Efficient Data-Driven MPC for Demand Response of Commercial Buildings
Marie-Christine Par'e
Vasken Dermardiros
Antoine Lesage-Landry
Model predictive control (MPC) has been shown to significantly improve the energy efficiency of buildings while maintaining thermal comfort.… (see more) Data-driven approaches based on neural networks have been proposed to facilitate system modelling. However, such approaches are generally nonconvex and result in computationally intractable optimization problems. In this work, we design a readily implementable energy management method for small commercial buildings. We then leverage our approach to formulate a real-time demand bidding strategy. We propose a data-driven and mixed-integer convex MPC which is solved via derivative-free optimization given a limited computational time of 5 minutes to respect operational constraints. We consider rooftop unit heating, ventilation, and air conditioning systems with discrete controls to accurately model the operation of most commercial buildings. Our approach uses an input convex recurrent neural network to model the thermal dynamics. We apply our approach in several demand response (DR) settings, including a demand bidding, a time-of-use, and a critical peak rebate program. Controller performance is evaluated on a state-of-the-art building simulation. The proposed approach improves thermal comfort while reducing energy consumption and cost through DR participation, when compared to other data-driven approaches or a set-point controller.
2024-01-28
ArXiv (preprint)
doi.org
arxiv.org
Graphylo: A deep learning approach for predicting regulatory DNA and RNA sites from whole-genome multiple alignments
Dongjoon Lim
Changhyun Baek
Mathieu Blanchette
2024-01-26
iScience (published)
doi.org
Black-Box Access is Insufficient for Rigorous AI Audits
Stephen Casper
Carson Ezell
Charlotte Siegmann
Noam Kolt
Taylor Lynn Curtis
Benjamin Bucknall
Andreas Haupt
Kevin Wei
Jérémy Scheurer
Marius Hobbhahn
Lee Sharkey
Satyapriya Krishna
Marvin Von Hagen
Silas Alberti
Alan Chan
Qinyi Sun
Michael Gerovitch
David Bau
Max Tegmark
David Scott Krueger … (see 1 more)
Dylan Hadfield-Menell
External audits of AI systems are increasingly recognized as a key mechanism for AI governance. The effectiveness of an audit, however, depe… (see more)nds on the degree of access granted to auditors. Recent audits of state-of-the-art AI systems have primarily relied on black-box access, in which auditors can only query the system and observe its outputs. However, white-box access to the system’s inner workings (e.g., weights, activations, gradients) allows an auditor to perform stronger attacks, more thoroughly interpret models, and conduct fine-tuning. Meanwhile, outside-the-box access to training and deployment information (e.g., methodology, code, documentation, data, deployment details, findings from internal evaluations) allows auditors to scrutinize the development process and design more targeted evaluations. In this paper, we examine the limitations of black-box audits and the advantages of white- and outside-the-box audits. We also discuss technical, physical, and legal safeguards for performing these audits with minimal security risks. Given that different forms of access can lead to very different levels of evaluation, we conclude that (1) transparency regarding the access and methods used by auditors is necessary to properly interpret audit results, and (2) white- and outside-the-box access allow for substantially more scrutiny than black-box access alone.
2024-01-25
ArXiv (preprint)
doi.org
arxiv.org
Black-Box Access is Insufficient for Rigorous AI Audits
Stephen Casper
Carson Ezell
Charlotte Siegmann
Noam Kolt
Taylor Lynn Curtis
Benjamin Bucknall
Andreas Haupt
Kevin Wei
Jérémy Scheurer
Marius Hobbhahn
Lee Sharkey
Satyapriya Krishna
Marvin Von Hagen
Silas Alberti
Alan Chan
Qinyi Sun
Michael Gerovitch
David Bau
Max Tegmark
David Scott Krueger … (see 1 more)
Dylan Hadfield-Menell
External audits of AI systems are increasingly recognized as a key mechanism for AI governance. The effectiveness of an audit, however, depe… (see more)nds on the degree of access granted to auditors. Recent audits of state-of-the-art AI systems have primarily relied on black-box access, in which auditors can only query the system and observe its outputs. However, white-box access to the system’s inner workings (e.g., weights, activations, gradients) allows an auditor to perform stronger attacks, more thoroughly interpret models, and conduct fine-tuning. Meanwhile, outside-the-box access to training and deployment information (e.g., methodology, code, documentation, data, deployment details, findings from internal evaluations) allows auditors to scrutinize the development process and design more targeted evaluations. In this paper, we examine the limitations of black-box audits and the advantages of white- and outside-the-box audits. We also discuss technical, physical, and legal safeguards for performing these audits with minimal security risks. Given that different forms of access can lead to very different levels of evaluation, we conclude that (1) transparency regarding the access and methods used by auditors is necessary to properly interpret audit results, and (2) white- and outside-the-box access allow for substantially more scrutiny than black-box access alone.
2024-01-25
ArXiv (preprint)
doi.org
arxiv.org
Black-Box Access is Insufficient for Rigorous AI Audits
Stephen Casper
Carson Ezell
Charlotte Siegmann
Noam Kolt
Taylor Lynn Curtis
Benjamin Bucknall
Andreas Haupt
Kevin Wei
Jérémy Scheurer
Marius Hobbhahn
Lee Sharkey
Satyapriya Krishna
Marvin Von Hagen
Silas Alberti
Alan Chan
Qinyi Sun
Michael Gerovitch
David Bau
Max Tegmark
David Scott Krueger … (see 1 more)
Dylan Hadfield-Menell
External audits of AI systems are increasingly recognized as a key mechanism for AI governance. The effectiveness of an audit, however, depe… (see more)nds on the degree of access granted to auditors. Recent audits of state-of-the-art AI systems have primarily relied on black-box access, in which auditors can only query the system and observe its outputs. However, white-box access to the system’s inner workings (e.g., weights, activations, gradients) allows an auditor to perform stronger attacks, more thoroughly interpret models, and conduct fine-tuning. Meanwhile, outside-the-box access to training and deployment information (e.g., methodology, code, documentation, data, deployment details, findings from internal evaluations) allows auditors to scrutinize the development process and design more targeted evaluations. In this paper, we examine the limitations of black-box audits and the advantages of white- and outside-the-box audits. We also discuss technical, physical, and legal safeguards for performing these audits with minimal security risks. Given that different forms of access can lead to very different levels of evaluation, we conclude that (1) transparency regarding the access and methods used by auditors is necessary to properly interpret audit results, and (2) white- and outside-the-box access allow for substantially more scrutiny than black-box access alone.
2024-01-25
ArXiv (preprint)
doi.org
arxiv.org
Human-Centered AI
Catherine Régis
Jean-Louis Denis
Maria Luciana Axente
Atsuo Kishimoto
2024-01-25
(published)
doi.org
Human-Centered AI
Catherine Régis
Jean-Louis Denis
Maria Luciana Axente
Atsuo Kishimoto
2024-01-25
(published)
doi.org
Neighbor-Aware Calibration of Segmentation Networks with Penalty-Based Constraints
Balamurali Murugesan
Sukesh Adiga Vasudeva
Bingyuan Liu
Hervé Lombaert
Ismail Ben Ayed
Jose Dolz
Ensuring reliable confidence scores from deep neural networks is of paramount significance in critical decision-making systems, particularly… (see more) in real-world domains such as healthcare. Recent literature on calibrating deep segmentation networks has resulted in substantial progress. Nevertheless, these approaches are strongly inspired by the advancements in classification tasks, and thus their uncertainty is usually modeled by leveraging the information of individual pixels, disregarding the local structure of the object of interest. Indeed, only the recent Spatially Varying Label Smoothing (SVLS) approach considers pixel spatial relationships across classes, by softening the pixel label assignments with a discrete spatial Gaussian kernel. In this work, we first present a constrained optimization perspective of SVLS and demonstrate that it enforces an implicit constraint on soft class proportions of surrounding pixels. Furthermore, our analysis shows that SVLS lacks a mechanism to balance the contribution of the constraint with the primary objective, potentially hindering the optimization process. Based on these observations, we propose NACL (Neighbor Aware CaLibration), a principled and simple solution based on equality constraints on the logit values, which enables to control explicitly both the enforced constraint and the weight of the penalty, offering more flexibility. Comprehensive experiments on a wide variety of well-known segmentation benchmarks demonstrate the superior calibration performance of the proposed approach, without affecting its discriminative power. Furthermore, ablation studies empirically show the model agnostic nature of our approach, which can be used to train a wide span of deep segmentation networks.
2024-01-25
ArXiv (preprint)
doi.org
arxiv.org
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 … (see 480 more)
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
Z. 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. Gottel
Marco Grassi
Maxim Gromov
Vasily Gromov
Minghao Gu
X. 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
Y. 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
X. 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
Xiaonan Li
Xinglong Li
Yi Li
Yichen Li
Yufeng Li
Zhaohan Li
Zhibing Li
Ziyuan Li
Zonghui Li
Hao Liang
Jiaming Yan
Ayut Limphirat
G. Lin
Shengxin Lin
Tao Lin
Jiajie Ling
Xin Ling
Ivano Lippi
Caimei Liu
Yang Liu
Fengcheng Liu
Haidong Liu
Hao Liu
Hongbang Liu
Hongjuan Liu
Hongtao Liu
Hui Liu
Jianglai Liu
Jia-xing Liu
Jinchang Liu
Min Liu
Qian Liu
Q. Liu
Runxuan Liu
Sheng Liu
Shubin Liu
Shulin Liu
Xiaowei Liu
Xiwen Liu
Xue (Steve) 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
F. Luo
Guang Luo
Jianyi Luo
Shu Luo
Wuming Luo
Xiaojie Luo
Vladimir Lyashuk
B. 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 Muller
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
X. 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
X. 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
W. Wang
Wenshuai Wang
Xi Wang
Xiangyue Wang
Yangfu Wang
Yaoguang Wang
Yi Xing Wang
Yifang Wang
Yuanqing Wang
Yuyi Wang
Zhe Wang
Zheng 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… (see more)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 (published)
doi.org
arxiv.org
Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends
Mina Taraghi
Gianolli Dorcelus
Armstrong Foundjem
Florian Tambon
Foutse Khomh
The ubiquity of large-scale Pre-Trained Models (PTMs) is on the rise, sparking interest in model hubs, and dedicated platforms for hosting P… (see more)TMs. Despite this trend, a comprehensive exploration of the challenges that users encounter and how the community leverages PTMs remains lacking. To address this gap, we conducted an extensive mixed-methods empirical study by focusing on discussion forums and the model hub of HuggingFace, the largest public model hub. Based on our qualitative analysis, we present a taxonomy of the challenges and benefits associated with PTM reuse within this community. We then conduct a quantitative study to track model-type trends and model documentation evolution over time. Our findings highlight prevalent challenges such as limited guidance for beginner users, struggles with model output comprehensibility in training or inference, and a lack of model understanding. We also identified interesting trends among models where some models maintain high upload rates despite a decline in topics related to them. Additionally, we found that despite the introduction of model documentation tools, its quantity has not increased over time, leading to difficulties in model comprehension and selection among users. Our study sheds light on new challenges in reusing PTMs that were not reported before and we provide recommendations for various stakeholders involved in PTM reuse.
2024-01-24
ArXiv (preprint)
doi.org
arxiv.org
Marc Bellemare
Marc Gendron-Bellemare
2024-01-24
American Journal of Agricultural Economics (published)
doi.org
Beyond Predictive Algorithms in Child Welfare
Erina Seh-Young Moon
Erin Moon
Devansh Saxena
Tegan Maharaj
Shion Guha
2024-01-23
graphicsinterface.org/Graphics_Interface/2024/Conference (published)
doi.org
openreview.net