December 2025 arXiv papers — page 21

Xi Mou, Xiaopeng Peng, Tongsheng Mou

Direct-view LED displays are widely adopted in large-format applications due to their high luminance and reliability. However, visual comfort and accurate performance evaluation remain challenging due to the complex interaction between pixel luminance, human visual perception, and measurement artifacts. In this work, we introduce a novel 2D imaging luminance

Gianluca Cuffaro, Matthew B. Weiss

We introduce the Clifford entropy, a measure of how close an arbitrary unitary is to a Clifford unitary, which generalizes the stabilizer entropy for states. We show that this quantity vanishes if and only if a unitary is Clifford, is invariant under composition with Clifford unitaries, and is subadditive under tensor products. Rewriting the Clifford entropy

TJ Bai, Jason Eisner

Large language models are increasingly deployed in multi-agent workflows. We introduce Prompt Choreography, a framework that efficiently executes LLM workflows by maintaining a dynamic, global KV cache. Each LLM call can attend to an arbitrary, reordered subset of previously encoded messages. Parallel calls are supported. Though caching messages' encodings s

Giuseppe Rossi

We re-examine the problem of vacuum decay in the presence of spherically symmetric black holes. Within the semiclassical approximation, we study configurations describing a bubble of true vacuum propagating outside a black hole formed from gravitational collapse. We find that the saddle point is dominated by a single energy state and that the dependence on t

Sayantan Banerjee

High-dimensional Bayesian procedures often exhibit behavior that is effectively low dimensional, even when the ambient parameter space is large or infinite-dimensional. This phenomenon underlies the success of shrinkage priors, regularization, and approximate Bayesian methods, yet it is typically described only informally through notions such as sparsity, in

Maryam Olyaee, Giovanni Interdonato, Stefano Buzzi

In this paper, we investigate cell-free massive MIMO (CF-mMIMO) systems in which access points (APs) are equipped with fluid antennas (FAs) and develop a comprehensive framework for channel estimation, antenna port selection, and uplink spectral efficiency (SE) optimization. We propose a generalized LMMSE-based uplink channel estimation scheme that dynamical

Vaibhav Kumar, Anastasios Papazafeiropoulos, Pandelis Kourtessis, John Senior

Flexible intelligent metasurface (FIM) is a recently developed, groundbreaking hardware technology with promising potential for 6G wireless systems. Unlike conventional rigid antenna array (RAA)-based transmitters, FIM-assisted transmitters can dynamically alter their physical surface through morphing, offering new degrees of freedom to enhance system perfor

Zhengyang Liang, Yan Shu, Xiangrui Liu, Minghao Qin

The evolution of autonomous agents is redefining information seeking, transitioning from passive retrieval to proactive, open-ended web research. However, a significant modality gap remains in processing the web's most dynamic and information-dense modality: video. In this paper, we first formalize the task of Agentic Video Browsing and introduce Video-Brows

Ryousuke Yamada, Kohsuke Ide, Yoshihiro Fukuhara, Hirokatsu Kataoka

Despite recent progress in 3D self-supervised learning, collecting large-scale 3D scene scans remains expensive and labor-intensive. In this work, we investigate whether 3D representations can be learned from unlabeled videos recorded without any real 3D sensors. We present Laplacian-Aware Multi-level 3D Clustering with Sinkhorn-Knopp (LAM3C), a self-supervi

Mboyo Esole, Monica Jinwoo Kang

For simple, simply-connected compact Lie groups, Dynkin embedding indices obey a universal scaling law with a direct topological meaning. Given an inclusion $f:G\hookrightarrow H$, the Dynkin embedding index $j_f$ is characterized equivalently by the induced maps on $\pi_3$ and on the canonical generators of $H^3$, $H^4(B{-})$, and $H^4(\Sigma{-})$. Conseque

Sahar Shahaf, Barak Zackay

We generalize the short-time Fourier transform (STFT) formalism for radial velocity extraction to cases where the underlying spectral components are unknown. The method factorizes a spectroscopic time series into principal spectra and time-dependent kernels, enabling simultaneous recovery of both. In Fourier space, each inverse-wavelength slice is decomposed

Laura Baudis

Direct dark matter detection experiments search for rare signals induced by hypothetical, galactic dark matter particles in low-background detectors operated deep underground. I will briefly review the direct detection principles, the expected signals and backgrounds, and the main experimental techniques. I will then discuss the status of ongoing experiments

Sarah Packman, Justin Finkel, Dorian S. Abbot, Eli Tziperman

Extreme El Ni\~no events, such as occurred in 1997--1998, can induce severe weather on a global scale, with significant socioeconomic impacts that motivate efforts to understand them better. However, extreme El Ni\~no events are rare, and even in a very long direct numerical simulation (DNS) occur too infrequently for robust statistical characterization. Thi

Riling Li, Keli Zheng, Yiming Zhang, Huazhe Lou

Circuit simulation tools are critical for developing and assessing quantum-error-correcting and fault-tolerant strategies. In this work, we present SOFT, a high-performance SimulatOr for universal Fault-Tolerant quantum circuits. Integrating the generalized stabilizer formalism and highly optimized GPU parallelization, SOFT enables the simulation of noisy qu

Danial Hooshyar, Yeongwook Yang, Gustav Šíř, Tommi Kärkkäinen

The rapid rise of large language model (LLM)-based tutors in K--12 education has fostered a misconception that generative models can replace traditional learner modelling for adaptive instruction. This is especially problematic in K--12 settings, which the EU AI Act classifies as high-risk domain requiring responsible design. Motivated by these concerns, thi

Yi Zhou, Xuechao Zou, Shun Zhang, Kai Li

Semi-supervised remote sensing (RS) image semantic segmentation offers a promising solution to alleviate the burden of exhaustive annotation, yet it fundamentally struggles with pseudo-label drift, a phenomenon where confirmation bias leads to the accumulation of errors during training. In this work, we propose Co2S, a stable semi-supervised RS segmentation

L. Taras, C. Bailly, T. Dufour

The aim of this study was to investigate the impact of ambient air plasma treatment on both sunflower seed germination and early steps of seedling development under water stress. Dry seeds were exposed to a cold atmospheric plasma (CAP) generated in a dielectric barrier device where excited molecular nitrogen and ozone were detected by optical emission spect

Fuyad Hasan Bhoyan, Prashanta Sarker, Parsia Noor Ethila, Md. Emon Hossain

Early and accurate detection of gallbladder diseases is crucial, yet ultrasound interpretation is challenging. To address this, an AI-driven diagnostic software integrates our hybrid deep learning model MobResTaNet to classify ten categories, nine gallbladder disease types and normal directly from ultrasound images. The system delivers interpretable, real-ti

Kerem Zaman, Shashank Srivastava

Recent work, using the Biasing Features metric, labels a CoT as unfaithful if it omits a prompt-injected hint that affected the prediction. We argue this metric adopts a narrow notion of faithfulness and confuses unfaithfulness with incompleteness, the lossy compression needed to turn distributed transformer computation into a linear natural language narrati

D. Bazeia, R. Menezes, G. S. Santiago

We report on the presence of families of exact solutions for a complex scalar field that behaves according to the rules of discrete $Z_N$ symmetry. Since the family of models is exactly solved, the results appear to be of interest to integrability, to build junctions and networks of localized structures and to describe scalar dark matter in high energy physi

S. S. Agaev, K. Azizi, H. Sundu

Hadronic molecules $\mathfrak{M}=J/\psi B_{c}^{+}$ and $\widetilde{\mathfrak{ \ M}}=\eta _{c}B_{c}^{\ast +}$ are investigated in the framework of QCD sum rule method. These particles with spin-parities $J^{\mathrm{P}}=1^+$ have the quark contents $cc \overline{c}\overline{b}$. We compute their masses and current couplings and find that they are numerically v

Alex Khalil, Guillaume Heilles, Maria Parraga, Simon Heilles

The proliferation of Large Language Models (LLMs) has been accompanied by a reliance on cloud-based, proprietary systems, raising significant concerns regarding data privacy, operational sovereignty, and escalating costs. This paper investigates the feasibility of deploying a high-performance, private LLM inference server at a cost accessible to Small and Me

Thomson Tong, Diba Darooneh

This report provides an architecture-led analysis of two modern vision-language models (VLMs), Qwen2.5-VL-7B-Instruct and Llama-4-Scout-17B-16E-Instruct, and explains how their architectural properties map to a practical video-to-artifact pipeline implemented in the BodyLanguageDetection repository [1]. The system samples video frames, prompts a VLM to detec

Sudhi Sharma Padillath Vasudevan

An acoustic wave propagation problem with a log normal random field approximation for wave speed is solved using a sampling-free intrusive stochastic Galerkin approach. The stochastic partial differential equation with the inputs and outputs expanded using polynomial chaos expansion (PCE) is transformed into a set of deterministic PDEs and further to a syste

Wenxuan Xu, Arvind Pillai, Subigya Nepal, Amanda C Collins

Multimodal health sensing offers rich behavioral signals for assessing mental health, yet translating these numerical time-series measurements into natural language remains challenging. Current LLMs cannot natively ingest long-duration sensor streams, and paired sensor-text datasets are scarce. To address these challenges, we introduce LENS, a framework that

Ciprian Constantinescu, Marius Leordeanu

Humans effortlessly identify objects by leveraging a rich understanding of the surrounding scene, including spatial relationships, material properties, and the co-occurrence of other objects. In contrast, most computational object recognition systems operate on isolated image regions, devoid of meaning in isolation, thus ignoring this vital contextual inform

Andrzej Czarnecki, A. O. Davydov, M. Y. Kaygorodov

We revisit the total decay rate of a muon being in the ground state of a Coulomb potential with atomic charge numbers $4\leq Z \leq 9$. The discrepancy between the perturbative $(\alpha Z)^2$ result of [Phys. Rev. \textbf{119}, 365 (1960)] and the fully relativistic partial-wave calculation of [At. Data Nucl. Data Tables \textbf{54}, 165 (1993)] for oxygen (

Ranjit Vohra

We prove a new fixed - point result for the image Im(j) of any continuous function j from K to (K x K), where K is a compact convex subset of a Hausdorff locally convex space, provided that the projection of Im(j) to the first factor is onto, and a condition on the convex hull of Im(j) holds. A special case of our result is the Brouwer - Schauder - Tychonoff

Layla Abu Khalaf, William Smyth

We revisit Gerber's Informational Quality (IQ) framework, a data-driven approach for constructing correlation matrices from co-movement evidence, and address two obstacles that limit its use in portfolio optimization: guaranteeing positive semidefinite ness (PSD) and controlling spectral conditioning. We introduce a squeezing identity that represents IQ esti

Afshin Yaghoubi, Esmaile Khorram, Omid Naghshineh Arjmand

Cold standby 1-out-of-n redundant systems are well-established models in system reliability engineering. To date, reliability analyses of such systems have predominantly assumed exponential, Erlang, or Weibull failure distributions for their components. The Lindley distribution and its generalizations represent a significant class of statistical distribution

François Clément, Stefan Steinerberger

It is widely believed that the energy functional $E_p:(\mathbb{S}^2)^n \rightarrow \mathbb{R}$ $$ E_p = \sum_{i,j=1 \atop i \neq j}^{n} \frac{1}{\|x_i-x_j\|^p}$$ has a number of critical points, $\nabla E(x) = 0$, that grows exponentially in $n$. Despite having been extensively tested and being physically well motivated, no rigorous result in this direction

Fuli Qiao, Mehrdad Mahdavi

Parameter-efficient continual learning has emerged as a promising approach for large language models (LLMs) to mitigate catastrophic forgetting while enabling adaptation to new tasks. Current Low-Rank Adaptation (LoRA) continual learning techniques often retain and freeze previously learned LoRAs or generate data representations to overcome forgetting, typic

Spyridon G. Kosionis, Sutirtha Biswas, Christina Fouseki, Dionisis Stefanatos

In the present study, we apply shortcut to adiabaticity pulses (time-dependent Rabi frequency and detuning) for the efficient population transfer from the ground to the exciton state in a GaAs/InGaAs quantum dot with phonon-induced dephasing. We use the time-evolving matrix product operator (TEMPO) method to propagate system in time and find that, for temper

Tao Yu, Yongqi An, Kuan Zhu, Guibo Zhu

Large Language Models (LLMs) demonstrate impressive performance across natural language tasks but incur substantial computational and storage costs due to their scale. Post-training structured pruning offers an efficient solution. However, when few-shot calibration sets fail to adequately reflect the pretraining data distribution, existing methods exhibit li

Simone Cepollaro, Gianluca Cuffaro, Matthew B. Weiss, Stefano Cusumano

We consider the costs and benefits of embedding the states of one quantum system within those of another. Such embeddings are ubiquitous, e.g., in error correcting codes and in symmetry-constrained systems. In particular we investigate the impact of embeddings in terms of the resource theory of nonstabilizerness (also known as magic) quantified via the stabi

Kurt Miller, Qiuhao Lu, William Hersh, Kirk Roberts

Clinical document metadata, such as document type, structure, author role, medical specialty, and encounter setting, is essential for accurate interpretation of information captured in clinical documents. However, vast documentation heterogeneity and drift over time challenge harmonization of document metadata. Automated extraction methods have emerged to co

Nikolas Kuhn, Henry Liu, Felix Thimm

We provide a wall-crossing framework for operational enumerative invariants of equivariant 3-Calabi--Yau categories arising from virtual cycles. The strategy follows ideas of Joyce's ``universal'' wall-crossing framework arXiv:2111.04694, using the authors' symmetrized pullback technique to preserve the symmetry of the (almost-perfect) obstruction theories t

Jie Ma, Mingyuan Rong

The codegree Tur\'an density $\gamma(F)$ of a $k$-uniform hypergraph $F$ is the minimum real number $\gamma \ge 0$ such that every $k$-uniform hypergraph on sufficiently many $n$ vertices, in which every set of $k-1$ vertices is contained in at least $(\gamma+o(1))n$ edges, contains a copy of $F$. A recent result of Piga, Sanhueza-Matamala, and Schacht deter

Guanghen Liu, Songge Yang, Yu Zhong

Density functional theory (DFT) and machine learning potentials (MLPs) are essential for predicting and understanding materials properties, yet preparing, executing, and analyzing these simulations typically requires extensive scripting, multi-step procedures, and significant high-performance computing (HPC) expertise. These challenges hinder reproducibility

Rudraksh Sharma

Variational quantum algorithms are among the most promising approaches for simulating interacting quantum many-body systems on noisy intermediate-scale quantum (NISQ) devices. However, the practical success of variational quantum eigensolvers (VQE) critically depends on the structure of the chosen variational ansatz. In this work, we investigate the ground-s

Tobias Gebäck, Ioanna Motschan-Armen, Irina Pettersson

We study a phenomenological electropermeabilization model in a periodic medium representing biological tissue. Starting from a cell-level model describing the electric potential and the degree of porosity, we perform dimension analysis to identify a relevant scaling in terms of a small parameter $\ve$ - the ratio between the cell and the tissue size. The ele

Carolina Benedetti

In this manuscript we study the subdivisions of the permutahedron $\Pi_n$ into two subpolytopes corresponding to flags of positroids, which are in particular flags of lattice path matroids (LPFMs). A subpolytope $P_{[u,v]}$ of $\Pi_n$ is a Bruhat Interval Polytope (BIP) if $P_{[u,v]}$ is the convex hull of all the permutations (viewed as points in $\RR^n$) i

Yehya Sleiman Tellawi, Abhishek K. Umrawal

This experience report explores the effects of ungraded assignments on the learning experience of students in an introductory computing course. Our study examines the impact of ungraded assignments on student engagement, understanding, and overall academic performance. We developed and administered new ungraded assignments for a required course in the first

Mirna Džamonja

A celebrated 1969 theorem of Michael Rabin is that the MSO theory of the real order where the monadic quantifier is allowed only to range over the sets of rational numbers, is decidable. In 1975 Saharon Shelah proved that if the monadic quantifier is allowed to range over all subsets of the reals, the resulting MSO theory is undecidable. He conjectured that

Leon A. Abdillah, Aisyah, Wahdyta Putri Panggabean, Sayfiyev Eldor Erkinovich

This article examines the knowledge of digital transformation of Small and Medium Enterprises (SMEs) that specialize in traditional handicrafts, with a specific emphasis on the Songket textile sector. The study investigates the use of digital technologies, notably blog platforms and the e-commerce site Shopee, to improve and streamline several business proce

Jean-Jacq du Plessis, Malcolm Hillebrand, Charalampos Skokos

Covariant Lyapunov vectors (CLVs) are useful in multiple applications, but the optimal time windows needed to accurately compute these vectors are yet unclear. To remedy this, we investigate two methods for determining when to safely terminate the forward and backward transient phases of the CLV computation algorithm by Ginelli et al.~\cite{GinelliEtAl2007}

Sergey Sadov

The error of approximation of the $2\pi$-periodic sawtooth function $(\pi-x)/2$, $0\leq x<2\pi$, by its $n$-th Fourier polynomial is shown to be bounded by arccot$((2n+1)\sin(x/2))$. Related asymptotically tight inequalities with explicit constants are given for the integral of the Dirichlet kernel interpolated to non-integer values of frequency parameter an

Mohammed Salah, Eman Ouda, Stefano Sfarra, Davor Svetinovic

Active infrared thermography (AIRT) became a crucial tool in aerospace non-destructive testing (NDT), enabling the detection of hidden defects and anomalies in materials by capturing thermal responses over time. In AIRT, autoencoders are widely used to enhance defect detection by reducing the dimensionality of thermal data and improving the signal-to-noise r

Dionisis Stefanatos, Ioannis Thanopulos, Emmanuel Paspalakis

In this work, we formulate the problem of achieving in minimum-time perfect chiral resolution with bounded control fields, as an optimal control problem on two non-interacting spins-$1/2$. We assume the same control bound for the two Raman fields (pump and Stokes) and a different bound for the field connecting directly the two lower-energy states. Using cont

Simon Ekhammar, Nikolay Gromov, Fedor Levkovich-Maslyuk, Paul Ryan

We formulate a closed set of equations for the spectrum of two-dimensional bi-scalar fishnet conformal field theory, comprising Baxter equations and quantisation conditions, which we derive operatorially from the underlying sl(2) spin chain. These equations are reminiscent of the Quantum Spectral Curve (QSC) framework found in other holographic CFTs and are

Prudhvi Gadupudi, Suman Saha

Buffer management remains a critical component of database and operating system performance, serving as the primary mechanism for bridging the persistent latency gap between CPU processing speeds and storage access times. This paper provides a comprehensive survey of buffer management evolution spanning four decades of research. We systematically analyze the

F. Xavier Trias, Xavier Álvarez-Farré, Àdel Alsalti-Baldellou, Andrey Gorobets

Direct and large-eddy simulations of turbulence are often solved using explicit temporal schemes. However, this imposes very small time-steps because the eigenvalues of the (linearized) dynamical system, re-scaled by the time-step, must lie inside the stability region. In practice, fast and accurate estimations of the spectral radii of both the discrete conv

Tohida Rehman

Financial reports and earnings communications contain large volumes of structured and semi structured information, making detailed manual analysis inefficient. Earnings conference calls provide valuable evidence about a firm's performance, outlook, and strategic priorities. The manual analysis of lengthy call transcripts requires substantial effort and is su

C. N. Pope

In the paper arXiv:2501.04771, a novel compactification of heterotic supergravity on a warped product of $\R\times T^{1,1}$ was constructed, where $T^{1,1}$ is a five-dimensional coset space $(SU(2)\times SU(2))/U(1)$. It was shown that this admits a four dimensional Minkowski vacuum solution with ${\cal N}=1$ supersymmetry, and furthermore that in the boson

Tanmoy Pati, Narayan Rana, V. Ravindran

We present the analytic results for the non-singlet contributions to the three-loop mixed strong-electroweak ${\mathcal{O}}(\alpha_s^2\alpha)$ virtual corrections to the quark form factors. The primary challenge of this computation arises from the presence of massive vector bosons within the loops. This significantly increases the complexity of the integrati

Tillmann Rheude, Roland Eils, Benjamin Wild

Multimodal learning has become a prominent research area, with the potential of substantial performance gains by combining information across modalities. At the same time, model development has trended toward increasingly complex deep learning architectures, motivated by the assumption that multimodal-specific methods improve performance. We challenge this a

Soham Dutta, Soham Banerjee, Sneha Mahata, Anindya Sen

Apple orchards require timely disease detection, fruit quality assessment, and yield estimation, yet existing UAV-based systems address such tasks in isolation and often rely on costly multispectral sensors. This paper presents a unified, low-cost RGB-only UAV-based orchard intelligent pipeline integrating ResNet50 for leaf disease detection, VGG 16 for appl

Indra Kumar Banerjee, Ujjal Kumar Dey, Shaaban Khalil

We review the creation mechanism of primordial black holes from first order phase transitions. We discuss various model-dependent and independent mechanisms and relate the properties of these mechanisms to the properties of primordial black holes. For each of these mechanisms, we provide model-specific examples.

Roman Drnovšek, Marko Kandić

In this paper we first consider the question which nonnegative matrices are commutators of nonnegative square-zero matrices. Then, we treat infinite-dimensional analogues of these results for operators on the Banach lattices $L^p[0,1]$ and $\ell^p$ ($ 1 \leq p < \infty$). In the last setting we need to extend the notion of the nonnegative rank of a nonnegati

Siyi Wang, Zifan Wang, Karl H. Johansson

In safety-critical decision-making, the environment may evolve over time, and the learner adjusts its risk level accordingly. This work investigates risk-averse online optimization in dynamic environments with varying risk levels, employing Conditional Value-at-Risk (CVaR) as the risk measure. To capture the dynamics of the environment and risk levels, we em

Michael J. Larsen

Given a finite-dimensional faithful representation $V$ of a linearly reductive group $G$ over a field $K=\bar K$, we consider the growth of the number of irreducible factors of $V^{\otimes n}$ when $n$ is large. We prove that there exist upper and lower bounds which are constant multiples of $n^{-u/2} (\dim V)^n$, where $u$ is the dimension of any maximal un

Ayda Aghaei Nia

While Deep Learning has improved Brain-Computer Interface (BCI) decoding accuracy, clinical adoption is hindered by the "Black Box" nature of these algorithms, leading to user frustration and poor neuroplasticity outcomes. We propose OmniNeuro, a novel HCI framework that transforms the BCI from a silent decoder into a transparent feedback partner. OmniNeuro

Han-Wei Kung, Tuomas Varanka, Nicu Sebe

Recent text-to-image diffusion models have demonstrated remarkable generation of realistic facial images conditioned on textual prompts and human identities, enabling creating personalized facial imagery. However, existing prompt-based methods for removing or modifying identity-specific features rely either on the subject being well-represented in the pre-tr

Shuanghao Bai, Wenxuan Song, Jiayi Chen, Yuheng Ji

Recent advances in vision, language, and multimodal learning have significantly accelerated progress in robotic foundation models, with robotic manipulation remaining one of the most challenging embodied tasks. Its difficulty lies in integrating perception, semantic understanding, task reasoning, physically grounded action generation, and reliable execution.

Linglin Liao, Qichuan Geng, Yu Liu

Text-guided Medical Image Segmentation has shown considerable promise for medical image segmentation, with rich clinical text serving as an effective supplement for scarce data. However, current methods have two key bottlenecks. On one hand, they struggle to process diagnostic and descriptive texts simultaneously, making it difficult to identify lesions and

Runchen Zhang, Tade Marozsak, An Aloysius Wang, Yunqi Zhang

Structured light, in which the amplitude, phase, and polarization of an optical field are deliberately tailored in space and time, has enabled unprecedented control over optical fields, paving the way for diverse applications across photonics and optical engineering. However, the prevailing design philosophy, which predominantly focuses on converting a singl

Huiming Yang, Linglin Liao, Fei Ding, Sibo Wang

Six degree of freedom (6DoF) pose estimation for novel objects is a critical task in computer vision, yet it faces significant challenges in high-speed and low-light scenarios where standard RGB cameras suffer from motion blur. While event cameras offer a promising solution due to their high temporal resolution, current 6DoF pose estimation methods typically

Hoa Dinh Thi, Matthew G. Baring, Kun Hu, Alice K. Harding

Intense magnetic fields in the atmospheres of neutron stars render non-trivial angular dependence of intensity and polarization of soft X-ray emission originating from their surfaces. By tracking the complex electric field vector for each photon during its atmospheric transport and propagation in general relativistic and birefringent magnetospheres, our Mont

Wensheng Sun, Yujun Yang, Shou-Jun Xu

A graph $G$ is called equiarboreal if the number of spanning trees containing a given edge in $G$ is independent of the choice of edge. In [Combinatorica 1(2) (1981) 163--167], Godsil proved that any graph which is a colour class in an association scheme is equiarboreal, and further conjectured that the edge-connectivity of a connected graph which is a colou

Natalia Abarca, Andrés Carvallo, Claudia López Moncada, Felipe Bravo-Marquez

The increasing use of Machine Learning (ML) in sensitive domains such as healthcare, finance, and public policy has raised concerns about the transparency of automated decisions. Explainable AI (XAI) addresses this by clarifying how models generate predictions, yet most methods demand technical expertise, limiting their value for novices. This gap is especia

Amirali Vakili, Salar Jahanshiri, Armin Salimi-Badr

Automatic sleep staging is a critical task in healthcare due to the global prevalence of sleep disorders. This study focuses on single-channel electroencephalography (EEG), a practical and widely available signal for automatic sleep staging. Existing approaches face challenges such as class imbalance, limited receptive-field modeling, and insufficient interp

Flavia Bonomo-Braberman, Eric Brandwein, Ignasi Sau

Recently, Lafond and Luo [MFCS 2023] defined the $\mathcal{G}$-modular cardinality of a graph $G$ as the minimum size of a partition of $V(G)$ into modules that belong to a graph class $\mathcal{G}$. We analyze the complexity of calculating parameters that generalize interval graphs when parameterized by the $\mathcal{G}$-modular cardinality, where $\mathcal

Chunyuan Chen, Yunuo Cai, Shujuan Li, Weiyun Liang

Camouflaged image generation (CIG) has recently emerged as an efficient alternative for acquiring high-quality training data for camouflaged object detection (COD). However, existing CIG methods still suffer from a substantial gap to real camouflaged imagery: generated images either lack sufficient camouflage due to weak visual similarity, or exhibit clutter

Shizhou Zhang, Xueqiang Lv, Yinghui Xing, Qirui Wu

Current methods for incremental object detection (IOD) primarily rely on Faster R-CNN or DETR series detectors; however, these approaches do not accommodate the real-time YOLO detection frameworks. In this paper, we first identify three primary types of knowledge conflicts that contribute to catastrophic forgetting in YOLO-based incremental detectors: foregr

Runwei Guan, Jianan Liu, Shaofeng Liang, Fangqiang Ding

4D millimeter-wave (mmWave) radar has been widely adopted in autonomous driving and robot perception due to its low cost and all-weather robustness. However, point-cloud-based radar representations suffer from information loss due to multi-stage signal processing, while directly utilizing raw 4D radar tensors incurs prohibitive computational costs. To addres

A. Yu. Korchin, E. Richter-Was, Z. Was

The possible anomalous New Physics contributions to dipole and weak dipole moments of the $τ$ lepton bring renewed interest in development and revisiting charge-parity violating signatures in $τ$-pair production in $Z$-boson decay at energies of the LHC. In this paper, we discuss effects of anomalous contributions to polarisation and spin correlations in the

Kazumichi Nakamura

The $\Delta$-unknotting number for a knot is defined as the minimum number of $\Delta$-moves needed to deform the knot into the trivial knot. We determine the $\Delta$-unknotting numbers for two-bridge knots of type $C(2\beta_1, 2\beta_2, ... , 2\beta_n)$ and type $C(2\beta_1, 2\beta_2, ... , 2\beta_{n-1}, 2\beta_n-1)$, where $\beta_i$ is a positive integer

Kirill F. Sheberstov

Spin waves are propagating disturbances of spin order in lattices with nearest-neighbor interactions. They are traditionally observed in magnetically ordered solids using inelastic neutron, light, or electron scattering, and ferromagnetic resonance. Here, we show that analogous spin dynamics can arise in liquid-state nuclear magnetic resonance (NMR) of molec

Carlos Arturo Saldarriaga-Cortes, Carlos Adrian Correa-Florez, Maximiliano Bueno-Lopez, Maria Victoria Gasca-Segura

The Alternating Current Optimal Power Flow (ACOPF) problem remains one of the most fundamental yet computationally challenging tasks in power systems operation and planning due to its nonconvex, nonlinear, and multimodal nature. This paper proposes a convex reformulation of the AC power flow problem by introducing auxiliary variables to isolate nonlinear ter

Andrei Mironov, Hisham Sati, Vivek Kumar Singh, Alexander Stoimenov

We use a decomposition of the tensor of the fundamental representation of the quantum group $U_q(\mathfrak{sl}_N)$ and the Rosso-Jones formula to establish a peculiar ``panhandle'' shape of the HOMFLY-PT polynomial of the reverse parallel of torus knots and links. Due to their panhandle-like intrinsic properties, the HOMFLY-PT polynomial is referred to as a

Mengdi Chai, Ali R. Zomorrodi

Large Language Models (LLMs) have demonstrated promise in medical knowledge assessments, yet their practical utility in real-world clinical decision-making remains underexplored. In this study, we evaluated the performance of three state-of-the-art LLMs-ChatGPT-4o, Gemini 1.5 Pro, and LIama 3.3 70B-in clinical decision support across the entire clinical reas

Philippe Grangier

In a recent preprint [1] Jeffrey Bub presents a discussion of neo-Bohrian interpretations of quantum mechanics, and also of von Neumann's work on infinite tensor products [2]. He rightfully writes that this work provides a theoretical framework that deflates the measurement problem and justifies Bohr's insistence on the primacy of classical concepts. But the

Elena Tomasetti, Michele Moresco, Nicola Borghi, Dinko Milaković

The current tension between early- and late-Universe measurements of the Hubble constant ($H_0$), along with the still elusive nature of dark matter and dark energy, calls for model-independent probes of the Universe's expansion history. The cosmic chronometers (CC) method offers a unique opportunity to directly measure the Hubble parameter $H(z)$ without re

Wenyu Xin, Ken'ichi Nomoto, Xianfei Zhang, Shaolan Bi

We first present a systematic investigation into the effect of the $^{16}$O($^{16}$O, n)$^{31}$S reaction rate on the evolution and nucleosynthesis of Population III (Pop III) stars. We simulate the evolution of a 15 M$_\odot$ Pop III star from the zero-age main sequence through to core collapse, while varying the $^{16}$O($^{16}$O, n)$^{31}$S reaction rate

Yankai Zhang, Yoshitaka Tanimura, So Hirata

Studies of quantum thermal effects on molecular excitation dynamics have often relied on oversimplified models, such as energy eigenstates or low-dimensional potentials, which fail to capture the complexity of real chemical systems. In reality, molecules are spatially extended and embedded in anisotropic environments, where molecular orbitals (MOs) play a ce

Mathieu Laurière, Mehdi Talbi

This paper presents a novel deep learning framework for solving multiple optimal stopping problems in high dimensions. While deep learning has recently shown promise for single stopping problems, the multiple exercise case involves complex recursive dependencies that remain challenging. We address this by combining the Dynamic Programming Principle with neur

Samaël Mackowiak

In this paper, we study standing waves for the Anderson-Gross-Pitaevskii equation in dimension 1 and 2. The Anderson-Gross-Pitaevskii equation is a nonlinear Schr\"odinger equation with a confining potential and a multiplicative spatial white noise. Standing waves are characterized by a profile which is invariant by the dynamic and solves a nonlinear ellipti

Ziyuan Dong, Xiang Fan, Tengxun Zhong, Daowen Qiu

We revisit the finite Abelian hidden subgroup problem (AHSP) from a mathematical perspective and make the following contributions. First, by employing amplitude amplification, we present an exact quantum algorithm for the finite AHSP, our algorithm is more concise than the previous exact algorithm and applies to any finite Abelian group. Second, utilizing th

Tomislav Prokopec

We consider the gravitational Noether-Ward identities for the evolution of general metric perturbations on quantum matter backgrounds. In this work we consider Einstein's gravity covariantly coupled to a massive, non-minimally coupled, quantum scalar field in general curved backgrounds. We find that each term in the equation of motion for gravitational pertu

Mengyu Ji, Shiliang Guo, Zhengzhen Li, Jiahao Shen

An aerial manipulator, comprising a multirotor base and a robotic arm, is subject to significant dynamic coupling between these two components. Therefore, achieving precise and robust motion control is a challenging yet important objective. Here, we propose a novel prescribed performance motion control framework based on variable-gain extended state observer

Wafaa El Husseini

Access to longitudinal, individual-level data on work-life balance and wellbeing is limited by privacy, ethical, and logistical constraints. This poses challenges for reproducible research, methodological benchmarking, and education in domains such as stress modeling, behavioral analysis, and machine learning. We introduce FLOW, a synthetic longitudinal data

Haoyuan Wu, Hai Wang, Jiajia Wu, Jinxiang Ou

Recent advancements have shown that reinforcement learning (RL) can substantially improve the reasoning abilities of large language models (LLMs). The effectiveness of such RL training, however, depends critically on the exploration space defined by the pre-trained model's token-output distribution. In this paper, we revisit the standard cross-entropy loss,

Emiliano Liwski

Given a finite simple graph $G$ and a positive integer $d$, one can associate to $G$ the Lov\'asz--Saks--Schrijver ideal $L_{G}(d)$, an ideal generated by quadratic polynomials coming from orthogonality conditions. The corresponding variety $\mathbb{V}(L_{G}(d))$, denoted $\mathrm{OR}_{d}(\overline{G})$, is the variety of orthogonal representations of the co

Wang Zixian

Two divergence regimes dominate modern alignment practice. Supervised fine-tuning and many distillation-style objectives implicitly minimize the forward KL divergence KL(q || pi_theta), yielding stable mode-covering updates but often under-exploiting high-reward modes. In contrast, PPO-style online reinforcement learning from human feedback behaves closer to

Santosh Kumar Maurya, Ilan Goldberg, Rita Schmidt

Emerging concepts of metamaterials for MRI offer improved image quality through enhanced RF field control. However, current designs are often limited by bulky configurations, dielectric losses, and limited adaptability. Patient-specific optimization - essential for improving field homogeneity, boosting local SNR, and addressing anatomical variability - requi

Jagjit Singh, J. Casal, L. Fortunato, N. R. Walet

The role of the inversion of the $p$ and $f$ shell-model orbits in the emergence of halo structures in the ground states of neutron-rich $^{34,37,39}$Na is investigated. Families of two- and three-body models are constructed with effective core-neutron interactions, with parameter choices based on a combination of the available experimental data and systemat

Alexander Sturges, Hugo Smith, Matteo Marcuzzi

Inspired by previous works on epidemic-like processes in open quantum systems, we derive an elementary quantum epidemic model that is simple enough to be studied via Quantum Jump Monte Carlo simulations at reasonably large system sizes. We show how some weak symmetries of the Lindblad equation allow us to map the dynamics onto a classical Kinetic Monte Carlo

Zhicheng Zhao, Xuanang Fan, Lingma Sun, Chenglong Li

High-resolution remote sensing imagery increasingly contains dense clusters of tiny objects, the detection of which is extremely challenging due to severe mutual occlusion and limited pixel footprints. Existing detection methods typically allocate computational resources uniformly, failing to adaptively focus on these density-concentrated regions, which hind

Mahir Bilen Can, Benjamin Horowitz

This article introduces Generalized Hyperderivative Reed-Solomon codes (GHRS codes), which generalize NRT Reed-Solomon codes. Its main results are as follows: 1) every GHRS code is MDS, 2) the dual of a GHRS code is also an GHRS code, 3) determine subfamilies of GHRS codes whose members are low-density parity-check codes (LDPCs), and 4) determine a family of

Riccardo Battistelli, Lukas Körber, Kai Litzius, Matthieu Grelier

Topological magnetic textures are particle-like spin configurations stabilized by competing interactions. Their formation is commonly attributed to fluctuation-driven, first-order nucleation processes requiring activation over a topological energy barrier. Here, we demonstrate an alternative barrier- and fluctuation-free pathway for nucleating topological ma