December 2025 arXiv papers — page 31

Jianmin Chen, Huiyuan Wang, Thomas Lumley, Xiaowu Dai

High-quality labeled data are essential for reliable statistical inference, but are often limited by validation costs. While surrogate labels provide cost-effective alternatives, their noise can introduce non-negligible bias. To address this challenge, we propose the surrogate-powered inference (SPI) toolbox, a unified framework that leverages both the valid

Nathan M. Dunfield, Sherry Gong

There are 352.2 million prime knots in the 3-sphere with at most 19 crossings. We study which of these knots are slice, in both the smooth and topological categories. While no algorithm is known for deciding whether a given knot is slice in either setting, we are able to determine it smoothly for all but about 11,400 knots (0.003% or 1 in 30,000) and topolog

Yilong Ma, Yamin Xiao

This paper studies the orbital stability of solitary waves for the following Schr\"{o}dinger-Boussinesq system \begin{equation*} \begin{cases} { \begin{array}{ll} i\varepsilon_t+\varepsilon_{xx}=n\varepsilon+\gamma |\varepsilon|^2\varepsilon, \\ n_{tt}-n_{xx}+ \alpha n_{xxxx}-\beta(n^2)_{xx}=|\varepsilon|^2_{xx}, \end{array} } (t,x)\in \mathbb{R}^2. \end{cas

Guangxi Fan, Tianliang Ma, Xuguang Sun, Xun Wang

With the continued scaling of advanced technology nodes, the design-technology co-optimization (DTCO) paradigm has become increasingly critical, rendering efficient device design and optimization essential. In the domain of TCAD simulation, however, the scarcity of open-source resources hinders language models from generating valid TCAD code. To overcome thi

Siddhartha Srinivas Rentala

This study investigates the efficacy of Conditional Restricted Boltzmann Machines (CRBMs) for modeling high-dimensional financial time series and detecting systemic risk regimes. We extend the classical application of static Restricted Boltzmann Machines (RBMs) by incorporating autoregressive conditioning and utilizing Persistent Contrastive Divergence (PCD)

Judah Immanuel, Avik Mahata, Aniruddha Maiti

We present a graph neural network (GNN) based surrogate framework for molecular dynamics simulations that directly predicts atomic displacements and learns the underlying evolution operator of an atomistic system. Unlike conventional molecular dynamics, which relies on repeated force evaluations and numerical time integration, the proposed surrogate model pr

Lingyun Qiu, Shenwen Yu

We establish quantitative global stability estimates, formulated in terms of optimal transport (OT) cost, for inverse point-source problems governed by elliptic and parabolic equations with spatially varying coefficients. The key idea is that the Kantorovich dual potential can be represented as a boundary functional of suitable adjoint solutions, thereby lin

Jun-Hao Chen, Ming-Kai Hung, Yun-Cheng Tsai, Samuel Yen-Chi Chen

We compare two quantum sequence models, QLSTM and QFWP, under an Equal Parameter Count (EPC) and adjoint differentiation setup on daily EUR USD forecasting as a controlled one dimensional time series case study. Across 10 random seeds and batch sizes from 4 to 64, we measure component wise runtimes including train forward, backward, full train, and inference

Yen-Ku Liu, Yun-Cheng Tsai, Samuel Yen-Chi Chen

Traditional ETF stock selection methods and reinforcement learning models such as the Asynchronous Advantage Actor-Critic (A3C) often suffer from high-dimensional feature spaces and overfitting when applied to complex financial markets. Moreover, static clustering algorithms fail to capture evolving market regimes, as the cluster with higher returns in one p

Brian Bowers, Smita Khapre, Jugal Kalita

Agentic AI and Multi-Agent Systems are poised to dominate industry and society imminently. Powered by goal-driven autonomy, they represent a powerful form of generative AI, marking a transition from reactive content generation into proactive multitasking capabilities. As an exemplar, we propose an architecture of a multi-agent system for the implementation p

Hong Su

Intelligent IoT systems increasingly rely on large language models (LLMs) to generate task-execution methods for dynamic environments. However, existing approaches lack the ability to systematically produce new methods when facing previously unseen situations, and they often depend on fixed, device-specific logic that cannot adapt to changing environmental c

Lucas Burns, Sacha Greenfield, Justin Dressel

A continuously monitored quantum bit (qubit) exhibits competition between unitary Hamiltonian dynamics and non-unitary measurement-collapse dynamics, which for diffusive measurements form an enlarged transformation group equivalent to the Lorentz group of spacetime. We leverage this equivalence to develop a four-dimensional generalization of the three-dimens

Tianjiao Wang, Xiang Xu, Yue Zhao

This paper is concerned with an inverse random potential problem for the Schr\"odinger equation. The random potential is assumed to be a generalized Gaussian random function, whose covariance operator is a classical pseudo-differential operator. For the direct problem, the meromorphic continuation of the resolvent of the Schr\"odinger operator with rough pot

Vesal Ahsani, Babak Hossein Khalaj, Hamed Shah-Mansouri

In-cabin driver monitoring systems (DMS) must recognize distraction- and drowsiness-related behaviors with low latency under strict constraints on compute, power, and cost. We present a single-camera in-cabin driver behavior recognition system designed for deployment on two low-cost edge platforms: Raspberry Pi 5 (CPU-only) and the Google Coral development b

Nimra Akram, Atif Ahmad, Sean B Maynard

The Advanced Dynamic Security Learning (DSL) Process Model is an Industry 4.0 cybersecurity incident response architecture proposed in this paper. This model addresses proactive and reflective cybersecurity governance across complex cyber-physical systems by combining Argyris and Sch\"on's double-loop learning theory with Crossan's 4I organizational learning

James Saunderson

Let $x_1,x_2,\ldots,x_m$ be elements of a convex cone $K$ such that their sum, $e$, is in the relative interior of $K$. An $\epsilon$-sparsification of the sum involves taking a subset of the $x_i$ and reweighting them by positive scalars, so that the resulting sum is $\epsilon$-close to $e$, where error is measured in a relative sense with respect to the or

Qiaolin Qin, Jianchen Zhao, Heng Li, Weiyi Shang

Log parsing converts log messages into structured event templates, allowing for automated log analysis and reducing manual inspection effort. To select the most compatible parser for a specific system, multiple evaluation metrics are commonly used for performance comparisons. However, existing evaluation metrics heavily rely on labeled log data, which limits

Aalok Tiwari, Karamjyoti Panigrahi, Mrinmay Sahu, Sayan Bhattacharyya

Understanding lattice dynamics and structural transitions in vacancy-ordered double perovskites is crucial for developing lead-free optoelectronic materials, yet the role of dopants in modulatingthese properties remains poorly understood. We investigate the vibrational and optical properties of pristine and Antimony(Sb)-doped Cs$_2$TiCl$_6$ vacancy-ordered d

Vitthal Bhandari

Research has shown that while large language models (LLMs) can generate their responses based on cultural context, they are not perfect and tend to generalize across cultures. However, when evaluating the cultural bias of a language technology on any dataset, researchers may choose not to engage with stakeholders actually using that technology in real life,

Paul A. Pearce, Jared Heymann, Thomas Quella

We consider both unitary and nonunitary A-D-E minimal models on the cylinder with topological defects along the non-contractible cycle of the cylinder. We define the coset graph $A \otimes G/\mathbb{Z}_2$ and argue that it encodes not only the (i) coset graph fusion algebra, but also (ii) the Affleck-Ludwig boundary g-factors; (iii) the defect g-factors (qua

Vahideh Zolfaghari

Background Large language models (LLMs) are increasingly deployed in medical consultations, yet their safety under realistic user pressures remains understudied. Prior assessments focused on neutral conditions, overlooking vulnerabilities from anxious users challenging safeguards. This study evaluated LLM safety under parental anxiety-driven adversarial pres

Tim Ziemer, Simon Linke

Many documentaries on early house and techno music exist. Here, protagonists from the scenes describe key elements and events that affected the evolution of the music. In the research community, there is consensus that such descriptions have to be examined critically. Yet, there have not been attempts to validate such statements on the basis of audio analyse

Koyena Ghosh, Suchismita Banerjee, Urna Basu, Banasri Basu

Understanding pollutant meteorology interactions is essential for environmental risk assessment. This study develops an entropy-based statistical framework to analyze static and temporal dependencies between urban air pollutants and meteorological variables across multiple Indian cities. Dependence is quantified using complementary linear and nonlinear measu

Sunil Arora, John Hastings

Natural Language Processing (NLP) systems are increasingly used in sensitive domains such as healthcare, finance, and government, where they handle large volumes of personal and regulated data. However, these systems introduce distinct risks related to security, privacy, and regulatory compliance that are not fully addressed by existing AI governance framewo

Danilo Nikolić, Niklas L. Schulz, Matthias Eschrig

We present a set of necessary conditions for the appearance of charge and spin Josephson diode effects across strongly spin-polarized inhomogeneous magnetic materials (FM) placed between two spin-singlet superconductors. Noncoplanarity of the FM's spin texture gives rise to quantum geometric phases, $Δφ'$, that enter the Josephson current-phase relat

Pablo Benavent-Ocejo, Delfina Gómez, Maria-Eugenia Pérez-Martínez

We consider spectral problems for Laplace operator in 3D rod structures with a small cross section of diameter $O(\varepsilon)$, $\varepsilon$ being a positive parameter. The boundary conditions are Dirichlet (Neumann, respectively) on the bases of this structure and Neumann on the lateral boundary. As $\varepsilon\to 0$, we show the convergence of the spect

Adrien Busnot Laurent, Yunnan Li, Yunhe Sheng

In this paper, we introduce the notion of post-Hopf algebroids, generalizing the pre-Hopf algebroids introduced in [Bronasco, Laurent, 2025] in the study of exotic aromatic S-series. We construct action post-Hopf algebroids through actions of post-Hopf algebras. We show that the universal enveloping algebra of a post-Lie-Rinehart algebra (post-Lie algebroid)

Deniz Elbek, Kamer Kaya

Breadth-First Search (BFS) is a fundamental graph kernel that underpins a wide range of applications. While modern GPUs provide specialised Matrix-Multiply-Accumulate (MMA) units, e.g., Tensor Cores (TC), with extremely high throughput, they target dense operations, making it non-trivial to exploit them for irregular, unstructured graph computations. In part

Salome Hayes-Shuptar, Daniel Bhatti, Ana Belen Sainz, David Elkouss

Network nonlocality extends Bell nonlocality to settings with multiple independent sources and parties. Certifying it in quantum information processing tasks requires suitable witnesses. However, in contrast to local correlations, the set of network-local correlations is non-convex. This non-convexity makes certifying network nonlocality a highly non-trivial

Łukasz Baran, Luis G. MacDowell

Hypothesis: Seawater ice and frozen aqueous solutions in contact with air can exhibit a thin quasi-brine surface layer intruding between ice and vapor, but a detailed characterization of surface properties and its relation to three phase coexistence has been lacking. Using thermodynamic arguments we show how it is possible to characterize the surface layers

Tongqi Liang, Dong Bai, Zhongzhou Ren

Nucleon momentum distributions (NMDs) reveal essential information about Fermi motion and short-range correlations (SRCs). In extracting NMDs from inclusive electron scattering data, theoretical analyses, such as the scaling analysis, are typically employed. For complex nuclei, consistently treating the excitation energy of the residual system is a complicat

Cheng-Jie Wang, Fu-Quan Dou

Ergotropy--a key figure of merit for quantum battery (QB) performance--plays a crucial role. However, the dynamics and physical mechanisms governing ergotropy evolution remain open challenges. Here, we investigate the ergotropy of a general QB model and find that the charging process is accompanied by the variation and inversion of the energy level populatio

Ian B. Logue, Sandaruka Jayasooriya Arachchilage, Lance M. Griswold, Moses B. Gaither-Ganim

High-quality metallocene single crystals with a low density of impurities and high homogeneity were prepared using the physical vapor transport method. These crystals were then characterized using various spectroscopic tools and X-ray diffraction. Laser-induced breakdown spectroscopy confirmed the presence of metal ions in each freshly grown sample despite a

Jitender Singh

Using Newton polygons, a key factorization result for polynomials over discrete valuation domains is proved, which in particular yields new irreducibility criteria including a generalization of the classical irreducibility criterion of Dumas.

Xian-Peng Zhang, Chuanchang Zeng, Zhen-Biao Yang, Jose Carlos Egues

Nondemolition protocols use ancilla qubits to identify the fragile quantum state of a qubit without destroying its encoded information, thus playing a crucial role in nondestructive quantum measurements particularly relevant for quantum error correction. However, the multitude of ancilla preparations, information transfers, and ancilla measurements in these

Hao Gao, Yumeng Zhu, Zhilong Yu, Yuhui Hu

Compact, lightweight, and energy-efficient cold atom systems are crucial for advancing quantum technologies, yet their realization remains constrained by the bulky optical and magnetic components required in current atom trapping architectures. Here, we demonstrate a low-SWaP magneto-optical trap that seamlessly integrates planar optical and magnetic compone

Douglas P. Wiens

Designs which are minimax in the presence of model misspecifications have been constructed so as to minimize the maximum, over classes of alternate response models, of the integrated mean squared error of the predicted values. This mean squared error decomposes into a term arising solely from variation, and a bias term arising from the model errors. Here we

Daniel Winney, Adam P. Szczepaniak

We provide a pedagogical introduction to Regge theory as it pertains to the study of hadrons and their interactions. We clarify the fundamental concepts of analyticity in the complex angular momentum plane and their implications for scattering amplitudes. We highlight historical developments that significantly shaped our understanding of scattering theory an

Rahul Gupta

This paper is about predicting the movement of stock consist of S&P 500 index. Historically there are many approaches have been tried using various methods to predict the stock movement and being used in the market currently for algorithm trading and alpha generating systems using traditional mathematical approaches [1, 2]. The success of artificial neural n

Ruochen Liu, Yi Tian, Jiahao Wang, Hongbin Liu

Cell detection in pathological images presents unique challenges due to densely packed objects, subtle inter-class differences, and severe background clutter. In this paper, we propose CellMamba, a lightweight and accurate one-stage detector tailored for fine-grained biomedical instance detection. Built upon a VSSD backbone, CellMamba integrates CellMamba Bl

Kensuke Yoshizawa

In this note, we study an obstacle problem for the elastic flow. We prove the local-in-time existence of weak solutions and discuss their relation to classical solutions when additional regularity is obtained. Related results concerning obstacle problems for the bending energy are also collected.

Krishna Chaitanya Sunkara, Rambabu Konakanchi

AI data centers which are GPU centric, have adopted liquid cooling to handle extreme heat loads, but coolant leaks result in substantial energy loss through unplanned shutdowns and extended repair periods. We present a proof-of-concept smart IoT monitoring system combining LSTM neural networks for probabilistic leak forecasting with Random Forest classifiers

Alexander Engström

The chromatic number $\chi$ of a graph is bounded from below by its clique number $\omega,$ but it can be arbitrary large. Perfect graphs are defined by $\chi=\omega$ for all induced subgraphs. An interesting relaxation are $\chi$-bounded graph classes, where $\chi\leq f(\omega).$ It is not always possible to achieve this with a polynomial $f.$ The edge idea

Hung-Nghiep Tran, Atsuhiro Takasu

In this paper, we present KG20C and KG20C-QA, two curated datasets for advancing question answering (QA) research on scholarly data. KG20C is a high-quality scholarly knowledge graph constructed from the Microsoft Academic Graph through targeted selection of venues, quality-based filtering, and schema definition. Although KG20C has been available online in n

Christophe D. Hounwanou, Yae Ulrich Gaba

Synthetic financial data provides a practical solution to the privacy, accessibility, and reproducibility challenges that often constrain empirical research in quantitative finance. This paper investigates the use of deep generative models, specifically Time-series Generative Adversarial Networks (TimeGAN) and Variational Autoencoders (VAEs) to generate real

Abu Hanif Muhammad Syarubany

This report studies diffusion posterior sampling (DPS) for single-image super-resolution (SISR) under a known degradation model. We implement a likelihood-guided sampling procedure that combines an unconditional diffusion prior with gradient-based conditioning to enforce measurement consistency for $4\times$ super-resolution with additive Gaussian noise. We

Hye-Young Jo, Ada Yi Zhao, Xiaoan Liu, Ryo Suzuki

We introduce Generative Lecture, a concept that makes existing lecture videos interactive through generative AI and AI clone instructors. By leveraging interactive avatars powered by HeyGen, ElevenLabs, and GPT-5, we embed an AI instructor into the video and augment the video content in response to students' questions. This allows students to personalize the

Soumik Chandra

The charm quark is formed almost exclusively during the initial stages of the collision, and a significant fraction of the charm quarks fragment into the $\mathrm{D}^{0}$ meson, the lightest open-charm hadron. We can gain insights into the interactions between the charm quark and the quark-gluon plasma (QGP) medium by studying the production and the flow of

Qiushi Han, David Simchi-Levi, Renfei Tan, Zishuo Zhao

We study a sequential mechanism design problem in which a principal seeks to elicit truthful reports from multiple rational agents while starting with no prior knowledge of agents' beliefs. We introduce Distributionally Robust Adaptive Mechanism (DRAM), a general framework combining insights from both mechanism design and online learning to jointly address t

Federico Bobbio, Randall A. Berry, Michael L. Honig, Thanh Nguyen

The radio spectrum suitable for commercial wireless services is limited. A portion of the radio spectrum has been reserved for institutions using it for non-commercial purposes such as federal agencies, defense, public safety bodies and scientific institutions. In order to operate efficiently, these incumbents need clean spectrum access. However, commercial

Hyam Omar Ali, Sahar Alhesseen, Lamis Elkhair, Adrian Galdran

Mycetoma is a neglected tropical disease caused by fungi or bacteria leading to severe tissue damage and disabilities. It affects poor and rural communities and presents medical challenges and socioeconomic burdens on patients and healthcare systems in endemic regions worldwide. Mycetoma diagnosis is a major challenge in mycetoma management, particularly in

Christophe D. Hounwanou, Yae Ulrich Gaba, Pierre Ntakirutimana

Data scarcity and confidentiality in finance often impede model development and robust testing. This paper presents a unified multi-criteria evaluation framework for synthetic financial data and applies it to three representative generative paradigms: the statistical ARIMA-GARCH baseline, Variational Autoencoders (VAEs), and Time-series Generative Adversaria

Ayoub Bahlaoui, Youness Zahidi, Ahmed Naddami

We investigate quantum tunneling of charge carriers through a periodic superlattice in twisted bilayer graphene (TBG) with rectangular potential barriers, including the presence of a defect, using a low-energy continuum model. Transmission probabilities are numerically analyzed depending on the parameters of the problem, highlighting the roles of twist angle

Ting-Hao 'Kenneth' Huang, Ryan A. Rossi, Sungchul Kim, Tong Yu

Between 2021 and 2025, the SciCap project grew from a small seed-funded idea at The Pennsylvania State University (Penn State) into one of the central efforts shaping the scientific figure-captioning landscape. Supported by a Penn State seed grant, Adobe, and the Alfred P. Sloan Foundation, what began as our attempt to test whether domain-specific training,

Jinqi Xiao, Qing Yan, Liming Jiang, Zichuan Liu

Parameter-Efficient Fine-Tuning of Diffusion Transformers (DiTs) for diverse, multi-conditional tasks often suffers from task interference when using monolithic adapters like LoRA. The Mixture of Low-rank Experts (MoLE) architecture offers a modular solution, but its potential is usually limited by routing policies that operate at a token level. Such local r

Abdullah Alabdullah, Lifeng Han, Chenghua Lin

Dialectal Arabic to Modern Standard Arabic (DA-MSA) translation is a challenging task in Machine Translation (MT) due to significant lexical, syntactic, and semantic divergences between Arabic dialects and MSA. Existing automatic evaluation metrics and general-purpose human evaluation frameworks struggle to capture dialect-specific MT errors, hindering progr

Aicha Boutorh, Kamar Hibatallah Baghdadi, Anais Daoud

Genomic prediction of drug resistance in Mycobacterium tuberculosis is often hindered by complex epistatic interactions and variable sequencing quality. We present the Interpretable Variant-Aware Multi-Path Network (VAMP-Net), a novel architecture addressing these challenges through a dual-pathway approach. Path-1 utilizes a Set Attention Transformer to mode

Dean Rubine

The closed form for the hyper-Catalan number C[m2,m3,m4,...], which counts the number of subdivisions of a roofed polygon into m2 triangles, m3 quadrilaterals, m4 pentagons, etc., has been known since 1940. In 2025, Wildberger and Rubine showed its generating series S[t2,t3,t4,...] is a zero of the general geometric univariate polynomial. They note the facto

Gözde Özbal Sargin, Kai Gong, V. Ongun Özçelik

After the synthesis of the carbon biphenylene network (C-BPN), research has increasingly focused on adapting elements from other groups of the periodic table to this lattice structure. In this study, the direction-dependent electronic, thermal, and thermoelectric (TE) properties of semiconducting group-III (group-III = B, Al, Ga, In) nitride biphenylene netw

Reda Heddad, Lamiae Bouanane

The Mixture-of-Experts (MoE) architecture has emerged as a powerful paradigm for scaling deep learning models, yet it is fundamentally limited by challenges such as expert imbalance and the computational complexity of classical routing mechanisms. This paper investigates the potential of Quantum Machine Learning (QML) to address these limitations through a n

D. V. Karlovets, A. A. Shchepkin, A. D. Chaikovskaia, D. V. Grosman

Interactions between charged particles and light occur in real space and time, yet quantum field theory usually describes them in momentum space. Whereas this approach is well suited for calculating emission probabilities and cross sections, it is insensitive to spatial and temporal phenomena such as, for instance, radiation formation, quantum coherence, and

Tangtang Wang, Kaijie Zhang, Kuangcong Liu

In modern digital marketing, the growing complexity of advertisement data demands intelligent systems capable of understanding semantic relationships among products, audiences, and advertising content. To address this challenge, this paper proposes a Knowledge Graph and Deep Learning-Based Semantic Recommendation Database System (KGSR-ADS) for advertisement

Yuanqi Du, Botao Yu, Tianyu Liu, Tony Shen

There has been unprecedented interest in developing agents that expand the boundary of scientific discovery, primarily by optimizing quantitative objective functions specified by scientists. However, for grand challenges in science, these objectives may only be imperfect proxies. We argue that automating objective function design is a central, yet unmet need

Tian Guo, Hui Yuan, Philip Xu, David Elizondo

We propose SirenPose, a novel loss function that combines the periodic activation properties of sinusoidal representation networks with geometric priors derived from keypoint structures to improve the accuracy of dynamic 3D scene reconstruction. Existing approaches often struggle to maintain motion modeling accuracy and spatiotemporal consistency in fast mov

Qikang Feng, Yang Huang, Huawei Zhang, Jifeng Liu

We determine the rotation curve (RC) of the Milky Way in the range 6 < R < 18 kpc using a sample of 903 carefully selected classical Cepheids with precise proper motions and high-quality radial velocities from \emph{Gaia} DR3. Their distances can be accurately measured from the well-known Period-Wesenheit relations. The RC is computed from the three-dimensio

Viet H. Do, Hoi H. Nguyen, Kiet H. Phan, Tuan Tran

The anti-concentration phenomenon in probability theory has been intensively studied in recent years, with applications across many areas of mathematics. In most existing works, the ambient probability space is a product space generated by independent random variables. In this paper, we initiate a systematic study of anti-concentration when the ambient space

Nimrod Berman, Adam Botach, Emanuel Ben-Baruch, Shunit Haviv Hakimi

Segmenting long-form videos into semantically coherent scenes is a fundamental task in large-scale video understanding. Existing encoder-based methods are limited by visual-centric biases, classify each shot in isolation without leveraging sequential dependencies, and lack both narrative understanding and explainability. In this paper, we present Scene-VLM,

Philip Xu

We introduce Uni4D, a unified framework for large scale open vocabulary 3D retrieval and controlled 4D generation based on structured three level alignment across text, 3D models, and image modalities. Built upon the Align3D 130 dataset, Uni4D employs a 3D text multi head attention and search model to optimize text to 3D retrieval through improved semantic a

Zhenya Zang, Xingda Li, David Day Uei Li

We propose a simplified, biologically inspired predictive local learning rule that eliminates the need for global backpropagation in conventional neural networks and membrane integration in event-based training. Weight updates are triggered only on prediction errors and are performed using sparse, binary-driven vector additions. We integrate this rule into a

Jingbo Yang, Adrian G. Bors

Video generation has seen remarkable progress thanks to advancements in generative deep learning. However, generating long sequences remains a significant challenge. Generated videos should not only display coherent and continuous movement but also meaningful movement in successions of scenes. Models such as GANs, VAEs, and Diffusion Networks have been used

Matyas Bohacek, Ignacio Vilanova Echavarri

Generative Artificial Intelligence (GAI) has experienced exponential growth in recent years, partly facilitated by the abundance of large-scale open-source datasets. These datasets are often built using unrestricted and opaque data collection practices. While most literature focuses on the development and applications of GAI models, the ethical and legal con

Tsogt-Ochir Enkhbayar

Warning-framed content in training data (e.g., "DO NOT USE - this code is vulnerable") does not, it turns out, teach language models to avoid the warned-against behavior. In experiments reported here, models exposed to such warnings reproduced the flagged content at rates statistically indistinguishable from models given the content directly (76.7% vs. 83.3%

Luca Griguolo, Jacopo Papalini, Lorenzo Russo, Domenico Seminara

The formulation of two-dimensional quantum gravity at finite cutoff remains an open problem. We revisit this question in JT gravity from two perspectives: the closed-channel bulk path integral and the path integral over boundary curves. First, we study the radial evolution of a closed universe and derive the trumpet wavefunction as a transition amplitude bet

Biswajit Khara, Suresh Murugaiyan, Suriya Dhakshinamoorthy, Makrand Khanwale

A semi-implicit, residual-based variational multiscale (VMS) formulation is developed for the incompressible Navier--Stokes equations. The approach linearizes convection using an extrapolated (Oseen-type) convecting velocity, producing a linear advection operator at each time step. For this operator, the adjoint can be written exactly. Exploiting this exact

L. D. Silakov, Ya. S. Lyakhova, A. N. Rubtsov

We formulate a time-dependent Fluctuating Local Field (TD-FLF) method for correlated fermion dynamics, extending the stationary FLF approach. The wavefunction is approximated as an ensemble of non-interacting states subject to a classical fluctuating field, with dynamics encoded in the field's time-dependent distribution. This reduces the time-dependent Schr

Nihar Ranjan Saha

Charm quarks serve as a sensitive probe of the Quark-Gluon Plasma (QGP), providing direct insights into its formation, evolution, and properties. In this proceeding, we present new high-precision measurements from the CMS experiment in Pb-Pb collisions at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV. The elliptic ($v_2$) and triangular ($v_3$) flow of prompt $\mathrm{D

Chandrasekhar Gokavarapu

The operator-theoretic dichotomy underlying diffusion on directed networks is \emph{symmetry versus non-self-adjointness} of the Markov transition operator. In the reversible (detailed-balance) regime, a directed random walk $P$ is self-adjoint in a stationary $\pi$-weighted inner product and admits orthogonal spectral coordinates; outside reversibility, $P$

Evgeny Alves Limarenko, Anastasiia Studenikina

The application of self-supervised learning (SSL) and Vision Transformers (ViTs) approaches demonstrates promising results in the field of 2D medical imaging, but the use of these methods on 3D volumetric images is fraught with difficulties. Standard Masked Autoencoders (MAE), which are state-of-the-art solution for 2D, have a hard time capturing three-dimen

P. O. Michel, C. Sun, S. Jaffard, D. Longrois

Stochastic burst-like oscillations are common in physiological signals, yet there are few compact generative models that capture their transient structure. We propose a numerical-twin framework that represents transient narrowband activity as a two-dimensional Ornstein-Uhlenbeck (OU) process with three interpretable parameters: decay rate, mean frequency, an

Jing Gao, Junhan Chang, Haohui Que, Yanfei Xiong

Autonomous laboratories promise to accelerate discovery by coupling learning algorithms with robotic experimentation, yet adoption remains limited by fragmented software that separates high-level planning from low-level execution. Here we present UniLabOS, an AI-native operating system for autonomous laboratories that bridges digital decision-making and embo

Samir Bhatt, John Sabol, Papri Dey, Matthew J. Penn

Phylogenetic trees provide a fundamental representation of evolutionary relationships, yet the combinatorial explosion of possible tree topologies renders inference computationally challenging. Classical approaches to characterizing tree space, such as the Billera-Holmes-Vogtmann (BHV) space, offer elegant geometric structure but suffer from statistical and

Edward A. Hirsch, Ilya Volkovich

A recent result of Ghentiyala, Li, and Stephens-Davidowitz (ECCC TR 25-210) shows that any language reducible to the Range Avoidance Problem via deterministic or randomized Turing reductions is contained in AM $\cap$ coAM. In this note, we present a different potential avenue for obtaining the same result via the Minimal Circuit Size Problem.

Oles Matsyshyn, Li-kun Shi, Inti Sodemann Villadiego

A periodically driven Fermi gas coupled to a simple boson bath reaches a non-equilibrium steady-state occupation with sharp non-analyticities at certain momenta. Here, we demonstrate that these non-analyticities behave as emergent Fermi surfaces by showing that they give rise to quantum oscillations of observables with a period controlled by the effective Fe

Kurtis Chow, Omar Samiullah, Vinesh Sridhar, Hewen Zhang

Generative AI systems are quickly improving, now able to produce believable output in several modalities including images, text, and audio. However, this fast development has prompted increased scrutiny concerning user privacy and the use of copyrighted works in training. A recent attack on machine-learning models called membership inference lies at the cros

Arunkumar V, Firos V M, Senthilkumar S, Gangadharan G R

Multimodal medical imaging provides complementary information that is crucial for accurate delineation of pathology, but the development of deep learning models is limited by the scarcity of large datasets in which different modalities are paired and spatially aligned. This paper addresses this fundamental limitation by proposing an Adaptive Quaternion Cross

Nabarun Chakrabarty, Indrani Chakraborty

A detailed calculation of the radiatively induced $H_{1,2}^+ W^- Z$ vertices is carried out in the context of flavour conserving three Higgs doublet models (3HDMs). The Type-II, lepton specific and democratic versions of the 3HDM are chosen as representative cases and the \emph{alignment limit} is adopted. We arrange the amplitudes in UV-finite and gauge-inv

Fernando Temprano-Coleto, Jeongmin Kim, Marcel M. Louis, Howard A. Stone

The separation of colloidal particles from fluids is essential to ensure a safe global supply of drinking water, yet in the case of microscopic particles, it remains a highly energy-intensive process when using traditional filtration methods. Water cleaning through diffusiophoresis, spontaneous colloid migration in chemical gradients, effectively circumvents

Huiyun Peng, Antonio Zhong, Ricardo Andrés Calvo Méndez, Kelechi G. Kalu

Performance optimization is a critical yet challenging aspect of software development, often requiring a deep understanding of system behavior, algorithmic tradeoffs, and careful code modifications. Although recent advances in AI coding agents have accelerated code generation and bug fixing, little is known about how these agents perform on real-world perfor

Anupriya Dutta Roy, Smita S. Sontakke, Arvind Kumar, Ranabir Dey

Symmetry breaking is essential for biological microswimmers to achieve locomotion in viscous environments. Such asymmetry in the swimming mechanism enables the generation of directional forces that overcome fluid resistance, leading to efficient motion and complex interactions. As synthetic analogues, autophoretic microswimmers including isotropic active col

Himanshu Chandrakar

The $k$-cut complex was recently introduced by Bayer et al. as a generalization of earlier work of Fr{\"o}berg (1990) and Eagon and Reiner (1998), and was shown to be shellable for several classes of graphs. In this article, we prove that the $3$-cut complexes of the hexagonal grid graphs $H_{1 \times m \times n}$ are shellable for all $m,n \geq 1$, by const

Khaled Bin Walid, Feng Ye, Jiaxiang Ji, Ahmed Aziz Ezzat

This study investigates the economic and reliability benefits of improved offshore wind forecasting for grid operations along the U.S. East Coast. We introduce and evaluate a state-of-the-art, machine-learning-based offshore wind forecasting model tailored for this region by integrating its improved forecasts into a dynamic reserve procurement framework alig

Manfred Buchacher

The purpose of these notes is to introduce some of the problems the enumeration of lattice walks is dedicated to and familiarize with some of the arguments they can be addressed with. We discuss the enumeration of lattice walks, their generating functions, and the functional equations they satisfy. We focus on algebraic methods for manipulating and solving t

Subha Pal, Sarath Sasi

The spectral theory of the p-Laplacian is well developed for classical Dirichlet and Neumann boundary conditions, but the transitional Robin regime on exterior domains remains largely unexplored. This paper studies a weighted p-Laplacian eigenvalue problem with Robin boundary conditions on the exterior of the unit ball in Euclidean space of dimension N, with

Arham Deep

We compute an explicit constant for an injectivity estimate on the 3-Torus involving the Laplace Operator. First, we provide motivation for such explicit estimates. We perform the computation for the 3-Torus endowed with the flat metric before generalising to perturbed metrics. Finally, we apply these results to show existence of a nowhere vanishing harmonic

B. Feigin, M. Jimbo, E. Mukhin

We introduce a family of algebras $\mathcal{A}_{M,N}$, $M,N\in\mathbb{Z}$, as an extension of a pair of commuting quantum toroidal $\mathfrak{gl}_1$ subalgebras $\mathcal{E}_1,\check{\mathcal{E}}_1$, wherein the parameters are tuned in a specific way according to $M,N$. In the case $M=\pm 1$, algebra $\mathcal{A}_{\pm1,N}$ is a shifted quantum toroidal $\mat

Konstantin Yakovlev, Nikita Puchkin

We derive an approximation error bound that holds simultaneously for a function and all its derivatives up to any prescribed order. The bounds apply to elementary functions, including multivariate polynomials, the exponential function, and the reciprocal function, and are obtained using feedforward neural networks with the Gaussian Error Linear Unit (GELU) a

Swapna Mahapatra, Rudra Majhi, Jahangir Mohammed, Subhashree Mohanty

In this paper, we have studied the critical temperature $T_c$ of continuous spin $2d$ square-lattice Ising model using Monte-Carlo simulation. We have considered spins $s$ in a bounded interval, where $s \in [-1,+1]$ in square-lattice configuration with periodic boundary condition. We have observed that the critical temperature $T_c$ is approximately $0.925$

Gourav Siddhad, Anurag Singh, Rajkumar Saini, Partha Pratim Roy

Driver drowsiness is a leading cause of traffic accidents, necessitating real-time, reliable detection systems to ensure road safety. This study proposes a Modified TSception architecture for robust assessment of driver fatigue and mental workload using Electroencephalography (EEG). The model introduces a five-layer hierarchical temporal refinement strategy

Takashi Isozaki, Masahiro Yamamoto, Atsushi Noda

The problem of explaining the results produced by machine learning methods continues to attract attention. Neural network (NN) models, along with gradient boosting machines, are expected to be utilized even in tabular data with high prediction accuracy. This study addresses the related issues of pseudo-correlation, causality, and combinatorial reasons for ta

Katherina Bick, Reinhard Nabben

Here we consider the factorized sparse approximate inverse (FSAI) preconditioner. We apply the FSAI preconditioner to singular irreducible M-matrices. These matrices arise e.g. in discrete Markov chain modeling or as graph Laplacians. We show, that there are some restrictions on the nonzero pattern needed for a stable construction of the FSAI preconditioner