Publications

Topological Analysis of Single-Cell Hierarchy Reveals Inflammatory Glial Landscape of Macular Degeneration
Manik Kuchroo
Marcello DiStasio
Eric Song
Eda Calapkulu
Le Zhang
Maryam Ige
Amar H. Sheth
Madhvi Menon
Alexander Tong
Abhinav Godavarthi
Yu Xing
Scott Gigante
Holly Steach
Jessie Huang
Guillaume Huguet
Janhavi Narain
George Mourgkos
Rahul M. Dhodapkar
Matthew J. Hirn
Bastian Rieck … (voir 3 de plus)
Guy Wolf
Smita Krishnaswamy
Brian P. Hafler
2020-12-31
SSRN Electronic Journal (publié)
doi.org
Toward Tweet-Mining Framework for Extracting Terrorist Attack-Related Information and Reporting
Farkhund Iqbal
Rabia Batool
Benjamin C. M. Fung
Saiqa Aleem
Ahmed Abbasi
Abdul Rehman Javed
The widespread popularity of social networking is leading to the adoption of Twitter as an information dissemination tool. Existing research… (voir plus) has shown that information dissemination over Twitter has a much broader reach than traditional media and can be used for effective post-incident measures. People use informal language on Twitter, including acronyms, misspelled words, synonyms, transliteration, and ambiguous terms. This makes incident-related information extraction a non-trivial task. However, this information can be valuable for public safety organizations that need to respond in an emergency. This paper proposes an early event-related information extraction and reporting framework that monitors Twitter streams synthesizes event-specific information, e.g., a terrorist attack, and alerts law enforcement, emergency services, and media outlets. Specifically, the proposed framework, Tweet-to-Act (T2A), employs word embedding to transform tweets into a vector space model and then utilizes the Word Mover’s Distance (WMD) to cluster tweets for the identification of incidents. To extract reliable and valuable information from a large dataset of short and informal tweets, the proposed framework employs sequence labeling with bidirectional Long Short-Term Memory based Recurrent Neural Networks (bLSTM-RNN). Extensive experimental results suggest that our proposed framework, T2A, outperforms other state-of-the-art methods that use vector space modeling and distance calculation techniques, e.g., Euclidean and Cosine distance. T2A achieves an accuracy of 96% and an F1-score of 86.2% on real-life datasets.
2020-12-31
IEEE Access (publié)
doi.org
Towards a Trace-Preserving Tensor Network Representation of Quantum Channels
Siddarth Srinivasan
Sandesh M. Adhikary
Jacob Miller
Bibek Pokharel
Guillaume Rabusseau
Byron Boots
The problem of characterizing quantum channels arises in a number of contexts such as quantum process tomography and quantum error correctio… (voir plus)n. However, direct approaches to parameterizing and optimizing the Choi matrix representation of quantum channels face a curse of dimensionality: the number of parameters scales exponentially in the number of qubits. Recently, Torlai et al. [2020] proposed using locally purified density operators (LPDOs), a tensor network representation of Choi matrices, to overcome the unfavourable scaling in parameters. While the LPDO structure allows it to satisfy a ‘complete positivity’ (CP) constraint required of physically valid quantum channels, it makes no guarantees about a similarly required ‘trace preservation’ (TP) constraint. In practice, the TP constraint is violated, and the learned quantum channel may even be trace-increasing, which is non-physical. In this work, we present the problem of optimizing over TP LPDOs, discuss two approaches to characterizing the TP constraints on LPDOs, and outline the next steps for developing an optimization scheme.
2020-12-31
(publié)
www.semanticscholar.org
qu an tph ] 10 O ct 2 01 1 Quantum Communication in Rindler Spacetime
Kamil Brádler
P. Hayden
Prakash Panangaden
A state that an inertial observer in Minkowski space perceiv es to be the vacuum will appear to an accelerating observer to be a thermal ba … (voir plus)th of radiation. We study the impact of this Davies-Fulling-Unruh noise on comm unication, particularly quantum communication from an inertial sender to an ac celerating observer and private communication between two inertial observers i n the presence of an accelerating eavesdropper. In both cases, we establish com pact, tractable formulas for the associated communication capacities assuming enco dings that allow a single excitation in one of a fixed number of modes per use of the co mmunications channel. Our contributions include a rigorous presentatio n of the general theory of the private quantum capacity as well as a detailed analysis o f the structure of these channels, including their group-theoretic properties and proof that they are conjugate degradable. Connections between the Unruh channel a d optical amplifiers are also discussed.
2020-12-31
(publié)
www.semanticscholar.org
Uncovering the Folding Landscape of RNA Secondary Structure Using Deep Graph Embeddings
Egbert Castro
Andrew Benz
Alexander Tong
Guy Wolf
Smita Krishnaswamy
Biomolecular graph analysis has recently gained much attention in the emerging field of geometric deep learning. Here we focus on organizing… (voir plus) biomolecular graphs in ways that expose meaningful relations and variations between them. We propose a geometric scattering autoencoder (GSAE) network for learning such graph embeddings. Our embedding network first extracts rich graph features using the recently proposed geometric scattering transform. Then, it leverages a semi-supervised variational autoencoder to extract a low-dimensional embedding that retains the information in these features that enable prediction of molecular properties as well as characterize graphs. We show that GSAE organizes RNA graphs both by structure and energy, accurately reflecting bistable RNA structures. Also, the model is generative and can sample new folding trajectories.
2020-12-31
IEEE International Conference on Big Data (inconnu)
doi.org
arxiv.org
Understanding Quantum Software Engineering Challenges An Empirical Study on Stack Exchange Forums and GitHub Issues
Mohamed Raed El aoun
Heng Li
Foutse Khomh
Moses Openja
With the advance in quantum computing, quantum software becomes critical for exploring the full potential of quantum computing systems. Rece… (voir plus)ntly, quantum software engineering (QSE) becomes an emerging area attracting more and more attention. However, it is not clear what are the challenges and opportunities of quantum computing facing the software engineering community. This work aims to understand the QSE-related challenges perceived by developers. We perform an empirical study on Stack Exchange forums where developers post-QSE-related questions & answers and Github issue reports where developers raise QSE-related issues in practical quantum computing projects. Based on an existing taxonomy of question types on Stack Overflow, we first perform a qualitative analysis of the types of QSE-related questions asked on Stack Exchange forums. We then use automated topic modeling to uncover the topics in QSE-related Stack Exchange posts and GitHub issue reports. Our study highlights some particularly challenging areas of QSE that are different from that of traditional software engineering, such as explaining the theory behind quantum computing code, interpreting quantum program outputs, and bridging the knowledge gap between quantum computing and classical computing, as well as their associated opportunities.
2020-12-31
IEEE International Conference on Software Maintenance and Evolution (publié)
doi.org
arxiv.org
Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond
Dinghuai Zhang
Jie Fu
Yoshua Bengio
Aaron Courville
2020-12-31
arXiv.org (prépublication)
dblp.uni-trier.de
Using Artificial Intelligence to Visualize the Impacts of Climate Change
Alexandra Luccioni
Victor Schmidt
Vahe Vardanyan
Yoshua Bengio
Public awareness and concern about climate change often do not match the magnitude of its threat to humans and our environment. One reason f… (voir plus)or this disagreement is that it is difficult to mentally simulate the effects of a process as complex as climate change and to have a concrete representation of the impact that our individual actions will have on our own future, especially if the consequences are long term and abstract. To overcome these challenges, we propose to use cutting-edge artificial intelligence (AI) approaches to develop an interactive personalized visualization tool, the AI climate impact visualizer. It will allow a user to enter an address—be it their house, their school, or their workplace—-and it will provide them with an AI-imagined possible visualization of the future of this location in 2050 following the detrimental effects of climate change such as floods, storms, and wildfires. This image will be accompanied by accessible information regarding the science behind climate change, i.e., why extreme weather events are becoming more frequent and what kinds of changes are happening on a local and global scale.
2020-12-31
IEEE Computer Graphics and Applications (publié)
doi.org
What Makes Machine Reading Comprehension Questions Difficult? Investigating Variation in Passage Sources and Question Types
Susan Bartlett
Grzegorz Kondrak
Max Bartolo
Alastair Roberts
Johannes Welbl
Steven Bird
Ewan Klein
Edward Loper
Samuel R. Bowman
George Dahl. 2021
What
Chao Pang
Junyuan Shang
Jiaxiang Liu
Xuyi Chen
Yanbin Zhao
Yuxiang Lu
Weixin Liu
Zhi-901 hua Wu
Weibao Gong … (voir 21 de plus)
Jianzhong Liang
Zhizhou Shang
Peng Sun
Ouyang Xuan
Dianhai
Houwen Tian
Hua Wu
Haifeng Wang
Adam Trischler
Tong Wang
Xingdi Yuan
Justin Har-908
Alessandro Sordoni
Philip Bachman
Adina Williams
Nikita Nangia
Zhilin Yang
Peng Qi
Saizheng Zhang
Yoshua Bengio
ing. In
For a natural language understanding bench-001 mark to be useful in research, it has to con-002 sist of examples that are diverse and diffi… (voir plus)-003 cult enough to discriminate among current and 004 near-future state-of-the-art systems. However, 005 we do not yet know how best to select pas-006 sages to collect a variety of challenging exam-007 ples. In this study, we crowdsource multiple-008 choice reading comprehension questions for 009 passages taken from seven qualitatively dis-010 tinct sources, analyzing what attributes of pas-011 sages contribute to the difficulty and question 012 types of the collected examples. To our sur-013 prise, we find that passage source, length, and 014 readability measures do not significantly affect 015 question difficulty. Through our manual anno-016 tation of seven reasoning types, we observe 017 several trends between passage sources and 018 reasoning types, e.g., logical reasoning is more 019 often required in questions written for techni-020 cal passages. These results suggest that when 021 creating a new benchmark dataset, selecting a 022 diverse set of passages can help ensure a di-023 verse range of question types, but that passage 024 difficulty need not be a priority. 025
2020-12-31
(publié)
www.semanticscholar.org
Where Did You Learn That From? Surprising Effectiveness of Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning
Maziar Gomrokchi
Susan Amin
Hossein Aboutalebi
Alexander Wong
Doina Precup
to
2020-12-31
arXiv.org (prépublication)
dblp.uni-trier.de
Bijective-Contrastive Estimation
Jae Hyun Lim
Chin-wei Huang
Aaron Courville
Christopher Pal
In this work, we propose Bijective-Contrastive Estimation (BCE), a classification-based learning criterion for energy-based models. We gener… (voir plus)ate a collection of contrasting distributions using bijections, and solve all the classification problems between the original data distribution and the distributions induced by the bijections using a classifier parameterized by an energy model. We show that if the classification objective is minimized, the energy function will uniquely recover the data density up to a normalizing constant. This has the benefit of not having to explicitly specify a contrasting distribution, like noise contrastive estimation. Experimentally, we demonstrate that the proposed method works well on 2D synthetic datasets. We discuss the difficulty in high dimensional cases, and propose potential directions to explore for future work.
2020-12-20
Symposium on Advances in Approximate Bayesian Inference (accepté)
openreview.net
openreview.net
Functional idiosyncrasy has a shared topography with group-level connectivity alterations in autism
Oualid Benkarim
Casey Paquola
Bo-yong Park
Seok-Jun Hong
Jessica Royer
Reinder Vos de Wael
Sara Larivière
Sofie Valk
Danilo Bzdok
Laurent Mottron
Boris Bernhardt
Autism spectrum disorder (ASD) is commonly understood as a network disorder, yet case-control analyses against typically-developing controls… (voir plus) (TD) have yielded somewhat inconsistent patterns of results. The current work was centered on a novel approach to profile functional network idiosyncrasy, the inter-individual variability in the association between functional network organization and brain anatomy, and we tested the hypothesis that idiosyncrasy contributes to connectivity alterations in ASD. Studying functional network idiosyncrasy in a multi-centric dataset with 157 ASD and 172 TD, our approach revealed higher idiosyncrasy in ASD in the default mode, somatomotor and attention networks together with reduced idiosyncrasy in the lateral temporal lobe. Idiosyncrasy was found to increase with age in both ASD and TD, and was significantly correlated with symptom severity in the former group. Association analysis with structural and molecular brain features indicated that patterns of functional network idiosyncrasy were not correlated with ASD-related cortical thickness alterations, but closely with the spatial expression patterns of intracortical ASD risk genes. In line with our main hypothesis, we could demonstrate that idiosyncrasy indeed plays a strong role in the manifestation of connectivity alterations that are measurable with conventional case-control designs and may, thus, be a principal driver of inconsistency in the autism connectomics literature. These findings support important interactions between the heterogeneity of individuals with an autism diagnosis and group-level functional signatures, and help to consolidate prior research findings on the highly variable nature of the functional connectome in ASD. Our study promotes idiosyncrasy as a potential individualized diagnostic marker of atypical brain network development.
2020-12-17
bioRxiv (prépublication)
doi.org