Publications

Assessing the Impact of Aircraft Arrival on Ambient Ultrafine Particle Number Concentrations in Near-Airport Communities in Boston, Massachusetts
Chloe S. Chung
Chloe S. Kim
Kevin James Lane
K. Lane
Flannery Black-Ingersoll
Eric D. Kolaczyk
Claire Schollaert
Sijia Li
Matthew C. Simon
Jonathan I. Levy
Jerrold H. Levy
2023-02-28
Environmental Research (publié)
doi.org
Automated Detection of Ileocecal Valve, Appendiceal Orifice, and Polyp During Colonoscopy Using a Deep Learning Model
M. Taghiakbari
S. Hamidi Ghalehjegh
E. Jehanno
T. Berthier
L. di Jorio
A. N. Barkun
E. Deslandres
S. Bouchard
S. Sidani
Y. Bengio
D. von Renteln
Identification and photo-documentation of the ileocecal valve (ICV) and appendiceal orifice (AO) confirm completeness of colonoscopy examina… (voir plus)tions. We hypothesized that an artificial intelligence (AI)-empowered solution could help us automatically differentiate anatomical landmarks such as AO and ICV from polyps and normal colon mucosa. We aimed to develop and test a deep convolutional neural network (DCNN) model that can automatically identify ICV and AO, and differentiate these landmarks from normal mucosa and colorectal polyps. We prospectively collected annotated full-length colonoscopy videos of 318 patients undergoing outpatient colonoscopies. We created three non-overlapping training, validation, and test datasets with 25,444 unaltered frames extracted from the colonoscopy videos showing four landmarks/image classes (AO, ICV, normal mucosa, and polyps). For each landmark, we extracted an average of 30 frames for each time of its appearance. All the extracted frames were reviewed and annotated by a team of three clinicians. Using a quality assessment tool, the clinicians examined a total of 86,754 frames (7982 AO, 8374 ICV, 32,971 polyps, and 37,427 normal mucosa) and verified whether or not the frame contained one unique landmark. For this research, all frames were extracted from the white-light colonoscopies, and all narrow-band imaging frames were excluded. A DCNN classification model was developed, validated, and tested in separate datasets of images. The primary outcome was the proportion of patients in whom the AI model could identify both ICV and AO, and differentiate them from polyps and normal mucosa, with an accuracy of detecting both AO and ICV above a threshold of 40% (representing a value in which reliable identification of the landmarks can be assumed without increasing false-positive alerts). We trained a DCNN AI model on 21,503 unaltered frames extracted from the recorded colonoscopy videos of 272 patients, and validated and tested the model on 1,924 (25 patients) and 2,017 (21 patients) unaltered frames, respectively. We applied a transfer learning technique to fine-tune the model parameters to the endoscopic images using a cross-entropy loss function and back-propagation algorithm. After training and validation, the DCNN model could identify both AO and ICV in 18 out of 21 patients (85.71%), if accuracies were above the threshold of 40%. The accuracy of the model for differentiating AO from normal mucosa, and ICV from normal mucosa were 86.37% (95% CI 84.06% to 88.45%), and 86.44% (95% CI 84.06% to 88.59%), respectively. Furthermore, the accuracy of the model for differentiating polyps from normal mucosa was 88.57% (95% CI 86.60% to 90.33%). The model can reliably distinguish these anatomical landmarks from normal mucosa and colorectal polyps. It can be implemented into automated colonoscopy report generation, photo-documentation, and quality auditing solutions to improve colonoscopy reporting quality. Other MEDTEQ M. Taghiakbari: None Declared, S. Hamidi Ghalehjegh Employee of: Imagia Canexia Health Inc. , E. Jehanno Employee of: Imagia Canexia Health Inc. , T. Berthier Employee of: Imagia Canexia Health Inc. , L. di Jorio Employee of: Imagia Canexia Health Inc. , A. N. Barkun Grant / Research support from: co-awardee in funded research projects with Imagia Canexia Health Inc., Consultant of: Medtronic Inc. and A.I. VALI Inc, E. Deslandres: None Declared, S. Bouchard: None Declared, S. Sidani: None Declared, Y. Bengio: None Declared, D. von Renteln Grant / Research support from: ERBE, Ventage, Pendopharm, and Pentax, Consultant of: Boston Scientific and Pendopharm
2023-02-28
Journal of the Canadian Association of Gastroenterology (publié)
doi.org
A Convex Reformulation and an Outer Approximation for a Large Class of Binary Quadratic Programs
Borzou Rostami
Fausto Errico
Andrea Lodi
2023-02-28
Operations Research (publié)
doi.org
Design and Implementation of Smooth Renewable Power in Cloud Data Centers
Xinxin Liu
Yu Hua
Xue Liu
Ling Yang
Yuanyuan Sun
The renewable power has been widely used in modern cloud data centers, which also produce large electricity bills and the negative impacts o… (voir plus)n environments. However, frequent fluctuation and intermittency of renewable power often cause the challenges in terms of the stability of both electricity grid and data centers, as well as decreasing the utilization of renewable power. Existing schemes fail to alleviate the renewable power fluctuation, which is caused by the essential properties of renewable power. In order to address this problem, we propose an efficient and easy-to-use smooth renewable power-aware scheme, called Smoother, which consists of Flexible Smoothing (FS) and Active Delay (AD). First, in order to smooth the fluctuation of renewable power, FS carries out the optimized charge/discharge operation via computing the minimum variance of the renewable power that is supplied to data centers per interval. Second, AD improves the utilization of renewable power via actively adjusting the execution time of deferrable workloads. Extensive experimental results via examining the traces of real-world data centers demonstrate that Smoother significantly reduces the negative impact of renewable power fluctuations on data centers and improves the utilization of renewable power by 250.88 percent on average. We have released the source codes for public use.
2023-02-28
IEEE Transactions on Cloud Computing (publié)
doi.org
Dynamic Shimming in the Cervical Spinal Cord for Multi-Echo Gradient-Echo Imaging at 3 T
E. Alonso-Ortiz
D. Papp
A. D’Astous
J. Cohen-Adad
Obtaining high quality images of the spinal cord with MRI is difficult, partly due to the fact that the spinal cord is surrounded by a numbe… (voir plus)r of structures that have differing magnetic susceptibility. This causes inhomogeneities in the magnetic field, which in turn lead to image artifacts. In order to address this issue, linear compensation gradients can be employed. The latter can be generated using an MRI scanner's first order gradient coils and adjusted on a per-slice basis, in order to correct for through-plane ("z") magnetic field gradients. This approach is referred to as z-shimming. The aim of this study is two-fold. The first aim was to replicate aspects of a previous study wherein z-shimming was found to improve image quality in T2*-weighted echo-planar imaging. Our second aim was to improve upon the z-shimming approach by including in-plane compensation gradients and adjusting the compensation gradients during the image acquisition process so that they take into account respiration-induced magnetic field variations. We refer to this novel approach as realtime dynamic shimming. Measurements performed in a group of 12 healthy volunteers at 3 T show improved signal homogeneity along the spinal cord when using z-shimming. Signal homogeneity may be further improved by including realtime compensation for respiration-induced field gradients and by also doing this for gradients along the in-plane axes.
2023-02-28
Neuroimage. Reports (publié)
doi.org
arxiv.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
Imaging of Neck Nodes in Head and Neck Cancers – a Comprehensive Update
K. Bhattacharya
A. Mahajan
R. Vaish
S. Rane
S. Shukla
A.K. D'Cruz
2023-02-28
Clinics in oncology (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
Proactive Contact Tracing
Prateek Gupta
Tegan Maharaj
Martin Weiss
Nasim Rahaman
Hannah Alsdurf
Nanor Minoyan
Soren Harnois-Leblanc
Joanna Merckx
Andrew Williams
Victor Schmidt
Pierre-Luc St-Charles
Akshay Patel
Yang Zhang
David L. Buckeridge
Christopher Pal
Bernhard Schölkopf
Yoshua Bengio
The COVID-19 pandemic has spurred an unprecedented demand for interventions that can reduce disease spread without excessively restricting d… (voir plus)aily activity, given negative impacts on mental health and economic outcomes. Digital contact tracing (DCT) apps have emerged as a component of the epidemic management toolkit. Existing DCT apps typically recommend quarantine to all digitally-recorded contacts of test-confirmed cases. Over-reliance on testing may, however, impede the effectiveness of such apps, since by the time cases are confirmed through testing, onward transmissions are likely to have occurred. Furthermore, most cases are infectious over a short period; only a subset of their contacts are likely to become infected. These apps do not fully utilize data sources to base their predictions of transmission risk during an encounter, leading to recommendations of quarantine to many uninfected people and associated slowdowns in economic activity. This phenomenon, commonly termed as “pingdemic,” may additionally contribute to reduced compliance to public health measures. In this work, we propose a novel DCT framework, Proactive Contact Tracing (PCT), which uses multiple sources of information (e.g. self-reported symptoms, received messages from contacts) to estimate app users’ infectiousness histories and provide behavioral recommendations. PCT methods are by design proactive, predicting spread before it occurs. We present an interpretable instance of this framework, the Rule-based PCT algorithm, designed via a multi-disciplinary collaboration among epidemiologists, computer scientists, and behavior experts. Finally, we develop an agent-based model that allows us to compare different DCT methods and evaluate their performance in negotiating the trade-off between epidemic control and restricting population mobility. Performing extensive sensitivity analysis across user behavior, public health policy, and virological parameters, we compare Rule-based PCT to i) binary contact tracing (BCT), which exclusively relies on test results and recommends a fixed-duration quarantine, and ii) household quarantine (HQ). Our results suggest that both BCT and Rule-based PCT improve upon HQ, however, Rule-based PCT is more efficient at controlling spread of disease than BCT across a range of scenarios. In terms of cost-effectiveness, we show that Rule-based PCT pareto-dominates BCT, as demonstrated by a decrease in Disability Adjusted Life Years, as well as Temporary Productivity Loss. Overall, we find that Rule-based PCT outperforms existing approaches across a varying range of parameters. By leveraging anonymized infectiousness estimates received from digitally-recorded contacts, PCT is able to notify potentially infected users earlier than BCT methods and prevent onward transmissions. Our results suggest that PCT-based applications could be a useful tool in managing future epidemics.
2023-02-28
PLOS Digital Health (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