November 2025 arXiv papers — page 188

Cheng-Hung Chou, Pepijn G. Moerman, Sikao Guo, Yiben Fu

The self-assembly of DNA-coated colloids controlled by enzymatic reactions has the potential to enable the formation of materials with hierarchical organization and switchable configurations. However, the problem of designing such self-assembly is complex, and an effective simulation is necessary to assist in searching for appropriate design protocols. Typic

Subash Gautam, Alejandro Vargas-Uscategui, Peter King, Hans Lohr

Additive manufacturing (AM) is an emerging digital manufacturing technology to produce complex and freeform objects through a layer-wise deposition. High deposition rate robotic AM (HDRRAM) processes, such as cold spray additive manufacturing (CSAM), offer significantly increased build speeds by delivering large volumes of material per unit time. However, ma

Fan Zhang, Nisheeta Desai, Wenan Guo, Ribhu K. Kaul

We study the $T=0$ phase diagrams of models of bilayers of $S=1/2$ square lattices antiferromagnets with SU(2) Heisenberg symmetry that have 2, 4, and 6 spin exchanges. We study two families of bilayer models with distinct internal symmetries and, hence, different phase diagram topologies. A traditional bilayer model in which the interlayer interaction is He

J. M. Flagg, Louis H. Kauffman, Divyamaan Sahoo

This paper provides a commentary and guide to Appendix 8 of Laws Of Form, which is a chapter (appendix) on number theory in the book Laws of Form by Spencer-Brown. (Spencer-Brown,Laws Of Form,Revised Seventh English edition. Bohmeier Verlag. 2020) This chapter in the book provides Spencer-Brown's proofs of the conjecture that there are at least two prime num

Qi Xiong, Yan Gong, Junhui Yan, Furen Deng

We investigate the cosmological constraints from the void-lensing cross-correlation assuming the $w$CDM model for the Chinese Space Station Survey Telescope (CSST) photometric survey. Using Jiutian simulations, we construct a mock galaxy catalog to $z=3$ covering 100 deg$^2$, which incorporates the instrumental and observational effects of the CSST. We divid

Takako Endo, Yohei Matsumoto, Hiromichi Ohno, Akito Suzuki

Existence of the eigenvalues of the discrete-time quantum walks is deeply related to localization. Also, for the study of open quantum systems, non-Hermitian systems have attracted much attention. As mathematical models for such systems, non-unitary quantum walks with the chiral symmetry are essential for the study of the topological insulator. In this paper

James Austin Orgeron, Malbor Asllani

Habitat fragmentation, often driven by human activities, alters ecological landscapes by disrupting connectivity and reshaping species interactions. In such fragmented environments, habitats can be modeled as networks, where individuals disperse across interconnected patches. We consider an intraspecific competition model, where individuals compete for space

Xing Gao, Dazhong Rong, Qinming He

Investigating the mapping between visual stimuli and neural responses in the visual cortex contributes to a deeper understanding of biological visual processing mechanisms. Most existing studies characterize this mapping by training models to directly encode visual stimuli into neural responses or decode neural responses into visual stimuli. However, due to

Seokcheon Lee

Recent analyses of the Master Type Ia supernova (SN Ia) sample have revealed a mild redshift dependence in the inferred local Hubble parameter, often expressed as tilde{H}_0(z) = H_0 (1+z)^{-\alpha}, where \alpha quantifies possible departures from the standard cosmological time dilation relation. In this work, we show that such an empirical scaling can be i

Hang Yuan

For an open-closed homotopy algebra (OCHA), the previous work indicates that there is an open-closed version of Hochschild cohomology with a canonical Gerstenhaber algebra structure. If this OCHA is further cyclic and unital in the sense of Kajiura and Stasheff, we produce a BV algebra structure on this cohomology via a cochain-level identity formulated with

Hyungjong Na, Wonho Song, Seungyong Han, Donghyeon Jo

This study introduces the Korean Tax Avoidance Panel (KoTaP), a long-term panel dataset of non-financial firms listed on KOSPI and KOSDAQ between 2011 and 2024. After excluding financial firms, firms with non-December fiscal year ends, capital impairment, and negative pre-tax income, the final dataset consists of 12,653 firm-year observations from 1,754 firm

Yuanning Cui, Zequn Sun, Wei Hu, Zhangjie Fu

Large language models (LLMs) excel at reasoning but struggle with knowledge-intensive questions due to limited context and parametric knowledge. However, existing methods that rely on finetuned LLMs or GNN retrievers are limited by dataset-specific tuning and scalability on large or unseen graphs. We propose the LLM-KGFR collaborative framework, where an LLM

Yang Xu, Peiyao Liu, Shuwei Chen, Jun Liu

Currently, there is a lack of rigorous theoretical system for systematically generating non-trivial and logically valid theorems. Addressing this critical gap, this paper conducts research to propose a novel automated theorem generation theory and tool. Based on the concept of standard contradiction which possesses unique deductive advantages, this paper def

Michael Poon, Marta L. Bryan, Hanno Rein, Jiayin Dong

This decade has seen the first measurements of extrasolar planetary obliquities, characterizing how an exoplanet's spin axis is oriented relative to its orbital axis. These measurements are enabled by combining projected rotational velocities, planetary rotation periods, and astrometric orbits for directly-imaged super-Jupiters. This approach constrains both

Brigitte A. Mora-Reyes, Jennifer A. Drewyor, Abel A. Reyes-Angulo

Artificial intelligence (AI) systems often reflect biases from economically advanced regions, marginalizing contexts in economically developing regions like Latin America due to imbalanced datasets. This paper examines AI representations of diverse Latin American contexts, revealing disparities between data from economically advanced and developing regions.

Yuanjie Liu, Wenpeng Xing, Ye Zhou, Gaowei Chang

The absence of a fully decentralized, verifiable, and privacy-preserving communication protocol for autonomous agents remains a core challenge in decentralized computing. Existing systems often rely on centralized intermediaries, which reintroduce trust bottlenecks, or lack decentralized identity-resolution mechanisms, limiting persistence and cross-network

Ken Yamamoto

This study analyzes pass networks in football (soccer) using a stochastic model known as the P\'olya urn. By focusing on preferential selection, it theoretically demonstrates that the time evolution of networks can be characterized by a single parameter. Building on this result, a data analysis method is proposed and applied to a large-scale public dataset o

Pranav Joshi, Daniel McMorrow, Yihan Zhang, Amitalok J. Budkuley

Given a channel with length-$n$ inputs and outputs over the alphabet $\{0,1,\ldots,q-1\}$, and of which a fraction $\varrho \in (0,1-1/q)$ of symbols can be arbitrarily corrupted by an adversary, a fundamental problem is that of communicating at rates close to the information-theoretically optimal values, while ensuring the receiver can infer that the transm

Ge Zhang, Rohan Deepak Ajwani, Yaochen Hu, Tony Zheng

Finding relevant products given a user query is pivotal to an e-commerce platform, as it can drive shopping behavior and generate revenue. The challenge lies in accurately predicting the correlation between queries and products. Recently, mining commonsense knowledge between queries and products using Large Language Models (LLMs) has shown promising results

Qingfeng Chen, Haojin Zeng, Jingyi Jie, Shichao Zhang

With the rapid growth of graph-structured data in critical domains, unsupervised graph-level anomaly detection (UGAD) has become a pivotal task. UGAD seeks to identify entire graphs that deviate from normal behavioral patterns. However, most Graph Neural Network (GNN) approaches implicitly assume that the training set is clean, containing only normal graphs,

Harrison Hartle, P. L. Krapivsky

Leaves, i.e., vertices of degree one, can play a significant role in graph structure, especially in sparsely connected settings in which leaves often constitute the largest fraction of vertices. We consider a leaf-based counterpart of the degree, namely, the leaf degree -- the number of leaves a vertex is connected to -- and the associated leaf degree distri

Nishchal Sapkota, Haoyan Shi, Yejia Zhang, Xianshi Ma

Medical image segmentation is critical for accurate diagnostics and treatment planning, but remains challenging due to complex anatomical structures and limited annotated training data. CNN-based segmentation methods excel at local feature extraction, but struggle with modeling long-range dependencies. Transformers, on the other hand, capture global context

Abu Hanif Muhammad Syarubany

We study CT image denoising in the unpaired and self-supervised regimes by evaluating two strong, training-data-efficient paradigms: a CycleGAN-based residual translator and a Noise2Score (N2S) score-matching denoiser. Under a common evaluation protocol, a configuration sweep identifies a simple standard U-Net backbone within CycleGAN (lambda_cycle = 30, lam

M Sadik Batcha, S Roselin Jahina, Muneer Ahmad

DESIDOC Journal of Library & Information Technology (DJLIT) formerly known as DESIDOC Bulletin of Information Technology is a peer-reviewed, open access, bimonthly journal. This paper presents a Scientometric analysis of the DESIDOC Journal. The paper analyses the pattern of growth of the research output published in the journal, pattern of authorship, autho

Kyrie Zhixuan Zhou, Justin Eric Chen, Xiang Zheng, Yaoyao Qian

Preprinting has become a norm in fast-paced computing fields such as artificial intelligence (AI) and human-computer interaction (HCI). In this paper, we conducted semistructured interviews with 15 academics in these fields to reveal their motivations and perceptions of preprinting. The results found a close relationship between preprinting and characteristi

Eva Prakash, Maayane Attias, Pierre Chambon, Justin Xu

Objective: To enhance automated de-identification of radiology reports by scaling transformer-based models through extensive training datasets and benchmarking performance against commercial cloud vendor systems for protected health information (PHI) detection. Materials and Methods: In this retrospective study, we built upon a state-of-the-art, transformer-

Zehui Feng, Chenqi Zhang, Mingru Wang, Minuo Wei

Unveiling visual semantics from neural signals such as EEG, MEG, and fMRI remains a fundamental challenge due to subject variability and the entangled nature of visual features. Existing approaches primarily align neural activity directly with visual embeddings, but visual-only representations often fail to capture latent semantic dimensions, limiting interp

Špela Vintar, Jan Jona Javoršek

Human word associations are a well-known method of gaining insight into the internal mental lexicon, but the responses spontaneously offered by human participants to word cues are not always predictable as they may be influenced by personal experience, emotions or individual cognitive styles. The ability to form associative links between seemingly unrelated

Hao Li, Haotian Chen, Ruoyuan Gong, Juanjuan Wang

Redistricting plays a central role in shaping how votes are translated into political power. While existing computational methods primarily aim to generate large ensembles of legally valid districting plans, they often neglect the strategic dynamics involved in the selection process. This oversight creates opportunities for partisan actors to cherry-pick map

Muneer Ahmad, Samuel Ibor Ubi

This paper presents a scientometric analysis of research output from the University of Lagos, focusing on the two decades spanning 2004 to 2023. Using bibliometric data retrieved from the Web of Science, we examine trends in publication volume, collaboration patterns, citation impact, and the most prolific authors, departments, and research domains at the un

Ananya Sutradhar, Suryansh Gupta, Ravishankar Krishnaswamy, Haiyang Xu

Filtered Approximate Nearest Neighbor (ANN) search retrieves the closest vectors for a query vector from a dataset. It enforces that a specified set of discrete labels $S$ for the query must be included in the labels of each retrieved vector. Existing graph-based methods typically incorporate filter awareness by assigning fixed penalties or prioritizing node

Xinying Qian, Ying Zhang, Yu Zhao, Baohang Zhou

Temporal Knowledge Graph Question Answering (TKGQA) aims to answer time-sensitive questions by leveraging factual information from Temporal Knowledge Graphs (TKGs). While previous studies have employed pre-trained TKG embeddings or graph neural networks to inject temporal knowledge, they fail to fully understand the complex semantic information of time const

Fatemeh Hosseinabadi, Seyedhassan Sharifi

Accurate segmentation of the left atrium (LA) from cardiac MRI is critical for guiding atrial fibrillation (AF) ablation and constructing biophysical cardiac models. Manual delineation is time-consuming, observer-dependent, and impractical for large-scale or time-sensitive clinical workflows. Deep learning methods, particularly convolutional architectures, h

Fatemeh Hosseinabadi, Seyedhassan Sharifi

Pediatric appendicitis remains one of the most common causes of acute abdominal pain in children, and its diagnosis continues to challenge clinicians due to overlapping symptoms and variable imaging quality. This study aims to develop and evaluate a deep learning model based on a pretrained ResNet architecture for automated detection of appendicitis from ult

Lei Zhang, Jinhai Zhang, Han Yan, Xian Chen

The Moon has been long regarded as a natural resonator of gravitational waves (GWs) since 1960, showing great potential to fill the frequency gap left behind GW detections by ground- or space-based laser interferometry. However, the spatial variation of this amplification capacity on the Moon remains unclear. Here, we numerically simulate the lunar response

Xiaonan Liu

The rainbow Tur{\'a}n number of a fixed graph $H$, denoted by ${\text{ex}}^*(n,H)$, is the maximum number of edges in an $n$-vertex graph such that it admits a proper edge coloring with no rainbow $H$. We study this problem in planar setting. The rainbow planar Tur{\'a}n number of a graph $H$, denoted by ${\text{ex}_{\mathcal{P}}}^*(n,H)$, is the maximum num

Zhengran Zeng, Yixin Li, Rui Xie, Wei Ye

The development of LLM-based autonomous agents for end-to-end software development represents a significant paradigm shift in software engineering. However, the scientific evaluation of these systems is hampered by significant challenges, including overly simplistic benchmarks and the difficulty of conducting fair comparisons between different agent architec

Yuantian Shao, Yuanteng Chen, Peisong Wang, Jianlin Yu

Quantization plays a crucial role in accelerating the inference of large-scale models, and rotational matrices have been shown to effectively improve quantization performance by smoothing outliers. However, end-to-end fine-tuning of rotational optimization algorithms incurs high computational costs and is prone to overfitting. To address this challenge, we p

Beichen Zheng, Ying Chen, Lili Wen, Xiaofei Wu

This paper presents an enhanced version of the subgroup method for resonance self-shielding treatment, termed the robust subgroup method, which integrates Robust Estimation (RE) with a Differential Evolution (DE) algorithm. The RE approach is employed to handle model misspecification and data contamination, while the DE algorithm serves as an optimization to

Denuwan Vithanage, Skyler Wright, Edith Luveina-Joseph, Christopher Larson

We numerically study the fast spatial transport of a trapped Bose-Einstein condensate (BEC) using shortcuts-to-adiabaticity (STA) by counterdiabatic driving (CD). The trapping potential and the required auxiliary potential were simulated as painted potentials. We compared STA transport to transport that follows a constant-acceleration scheme (CA). Experiment

Debraj Chandra, Nur Alam, Dipika Roy

A space $X$ is M-separable (selectively separable) (Scheepers, 1999; Bella et al., 2009) if for every sequence $(Y_n)$ of dense subspaces of $X$ there exists a sequence $(F_n)$ such that for each $n$ $F_n$ is a finite subset of $Y_n$ and $\cup_{n\in \mathbb{N}} F_n$ is dense in $X$. In this paper, we introduce and study a strengthening of M-separability situ

Anastasia Bednyakova, Artem Gemuzov, Mikhail Mishevsky, Karina Saraeva

A neural network model based on the Transformer architecture has been developed to predict the nonlinear evolution of optical pulses in Er-doped fiber amplifier under conditions of limited experimental data. To address data scarcity, a two-stage training strategy is employed. In the first stage, the model is pretrained on a synthetic dataset generated throug

Andreas Tataris, Alexander V. Mamonov

Recently, reduced order modeling methods have been applied to solving inverse boundary value problems arising in frequency domain scattering theory. A key step in projection-based reduced order model methods is the use of a sesquilinear form associated with the forward boundary value problem. However, in contrast to scattering problems posed in $\mathbb{R}^d

Chenlei Xu, Tianhao Su, Jie Xiong, Yue Wu

Accurate determination of crystal structures is central to materials science, underpinning the understanding of composition-structure-property relationships and the discovery of new materials. Powder X-ray diffraction is a key technique in this pursuit due to its versatility and reliability. However, current analysis pipelines still rely heavily on expert kn

Hirohane Takagi, Gouki Minegishi, Shota Kizawa, Issey Sukeda

Although behavioral studies have documented numerical reasoning errors in large language models (LLMs), the underlying representational mechanisms remain unclear. We hypothesize that numerical attributes occupy shared latent subspaces and investigate two questions:(1) How do LLMs internally integrate multiple numerical attributes of a single entity? (2)How d

Kyongsik Yun, David Bayard, Gerik Kubiak, Austin Owens

Future planetary exploration missions demand high-performance, fault-tolerant computing to enable autonomous Guidance, Navigation, and Control (GNC) and Lander Vision System (LVS) operations during Entry, Descent, and Landing (EDL). This paper evaluates the deployment of GNC and LVS algorithms on next-generation multi-core processors--HPSC, Snapdragon VOXL2,

Zenan Chen, Ruijiang Gao, Yingzhi Liang

Effective human-AI collaboration requires humans to accurately gauge AI capabilities and calibrate their trust accordingly. Humans often have context-dependent private information, referred to as Unique Human Knowledge (UHK), that is crucial for deciding whether to accept or override AI's recommendations. We examine how displaying AI reasoning affects trust

Debraj Chandra, Nur Alam, Dipika Roy

A space $X$ is H-separable (Bella et al., 2009) if for every sequence $(Y_n)$ of dense subspaces of $X$ there exists a sequence $(F_n)$ such that for each $n$ $F_n$ is a finite subset of $Y_n$ and every nonempty open set of $X$ intersects $F_n$ for all but finitely many $n$. In this paper, we introduce and study an ideal variant of H-separability, called $\m

Ayaan Bedi, Karoliina Lehtinen

We study explorability, a measure of nondeterminism in pushdown automata, which generalises history-determinism. An automaton is k-explorable if, while reading the input, it suffices to follow k concurrent runs, built step-by-step based only on the input seen so far, to construct an accepting one, if it exists. We show that the class of explorable PDAs lies

Guoming Zhang

We consider the Kato problem and extensions for degenerate elliptic operators of arbitrary order $2m$ ($m\geq 1$), whose coefficients are measurable, complex-valued and satisfy the G$\mathring{a}$rding inequality with respect to a Muckenhoupt $A_{2}$-weight; this generalizes the work of [Cruz-Uribe, Martell and Rios 2018]. As an application, the unweighted $

Ankush, Sangeeta Dey, Rishu Verma, Manoj Kumar

In this work, we investigate a predictive class of neutrino mass matrices characterized by one texture zero and one vanishing sub-trace within the framework of the scotogenic model, wherein neutrino masses, dark matter, and neutrinoless double beta decay are intrinsically correlated. We analyze twelve viable texture structures -- namely $B_{1,4,5}$, $C_{1,2,

Nareg Ghazikhanian, David J. Alspaugh, Pavel Salev, Lorenzo Fratino

Insulator-to-metal transition materials are highly sensitive to even minute deviations of stoichiometry, lattice defects, and disorder, which provides opportunities to engineer their electrical switching characteristics. Using V2O3 as a prototypical metal-insulator transition resistive switching material, we demonstrate that localized focused ion beam irradi

Kailun Ji, Xiaoyu Hu, Xinyu Zhang, Jun Chen

Large-scale disaster Search And Rescue (SAR) operations are persistently challenged by complex terrain and disrupted communications. While Unmanned Aerial Vehicle (UAV) swarms offer a promising solution for tasks like wide-area search and supply delivery, yet their effective coordination places a significant cognitive burden on human operators. The core huma

Lei Li, Jian-Guo Liu, Yuliang Wang

We study the implicit Langevin Monte Carlo (iLMC) method, which simulates the overdamped Langevin equation via an implicit iteration rule. In many applications, iLMC is favored over other explicit schemes such as the (explicit) Langevin Monte Carlo (LMC). LMC may blow up when the drift field $\nabla U$ is not globally Lipschitz, while iLMC has convergence gu

Xiaoling Luo, Peng Chen, Chengliang Liu, Xiaopeng Jin

Multimodal protein features play a crucial role in protein function prediction. However, these features encompass a wide range of information, ranging from structural data and sequence features to protein attributes and interaction networks, making it challenging to decipher their complex interconnections. In this work, we propose a multimodal protein functi

Bobo Hua, Lili Wang

In this paper, we introduce isocapacitary constants for the $p$-Laplacian on graphs and apply them to derive estimates for the first eigenvalues of the Dirichlet $p$-Laplacian, the Neumann $p$-Laplacian, and the $p$-Steklov problem.

Debraj Chandra, Subhankar Das, Nur Alam

We study selection principles related to bornological covers in a topological space $X$ following the work of Aurichi et al., 2019, where selection principles have been investigated in the function space $C_\mathfrak{B}(X)$ endowed with the topology $\tau_\mathfrak{B}$ of uniform convergence on bornology $\mathfrak{B}$. We show equivalences among certain sel

Arfina Rahman, Mahesh Banavar

Photoplethysmography (PPG) signals, which measure changes in blood volume in the skin using light, have recently gained attention in biometric authentication because of their non-invasive acquisition, inherent liveness detection, and suitability for low-cost wearable devices. However, PPG signal quality is challenged by motion artifacts, illumination changes

Yue Jiet Chong, Yimin Wang, Zhen Wu, Xuanyao Fong

This paper presents a 3D-stacked chiplets based large language model (LLM) inference accelerator, consisting of non-volatile in-memory-computing processing elements (PEs) and Inter-PE Computational Network (IPCN), interconnected via silicon photonic to effectively address the communication bottlenecks. A LLM mapping scheme was developed to optimize hardware

Dongji Gao, Chenda Liao, Changliang Liu, Matthew Wiesner

The Recurrent Neural Network-Transducer (RNN-T) is widely adopted in end-to-end (E2E) automatic speech recognition (ASR) tasks but depends heavily on large-scale, high-quality annotated data, which are often costly and difficult to obtain. To mitigate this reliance, we propose a Weakly Supervised Transducer (WST), which integrates a flexible training graph d

Nilanjan Chaudhuri, Sourav Duari, Pradip Roy, Sourav Sarkar

We evaluate the thermal photon emission rate from a chirally asymmetric quark gluon plasma using the Hard Thermal Loop approximation. The quasiparticle and plasmino modes prevalent at finite temperature split into L and R-modes in the presence of chiral imbalance and are found to disperse differently acquiring different thermal masses. The soft contribution

Ashly Joseph, Albin Joseph, Christina Terese Joseph, John Paul Martin

We present CosmicANNEstimator (Cosmological Parameters Artificial Neural Network Estimator), a machine learning approach for constraining cosmological parameters within the Lambda Cold Dark Matter ($\Lambda$CDM) framework. Our methodology employs two specialized artificial neural networks (ANNs) designed to analyze Hubble parameter and Supernova data indepen

Divya Pathak, Harshit Kumar, Anuska Roy, Felix George

Multi-Agentic AI systems, powered by large language models (LLMs), are inherently non-deterministic and prone to silent failures such as drift, cycles, and missing details in outputs, which are difficult to detect. We introduce the task of anomaly detection in agentic trajectories to identify these failures and present a dataset curation pipeline that captur

Ashok Yacham, Tarak K. Patra, Jithin John Varghese

Understanding how gas mixtures diffuse and distribute within porous frameworks is central to designing advanced separation and storage materials. Here, we investigate the transport and spatial distribution of binary gas mixtures in a porous metal organic framework, viz., ZIF-90, using molecular simulations. We perform grand canonical Monte Carlo (GCMC) simul

Mostafa Amini, Alireza Vahidi, Fatemeh Ghanavati

Let $S$ and $R$ be rings, $n, d\geq 0$ be two integers or $n=\infty$. In this paper, first we introduce special (faithfully) semidualizing bimodule $_S(K_{d-1})_R$, and then introduce and study the concepts of $K_{d-1}$-$(n,d)$-injective (resp. $K_{d-1}$-$(n,d)$-flat) modules as a common generalization of some known modules such as $C$-injective, $C$-weak in

Yihao Luo, Xianglong He, Chuanyu Pan, Yiwen Chen

Accurate and efficient voxelized representations of 3D meshes are the foundation of 3D reconstruction and generation. However, existing representations based on iso-surface heavily rely on water-tightening or rendering optimization, which inevitably compromise geometric fidelity. We propose Faithful Contouring, a sparse voxelized representation that supports

Qing-Feng Xue, Qi Zhang, Xu-Cai Zhuang, Yun-Jie Xia

The principle that heat spontaneously flows from higher temperature to lower temperature is a cornerstone of classical thermodynamics, often assumed to be independent of the sequence of interactions. While this holds true for macroscopic systems at equilibrium, here we show that, when the order of interactions between two identical thermalization channels is

Zi-Yan Yang, Wei Chen

In this work we investigate the decay and production properties of the strange double-charm pentaquark $P_{ccs}^{++}$ with strangeness $S=-1$. Building upon our previous work predicting its $J^P=1/2^-$ molecular configuration, we employ three-point QCD sum rules to calculate its strong decay widths and estimate its production branching ratio via $\Xi_{bc}^+$

M. Sharif, Madiha Ajmal

In this study, we explore the reconstruction of a new agegraphic dark energy model in a flat Friedmann-Robertson-Walker spacetime by $f(Q)$ gravity framework, where $Q$ represents non-metricity. We assume that the scale factor follows a power-law and explore how this model aligns with the expanding universe. In this perspective, we develop a new agegraphic $

Marina Buzzi, Barbara Leporini, Angelica Lo Duca

Accessible teaching has been extensively investigated in computer science, yet its integration into other disciplines, such as data literacy, remains limited. This paper examines the potential of data storytelling, defined as the integration of data, visualizations, and narrative, as a possible strategy for making complex information accessible to diverse le

Shiyin Lin

Static analysis is effective for discovering software vulnerabilities but notoriously suffers from incomplete source--sink specifications and excessive false positives (FPs). We present \textsc{AdaTaint}, an LLM-driven taint analysis framework that adaptively infers source/sink specifications and filters spurious alerts through neuro-symbolic reasoning. Unli

Justin Bennett

Transport networks must balance viscous pumping losses with the energetic cost of maintaining an operative architecture. This paper formulates that trade-off as an entropy-per-information-cost (EPIC) extremum that prices structural upkeep in calibrated units (joules per bit). An upkeep law r^m distinguishes volume-priced (m = 2) from surface-priced (m = 1) m

Sergio Demian Lerner, Ariel Futoransky

We present a new type of bidirectional payment channel based on One-Time Signatures on state sequence numbers. This new construction is simpler than the Poon-Dryja construction, but provides a number of benefits such as $O(1)$ storage per channel, minimal information leakage, and compatibility with Lightning Network routing.

Shiyin Lin

Large Language Models (LLMs) enhanced with retrieval -- commonly referred to as Retrieval-Augmented Generation (RAG) -- have demonstrated strong performance in knowledge-intensive tasks. However, RAG pipelines often fail when retrieved evidence is incomplete, leaving gaps in the reasoning process. In such cases, \emph{abductive inference} -- the process of g

Qiyang Pei, Lihu Xu

We consider the Euler--Maruyama (EM) scheme of a family of dissipative SDEs, whose step sizes $η_{1}\geη_{2}\ge \cdots$ are decreasing, and prove that the EM scheme weakly converges to a subordinated Brownian motion $\{B_{a(t)}\}_{0\le t\le 1}$ rather than $\{B_{t}\}_{0\le t\le 1}$, where $a(t)$ is an increasing function depending on $\{η_{k}\}_{k \ge 1}$, f

Mauricio Gutiérrez, Chiranjib Mukhopadhyay, Victor Montenegro, Abolfazl Bayat

For single-parameter sensing, Greenberger-Horne-Zeilinger (GHZ) probes achieve optimal quantum-enhanced precision across the unknown parameter range, solely relying on parameter-independent separable measurement strategies for all values of the unknown parameter. However, in the multiparameter setting, a single GHZ probe not only fails to achieve quantum adv

Wenxiang Ying, Abraham Nitzan

With the rapid development of nanophotonics and cavity quantum electrodynamics, there has been growing interest in how confined electromagnetic fields modify fundamental molecular processes such as electron transfer. In this paper, we revisit the problem of nonadiabatic electron transfer (ET) in confined electromagnetic fields studied in [J. Chem. Phys. 150,

Mahmoud Soliman, Islam Osman, Mohamed S. Shehata, Rasika Rajapakshe

The performance of vision models in medical imaging is often hindered by the prevailing paradigm of fine-tuning backbones pre-trained on out-of-domain natural images. To address this fundamental domain gap, we propose MedDChest, a new foundational Vision Transformer (ViT) model optimized specifically for thoracic imaging. We pre-trained MedDChest from scratc

Haotian Zhang, Shijian Gao, Xiang Cheng

The massive scale of Wireless Foundation Models (FMs) hinders their real-time deployment on edge devices. This letter moves beyond standard knowledge distillation by introducing a novel Multi-Component Adaptive Knowledge Distillation (MCAKD) framework. Key innovations include a Cross-Attention-Based Knowledge Selection (CA-KS) module that selectively identif

Cheng-Rong Xie, Hiroki Tsuchiura, Manfred Sigrist

We study the Josephson diode effect realized purely by geometry in a Kitaev-ladder Josephson junction composed of two parallel spinless $p$-wave chains coupled by an interleg hopping $t_\perp$. The junction is governed by two phases: the superconducting phase difference across the weak link, $\theta$, and the leg-to-leg phase difference, $\phi$. For $\phi\no

Hao Zhu, Jia Li, Cuiyun Gao, Jiaru Qian

Large language models (LLMs) have achieved remarkable progress in code understanding tasks. However, they demonstrate limited performance in vulnerability detection and struggle to distinguish vulnerable code from patched code. We argue that LLMs lack understanding of security specifications -- the expectations about how code should behave to remain safe. Wh

Binbin Nie, Yuanlei Wang, Du Qian, Yiwen Yang

Soliton microcombs are evolving towards octave-spanning for $f$-$2f$ self-referencing and expanding applications in spectroscopy and timekeeping. As spectra broaden and pulses shorten, the Raman-induced soliton self-frequency shift (SSFS) becomes a principal limitation: it reduces pump-to-comb conversion efficiency, constrains achievable span, and can, in ex

Yongxi Chen, Lei Chen

Design-to-code generation has emerged as a promising approach to bridge the gap between design prototypes and deployable frontend code. However, existing methods often suffer from structural inconsistencies, asset misalignment, and limited production readiness. This paper presents PSD2Code, a novel multi-modal approach that leverages PSD file parsing and ass

Higo T. P. Da Silva, Hugerles S. Silva, Felipe A. P. Figueiredo, Andre A. Dos Anjos

The proliferation of sixth-generation (6G) networks and the massive Internet of Things (IoT) demand wireless communication technologies that are ultra-low-power, secure, and covert. Noise-based communication has emerged as a transformative paradigm that meets these demands by encoding information directly into the statistical properties of noise, rather than

Tian-Yong Cao, Shu-Xu Yi

According to General Relativity (GR), gravitational waves (GWs) should travel at the speed of light $c$. However, some theories beyond GR predict deviations of the velocity of GWs $c_{\rm gw}$ from $c$, and some of those expect vacuum dispersion. Therefore, probing the propagation effects of GWs by comparing the wave format detectors against the one at emiss

Chenzui Li, Yiming Chen, Xi Wu, Giacinto Barresi

This paper introduces an upper limb postural optimization method for enhancing physical ergonomics and force manipulability during bimanual human-robot co-carrying tasks. Existing research typically emphasizes human safety or manipulative efficiency, whereas our proposed method uniquely integrates both aspects to strengthen collaboration across diverse condi

Mahmoud Soliman, Omar Abdelaziz, Ahmed Radwan, Anand

Domain generalization (DG) seeks robust Vision Transformer (ViT) performance on unseen domains. Efficiently adapting pretrained ViTs for DG is challenging; standard fine-tuning is costly and can impair generalization. We propose GNN-MoE, enhancing Parameter-Efficient Fine-Tuning (PEFT) for DG with a Mixture-of-Experts (MoE) framework using efficient Kronecke

Zhiming Shuai, Xiangdong Zhang, Gui-Rong Liang

We numerically investigate the superradiant amplification effect of a charged scalar filed in the scattering experiment and the black hole bomb model in a charged black-bounce spacetime. Due to the shallowing effect on the effective potential by the introduced quantum parameter $\l$, superradiance in both the above cases are verified to be weakened. In a sca

Jonah S. Gannon, Arianna Di Cintio, Duncan A. Forbes, Guacimara García-Bethencourt

In this work, we compare galaxies from the NIHAO and HESTIA simulation suites to ultra-diffuse galaxies (UDGs) with spectroscopically measured dynamical masses. For each observed UDG, we identify the simulated dark matter halo that best matches its dynamical mass. In general, observed UDGs are matched to simulated galaxies with lower stellar masses than they

Hukmashabiyya Ariq Gumilar, Al Azhary Masta, Siti Fatimah

The Orlicz-Morrey spaces, which were introduced through the research of Nakai in 2006, are a generalization and combination of Orlicz and Morrey spaces. There are two types of Orlicz-Morrey spaces, such as continuous Orlicz-Morrey spaces and discrete Orlicz-Morrey spaces. Some properties that apply to Orlicz-Morrey spaces have been studied correspondingly to

Jiangtao Li

In this note, we introduce a new curvature condition called the $2-$positive bisectional curvature on compact K\"{a}hler manifolds. We then deduce a characterization theorem for manifolds with $2-$positive bisectional curvature, which can be regarded as a variant of the classical Frankel conjecture (cf.\cite{Fra61,SY80}) and its generalizations (cf.\cite{Siu

Mingyu Sung, Vikas Palakonda, Suhwan Im, Sunghwan Moon

Large language models (LLMs) have achieved near-human performance across diverse reasoning tasks, yet their deployment on resource-constrained Internet-of-Things (IoT) devices remains impractical due to massive parameter footprints and memory-intensive autoregressive decoding. While split computing offers a promising solution by partitioning model execution

J. Nathan Kutz, Peter Battaglia, Michael Brenner, Kevin Carlberg

Machine learning (ML) and artificial intelligence (AI) algorithms are transforming and empowering the characterization and control of dynamic systems in the engineering, physical, and biological sciences. These emerging modeling paradigms require comparative metrics to evaluate a diverse set of scientific objectives, including forecasting, state reconstructi

Kyaw Hpone Myint, Zhe Wu, Alexandre G. R. Day, Giri Iyengar

Decision trees are widely used in high-stakes fields like finance and healthcare due to their interpretability. This work introduces an efficient, scalable method for generating synthetic pre-training data to enable meta-learning of decision trees. Our approach samples near-optimal decision trees synthetically, creating large-scale, realistic datasets. Using

Han-Jin Noh, En-Jin Cho, Byeong-Gyu Park, Hyowon Park

We report ARPES studies combined with DFT+DMFT calculations to confirm that the magnetic ordering vector transition from \textbf{Q}=(1/2,0,0) to \textbf{Q}=(1/3,0,0) in the metallic triangular antiferromagnets Co$_{1/3\pm\epsilon}$TaS$_2$ ($\epsilon\approx$0.007) is induced by the electronic structure change in the system. The ARPES-measured Fermi surface (F

Peiyao Wang, Weining Wang, Qi Li

Recent advances in text-to-video generation have achieved impressive perceptual quality, yet generated content often violates fundamental principles of physical plausibility - manifesting as implausible object dynamics, incoherent interactions, and unrealistic motion patterns. Such failures hinder the deployment of video generation models in embodied AI, rob

Yushi Wang, Changsheng Luo, Penghui Chen, Jianran Liu

Humanoid soccer poses a representative challenge for embodied intelligence, requiring robots to coordinate agile locomotion with unreliable visual perception in dynamic environments. However, existing systems typically rely on modular pipelines that separate perception from control or assume ideal sensing, making it difficult to achieve coherent and reactive

Shiyin Lin

Software fuzzing has become a cornerstone in automated vulnerability discovery, yet existing mutation strategies often lack semantic awareness, leading to redundant test cases and slow exploration of deep program states. In this work, I present a hybrid fuzzing framework that integrates static and dynamic analysis with Large Language Model (LLM)-guided input

Mahek Desai, Apoorva Rumale, Marjan Asadinia

As DRAM and other transistor-based memory technologies approach their scalability limits, alternative storage solutions like Phase-Change Memory (PCM) are gaining attention for their scalability, fast access times, and zero leakage power. However, current memory-intensive algorithms, especially those used in big data systems, often overlook PCM's endurance l

Berk Iskar, Michael Taynnan Barros

Network anomaly detection systems encounter several challenges with traditional detectors trained offline. They become susceptible to concept drift and new threats such as zero-day or polymorphic attacks. To address this limitation, we propose a Ca$^{2+}$-modulated learning framework that draws inspiration from astrocytic Ca$^{2+}$ signaling in the brain, wh