pwc-archive/papers-with-abstracts huggingface dataset:
Self-Attention Enhanced Patient Journey Understanding in Healthcare System
Authors: Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang
Date: 2020-06-15 00:00:00

Understanding patients' journeys in healthcare system is a fundamental prepositive task for a broad range of AI-based healthcare applications. This task aims to learn an informative representation that can comprehensively encode hidden dependencies among medical events and its inner entities, and then the use of encoding outputs can greatly benefit the downstream application-driven tasks. A patient journey is a sequence of electronic health records (EHRs) over time that is organized at multiple levels: patient, visits and medical codes. The key challenge of patient journey understanding is to design an effective encoding mechanism which can properly tackle the aforementioned multi-level structured patient journey data with temporal sequential visits and a set of medical codes. This paper proposes a novel self-attention mechanism that can simultaneously capture the contextual and temporal relationships hidden in patient journeys. A multi-level self-attention network (MusaNet) is specifically designed to learn the representations of patient journeys that is used to be a long sequence of activities. The MusaNet is trained in end-to-end manner using the training data derived from EHRs. We evaluated the efficacy of our method on two medical application tasks with real-world benchmark datasets. The results have demonstrated the proposed MusaNet produces higher-quality representations than state-of-the-art baseline methods. The source code is available in https://github.com/xueping/MusaNet.
Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention
Authors: Ce Ju, Ruihui Zhao, Jichao Sun, Xiguang Wei, Bo Zhao, Yang Liu, Hongshan Li, Tianjian Chen, Xinwei Zhang, Dashan Gao, Ben Tan, Han Yu, Chuning He, Yuan Jin
Date: 2020-06-15 00:00:00
Tasks: Prediction, Privacy Preserving

Prevention of stroke with its associated risk factors has been one of the public health priorities worldwide. Emerging artificial intelligence technology is being increasingly adopted to predict stroke. Because of privacy concerns, patient data are stored in distributed electronic health record (EHR) databases, voluminous clinical datasets, which prevent patient data from being aggregated and restrains AI technology to boost the accuracy of stroke prediction with centralized training data. In this work, our scientists and engineers propose a privacy-preserving scheme to predict the risk of stroke and deploy our federated prediction model on cloud servers. Our system of federated prediction model asynchronously supports any number of client connections and arbitrary local gradient iterations in each communication round. It adopts federated averaging during the model training process, without patient data being taken out of the hospitals during the whole process of model training and forecasting. With the privacy-preserving mechanism, our federated prediction model trains over all the healthcare data from hospitals in a certain city without actual data sharing among them. Therefore, it is not only secure but also more accurate than any single prediction model that trains over the data only from one single hospital. Especially for small hospitals with few confirmed stroke cases, our federated model boosts model performance by 10%~20% in several machine learning metrics. To help stroke experts comprehend the advantage of our prediction system more intuitively, we developed a mobile app that collects the key information of patients' statistics and demonstrates performance comparisons between the federated prediction model and the single prediction model during the federated training process.
Optimization of Fuzzy Controller of a Wind Power Plant Based on the Swarm Intelligence
Authors: Vadim Manusov, Pavel Matrenin
Date: 2020-06-14 00:00:00

The article considers the problem of the optimal control of a wind power plant based on fuzzy control and automation of generating the fuzzy rule base. Fuzzy rules by experts do not always provide a maximum power output of the wind plant and fuzzy rule bases require an adjustment in the case of changing the parameters of the wind power plant or the environment. This research proposes the method for optimizing the fuzzy rules base compiled by various experts. The method is based on balancing weights of fuzzy rules into the base by the Particle Swarm Optimization algorithm. The experiment has shown that the proposed method allows forming the fuzzy rule base as an exemplary optimal base from a non-optimized set of fuzzy rules. The optimal fuzzy rule base has been taken under consideration for the concrete control loop of wind power plant and the concrete fuzzy model of the wind.
COVID-ABS: An Agent-Based Model of COVID-19 Epidemic to Simulate Health and Economic Effects of Social Distancing Interventions
Authors: Petrônio C. L. Silva, Paulo V. C. Batista, Hélder S. Lima, Marcos A. Alves, Frederico G. Guimarães, Rodrigo C. P. Silva
Date: 2020-06-09 00:00:00

The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has directly impacted the public health and economy worldwide. To overcome this problem, countries have adopted different policies and non-pharmaceutical interventions for controlling the spread of the virus. This paper proposes the COVID-ABS, a new SEIR (Susceptible-Exposed-Infected-Recovered) agent-based model that aims to simulate the pandemic dynamics using a society of agents emulating people, business and government. Seven different scenarios of social distancing interventions were analyzed, with varying epidemiological and economic effects: (1) do nothing, (2) lockdown, (3) conditional lockdown, (4) vertical isolation, (5) partial isolation, (6) use of face masks, and (7) use of face masks together with 50% of adhesion to social isolation. In the impossibility of implementing scenarios with lockdown, which present the lowest number of deaths and highest impact on the economy, scenarios combining the use of face masks and partial isolation can be the more realistic for implementation in terms of social cooperation. The COVID-ABS model was implemented in Python programming language, with source code publicly available. The model can be easily extended to other societies by changing the input parameters, as well as allowing the creation of a multitude of other scenarios. Therefore, it is a useful tool to assist politicians and health authorities to plan their actions against the COVID-19 epidemic.
M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients
Authors: Tao Zhou, Huazhu Fu, Yu Zhang, Changqing Zhang, Xiankai Lu, Jianbing Shen, Ling Shao
Date: 2020-06-01 00:00:00
Tasks: Prediction

Early and accurate prediction of overall survival (OS) time can help to obtain better treatment planning for brain tumor patients. Although many OS time prediction methods have been developed and obtain promising results, there are still several issues. First, conventional prediction methods rely on radiomic features at the local lesion area of a magnetic resonance (MR) volume, which may not represent the full image or model complex tumor patterns. Second, different types of scanners (i.e., multi-modal data) are sensitive to different brain regions, which makes it challenging to effectively exploit the complementary information across multiple modalities and also preserve the modality-specific properties. Third, existing methods focus on prediction models, ignoring complex data-to-label relationships. To address the above issues, we propose an end-to-end OS time prediction model; namely, Multi-modal Multi-channel Network (M2Net). Specifically, we first project the 3D MR volume onto 2D images in different directions, which reduces computational costs, while preserving important information and enabling pre-trained models to be transferred from other tasks. Then, we use a modality-specific network to extract implicit and high-level features from different MR scans. A multi-modal shared network is built to fuse these features using a bilinear pooling model, exploiting their correlations to provide complementary information. Finally, we integrate the outputs from each modality-specific network and the multi-modal shared network to generate the final prediction result. Experimental results demonstrate the superiority of our M2Net model over other methods.
LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection
Authors: Jinmeng Rao, Song Gao, Yuhao Kang, Qunying Huang
Date: 2020-06-14 00:00:00
Tasks: Deep Learning, Privacy Preserving

The prevalence of location-based services contributes to the explosive growth of individual-level trajectory data and raises public concerns about privacy issues. In this research, we propose a novel LSTM-TrajGAN approach, which is an end-to-end deep learning model to generate privacy-preserving synthetic trajectory data for data sharing and publication. We design a loss metric function TrajLoss to measure the trajectory similarity losses for model training and optimization. The model is evaluated on the trajectory-user-linking task on a real-world semantic trajectory dataset. Compared with other common geomasking methods, our model can better prevent users from being re-identified, and it also preserves essential spatial, temporal, and thematic characteristics of the real trajectory data. The model better balances the effectiveness of trajectory privacy protection and the utility for spatial and temporal analyses, which offers new insights into the GeoAI-powered privacy protection.
Reinforcement Learning as Iterative and Amortised Inference
Authors: Beren Millidge, Alexander Tschantz, Anil. K. Seth, Christopher L. Buckley
Date: 2020-06-13 00:00:00
Tasks: General Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

There are several ways to categorise reinforcement learning (RL) algorithms, such as either model-based or model-free, policy-based or planning-based, on-policy or off-policy, and online or offline. Broad classification schemes such as these help provide a unified perspective on disparate techniques and can contextualise and guide the development of new algorithms. In this paper, we utilise the control as inference framework to outline a novel classification scheme based on amortised and iterative inference. We demonstrate that a wide range of algorithms can be classified in this manner providing a fresh perspective and highlighting a range of existing similarities. Moreover, we show that taking this perspective allows us to identify parts of the algorithmic design space which have been relatively unexplored, suggesting new routes to innovative RL algorithms.
StarCraft II Build Order Optimization using Deep Reinforcement Learning and Monte-Carlo Tree Search
Authors: Islam Elnabarawy, Kristijana Arroyo, Donald C. Wunsch II
Date: 2020-06-12 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft, Starcraft II

The real-time strategy game of StarCraft II has been posed as a challenge for reinforcement learning by Google's DeepMind. This study examines the use of an agent based on the Monte-Carlo Tree Search algorithm for optimizing the build order in StarCraft II, and discusses how its performance can be improved even further by combining it with a deep reinforcement learning neural network. The experimental results accomplished using Monte-Carlo Tree Search achieves a score similar to a novice human player by only using very limited time and computational resources, which paves the way to achieving scores comparable to those of a human expert by combining it with the use of deep reinforcement learning.
Interpreting the Latent Space of GANs via Correlation Analysis for Controllable Concept Manipulation
Authors: Ziqiang Li, Rentuo Tao, Hongjing Niu, Bin Li
Date: 2020-05-23 00:00:00
Tasks: Drug Discovery, Image Generation, Super-Resolution

Generative adversarial nets (GANs) have been successfully applied in many fields like image generation, inpainting, super-resolution and drug discovery, etc., by now, the inner process of GANs is far from been understood. To get deeper insight of the intrinsic mechanism of GANs, in this paper, a method for interpreting the latent space of GANs by analyzing the correlation between latent variables and the corresponding semantic contents in generated images is proposed. Unlike previous methods that focus on dissecting models via feature visualization, the emphasis of this work is put on the variables in latent space, i.e. how the latent variables affect the quantitative analysis of generated results. Given a pretrained GAN model with weights fixed, the latent variables are intervened to analyze their effect on the semantic content in generated images. A set of controlling latent variables can be derived for specific content generation, and the controllable semantic content manipulation be achieved. The proposed method is testified on the datasets Fashion-MNIST and UT Zappos50K, experiment results show its effectiveness.
Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming
Authors: Itay Hubara, Yury Nahshan, Yair Hanani, Ron Banner, Daniel Soudry
Date: 2020-06-14 00:00:00
Tasks: Model Compression, Quantization

Lately, post-training quantization methods have gained considerable attention, as they are simple to use, and require only a small unlabeled calibration set. This small dataset cannot be used to fine-tune the model without significant over-fitting. Instead, these methods only use the calibration set to set the activations' dynamic ranges. However, such methods always resulted in significant accuracy degradation, when used below 8-bits (except on small datasets). Here we aim to break the 8-bit barrier. To this end, we minimize the quantization errors of each layer separately by optimizing its parameters over the calibration set. We empirically demonstrate that this approach is: (1) much less susceptible to over-fitting than the standard fine-tuning approaches, and can be used even on a very small calibration set; and (2) more powerful than previous methods, which only set the activations' dynamic ranges. Furthermore, we demonstrate how to optimally allocate the bit-widths for each layer, while constraining accuracy degradation or model compression by proposing a novel integer programming formulation. Finally, we suggest model global statistics tuning, to correct biases introduced during quantization. Together, these methods yield state-of-the-art results for both vision and text models. For instance, on ResNet50, we obtain less than 1\% accuracy degradation --- with 4-bit weights and activations in all layers, but the smallest two. We open-sourced our code.
Multi-view Low-rank Preserving Embedding: A Novel Method for Multi-view Representation
Authors: Xiangzhu Meng, Lin Feng, Huibing Wang
Date: 2020-06-14 00:00:00
Tasks: MULTI-VIEW LEARNING, Representation Learning

In recent years, we have witnessed a surge of interest in multi-view representation learning, which is concerned with the problem of learning representations of multi-view data. When facing multiple views that are highly related but sightly different from each other, most of existing multi-view methods might fail to fully integrate multi-view information. Besides, correlations between features from multiple views always vary seriously, which makes multi-view representation challenging. Therefore, how to learn appropriate embedding from multi-view information is still an open problem but challenging. To handle this issue, this paper proposes a novel multi-view learning method, named Multi-view Low-rank Preserving Embedding (MvLPE). It integrates different views into one centroid view by minimizing the disagreement term, based on distance or similarity matrix among instances, between the centroid view and each view meanwhile maintaining low-rank reconstruction relations among samples for each view, which could make more full use of compatible and complementary information from multi-view features. Unlike existing methods with additive parameters, the proposed method could automatically allocate a suitable weight for each view in multi-view information fusion. However, MvLPE couldn't be directly solved, which makes the proposed MvLPE difficult to obtain an analytic solution. To this end, we approximate this solution based on stationary hypothesis and normalization post-processing to efficiently obtain the optimal solution. Furthermore, an iterative alternating strategy is provided to solve this multi-view representation problem. The experiments on six benchmark datasets demonstrate that the proposed method outperforms its counterparts while achieving very competitive performance.
Neural Path Features and Neural Path Kernel : Understanding the role of gates in deep learning
Authors: Chandrashekar Lakshminarayanan, Amit Vikram Singh
Date: 2020-06-11 00:00:00
Proceeding: NeurIPS 2020 12

Rectified linear unit (ReLU) activations can also be thought of as 'gates', which, either pass or stop their pre-activation input when they are 'on' (when the pre-activation input is positive) or 'off' (when the pre-activation input is negative) respectively. A deep neural network (DNN) with ReLU activations has many gates, and the on/off status of each gate changes across input examples as well as network weights. For a given input example, only a subset of gates are 'active', i.e., on, and the sub-network of weights connected to these active gates is responsible for producing the output. At randomised initialisation, the active sub-network corresponding to a given input example is random. During training, as the weights are learnt, the active sub-networks are also learnt, and potentially hold very valuable information. In this paper, we analytically characterise the role of active sub-networks in deep learning. To this end, we encode the on/off state of the gates of a given input in a novel 'neural path feature' (NPF), and the weights of the DNN are encoded in a novel 'neural path value' (NPV). Further, we show that the output of network is indeed the inner product of NPF and NPV. The main result of the paper shows that the 'neural path kernel' associated with the NPF is a fundamental quantity that characterises the information stored in the gates of a DNN. We show via experiments (on MNIST and CIFAR-10) that in standard DNNs with ReLU activations NPFs are learnt during training and such learning is key for generalisation. Furthermore, NPFs and NPVs can be learnt in two separate networks and such learning also generalises well in experiments.
Sustainable Recreational Fishing Using a Novel Electrical Muscle Stimulation (EMS) Lure and Ensemble Network Algorithm to Maximize Catch and Release Survivability
Authors: Petteri Haverinen, Krithik Ramesh, Nathan Wang
Date: 2020-05-13 00:00:00

With 200-700 million anglers in the world, sportfishing is nearly five times more common than commercial trawling. Worldwide, hundreds of thousands of jobs are linked to the sportfishing industry, which generates billions of dollars for water-side communities and fisheries conservatories alike. However, the sheer popularity of recreational fishing poses threats to aquatic biodiversity that are hard to regulate. For example, as much as 25% of overfished populations can be traced to anglers. This alarming statistic is explained by the average catch and release mortality rate of 43%, which primarily results from hook-related injuries and careless out-of-water handling. The provisional-patented design proposed in this paper addresses both these problems separately First, a novel, electrical muscle stimulation based fishing lure is proposed as a harmless and low cost alternative to sharp hooks. Early prototypes show a constant electrical current of 90 mA applied through a 200g European perch's jaw can support a reeling tension of 2N - safely within the necessary ranges. Second, a fish-eye camera bob is designed to wirelessly relay underwater footage to a smartphone app, where an ensemble convolutional neural network automatically classifies the fish's species, estimates its length, and cross references with local and state fishing regulations (ie. minimum size, maximum bag limit, and catch season). This capability reduces overfishing by helping anglers avoid accidentally violating guidelines and eliminates the need to reel the fish in and expose it to negligent handling. IN conjunction, this cheap, lightweight, yet high-tech invention is a paradigm shift in preserving a world favorite pastime; while at the same time making recreational fishing more sustainable.
Real-Time Monocular 4D Face Reconstruction using the LSFM models
Authors: Mohammad Rami Koujan, Nikolai Dochev, Anastasios Roussos
Date: 2020-05-22 00:00:00
Tasks: 4D reconstruction, Face Reconstruction

4D face reconstruction from a single camera is a challenging task, especially when it is required to be performed in real time. We demonstrate a system of our own implementation that solves this task accurately and runs in real time on a commodity laptop, using a webcam as the only input. Our system is interactive, allowing the user to freely move their head and show various expressions while standing in front of the camera. As a result, the put forward system both reconstructs and visualises the identity of the subject in the correct pose along with the acted facial expressions in real-time. The 4D reconstruction in our framework is based on the recently-released Large-Scale Facial Models (LSFM) \cite{LSFM1, LSFM2}, which are the largest-scale 3D Morphable Models of facial shapes ever constructed, based on a dataset of more than 10,000 facial identities from a wide range of gender, age and ethnicity combinations. This is the first real-time demo that gives users the opportunity to test in practice the capabilities of the recently-released Large-Scale Facial Models (LSFM)
ReenactNet: Real-time Full Head Reenactment
Authors: Mohammad Rami Koujan, Michail Christos Doukas, Anastasios Roussos, Stefanos Zafeiriou
Date: 2020-05-22 00:00:00
Tasks: Translation, Video-to-Video Synthesis

Video-to-video synthesis is a challenging problem aiming at learning a translation function between a sequence of semantic maps and a photo-realistic video depicting the characteristics of a driving video. We propose a head-to-head system of our own implementation capable of fully transferring the human head 3D pose, facial expressions and eye gaze from a source to a target actor, while preserving the identity of the target actor. Our system produces high-fidelity, temporally-smooth and photo-realistic synthetic videos faithfully transferring the human time-varying head attributes from the source to the target actor. Our proposed implementation: 1) works in real time ($\sim 20$ fps), 2) runs on a commodity laptop with a webcam as the only input, 3) is interactive, allowing the participant to drive a target person, e.g. a celebrity, politician, etc, instantly by varying their expressions, head pose, and eye gaze, and visualising the synthesised video concurrently.