pwc-archive/papers-with-abstracts huggingface dataset:
PlenoptiCam v1.0: A light-field imaging framework
Authors: Christopher Hahne, Amar Aggoun
Date: 2020-10-14 00:00:00
Tasks: Camera Calibration, Information Retrieval, Retrieval

Light-field cameras play a vital role for rich 3-D information retrieval in narrow range depth sensing applications. The key obstacle in composing light-fields from exposures taken by a plenoptic camera is to computationally calibrate, align and rearrange four-dimensional image data. Several attempts have been proposed to enhance the overall image quality by tailoring pipelines dedicated to particular plenoptic cameras and improving the consistency across viewpoints at the expense of high computational loads. The framework presented herein advances prior outcomes thanks to its novel micro image scale-space analysis for generic camera calibration independent of the lens specifications and its parallax-invariant, cost-effective viewpoint color equalization from optimal transport theory. Artifacts from the sensor and micro lens grid are compensated in an innovative way to enable superior quality in sub-aperture image extraction, computational refocusing and Scheimpflug rendering with sub-sampling capabilities. Benchmark comparisons using established image metrics suggest that our proposed pipeline outperforms state-of-the-art tool chains in the majority of cases. Results from a Wasserstein distance further show that our color transfer outdoes the existing transport methods. Our algorithms are released under an open-source license, offer cross-platform compatibility with few dependencies and different user interfaces. This makes the reproduction of results and experimentation with plenoptic camera technology convenient for peer researchers, developers, photographers, data scientists and others working in this field.
Automatic Data Augmentation for 3D Medical Image Segmentation
Authors: Ju Xu, Mengzhang Li, Zhanxing Zhu
Date: 2020-10-07 00:00:00
Tasks: Data Augmentation, Diversity, Image Segmentation, Medical Image Segmentation, Semantic Segmentation

Data augmentation is an effective and universal technique for improving generalization performance of deep neural networks. It could enrich diversity of training samples that is essential in medical image segmentation tasks because 1) the scale of medical image dataset is typically smaller, which may increase the risk of overfitting; 2) the shape and modality of different objects such as organs or tumors are unique, thus requiring customized data augmentation policy. However, most data augmentation implementations are hand-crafted and suboptimal in medical image processing. To fully exploit the potential of data augmentation, we propose an efficient algorithm to automatically search for the optimal augmentation strategies. We formulate the coupled optimization w.r.t. network weights and augmentation parameters into a differentiable form by means of stochastic relaxation. This formulation allows us to apply alternative gradient-based methods to solve it, i.e. stochastic natural gradient method with adaptive step-size. To the best of our knowledge, it is the first time that differentiable automatic data augmentation is employed in medical image segmentation tasks. Our numerical experiments demonstrate that the proposed approach significantly outperforms existing build-in data augmentation of state-of-the-art models.
OCT-GAN: Single Step Shadow and Noise Removal from Optical Coherence Tomography Images of the Human Optic Nerve Head
Authors: Haris Cheong, Sripad Krishna Devalla, Thanadet Chuangsuwanich, Tin A. Tun, Xiaofei Wang, Tin Aung, Leopold Schmetterer, Martin L. Buist, Craig Boote, Alexandre H. Thiéry, Michaël J. A. Girard
Date: 2020-10-06 00:00:00
Tasks: SSIM

Speckle noise and retinal shadows within OCT B-scans occlude important edges, fine textures and deep tissues, preventing accurate and robust diagnosis by algorithms and clinicians. We developed a single process that successfully removed both noise and retinal shadows from unseen single-frame B-scans within 10.4ms. Mean average gradient magnitude (AGM) for the proposed algorithm was 57.2% higher than current state-of-the-art, while mean peak signal to noise ratio (PSNR), contrast to noise ratio (CNR), and structural similarity index metric (SSIM) increased by 11.1%, 154% and 187% respectively compared to single-frame B-scans. Mean intralayer contrast (ILC) improvement for the retinal nerve fiber layer (RNFL), photoreceptor layer (PR) and retinal pigment epithelium (RPE) layers decreased from 0.362 \pm 0.133 to 0.142 \pm 0.102, 0.449 \pm 0.116 to 0.0904 \pm 0.0769, 0.381 \pm 0.100 to 0.0590 \pm 0.0451 respectively. The proposed algorithm reduces the necessity for long image acquisition times, minimizes expensive hardware requirements and reduces motion artifacts in OCT images.
Image Smoothing Algorithm Based on Gradient Analysis
Authors: Vladimir Gudkov, Ilia Moiseev
Date: 2020-06-16 00:00:00
Tasks: image smoothing

In this paper image smoothing algorithm based on gradient analysis is proposed. Our algorithm uses filtering and to achieve edge-preserving smoothing it uses two components of gradient vectors: their magnitudes (or lengths) and directions. Our method discriminates between two types of boundaries in given neighborhood: regular and irregular ones. Regular boundaries have small deviations of gradient angles and the opposite for irregular ones. To measure closeness of angles cosine of doubled difference is used. As additional measure that helps to discriminate the types of boundaries inverted gradient values were used. When gradient magnitudes are inverted bigger values refer to textures (insignificant changes in gradient) and smaller refer to strong boundaries. So textures would have bigger weights and hence they would appear smoother. We also propose to filter image of gradient magnitudes with median filter to enhance visual quality of results. The method proposed in this paper is easy to implement and compute and it gives good results in comparison with other techniques like bilateral filter.
Efficient and flexible methods for time since infection models
Authors: Joseph D. Peterson, Ronojoy Adhikari
Date: 2020-10-21 00:00:00

Epidemic models are useful tools in the fight against infectious diseases, as they allow policy makers to test and compare various strategies to limit disease transmission while mitigating collateral damage on the economy. Epidemic models that are more faithful to the microscopic details of disease transmission can offer more reliable projections, which in turn can lead to more reliable control strategies. For example, many epidemic models describe disease progression via a series of artificial 'stages' or 'compartments' (e.g. exposed, activated, infectious, etc.) but an epidemic model that explicitly tracks time since infection (TSI) can provide a more precise description. At present, epidemic models with 'compartments' are more common than TSI models , largely due to higher computational cost and complexity typically associated with TSI models. Here, however, we show that with the right discretization scheme a TSI model is not much more difficult to solve than a comparment model with three or four 'stages' for the infected class. We also provide a new perspective for adding 'stages' to a TSI model in a way that decouples the disease transmission dynamics from the residence time distributions at each stage. These results are also generalized for age-structured TSI models in an appendix. Finally, as proof-of-principle for the efficiency of the proposed numerical methods, we provide calculations for optimal epidemic control by non-pharmaceutical intervention. Many of the tools described in this report are available through the software package 'pyross'
Binary Choice Models with High-Dimensional Individual and Time Fixed Effects
Authors: Daniel Czarnowske, Amrei Stammann
Date: 2019-04-08 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Empirical economists are often deterred from the application of nonlinear fixed effects models mainly for two reasons: the incidental parameter problem and the computational challenge even in moderately large panels. Using the example of binary choice models with individual and time fixed effects, we show how both issues can be alleviated by combining the asymptotic bias corrections of Fern\'andez-Val and Weidner (2016) with an estimation algorithm proposed by Stammann (2018). We conduct extensive simulation experiments to analyze the statistical properties of various (bias-corrected) estimators in large and even unbalanced panels. Our simulation results provide new insights that are relevant for the application of bias corrections in empirical work. Overall, we find that analytically bias corrected estimators are clearly preferable, especially for unbalanced panels.
Deep iterative vessel segmentation in OCT angiography
Authors: Theodoros Pissas, Edward Bloch, M. Jorge Cardoso, Blanca Flores, Odysseas Georgiadis, Sepehr Jalali, Claudio Ravasio, Danail Stoyanov, Lyndon Da Cruz, Christos Bergeles
Date: 2020-04-01 00:00:00
Tasks: Retinal Vessel Segmentation

This paper addresses retinal vessel segmentation on optical coherence tomography angiography (OCT-A) images of the human retina. Our approach is motivated by the need for high precision image-guided delivery of regenerative therapies in vitreo-retinal surgery. OCT-A visualizes macular vasculature, the main landmark of the surgically targeted area, at a level of detail and spatial extent unattainable by other imaging modalities. Thus, automatic extraction of detailed vessel maps can ultimately inform surgical planning. We address the task of delineation of the Superficial Vascular Plexus in 2D Maximum Intensity Projections (MIP) of OCT-A using convolutional neural networks that iteratively refine the quality of the produced vessel segmentations. We demonstrate that the proposed approach compares favourably to alternative network baselines and graph-based methodologies through extensive experimental analysis, using data collected from 50 subjects, including both individuals that underwent surgery for structural macular abnormalities and healthy subjects. Additionally, we demonstrate generalization to 3D segmentation and narrower field-of-view OCT-A. In the future, the extracted vessel maps will be leveraged for surgical planning and semi-automated intraoperative navigation in vitreo-retinal surgery.
Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics
Authors: Zekun Ren, Felipe Oviedo, Maung Thway, Siyu I. P. Tian, Yue Wang, Hansong Xue, Jose Dario Perea, Mariya Layurova, Thomas Heumueller, Erik Birgersson, Armin G. Aberle, Christoph J. Brabec, Rolf Stangl, Qianxiao Li, Shijing Sun, Fen Lin, Ian Marius Peters & Tonio Buonassisi
Date: 2020-01-31 00:00:00
Tasks: Bayesian Inference, Physical Simulations, Physics-informed machine learning

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.
A method of the transfer graphene based on sublimed method 2015 patent CN106904605B
Authors: Yu-Hao Deng
Date: 2015-12-21 00:00:00

The invention discloses a kind of methods of transfer graphene.This method is using the substance easily to distil as the supporting layer of transfer graphene, first by heating easy sublimate, its graphene surface on being grown on initial substrate is set to sublimate to form fine and close supporting layer, then it removes initial substrate and graphene/supporting layer is transferred to target substrate, then supporting layer is distilled.Compared to residue glue problem existing for existing PMMA methods, PDMS methods and hot stamping, heat release tape method to target substrate planarization, adhesion requirement is higher the problems such as, the present invention uses easy sublimate as supporting layer, transfer process is simpler, convenient, can be achieved large area from graphene to arbitrary target substrate, it is lossless, shifted without residue glue, graphene will be expanded significantly in terms of flexible electronic, organic nano electronics, organic solar batteries, organic sensor, the application in the fields such as organic High-performance micro-nano electronic device, organic material energy stores.
CN106904605 Method for transferring graphene based on sublimation method
Authors: Yu-Hao Deng
Date: 2015-12-21 00:00:00

The invention discloses a method for transferring graphene. According to the method, a substance which is easily sublimated is utilized as a supporting layer for transferring the graphene; first, through heating the substance which is easily sublimated, the substance is desublimated to form a compact supporting layer on the surface of the graphene growing on an initial matrix, then, the initial matrix is removed and the graphene/supporting layer is transferred to a target substrate, and afterwards, the supporting layer is sublimated. In comparison with adhesive residue problems existing in existing PMMA (Polymethyl Methacrylate) method and PDMS (Polydimethylsiloxane) method and problems that a hot padding method and a heat release adhesive tape method have relative high requirements on the flatness and the adhesiveness of the target substrate, and the like, according to the method, the substance which is easily sublimated is used as the supporting layer; the transfer process is simpler, more convenient and quicker; the large-area, damage-free and adhesive-residue-free transfer of the graphene to any target substrate can be realized; the application of the graphene to the fields of flexible electronics, organic nanoelectronics, organic solar cells, organic sensor aspects, organic high-performance micro-nano electronic devices, organic-material energy storage and the like is greatly expanded.
Improving Document-level Relation Extraction via Contextualizing Mention Representations and Weighting Mention Pairs
Authors: Ping Jiang;Xian-Ling Mao;Binbin Bian;Heyan Huang
Date: 2020-08-09 00:00:00
Tasks: Document-level Relation Extraction, Relation, Relation Extraction

Document-level relation extraction (RE) has attracted considerable attention, because a large number of relational facts are expressed in multiple sentences. Recently, encoder-aggregator based models have become promising for document-level RE. However, these models have two shortcomings: (i) they cannot obtain contextualized representations of a mention by low computational cost, when the mention is involved in different entity pairs; (ii) they ignore the different weights for the mention pairs of a target entity pair. To tackle the above two problems, in this paper, we propose a novel encoder-attender-aggregator model, which introduces two attenders between the encoder and aggregator. Specifically, a mutual attender is first employed on the selected head and tail mentions to efficiently produce contextualized mention representations. Then, an integration attender is utilized to weight the mention pairs of a target entity pair. Extensive experiments on two document-level RE datasets show that the proposed model performs better than the state-of-the-art baselines. Our codes are publicly available at "https://github.com/nefujiangping/EncAttAgg".
Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion
Authors: Qianqian Ma, Alex Olshevsky
Date: 2020-10-23 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Matrix Completion

We consider the problem of reconstructing a rank-one matrix from a revealed subset of its entries when some of the revealed entries are corrupted with perturbations that are unknown and can be arbitrarily large. It is not known which revealed entries are corrupted. We propose a new algorithm combining alternating minimization with extreme-value filtering and provide sufficient and necessary conditions to recover the original rank-one matrix. In particular, we show that our proposed algorithm is optimal when the set of revealed entries is given by an Erd\H{o}s-R\'enyi random graph. These results are then applied to the problem of classification from crowdsourced data under the assumption that while the majority of the workers are governed by the standard single-coin David-Skene model (i.e., they output the correct answer with a certain probability), some of the workers can deviate arbitrarily from this model. In particular, the "adversarial" workers could even make decisions designed to make the algorithm output an incorrect answer. Extensive experimental results show our algorithm for this problem, based on rank-one matrix completion with perturbations, outperforms all other state-of-the-art methods in such an adversarial scenario.
Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection
Authors: Lucas Tabelini, Rodrigo Berriel, Thiago M. Paixão, Claudine Badue, Alberto F. de Souza, Thiago Oliveira-Santos
Date: 2020-10-22 00:00:00
Proceeding: CVPR 2021 1
Tasks: Autonomous Vehicles, Lane Detection

Modern lane detection methods have achieved remarkable performances in complex real-world scenarios, but many have issues maintaining real-time efficiency, which is important for autonomous vehicles. In this work, we propose LaneATT: an anchor-based deep lane detection model, which, akin to other generic deep object detectors, uses the anchors for the feature pooling step. Since lanes follow a regular pattern and are highly correlated, we hypothesize that in some cases global information may be crucial to infer their positions, especially in conditions such as occlusion, missing lane markers, and others. Thus, this work proposes a novel anchor-based attention mechanism that aggregates global information. The model was evaluated extensively on three of the most widely used datasets in the literature. The results show that our method outperforms the current state-of-the-art methods showing both higher efficacy and efficiency. Moreover, an ablation study is performed along with a discussion on efficiency trade-off options that are useful in practice.
Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning
Authors: Guangxiang Zhu, Minghao Zhang, Honglak Lee, Chongjie Zhang
Date: 2020-10-23 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Deep Reinforcement Learning, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Sample efficiency has been one of the major challenges for deep reinforcement learning. Recently, model-based reinforcement learning has been proposed to address this challenge by performing planning on imaginary trajectories with a learned world model. However, world model learning may suffer from overfitting to training trajectories, and thus model-based value estimation and policy search will be pone to be sucked in an inferior local policy. In this paper, we propose a novel model-based reinforcement learning algorithm, called BrIdging Reality and Dream (BIRD). It maximizes the mutual information between imaginary and real trajectories so that the policy improvement learned from imaginary trajectories can be easily generalized to real trajectories. We demonstrate that our approach improves sample efficiency of model-based planning, and achieves state-of-the-art performance on challenging visual control benchmarks.
On Projection Based Operators in Lp space for Exact Similarity Search
Authors: Andreas Wichert, Catarina Moreira
Date: 2015-02-12 00:00:00

We investigate exact indexing for high dimensional Lp norms based on the 1-Lipschitz property and projection operators. The orthogonal projection that satisfies the 1-Lipschitz property for the Lp norm is described. The adaptive projection defined by the first principal component is introduced.
KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi
Authors: Rubungo Andre Niyongabo, Hong Qu, Julia Kreutzer, Li Huang
Date: 2020-10-23 00:00:00
Proceeding: COLING 2020 8
Tasks: Articles, Benchmarking, Cross-Lingual Transfer, General Classification, Multi-class Classification, NER, News Classification, POS, POS Tagging, Representation Learning, text-classification, Text Classification

Recent progress in text classification has been focused on high-resource languages such as English and Chinese. For low-resource languages, amongst them most African languages, the lack of well-annotated data and effective preprocessing, is hindering the progress and the transfer of successful methods. In this paper, we introduce two news datasets (KINNEWS and KIRNEWS) for multi-class classification of news articles in Kinyarwanda and Kirundi, two low-resource African languages. The two languages are mutually intelligible, but while Kinyarwanda has been studied in Natural Language Processing (NLP) to some extent, this work constitutes the first study on Kirundi. Along with the datasets, we provide statistics, guidelines for preprocessing, and monolingual and cross-lingual baseline models. Our experiments show that training embeddings on the relatively higher-resourced Kinyarwanda yields successful cross-lingual transfer to Kirundi. In addition, the design of the created datasets allows for a wider use in NLP beyond text classification in future studies, such as representation learning, cross-lingual learning with more distant languages, or as base for new annotations for tasks such as parsing, POS tagging, and NER. The datasets, stopwords, and pre-trained embeddings are publicly available at https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus .
Object-aware Feature Aggregation for Video Object Detection
Authors: Qichuan Geng, Hong Zhang, Na Jiang, Xiaojuan Qi, Liangjun Zhang, Zhong Zhou
Date: 2020-10-23 00:00:00
Tasks: Object, object-detection, Object Detection, Object Recognition, Video Object Detection

We present an Object-aware Feature Aggregation (OFA) module for video object detection (VID). Our approach is motivated by the intriguing property that video-level object-aware knowledge can be employed as a powerful semantic prior to help object recognition. As a consequence, augmenting features with such prior knowledge can effectively improve the classification and localization performance. To make features get access to more content about the whole video, we first capture the object-aware knowledge of proposals and incorporate such knowledge with the well-established pair-wise contexts. With extensive experimental results on the ImageNet VID dataset, our approach demonstrates the effectiveness of object-aware knowledge with the superior performance of 83.93% and 86.09% mAP with ResNet-101 and ResNeXt-101, respectively. When further equipped with Sequence DIoU NMS, we obtain the best-reported mAP of 85.07% and 86.88% upon the paper submitted. The code to reproduce our results will be released after acceptance.