November 2025 arXiv papers — page 55

Shuo Wen, Edwin Meriaux, Mariana Sosa Guzmán, Zhizun Wang

Stable and accurate tracking is essential for marine robotics, yet Global Navigation Satellite System (GNSS) signals vanish immediately below the sea surface. Traditional alternatives suffer from error accumulation, high computational demands, or infrastructure dependence. In this work, we present a multi-drone GNSS-based tracking system for surface and near

Lingfeng Xie, Jiajun Liu, Yifei Zhao, Chang Cai

The RELICS (REactor neutrino LIquid xenon Coherent elastic Scattering) experiment aims to detect coherent elastic neutrino-nucleus scattering from reactor antineutrinos using a dual-phase xenon time projection chamber. To validate the detector concept and ensure technical reliability for the full-scale experiment, a dedicated prototype was designed, construc

Kichang Yang, Seonjun Kim, Minjae Kim, Nairan Zhang

Edge deployment of large Vision-Language Models (VLMs) increasingly relies on flash-based weight offloading, where activation sparsification is used to reduce I/O overhead. However, conventional sparsification remains model-centric, selecting neurons solely by activation magnitude and neglecting how access patterns influence flash performance. We present Neu

Andrew J. Hanson, Sonya M. Hanson

Pose estimation is a general problem in computer vision with wide applications. The relative orientation of a 3D reference object can be determined from a 3D rotated version of that object, or from a projection of the rotated object to a 2D planar image. This projection can be a perspective projection (the PnP problem) or an orthographic projection (the OnP

Kazi Reyazul Hasan, Md Nafiu Rahman, Wasif Jalal, Sadif Ahmed

Hybrid vision architectures combining Transformers and CNNs have significantly advanced image classification, but they usually do so at significant computational cost. We introduce EVCC (Enhanced Vision Transformer-ConvNeXt-CoAtNet), a novel multi-branch architecture integrating the Vision Transformer, lightweight ConvNeXt, and CoAtNet through key innovation

Xinyu Pan, Boxun Liu, Xiang Cheng, Chen Chen

Adaptive modulation and coding (AMC) is a key technology in 5G new radio (NR), enabling dynamic link adaptation by balancing transmission efficiency and reliability based on channel conditions. However, traditional methods often suffer from performance degradation due to the aging issues of channel quality indicator (CQI). Recently, the emerging capabilities

Kasidis Arunruangsirilert, Jiro Katto

The demand for high-quality, real-time video streaming has grown exponentially, with 4K Ultra High Definition (UHD) becoming the new standard for many applications such as live broadcasting, TV services, and interactive cloud gaming. This trend has driven the integration of dedicated hardware encoders into modern Graphics Processing Units (GPUs). Nowadays, t

Kasidis Arunruangsirilert, Jiro Katto

NVIDIA Encoder (NVENC) features in modern NVIDIA GPUs, offer significant advantages over software encoders by providing comparable Rate-Distortion (RD) performance while consuming considerably less power. The increasing capability of consumer devices to capture footage in Ultra High-Definition (UHD) at 4K and 8K resolutions necessitates high-performance vide

Kasidis Arunruangsirilert, Jiro Katto

Many GPUs have incorporated hardware-accelerated video encoders, which allow video encoding tasks to be offloaded from the main CPU and provide higher power efficiency. Over the years, many new video codecs such as H.265/HEVC, VP9, and AV1 were added to the latest GPU boards. Recently, the rise of live video content such as VTuber, game live-streaming, and l

Yunqi Zhang, Kuangyu Shi, Biao Li

Emerging from NASA's spacecraft simulations in the 1960s, digital twin technology has advanced through industrial adoption to spark a healthcare transformation. A digital twin is a dynamic, data-driven virtual counterpart of a physical system, continuously updated through real-time data streams and capable of bidirectional interaction. In medicine, digital t

Shristi Das Biswas, Arani Roy, Kaushik Roy

Robust concept removal for text-to-image (T2I) and text-to-video (T2V) models is essential for their safe deployment. Existing methods, however, suffer from costly retraining, inference overhead, or vulnerability to adversarial attacks. Crucially, they rarely model the latent semantic overlap between the target erase concept and surrounding content -- causin

Yinan Dong, Ziyu Xu, Tsimafei Lazouski, Sangli Teng

Autonomous surface vehicles (ASVs) are influenced by environmental disturbances such as wind and waves, making accurate trajectory tracking a persistent challenge in dynamic marine conditions. In this paper, we propose an efficient controller for trajectory tracking of marine vehicles under unknown disturbances by combining a convex error-state MPC on the Li

Xiang Gao, Xinmu Wang, Zhou Zhao, Junqi Huang

Recent years have witnessed rapid advancements in 3D scanning technologies, with applications spanning VR/AR, digital human creation, and medical imaging. Structured-light scanning with phase-shifting techniques is preferred for its use of low-intensity visible light and high accuracy, making it well suited for capturing 4D facial dynamics. A key step is pha

Xiang Gao, Xinmu Wang, Xiaolong Wu, Jiazhi Li

We present a topology-informed inverse rendering approach for reconstructing high-genus surface meshes from multi-view images. Compared to 3D representations like voxels and point clouds, mesh-based representations are preferred as they enable the application of differential geometry theory and are optimized for modern graphics pipelines. However, existing i

Xiang Gao, Yuanpeng Liu, Xinmu Wang, Jiazhi Li

Neural representations for 3D meshes are emerging as an effective solution for compact storage and efficient processing. Existing methods often rely on neural overfitting, where a coarse mesh is stored and progressively refined through multiple decoder networks. While this can restore high-quality surfaces, it is computationally expensive due to successive d

Cheyanne Shariat, Claire S. Ye, Smadar Naoz, Sanaea Rose

The detection of fast radio bursts (FRBs) in both young and old stellar populations suggests multiple formation pathways, beyond just young magnetars from core-collapse supernovae. A promising delayed channel involves the formation of FRB-emitting neutron stars through merger- or accretion-induced collapse of a massive white dwarf (WD). By simulating a reali

Yunpeng Gong, Yongjie Hou, Jiangming Shi, Kim Long Diep

Sketch based person re-identification aims to match hand-drawn sketches with RGB surveillance images, but remains challenging due to significant modality gaps and limited annotated data. To address this, we introduce KTCAA, a theoretically grounded framework for few-shot cross-modal generalization. Motivated by generalization theory, we identify two key fact

J. D. Vega-Sánchez, V. H. Garzón Pacheco, N. V. Orozco Garzón, D. A. Riofrío Almeida

This work examines the secrecy outage probability (SOP) in Fluid Reconfigurable Intelligent Surfaces (FRIS) and contrasts their performance against two alternative RIS architectures: a traditional planar RIS and a compact RIS layout. To characterize the end-to-end FRIS channel, a maximum likelihood estimation (MLE) approach is introduced, while a Q-learning

Alfredo Metere

Large matrix multiplication is a cornerstone of modern machine learning workloads, yet traditional approaches suffer from cubic computational complexity (e.g., $\mathcal{O}(n^3)$ for a matrix of size $n\times n$). We present Low-Rank GEMM, a novel approach that leverages low-rank matrix approximations to achieve sub-quadratic complexity while maintaining har

Yiqing Shi, Yiren Song, Mike Zheng Shou

Recent advances in diffusion transformers have shown remarkable generalization in visual synthesis, yet most dense perception methods still rely on text-to-image (T2I) generators designed for stochastic generation. We revisit this paradigm and show that image editing diffusion models are inherently image-to-image consistent, providing a more suitable foundat

Yuchen Xia, Souvik Kundu, Mosharaf Chowdhury, Nishil Talati

Novel View Synthesis (NVS) is the task of generating new images of a scene from viewpoints that were not part of the original input. Diffusion-based NVS can generate high-quality, temporally consistent images, however, remains computationally prohibitive. Conversely, regression-based NVS offers suboptimal generation quality despite requiring significantly lo

Asif Zaman, Kallol Naha, Khalid Belhajjame, Hasan M. Jamil

Scientific workflows encode valuable domain expertise and computational methodologies. Yet studies consistently show that a significant proportion of published workflows suffer from decay over time. This problem is particularly acute for legacy workflow systems like Taverna, where discontinued services, obsolete dependencies, and system retirement render pre

Yan Wang, Ke Deng, Yongli Ren

Cooperative multi-agent reinforcement learning (MARL) commonly adopts centralized training with decentralized execution (CTDE), where centralized critics leverage global information to guide decentralized actors. However, centralized-decentralized mismatch (CDM) arises when the suboptimal behavior of one agent degrades others' learning. Prior approaches miti

Rowan Bradbury, Aniket Srinivasan Ashok, Sai Ram Kasanagottu, Gunmay Jhingran

Replacing modules in pretrained models, especially swapping quadratic self-attention for efficient attention alternatives, poses a hard optimization problem: cold-start reinitialization destabilizes frozen backbones. We isolate this core stability challenge in a controlled study. Deterministic Continuous Replacement (DCR) blends teacher and student outputs w

Roberto C. G. Porto, Rodrigo C. de Lamare

The use of multiple Reconfigurable Intelligent Sur- faces (RIS) has gained attention in 6G networks to enhance coverage. However, the feasibility of deploying multiple RIS relies on efficient channel estimation and reduced pilot overhead. To address these challenges, this work proposes an iterative channel estimation scheme that exploits low-density parity-c

Flora Lian, Dinh Quang Huynh, Hector Penades, J. Stephany Berrio Perez

Robust lane detection is essential for advanced driver assistance and autonomous driving, yet models trained on public datasets such as CULane often fail to generalise across different camera viewpoints. This paper addresses the challenge of domain shift for side-mounted cameras used in lane-wheel monitoring by introducing a generative AI-based data enhancem

Kailin Lyu, Long Xiao, Jianing Zeng, Junhao Dong

Traditional vision-based material perception methods often experience substantial performance degradation under visually impaired conditions, thereby motivating the shift toward non-visual multimodal material perception. Despite this, existing approaches frequently perform naive fusion of multimodal inputs, overlooking key challenges such as modality-specifi

Alexander Mehta, Ruangrawee Kitichotkul, Vivek K Goyal, Julián Tachella

Equivariant imaging (EI) enables training signal reconstruction models without requiring ground truth data by leveraging signal symmetries. Deep equilibrium models (DEQs) are a powerful class of neural networks where the output is a fixed point of a learned operator. However, training DEQs with complex EI losses requires implicit differentiation through fixe

Frederic Koehler, Joonhyung Shin

Two directions in algorithms and complexity involve: (1) classifying which optimization problems can be solved in polynomial time, and (2) understanding which computational problems are hard to solve \emph{on average} in addition to the worst case. For many average-case problems, there does not currently exist strong evidence via reductions that they are har

Jeremy Beard

We prove the uniqueness of high cofinality limit models in stable abstract elementary classes (AECs) with amalgamation, assuming the existence of a rather weak independence relation. $\textbf{Theorem.}$ Suppose $\mathbf{K}$ is a $\lambda$-stable AEC, where $\operatorname{LS}(\mathbf{K}) \leq \lambda$, $\kappa < \lambda^+$ is regular, and $\mathbf{K}_\lambda$

Lawrence W. Abrams

By clarifying the bases for the Verizon Communications Inc. v. Law Offices of Curtis V. Trinko, LLP, 2004 opinion, we hope to reduce two distinct errors. The false positive error is citing Trinko as precedent when it is not. This error is so prevalent it has earned the nickname of Trinko Creep. The false negative error is not citing Trinko when it should be.

J. D. Vega-Sánchez, V. H. Garzón Pacheco, N. V. Orozco Garzón, H. R. Carvajal Mora

Fluid Reconfigurable Intelligent Surfaces (FRISs) are gaining momentum as an improved alternative over classical RIS. However, it remains unclear whether their performance gains can be entirely attributed to spatial flexibility, or instead to differences in equivalent aperture or phase design. In this work, we shed light onto this problem by benchmarking FRI

Itai Arieli, Colin Stewart

We introduce a model of persuasion in which a sender without any commitment power privately gathers information about an unknown state of the world and then chooses what to verifiably disclose to a receiver. The receiver does not know how many experiments the sender is able to run, and may therefore be uncertain as to whether the sender disclosed all of her

Salome Mtchedlidze, Franco Vazza, Xiaolong Du, Ettore Carretti

Primordial Magnetic Fields (PMFs) -- magnetic fields originating in the early Universe and permeating the cosmological scales today -- can explain the observed microGauss-level magnetisation of galaxies and their clusters. In light of current and upcoming all-sky radio surveys, PMFs have drawn attention not only as major candidates for explaining the large-s

Guillaume Braun, Bruno Loureiro, Ha Quang Minh, Masaaki Imaizumi

Scaling laws describe how learning performance improves with data, compute, or training time, and have become a central theme in modern deep learning. We study this phenomenon in a canonical nonlinear model: phase retrieval with anisotropic Gaussian inputs whose covariance spectrum follows a power law. Unlike the isotropic case, where dynamics collapse to a

Mostafa Mozafari, Farooq Ahmad Wani, Maria Sofia Bucarelli, Fabrizio Silvestri

Corrupted training data are ubiquitous. Corrective Machine Unlearning (CMU) seeks to remove the influence of such corruption post-training. Prior CMU typically assumes access to identified corrupted training samples (a "forget set"). However, in many real-world scenarios the training data are no longer accessible. We formalize source-free CMU, where the orig

Jie He, Richard He Bai, Sinead Williamson, Jeff Z. Pan

Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge but still suffers from long contexts and disjoint retrieval-generation optimization. In this work, we propose CLaRa (Continuous Latent Reasoning), a unified framework that performs embedding-based compression and joint optimization in a shared continuous space.

Alexander M. McKillop, Liying Chen, Ashley P. Fidler, Marissa L. Weichman

The mechanisms governing molecular photophysics under electronic strong coupling (ESC) remain elusive to date. Here, we use ultrafast pump-probe spectroscopy to study the nonradiative excited state relaxation dynamics of chlorin e6 trimethyl ester (Ce6T) under strong coupling of its transition from the electronic ground state to the Qy band. We use dichroic

Amit Lavon

Blini is a tool for quick lookup of nucleotide sequences in databases, and for quick dereplication of sequence collections. It is meant to help clean and characterize large collections of assembled contigs or long sequences that would otherwise be too big to search with online tools, or too demanding for a local machine to process. Benchmarks on simulated da

Xian Xu, Diana Y. Qiu

Circular dichroism (CD) and other chiroptical responses are a key probe of both chirality and momentum-space geometry in solids, but first-principles calculations are still challenging in periodic systems with strong exciton effects. Here, we develop a gauge-invariant first-principles framework for CD including exciton effects based on full minimal coupling

George E. Andrews, Brian Hopkins

The crank-mex theorem states that the number of integer partitions of $n$ with nonnegative crank equals the number with odd minimal excludant (mex). Andrews and M. Newman recently refined that result in terms of the number of parts greater than one. Here, we establish and expand a complementary result connecting the partitions with even mex, having fixed poi

Jens Niklas Eberhardt, Arnaud Eteve

We define and study the parabolic K-motivic Hecke category of a (possibly disconnected) Kac-Moody group. Our main result is a combinatorial description via singular K-theory Soergel bimodules which arise from the equivariant algebraic K-theory of parabolic Bott-Samelson resolutions. In the spherical affine case, the K-motivic Hecke category serves as one sid

Xiaohan Wang, S. Cantalupo, Weichen Wang, M. Galbiati

We present the mass-metallicity relation for star-forming galaxies in the MUSE Quasar Nebula 01 (MQN01) field, a massive cosmic web node at $z \sim 3.245$, hosting one of the largest overdensities of galaxies and AGNs found so far at $z > 3$. Through James Webb Space Telescope (JWST) Near Infrared Spectrograph (NIRSpec) spectra and images from JWST and Hubbl

Joseph Vovrosh, Sergi Julià-Farré, Wladislaw Krinitsin, Michael Kaicher

The quantum dynamics of many-qubit systems is an outstanding problem that has recently driven significant advances in both numerical methods and programmable quantum processing units. In this work, we employ a comprehensive toolbox of state-of-the-art numerical approaches to classically simulate the dynamics of the two-dimensional transverse field Ising mode

Samapan Bhadury, Zbigniew Drogosz, Wojciech Florkowski, Sudip Kumar Kar

Four formulations of perfect spin hydrodynamics for spin-1/2 particles, distinguished by their treatment of spin (classical vs. quantum) and by the underlying particle statistics (Boltzmann vs. Fermi-Dirac), are analyzed and shown to satisfy the requirements of a divergence-type theory. Moreover, for all the formulations, we define the generating functions a

Darius Köhnke, Hans-Christian Ahlswede, Tim Bayer, Matthias Wollenhaupt

We report the first observation of free-electron angular momentum wave packets generated by atomic multiphoton ionization with bichromatic three-dimensional (3D) polarization-tailored ultrashort laser fields. These fields, created by the non-collinear superposition of two polarization-shaped pulses of different colors from a supercontinuum polarization pulse

Morgan A. Mitchell, Paul Chote, James McCormac, Don Pollacco

We present the commissioning results and first scientific observations from the newly installed TWIST observatory - a 50 cm telescope equipped with an sCMOS camera providing a $36.1\times24.1$ arcmin$^2$ field of view - housed in the former SuperWASP-North enclosure. We conducted a 67-night, 199-day baseline white-light monitoring campaign centred on the glo

Nguyen Minh Duc, Vu Tuan Hai, Le Bin Ho, Tran Nguyen Lan

Quantum Support Vector Machines (QSVM) is one of the most promising frameworks in quantum machine learning, yet their performance depends on the design of the feature map. Conventional approaches rely on fixed quantum circuits, which often fail to generalize across datasets. To address this limitation, we propose GA-QSVM, a hybrid framework that employs Gene

Stephanie Matern, Alberto Biella, Pasquale Scarlino, Iacopo Carusotto

Within a circuit quantum electrodynamics architecture, we theoretically investigate the detection of a single propagating microwave photon traveling through a resonant microwave cavity dispersively interacting with a double quantum dot tunnel-coupled to a lead. Under suitable conditions, a single photon in the cavity can induce a measurable change in the ele

E. Quintin, E. Russeil, M. Llamas Lanza, S. Karpov

Tidal disruption events (TDEs) correspond to the destruction of a star by the tidal forces around a black hole, leading to outbursts which can last from months to years. These transients are rare, and increasing the current sample is paramount to understand them. As part of the Fink alert broker, we have developed an early detection system for TDEs for the Z

Hyeong-Gun Joo, Songnam Hong, Seunghwan Lee, Dong-Joon Shin

Federated learning (FL) faces challenges in ensuring both privacy and communication efficiency, particularly in resource-constrained environments such as Internet of Things (IoT) and edge networks. While sign-based methods, such as sign stochastic gradient descent with majority voting (SIGNSGD-MV), offer substantial bandwidth savings, they remain vulnerable

V. Gareyan, Zh. Gevorkian

Radiation of a charged particle crossing an alternating stack of slabs in the optical region is considered. Both disordered and periodic stacks are investigated. It is shown that for special type of alternating disordered and periodic stacks the radiation problem can be solved exactly for backward and forward Brewster observation angles. Strong $N^2$ depende

Canon Sun, Marcel Franz, Joseph Maciejko

We study the topology of the order parameter in the intermediate phase between the superconducting and time-reversal symmetry breaking transitions of a $p_x+ip_y$ superconductor under strain. The application of in-plane strain reduces the underlying crystal symmetry and lifts the degeneracy of the critical temperature between the $p_x$ and $p_y$ orbitals, re

D. V. Brazhnikov, A. O. Makarov, K. S. Kozlova, A. N. Goncharov

We investigate the ground-state Hanle effect in alkali-metal vapor irradiating by a resonant elliptically polarized light wave. The magneto-optical resonances are observed as a change in the ellipticity parameter of the light wave polarization when scanning the transverse magnetic field near zero. We use a miniature ($\approx\,$$0.125$ cm$^3$) glass cesium v

Guo-Ying Li, Mei-Hui Xiao, Song He, Jia-Rui Sun

We extend the entanglement first law of conformal field theory (CFT) to timelike subregions. Focusing on intervals along the time direction of the boundary CFT, we show that the associated timelike entanglement entropy obeys a first-law-like relation, with an effective entanglement temperature inversely proportional to the temporal size of the interval. By i

Shu-Chiuan Chang, Robert Shrock

We calculate the continuous accumulation set ${\cal B}_q(p,\ell)$ of zeros of the chromatic polynomial $P(G^{(p,\ell)}_m,q)$ in the limit $m \to \infty$, on a family of graphs $G^{(p,\ell)}_m$ defined such that $G^{(p,\ell)}_m$ is obtained from $G^{(p,\ell)}_{m-1}$ by replacing each edge (i.e., bond) on $G^{(p,\ell)}_m$ by $p$ paths each of length $\ell$ edg

Yuqi Qian, Yun Cao, Meiyang Lv, Haocheng Fu

Generative image steganography aims to conceal secret information in generated images without arousing suspicion. However, in practical scenarios involving high-capacity embedding or lossy transmission, existing methods still suffer from limited extraction accuracy. The main challenge lies in accurately recovering the secret-embedded latent vectors from steg

Hayley J. Macpherson, Asta Heinesen

Cosmography is a widely applied method to infer kinematics of the Universe at small cosmological scales while remaining agnostic about the theory of gravity at play. Usually cosmologists invoke the Friedmann-Lemaitre-Robertson-Walker (FLRW) metric in cosmographic analyses, however generalised approaches allow for analyses outside of any assumed geometrical m

Alvis Zahl

We study the minimizers of \begin{equation} λ_k^s(A) + |A| \end{equation} where $λ^s_k(A)$ is the $k$-th Dirichlet eigenvalue of the fractional Laplacian on $A$. Unlike in the case of the Laplacian, the free boundary of minimizers exhibit distinct global behavior. Our main results include: the existence of minimizers, optimal Hölder regularity for the corres

Jan Niklas Böhm, Marius Keute, Alica Guzmán, Sebastian Damrich

Node embeddings are a paradigm in non-parametric graph representation learning, where graph nodes are embedded into a given vector space to enable downstream processing. State-of-the-art node-embedding algorithms, such as DeepWalk and node2vec, are based on random-walk notions of node similarity and on contrastive learning. In this work, we introduce the gra

Emre Anakok, Pierre Barbillon, Colin Fontaine, Elisa Thebault

Pollinators play a crucial role for plant reproduction, either in natural ecosystem or in human-modified landscape. Global change drivers,including climate change or land use modifications, can alter the plant-pollinator interactions. To assess the potential influence of global change drivers on pollination, large-scale interactions, climate and land use dat

L. F. Deeg, D. Zoepfl, N. Diaz-Naufal, M. L. Juan

With a variety of realisations, optomechanics utilizes its light matter interaction to test fundamental physics. By coupling the phonons of a mechanical resonator to the photons in a high quality cavity, control of increasingly macroscopic objects has become feasible. In such systems, state manipulation of the mechanical mode is achieved by driving the cavit

Karolina Drabent, Ondřej Kubíček, Viliam Lisý

In large-scale games, approximating the opponent's strategy space with a small portfolio of representative strategies is a common and powerful technique. However, the construction of these portfolios often relies on domain-specific knowledge or heuristics with no theoretical guarantees. This paper establishes a formal foundation for portfolio-based strategy

Mohammad Malekjani, Saeed Pourojaghi, Zahra Davari

The nature of dark energy is one of the big puzzling issues in cosmology. While $\Lambda$CDM provides a good fit to the observational data, evolving dark energy scenarios, such as the CPL parametrization, offer a compelling alternative. In this paper, we present a Bayesian model comparison of various dark energy parametrizations using a joint analysis of Cos

Harrison Bagley, Will Meakin, Simon Lucey, Yee Wei Law

Physical adversarial attacks on deep learning systems is concerning due to the ease of deploying such attacks, usually by placing an adversarial patch in a scene to manipulate the outcomes of a deep learning model. Training such patches typically requires regularization that improves physical realizability (e.g., printability, smoothness) and/or robustness t

Loren E Held, Gordon I. Ogilvie

We present the first local simulations of disc breaking/tearing in a warped accretion disc. Warps can arise due to a misalignment between the disc and the rotation axis of the central object, or a misalignment with the orbital plane of a binary (or planetary) companion. Warped discs can break into rings, as found in observations of circumbinary protoplanetar

Nayu Dong, Townim Chowdhury, Hieu Phan, Mark Jenkinson

The scarcity of annotated Magnetic Resonance Imaging (MRI) tumor data presents a major obstacle to accurate and automated tumor segmentation. While existing data synthesis methods offer promising solutions, they often suffer from key limitations: manual modeling is labor intensive and requires expert knowledge. Deep generative models may be used to augment d

Nuo Xu, Zhaoting Gong, Ran Ran, Jinwei Tang

Fully Homomorphic Encryption (FHE), particularly the CKKS scheme, is a promising enabler for privacy-preserving MLaaS, but its practical deployment faces a prohibitive barrier: it heavily relies on domain expertise. Configuring CKKS involves a tightly coupled space of ring dimensions, modulus chains, and packing layouts. Without deep cryptographic knowledge

M. Mavin De Silva, Callie Clark, Tadachika Nakayama, Takahiro Yabe

Understanding the economic impacts of the placement of electric vehicle charging stations (EVCSs) is crucial for planning infrastructure systems that benefit the broader community. Theoretical models have been used to predict human behavior during charging events, however, these models have often neglected the complexity of trip patterns, and have underestim

Chidi Elijah Nwakpa, Austine Efut Ofem, Kalu Okam Okorie, Chinedu Izuchukwu

We propose in this paper a proximal and contraction method for solving a convex mixed variational inequality problem in a real Hilbert space. To accelerate the convergence of our proposed method, we incorporate an inertial extrapolation term, two correction terms, and a relaxation technique. We therefore obtain a weak convergence result under some mild assum

Atena Khoshkonesh, Mohsen Mohammadagha, Navid Ebrahimi, Narges Sadeghigolshan

This paper introduces Lean 5.0, a human-centric evolution of Lean-Digital integration that connects predictive analytics, AI collaboration, and continuous learning within Industry 5.0 and Construction 5.0 contexts. A systematic literature review (2019-2024) and a 12-week empirical validation study demonstrate measurable performance gains, including a 13% inc

K. Aivazelis, D. Attié, P. Billoir, A. Blanchet

The off-axis magnetic near detector of the T2K experiment has undergone a significant upgrade, including the construction and installation of two new Time Projection Chambers featuring innovative resistive Micromegas technology and a field cage composed of thin composite walls. This paper provides a detailed description of the new components of the chambers,

Goun Pyeon, Inbum Heo, Jeesu Jung, Taewook Hwang

This study systematically evaluated the mathematical reasoning capabilities of Large Language Models (LLMs) using the 2026 Korean College Scholastic Ability Test (CSAT) Mathematics section, ensuring a completely contamination-free evaluation environment. To address data leakage issues in existing benchmarks, we digitized all 46 questions (22 common and 24 el

Dario Javier Zamora

Quantifying complexity in physical systems remains a fundamental challenge, and many proposed measures fail to capture the structural features that intuitive or theoretical considerations would demand. Among them, the Lopez-Ruiz-Mancini-Calbet (LMC) statistical complexity has been widely cited due to its simplicity and analytic tractability. Here, we examine

Tianning Wang, Evan Grohs, Laura Mersini-Houghton

Primordial Black Holes (PBHs) provide a powerful probe of the early universe physics, linking inflationary fluctuations to observable cosmological phenomena. In this work, we use a bottom-up approach to study how PBHs with masses in the range $10^{8} \leq M \leq 10^{13}\,\mathrm{g}$ modify Big Bang Nucleosynthesis (BBN) through Hawking radiation. We incorpor

Raoul H. Kutil, Georg Zimmermann, Christian Borgelt

Diagnosing cognitive (mental health) disorders is a delicate and complex task. Identifying the next most informative symptoms to assess, in order to distinguish between possible disorders, presents an additional challenge. This process requires comprehensive knowledge of diagnostic criteria and symptom overlap across disorders, making it difficult to navigat

Hong-Chen Jiang, Thomas P. Devereaux, Steven A. Kivelson

The issue of whether $d$-wave superconductivity (SC) occurs in the square-lattice Hubbard model with $U$ of order of the bandwidth has been one of the most debated issues to emerge from the study of high temperature SC. Here, we report variational results on eight-leg cylinders with next-nearest-neighbor hopping in the range $-0.5 t \leq t'\leq 0.25 t$ with

Haojun Xia, Xiaoxia Wu, Jisen Li, Robert Wu

The KV cache is a dominant memory bottleneck for LLM inference. While 4-bit KV quantization preserves accuracy, 2-bit often degrades it, especially on long-context reasoning. We close this gap via an algorithm-system co-design for mixed-precision KV caching: Kitty. On the algorithm side, extensive experiments show that Dynamic Channel-wise Precision Boost --

Chidi Elijah Nwakpa, Chinedu Izuchukwu, Chibueze CHristian Okeke, Dilber Uzun Ozsahin

We propose in this work a subgradient extragradient method with inertial and correction terms for solving equilibrium problems in a real Hilbert space. We obtain that the sequence generated by our proposed method converges weakly to a point in the solutions set of the equilibrium problem when the associated bivariate function is pseudomonotone and satisfies

Raoul H. Kutil, Georg Zimmermann, Barbara Strasser-Kirchweger, Christian Borgelt

Mental health disorders, particularly cognitive disorders defined by deficits in cognitive abilities, are described in detail in the DSM-5, which includes definitions and examples of signs and symptoms. A simplified, machine-actionable representation was developed to assess the similarity and separability of these disorders, but it is not suited for the most

Yuefeng Han, Likai Chen, Wei Biao Wu

High-dimensional vector autoregressive (VAR) models have numerous applications in fields such as econometrics, biology, climatology, among others. While prior research has mainly focused on linear VAR models, these approaches can be restrictive in practice. To address this, we introduce a high-dimensional non-parametric sparse additive model, providing a mor

Akhil Kondepudi, Akshay Rao, Chenhui Zhao, Yiwei Lyu

Frontier artificial intelligence (AI) models, such as OpenAI's GPT-5 and Meta's DINOv3, have advanced rapidly through training on internet-scale public data, yet such systems lack access to private clinical data. Neuroimaging, in particular, is underrepresented in the public domain due to identifiable facial features within MRI and CT scans, fundamentally re

Chidi Elijah Nwakpa, Chinedu Izuchukwu, Chibueze Christian Okeke

We study in this paper a forward-backward-forward dynamical system for solving a mixed variational inequality problem in a real Hilbert space. For the convergence analysis of our proposed system, we apply the Lyapunov analysis to obtain the weak convergence of the generated trajectories when the associated operator is Lipschitz continuous and satisfies the g

Alfredo Guevara, Uri Kol

Rotating and charged black holes are known to exhibit remarkable properties close to extremality, including emergent hidden symmetries and holographic duality to 2D theories. In this note, we introduce a new class of near-extremal black holes living in $(2,2)$ signature, strongly resembling the Lorentzian ones but with an exact integrable structure $SL(2,\ma

Wei Wang, Devi Karolita, Hourieh Khalajzadeh, John Grundy

Mobile health (mHealth) applications are increasingly adopted for chronic disease management, yet they face persistent challenges related to accessibility, inclusivity, and sustained engagement. Patients' needs evolve dynamically with their health progression, adherence, and caregiver support, creating unique requirements engineering (RE) challenges that tra

Yildiz Culcu

Machine learning models increasingly function as representational systems, yet the philosoph- ical assumptions underlying their internal structures remain largely unexamined. This paper develops a structuralist decision framework for classifying the implicit ontological commitments made in machine learning research on neural network representations. Using a

Jan Benedikt Ruhland, Doguhan Bahcivan, Jan-Peter Sowa, Ali Canbay

Recent advances in large language models made it possible to achieve high conversational performance with substantially reduced computational demands, enabling practical on-site deployment in clinical environments. Such progress allows for local integration of AI systems that uphold strict data protection and patient privacy requirements, yet their secure im

Kiyan Rezaee, Morteza Ziabakhsh, Niloofar Nikfarjam, Mohammad M. Ghassemi

Interdisciplinary scientific breakthroughs mostly emerge unexpectedly, and forecasting the formation of novel research fields remains a major challenge. We introduce FOS (Future Of Science), a comprehensive time-aware graph-based benchmark that reconstructs annual co-occurrence graphs of 65,027 research sub-fields (spanning 19 general domains) over the perio

Amin Rakhsha, Kanika Madan, Tianyu Zhang, Amir-massoud Farahmand

Sampling multiple outputs from a Large Language Model (LLM) and selecting the most frequent (Self-consistency) or highest-scoring (Best-of-N) candidate is a popular approach to achieve higher accuracy in tasks with discrete final answers. Best-of-N (BoN) selects the output with the highest reward, and with perfect rewards, it often achieves near-perfect accu

Jack Yarndley

Spacecraft equipped with multiple propulsion modes or systems can offer enhanced performance and mission flexibility compared with traditional configurations. Despite these benefits, the trajectory optimization of spacecraft utilizing such configurations remains a complex challenge. This paper presents a sequential convex programming (SCP) approach for the o

Kun Liu

In this short note, we prove a result about the non-generic part of the cohomology of certain compact unitary Shimura varieties for good $p$, partially extending a result of Boyer in the case of Harris--Taylor unitary Shimura varieties. Our arguments are different to those of Boyer -- we work in the context of the work of Fargues--Scholze, using ideas introd

Joakim Cronvall, Aron Wennman

We develop a unified approach to universality of local scaling limits for eigenvalues of random normal matrices, or equivalently for planar Coulomb gases at inverse temperature $\beta=2$. The approach is direct in that it does not rely on expressing the kernels in terms of orthogonal polynomials. There are three main results. The first is a proof of universa

Jan Benedikt Ruhland, Thorsten Papenbrock, Jan-Peter Sowa, Ali Canbay

Reliable detection of retinal diseases from fundus images is challenged by the variability in imaging quality, subtle early-stage manifestations, and domain shift across datasets. In this study, we systematically evaluated a Vision Transformer (ViT) classifier under multiple augmentation and enhancement strategies across several heterogeneous public datasets

François X. P. Bourassa, Sooraj Achar, Grégoire Altan-Bonnet, Paul François

T cells are central to the adaptive immune response, capable of detecting pathogenic antigens while ignoring healthy tissues with remarkable specificity and sensitivity. Quantitatively understanding how T cell receptors (TCRs) discriminate among antigens requires biophysical models and theoretical analysis of signaling networks. Here, we review current theor

Wei Wang, Hourieh Khalajzadeh, John Grundy, Anuradha Madugalla

Mobile health (mHealth) applications are widely used for chronic disease management, but usability and accessibility challenges persist due to the diverse needs of users. Adaptive User Interfaces (AUIs) offer a personalized solution to enhance user experience, yet barriers to adoption remain. Understanding user preferences and trade-offs is essential to ensu

B. Dalla Barba, L. Foschini, M. Berton, A. Lähteenmäki

The analysis of the optical spectra of PMN J0948+0022 showed significant variations in the spectral lines that, when combined with the Fermi $\gamma$-ray light curve and radio observations reported by other authors, were interpreted as the result of interactions between the relativistic jet and the narrow-line region (NLR). In this work, we present order-of-

Luke Peilen, Sylvia Serfaty

We study the local statistical behavior of the super-Coulombic Riesz gas of particles in Euclidean space of arbitrary dimension, with inverse power distance repulsion integrable near $0$, and with a general confinement potential, in a certain regime of inverse temperature. Using a bootstrap procedure, we prove local laws on the next order energy and control

Michael J. Bommarito

We present OpenGloss, a synthetic encyclopedic dictionary and semantic knowledge graph for English that integrates lexicographic definitions, encyclopedic context, etymological histories, and semantic relationships in a unified resource. OpenGloss contains 537K senses across 150K lexemes, on par with WordNet 3.1 and Open English WordNet, while providing more

Mikhail Mironov

We study complete interpolating sequences in two types of small Fock spaces, $\mathcal{F}^p_{\alpha +}$ and $\mathcal{F}^p_{\alpha}$, for $0 < p \le \infty$. One-sided small Fock spaces $\mathcal{F}^p_{\alpha +}$ are well-studied spaces of entire functions with sub-exponential growth, while $\mathcal{F}^p_{\alpha}$ are their two-sided analogue with a symmetr

Maanas Taneja

Language Models are extremely susceptible to performance collapse with even small changes to input prompt strings. Libraries such as DSpy (from Stanford NLP) avoid this problem through demonstration-based prompt optimisation. Inspired by this, I propose an alternative approach that treats prompt optimisation as a classical state-space search problem. I model