Publications associées à Algorithmic efficiency

Biomembrane modeling with isogeometric analysis

We consider the numerical approximation of lipid biomembranes at equilibrium described by the Canham-Helfrich model, according to which the bending energy is minimized under area and volume constraints. Energy minimization is performed via L-2-gradient flo ...

ELSEVIER SCIENCE SA2019

Variance-Reduced Stochastic Learning by Networked Agents Under Random Reshuffling

Ali H. Sayed, Bicheng Ying, Kun Yuan

This paper develops a distributed variance-reduced strategy for a collection of interacting agents that are connected by a graph topology. The resulting diffusion-AVRG (where AVRG stands for "amortized variance-reduced gradient") algorithm is shown to have ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Supplementary Material for "Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications"

Sandro Carrara, Danilo Demarchi, Simone Aiassa

The Voltammetry-Based Sensing (VBS) methods are extremely interesting due to high specificity in several biochemical applications. Several considerations can be applied to use this method to measure different analytes, and implement efficient and optimized ...

2019

Load Balancing Video Clients in Enterprise Wireless Networks

Dan-Cristian Tomozei, Lorenzo Granai, Aswin Suresh, Sergio Mena

Given the increasing popularity of real-time video communication over enterprise wireless networks, ensuring good quality of experience is becoming critical. A common problem in such networks is that the clients typically adopt the strategy of associating ...

IEEE2018

Code Generation in Computational Neuroscience: A Review of Tools and Techniques

Michael Lee Hines, Pramod Shivaji Kumbhar, Romain Brette

Advances in experimental techniques and computational power allowing researchers to gather anatomical and electrophysiological data at unprecedented levels of detail have fostered the development of increasingly complex models in computational neuroscience ...

2018

Detecting troubled-cells on two-dimensional unstructured grids using a neural network

Jan Sickmann Hesthaven, Deep Ray

In a recent paper [Ray and Hesthaven, J. Comput. Phys. 367 (2018), pp 166-191], we proposed a new type of troubled-cell indicator to detect discontinuities in the numerical solutions of one-dimensional conservation laws. This was achieved by suitably train ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2018

Near-Optimal Noisy Group Testing via Separate Decoding of Items

Volkan Cevher, Jonathan Mark Scarlett

In this paper, we revisit an efficient algorithm for noisy group testing in which each item is decoded separately (Malyutov and Mateev, 1980), and develop novel performance guarantees via an information-theoretic framework for general noise models. For the ...

IEEE2018

Detailed Heap Profiling

James Richard Larus, Stuart Anthony Byma

Modern software systems heavily use the memory heap. As systems grow more complex and compute with increasing amounts of data, it can be difficult for developers to understand how their programs actually use the bytes that they allocate on the heap and whe ...

ACM2018

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Algorithmic efficiency

Biomembrane modeling with isogeometric analysis

Variance-Reduced Stochastic Learning by Networked Agents Under Random Reshuffling

Using the new VPMADD instructions for the new post quantum key encapsulation mechanism SIKE

Supplementary Material for "Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications"

Adaptive numerical method algorithms for nonlinear viscous and bilinear oil damper models subjected to dynamic loading

Load Balancing Video Clients in Enterprise Wireless Networks

Code Generation in Computational Neuroscience: A Review of Tools and Techniques

Detecting troubled-cells on two-dimensional unstructured grids using a neural network

Near-Optimal Noisy Group Testing via Separate Decoding of Items

Detailed Heap Profiling

Graph Chatbot

Chattez avec Graph Search

Detailed Heap Profiling

Variance-Reduced Stochastic Learning by Networked Agents Under Random Reshuffling

Detecting troubled-cells on two-dimensional unstructured grids using a neural network

Code Generation in Computational Neuroscience: A Review of Tools and Techniques

Near-Optimal Noisy Group Testing via Separate Decoding of Items

Biomembrane modeling with isogeometric analysis

Supplementary Material for "Optimized Sampling Rate for Voltammetry-Based Electrochemical Sensing in Wearable and IoT Applications"

Adaptive numerical method algorithms for nonlinear viscous and bilinear oil damper models subjected to dynamic loading

Using the new VPMADD instructions for the new post quantum key encapsulation mechanism SIKE

Load Balancing Video Clients in Enterprise Wireless Networks