Tootfinder

On Performance of IoT Networks with Coordinated NOMA Transmission: Covert Monitoring and Information Decoding
Thai-Hoc Vu, Anh-Tu Le, Ngo Hoang Tu, Tan N. Nguyen, Miroslav Voznak
https://arxiv.org/abs/2509.01935

On Performance of IoT Networks with Coordinated NOMA Transmission: Covert Monitoring and Information Decoding
This work investigates the covertness and security performance of Internet-of-Things (IoTs) networks under Rayleigh fading environments. Specifically, a cellular source transmits covert information to cell-edge users with the assistance of an IoT master node, employing a coordinated direct and relay transmission strategy combined with non-orthogonal multiple access (NOMA). This approach not only enhances spectrum utilization but also generates friendly interference to complicate a warden's surv…

Overview of SCIDOCA 2025 Shared Task on Citation Prediction, Discovery, and Placement
An Dao, Vu Tran, Le-Minh Nguyen, Yuji Matsumoto
https://arxiv.org/abs/2509.24283 https://…

Overview of SCIDOCA 2025 Shared Task on Citation Prediction, Discovery, and Placement
We present an overview of the SCIDOCA 2025 Shared Task, which focuses on citation discovery and prediction in scientific documents. The task is divided into three subtasks: (1) Citation Discovery, where systems must identify relevant references for a given paragraph; (2) Masked Citation Prediction, which requires selecting the correct citation for masked citation slots; and (3) Citation Sentence Prediction, where systems must determine the correct reference for each cited sentence. We release a…

Empart: Interactive Convex Decomposition for Converting Meshes to Parts
Brandon Vu, Shameek Ganguly, Pushkar Joshi
https://arxiv.org/abs/2509.22847 https://

Empart: Interactive Convex Decomposition for Converting Meshes to Parts
Simplifying complex 3D meshes is a crucial step in robotics applications to enable efficient motion planning and physics simulation. Common methods, such as approximate convex decomposition, represent a mesh as a collection of simple parts, which are computationally inexpensive to simulate. However, existing approaches apply a uniform error tolerance across the entire mesh, which can result in a sub-optimal trade-off between accuracy and performance. For instance, a robot grasping an object nee…

From latent factors to language: a user study on LLM-generated explanations for an inherently interpretable matrix-based recommender system
Maxime Manderlier, Fabian Lecron, Olivier Vu Thanh, Nicolas Gillis
https://arxiv.org/abs/2509.18980

#WritersCoffeeClub
22-23-24. I gave a gnawing deja vu. I have answered these before.
25. Does 'destiny' have a rôle in modern fiction?
26. How culturally diverse do your casts tend to be?
27. Recommend a book that had an impact on your prose.
---
25. It is now a trivial plot device. As such, it — or its denial/deconstruction — begs for originality. Warcr…

xOffense: An AI-driven autonomous penetration testing framework with offensive knowledge-enhanced LLMs and multi agent systems
Phung Duc Luong, Le Tran Gia Bao, Nguyen Vu Khai Tam, Dong Huu Nguyen Khoa, Nguyen Huu Quyen, Van-Hau Pham, Phan The Duy
https://arxiv.org/abs/2509.13021

xOffense: An AI-driven autonomous penetration testing framework with offensive knowledge-enhanced LLMs and multi agent systems
This work introduces xOffense, an AI-driven, multi-agent penetration testing framework that shifts the process from labor-intensive, expert-driven manual efforts to fully automated, machine-executable workflows capable of scaling seamlessly with computational infrastructure. At its core, xOffense leverages a fine-tuned, mid-scale open-source LLM (Qwen3-32B) to drive reasoning and decision-making in penetration testing. The framework assigns specialized agents to reconnaissance, vulnerability sc…

NeuFACO: Neural Focused Ant Colony Optimization for Traveling Salesman Problem
Tran Thanh Dat, Tran Quang Khai, Pham Anh Khoi, Vu Van Khu, Do Duc Dong
https://arxiv.org/abs/2509.16938

NeuFACO: Neural Focused Ant Colony Optimization for Traveling Salesman Problem
This study presents Neural Focused Ant Colony Optimization (NeuFACO), a non-autoregressive framework for the Traveling Salesman Problem (TSP) that combines advanced reinforcement learning with enhanced Ant Colony Optimization (ACO). NeuFACO employs Proximal Policy Optimization (PPO) with entropy regularization to train a graph neural network for instance-specific heuristic guidance, which is integrated into an optimized ACO framework featuring candidate lists, restricted tour refinement, and sc…

IPA: An Information-Preserving Input Projection Framework for Efficient Foundation Model Adaptation
Yuan Yin, Shashanka Venkataramanan, Tuan-Hung Vu, Andrei Bursuc, Matthieu Cord
https://arxiv.org/abs/2509.04398

IPA: An Information-Preserving Input Projection Framework for Efficient Foundation Model Adaptation
Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, reduce adaptation cost by injecting low-rank updates into pretrained weights. However, LoRA's down-projection is randomly initialized and data-agnostic, discarding potentially useful information. Prior analyses show that this projection changes little during training, while the up-projection carries most of the adaptation, making the random input compression a performance bottleneck. We propose IPA, a feature-aware projection framewo…

Privacy Preserving Inference of Personalized Content for Out of Matrix Users
Michael Sun, Tai Vu, Andrew Wang
https://arxiv.org/abs/2508.14905 https://arxi…

Privacy Preserving Inference of Personalized Content for Out of Matrix Users
Recommender systems for niche and dynamic communities face persistent challenges from data sparsity, cold start users and items, and privacy constraints. Traditional collaborative filtering and content-based approaches underperform in these settings, either requiring invasive user data or failing when preference histories are absent. We present DeepNaniNet, a deep neural recommendation framework that addresses these challenges through an inductive graph-based architecture combining user-item in…

Benchmark Dataset Generation and Evaluation for Excel Formula Repair with LLMs
Ananya Singha, Harshita Sahijwani, Walt Williams, Emmanuel Aboah Boateng, Nick Hausman, Miguel Di Luca, Keegan Choudhury, Chaya Binet, Vu Le, Tianwei Chen, Oryan Rokeah Chen, Sulaiman Vesal, Sadid Hasan
https://arxiv.org/abs/2508.11715

Benchmark Dataset Generation and Evaluation for Excel Formula Repair with LLMs
Excel is a pervasive yet often complex tool, particularly for novice users, where runtime errors arising from logical mistakes or misinterpretations of functions pose a significant challenge. While large language models (LLMs) offer promising assistance by explaining formula errors, the automated correction of these semantic runtime errors remains an open problem. A primary challenge to advancing models for such scenarios is the severe lack of high-quality, comprehensive datasets for training a…

ASPEN: An Additional Sampling Penalty Method for Finite-Sum Optimization Problems with Nonlinear Equality Constraints
Nata\v{s}a Kreji\'c, Nata\v{s}a Krklec Jerinki\'c, Tijana Ostoji\'c, Nemanja Vu\v{c}i\'cevi\'c
https://arxiv.org/abs/2508.02299

ASPEN: An Additional Sampling Penalty Method for Finite-Sum Optimization Problems with Nonlinear Equality Constraints
We propose a novel algorithm for solving non-convex, nonlinear equality-constrained finite-sum optimization problems. The proposed algorithm incorporates an additional sampling strategy for sample size update into the well-known framework of quadratic penalty methods. Thus, depending on the problem at hand, the resulting method may exhibit a sample size strategy ranging from a mini-batch on one end, to increasing sample size that achieves the full sample eventually, on the other end of the spec…

HPQEA: A Scalable and High-Performance Quantum Emulator with High-Bandwidth Memory for Diverse Algorithms Support
Tran Van Duy, Tuan Hai Vu, Vu Trung Duong Le, Hoai Luan Pham, Yasuhiko Nakashima
https://arxiv.org/abs/2510.07110

HPQEA: A Scalable and High-Performance Quantum Emulator with High-Bandwidth Memory for Diverse Algorithms Support
In recent years, there has been a growing interest in the development of quantum emulation. However, existing studies often struggle to achieve broad applicability, high performance, and efficient resource and memory utilization. To address these challenges, we provide HPQEA, a quantum emulator based on the state-vector emulation approach. HPQEA includes three main features: a high-performance computing core, an optimized controlled-NOT gate computation strategy, and effective utilization of hi…

LubDubDecoder: Bringing Micro-Mechanical Cardiac Monitoring to Hearables
Siqi Zhang, Xiyuxing Zhang, Duc Vu, Tao Qiang, Clara Palacios, Jiangyifei Zhu, Yuntao Wang, Mayank Goel, Justin Chan
https://arxiv.org/abs/2509.10764

LubDubDecoder: Bringing Micro-Mechanical Cardiac Monitoring to Hearables
We present LubDubDecoder, a system that enables fine-grained monitoring of micro-cardiac vibrations associated with the opening and closing of heart valves across a range of hearables. Our system transforms the built-in speaker, the only transducer common to all hearables, into an acoustic sensor that captures the coarse "lub-dub" heart sounds, leverages their shared temporal and spectral structure to reconstruct the subtle seismocardiography (SCG) and gyrocardiography (GCG) waveforms, and extr…

Isomorphism of Clean Graphs over $\mathbb{Z}_n$ and Structural Insight into $M_2(\mathbb{Z}_p)$
Felicia Servina Djuang, Indah Emilia Wijayanti, Yeni Susanti
https://arxiv.org/abs/2509.12004

Isomorphism of Clean Graphs over $\mathbb{Z}_n$ and Structural Insight into $M_2(\mathbb{Z}_p)$
Let $R$ be a finite ring with identity. The clean graph $Cl(R)$ of a ring $R$ is a graph whose vertices are pairs $(e, u)$, where $e$ is an idempotent element and $u$ is a unit of $R$. Two distinct vertices $(e, u)$ and $(f, v)$ are adjacent if and only if $ef = fe = 0$ or $uv = vu = 1$. The graph $Cl_2(R)$ is the induced subgraph of $Cl(R)$ induced by the set $\{(e, u): e \text{ is a nonzero idempotent and } u \text{ is a unit of } R\}$. In this study, we present properties that arise from the…

Singularity of non-pluripolar cohomology classes
Duc-Bao Nguyen, Shuang Su, Duc-Viet Vu
https://arxiv.org/abs/2508.14669 https://arxiv.org/pdf/2508.14669…

Singularity of non-pluripolar cohomology classes
We establish a relation between Lelong numbers and the full mass property of relative non-pluripolar products. We use it to show that if the restricted volume of a big cohomology class $α$ in a compact Kähler $n$-dimensional manifold $X$ to an effective divisor $D$ is of full mass, then the Lelong numbers of the non-pluripolar class $\langle α^{n-1}\rangle$ at every point in the support of $D$ is zero. In particular, we obtain that on projective manifolds, the Lelong numbers of the non-pluri…

Long time behavior of the Vlasov-Maxwell-Fokker-Planck system with a strong external magnetic field
Anh-Tuan Vu
https://arxiv.org/abs/2508.12231 https://ar…

Long time behavior of the Vlasov-Maxwell-Fokker-Planck system with a strong external magnetic field
The subject matter of this paper concerns the magnetic confinement of plasma. We investigate the long time behavior of the Vlasov-Maxwell-Fokker-Planck system under the effect of a strong external magnetic field. We first formally derive an asymptotically reduced model. In three space dimensions, a constraint occurs along the parallel direction to the magnetic field. To eliminate the corresponding Lagrange multiplier, we then study the Vlasov-Maxwell-Fokker-Planck system in two spatial dimensio…

SCA-LLM: Spectral-Attentive Channel Prediction with Large Language Models in MIMO-OFDM
Ke He, Le He, Lisheng Fan, Xianfu Lei, Thang X. Vu, George K. Karagiannidis, Symeon Chatzinotas
https://arxiv.org/abs/2509.08139

SCA-LLM: Spectral-Attentive Channel Prediction with Large Language Models in MIMO-OFDM
In recent years, the success of large language models (LLMs) has inspired growing interest in exploring their potential applications in wireless communications, especially for channel prediction tasks. However, directly applying LLMs to channel prediction faces a domain mismatch issue stemming from their text-based pre-training. To mitigate this, the ``adapter + LLM" paradigm has emerged, where an adapter is designed to bridge the domain gap between the channel state information (CSI) data and …

TotalRegistrator: Towards a Lightweight Foundation Model for CT Image Registration
Xuan Loc Pham, Gwendolyn Vuurberg, Marjan Doppen, Joey Roosen, Tip Stille, Thi Quynh Ha, Thuy Duong Quach, Quoc Vu Dang, Manh Ha Luu, Ewoud J. Smit, Hong Son Mai, Mattias Heinrich, Bram van Ginneken, Mathias Prokop, Alessa Hering
https://arxiv.org/abs/2508.0…

TotalRegistrator: Towards a Lightweight Foundation Model for CT Image Registration
Image registration is a fundamental technique in the analysis of longitudinal and multi-phase CT images within clinical practice. However, most existing methods are tailored for single-organ applications, limiting their generalizability to other anatomical regions. This work presents TotalRegistrator, an image registration framework capable of aligning multiple anatomical regions simultaneously using a standard UNet architecture and a novel field decomposition strategy. The model is lightweight…

Bridging Simulation and Experiment: A Self-Supervised Domain Adaptation Framework for Concrete Damage Classification
Chen Xu, Giao Vu, Ba Trung Cao, Zhen Liu, Fabian Diewald, Yong Yuan, G\"unther Meschke
https://arxiv.org/abs/2508.04538

Bridging Simulation and Experiment: A Self-Supervised Domain Adaptation Framework for Concrete Damage Classification
Reliable assessment of concrete degradation is critical for ensuring structural safety and longevity of engineering structures. This study proposes a self-supervised domain adaptation framework for robust concrete damage classification using coda wave signals. To support this framework, an advanced virtual testing platform is developed, combining multiscale modeling of concrete degradation with ultrasonic wave propagation simulations. This setup enables the generation of large-scale labeled syn…

Comparing Methodologies for Ranking Alternatives: A case study in assessing bank financial performance
Dong Trung Chinh, Nguyen Thi Thu Hien, Pham Huong Quynh, Vu Quang Minh
https://arxiv.org/abs/2509.08451

Comparing Methodologies for Ranking Alternatives: A case study in assessing bank financial performance
Bank financial performance encapsulates an institution's capacity to effectively manage its assets, capital, and operational activities to generate profits and ensure stability. Evaluating this performance necessitates the integration of diverse metrics, including profitability indicators, loan growth rates, capital utilization efficiency, and more. Nevertheless, directly comparing the financial performance across different banks presents a complex challenge due to inherent disparities in their…

Expanding a 4-qubit Dicke State a to 5-qubit Dicke State with Limited Qubit Access
Bibhuti Thapa, Oberon Moran, Duc-Kha Vu, Fatih Ozaydin
https://arxiv.org/abs/2508.07977 https:…

Expanding a 4-qubit Dicke State a to 5-qubit Dicke State with Limited Qubit Access
In scenarios where full access to all qubits of a multipartite quantum system is available and global operations can be implemented, the preparation of arbitrary entangled states - including multipartite Dicke states - is theoretically straightforward. However, practical constraints often limit direct control over all qubits. In this work, we first present an efficient method for preparing a four-qubit Dicke state, and then demonstrate how a four-qubit Dicke state can be expanded to a five-qubi…

BigCodeArena: Unveiling More Reliable Human Preferences in Code Generation via Execution
Terry Yue Zhuo, Xiaolong Jin, Hange Liu, Juyong Jiang, Tianyang Liu, Chen Gong, Bhupesh Bishnoi, Vaisakhi Mishra, Marek Suppa, Noah Ziems, Saiteja Utpala, Ming Xu, Guangyu Song, Kaixin Li, Yuhan Cao, Bo Liu, Zheng Liu, Sabina Abdurakhmanova, Wenhao Yu, Mengzhao Jia, Jihan Yao, Kenneth Hamilton, Kumar Shridhar, Minh Chien Vu, Dingmin Wang, Jiawei Liu, Zijian Wang, Qian Liu, Binyuan Hui, Meg Risdal, …

BigCodeArena: Unveiling More Reliable Human Preferences in Code Generation via Execution
Crowdsourced model evaluation platforms, such as Chatbot Arena, enable real-time evaluation from human perspectives to assess the quality of model responses. In the coding domain, manually examining the quality of LLM-generated content is extremely challenging, as it requires understanding long chunks of raw code and deliberately simulating code execution. To this end, we introduce BigCodeArena, an open human evaluation platform for code generation backed by a comprehensive and on-the-fly execu…

Vision-Guided Targeted Grasping and Vibration for Robotic Pollination in Controlled Environments
Jaehwan Jeong, Tuan-Anh Vu, Radha Lahoti, Jiawen Wang, Vivek Alumootil, Sangpil Kim, M. Khalid Jawed
https://arxiv.org/abs/2510.06146

Vision-Guided Targeted Grasping and Vibration for Robotic Pollination in Controlled Environments
Robotic pollination offers a promising alternative to manual labor and bumblebee-assisted methods in controlled agriculture, where wind-driven pollination is absent and regulatory restrictions limit the use of commercial pollinators. In this work, we present and validate a vision-guided robotic framework that uses data from an end-effector mounted RGB-D sensor and combines 3D plant reconstruction, targeted grasp planning, and physics-based vibration modeling to enable precise pollination. First…

Gradient Flows of Interfacial Energies: Curvature Agents and Incompressibility
Keith Promislow, Truong Vu, Brian Wetton
https://arxiv.org/abs/2509.07380 https://

Gradient Flows of Interfacial Energies: Curvature Agents and Incompressibility
We present a framework for the gradient flow of sharp-interface surface energies that couple to embedded curvature active agents. We use a penalty method to develop families of locally incompressible gradient flows that couple interface stretching or compression to local flux of interfacial mass. We establish the convergence of the penalty method to an incompressible flow both formally for a broad family of surface energies and rigorously for a more narrow class of surface energies. We present …