pwc-archive/papers-with-abstracts huggingface dataset:
Photodynamically Active Electrospun Fibres for Antibiotic-Free Infection Control
Authors: Amy Contreras, Michael J. Raxworthy, Simon Wood, Jessica D. Schiffman, Giuseppe Tronci
Date: 2021-01-05 00:00:00

Antimicrobial biomaterials are critical to aid in the regeneration of oral soft tissue and prevent or treat localised bacterial infections. With the rising trend in antibiotic resistance, there is a pressing clinical need for new antimicrobial chemistries and biomaterial design approaches enabling on-demand activation of antibiotic-free antimicrobial functionality following an infection that are environment-friendly, flexible and commercially-viable. This study explores the feasibility of integrating a bioresorbable electrospun polymer scaffold with localised antimicrobial photodynamic therapy (aPDT) capability. To enable aPDT, we encapsulated a photosensitiser (PS) in polyester fibres in the PS inert state, so that the antibacterial function would be activated on-demand via a visible light source. Fibrous scaffolds were successfully electrospun from FDA-approved polyesters, either poly(epsilon-caprolactone (PCL) or poly[(rac-lactide)-co-glycolide] (PLGA) with encapsulated PS (either methylene blue (MB) or erythrosin B (ER)). The electrospun fibres achieved an ~100 wt.% loading efficiency of PS, which significantly increased their tensile modulus and reduced their average fibre diameter and pore size with respect to PS-free controls. In vitro, PS release varied between a burst release profile to limited release within 100 hours depending on the selected scaffold formulation. Exposure of PS-encapsulated PCL fibres to visible light successfully led to at least a 1 log reduction in E. coli viability after 60 minutes of light exposure whereas PS-free electrospun controls did not inactive microbes. This study successfully demonstrates the significant potential of PS-encapsulated electrospun fibres as photodynamically active biomaterial for antibiotic-free infection control.
VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations
Authors: Ha Q. Nguyen, Khanh Lam, Linh T. Le, Hieu H. Pham, Dat Q. Tran, Dung B. Nguyen, Dung D. Le, Chi M. Pham, Hang T. T. Tong, Diep H. Dinh, Cuong D. Do, Luu T. Doan, Cuong N. Nguyen, Binh T. Nguyen, Que V. Nguyen, Au D. Hoang, Hien N. Phan, Anh T. Nguyen, Phuong H. Ho, Dat T. Ngo, Nghia T. Nguyen, Nhan T. Nguyen, Minh Dao, Van Vu
Date: 2020-12-30 00:00:00

Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available (https://www.physionet.org/content/vindr-cxr/1.0.0/) in DICOM format along with the labels of both the training set and the test set.
Interpretable Recommender System With Heterogeneous Information: A Geometric Deep Learning Perspective
Authors: Yan Leng, Rodrigo Ruiz, Xiaowen Dong, Alex Pentland
Date: 2020-09-20 00:00:00
Tasks: Decision Making, Deep Learning, Graph Attention, Management, Recommendation Systems

Recommender systems (RS) are ubiquitous in the digital space. This paper develops a deep learning-based approach to address three practical challenges in RS: complex structures of high-dimensional data, noise in relational information, and the black-box nature of machine learning algorithms. Our method—Multi-Graph Graph Attention Network (MG-GAT)—learns latent user and business representations by aggregating a diverse set of information from neighbors of each user (business) on a neighbor importance graph. MG-GAT out-performs state-of-the-art deep learning models in the recommendation task using two large-scale datasets collected from Yelp and four other standard datasets in RS. The improved performance highlights MG-GAT’s advantage in incorporating multi-modal features in a principled manner. The features importance, neighbor importance graph and latent representations reveal business insights on predictive features and explainable characteristics of business and users. Moreover, the learned neighbor importance graph can be used in a variety of management applications, such as targeting customers, promoting new businesses, and designing information acquisition strategies. Our paper presents a quintessential big data application of deep learning models in management while providing interpretability essential for real-world decision-making.
Improving Training Result of Partially Observable Markov Decision Process by Filtering Beliefs
Authors: Oscar LiJen Hsu
Date: 2021-01-05 00:00:00

In this study I proposed a filtering beliefs method for improving performance of Partially Observable Markov Decision Processes(POMDPs), which is a method wildly used in autonomous robot and many other domains concerning control policy. My method search and compare every similar belief pair. Because a similar belief have insignificant influence on control policy, the belief is filtered out for reducing training time. The empirical results show that the proposed method outperforms the point-based approximate POMDPs in terms of the quality of training results as well as the efficiency of the method.
A New Weighting Scheme for Fan-beam and Circle Cone-beam CT Reconstructions
Authors: Wei Wang, Xiang-Gen Xia, Chuanjiang He, Zemin Ren, Jian Lu, Tianfu Wang, Baiying Lei
Date: 2021-01-06 00:00:00
Tasks: ARC, Computed Tomography (CT), CT Reconstruction, SSIM

In this paper, we first present an arc based algorithm for fan-beam computed tomography (CT) reconstruction via applying Katsevich's helical CT formula to 2D fan-beam CT reconstruction. Then, we propose a new weighting function to deal with the redundant projection data. By extending the weighted arc based fan-beam algorithm to circle cone-beam geometry, we also obtain a new FDK-similar algorithm for circle cone-beam CT reconstruction. Experiments show that our methods can obtain higher PSNR and SSIM compared to the Parker-weighted conventional fan-beam algorithm and the FDK algorithm for super-short-scan trajectories.
Statistical learning for accurate and interpretable battery lifetime prediction
Authors: Peter M. Attia, Kristen A. Severson, Jeremy D. Witmer
Date: 2021-01-06 00:00:00
Tasks: BIG-bench Machine Learning, CyberBattleSim, Feature Engineering

Data-driven methods for battery lifetime prediction are attracting increasing attention for applications in which the degradation mechanisms are poorly understood and suitable training sets are available. However, while advanced machine learning and deep learning methods promise high performance with minimal data preprocessing, simpler linear models with engineered features often achieve comparable performance, especially for small training sets, while also providing physical and statistical interpretability. In this work, we use a previously published dataset to develop simple, accurate, and interpretable data-driven models for battery lifetime prediction. We first present the "capacity matrix" concept as a compact representation of battery electrochemical cycling data, along with a series of feature representations. We then create a number of univariate and multivariate models, many of which achieve comparable performance to the highest-performing models previously published for this dataset. These models also provide insights into the degradation of these cells. Our approaches can be used both to quickly train models for a new dataset and to benchmark the performance of more advanced machine learning methods.
Boarding House Renting Price Prediction Using Deep Neural Network Regression on Mobile Apps
Authors: Malik Abdul Aziz, Fahmi Nurrahim, Prastyo Eko Susanto, Yurio Windiatmoko
Date: 2020-12-31 00:00:00
Tasks: regression, Survey

Boarding house is the most important requirement, especially for college students who live far away from the city, place of his origin or house. However, the problem we see now is the uneven distribution of study places in Indonesia which 75% of the best top educational institutions come from the island of Java. So, students who are looking for boarding houses rent requires more effort in comparing the various aspects desired. They need to survey one by one to the boarding house they want, even though they can survey online, it still requires more effort to pay attention to the desired facilities one by one. Therefore, we then created an Mobile Application that can predict prices based on student needs by comparing several variables, namely city, area, type of boarding house, and facilities. So, students can easily estimate the ideal price. The results of this study prove that we have succeeded in predicting prices for boarding houses rent well based on the variables we have determined, and modeling that variables using Deep Neural Network Regression.
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints
Authors: Tianhao Wang, Dongruo Zhou, Quanquan Gu
Date: 2021-01-06 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: reinforcement-learning, Reinforcement Learning (RL)

We study reinforcement learning (RL) with linear function approximation under the adaptivity constraint. We consider two popular limited adaptivity models: the batch learning model and the rare policy switch model, and propose two efficient online RL algorithms for episodic linear Markov decision processes, where the transition probability and the reward function can be represented as a linear function of some known feature mapping. In specific, for the batch learning model, our proposed LSVI-UCB-Batch algorithm achieves an $\tilde O(\sqrt{d^3H^3T} + dHT/B)$ regret, where $d$ is the dimension of the feature mapping, $H$ is the episode length, $T$ is the number of interactions and $B$ is the number of batches. Our result suggests that it suffices to use only $\sqrt{T/dH}$ batches to obtain $\tilde O(\sqrt{d^3H^3T})$ regret. For the rare policy switch model, our proposed LSVI-UCB-RareSwitch algorithm enjoys an $\tilde O(\sqrt{d^3H^3T[1+T/(dH)]^{dH/B}})$ regret, which implies that $dH\log T$ policy switches suffice to obtain the $\tilde O(\sqrt{d^3H^3T})$ regret. Our algorithms achieve the same regret as the LSVI-UCB algorithm (Jin et al., 2019), yet with a substantially smaller amount of adaptivity. We also establish a lower bound for the batch learning model, which suggests that the dependency on $B$ in our regret bound is tight.
Federated Learning over Noisy Channels: Convergence Analysis and Design Examples
Authors: Xizixiang Wei, Cong Shen
Date: 2021-01-06 00:00:00
Tasks: Federated Learning

Does Federated Learning (FL) work when both uplink and downlink communications have errors? How much communication noise can FL handle and what is its impact to the learning performance? This work is devoted to answering these practically important questions by explicitly incorporating both uplink and downlink noisy channels in the FL pipeline. We present several novel convergence analyses of FL over simultaneous uplink and downlink noisy communication channels, which encompass full and partial clients participation, direct model and model differential transmissions, and non-independent and identically distributed (IID) local datasets. These analyses characterize the sufficient conditions for FL over noisy channels to have the same convergence behavior as the ideal case of no communication error. More specifically, in order to maintain the O(1/T) convergence rate of FedAvg with perfect communications, the uplink and downlink signal-to-noise ratio (SNR) for direct model transmissions should be controlled such that they scale as O(t^2) where t is the index of communication rounds, but can stay constant for model differential transmissions. The key insight of these theoretical results is a "flying under the radar" principle - stochastic gradient descent (SGD) is an inherent noisy process and uplink/downlink communication noises can be tolerated as long as they do not dominate the time-varying SGD noise. We exemplify these theoretical findings with two widely adopted communication techniques - transmit power control and diversity combining - and further validating their performance advantages over the standard methods via extensive numerical experiments using several real-world FL tasks.
Generating Masks from Boxes by Mining Spatio-Temporal Consistencies in Videos
Authors: Bin Zhao, Goutam Bhat, Martin Danelljan, Luc van Gool, Radu Timofte
Date: 2021-01-06 00:00:00
Proceeding: ICCV 2021 10
Tasks: Object, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Segmentation, Video Semantic Segmentation

Segmenting objects in videos is a fundamental computer vision task. The current deep learning based paradigm offers a powerful, but data-hungry solution. However, current datasets are limited by the cost and human effort of annotating object masks in videos. This effectively limits the performance and generalization capabilities of existing video segmentation methods. To address this issue, we explore weaker form of bounding box annotations. We introduce a method for generating segmentation masks from per-frame bounding box annotations in videos. To this end, we propose a spatio-temporal aggregation module that effectively mines consistencies in the object and background appearance across multiple frames. We use our resulting accurate masks for weakly supervised training of video object segmentation (VOS) networks. We generate segmentation masks for large scale tracking datasets, using only their bounding box annotations. The additional data provides substantially better generalization performance leading to state-of-the-art results in both the VOS and more challenging tracking domain.
Regularization-Agnostic Compressed Sensing MRI Reconstruction with Hypernetworks
Authors: Alan Q. Wang, Adrian V. Dalca, Mert R. Sabuncu
Date: 2021-01-06 00:00:00
Tasks: compressed sensing, MRI Reconstruction

Reconstructing under-sampled k-space measurements in Compressed Sensing MRI (CS-MRI) is classically solved with regularized least-squares. Recently, deep learning has been used to amortize this optimization by training reconstruction networks on a dataset of under-sampled measurements. Here, a crucial design choice is the regularization function(s) and corresponding weight(s). In this paper, we explore a novel strategy of using a hypernetwork to generate the parameters of a separate reconstruction network as a function of the regularization weight(s), resulting in a regularization-agnostic reconstruction model. At test time, for a given under-sampled image, our model can rapidly compute reconstructions with different amounts of regularization. We analyze the variability of these reconstructions, especially in situations when the overall quality is similar. Finally, we propose and empirically demonstrate an efficient and data-driven way of maximizing reconstruction performance given limited hypernetwork capacity. Our code is publicly available at https://github.com/alanqrwang/RegAgnosticCSMRI.
A Robust Illumination-Invariant Camera System for Agricultural Applications
Authors: Abhisesh Silwal, Tanvir Parhar, Francisco Yandun, George Kantor
Date: 2021-01-06 00:00:00
Tasks: Data Augmentation, Object, object-detection, Object Detection, Semantic Segmentation, Transfer Learning

Object detection and semantic segmentation are two of the most widely adopted deep learning algorithms in agricultural applications. One of the major sources of variability in image quality acquired in the outdoors for such tasks is changing lighting condition that can alter the appearance of the objects or the contents of the entire image. While transfer learning and data augmentation to some extent reduce the need for large amount of data to train deep neural networks, the large variety of cultivars and the lack of shared datasets in agriculture makes wide-scale field deployments difficult. In this paper, we present a high throughput robust active lighting-based camera system that generates consistent images in all lighting conditions. We detail experiments that show the consistency in images quality leading to relatively fewer images to train deep neural networks for the task of object detection. We further present results from field experiment under extreme lighting conditions where images without active lighting significantly lack to provide consistent results. The experimental results show that on average, deep nets for object detection trained on consistent data required nearly four times less data to achieve similar level of accuracy. This proposed work could potentially provide pragmatic solutions to computer vision needs in agriculture.
Predicting Illness for a Sustainable Dairy Agriculture: Predicting and Explaining the Onset of Mastitis in Dairy Cows
Authors: Cathal Ryan, Christophe Guéret, Donagh Berry, Medb Corcoran, Mark T. Keane, Brian Mac Namee
Date: 2021-01-06 00:00:00
Tasks: Decision Making

Mastitis is a billion dollar health problem for the modern dairy industry, with implications for antibiotic resistance. The use of AI techniques to identify the early onset of this disease, thus has significant implications for the sustainability of this agricultural sector. Current approaches to treating mastitis involve antibiotics and this practice is coming under ever increasing scrutiny. Using machine learning models to identify cows at risk of developing mastitis and applying targeted treatment regimes to only those animals promotes a more sustainable approach. Incorrect predictions from such models, however, can lead to monetary losses, unnecessary use of antibiotics, and even the premature death of animals, so it is important to generate compelling explanations for predictions to build trust with users and to better support their decision making. In this paper we demonstrate a system developed to predict mastitis infections in cows and provide explanations of these predictions using counterfactuals. We demonstrate the system and describe the engagement with farmers undertaken to build it.
Adaptive Synthetic Characters for Military Training
Authors: Volkan Ustun, Rajay Kumar, Adam Reilly, Seyed Sajjadi, Andrew Miller
Date: 2021-01-06 00:00:00
Tasks: BIG-bench Machine Learning, Imitation Learning, Unity

Behaviors of the synthetic characters in current military simulations are limited since they are generally generated by rule-based and reactive computational models with minimal intelligence. Such computational models cannot adapt to reflect the experience of the characters, resulting in brittle intelligence for even the most effective behavior models devised via costly and labor-intensive processes. Observation-based behavior model adaptation that leverages machine learning and the experience of synthetic entities in combination with appropriate prior knowledge can address the issues in the existing computational behavior models to create a better training experience in military training simulations. In this paper, we introduce a framework that aims to create autonomous synthetic characters that can perform coherent sequences of believable behavior while being aware of human trainees and their needs within a training simulation. This framework brings together three mutually complementary components. The first component is a Unity-based simulation environment - Rapid Integration and Development Environment (RIDE) - supporting One World Terrain (OWT) models and capable of running and supporting machine learning experiments. The second is Shiva, a novel multi-agent reinforcement and imitation learning framework that can interface with a variety of simulation environments, and that can additionally utilize a variety of learning algorithms. The final component is the Sigma Cognitive Architecture that will augment the behavior models with symbolic and probabilistic reasoning capabilities. We have successfully created proof-of-concept behavior models leveraging this framework on realistic terrain as an essential step towards bringing machine learning into military simulations.
Quick Annotator: an open-source digital pathology based rapid image annotation tool
Authors: Runtian Miao, Robert Toth, Yu Zhou, Anant Madabhushi, Andrew Janowczyk
Date: 2021-01-06 00:00:00
Tasks: whole slide images

Image based biomarker discovery typically requires an accurate segmentation of histologic structures (e.g., cell nuclei, tubules, epithelial regions) in digital pathology Whole Slide Images (WSI). Unfortunately, annotating each structure of interest is laborious and often intractable even in moderately sized cohorts. Here, we present an open-source tool, Quick Annotator (QA), designed to improve annotation efficiency of histologic structures by orders of magnitude. While the user annotates regions of interest (ROI) via an intuitive web interface, a deep learning (DL) model is concurrently optimized using these annotations and applied to the ROI. The user iteratively reviews DL results to either (a) accept accurately annotated regions, or (b) correct erroneously segmented structures to improve subsequent model suggestions, before transitioning to other ROIs. We demonstrate the effectiveness of QA over comparable manual efforts via three use cases. These include annotating (a) 337,386 nuclei in 5 pancreatic WSIs, (b) 5,692 tubules in 10 colorectal WSIs, and (c) 14,187 regions of epithelium in 10 breast WSIs. Efficiency gains in terms of annotations per second of 102x, 9x, and 39x were respectively witnessed while retaining f-scores >.95, suggesting QA may be a valuable tool for efficiently fully annotating WSIs employed in downstream biomarker studies.
Maximum a Posteriori Inference of Random Dot Product Graphs via Conic Programming
Authors: David Wu, David R. Palmer, Daryl R. Deford
Date: 2021-01-06 00:00:00
Tasks: Bayesian Inference

We present a convex cone program to infer the latent probability matrix of a random dot product graph (RDPG). The optimization problem maximizes the Bernoulli maximum likelihood function with an added nuclear norm regularization term. The dual problem has a particularly nice form, related to the well-known semidefinite program relaxation of the MaxCut problem. Using the primal-dual optimality conditions, we bound the entries and rank of the primal and dual solutions. Furthermore, we bound the optimal objective value and prove asymptotic consistency of the probability estimates of a slightly modified model under mild technical assumptions. Our experiments on synthetic RDPGs not only recover natural clusters, but also reveal the underlying low-dimensional geometry of the original data. We also demonstrate that the method recovers latent structure in the Karate Club Graph and synthetic U.S. Senate vote graphs and is scalable to graphs with up to a few hundred nodes.