Xue (Steve) Liu

Ralph Cui

Postdoctorat

Linfeng Du

Doctorat - McGill

Lyu Fuyuan

Doctorat - McGill

Co-superviseur⋅e :

Kaiyuan Hu

Doctorat - McGill

Doctorat - McGill

Doctorat - McGill

Yili Jin

Doctorat - McGill

Hong Kang

Doctorat - McGill

Zonglun Li

Doctorat - McGill

Yuyan Lin

Maîtrise recherche - McGill

Junliang Luo

Doctorat - McGill

Shaoxiang Qin

Doctorat - McGill

Alejandro Salinas-Medina

Doctorat - McGill

Zipeng Sun

Doctorat - McGill

Dheeraj Vattikonda

Maîtrise recherche - McGill

Haolun Wu

Doctorat - McGill

Co-superviseur⋅e :

Fernando Diaz

Ye Yuan

Doctorat - McGill

Dun Yuan

Doctorat - McGill

Co-enseignement sensible à l’importance pour optimisation hors-ligne fondée sur un modèle

Weixu Zhang

Doctorat - McGill

Billets de blogue

1 juillet 2024

par

Ye Yuan

Can Chen

Zixuan Liu

Willie Neiswanger

Xue Liu

Lire l'article

Bidirectional Learning for Offline Model-based Optimization

20 septembre 2023

Apprentissage bidirectionnel pour l’optimisation hors ligne basée sur un modèle

par

Can Chen

Yingxue Zhang

Xue Liu

Mark Coates

Lire l'article

Publications

FedSwarm: An Adaptive Federated Learning Framework for Scalable AIoT

Haizhou Du

Chengdong Ni

Chaoqian Cheng

Qiao Xiang

X. T. Chen

Federated learning (FL) is a key solution for datadriven the Artificial Intelligence of Things (AIoT). Although much progress has been made,… (voir plus) scalability remains a core challenge for real-world FL deployments. Existing solutions either suffer from accuracy loss or do not fully address the connectivity dynamicity of FL systems. In this article, we tackle the scalability issue with a novel, adaptive FL framework called FedSwarm, which improves system scalability for AIoT by deploying multiple collaborative edge servers. FedSwarm has two novel features: 1) adaptiveness on the number of local updates and 2) dynamicity of the synchronization between edge devices and edge servers. We formulate FedSwarm as a local update adaptation and perdevice dynamic server selection problem and prove FedSwarm‘s convergence bound. We further design a control mechanism consisting of a learning-based algorithm for collaboratively providing local update adaptation on the servers’ side and a bonus-based strategy for spurring dynamic per-device server selection on the devices’ side. Our extensive evaluation shows that FedSwarm significantly outperforms other studies with better scalability, lower energy consumption, and higher model accuracy.

2024-03-01

IEEE Internet of Things Journal (publié)

FedSwarm: An Adaptive Federated Learning Framework for Scalable AIoT

Haizhou Du

Chengdong Ni

Chaoqian Cheng

Qiao Xiang

Xi Chen

2024-03-01

IEEE Internet of Things Journal (publié)

ICE-SEARCH: A Language Model-Driven Feature Selection Approach

Tianze Yang

Tianyi Yang

Shaoshan Liu

Fuyuan Lyu

This study unveils the In-Context Evolutionary Search (ICE-SEARCH) method, the first work that melds language models (LMs) with evolutionary… (voir plus) algorithms for feature selection (FS) tasks and demonstrates its effectiveness in Medical Predictive Analytics (MPA) applications. ICE-SEARCH harnesses the crossover and mutation capabilities inherent in LMs within an evolutionary framework, significantly improving FS through the model's comprehensive world knowledge and its adaptability to a variety of roles. Our evaluation of this methodology spans three crucial MPA tasks: stroke, cardiovascular disease, and diabetes, where ICE-SEARCH outperforms traditional FS methods in pinpointing essential features for medical applications. ICE-SEARCH achieves State-of-the-Art (SOTA) performance in stroke prediction and diabetes prediction; the Decision-Randomized ICE-SEARCH ranks as SOTA in cardiovascular disease prediction. Our results not only demonstrate the efficacy of ICE-SEARCH in medical FS but also underscore the versatility, efficiency, and scalability of integrating LMs in FS tasks. The study emphasizes the critical role of incorporating domain-specific insights, illustrating ICE-SEARCH's robustness, generalizability, and swift convergence. This opens avenues for further research into comprehensive and intricate FS landscapes, marking a significant stride in the application of artificial intelligence in medical predictive analytics.

2024-02-28

ArXiv (prépublication)

AICOM-MP: an AI-based monkeypox detector for resource-constrained environments

Tianyi Yang

Tianze Yang

Andrew Liu

Na An

Shaoshan Liu

2024-02-06

Connection science (publié)

AICOM-MP: an AI-based Monkeypox Detector for Resource-Constrained Environments

Tianyi Yang

Tianze Yang

Andrew Liu

Na An

Jie Tang

Shaoshan Liu

2024-02-06

Connection Science (publié)

Real-time monitoring for the next core-collapse supernova in JUNO

Angel Abusleme

Thomas Adam

Shakeel Ahmad

Rizwan Ahmed

Sebastiano Aiello

Muhammad Akram

Abid Aleem

Fengpeng An

Qi An

Giuseppe Andronico

Nikolay Anfimov

Vito Antonelli

Tatiana Antoshkina

Burin Asavapibhop

João Pedro Athayde Marcondes de André

Didier Auguste

Weidong Bai

Nikita Balashov

Wander Baldini

Andrea Barresi … (voir 479 de plus)

Davide Basilico

Eric Baussan

Marco Bellato

Marco Beretta

Antonio Bergnoli

Daniel Bick

Lukas Bieger

Svetlana Biktemerova

Thilo Birkenfeld

Iwan Morton-blake

David Blum

Simon Blyth

Anastasia Bolshakova

Mathieu Bongrand

Clément Bordereau

Dominique Breton

Augusto Brigatti

Riccardo Brugnera

Riccardo Bruno

Antonio Budano

Jose Busto

Anatael Cabrera

Barbara Caccianiga

Hao Cai

Xiao Cai

Yanke Cai

Zucong Cai

Stéphane Callier

Antonio Cammi

Agustin Campeny

C. Cao

Guofu Cao

Jun Cao

Rossella Caruso

C. Cerna

Vanessa Cerrone

Chi Chan

Jinfan Chang

Yun Chang

Auttakit Chatrabhuti

Chao Chen

Guoming Chen

Pingping Chen

Shaomin Chen

Yixue Chen

Yu Chen

Zhangming Chen

Zhiyuan Chen

Zikang Chen

Jie Cheng

Yaping Cheng

Yuanyuan Zhang

Alexander Chepurnov

Alexey Chetverikov

Davide Chiesa

Pietro Chimenti

Yen-Ting Chin

Ziliang Chu

Artem Chukanov

Gérard Claverie

Catia Clementi

Barbara Clerbaux

Marta Colomer Molla

Selma Conforti Di Lorenzo

Alberto Coppi

Daniele Corti

Simon Csakli

Flavio Dal Corso

Olivia Dalager

Jaydeep Datta

C. Taille

Zhi Deng

Ziyan Deng

Xiaoyu Ding

Xuefeng Ding

Yayun Ding

Bayu Dirgantara

Carsten Dittrich

Sergey Dmitrievsky

Tadeas Dohnal

Dmitry Dolzhikov

Georgy Donchenko

Jianmeng Dong

Evgeny Doroshkevich

Wei Dou

Marcos Dracos

Frédéric Druillole

Ran Du

S. X. Du

K. Dugas

Stefano Dusini

Hongyue Duyang

J. Eck

Timo Enqvist

Andrea Fabbri

Ulrike Fahrendholz

Lei Fan

Jian Fang

Wen Fang

Marco Fargetta

Dmitry Fedoseev

Zhengyong Fei

Li-Cheng Feng

Qichun Feng

Federico Ferraro

Amélie Fournier

H. Gan

Feng Gao

Alberto Garfagnini

Arsenii Gavrikov

Marco Giammarchi

Nunzio Giudice

Maxim Gonchar

G. Gong

Hui Gong

Yuri Gornushkin

A. Göttel

Marco Grassi

Maxim Gromov

Vasily Gromov

Minghao Gu

Xiang Gu

Yunting Gu

M. Guan

Yuduo Guan

Nunzio Guardone

Cong Guo

Wanlei Guo

Xinheng Guo

Caren Hagner

Ran Han

Yang Han

Miao He

W. He

Tobias Heinz

Patrick Hellmuth

Yue-kun Heng

Rafael Herrera

Yuenkeung Hor

Shaojing Hou

Yee Hsiung

Bei-Zhen Hu

Hang Hu

Jianrun Hu

Jun Hu

Shouyang Hu

Tao Hu

Yuxiang Hu

Zhuojun Hu

Guihong Huang

Hanxiong Huang

Jinhao Huang

Jun-Hao Huang

Kaixuan Huang

Wenhao Huang

Xinting Huang

X. T. Huang

Yongbo Huang

Jiaqi Hui

Lei Huo

Wenju Huo

Cédric Huss

Safeer Hussain

Leonard Imbert

Ara Ioannisian

Roberto Isocrate

Arshak Jafar

Beatrice Jelmini

Ignacio Jeria

Xiaolu Ji

Huihui Jia

Junji Jia

Siyu Jian

Cailian Jiang

Di Jiang

Wei Jiang

Xiaoshan Jiang

Xiang Jing

Cécile Jollet

Philipp Kampmann

Li Kang

Rebin Karaparambil

Narine Kazarian

Ali Khan

Amina Khatun

Khanchai Khosonthongkee

Denis Korablev

K. Kouzakov

Alexey Krasnoperov

Sergey Kuleshov

Nikolay Kutovskiy

Loïc Labit

Tobias Lachenmaier

Cecilia Landini

Sébastien Leblanc

Victor Lebrin

Frederic Lefevre

Rui Li

Rupert Leitner

Jason Leung

Demin Li

Fei Li

Fule Li

Gaosong Li

Huiling Li

Jiajun Li

Mengzhao Li

Min Li

Nan Li

Qingjiang Li

Ruhui Li

Ruiting Lei

Shanfeng Li

Tao Li

Teng Li

Weidong Li

Weiguo Li

Xiaomei Li

Xiao-Nan Li

Xinglong Li

Yi Li

Yichen Li

Yufeng Li

Zhaohan Li

Zhibing Li

Ziyuan Li

Zonghui Li

Hao Liang

Jiaming Yan

Ayut Limphirat

Gen Lin

Shengxin Lin

Tao Lin

Jiajie Ling

Xin Ling

Ivano Lippi

Caimei Liu

Yang Liu

Fengcheng Liu

Haidong Liu

Hongbang Liu

Hongjuan Liu

Hongtao Liu

H. Liu

Jianglai Liu

Jia-xing Liu

Jinchang Liu

Min Liu

Qian Liu

Qi Liu

Runxuan Liu

Sheng Liu

Shubin Liu

Shulin Liu

Xiaowei Liu

Xiwen Liu

Yankai Liu

Zhen Liu

Alexey Lokhov

Paolo Lombardi

Claudio Lombardo

Kai Loo

Chuan Lu

Haoqi Lu

Jingbin Lu

Junguang Lu

Peizhi Lu

Shuxiang Lu

Xianguo Lu

Bayarto Lubsandorzhiev

Sultim Lubsandorzhiev

Livia Ludhova

Arslan Lukanov

Daibin Luo

Feijun Luo

Guang Luo

Jianyi Luo

Shu Luo

Wuming Luo

Xiaojie Luo

Vladimir Lyashuk

Biao Ma

Bing Ma

R. Q. Ma

Si Ma

Xiaoyan Ma

Xubo Ma

Jihane Maalmi

Marco Magoni

Jingyu Mai

Yury Malyshkin

Roberto Carlos Mandujano

Fabio Mantovani

Xin Mao

Yajun Mao

S. Mari

F. Marini

Agnese Martini

Matthias Mayer

Davit Mayilyan

Ints Mednieks

Yu Meng

Anita Meraviglia

Anselmo Meregaglia

Emanuela Meroni

David J. Meyhofer

Lino Miramonti

Nikhil Mohan

Michele Montuschi

Axel Müller

M. Nastasi

Dmitry V. Naumov

Elena Naumova

Diana Navas-Nicolas

Igor Nemchenok

Minh Thuan Nguyen Thi

Alexey Nikolaev

F. Ning

Zhe Ning

Hiroshi Nunokawa

Lothar Oberauer

Juan Pedro Ochoa-Ricoux

Alexander Olshevskiy

Domizia Orestano

Fausto Ortica

Rainer Othegraven

A. Paoloni

Sergio Parmeggiano

Y. P. Pei

Luca Pelicci

Anguo Peng

Yu Peng

Yuefeng Peng

Z-R Peng

Frédéric Perrot

P. Petitjean

Fabrizio Petrucci

Oliver Pilarczyk

Luis Felipe Piñeres Rico

Artyom Popov

Pascal Poussot

Ezio Previtali

Fazhi Qi

M. Qi

Xiaohui Qi

Sen Qian

Xiangyang Qian

Zhen Qian

Hao-xue Qiao

Zhonghua Qin

S. Qiu

Manhao Qu

Z. Qu

Gioacchino Ranucci

Reem Rasheed

A. Re

Abdel Rebii

Mariia Redchuk

Bin Ren

Jie Ren

Barbara Ricci

Komkrit Rientong

Mariam Rifai

Mathieu Roche

Narongkiat Rodphai

Aldo M. Romani

Bedřich Roskovec

Xianhui Ruan

Arseniy Rybnikov

Andrey Sadovsky

Paolo Saggese

Deshan Sandanayake

Anut Sangka

G. Sava

Utane Sawangwit

Michaela Schever

Cédric Schwab

Konstantin Schweizer

Alexandr Selyunin

Andrea Serafini

M. Settimo

V. Sharov

Arina Shaydurova

Jingyan Shi

Yanan Shi

Vitaly Shutov

Andrey Sidorenkov

Fedor Šimkovic

Apeksha Singhal

Chiara Sirignano

Jaruchit Siripak

Monica Sisti

Mikhail Smirnov

Oleg Smirnov

Thiago Sogo-Bezerra

Sergey Sokolov

Julanan Songwadhana

Boonrucksar Soonthornthum

Albert Sotnikov

Ondvrej vSr'amek

Warintorn Sreethawong

Achim Stahl

Luca Stanco

Konstantin Stankevich

Hans Steiger

Jochen Steinmann

Tobias Sterr

M. Stock

Virginia Strati

Alexander Studenikin

Aoqi Su

Jun Su

Shifeng Sun

Xilei Sun

Yongjie Sun Sun

Yongzhao Sun

Zhengyang Sun

Narumon Suwonjandee

Michal Szelezniak

Akira Takenaka

Jian Tang

Qiang Tang

Quan Tang

Xiao Tang

Vidhya Thara Hariharan

Eric Theisen

Alexander Tietzsch

Igor Tkachev

Tomas Tmej

M. Torri

Francesco Tortorici

K. Treskov

Andrea Triossi

Riccardo Triozzi

Wladyslaw Trzaska

Y. Tung

Cristina Tuve

Nikita Ushakov

Vadim Vedin

Carlo Venettacci

Giuseppe Verde

Maxim Vialkov

Benoit Viaud

Cornelius Moritz Vollbrecht

Katharina von Sturm

Vit Vorobel

Dmitriy Voronin

Lucia Votano

Pablo Walker

Caishen Wang

Chung-Hsiang Wang

En Wang

Guoli Wang

Jian Wang

Jun Wang

Li Wang

Lucinda W. Wang

Meng Wang

Ruiguang Wang

Siguang Wang

Wei David Wang

Wenshuai Wang

Xi Wang

Xiangyue Wang

Yangfu Wang

Yaoguang Wang

Yi Xing Wang

Yifang Wang

Yuanqing Wang

Yuyi Wang

Zhe Wang

Z. Wang

Zhimin Wang

Apimook Watcharangkool

Wei Wei

Wenlu Wei

Yadong Wei

Yuehuan Wei

The core-collapse supernova (CCSN) is considered one of the most energetic astrophysical events in the universe. The early and prompt detect… (voir plus)ion of neutrinos before (pre-SN) and during the supernova (SN) burst presents a unique opportunity for multi-messenger observations of CCSN events. In this study, we describe the monitoring concept and present the sensitivity of the system to pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton liquid scintillator detector currently under construction in South China. The real-time monitoring system is designed to ensure both prompt alert speed and comprehensive coverage of progenitor stars. It incorporates prompt monitors on the electronic board as well as online monitors at the data acquisition stage. Assuming a false alert rate of 1 per year, this monitoring system exhibits sensitivity to pre-SN neutrinos up to a distance of approximately 1.6 (0.9) kiloparsecs and SN neutrinos up to about 370 (360) kiloparsecs for a progenitor mass of 30 solar masses, considering both normal and inverted mass ordering scenarios. The pointing ability of the CCSN is evaluated by analyzing the accumulated event anisotropy of inverse beta decay interactions from pre-SN or SN neutrinos. This, along with the early alert, can play a crucial role in facilitating follow-up multi-messenger observations of the next galactic or nearby extragalactic CCSN.

2024-01-25

Journal of Cosmology and Astroparticle Physics (publié)

AIoT Smart Home via Autonomous LLM Agents

Dmitriy Rivkin

Francois Hogan

Amal Feriani

Abhisek Konar

Adam Sigal

2024-01-01

IEEE Internet of Things Journal (publié)

Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation

Chengming Hu

Haolun Wu

Xuan Li

Chen Ma

X. T. Chen

Jun Yan

Boyu Wang

2023-12-22

ArXiv (prépublication)

AdaTeacher: Adaptive Multi-Teacher Weighting for Communication Load Forecasting

Chengming Hu

Ju Wang

Di Wu

Yan Xin

Charlie Zhang

To deal with notorious delays in communication systems, it is crucial to forecast key system characteristics, such as the communication load… (voir plus). Most existing studies aggregate data from multiple edge nodes for improving the forecasting accuracy. However, the bandwidth cost of such data aggregation could be unacceptably high from the perspective of system operators. To achieve both the high forecasting accuracy and bandwidth efficiency, this paper proposes an Adaptive Multi-Teacher Weighting in Teacher-Student Learning approach, namely AdaTeacher, for communication load forecasting of multiple edge nodes. Each edge node trains a local model on its own data. A target node collects multiple models from its neighbor nodes and treats these models as teachers. Then, the target node trains a student model from teachers via Teacher-Student (T-S) learning. Unlike most existing T-S learning approaches that treat teachers evenly, resulting in a limited performance, AdaTeacher introduces a bilevel optimization algorithm to dynamically learn an importance weight for each teacher toward a more effective and accurate T-S learning process. Compared to the state-of-the-art methods, Ada Teacher not only reduces the bandwidth cost by 53.85%, but also improves the load forecasting accuracy by 21.56% and 24.24% on two real-world datasets.

2023-12-04

Global Communications Conference (publié)

Energy Saving in Cellular Wireless Networks via Transfer Deep Reinforcement Learning

Di Wu

Yi Tian Xu

M. Jenkin

Seowoo Jang

Ekram Hossain

With the increasing use of data-intensive mobile applications and the number of mobile users, the demand for wireless data services has been… (voir plus) increasing exponentially in recent years. In order to address this demand, a large number of new cellular base stations are being deployed around the world, leading to a significant increase in energy consumption and greenhouse gas emission. Consequently, energy consumption has emerged as a key concern in the fifth-generation (5G) network era and beyond. Reinforcement learning (RL), which aims to learn a control policy via interacting with the environment, has been shown to be effective in addressing network optimization problems. However, for reinforcement learning, especially deep reinforcement learning, a large number of interactions with the environment are required. This often limits its applicability in the real world. In this work, to better deal with dynamic traffic scenarios and improve real-world applicability, we propose a transfer deep reinforcement learning framework for energy optimization in cellular communication networks. Specifically, we first pre-train a set of RL-based energy-saving policies on source base stations and then transfer the most suitable policy to the given target base station in an unsupervised learning manner. Experimental results demonstrate that base station energy consumption can be reduced significantly using this approach.

2023-12-04

Global Communications Conference (publié)

Learning to Adapt: Communication Load Balancing via Adaptive Deep Reinforcement Learning

Di Wu

Yi Tian Xu

Jimmy Li

M. Jenkin

Ekram Hossain

Seowoo Jang

Yan Xin

Charlie Zhang

The association of mobile devices with network resources (e.g., base stations, frequency bands/channels), known as load balancing, is critic… (voir plus)al to reduce communication traffic congestion and network performance. Reinforcement learning (RL) has shown to be effective for communication load balancing and achieves better performance than currently used rule-based methods, especially when the traffic load changes quickly. However, RL-based methods usually need to interact with the environment for a large number of time steps to learn an effective policy and can be difficult to tune. In this work, we aim to improve the data efficiency of RL-based solutions to make them more suitable and applicable for real-world applications. Specifically, we propose a simple, yet efficient and effective deep RL-based wireless network load balancing framework. In this solution, a set of good initialization values for control actions are selected with some cost-efficient approach to center the training of the RL agent. Then, a deep RL-based agent is trained to find offsets from the initialization values that optimize the load balancing problem. Experimental evaluation on a set of dynamic traffic scenarios demonstrates the effectiveness and efficiency of the proposed method.

2023-12-04

GLOBECOM 2023 - 2023 IEEE Global Communications Conference (publié)

Realizing XR Applications Using 5G-Based 3D Holographic Communication and Mobile Edge Computing

Dun Yuan

Ekram Hossain

Di Wu

3D holographic communication has the potential to revolutionize the way people interact with each other in virtual spaces, offering immersiv… (voir plus)e and realistic experiences. However, demands for high data rates, extremely low latency, and high computations to enable this technology pose a significant challenge. To address this challenge, we propose a novel job scheduling algorithm that leverages Mobile Edge Computing (MEC) servers in order to minimize the total latency in 3D holographic communication. One of the motivations for this work is to prevent the uncanny valley effect, which can occur when the latency hinders the seamless and real-time rendering of holographic content, leading to a less convincing and less engaging user experience. Our proposed algorithm dynamically allocates computation tasks to MEC servers, considering the network conditions, computational capabilities of the servers, and the requirements of the 3D holographic communication application. We conduct extensive experiments to evaluate the performance of our algorithm in terms of latency reduction, and the results demonstrate that our approach significantly outperforms other baseline methods. Furthermore, we present a practical scenario involving Augmented Reality (AR), which not only illustrates the applicability of our algorithm but also highlights the importance of minimizing latency in achieving high-quality holographic views. By efficiently distributing the computation workload among MEC servers and reducing the overall latency, our proposed algorithm enhances the user experience in 3D holographic communications and paves the way for the widespread adoption of this technology in various applications, such as telemedicine, remote collaboration, and entertainment.

2023-10-06

ArXiv (prépublication)