Publications related to SDSS-V Algorithms: Fast, Collision-free Trajectory Planning for Heavily Overlapping Robotic Fiber Positioners

Related publications (17)

Secure and federated genome-wide association studies for biobank-scale datasets

Sharing data across institutions for genome-wide association studies (GWAS) would enhance the discovery of genetic variation linked to health and disease1,2. However, existing data-sharing regulations limit the scope of such collaborations3. Although crypt ...

NATURE PORTFOLIO2025

How Far Can a 1-Pixel Camera Go? Solving Vision Tasks Using Photoreceptors and Computationally Designed Visual Morphology

Jiawei Fu, Isabella Lucile Yu, Amir Roshan Zamir, Andrei Atanov, Rishubh Singh

A de facto standard approach in solving computer vision tasks is to use a common high-resolution camera and choose its placement on an agent based on human intuition. On the other hand, extremely simple and well-designed visual sensors found throughout nat ...

Springer Science and Business Media Deutschland GmbH2025

Fair Decentralized Learning

Anne-Marie Kermarrec, Rishi Sharma, Marinus Abraham de Vos, Sayan Biswas

Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of hete ...

IEEE2025

Scalable kernel logistic regression with Nyström approximation: Theoretical analysis and application to discrete choice modelling

Michel Bierlaire

The application of kernel-based Machine Learning (ML) techniques to discrete choice modelling using large datasets often faces challenges due to memory requirements and the considerable number of parameters involved in these models. This complexity hampers ...

2025

Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-Constrained Cycles

Paolo Ienne, Kubilay Atasu, Jovan Blanusa

Finding cycles in directed graphs enables important applications in various domains such as finance, biology, chemistry, and network science. However, as the size of graph datasets continues to grow, it becomes increasingly difficult to discover cycles wit ...

arXiv2023

Performance of the local reconstruction algorithms for the CMS hadron calorimeter with Run 2 data

Jan Steggemann, Konstantin Androsov, Anna Mascellani

A description is presented of the algorithms used to reconstruct energy deposited in the CMS hadron calorimeter during Run 2 (2015-2018) of the LHC. During Run 2, the characteristic bunch-crossing spacing for proton-proton collisions was 25 ns, which resul ...

Iop Publishing Ltd2023

U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search

Pascal Frossard, Ahmet Caner Yüzügüler, Nikolaos Dimitriadis

Optimizing resource utilization in target platforms is key to achieving high performance during DNN inference. While optimizations have been proposed for inference latency, memory footprint, and energy consumption, prior hardware-aware neural architecture ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Putting ridesharing to the test: efficient and scalable solutions and the power of dynamic vehicle relocation

Boi Faltings, Panayiotis Danassis, Marija Sakota

We study the optimization of large-scale, real-time ridesharing systems and propose a modular design methodology, Component Algorithms for Ridesharing (CAR). We evaluate a diverse set of CARs (14 in total), focusing on the key algorithmic components of rid ...

SPRINGER2022

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks

Chen Liu

In the last decade, deep neural networks have achieved tremendous success in many fields of machine learning. However, they are shown vulnerable against adversarial attacks: well-designed, yet imperceptible, perturbations can make the state-of-the-art deep ...

EPFL2022

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

Michel Bierlaire, Virginie Janine Camille Lurkin, Timothy Michael Hillel, Gael Lederrey

The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the esti ...

2021

Assisted specification of discrete choice models

Michel Bierlaire, Timothy Michael Hillel, Nicola Marco Ortelli

Determining appropriate utility specifications for discrete choice models is time-consuming and prone to errors. With the availability of larger and larger datasets, as the number of possible specifications exponentially grows with the number of variables ...

ELSEVIER SCI LTD2021

LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads

Martin Jaggi, Sebastian Urban Stich

In distributed optimization, parameter updates from the gradient computing node devices have to be aggregated in every iteration on the orchestrating server. When these updates are sent over an arbitrary commodity network, bandwidth and latency can be limi ...

MICROTOME PUBLISHING2021

Three-Dimensional Optical Diffraction Tomography With Lippmann-Schwinger Model

Michaël Unser, Demetri Psaltis, Emmanuel Emilien Louis Soubies, Thanh-An Michel Pham, Joowon Lim, Ahmed Bassam Sayed Ayoub Mohamed Emam

A broad class of imaging modalities involve the resolution of an inverse-scattering problem. Among them, three-dimensional optical diffraction tomography (ODT) comes with its own challenges. These include a limited range of views, a large size of the sampl ...

2020

Detection of Bad PMU Data using Machine Learning Techniques

Sidi-Rachid Cherkaoui, Alexandra Cameron Karpilow

This project presents a pre-State Estimation method for the detection of Bad Data (BD) in Phasor Measurement Units (PMUs) using correlation analysis and a Neural Network Classifier. Presented in this paper is the algorithm design, the steps for generating ...

IEEE2020

Active Improvement of Control Policies with Bayesian Gaussian Mixture Model

Sylvain Calinon, Noémie Laure Gwendoline Jaquier, Emmanuel Pignat, Hakan Girgin

Learning from demonstration (LfD) is an intuitive framework allowing non-expert users to easily (re-)program robots. However, the quality and quantity of demonstrations have a great influence on the generalization performances of LfD approaches. In this pa ...

IEEE2020

Haldane Gap of the Three-Box Symmetric SU(3) Chain

Frédéric Mila, Pierre Marcel Nataf, Samuel Gozel

Motivated by the recent generalization of the Haldane conjecture to SU(3) chains [Lajko et al., Nucl. Phys. B924, 508 (2017)] according to which a Haldane gap should be present for symmetric representations if the number of boxes in the Young diagram is a ...

2020

Removing imaging systematics from galaxy clustering measurements with Obiwan: application to the SDSS-IV extended Baryon Oscillation Spectroscopic Survey emission-line galaxy sample

Cheng Zhao, Anand Stéphane Raichoor

This work presents the application of a new tool, Obiwan, which uses image simulations to determine the selection function of a galaxy redshift survey and calculate three-dimensional (3D) clustering statistics. Obiwan relies on a forward model of the proce ...

OXFORD UNIV PRESS2020