December 2025 arXiv papers — page 111

Y. Li, W. Zeng, R. Shen

Monolayer 1{\it T}$^{\prime}$-$\mathrm{MoS}_{2}$ exhibits spin- and valley-dependent massive tilted Dirac cones with two velocity correction terms in low-energy effective Hamiltonian. We theoretically investigate the longitudinal diffusive magneto-conductivity of monolayer 1{\it T}$^{\prime}$-$\mathrm{MoS}_{2}$ by using the linear response theory. It is show

Mikołaj Sienicki, Krzysztof Sienicki

Godfrey and Sichelman propose a quantum-inspired framework, legal entanglement, to model coupled legal relations and interpretations, with quantitative proxies for modularity and information cost. We identify a specific technical issue in their account of formulative entanglement: legislation is modeled as a local operation on subsystem A that changes the re

Jie Lin, Chengyu Liu, Jingjing Jiang

We undertake a study of transposed \delta-Poisson (super)algebra structures on the Virasoro-like algebra and its Kantor Lie-double -- the latter being constructed via Kantor's procedure. This work leads to the finding that, whereas non-trivial \delta-derivations exist solely at \delta=1, non-trivial transposed \delta-Poisson (super)algebra structures are ent

Tom Gur, Dor Minzer, Guy Weissenberg, Kai Zhe Zheng

We construct $3$-query relaxed locally decodable codes (RLDCs) with constant alphabet size and length $\tilde{O}(k^2)$ for $k$-bit messages. Combined with the lower bound of $\tilde{\Omega}(k^3)$ of [Alrabiah, Guruswami, Kothari, Manohar, STOC 2023] on the length of locally decodable codes (LDCs) with the same parameters, we obtain a separation between RLDCs

Mingda Cai, Yun Guo

We extend our previous work on the energy loss of a heavy fermion in a QED plasma to the Quark-Gluon plasma, using the same Bhatnagar-Gross-Krook collisional kernel. The calculation is carried out with a theoretical method where the hard-thermal-loop resummed gluon propagator is used for arbitrary momentum transfer in the scattering processes. Encoding the c

Zhijie Qu, Hsiao-Wen Chen, Eliana Schiller, Jing Wang

We investigate large-scale gas kinematics in the multiphase circumgalactic medium (CGM) using the observed correlation between line width (Doppler $b$ parameter) and column density ($N$) for H I and O VI absorbers. Leveraging extensive public galaxy survey data at $z\lesssim0.1$, we construct a new galaxy sample based on the availability of background Quasi-

Wolfgang Gatterbauer, Diandre Miguel Sabale

For decades, SQL has been the default language for composing queries, but it is increasingly used as an artifact to be read and verified rather than authored. With Large Language Models (LLMs), queries are increasingly machine-generated, while humans read, validate, and debug them. This shift turns relational query languages into interfaces for back-and-fort

Masashi Tokunaga, Kazuto Akiba, Hiroshi Yaguchi, Akira Matsuo

Graphite exhibits multi-stage phase transitions in the quantum-limit states realized by magnetic fields applied along the c-axis. Despite extensive studies on this phenomenon, the origin remains a matter of debate to this day. We performed high-field magnetotransport measurements on single crystals of graphite, focusing on the non-linear conductivity in puls

Jen-Chieh Peng, Ming-Xiong Liu, Guanghua Xu

A striking prediction of QCD on the properties of the novel Transverse Momentum Dependent (TMD) distribution functions is that the time-reversal odd Sivers and Boer-Mulders functions extracted from semi-inclusive deep-inelastic scattering (SIDIS) will undergo a sign reversal in the Drell-Yan (DY) process. This prediction has been tested by experiments that h

Madhav Sankaranarayanan, Yana Hrytsenko, Jerome I. Rotter, Tamar Sofer

We consider statistical inference in high-dimensional regression problems under affine constraints on the parameter space. The theoretical study of this is motivated by the study of genetic determinants of diseases, such as diabetes, using external information from mediating protein expression levels. Specifically, we develop rigorous methods for estimating

Krishna Srikar Durbha, Hassene Tmar, Ping-Hao Wu, Ioannis Katsavounidis

Over the past few years, per-title and per-shot video encoding techniques have demonstrated significant gains as compared to conventional techniques such as constant CRF encoding and the fixed bitrate ladder. These techniques have demonstrated that constructing content-gnostic per-shot bitrate ladders can provide significant bitrate gains and improved Qualit

Lingyi Meng, Maolin Liu, Hao Wang, Yilan Cheng

Accurately mapping legal terminology across languages remains a significant challenge, especially for language pairs like Chinese and Japanese, which share a large number of homographs with different meanings. Existing resources and standardized tools for these languages are limited. To address this, we propose a human-AI collaborative approach for building

Ziyu Huang, Yangjie Zhou, Zihan Liu, Xinhao Luo

The scaling of computation throughput continues to outpace improvements in memory bandwidth, making many deep learning workloads memory-bound. Kernel fusion is a key technique to alleviate this problem, but the fusion strategies of existing compilers and frameworks are limited to using local scratchpad memory. When the intermediate results exceed the limited

Haiying Xia, Zhongyi Huang, Yumei Tan, Shuxiang Song

Music emotion recognition is a key task in symbolic music understanding (SMER). Recent approaches have shown promising results by fine-tuning large-scale pre-trained models (e.g., MIDIBERT, a benchmark in symbolic music understanding) to map musical semantics to emotional labels. While these models effectively capture distributional musical semantics, they o

Anibal M. Medina-Mardones, Bruno Vallette

The search for algebraic foundations of colour-kinematics duality and the double-copy construction has brought into focus a generalization of Batalin--Vilkovisky algebras, referred to here as coexact BV-algebras and as $\textrm{BV}^\square$-algebras in other sources. While these structures capture the cubic sector, they fail to encode higher-valence phenomen

Nischal Subedi, Ember Kerstetter, Winnie Li, Silo Murphy

Graph Convolutional Networks (GCNs) have become a standard approach for semi-supervised node classification, yet practitioners lack clear guidance on when GCNs provide meaningful improvements over simpler baselines. We present a diagnostic study using the Amazon Computers co-purchase data to understand when and why GCNs help. Through systematic experiments w

Junmo Song

Structural changes and outliers often coexist, complicating statistical inference. This paper addresses the problem of testing for parameter changes in conditionally heteroscedastic time series models, particularly in the presence of outliers. To mitigate the impact of outliers, we introduce a two-step procedure comprising robust estimation and residual trun

Anja Sheppard, Parker Ewen, Joey Wilson, Advaith V. Sethuraman

This paper introduces SLIM-VDB, a new lightweight semantic mapping system with probabilistic semantic fusion for closed-set or open-set dictionaries. Advances in data structures from the computer graphics community, such as OpenVDB, have demonstrated significantly improved computational and memory efficiency in volumetric scene representation. Although OpenV

Huanhuan Tian, Jianguo Yang, Ming Liu

The imprint effect, a significant reliability challenge in ferroelectric memories, manifests as a shift in the coercive field during retention and endurance tests, ultimately degrading the usable memory window. \rv{While traditional models attribute imprint primarily to charge screening at the interface between the dead layer and the ferroelectric film, the

Kazuyuki Yoshida

We propose an intrinsic geometric framework on the space of operational contexts, specified by channels, stationary states, and self-preservation functionals. Each context C carries a pointer algebra, internal charges, and a self-consistent configuration minimizing a self-preservation functional. The Hessian of this functional yields an intrinsic metric on c

Changqing Guo, Letao Yang, Jing Wang, Houbing Huang

Flexible ferroelectrics possess significant potential for wearable electronics and bio-inspired devices, yet their electromechanical coupling mechanisms under dynamic bending remain elusive. This study employs phase-field simulations to investigate the effects of bending deformation on domain structures and macroscopic ferroelectric responses in (SrTiO3)10/(

Siyuan Dai, Lunxiao Li, Kun Zhao, Eardi Lila

With the rapid progress of large language models (LLMs), advanced multimodal large language models (MLLMs) have demonstrated impressive zero-shot capabilities on vision-language tasks. In the biomedical domain, however, even state-of-the-art MLLMs struggle with basic Medical Decision Making (MDM) tasks. We investigate this limitation using two challenging da

Elham Kiyani, Amit Makarand Deshpande, Madhura Limaye, Zhiwei Gao

Fiber reinforcement and polymer matrix respond differently to manufacturing conditions due to mismatch in coefficient of thermal expansion and matrix shrinkage during curing of thermosets. These heterogeneities generate residual stresses over multiple length scales, whose partial release leads to process-induced deformation (PID), requiring accurate predicti

Siyuan Yao, Dongxiu Liu, Taotao Li, Shengjie Li

Building extraction from remote sensing images is a challenging task due to the complex structure variations of the buildings. Existing methods employ convolutional or self-attention blocks to capture the multi-scale features in the segmentation models, while the inherent gap of the feature pyramids and insufficient global-local feature integration leads to

Feng Tao, Xiaoliang Zhang, Dawei Tang, Shigeo Maruyama

We combine machine learning (ML)-based neuroevolution potentials (NEP) with anharmonic lattice dynamics and the Boltzmann transport equation (ALD-BTE) to achieve a quantitative and mode-resolved description of thermal transport in individual (10, 0) zigzag single-walled carbon nanotubes (SWCNTs) and their bundles. Our analysis reveals a dual mechanism behind

Sebastien Tchitchek, Mohamed Kissi, Julien Tierny

We introduce the Continuous Edit Distance (CED), a geodesic and elastic distance for time-varying persistence diagrams (TVPDs). The CED extends edit-distance ideas to TVPDs by combining local substitution costs with penalized deletions/insertions, controlled by two parameters: \(\alpha\) (trade-off between temporal misalignment and diagram discrepancy) and \

Duy A. Nguyen, Hai H. Do, Minh Doan, Minh N. Do

The ability to extract value from historical data is essential for enterprise decision-making. However, much of this information remains inaccessible within large legacy file systems that lack structured organization and semantic indexing, making retrieval and analysis inefficient and error-prone. We introduce SPAR (Session-based Pipeline for Adaptive Retrie

Sayak Chatterjee, Anirban Chatterjee, Abhinav Chakraborty, Bhaswar B. Bhattacharya

In this paper, we derive the asymptotic distribution of the number of copies of a fixed graph $H$ in a random graph $G_n$ sampled from a sparse graphon model. Specifically, we provide a refined analysis that separates the contributions of edge randomness and vertex-label randomness, allowing us to identify distinct sparsity regimes in which each component do

Tiange Zhang, Xiandong Meng, Siwei Ma

Recent advances in end-to-end video compression have shown promising results owing to their unified end-to-end learning optimization. However, such generalized frameworks often lack content-specific adaptation, leading to suboptimal compression performance. To address this, this paper proposes a content adaptive based motion alignment framework that improves

Toan Le Ngo Thanh, Phat Ha Huu, Tan Nguyen Dang Duy, Thong Nguyen Le Minh

The exponential growth of video content has created an urgent need for efficient multimodal moment retrieval systems. However, existing approaches face three critical challenges: (1) fixed-weight fusion strategies fail across cross modal noise and ambiguous queries, (2) temporal modeling struggles to capture coherent event sequences while penalizing unrealis

Yizhong Chen, Zhibin Wang

The pseudopotential lattice Boltzmann method (LBM) is a prominent approach for simulating multiphase flows, valued for its physical intuitiveness and computational tractability. However, existing immiscible pseudopotential methods for modeling dynamic multi-component immiscible fluid systems involving open boundaries face persistent challenges, notably the i

Yufeng Shen, Zhiyu Song, Fenglin Yu, Leopold Wuhan Zhou

In this paper, we investigate three fundamental problems regarding cut complexes of graphs: their realizability, the uniqueness of graph reconstruction from them, and their algorithmic recognition. We define the parameter $m(d,n)$ as the minimum number of additional vertices needed to realize any $d$-dimensional simplicial complex on $n$ vertices as a cut co

Yifan Wu, Jiyue Jiang, Xichen Ye, Yiqi Wang

Biological foundation models (BioFMs), pretrained on large-scale biological sequences, have recently shown strong potential in providing meaningful representations for diverse downstream bioinformatics tasks. However, such models often rely on millions to billions of training sequences and billions of parameters, resulting in prohibitive computational costs

Xiuying Zhang, Qinsheng Zhu, Xiaodong Xing

We propose a Hybrid Spatio-Temporal Quantum Graph Convolutional Network (H-STQGCN) algorithm by combining the strengths of quantum computing and classical deep learning to predict the taxi destination within urban road networks. Our algorithm consists of two branches: spatial processing and time evolution. Regarding the spatial processing, the classical modu

Yusuke Isono

We study cocycle perturbations of state preserving actions on type $\mathrm{III}_1$ factors. Extending the theorem of Marrakchi and Vaes for type $\mathrm{II}_1$ factors, we show that a state preserving outer $\mathbb Z$-action on a type $\mathrm{III}_1$ factor with trivial bicentralizer admits a unitary cocycle whose perturbation becomes an ergodic action.

Huu-An Vu, Van-Khanh Mai, Trong-Tam Nguyen, Quang-Duc Dam

The rapid expansion of video content across online platforms has accelerated the need for retrieval systems capable of understanding not only isolated visual moments but also the temporal structure of complex events. Existing approaches often fall short in modeling temporal dependencies across multiple events and in handling queries that reference unseen or

Mai Qi, Eugenia Colafranceschi

We introduce an enriched entanglement structure for spin networks, inspired by tensor-network constructions, in which internal links can carry a controlled and discrete amount of entanglement. In the spin-network picture, vertices are dual to simplices and links are dual to their faces. Standard spin-network gluing corresponds to fully identifying two simpli

Simon Allzén

The 1974 papers by Ostriker et al. [1974] and Einasto et al. [1974] are considered by many to be pivotal in establishing the epistemic foundations for the dark matter hypothesis. From a theory confirmation point of view, the circumstances surrounding this pivot are difficult to reconcile with common approaches to epistemic support. First, the papers did not

Zhimao Peng, Enguang Wang, Fei Yang, Xialei Liu

Generalized category discovery (GCD) is an important and challenging task in open-world learning. Specifically, given some labeled data of known classes, GCD aims to cluster unlabeled data that contain both known and unknown classes. Current GCD methods based on parametric classification adopt the DINO-like pseudo-labeling strategy, where the sharpened proba

Udayon Sen, Alka Luqman, Anupam Chattopadhyay

Deepfake audio detection has progressed rapidly with strong pre-trained encoders (e.g., WavLM, Wav2Vec2, MMS). However, performance in realistic capture conditions - background noise (domestic/office/transport), room reverberation, and consumer channels - often lags clean-lab results. We survey and evaluate robustness for state-of-the-art audio deepfake dete

Gagan Deep, Akash Deep, William Lamptey

We develop a rigorous walk-forward validation framework for algorithmic trading designed to mitigate overfitting and lookahead bias. Our methodology combines interpretable hypothesis-driven signal generation with reinforcement learning and strict out-of-sample testing. The framework enforces strict information set discipline, employs rolling window validatio

Bangyu Li, Boping Gu, Ziyang Ding

In modern financial markets, investors increasingly seek personalized and adaptive portfolio strategies that reflect their individual risk preferences and respond to dynamic market conditions. Traditional rule-based or static optimization approaches often fail to capture the nonlinear interactions among investor behavior, market volatility, and evolving fina

Amy Chang, Tiffany Saade, Sanket Mendapara, Adam Swanda

Artificial intelligence (AI) systems are being readily and rapidly adopted, increasingly permeating critical domains: from consumer platforms and enterprise software to networked systems with embedded agents. While this has unlocked potential for human productivity gains, the attack surface has expanded accordingly: threats now span content safety failures (

Alexey Cruz-García, Joaquín Puig, Sergii Pylypenko, Gladys Nieva

Hyperuniform patterns present enhanced physical properties that make them the new generation of cutting-edge technological devices. Synthesizing devices with tens of thousands of components arranged in a hyperuniform fashion has thus become a breakthrough to achieve in order to implement these technologies. Here we provide evidence that extended two-dimensio

Nijesh Upreti, Vaishak Belle

Inductive Logic Programming (ILP) provides interpretable rule learning in relational domains, yet remains limited in its ability to induce and reason with numerical constraints. Classical ILP systems operate over discrete predicates and typically rely on discretisation or hand-crafted numerical predicates, making it difficult to infer thresholds or arithmeti

Yue Han, Jinguang Han, Jianying Zhou

With the rapid growth of smart devices and mobile internet, large-scale data processing is becoming increasingly important, while mobile devices remain resource-constrained. Mobile Cloud Computing (MCC) addresses this limitation by offloading tasks to the cloud. Nevertheless, the widespread adoption of MCC also raises challenges such as data privacy, selecti

Jiayin Li, Guoshuai Du, Lili Zhao, Wuxiao Han

Two-dimensional (2D) diamond has aroused tremendous interest in nanoelectronics and optoelectronics, owing to its superior properties and flexible characteristics compared to bulk diamond. Despite significant efforts, great challenges lie in the experimental synthesis and transformation conditions of 2D diamond. Herein, we have demonstrated the experimental

Abhik Pal

We present `liesuperalg` a SageMath package for representation-theoretic calculations involving Lie superalgebras in Type A. Our package introduces functionality to calculate invariants of weights and produce the associated cup diagrams. We expose functionality to calculate characters of irreducible representations, work with combinatorics of generalized Kaz

Shashie Dilhara Batan Arachchige, Benjamin Zi Hao Zhao, Hassan Jameel Asghar, Dinusha Vatsalan

Large Language Models (LLMs) are often fine-tuned to adapt their general-purpose knowledge to specific tasks and domains such as cyber threat intelligence (CTI). Fine-tuning is mostly done through proprietary datasets that may contain sensitive information. Owners expect their fine-tuned model to not inadvertently leak this information to potentially adversa

Chengyu Wang, Siddharth K. Singh, Chia-Tse Tai, Adbhut Gupta

Electronic stripe/nematic phases are fascinating strongly-correlated states characterized by spontaneous rotational symmetry breaking. In the quantum Hall regime, such phases typically emerge at half-filled, high-orbital-index ($N\geq2$) Landau levels (LLs) where the short-range Coulomb interaction is softened by the nodes of electron wave functions. In the

Liming Li, Zhenguo Zhao, Gongxiang Wei, Wenfei Zhang

Computer-generated hologram (CGH) allows for the on-demand scaling and projection of artificially designed target patterns, while incorporating benefits such as a lensless setup and high-frame-rate operation. In this work, we actively control the projection pattern using CGH and integrate two typical single-pixel imaging (SPI) schemes, thereby implementing a

Kohei Nishikawa, Koki Shimizu, Hiroki Hashiguchi

This study evaluates thresholds for removing singular values from singular value decomposition-based low-rank approximations of deep neural network weight matrices. Each weight matrix is modeled as the sum of signal and noise matrices. The low-rank approximation is obtained by removing noise-related singular values using a threshold based on random matrix th

David Yang, Yuan Gao, Tianyi Lin, Christian Kroer

We introduce, to our knowledge, the first direct second-order method for computing Nash equilibria in two-player zero-sum games. To do so, we construct a Douglas-Rachford-style splitting formulation, which we then solve with a semi-smooth Newton (SSN) method. We show that our algorithm enjoys local superlinear convergence. In order to augment the fast local

Weilun Xu, An Chang

A 1-planar graph refers to a graph that can be drawn on the plane such that each edge has at most one crossing. In this paper, focusing on the spectral Tur\'{a}n-type problems of $1$-planar graphs, we determine completely the unique spectral extremal graph among all $K_3$-free or $K_4$-free $1$-planar graphs, and provide a characterization of the spectral ex

Shuai He, Junxing Pan, Jinjun Zhang

Polymer brush-grafted nanoparticles have significant application value in fields such as gene therapy and targeted drug delivery. A profound understanding of the interaction mechanisms between these particles and cell membranes represents a critical scientific challenge in biophysics. Using the Self-Consistent Field Theory, this work systematically explores

Parthasarathy Nadarajan, Michael Botsch, Sebastian Sardina

This paper introduces a novel machine learning architecture for an efficient estimation of the probabilistic space-time representation of complex traffic scenarios. A detailed representation of the future traffic scenario is of significant importance for autonomous driving and for all active safety systems. In order to predict the future space-time represent

Zhimao Peng, Enguang Wang, Xialei Liu, Ming-Ming Cheng

Semantically coherent out-of-distribution detection (SCOOD) is a recently proposed realistic OOD detection setting: given labeled in-distribution (ID) data and mixed in-distribution and out-of-distribution unlabeled data as the training data, SCOOD aims to enable the trained model to accurately identify OOD samples in the testing data. Current SCOOD methods

Ruixin Guo, Ruoming Jin, Xinyu Li, Yang Zhou

Linear Autoencoders (LAEs) have shown strong performance in state-of-the-art recommender systems. However, this success remains largely empirical, with limited theoretical understanding. In this paper, we investigate the generalizability -- a theoretical measure of model performance in statistical learning -- of multivariate linear regression and LAEs. We fi

Ben Dong, Hui Feng, Qian Wang

As post-quantum cryptography (PQC) becomes increasingly critical for securing future communication systems, the performance overhead introduced by quantum-resistant algorithms presents a major computing challenge. HQC (Hamming Quasi-Cyclic) is a newly standardized code-based PQC scheme designed to replace classical key exchange methods. In this paper, we pro

Ken-ichi Kitayama

An emerging computing paradigm, so-called next-generation reservoir computing (NG-RC) is investigated. True to its namesake, NG-RC requires no actual reservoirs for input data mixing but rather computing the polynomial terms directly from the time series inputs. However, benchmark tests so far reported have been one-sided, limited to prediction tasks of temp

Panagiota Birmpa, Patrícia Gonçalves, Dimitrios Tsagkarogiannis

We consider the one-dimensional stirring process on the segment $\{-N,\ldots,N\}$, coupled to boundary dynamics that inject particles from the right reservoir and remove particles from the left reservoir, each acting on a window of size $K$. We investigate the non-equilibrium fluctuations of the system, starting from a product measure associated with a smoot

Parthasarathy Nadarajan, Michael Botsch, Sebastian Sardina

In this paper, a probabilistic space-time representation of complex traffic scenarios is predicted using machine learning algorithms. Such a representation is significant for all active vehicle safety applications especially when performing dynamic maneuvers in a complex traffic scenario. As a first step, a hierarchical situation classifier is used to distin

Mohit Daga

We present a \emph{deterministic exact algorithm} for the \emph{minimum $k$-cut problem} on simple graphs. Our approach combines the \emph{principal sequence of partitions (PSP)}, derived canonically from ideal loads, with a single level of \emph{Kawarabayashi--Thorup (KT)} contractions at the critical PSP threshold~$\lambda_j$. Let $j$ be the smallest index

Lewis Combes

We present methods to compute Selmer groups associated to mod p Galois representations rho over a number field K, with a particular focus on comparing their ranks with periods coming from cohomology classes associated to rho by Serre's conjecture. This provides evidence for a loose version of a "mod p Bloch-Kato conjecture", where the vanishing of a period i

Abhinav Kumar, Tristan Aumentado-Armstrong, Lazar Valkov, Gopal Sharma

Gaussian Splatting has revolutionized the field of Novel View Synthesis (NVS) with faster training and real-time rendering. However, its reconstruction fidelity still trails behind the powerful radiance models such as Zip-NeRF. Motivated by our theoretical result that both queries (such as coordinates) and neighborhood are important to learn high-fidelity si

Akito Futaki, Jianwei Shi

For holomorphic vector bundles over compact K\"ahler manifolds, we establish a formula for the asymptotic slope of the {\alpha}-K-energy associated with the Kahler-Yang-Mills equations.

Parthasarathy Nadarajan, Michael Botsch

This paper presents a method to predict the evolution of a complex traffic scenario with multiple objects. The current state of the scenario is assumed to be known from sensors and the prediction is taking into account various hypotheses about the behavior of traffic participants. This way, the uncertainties regarding the behavior of traffic participants can

Charilaos Pipis, Shivam Garg, Vasilis Kontonis, Vaishnavi Shrivastava

Reasoning models (e.g., DeepSeek-R1) generate long chains of thought to solve harder problems, but they often loop, repeating the same text at low temperatures or with greedy decoding. We study why this happens and what role temperature plays. With open reasoning models, we find that looping is common at low temperature. Larger models tend to loop less, and

S. Y. Lu, Y. F. Duan, D. X. Yu, H. M. Dong

Magnetic chiral bobbers (CBs) are three-dimensional (3D) topological spin textures that consist of a tapered skyrmion tube terminating in a Bloch point, promising applications in high-density spintronics. However, the mechanisms controlling their size and the dynamics of their annihilation are still not fully understood. In this study, we present an analytic

Sergei Artemov

Non-compact proofs are a class of reasoning that is used in mathematics but overlooked in the analysis of (un)provability of consistency. We focus on proofs of arithmetical statements (*) "for any natural number n, F(n)." A proof of (*) is called compact if all proofs of F(n)'s for n=0,1,2,... fit into some finitely axiomatized fragment of Peano Arithmetic P

Mahsa Nasri, Mahnoosh Jahanian, Wei Wu, Binyan Xu

This in-person studio explores how mixed reality (MR) and biometrics can make intangible emotional states tangible through embodied art practices. We begin with two well-established modalities, clay sculpting and free-form 2D drawing, to ground participants in somatic awareness and manual, reflective expression. Building on this baseline, we introduce an MR

Raffaele Marcovecchio

We construct a class of multiple Legendre polynomials and prove that they satisfy an Ap\'ery-like recurrence. We give new upper bounds of the approximation measures of logarithms of rational numbers by algebraic numbers of bounded degree. We prove e.g. that the nonquadraticity exponent of $\log 2$ is bounded from above by $12.841618...$, thus improving upon

Yansong Gao, Yu Sun

Discrete diffusion models (DDMs) are a powerful class of generative models for categorical data, but they typically require many function evaluations for a single sample, making inference expensive. Existing acceleration methods either rely on approximate simulators, such as $\tau$-leaping, or on distillation schemes that train new student models and auxilia

David Dang, Stuart Love, Meena Salib, Quynh Dang

Advancing artificial intelligence for physical sciences requires representations that are both interpretable and compatible with the underlying laws of nature. We introduce METASTRINGS, a symbolic language for photonics that expresses nanostructures as textual sequences encoding materials, geometries, and lattice configurations. Analogous to molecular textua

Tjom Arens, Sander J. W. Vonk, A. Willem Vlasblom, Margarita Samoli

Recently, nanocrystals in the regime of vanishing quantum confinement-termed bulk nanocrystals (BNCs)-have demonstrated remarkable optical gain characteristics. While their high-power lasing performance was demonstrated convincingly, the photophysics at low and intermediate powers-where charge-carrier populations are discrete-remain unexplored. Using single-

Abhishek Tiwari, Binoy Krishna Patra

We use total energy-momentum conservation and the Bianchi identity (magnetic-flux conservation) to construct second-order relativistic magnetohydrodynamics in a Zubarev's non-equilibrium statistical operator (NESO) framework. We obtain all dissipative tensors in the medium by focusing on a relativistic magnetized plasma that preserves parity and is symme

Léo Hein, Giovanni de Nunzio, Giovanni Chierchia, Aurélie Pirayre

Existing traffic volume estimation methods typically address either forecasting traffic on sensor-equipped roads or spatially imputing missing volumes using nearby sensors. While forecasting models generally disregard unmonitored roads by design, spatial imputation methods explicitly address network-wide estimation; yet this approach relies on volume data at

Roger de Belsunce, Boryana Hadzhiyska, Mikhail M. Ivanov

The Lyman-alpha (Lya) forest is a unique probe of cosmology and the intergalactic medium at high redshift and small scales. The statistical power of the ongoing Dark Energy Spectroscopic Instrument (DESI) demands precise theoretical tools to model the Lya forest. We present a hybrid effective field theory (HEFT) forward model in redshift space that leverages

Ryan W. Spangler, Jacob M. Shusterman, Anton V. Ievlev, Patrick E. Hopkins

In this work, we develop a rapid reactive vapor transport technique to efficiently utilize limited isotopically pure precursors, particularly gaseous 18O2, and synthesize mm-scale, high-quality crystals within few-minute growth durations. We unlock this capability by using metallic molybdenum precursors with high source temperatures (900 C) and total pressur

Zeki Hayran, John B. Pendry

Space-time modulation of refractive index can produce synthetically moving interfaces with arbitrary apparent velocities, including superluminal motion, offering new ways to control light in dynamic media. On the other hand, space-time wave packets are structured waves whose spatio-temporal spectra lie on tilted space-time planes, so their group velocity can

Daniel Erkensten, Alexey Chernikov, Ermin Malic

The strong Coulomb interaction in 2D materials facilitates the formation of tightly bound excitons and charge-ordered phases of matter. A prominent example is the formation of a crystalline phase from free charges due to mutual Coulomb repulsion, known as the Wigner crystal. While exciton-electron interactions have been used as a sensor for Wigner crystalliz

Mahdi Sarikhani, Alexander K. Hartmann

We study the large-deviation properties of minimum spanning trees for two ensembles of random graphs with $N$ nodes. First, we consider complete graphs. Second, we study Erdős-Rényi (ER) random graphs with edge probability $p=c/N$ conditioned to be connected. By using large-deviation Markov chain sampling, we are able to obtain the distribution $P(W)$ of the

Marcello B. Silva Neto, Chang Niu, Marcus V. O. Moutinho, Pierpaolo Fontana

Negative magnetoresistance in conventional two-dimensional electron gases is a well-known phenomenon, but its origin in complex and topological materials, especially those endowed with quantum geometry, remains largely elusive. Here, we report the discovery of a giant negative magnetoresistance, reaching a remarkable $- 90\%$ of the resistance at zero magnet

Batoul Hashemi, Manuel Arturo Méndez-Rosales, Parimal Edke, Mohammad Rezaul Islam

In this work, we demonstrate compact paper-clip spiral silicon photonic waveguides with ultra-low delay loss. We characterize the optical loss and group delay of single-mode and multi-mode silicon waveguides across the telecom O-, S-, C-, and L-bands. For spiral devices with 2.0-μm-wide waveguides, we measure propagation losses of 0.11 and 0.06 dB/cm at 1310

Árpád Pándy, Róbert Lakatos, András Hajdu

Recent advancements in artificial intelligence have sparked interest in industrial agents capable of supporting analysts in regulated sectors, such as finance and healthcare, within tabular data workflows. A key capability for such systems is performing accurate arithmetic operations on structured data while ensuring sensitive information never leaves secure

Sait Sovukluk, Johannes Englsberger, Christian Ott

This study proposes a step adaptation framework for running through spring-mass trajectories and deadbeat control gain libraries. It includes four main parts: (1) Automatic spring-mass trajectory library generation; (2) Deadbeat control gain library generation through an actively controlled template model that resembles the whole-body dynamics well; (3) Traj

Lukas Schnelle, Niklas Fehlemann, Ali O. M. Kilicsoy, Niklas Bechler

Scatter in properties resulting from manufacturing is a great challenge in lightweight design, requiring consideration of not only the average mechanical performance but also the variance which is done e.g., by conservative safety factors. One contributor to this variance is the inherent geometric variability in the formed part. To isolate and quantify this

Juedong Yang, Yuan Li, Wuhong Zhang, Lixiang Chen

The rotational Doppler effect, for which the frequency shift is proportional to the light's orbital angular momentum $\ell$ and the object's rotational speed ($Δf \propto\ell Ω$), has proven to be a powerful tool for detecting the speed of rotational objects. However, the current detection technique is mainly based on coherent laser sources. There is

Ivan Oliinyk, Andrey Sarikov

In this paper, a three-dimensional lattice model based on the Monte Carlo approach is presented. This model is developed to investigate the kinetics of morphology change during phase separation in nonstoichiometric Si oxide (SiOx, x < 2) films. The model takes into account the SiOx local atomic structure and probabilistic migration of oxygen atoms driven by

Maria Y. Rodriguez, Ehren Dohler, Jon Phillips, Melissa Villodas

Open-source data and tools are lauded as essential for replicable and usable social science, though little is known about their use in resource constrained human service provision. This paper examines the challenges and opportunities of open-source tools and data in human service development by using both to forecast failure to pay eviction filings in Bronx

Huai-de Chen, Hai-liang Yang

We compiled the first high-frequency rice price panel for Taiwan from August 1945 to March 1947, during the transition from Japanese rule to China rule. Using regression models, we found that the pattern of rice price changes could be divided into four stages, each with distinct characteristics. Based on different stages, we combined the policies formulated

Sandro Rodriguez Garzon, Awid Vaziry, Enis Mert Kuzu, Dennis Enrique Gehrmann

A fundamental limitation of current LLM-based AI agents is their inability to build differentiated trust among each other at the onset of an agent-to-agent dialogue. However, autonomous and interoperable trust establishment becomes essential once agents start to operate beyond isolated environments and engage in dialogues across individual or organizational

Geoffroy C. B. Berthelot, Brigitte Gelein, Eric Meinadier, Emmanuel Orhant

The increase in the collection of biological data allows for the individual and longitudinal monitoring of hematological or urine biomarkers. However, identifying abnormal behavior in these biological sequences is not trivial. Moreover, the complexity of the biological data (correlation between biomarkers, seasonal effects, etc.) is also an issue. Z-score me

Keegan R. Bunker, Ryan J. Caverly

This paper presents a static and dynamic torque analysis of the CABLESSail concept, which involves cables routed along the length of the flexible booms that hold the solar sail membrane in such a manner that tensioning the cables results in a bending deformation of the booms. This provides a mechanism where actuation of the cables can be used to create an im

D. Andrew Brown, Peter Kiessler, John Nicholson

Gaussian processes (GPs) are ubiquitous tools for modeling and predicting continuous processes in physical and engineering sciences. This is partly due to the fact that one may employ a Gaussian process as an interpolator while facilitating straightforward uncertainty quantification at other locations. In addition to training data, it is sometimes the case t

Jounglag Lim

In this paper, we prove that the open neighborhood ideal of a TD-unmixed tree is geometrically vertex decomposable. This result implies that the associated Stanley-Reisner complex is vertex decomposable. We further demonstrate that Cohen-Macaulay open neighborhood ideals of trees are special cases of Cohen-Macaulay facet ideals of simplicial trees. Finally,

Minghao Zhu, Zhihao Zhang, Anmol Sidhu, Keith Redmill

Automated road sign recognition is a critical task for intelligent transportation systems, but traditional deep learning methods struggle with the sheer number of sign classes and the impracticality of creating exhaustive labeled datasets. This paper introduces a novel zero-shot recognition framework that adapts the Retrieval-Augmented Generation (RAG) parad

Xiangzhong Liu, Jiajie Zhang, Hao Shen

In automotive sensor fusion systems, smart sensors and Vehicle-to-Everything (V2X) modules are commonly utilized. Sensor data from these systems are typically available only as processed object lists rather than raw sensor data from traditional sensors. Instead of processing other raw data separately and then fusing them at the object level, we propose an en

Masoud S. Sakha, Rushikesh Kamalapurkar

This paper develops an embedding-based approach to solve switched optimal control problems (SOCPs) with an arbitrary number of subsystems. Initially, the discrete switching signal is represented by a set of binary variables, encoding each mode in binary format. An embedded optimal control problem (EOCP) is then formulated by replacing these binary variables

DongKyu Kim, Han-Lin Hsieh, Maryam M. Shanechi

Neural population activity often exhibits regime-dependent non-stationarity in the form of switching dynamics. Learning accurate switching dynamical system models can reveal how behavior is encoded in neural activity. Existing switching approaches have primarily focused on learning models from a single neural modality, either continuous Gaussian signals or d