pwc-archive/papers-with-abstracts huggingface dataset:
Metastatic Cancer Image Classification Based On Deep Learning Method
Authors: Guanwen Qiu, Xiaobing Yu, Baolin Sun, Yunpeng Wang, Lipei Zhang
Date: 2020-11-13 00:00:00
Tasks: Classification, Deep Learning, General Classification, image-classification, Image Classification

Using histopathological images to automatically classify cancer is a difficult task for accurately detecting cancer, especially to identify metastatic cancer in small image patches obtained from larger digital pathology scans. Computer diagnosis technology has attracted wide attention from researchers. In this paper, we propose a noval method which combines the deep learning algorithm in image classification, the DenseNet169 framework and Rectified Adam optimization algorithm. The connectivity pattern of DenseNet is direct connections from any layer to all consecutive layers, which can effectively improve the information flow between different layers. With the fact that RAdam is not easy to fall into a local optimal solution, and it can converge quickly in model training. The experimental results shows that our model achieves superior performance over the other classical convolutional neural networks approaches, such as Vgg19, Resnet34, Resnet50. In particular, the Auc-Roc score of our DenseNet169 model is 1.77% higher than Vgg19 model, and the Accuracy score is 1.50% higher. Moreover, we also study the relationship between loss value and batches processed during the training stage and validation stage, and obtain some important and interesting findings.
Relative Drone-Ground Vehicle Localization using LiDAR and Fisheye Cameras through Direct and Indirect Observations
Authors: Jan Hausberg, Ryoichi Ishikawa, Menandro Roxas, Takeshi Oishi
Date: 2020-11-13 00:00:00
Tasks: 3D Reconstruction, Pose Estimation

Estimating the pose of an unmanned aerial vehicle (UAV) or drone is a challenging task. It is useful for many applications such as navigation, surveillance, tracking objects on the ground, and 3D reconstruction. In this work, we present a LiDAR-camera-based relative pose estimation method between a drone and a ground vehicle, using a LiDAR sensor and a fisheye camera on the vehicle's roof and another fisheye camera mounted under the drone. The LiDAR sensor directly observes the drone and measures its position, and the two cameras estimate the relative orientation using indirect observation of the surrounding objects. We propose a dynamically adaptive kernel-based method for drone detection and tracking using the LiDAR. We detect vanishing points in both cameras and find their correspondences to estimate the relative orientation. Additionally, we propose a rotation correction technique by relying on the observed motion of the drone through the LiDAR. In our experiments, we were able to achieve very fast initial detection and real-time tracking of the drone. Our method is fully automatic.
Automatic segmentation with detection of local segmentation failures in cardiac MRI
Authors: Jörg Sander, Bob D. de Vos, Ivana Išgum
Date: 2020-11-13 00:00:00
Tasks: Prognosis, Segmentation

Segmentation of cardiac anatomical structures in cardiac magnetic resonance images (CMRI) is a prerequisite for automatic diagnosis and prognosis of cardiovascular diseases. To increase robustness and performance of segmentation methods this study combines automatic segmentation and assessment of segmentation uncertainty in CMRI to detect image regions containing local segmentation failures. Three state-of-the-art convolutional neural networks (CNN) were trained to automatically segment cardiac anatomical structures and obtain two measures of predictive uncertainty: entropy and a measure derived by MC-dropout. Thereafter, using the uncertainties another CNN was trained to detect local segmentation failures that potentially need correction by an expert. Finally, manual correction of the detected regions was simulated. Using publicly available CMR scans from the MICCAI 2017 ACDC challenge, the impact of CNN architecture and loss function for segmentation, and the uncertainty measure was investigated. Performance was evaluated using the Dice coefficient and 3D Hausdorff distance between manual and automatic segmentation. The experiments reveal that combining automatic segmentation with simulated manual correction of detected segmentation failures leads to statistically significant performance increase.
Balancing Wildfire Risk and Power Outages through Optimized Power Shut-Offs
Authors: Noah Rhodes, Lewis Ntaimo, Line Roald
Date: 2020-04-15 00:00:00

Electric grid faults can be the source of catas-trophic wildfires, particularly in regions with high winds andlow humidity. In short-term operations, electric utilities are leftwith few actions to mitigate the risk of wildfires, leading touse of disruptive measures such as proactive de-energization ofequipment, frequently referred to as public safety power shut-offs. Decisions of how to operate the grid in situations withhigh wildfire risk has significant impacts on customers, who maylose access to electricity in an attempt to protect them from theoutbreak of fires. This work proposes the optimal power shut-offproblem, an optimization model to support operational decisionmaking in the context of extreme wildfire risk. Specifically, themodel optimizes the operation of the grid to maximize the amountof power that can be delivered, while proactively minimizing therisk of wildfire ignitions by selectively de-energizing componentsin the grid. This is the first optimization model to considerpreventive wildfire risk measures and their impact on powersystems reliability at a short-term, operational time-frame. Theeffectiveness of the method is demonstrated on an augmentedversion of the IEEE-RTS GMLC test case, located in SouthernCalifornia, and compared against two simpler approaches. Weobserve that the optimization-based model reduces both wildfirerisk and lost load shed relative to the benchmarks.
Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distribution using healthcare worker infection data
Authors: Peixiao Wang, Tao Hu, Hongqiang Liu, Xinyan Zhu
Date: 2020-11-10 00:00:00

A timely understanding of the spatiotemporal pattern and development trend of COVID-19 is critical for timely prevention and control. However, the under-reporting of cases is widespread in fields associated with public health. It is also possible to draw biased inferences and formulate inappropriate prevention and control policies if the phenomenon of under-reporting is not taken into account. Therefore, in this paper, a novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions, and empirical analysis was carried out using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan). The results show that (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time; (2) the estimated peak infection time of the reported cases lagged the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases. (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages, and the impact on the early onset area was greater than that on the late onset area. (4) Although the number of reported cases was lower than the actual number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases. The proposed framework of this study is highly extensible, and relevant researchers can use data sources from other counties to carry out similar research.
Improved Visual Relocalization byDiscovering Anchor Points
Authors: Soham Saha, Girish Varma, C.V.Jawahar
Date: 2018-09-06 00:00:00

We address the visual relocalization problem of predicting the location and cameraorientation or pose (6DOF) of the given input scene. We propose a method based on howhumans determine their location using the visible landmarks.  We define anchor pointsuniformly across the route map and propose a deep learning architecture which predictsthe most relevant anchor point present in the scene as well as the relative offsets withrespect to it. The relevant anchor point need not be the nearest anchor point to the groundtruth location, as it might not be visible due to the pose. Hence we propose a multi taskloss function, which discovers the relevant anchor point, without needing the ground truthfor it.  We validate the effectiveness of our approach by experimenting on CambridgeLandmarks (large scale outdoor scenes) as well as 7 Scenes (indoor scenes) using variousCNN feature extractors.   Our method improves the median error in indoor as well asoutdoor localization datasets compared to the previous best deep learning model knownas  PoseNet  (with  geometric  re-projection  loss)  using  the  same  feature  extractor.   Weimprove the median error in localization in the specific case of Street scene, by over 8m.
DG: A program analysis library
Authors: Marek Chalupa
Date: 2020-11-07 00:00:00

DG is a C++ library providing elements for building program analysis tools. Its main components are a points-to analysis, a data dependence analysis, a control dependence analysis and an analysis of relations between variables. DG contains also a set of tools for displaying and exporting the results of the analyses and a program slicer for LLVM bitcode. It has been successfully used in several research projects.
The optimal sequence for reset controllers
Authors: Chengwai Cai, Ali Ahmadi Dastjerdi, Niranjan Saikumar, S. H. HosseinNia
Date: 2020-05-28 00:00:00

PID controllers cannot satisfy the high performance requirements since they are restricted by the water-bed effect. Thus, the need for a better alternative to linear PID controllers increases due to the rising demands of the high-tech industry. This has led many researchers to explore nonlinear controllers like reset control. Although reset controllers have been widely used to overcome the limitations of linear controllers in literature, the performance of the system varies depending on the relative sequence of controller linear and nonlinear parts. In this paper, the optimal sequence is found using high order sinusoidal input describing functions (HOSIDF). By arranging controller parts according to this strategy, better performance in the sense of precision and control input is achieved. The performance of the proposed sequence is validated on a precision positioning setup. The experimental results demonstrate that the optimal sequence found in theory outperforms other sequences.
Graph Edge Partitioning via Neighborhood Heuristic
Authors: Chenzi Zhang, Fan Wei, Qin Liu, Zhihao Gavin Tang, Zhenguo Li
Date: 2017-08-13 00:00:00

We consider the edge partitioning problem that partitions the edges of an input graph into multiple balanced components, while minimizing the total number of vertices replicated (one vertex might appear in more than one partition). This problem is critical in minimizing communication costs and running time for several largescale distributed graph computation platforms (e.g., PowerGraph, Spark GraphX). We first prove that this problem is NP-hard, and then present a new partitioning heuristic with polynomial running time. We provide a worst-case upper bound of replication factor for our heuristic on general graphs. To our knowledge, we are the first to provide such bound for edge partitioning algorithms on general graphs. Applying this bound to random power-law graphs greatly improves the previous bounds of expected replication factor. Extensive experiments demonstrated that our partitioning algorithm consistently produces much smaller replication factors on various benchmark data sets than the state-of-the-art. When deployed in the production graph engine, PowerGraph, in average it reduces replication factor, communication, and running time by 54%, 66%, and 21%, respectively.
An Intelligent Investigation Into Prediction of Post Rupture Neurological Status After Aneurysmal Subarachnoid Hemorrhage
Authors: Nina Soultanian, Alireza Zali, Michael Brainin
Date: 2018-08-16 00:00:00

INTRODUCTION
Study of aneurysm- and patient-specific risk factors has paved the way for understanding the pathopysiology of cerebral aneurysm development, growth, and rupture. However, predictors of post rupture neurological status have not been previously investigated. In this study, we try to effectively predict post rupture initial condition using a neuroevolutionary algorithm.

METHODS
We hypothesized that a number of suggested determinants of cerebral aneurysm growth and rupture may also contribute to the occurrence of a poor post rupture neurological condition. The relationship between potential predictors and the outcome was proposed as nonlinear and stochastic. Poor post rupture status was defined as Hunt and Hess scale 4, 5 and WFNS grade 3–5 up to 4 d or until surgery, each one occurred earlier. A single hidden-layer feed forward neural network (FFNN) combined with a genetic algorithm (GA) is so constructed.

RESULTS
Risk factors suggested in the PHASES and ELAPSS scores (age, history of subarachnoid hemorrhage, hypertension and aneurysm location, size and shape), plus smoking, gender, and multiplicity were selected as design variables. The data of 163 eligible patients with ruptured cerebral aneurysms were divided into 2 sets: 122 patients (1098 datasets) for training the network and 41 patients (369 datasets) as testing the model. Using binary variable encoding, a GA generates the initial population with population size, mutation and crossover rates of 16, 0.5, and 0.01, respectively. Then, the generated population is fed into the FFNN for prediction purpose. New generation is constructed using reproduction and recombination operators. The predictability of the constructed networks is calculated using R-squared value and mean square error on MATLAB for both the training and testing datasets.

CONCLUSION
Currently, there is no existing approach that can predict severity of neurological status after cerebral aneurysm rupture. This work will demonstrate a real-world application of a hybrid neurogenetic algorithm to tackle this clinical problem.
Towards achieving robust universal neural vocoding
Authors: Jaime Lorenzo-Trueba, Thomas Drugman, Javier Latorre, Thomas Merritt, Bartosz Putrycz, Roberto Barra-Chicote, Alexis Moinet, Vatsal Aggarwal
Date: 2019-07-04 00:00:00

This paper explores the potential universality of neural vocoders. We train a WaveRNN-based vocoder on 74 speakers coming from 17 languages. This vocoder is shown to be capable of generating speech of consistently good quality (98% relative mean MUSHRA when compared to natural speech) regardless of whether the input spectrogram comes from a speaker or style seen during training or from an out-of-domain scenario when the recording conditions are studio-quality. When the recordings show significant changes in quality, or when moving towards non-speech vocalizations or singing, the vocoder still significantly outperforms speaker-dependent vocoders, but operates at a lower average relative MUSHRA of 75%. These results are shown to be consistent across languages, regardless of them being seen during training (e.g. English or Japanese) or unseen (e.g. Wolof, Swahili, Ahmaric).
Optimal investment for participating insurance contracts under VaR-Regulation
Authors: Thai Nguyen, Mitja Stadje
Date: 2018-05-23 00:00:00

This paper studies a Value-at-Risk (VaR)-regulated optimal portfolio problem of the equity holders of a participating life insurance contract. In a setting with unhedgeable mortality risk and complete financial market, the optimal solution is given explicitly for contracts with mortality risk using a martingale approach for constrained non-concave optimization problems. We show that regulatory VaR constraints for participating insurance contracts lead to more prudent investment than in the case of no regulation. This result is contrary to the situation where the insurer maximizes the utility of the total wealth of the company (without distinguishing between contributions of equity holders and policyholders), in which case a VaR constraint may induce the insurer to take excessive risks leading to higher losses than in the case of no regulation. Compared to the unregulated problem, the VaR-constrained strategy leads to a higher expected utility for the policyholders, highlighting the potential usefulness of a VaR-regulation in the context of insurance. The prudent investment behavior is more significant if a VaR-type regulation is replaced by a portfolio insurance (PI)-type regulation. Furthermore, a stricter regulation (a smaller allowed default probability in the VaR problem or a higher minimum guarantee level in the PI problem) enhances the benefit of the policyholder but deteriorates that of the insurer. For both types of regulation, the gains in terms of expected utility are greater for higher participation rates, while being smaller for higher bonus rates. We also extend our analysis to frameworks where dividend and premature death benefit payments are made at an intermediate time date.
Not again! Data Leakage in Digital Pathology
Authors: Nicole Bussola, Alessia Marcolini, Valerio Maggio, Giuseppe Jurman, Cesare Furlanello
Date: 2019-09-14 00:00:00
Tasks: Model Selection, Transfer Learning

Bioinformatics of high throughput omics data (e.g. microarrays and proteomics) has been plagued by uncountable issues with reproducibility at the start of the century. Concerns have motivated international initiatives such as the FDA's led MAQC Consortium, addressing reproducibility of predictive biomarkers by means of appropriate Data Analysis Plans (DAPs). For instance, repreated cross-validation is a standard procedure meant at mitigating the risk that information from held-out validation data may be used during model selection. We prove here that, many years later, Data Leakage can still be a non-negligible overfitting source in deep learning models for digital pathology. In particular, we evaluate the impact of (i) the presence of multiple images for each subject in histology collections; (ii) the systematic adoption of training over collection of subregions (i.e. "tiles" or "patches") extracted for the same subject. We verify that accuracy scores may be inflated up to 41%, even if a well-designed 10x5 iterated cross-validation DAP is applied, unless all images from the same subject are kept together either in the internal training or validation splits. Results are replicated for 4 classification tasks in digital pathology on 3 datasets, for a total of 373 subjects, and 543 total slides (around 27, 000 tiles). Impact of applying transfer learning strategies with models pre-trained on general-purpose or digital pathology datasets is also discussed.
Perceptual Loss Function for Neural Modelling of Audio Systems
Authors: Alec Wright, Vesa Välimäki
Date: 2019-11-20 00:00:00

This work investigates alternate pre-emphasis filters used as part of the loss function during neural network training for nonlinear audio processing. In our previous work, the error-to-signal ratio loss function was used during network training, with a first-order highpass pre-emphasis filter applied to both the target signal and neural network output. This work considers more perceptually relevant pre-emphasis filters, which include lowpass filtering at high frequencies. We conducted listening tests to determine whether they offer an improvement to the quality of a neural network model of a guitar tube amplifier. Listening test results indicate that the use of an A-weighting pre-emphasis filter offers the best improvement among the tested filters. The proposed perceptual loss function improves the sound quality of neural network models in audio processing without affecting the computational cost.
Local dendritic balance enables learning of efficient representations in networks of spiking neurons
Authors: Fabian Alexander Mikulasch, Lucas Rudelt, Viola Priesemann
Date: 2020-10-23 00:00:00
Tasks: Representation Learning

How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms? Classical models of representation learning assume that input weights are learned via pairwise Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity only works under unrealistic requirements on neural dynamics and input statistics. To overcome these limitations, we derive from first principles a learning scheme based on voltage-dependent synaptic plasticity rules. Here, inhibition learns to locally balance excitatory input in individual dendritic compartments, and thereby can modulate excitatory synaptic plasticity to learn efficient representations. We demonstrate in simulations that this learning scheme works robustly even for complex, high-dimensional and correlated inputs, and with inhibitory transmission delays, where Hebbian-like plasticity fails. Our results draw a direct connection between dendritic excitatory-inhibitory balance and voltage-dependent synaptic plasticity as observed in vivo, and suggest that both are crucial for representation learning.
SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation
Authors: Xingyu Lin, YuFei Wang, Jake Olkin, David Held
Date: 2020-11-14 00:00:00
Tasks: Benchmarking, Deep Reinforcement Learning, Deformable Object Manipulation, Object, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Manipulating deformable objects has long been a challenge in robotics due to its high dimensional state representation and complex dynamics. Recent success in deep reinforcement learning provides a promising direction for learning to manipulate deformable objects with data driven methods. However, existing reinforcement learning benchmarks only cover tasks with direct state observability and simple low-dimensional dynamics or with relatively simple image-based environments, such as those with rigid objects. In this paper, we present SoftGym, a set of open-source simulated benchmarks for manipulating deformable objects, with a standard OpenAI Gym API and a Python interface for creating new environments. Our benchmark will enable reproducible research in this important area. Further, we evaluate a variety of algorithms on these tasks and highlight challenges for reinforcement learning algorithms, including dealing with a state representation that has a high intrinsic dimensionality and is partially observable. The experiments and analysis indicate the strengths and limitations of existing methods in the context of deformable object manipulation that can help point the way forward for future methods development. Code and videos of the learned policies can be found on our project website.