probing systems at low shear rates. In the present study, TTCF was used to evaluate the shear rate dependence of the slip length in a high-slip system consisting of water confined between graphene walls at experimentally accessible shear rates, for whic

Yichen Fan, Jacob Z. Williams, Weitao Yang

Density functional theory (DFT) is the most promising method for calculating quantum properties of molecules and materials at moderate and large scales. However, commonly used density functional approximations (DFAs) have systematic delocalization error, as demonstrated by underestimated band gaps, over-delocalized charges, and energy level misalignment at i

Sayaka Ichihara, Samuele Fiorini, Yoshiyuki Tagawa, Outi Supponen

Shock wave-droplet interactions have been receiving increasing attention due to their relevance in aviation fuel combustion and minimally invasive medical treatments, yet quantifying them experimentally remains a challenge. In this study, we propose a background-oriented schlieren (BOS) technique for quantitative spatiotemporal measurements of shock wave-dro