Tootfinder

An Introduction to Zero-Order Optimization Techniques for Robotics
Armand Jordana, Jianghan Zhang, Joseph Amigo, Ludovic Righetti
https://arxiv.org/abs/2506.22087

An Introduction to Zero-Order Optimization Techniques for Robotics
Zero-order optimization techniques are becoming increasingly popular in robotics due to their ability to handle non-differentiable functions and escape local minima. These advantages make them particularly useful for trajectory optimization and policy optimization. In this work, we propose a mathematical tutorial on random search. It offers a simple and unifying perspective for understanding a wide range of algorithms commonly used in robotics. Leveraging this viewpoint, we classify many trajec…

A Brief Introduction to Quantum Query Complexity
Yassine Hamoudi
https://arxiv.org/abs/2508.08852 https://arxiv.org/pdf/2508.08852

A Brief Introduction to Quantum Query Complexity
Quantum query complexity is a fundamental model for analyzing the computational power of quantum algorithms. It has played a key role in characterizing quantum speedups, from early breakthroughs such as Grover's and Simon's algorithms to more recent developments in quantum cryptography and complexity theory. This document provides a structured introduction to quantum query lower bounds, focusing on four major techniques: the hybrid method, the polynomial method, the recording method, and the ad…

Fulfillment of the Work Games: Warehouse Workers' Experiences with Algorithmic Management
EunJeong Cheon, Ingrid Erickson
https://arxiv.org/abs/2508.09438 https://

Fulfillment of the Work Games: Warehouse Workers' Experiences with Algorithmic Management
The introduction of algorithms into a large number of industries has already restructured the landscape of work and threatens to continue. While a growing body of CSCW research centered on the future of work has begun to document these shifts, relatively little is known about workers' experiences beyond those of platform-mediated gig workers. In this paper, we turn to a traditional work sector, Amazon fulfillment centers (FC), to deepen our field's empirical examination of algorithmic managemen…

Stochastic Moving Anchor Algorithms and a Popov's Scheme with Moving Anchor
James Alcala, Yat Tin Chow, Mahesh Sunkula
https://arxiv.org/abs/2506.07290

Stochastic Moving Anchor Algorithms and a Popov's Scheme with Moving Anchor
Since their introduction, anchoring methods in extragradient-type saddlepoint problems have inspired a flurry of research due to their ability to provide order-optimal rates of accelerated convergence in very general problem settings. Such guarantees are especially important as researchers consider problems in artificial intelligence (AI) and machine learning (ML), where large problem sizes demand immense computational power. Much of the more recent works explore theoretical aspects of this new…

POD-based reduced order modeling of global-in-time iterative decoupled algorithms for Biot's consolidation model
Huipeng Gu, Francesco Ballarin, Mingchao Cai, Jingzhi Li
https://arxiv.org/abs/2508.04082

POD-based reduced order modeling of global-in-time iterative decoupled algorithms for Biot's consolidation model
This paper focuses on the efficient numerical algorithms of a three-field Biot's consolidation model. The approach begins with the introduction of innovative monolithic and global-in-time iterative decoupled algorithms, which incorporate the backward differentiation formulas for time discretization. In each iteration, these algorithms involve solving a diffusion subproblem over the entire temporal domain, followed by solving a generalized Stokes subproblem over the same time interval. To accele…

SpeechRefiner: Towards Perceptual Quality Refinement for Front-End Algorithms
Sirui Li, Shuai Wang, Zhijun Liu, Zhongjie Jiang, Yannan Wang, Haizhou Li
https://arxiv.org/abs/2506.13709

SpeechRefiner: Towards Perceptual Quality Refinement for Front-End Algorithms
Speech pre-processing techniques such as denoising, de-reverberation, and separation, are commonly employed as front-ends for various downstream speech processing tasks. However, these methods can sometimes be inadequate, resulting in residual noise or the introduction of new artifacts. Such deficiencies are typically not captured by metrics like SI-SNR but are noticeable to human listeners. To address this, we introduce SpeechRefiner, a post-processing tool that utilizes Conditional Flow Match…

Particle Swarm Optimization for Quantum Circuit Synthesis: Performance Analysis and Insights
Mirza Hizriyan Nubli Hidayat, Tan Chye Cheah
https://arxiv.org/abs/2507.02898

Particle Swarm Optimization for Quantum Circuit Synthesis: Performance Analysis and Insights
This paper discusses how particle swarm optimization (PSO) can be used to generate quantum circuits to solve an instance of the MaxOne problem. It then analyzes previous studies on evolutionary algorithms for circuit synthesis. With a brief introduction to PSO, including its parameters and algorithm flow, the paper focuses on a method of quantum circuit encoding and representation as PSO parameters. The fitness evaluation used in this paper is the MaxOne problem. The paper presents experimental…

Development of a defacing algorithm to protect the privacy of head and neck cancer patients in publicly-accessible radiotherapy datasets
Kayla O'Sullivan-Steben, Luc Galarneau, John Kildea
https://arxiv.org/abs/2508.13914

Development of a defacing algorithm to protect the privacy of head and neck cancer patients in publicly-accessible radiotherapy datasets
Introduction: The rise in public medical imaging datasets has raised concerns about patient reidentification from head CT scans. However, existing defacing algorithms often remove or distort Organs at Risk (OARs) and Planning Target Volumes (PTVs) in head and neck cancer (HNC) patients, and ignore DICOM-RT Structure Set and Dose data. Therefore, we developed and validated a novel automated defacing algorithm that preserves these critical structures while removing identifiable features from HNC …

This https://arxiv.org/abs/2204.04198 has been replaced.
link: https://scholar.google.com/scholar?q=a

Modern applications of machine learning in quantum sciences
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, gen…