Tootfinder

Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper
Xinyue Zhu, Binghao Huang, Yunzhu Li
https://arxiv.org/abs/2507.15062

Touch in the Wild: Learning Fine-Grained Manipulation with a Portable Visuo-Tactile Gripper
Handheld grippers are increasingly used to collect human demonstrations due to their ease of deployment and versatility. However, most existing designs lack tactile sensing, despite the critical role of tactile feedback in precise manipulation. We present a portable, lightweight gripper with integrated tactile sensors that enables synchronized collection of visual and tactile data in diverse, real-world, and in-the-wild settings. Building on this hardware, we propose a cross-modal representatio…

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles
Jiangfan Liu, Yongkang Guo, Fangzhi Zhong, Tianyuan Zhang, Zonglei Jing, Siyuan Liang, Jiakai Wang, Mingchuan Zhang, Aishan Liu, Xianglong Liu
https://arxiv.org/abs/2508.14527

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles
The generation of safety-critical scenarios in simulation has become increasingly crucial for safety evaluation in autonomous vehicles prior to road deployment in society. However, current approaches largely rely on predefined threat patterns or rule-based strategies, which limit their ability to expose diverse and unforeseen failure modes. To overcome these, we propose ScenGE, a framework that can generate plentiful safety-critical scenarios by reasoning novel adversarial cases and then amplif…

Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models
Maciej Wysocki, Pawe{\l} Sakowski
https://arxiv.org/abs/2508.14999 https://

Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models
This paper investigates an important problem of an appropriate variance-covariance matrix estimation in the Modern Portfolio Theory. We propose a novel framework for variancecovariance matrix estimation for purposes of the portfolio optimization, which is based on deep learning models. We employ the long short-term memory (LSTM) recurrent neural networks (RNN) along with two probabilistic deep learning models: DeepVAR and GPVAR to the task of one-day ahead multivariate forecasting. We then use …

Structure-preserving Optimal Kron-based Reduction of Radial Distribution Networks
Omid Mokhtari, Samuel Chevalier, Mads Almassalkhi
https://arxiv.org/abs/2508.15006 https://

Structure-preserving Optimal Kron-based Reduction of Radial Distribution Networks
Network reduction simplifies complex electrical networks to address computational challenges of large-scale transmission and distribution grids. Traditional network reduction methods are often based on a predefined set of nodes or lines to remain in the reduced network. This paper builds upon previous work on Optimal Kron-based Reduction of Networks (Opti-KRON), which was formulated as a mixed-integer linear program (MILP), to enhance the framework in two aspects. First, the scalability is impr…

A Lightweight Privacy-Preserving Smart Metering Billing Protocol with Dynamic Tariff Policy Adjustment
Farid Zaredar, Morteza Amini
https://arxiv.org/abs/2508.14815 https://

A Lightweight Privacy-Preserving Smart Metering Billing Protocol with Dynamic Tariff Policy Adjustment
The integration of information and communication technology (ICT) with traditional power grids has led to the emergence of smart grids. Advanced metering infrastructure (AMI) plays a crucial role in smart grids by facilitating two-way communication between smart meters and the utility provider. This bidirectional communication allows intelligent meters to report fine-grained consumption data at predefined intervals, enabling accurate billing, efficient grid monitoring and management, and rapid …

Portfolio construction using a sampling-based variational quantum scheme
Gabriele Agliardi, Dimitris Alevras, Vaibhaw Kumar, Roberto Lo Nardo, Gabriele Compostella, Sumit Kumar, Manuel Proissl, Bimal Mehta
https://arxiv.org/abs/2508.13557

Portfolio construction using a sampling-based variational quantum scheme
The efficient and effective construction of portfolios that adhere to real-world constraints is a challenging optimization task in finance. We investigate a concrete representation of the problem with a focus on design proposals of an Exchange Traded Fund. We evaluate the sampling-based CVaR Variational Quantum Algorithm (VQA), combined with a local-search post-processing, for solving problem instances that beyond a certain size become classically hard. We also propose a problem formulation tha…

Carbon-rich Sub-Neptune Interiors Are Compatible with JWST Observations
Zifan Lin, Sara Seager
https://arxiv.org/abs/2508.15117 https://arxiv.org/pdf/2508.…

Carbon-rich Sub-Neptune Interiors Are Compatible with JWST Observations
Many possible interior compositions exist for sub-Neptunes: ice-poor, ice-rich, and water-dominated interiors can all match the measured masses and radii. Motivated by recent theory of carbon-rich planet formation outside of the refractory organic carbon "soot line" and observations of carbon-rich protoplanetary disks around late M dwarfs, we propose another possible sub-Neptune composition: a carbon-rich composition consisting of an iron-silicate core, a carbon layer, and a hydrogen/helium-dom…

"Just a strange pic": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives
Ding Wang, Mark D\'iaz, Charvi Rastogi, Aida Davani, Vinodkumar Prabhakaran, Pushkar Mishra, Roma Patel, Alicia Parrish, Zoe Ashwood, Michela Paganini, Tian Huey Teh, Verena Rieser, Lora Aroyo
https://

"Just a strange pic": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives
Understanding what constitutes safety in AI-generated content is complex. While developers often rely on predefined taxonomies, real-world safety judgments also involve personal, social, and cultural perceptions of harm. This paper examines how annotators evaluate the safety of AI-generated images, focusing on the qualitative reasoning behind their judgments. Analyzing 5,372 open-ended comments, we find that annotators consistently invoke moral, emotional, and contextual reasoning that extends …

Multiclass Portfolio Optimization via Variational Quantum Eigensolver with Dicke State Ansatz
J. V. S. Scursulim, Gabriel Mattos Langeloh, Victor Leme Beltran, Samura\'i Brito
https://arxiv.org/abs/2508.13954

Multiclass Portfolio Optimization via Variational Quantum Eigensolver with Dicke State Ansatz
Combinatorial optimization is a fundamental challenge in various domains, with portfolio optimization standing out as a key application in finance. Despite numerous quantum algorithmic approaches proposed for this problem, most overlook a critical feature of realistic portfolios: diversification. In this work, we introduce a novel quantum framework for multiclass portfolio optimization that explicitly incorporates diversification by leveraging multiple parametrized Dicke states, simultaneously …

Design and Implementation of a Lightweight Object Detection System for Resource-Constrained Edge Environments
Jiyue Jiang, Mingtong Chen, Zhengbao Yang
https://arxiv.org/abs/2507.16155

Design and Implementation of a Lightweight Object Detection System for Resource-Constrained Edge Environments
This project aims to develop a system to run the object detection model under low power consumption conditions. The detection scene is set as an outdoor traveling scene, and the detection categories include people and vehicles. In this system, users data does not need to be uploaded to the cloud, which is suitable for use in environments with portable needs and strict requirements for data privacy. The MCU device used in this system is STM32H7, which has better performance among low power devic…