Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

no code implementations • 3 May 2022 • Henna Kokkonen, Lauri Lovén, Naser Hossein Motlagh, Juha Partala, Alfonso González-Gil, Ester Sola, Iñigo Angulo, Madhusanka Liyanage, Teemu Leppänen, Tri Nguyen, Panos Kostakos, Mehdi Bennis, Sasu Tarkoma, Schahram Dustdar, Susanna Pirttikangas, Jukka Riekki

We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence.

Applications and Techniques for Fast Machine Learning in Science

no code implementations • 25 Oct 2021 • Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bahr, Jurgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomas E. Muller Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Kyle J Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belinavon Krosigk, Thomas K. Warburton, Maria Acosta Flechas, Anthony Aportela, Thomas Calvet, Leonardo Cristella, Daniel Diaz, Caterina Doglioni, Maria Domenica Galati, Elham E Khoda, Farah Fahim, Davide Giri, Benjamin Hawks, Duc Hoang, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Iris Johnson, Raghav Kansal, Ryan Kastner, Erik Katsavounidis, Jeffrey Krupa, Pan Li, Sandeep Madireddy, Ethan Marx, Patrick McCormack, Andres Meza, Jovan Mitrevski, Mohammed Attia Mohammed, Farouk Mokhtar, Eric Moreno, Srishti Nagu, Rohin Narayan, Noah Palladino, Zhiqiang Que, Sang Eon Park, Subramanian Ramamoorthy, Dylan Rankin, Simon Rothman, ASHISH SHARMA, Sioni Summers, Pietro Vischia, Jean-Roch Vlimant, Olivia Weng

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.

Memory-Efficient Convex Optimization for Self-Dictionary Separable Nonnegative Matrix Factorization: A Frank-Wolfe Approach

no code implementations • 23 Sep 2021 • Tri Nguyen, Xiao Fu, Ruiyuan Wu

Our algorithm capitalizes on the special update rules of a classic algorithm from the 1950s, namely, the Frank-Wolfe (FW) algorithm.

Community Detection

StressNAS: Affect State and Stress Detection Using Neural Architecture Search

no code implementations • 26 Aug 2021 • Lam Huynh, Tri Nguyen, Thu Nguyen, Susanna Pirttikangas, Pekka Siirtola

Smartwatches have rapidly evolved towards capabilities to accurately capture physiological signals.

Decision Making Neural Architecture Search

Glassy Carbon Microelectrode Arrays Enable Voltage-Peak Separated Simultaneous Detection of Dopamine and Serotonin Using Fast Scan Cyclic Voltammetry

no code implementations • 25 Nov 2020 • Elisa Castagnola, Sanitta Thongpang, Mieko Hirabayashi, Giorgio Nava, Surabhi Nimbalkar, Tri Nguyen, Sandra Lara, Alexis Oyawale, James Bunnell, Chet Moritz, Sam Kassegne

Here, we combine the use of these GC microelectrodes with the fast scan cyclic voltammetry (FSCV) technique to optimize the co-detection of dopamine and serotonin in vitro and in vivo.

Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media

no code implementations • 16 Feb 2019 • Khuong Vo, Tri Nguyen, Dang Pham, Mao Nguyen, Minh Truong, Trung Mai, Tho Quan

However, when applied with real data obtained from social media, we notice that there is a high volume of short and informal messages posted by users on those channels.

Data Augmentation Sentiment Analysis +2

MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

11 code implementations • 28 Nov 2016 • Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang

The size of the dataset and the fact that the questions are derived from real user search queries distinguishes MS MARCO from other well-known publicly available datasets for machine reading comprehension and question-answering.

Machine Reading Comprehension Question Answering