Publications

Effects of incoming particle energy and cluster size on the G-value of hydrated electrons.
Alaina Bui
H. Bekerat
Lilian Childress
Jack C Sankey
Jan Seuntjens
S. Enger
2023-02-28
Physica Medica (publié)
doi.org
An Extended State Space Model for Aggregation of Large-Scale EVs Considering Fast Charging
Sina Kiani
Keyhan Sheshyekani
Hanane Dagdougui
This article presents an extended state space model for aggregation of large-scale electric vehicles (EVs) for frequency regulation and peak… (voir plus) load shaving in power systems. The proposed model systematically deals with the fast charging of EVs as an effective solution for immediate charging requirements. Furthermore, the proposed extended state space model increases the flexibility of the EV aggregator (EVA) by enabling the EVs to participate in ancillary services with both regular and fast charging/discharging rates. This will help the EVA to provide a prompt and efficient response to severe generation-consumption imbalances. A probabilistic control approach is developed which reduces the communication burden of the EVA. Furthermore, the uncertainties related to EV users' behavior are modeled in real-time. The simulations are conducted for a typical power system including a large population of EVs, a conventional generator (CG), and a wind generation system. It is shown that the proposed aggregation model can accurately describe the aggregated behavior of a large population of EVs enabling them to efficiently participate in frequency regulation and peak load shaving services. Finally, the performance of EVA is evaluated for different driving behaviors and state of charge (SOC) levels of the EV population.
2023-02-28
IEEE Transactions on Transportation Electrification (publié)
doi.org
Memory-Efficient FPGA Implementation of Stochastic Simulated Annealing
Duckgyu Shin
Naoya Onizawa
Warren J. Gross
Takahiro Hanyu
Simulated annealing (SA) is a well-known algorithm for solving combinatorial optimization problems. However, the computation time of SA incr… (voir plus)eases rapidly, as the size of the problem grows. Recently, a stochastic simulated annealing (SSA) algorithm that converges faster than conventional SA has been reported. In this paper, we present a hardware-aware SSA (HA-SSA) algorithm for memory-efficient FPGA implementations. HA-SSA can reduce the memory usage of storing intermediate results while maintaining the computing speed of SSA. For evaluation purposes, the proposed algorithm is compared with the conventional SSA and SA approaches on maximum cut combinatorial optimization problems. HA-SSA achieves a convergence speed that is up to 114-times faster than that of the conventional SA algorithm depending on the maximum cut problem selected from the G-set which is a dataset of the maximum cut problems. HA-SSA is implemented on a field-programmable gate array (FPGA) (Xilinx Kintex-7), and it achieves up to 6-times the memory efficiency of conventional SSA while maintaining high solution quality for optimization problems.
2023-02-28
IEEE Journal on Emerging and Selected Topics in Circuits and Systems (publié)
doi.org
arxiv.org
Organizing principles of astrocytic nanoarchitecture in the mouse cerebral cortex
Christopher K. Salmon
Tabish A. Syed
J. Benjamin Kacerovsky
Nensi Alivodej
Alexandra L. Schober
Tyler F.W. Sloan
Michael T. Pratte
Michael P. Rosen
Miranda Green
Adario Chirgwin-Dasgupta
Shaurya Mehta
Affan Jilani
Yanan Wang
Hojatollah Vali
Craig A. Mandato
Kaleem Siddiqi
Keith K. Murai
Using high-resolution serial electron microscopy datasets and computer vision, this study provides a systematic analysis of astrocytic nanoa… (voir plus)rchitecture from multiple samples of layer 2/3 of adult mouse neocortex, and presents quantitative evidence that astrocytes organize their morphology into purposeful, classifiable assemblies with unique structural and subcellular organelle adaptations related to their physiological functions.
2023-02-28
Current Biology (publié)
doi.org
Predicting histopathology markers of endometrial carcinoma with a quantitative image analysis approach based on spherical harmonics in multiparametric MRI
Thierry L. Lefebvre
Ozan Ciga
Sahir Rai Bhatnagar
Yoshiko Ueno
Sameh Saif
Eric Winter-Reinhold
Anthony Dohan
Philippe Soyer
Reza Forghani
Kaleem Siddiqi
Jan Seuntjens
Caroline Reinhold
Peter Savadjiev
2023-02-28
Diagnostic and Interventional Imaging (publié)
doi.org
A Probabilistic Framework for Mutation Testing in Deep Neural Networks
Florian Tambon
Foutse Khomh
Giuliano Antoniol
2023-02-28
Information and Software Technology (publié)
doi.org
arxiv.org
Processing of social and monetary rewards in autism spectrum disorders
Sarah Baumeister
Carolin Moessnang
Nico Bast
Sarah Hohmann
Pascal Aggensteiner
Anna Kaiser
Julian Tillmann
David Goyard
Tony Charman
Sara Ambrosino
Simon Baron-Cohen
Christian Beckmann
Sven Bölte
Thomas Bourgeron
Annika Rausch
Daisy Crawley
Flavio Dell’Acqua
Guillaume Dumas
Sarah Durston
Christine Ecker … (voir 21 de plus)
Dorothea L. Floris
Vincent Frouin
Hannah Hayward
Rosemary Holt
Mark Johnson
Emily J. H. Jones
Meng-Chuan Lai
Michael V. Lombardo
Luke Mason
Beth Oakley
Marianne Oldehinkel
Antonio Persico
Antonia San José Cáceres
Thomas Wolfers
Eva Loth
Declan Murphy
Jan K. Buitelaar
Heike Tost
Andreas Meyer-Lindenberg
Tobias Banaschewski
Daniel Brandeis
Background Reward processing has been proposed to underpin the atypical social feature of autism spectrum disorder (ASD). However, previous … (voir plus)neuroimaging studies have yielded inconsistent results regarding the specificity of atypicalities for social reward processing in ASD. Aims Utilising a large sample, we aimed to assess reward processing in response to reward type (social, monetary) and reward phase (anticipation, delivery) in ASD. Method Functional magnetic resonance imaging during social and monetary reward anticipation and delivery was performed in 212 individuals with ASD (7.6–30.6 years of age) and 181 typically developing participants (7.6–30.8 years of age). Results Across social and monetary reward anticipation, whole-brain analyses showed hypoactivation of the right ventral striatum in participants with ASD compared with typically developing participants. Further, region of interest analysis across both reward types yielded ASD-related hypoactivation in both the left and right ventral striatum. Across delivery of social and monetary reward, hyperactivation of the ventral striatum in individuals with ASD did not survive correction for multiple comparisons. Dimensional analyses of autism and attention-deficit hyperactivity disorder (ADHD) scores were not significant. In categorical analyses, post hoc comparisons showed that ASD effects were most pronounced in participants with ASD without co-occurring ADHD. Conclusions Our results do not support current theories linking atypical social interaction in ASD to specific alterations in social reward processing. Instead, they point towards a generalised hypoactivity of ventral striatum in ASD during anticipation of both social and monetary rewards. We suggest this indicates attenuated reward seeking in ASD independent of social content and that elevated ADHD symptoms may attenuate altered reward seeking in ASD.
2023-02-28
The British Journal of Psychiatry (publié)
doi.org
Real-time simulation of viscoelastic tissue behavior with physics-guided deep learning
Mohammad Karami
Hervé Lombaert
David Rivest‐henault
2023-02-28
Computerized Medical Imaging and Graphics (publié)
doi.org
arxiv.org
Scalable Regret for Learning to Control Network-Coupled Subsystems With Unknown Dynamics
Sagar Sudhakara
Aditya Mahajan
Ashutosh Nayyar
Yi Ouyang
In this article, we consider the problem of controlling an unknown linear quadratic Gaussian (LQG) system consisting of multiple subsystems … (voir plus)connected over a network. Our goal is to minimize and quantify the regret (i.e., loss in performance) of our learning and control strategy with respect to an oracle who knows the system model. Upfront viewing the interconnected subsystems globally and directly using existing LQG learning algorithms for the global system results in a regret that increases super-linearly with the number of subsystems. Instead, we propose a new Thompson sampling-based learning algorithm which exploits the structure of the underlying network. We show that the expected regret of the proposed algorithm is bounded by
2023-02-28
IEEE Transactions on Control of Network Systems (publié)
doi.org
arxiv.org
Studying the challenges of developing hardware description language programs
Fatemeh Yousefifeshki
Heng Li
Foutse Khomh
2023-02-28
Information and Software Technology (publié)
doi.org
Tackling Climate Change with Machine Learning
David Rolnick
Priya L. Donti
Lynn H. Kaack
Kelly Kochanski
Alexandre Lacoste
Kris Sankaran
Andrew Slavin Ross
Nikola Milojevic-Dupont
Natasha Jaques
Anna Waldman-Brown
Alexandra Luccioni
Tegan Maharaj
Evan D. Sherwin
S. Karthik Mukkavilli
Konrad P. Kording
Carla Gomes
Andrew Y. Ng
Demis Hassabis
John C. Platt
Felix Creutzig … (voir 2 de plus)
Jennifer Chayes
Yoshua Bengio
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we d… (voir plus)escribe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.
2023-02-28
ETH Zurich (publié)
doi.org
arxiv.org
Differentially Private Release of Heterogeneous Network for Managing Healthcare Data
Rashid Hussain Khokhar
Benjamin C. M. Fung
Farkhund Iqbal
Khalil Al-Hussaeni
Mohammed Hussain
With the increasing adoption of digital health platforms through mobile apps and online services, people have greater flexibility connecting… (voir plus) with medical practitioners, pharmacists, and laboratories and accessing resources to manage their own health-related concerns. Many healthcare institutions are connecting with each other to facilitate the exchange of healthcare data, with the goal of effective healthcare data management. The contents generated over these platforms are often shared with third parties for a variety of purposes. However, sharing healthcare data comes with the potential risk of exposing patients’ sensitive information to privacy threats. In this article, we address the challenge of sharing healthcare data while protecting patients’ privacy. We first model a complex healthcare dataset using a heterogeneous information network that consists of multi-type entities and their relationships. We then propose DiffHetNet, an edge-based differentially private algorithm, to protect the sensitive links of patients from inbound and outbound attacks in the heterogeneous health network. We evaluate the performance of our proposed method in terms of information utility and efficiency on different types of real-life datasets that can be modeled as networks. Experimental results suggest that DiffHetNet generally yields less information loss and is significantly more efficient in terms of runtime in comparison with existing network anonymization methods. Furthermore, DiffHetNet is scalable to large network datasets.
2023-02-27
ACM Transactions on Knowledge Discovery from Data (publié)
doi.org