March 2026 arXiv papers — page 10

Dharmesh Jain

We uncover an inconsistency in the uniform WKB quantization of deformed quantum mechanics.

Ryuta Moriyasu, Carmen Amo Alonso, Marco Pavone

Model predictive control (MPC) has been widely used in many fields, often in hierarchical architectures that combine controllers and decision-making layers at different levels. However, when such architectures are cast as bilevel optimization problems, standard KKT-based reformulations often introduce nonconvex and potentially nonsmooth structures that are u

Shafayeth Jamil, Rehan Kapadia

Linear dynamical systems are fully characterized by their eigenspectra, accessible directly from the generator of the dynamics. For nonlinear systems governed by partial differential equations, no equivalent theory exists. We introduce Lie Generator Network-Koopman (LGN-KM), a neural operator that lifts nonlinear dynamics into a linear latent space and learn

Ashish Seth, Sonal Kumar, Ramaneswaran Selvakumar, Nishit Anand

Large Audio Language Models (LALMs) achieve strong performance on audio-language tasks; however, their reliability in real-world settings remains underexplored. We introduce Audio Hallucination Attacks (AHA), an attack suite called AHA-Eval, comprising 6.5K QA pairs designed to test whether LALMs genuinely ground their responses in the audio input. AHA targe

Junjie Zhang, Zhen Shen, Gang Xiong, Xisong Dong

Grokking in modular arithmetic has established itself as the quintessential fruit fly experiment, serving as a critical domain for investigating the mechanistic origins of model generalization. Despite its significance, existing research remains narrowly focused on specific local circuits or optimization tuning, largely overlooking the global structural evol

Lakshya Garg, Sai Yaswanth, Deep Narayan Mishra, Karthik Kumaran

Item Price Elasticity is used to quantify the responsiveness of consumer demand to changes in item prices, enabling businesses to create pricing strategies and optimize revenue management. Sectors such as store retail, e-commerce, and consumer goods rely on elasticity information derived from historical sales and pricing data. This elasticity provides an und

Eugene Gorsky, Soyeon Kim, Melissa Sherman-Bennett

We prove that an open Richardson variety in the complete flag variety for $\mathrm{GL}_n$ is isomorphic to a torus if and only if the corresponding closed Richardson variety is toric. Such toric varieties can be classified in terms of the combinatorics of Bruhat intervals, and include many varieties of dimension larger than $n-1$. We give a combinatorial des

Takumi Taki, Masato Kobayashi, Yuki Uranishi

Autonomous mobile robots operating in human-shared indoor environments often require paths that reflect human spatial intentions, such as avoiding interference with pedestrian flow or maintaining comfortable clearance. However, conventional path planners primarily optimize geometric costs and provide limited support for explicit route specification by human

Hejin Huang, Jusheng Zhang, Kaitong Cai, Jian Wang

Preference-based alignment objectives have been widely adopted, from RLHF-style pairwise learning in large language models to emerging applications in recommender systems. Yet, existing work rarely examines how Direct Preference Optimization (DPO) behaves under implicit feedback, where unobserved items are not reliable negatives. We conduct systematic experi

Jingqi Xu

Vision-Language Models (VLMs) have demonstrated strong capabilities across a wide range of multimodal tasks. However, recent studies have shown that VLMs, such as CLIP, perform poorly in understanding negation expressions, which are common in natural language. In this work, we propose Omni-NegCLIP, a fine-tuned CLIP model that improves CLIP's understanding o

Zehao Zhou, Xiaojie Wu, Yanheng Li, Xinran Wei

We introduce a GPU-accelerated implementation of time-dependent density functional theory with the minimal auxiliary basis approach (TDDFT-risp) in GPU4PySCF, together with large system demonstrations carried out using the Tamm--Dancoff approximation (TDA-risp). The method combines GPU-accelerated three-center integral evaluation, tensor contractions, exchan

Osasumwen Cedric Ogiesoba-Eguakun, Kaveh Ashenayi, Suman Rath

Real-time monitoring of inverter-based microgrids is essential for stability, fault response, and operational decision-making. However, electromagnetic transient (EMT) simulations, required to capture fast inverter dynamics, are computationally intensive and unsuitable for real-time applications. This paper presents a data-driven surrogate modeling framework

Lijingze Xiao, Jinhong Du, Supeng Diao, Yu Ren

Robotic grasping from single-view observations remains a critical challenge in manipulation. However, existing methods still struggle to generate reliable grasp candidates and stably evaluate grasp feasibility under incomplete geometric information. To address these limitations, we present SuperGrasp, a new two-stage framework for single-view parallel-jaw gr

Jun Zhang, Antong Zhu

Parallel to the study of toric domains, symplectically convex, and dynamically convex domains in $(\mathbb R^4, \omega_{\rm std})$, we build an analogous framework and corresponding subclasses for Liouville domains in $(T^*\mathbb T^2,\omega_{\rm can})$. A key feature of this framework is the introduction of a new notion of convexity, based on systolic ratio

Tao Chen, Kun Zhang, Qiong Wu, Xiao Chen

Long video understanding is a key challenge that plagues the advancement of \emph{Multimodal Large language Models} (MLLMs). In this paper, we study this problem from the perspective of visual memory mechanism, and proposed a novel and training-free approach, termed \emph{Flexible Memory} (\textbf{FlexMem}). In principle, FlexMem aims to mimic human behavior

Meng Zhou, Furen Deng, Yidong Xu, Li Zhou

A lunar orbit interferometer array suffers from a number of systematics. Beyond systematics induced by the imaging algorithm itself and thermal noise considered in Paper I, phase errors due to instrumental inconsistency between receivers, geometric error in baseline determination, and clock synchronization error between satellites will also affect synthesis

Koji Nakamura, Yua Murayama, Issei Horikoshi, Mahiro Kobayashi

The Low-Gain Avalanche Diode (LGAD) is a semiconductor detector capable of achieving excellent timing resolution (~20 ps) for minimum ionizing particles (MIPs). To realize a pixelated detector with both high timing precision and spatial resolution, we have been developing Capacitive-Coupled LGADs (ACLGADs) for future collider experiments, such as the latter

Wei-Fan Hu, Shi-Xiang Zhong, Po-Wen Hsieh, Chung-Kai Chen

Singularly perturbed partial differential equations arise in many applications, including magnetohydrodynamic duct flows, chemical reaction transport systems, and Poisson Boltzmann electrostatics. These problems are characterized by sharp boundary layers and pronounced multiscale behavior, posing significant challenges for numerical methods. Existing approac

Lei Huang, Chuan Qiu, Kuan-Jui Su, Anqi Liu

Genotype imputation enables dense variant coverage for genome-wide association and risk-prediction studies, yet conventional reference-panel methods remain limited by ancestry bias and reduced rare-variant accuracy. We present Genotype Bidirectional Encoder Representations from Transformers (GenoBERT), a transformer-based, reference-free framework that token

Seonmi Choi, Semin Oh, Jeong Rye Park, Seung Yeop Yang

Persistent homology is a central tool in topological data analysis, but its application to large and noisy datasets is often limited by computational cost and the presence of spurious topological features. Noise not only increases data size but also obscures the underlying structure of the data. In this paper, we propose the Refined Characteristic Lattice Al

Miji Jeong, Young Sun Lee, Vinicius M. Placco, Yutaka Hirai

We present a detailed chemical-abundance analysis of an actinide-boost ($\log\epsilon$(Th/Dy) = -0.74) star, LAMOST J122216.85-063345.2 (J1222), a very metal-poor ([Fe/H] = -2.45) halo star with moderate enhancement in rapid neutron-capture ($r$-)process elements ([Eu/Fe] = +0.61). From high-resolution spectra (R $\sim$ 55,000) taken with Gemini-S/GHOST, we

Hillary Mutisya, John Mugane, Gavin Nyamboga, Brian Chege

We present the Thiomi Dataset, a large-scale multimodal corpus spanning ten African languages across four language families: Swahili, Kikuyu, Kamba, Kimeru, Luo, Maasai, Kipsigis, Somali (East Africa); Wolof (West Africa); and Fulani (West/Central Africa). The dataset contains over 601,000 approved sentence-level text annotations and over 385,000 audio recor

Martha Alvarez Ramírez

We present a rigorous reassessment of chaotic behavior in two-dimensional autonomous systems with singular or nonsmooth dynamics. For the Cummings-Dixon-Kaus (CDK) model, we show that blow-up regularization restores smoothness and renders the hypotheses of the Poincar\'e-Bendixson theorem applicable, thereby excluding chaotic attractors away from the singula

Chuhan Zhang, Mark R. Krumholz, Melissa K. Ness, Yuan-sen Ting

We present the Ripples of Stellar Enrichment (RoSE) simulations, which follow a Milky Way-like isolated disc galaxy with star-by-star feedback and nucleosynthesis from all significant channels -- Wolf-Rayet stars, type II supernovae, type Ia supernovae, asymptotic giant branch stars, and neutron star mergers. We use these simulations to test how elements' di

Maria R. D'Orsogna, Alan E. Lindsay, Thomas Hillen

First passage phenomena arise across physics, biology, and finance when stochastic processes first reach a threshold, triggering downstream events. Examples include the irreversible exit from a domain, a biochemical reaction, a financial selloff. While typical formulations involve diffusive motion, many stochastic processes are better described as velocity j

Stanley Wang, Velin Kojouharov, Long Yin Chung, Daniel Morton

Commercial lunar activity is accelerating the need for reliable surface infrastructure and routine operations to keep it functioning. Maintenance tasks such as inspection, cleaning, dust mitigation, and minor repair are essential to preserve performance and extend system life. A specific application is the cleaning of lunar solar arrays. Solar arrays are exp

Phonphrm Thawatdamrongkit, Sukit Seripanitkarn, Supasorn Suwajanakorn

Can a diffusion model produce its own "mental average" of a concept-one that is as sharp and realistic as a typical sample? We introduce Diffusion Mental Averages (DMA), a model-centric answer to this question. While prior methods aim to average image collections, they produce blurry results when applied to diffusion samples from the same prompt. These data-

Aakanksha Khandwaha, Edith Law

Despite AI tools becoming more prevalent and applicable to a variety of workplaces, workers consistently report uncertainty about where AI applies, what problems it can help solve, and how it fits into real workflows. In other words, there is a gap between `knowing' and `doing' when it comes to AI literacy. We propose an experiential form of AI literacy whic

Yusheng Zheng, Wenan Mao, Shuyi Cheng, Fuqiu Feng

Performance diagnosis in production-scale AI training is challenging because subtle OS-level issues can trigger cascading GPU delays and network slowdowns, degrading training efficiency across thousands of GPUs. Existing profiling tools are limited to single system layers, incur prohibitive overhead (10--30%), or lack continuous deployment capabilities, resu

Jihwan Kim, Chenglin Fan

The ski rental problem is a canonical model for online decision-making under uncertainty, capturing the fundamental trade-off between repeated rental costs and a one-time purchase. While classical algorithms focus on worst-case competitive ratios and recent "learning-augmented" methods leverage point-estimate predictions, neither approach fully exploits the

Zhuowen Liang, Xiaotian Lin, Zhengxuan Zhang, Yuyu Luo

Large language models (LLMs) are widely applied to data analytics over documents, yet direct reasoning over long, noisy documents remains brittle and error-prone. Hence, we study document question answering (QA) that consolidates dispersed evidence into a structured output (e.g., a table, graph, or chunks) to support reliable, verifiable QA. We propose a two

Jiazhou Zhou, Yucheng Chen, Hongyang Li, Qing Jiang

Multimodal Large Language Models (MLLMs) have achieved remarkable success, yet they remain prone to perception-related hallucinations in fine-grained tasks. This vulnerability arises from a fundamental limitation: their reasoning is largely restricted to the language domain, treating visual input as a static, reasoning-agnostic preamble rather than a dynamic

Aaditya Khanal, Yangyang Tao, Junxiu Zhou

Existing benchmarks measure capability -- whether a model succeeds on a single attempt -- but production deployments require reliability -- consistent success across repeated attempts on tasks of varying duration. We show these properties diverge systematically as task duration grows, and that pass@1 on short tasks is structurally blind to this divergence. W

J. Reily, Daniel J. King, Jonathan C. Marcks, M. A. Wolfe

The mapping between gate voltages applied to a double quantum dot, and the parameters of a Hubbard-like Hamiltonian, is of utmost importance for understanding and operating spin qubits. State-of-the-art techniques for measuring Hamiltonian parameters (e.g., detuning axis pulsed spectroscopy, DAPS) provide details about energy levels; however, tunnel coupling

Zikai Liao, Zhaozheng Yin

Infrared target detection (IRSTD) tasks have critical applications in areas like wilderness rescue and maritime search. However, detecting infrared targets is challenging due to their low contrast and tendency to blend into complex backgrounds, effectively camouflaging themselves. Additionally, other objects with similar features (distractors) can cause fals

Stanley Wang, Venny Kojouharov, Long Yin Chung, Daniel Morton

Future infrastructure construction on the lunar surface will require semi- or fully-autonomous operation from robots deployed at the build site. In particular, tasks such as electrical outfitting necessitate transport, routing, and fine manipulation of cables across large structures. To address this need, we present a compact and long-reach manipulator incor

Igor G. Vladimirov, Ian R. Petersen, Guodong Shi

This paper is concerned with finite-level quantum memory systems for retaining initial dynamic variables in the presence of external quantum noise. The system variables have an algebraic structure, similar to that of the Pauli matrices, and their Heisenberg picture evolution is governed by a quasilinear quantum stochastic differential equation. The latter in

Wenshuo Wang, Fan Zhang

Fine-scale-faithful neural simulation under fixed storage budgets remains challenging. Many existing methods reduce high-frequency error by improving architectures, training objectives, or rollout strategies. However, under budgeted coarsen-quantize-decode pipelines, fine detail can already be lost when the carried state is constructed. In the canonical peri

Mingzhi Xiao, Yuki Takayama

Time-of-use pricing is promoted to manage demand at public EV charging stations, yet its effectiveness depends on short run flexibility and local constraints. Using station by day by hour data from Shenzhen and Amsterdam, we estimate intraday price responsiveness on two margins, whether charging occurs in a station hour and, conditional on charging, delivere

Madeline Jennings, Novarun Deb, Ronnie de Souza Santos

As AI assistants become commonplace in daily life, the demand for solutions that reduce the cost of inference without sacrificing utility is increasing. Existing work on AI sustainability frequently emphasizes hardware and software optimizations; however, there may be comparable value in social approaches that shape user behavior and discourage unnecessary u

Ranidu Gurusinghe, Nevidu Jayatilleke

SiPaKosa is a comprehensive corpus of Sinhala and Pali doctrinal texts comprising approximately 786K sentences and 9.25M words, incorporating 16 copyright-cleared historical Buddhist documents alongside the complete web-scraped Tripitaka canonical texts. The corpus was created through high-quality OCR using Google Document AI on historical manuscripts, combi

Jingli Li, Yiyan Ma, Bo Ai, Wei Chen

Low-altitude wireless networks (LAWN) require drones to follow specific trajectories controlled by ground base stations (GBSs). However, given complex low-altitude channel conditions and limited spectrum and power resources, sensing errors and wireless link unreliability cannot be ignored, leading to trajectory deviations that threaten flight safety. To addr

Qin Yi, Ping Yang, Zilong Liu, Zeping Sui

This paper presents a dual-domain low-complexity expectation propagation (EP) detection framework for affine frequency division multiplexing (AFDM) systems. By analyzing the structural properties of the effective channel matrices in both the time and affine frequency (AF) domains, our key observation is the domain-specific quasi-banded sparsity patterns, inc

Lukuang Dong, Ziwei Li, Saierdaer Yusuyin, Xianyu Zhao

Phoneme-based ASR factorizes recognition into speech-to-phoneme (S2P) and phoneme-to-grapheme (P2G), enabling cross-lingual acoustic sharing while keeping language-specific orthography in a separate module. While large language models (LLMs) are promising for P2G, multilingual P2G remains challenging due to language-aware generation and severe cross-language

Miles Farmer, Ekincan Ufuktepe, Anne Watson, Hialo Muniz Carvalho

Large Language Models (LLMs) have emerged as a popular choice in vulnerability detection studies given their foundational capabilities, open source availability, and variety of models, but have limited scalability due to extensive compute requirements. Using the natural graph relational structure of code, we show that our proposed graph neural network (GNN)

Ruoxu Tan, Mingjie Jian, Yiming Zang

Despite its extensive development for multivariate data, semi-supervised learning remains underdeveloped for functional data, especially under discrete and noisy observations. We develop a density-sensitive semi-supervised framework for functional data supported on a low-dimensional manifold by adapting the Fermat distance to reconstructed trajectories. The

Ian Xul Belaustegui, Himani Sinhmar, Ling-Wei Kong, Andrew Michael Hein

The Linear Threshold Model (LTM) is widely used to study the propagation of collective behaviors as complex contagions. However, its dependence on discrete states and timesteps restricts its ability to capture the multiple time-scales inherent in decision-making, as well as the effects of subthreshold signaling. To address these limitations, we introduce a c

Yinxiao Tian, Ziyi Yang, Zinan Zhao, Zhen Kan

Dexterous hand teleoperation requires motion re-targeting methods that simultaneously achieve high-frequency real-time performance and enforcement of heterogeneous kinematic and safety constraints. Existing nonlinear optimization-based approaches often incur prohibitive computational cost, limiting their applicability to kilohertz-level control, while learni

Michael B. Lund

The high frequency of satellite launches, particularly over the last few years, has been a subject of significant concern, particularly relating to the future of observational astronomy, the stability of low Earth orbits, and environmental impacts. We call attention to the insufficiently-addressed silver lining of this looming satellite cloud. If the high ra

Zhiqian Zhang, Xu Zhao, Xiaoqing Xu, Guangdong Liang

In recent years, multimodal large models have continued to improve on general benchmarks. However, in real-world content moderation and adversarial settings, mainstream models still suffer from degraded generalization and catastrophic forgetting because of limited fine-grained visual perception and insufficient modeling of long-tail noise. In this paper, we

Stuart Marongwe, Stuart Kauffman

The baryonic Tully-Fisher relation (BTFR) and Faber-Jackson relation (FJR) represent fundamental scaling laws linking the baryonic mass of galaxies to their kinematics, yet their physical origin and apparent offsets between different galaxy populations have remained enigmatic. Here we present a unified theoretical framework demonstrating that both relations

Tianyu Huang, Zhenyang Ren, Zhenchen Wan, Jiyang Zheng

3D Gaussian Splatting (3DGS) enables high-fidelity reconstruction of scene geometry and appearance. Building on this capability, inserting external mesh objects into reconstructed 3DGS scenes enables interactive editing and content augmentation for immersive applications such as AR/VR, virtual staging, and digital content creation. However, achieving physica

Renrui Tian, Yahui Li, Xia Yin, Han Zhang

To mitigate BGP prefix hijacking, the Resource Public Key Infrastructure (RPKI) provides prefix origin authentication via Route Origin Validation (ROV). Despite extensive measurement efforts in IPv4, the protective impact of ROV in IPv6 has yet to be systematically assessed. Existing approaches suffer from limited observability into invalid route propagation

Dianxing Zhang, Gang Li, Sheng Li

Routing is widely used to scale large language models, from Mixture-of-Experts gating to multi-model/tool selection. A common belief is that routing to a task ``expert'' activates sparser internal computation and thus yields more certain and stable outputs (the Sparsity--Certainty Hypothesis). We test this belief by injecting routing-style meta prompts as a

Hongyu Zhu, Lin Chen, Mingsheng Shang

Multimodal Sentiment Analysis (MSA) that integrates Electroencephalogram (EEG) with peripheral physiological signals (PPS) is crucial for the development of brain-computer interface (BCI) systems. However, existing methods encounter three major challenges: (1) overlooking the region-specific characteristics of affective processing by treating EEG signals as

Weiren Zhao, Ruizhao Zi

In this paper, we study the optimal stability threshold for the Vlasov-Poisson equation with weak Fokker-Planck collision. We prove that if the initial perturbation is of size $ν^{\frac{1}{2}}$ in the critical weighted space $H_x^{\log}L^2_{v}(\langle v\rangle^m)$, then the solution remains the same size in the same space. Moreover, a space-time type Landau

E. L. Brakensiek, G. A. Bougas, S. I. Mistakidis

We propose a dynamical protocol to generate supersolids in dipolar quantum gases by sweeping a repulsive Gaussian barrier through an incoherent quasi-one-dimensional droplet array. Supersolidity is inferred by monitoring the ensuing dynamics of the density, momentum distribution, center-of-mass motion, and superfluid fraction within the framework of the exte

Thi Phuong Thao Nguyen, Kunihiko Yamauchi

We present systematic first-principles results for the electronic and magnetic properties of two-dimensional transition-metal trihalide monolayers MX3 (M = V, Cr, Mn, Fe, Ni, Pd; X = F, Cl, Br, I), focusing on their potential to host the quantum anomalous Hall effect. In particular, MnF3 and PdF3 exhibit a spin-polarized Dirac cone at the K point, spin-orbit

Kevin Ruck

In this article we consider two classical problems in Quantum Mechanics, namely the 'particle on a ring' and the 'particle in a box' from the viewpoint of symplectic topology. Interpreting the solutions of the corresponding time independent Schr\"odinger equation as orbits in a suitably chosen time dependent Hamiltonian system allows us to investigate them u

Qixiang Li, Yuan Zhou, Shuwei Huo, Chong Wang

Deep learning-based tropical cyclone (TC) forecasting methods have demonstrated significant potential and application advantages, as they feature much lower computational cost and faster operation speed than numerical weather prediction models. However, existing deep learning methods still have key limitations: they can only process a single type of sequenti

Harsh Mankodiya, Chase Gallik, Theodoros Galanos, Andriy Mulyar

The AEC-Bench is a multimodal benchmark for evaluating agentic systems on real-world tasks in the Architecture, Engineering, and Construction (AEC) domain. The benchmark covers tasks requiring drawing understanding, cross-sheet reasoning, and construction project-level coordination. This report describes the benchmark motivation, dataset taxonomy, evaluation

William J. Bensen

Large language models (LLMs) are increasingly deployed as partners in knowledge work, where the shared conversational record functions as the decision record that safeguards work continuity. We characterize a class of context failures we term trace mutations, in which distortions enter the shared record while presenting as grounded continuity. We describe tw

Po-Yen Chen, Teruyasu Mizoguchi

Bulk materials are governed by both short-range and long-range interactions, both of which are naturally captured in conventional density functional theory (DFT) calculations through Ewald summation of electrostatic contributions. In contrast, machine learning potentials (MLPs) typically rely on local atomic environment descriptors, and long-range interactio

Govind M. Chari, Behçet Açıkmeşe

We present a GPU-accelerated backend for QOCO, a C-based solver for quadratic objective second-order cone programs (SOCPs) based on a primal-dual interior point method. Our backend uses NVIDIA's cuDSS library to perform a direct sparse LDL factorization of the KKT system at each iteration. We also develop custom CUDA kernels for cone operations and show that

Thakur G. M. Hiranandani, Joseph J. Hope, Simon A. Haine

In this work, we propose new methods of parameter estimation using stochastic sampling quantum phase-space simulations. We show that it is possible to compute the quantum Fisher information (QFI) from semiclassical stochastic samples using the Truncated Wigner Approximation (TWA). This method extends the class of quantum systems whose fundamental sensitivity

Qiao Wang

Let $F$ be the thermodynamic free energy of a ferromagnetic Ising model,analytic on $\mathbb{C}^{*}\setminus\mathcal{Z}_{\beta}$. The Lee--Yang edge at $z_c\in\partial\mathcal{Z}_\beta$ is characterised by $F(z)=F(z_c)+B(z-z_c)^{\sigma+1}+o(|z-z_c|^{\sigma+1})$ with $\sigma\in(-1,0)$ and $B\neq 0$. We prove three results: Theorem A (Jensen slope): defining t

Sunil Tiwari, Payal Fofadiya

Long-horizon dialogue systems suffer from semanticdrift and unstable memory retention across extended sessions. This paper presents a Multi-Layer Memory Framework that decomposes dialogue history into working, episodic, and semantic layers with adaptive retrieval gating and retention regularization. The architecture controls cross-session drift while maintai

Payal Fofadiya, Sunil Tiwari

Large Language Models (LLMs) often experience performance degradation during long-running interactions due to increasing context length, memory saturation, and computational overhead. This paper presents an adaptive context compression framework that integrates importance-aware memory selection, coherence-sensitive filtering, and dynamic budget allocation to

Sen Wang, Huaiyi Dong, Jingyi Tian, Jiayi Li

Prevailing 2D-centric visuomotor policies exhibit a pronounced deficiency in novel view generalization, as their reliance on static observations hinders consistent action mapping across unseen views. In response, we introduce GenSplat, a feed-forward 3D Gaussian Splatting framework that facilitates view-generalized policy learning through novel view renderin

Sunil Tiwari, Payal Fofadiya, Vicky Vishwakarma

The aim of our paper is to render an object in 3-dimension using a set of its orthographic views. Corner detector (Harris Detector) is applied on the input views to obtain control points. These control points are projected perpendicular to respective views, in order to construct an envelope. A set of points describing the object in 3-dimension, are obtained

Yan He, Meihua Jin

In this paper, we introduce the concept of quasi-semi hyperbolic pseudo-orbits and prove that quasi-semi hyperbolicity implies quasi hyperbolicity provided the error magnitude are sufficiently small. We also have successively demonstrated that both finite quasi-hyperbolic pseudo-orbits and infinite quasi-semi hyperbolic pseudo-orbits possess the bi-shadowing

Haiyang Zheng, Ruilin Zhang, Hongpeng Wang

Image clustering is one of the crucial techniques in multimedia analytics and knowledge discovery. Recently, the Deep clustering method (DC), characterized by its ability to perform feature learning and cluster assignment jointly, surpasses the performance of traditional ones on image data. However, existing methods rarely consider the role of model learning

Nidhish Thiruthukkal Puthenveettil, Topojit Debnath, Clayton Mantz, Zahra Ebrahim Nataj

The quasi-one-dimensional material Bi4I4 hosts two crystallographically similar polymorphs that realize distinct topological insulating phases separated by a first-order structural transition near room temperature. This transition occurs without a change in space group, arising instead from a subtle rearrangement of chain stacking registry. Polarization-reso

Evgenii Barts, Takahiro Morimoto, Naoto Nagaosa

Quantum Fisher information (QFI) sets the ultimate precision of optical phase measurements and reveals multiphoton entanglement, but it is not accessible with conventional photodetection. We theoretically predict that a photodetector utilizing the shot noise of the quantum-geometric shift current of exciton polaritons can directly measure the QFI of nonclass

Chengzhen Meng, Chenming He, Yidong Jiang, Xiaoran Fan

The potential usage of UAVs in daily life has made monitoring them essential. However, existing systems for monitoring UAVs typically rely on cameras, LiDARs, or radars, whose limited sensing range or high deployment cost hinder large-scale adoption. In response, we develop BSense, the first system that tracks UAVs by leveraging point clouds from commercial

Ryosuke Matsuda, Keito Kudo, Haruto Yoshida, Nobuyuki Shimizu

This paper proposes the synthetic long-video meta-evaluation (SLVMEval), a benchmark for meta-evaluating text-to-video (T2V) evaluation systems. The proposed SLVMEval benchmark focuses on assessing these systems on videos of up to 10,486 s (approximately 3 h). The benchmark targets a fundamental requirement, namely, whether the systems can accurately assess

Huaqi Tao, Bingxi Liu, Guangcheng Chen, Fulin Tang

Visual relocalization is a fundamental task in the field of 3D computer vision, estimating a camera's pose when it revisits a previously known scene. While point-based hierarchical relocalization methods have shown strong scalability and efficiency, they are often limited by sparse image observations and weak feature matching. In this work, we propose SplatH

Anci Lin, Zhiwen Zhang, Wenju Zhao

Nonconvex multi-well energies in cell-induced phase transitions give rise to fine-scale microstructures, low-regularity transition layers and sharp interfaces, all of which pose numerical challenges for physics-informed learning. Here we introduce biomimetic physics-informed neural networks (Bio-PINNs), which implement a near-to-far curriculum by progressive

Yunrui Yu, Xuxiang Feng, Pengda Qin, Pengyang Wang

Adversarial robustness evaluation faces a critical challenge as new defense paradigms emerge that can exploit limitations in existing assessment methods. This paper reveals that Dummy Classes-based defenses, which introduce an additional "dummy" class as a safety sink for adversarial examples, achieve significantly overestimated robustness under conventional

Shashwat Jha, Vishvaditya Luhach, Raju Poddar

Macular Holes, Central serous retinopathy and Diabetic Retinopathy are one of the most widespread maladies of the eyes responsible for either partial or complete vision loss, thus making it clear that early detection of the mentioned defects is detrimental for the well-being of the patient. This study intends to introduce the application of Vision Transforme

Kong Junran, Mao Mang, Liu Huan, Wang Chen

Nonequilibrium heat transport and quantum thermodynamics in light-matter interacting systems have received increasing attention. Quantum thermal devices, e.g., heat valve and head diode, have been realized. Recently, it has been discovered that the anisotropic light-matter interactions can greatly modify the eigenvalues and eigenvectors of hybrid quantum sys

Vishvaditya Luhach, Shashwat Jha

The long-term forecasting of electricity demand has been a prevalent research topic, primarily because of its economic and strategic relevance. Several machine learning as well as deep learning techniques have been developed in parallel with the growing complexity of the peak demand, planning for generation facilities and transmission augmentation in future.

Luning Sun, Van Hai Nguyen, Shusen Liu, John Klepeis

The non-equilibrium dynamics of mesoscale phase transitions are fundamentally shaped by thermal fluctuations, which not only seed instabilities but actively control kinetic pathways, including rare barrier-crossing events such as nucleation that are entirely inaccessible to deterministic models. Machine-learning surrogates for such systems must therefore rep

Roberto Albarrán-García, Martha Alvarez-Ramírez, Carlos García-Azpeitia

We analyze a three-dimensional Keen--Goodwin model that couples wage--employment dynamics with Minsky-style private debt. At zero real interest the interior equilibrium is nonhyperbolic and organized by a two-dimensional center manifold foliated by neutral Goodwin cycles. Introducing a small positive interest rate unfolds this degeneracy: we derive an explic

Souvik Roy, Ranjini Bhattacharya

We study localization in a quasiperiodic spinful antiferromagnetic Hubbard ring within a self-consistent Hartree-Fock framework, emphasizing the interplay of quasiperiodicity, staggered Zeeman-field-induced antiferromagnetic order, and electron correlations. Localization properties are characterized through inverse participation ratios, normalized participat

Siyuan Du, Siyi Li, Shuwei Bai, Ang Li

Parkinson's disease (PD) affects over ten million people worldwide. Although temporal interference (TI) and deep brain stimulation (DBS) are promising therapies, inter-individual variability limits empirical treatment selection, increasing non-negligible surgical risk and cost. Previous explorations either resort to limited statistical biomarkers that are in

Guohui Dong, Mengqi Yu, Yao Yao

Nowadays, quantum batteries (QBs) have been designed to outperform their classical counterparts by leveraging quantum advantages. For instance, the charging power greatly benefits from the entanglement generation of a collective charging scheme (e.g., the Dicke QB), especially in the ultrastrong coupling (USC) regime or even larger. However, apart from the f

Gyungho Maeng, Subeen Lim, Mi Gyoung Lee, Bonggeun Shong

Mitigating the RC delay from transistor miniaturization is essential for next-generation devices, driving a focus on interconnect electrical performance. Current copper-based interconnects face a critical challenge, that their resistivity sharply increases at the nanometer-scale due to surface and grain boundary scattering. Therefore, there is a pressing nee

Ranjini Bhattacharya, Souvik Roy

We investigate localization in a quasiperiodically engineered diamond lattice with strand-dependent Aubry-Andr\'e-Harper onsite modulations, highlighting the decisive roles of the modulation ratio $s$ and the averaged potential on the middle strand. The upper strand hosts the primary potential $\lambda$, the lower strand carries a weaker modulation $\lambda/

Zhong-Yi Wang, Ya-Min Quan, Yu-Xuan Sun, Liang-Jian Zou

Understanding the interplay of band topology, strong electron correlation, and magnetic order is the fundamental core bottleneck for realizing robust high-temperature quantum anomalous Hall effect (QAHE). Conventional two-band Anderson models are limited to paramagnetic Kondo topological insulators, failing to capture coupled topological-magnetic phase evolu

Chang Sun, Rui Shi, Tsukasa Koike, Tetsuro Sekine

Accurate segmentation of brain tissues such as gray matter and white matter from magnetic resonance imaging is essential for studying brain anatomy, diagnosing neurological disorders, and monitoring disease progression. Traditional methods, such as FSL FAST, produce tissue probability maps but often require task-specific adjustments and face challenges with

Jinlu Li

Differentiation in mathematical analysis is commonly built by using {\epsilon}-{\delta}-language. This approach also works similarly for defining continuity, Gateaux (directional) derivative and Frechet derivative in normed vector spaces, in particular, in Banach spaces, where Frechet derivatives are defined as limits of ratios with respect to the norms in t

Priyam Das, Trambak Banerjee, Prajamitra Bhuyan

We introduce BLOC (Black-box Optimization over Correlation matrices), a general framework for sparse covariance estimation with non-convex penalties. BLOC operates on the manifold of correlation matrices and reparameterizes it via an angular Cholesky mapping, transforming the positive-definite, unit-diagonal constraint into an unconstrained search over a Euc

Eric Tong, Salvador V. Balkus

In causal inference, interference occurs when the treatment of one unit may affect the outcomes of other units. The goal of this work is to serve as a guide to the use of linear outcome modeling for estimating causal effects in settings where interference may pose a challenge to identification and estimation, such as spatial and network data. We demonstrate

Zeyu Jin, Xiaoyu Qin, Songtao Zhou, Kaifeng Yun

Soccer commentary plays a crucial role in enhancing the soccer game viewing experience for audiences. Previous studies in automatic soccer commentary generation typically adopt an end-to-end method to generate anonymous live text commentary. Such generated commentary is insufficient in the context of real-world live televised commentary, as it contains anony

Duc V. Nguyen, Nguyen Thi Quynh Ly, Truong Thu Huong

A dynamic 3D mesh is a key component in Virtual Reality applications. However, this type of content demands a significant processing resource for real-time rendering. To reduce processing requirements while preserving the user experience, adjusting the level of detail of 3D meshes based on viewing distance has been proposed. In this paper, we conduct an exte

Haihong Hao, Lei Chen, Mingfei Han, Changlin Li

Existing vision-and-language navigation (VLN) models primarily reason over past and current visual observations, while largely ignoring the future visual dynamics induced by actions. As a result, they often lack an effective understanding of the causal relationship between actions and how the visual world changes, limiting robust decision-making. Humans, in

Sayyid Irsyadul Ibad, Yusaku Suzuki, Masahiro Tadokoro, Tokio Futaya

In this work, we examine microwave responses of the Pauli spin blockade (PSB) leakage current through a p-type silicon double quantum dot. We observe more than the expected two resonance lines with the main resonance line exhibits both positive and negative peaks as a function of the magnetic field, corresponding to enhancement and suppression of the PSB lea

Wenchao Sun, Xuewu Lin, Keyu Chen, Zixiang Pei

End-to-end multi-modal planning has been widely adopted to model the uncertainty of driving behavior, typically by scoring candidate trajectories and selecting the optimal one. Existing approaches generally fall into two categories: scoring a large static trajectory vocabulary, or scoring a small set of dynamically generated proposals. While static vocabular

Zeyu Jin, Songtao Zhou, Haoyu Wang, Minghao Tian

The recent advancement of Artificial Intelligence Generated Content (AIGC) has led to significant strides in modeling human interaction, particularly in the context of multimodal dialogue. While current methods impressively generate realistic dialogue in isolated modalities like speech or vision, challenges remain in controllable Multimodal Dialogue Generati