pwc-archive/papers-with-abstracts huggingface dataset:
ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration
Date: 2020-11-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Denoising, Speech Enhancement, speech-recognition, Speech Recognition, Speech Separation

We present ESPnet-SE, which is designed for the quick development of speech enhancement and speech separation systems in a single framework, along with the optional downstream speech recognition module. ESPnet-SE is a new project which integrates rich automatic speech recognition related models, resources and systems to support and validate the proposed front-end implementation (i.e. speech enhancement and separation).It is capable of processing both single-channel and multi-channel data, with various functionalities including dereverberation, denoising and source separation. We provide all-in-one recipes including data pre-processing, feature extraction, training and evaluation pipelines for a wide range of benchmark datasets. This paper describes the design of the toolkit, several important functionalities, especially the speech recognition integration, which differentiates ESPnet-SE from other open source toolkits, and experimental results with major benchmark datasets.
Space-confined growth of large-scale perovskite single-crystalline thin films via surface engineering
Authors: perovskite
Date: 2020-07-20 00:00:00

Modern electronic and photonic devices rely on single-crystalline thin film semiconductors for high performance and reproducibility. The emerging halide perovskites have extraordinary electronic and photonic properties and can be synthesized via low cost solution-based methods. They have been used in a variety of devices with performance approaching or over the devices based on conventional materials. However, their solution based growth method is intrinsically challenge to grow large scale single-crystalline thin film due to the random nucleation and isotropous growth of the crystal. Here, we report the growth of centimeter-scale perovskite single-crystalline thin films by controlling the nucleation density and growth rate of the crystal under a spatially confined growth condition. The hydrophobic treatment on substrates inhibits nucleation and accelerates the growth of single-crystalline thin film, providing enough space for initial nucleus growing up quickly without touching each other. Single-crystalline perovskite thin-film with an aspect ratio of 1000 (1 cm in side length, 10 μm in thickness) has been successfully grown. The low trap density and the high mobility of the as-grown thin film show a high crystallinity. The photodetector based on the perovskite thin film has achieved a gain ~ 104, benefitting from the short transit time of the carries due to the high mobility and thin thickness of the active layer. Our work opens up a new route to grow large scale perovskite single-crystalline thin films, providing a platform to develop high- performance devices.
【Article：Deng, YH., Yang, ZQ. & Ma, RM. Growth of centimeter-scale perovskite single-crystalline thin film via surface engineering. Nano Convergence 7, 25 (2020).  https://doi.org/10.1186/s40580-020-00236-5】
Common Misidentifications in High-Resolution Imaging of Halide Perovskites Using Electron Microscopy
Authors: Halide Perovskites
Date: 2020-12-29 00:00:00

【Abstract】High-resolution TEM (HRTEM) is a powerful tool for structure characterization. However, methylammonium lead iodide (MAPbI3) perovskite is highly sensitive to electron beams and easily decomposes into lead iodide (PbI2). Misidentifications, such as PbI2 being incorrectly labeled as perovskite, are widely present in HRTEM characterization and would negatively affect the development of perovskite research field. Here misidentifications in MAPbI3 perovskite are summarized, classified, and corrected based on low-dose imaging and electron diffraction (ED) simulations. Corresponding crystallographic parameters of intrinsic tetragonal MAPbI3 and the confusable hexagonal PbI2 are presented unambiguously. Finally, the method of proper phase identification and some strategies to control the radiation damage in HRTEM are provided. This warning paves the way to avoid future misinterpretations in HRTEM characterization of perovskite and other electron beam-sensitive materials.
【Keywords】: MAPbI3 perovskite; transmission electron microscopy (TEM); electron diffraction (ED); phase identification; electron beam-sensitive.
【Article: Deng, Y.-H. Common Phase and Structure Misidentifications in High-Resolution TEM Characterization of Perovskite Materials. Condens. Matter 2021, 6, 1.  https://doi.org/10.3390/condmat6010001】
Strategies for Proper Identifications of Halide Perovskites in Transmission Electron Microscopy
Authors: HRTEM
Date: 2020-12-25 00:00:00

Abstract:
High-resolution TEM (HRTEM) is a powerful tool for structure characterization. However, methylammonium lead iodide (MAPbI3) perovskite is highly sensitive to electron beams and easily decomposes into lead iodide (PbI2). Misidentifications, such as PbI2 being incorrectly labeled as perovskite, are widely present in HRTEM characterization and would negatively affect the development of perovskite research field. Here misidentifications in MAPbI3 perovskite are summarized, classified, and corrected based on low-dose imaging and electron diffraction (ED) simulations. Corresponding crystallographic parameters of intrinsic tetragonal MAPbI3 and the confusable hexagonal PbI2 are presented unambiguously. Finally, the method of proper phase identification and some strategies to control the radiation damage in HRTEM are provided. This warning paves the way to avoid future misinterpretations in HRTEM characterization of perovskite and other electron beam-sensitive materials. 
Keywords: MAPbI3 perovskite; transmission electron microscopy (TEM); electron diffraction (ED); phase identification; electron beam-sensitive
【MDPI and ACS Style
Deng, Y.-H. Common Phase and Structure Misidentifications in High-Resolution TEM Characterization of Perovskite Materials. Condens. Matter 2021, 6, 1.

AMA Style
Deng Y-H. Common Phase and Structure Misidentifications in High-Resolution TEM Characterization of Perovskite Materials. Condensed Matter. 2021; 6(1):1.

Chicago/Turabian Style
Deng, Yu-Hao. 2021. "Common Phase and Structure Misidentifications in High-Resolution TEM Characterization of Perovskite Materials" Condens. Matter 6, no. 1: 1.】
Monitoring of Railpad Long-term Condition in Turnouts Using Extreme Value Distributions
Authors: Pegah Barkhordari, Roberto Galeazzi, Mogens Blanke
Date: 2021-01-07 00:00:00

The railpad is a key element in railway infrastructures that plays an essential role in the train-track dynamics. Presence of worn or defective railpads along railway track may lead to large wheel/rail interaction forces, and a high rate of deterioration for track components. Despite the importance of railpad, the track infrastructure managers use no inspection tool for monitoring in-service railpads over time. In this paper, a novel data-driven monitoring tool for long-term performance analysis of in-service railpads is developed based on train-induced vibration data collected by a track-side measurement system. The monitoring tool consists of a method for track resonance frequencies estimation, a temperature-frequency model for describing railpad behavior with respect to ambient temperature, and a generalized likelihood ratio test based on the generalized extreme value distribution for detecting changes in the railpad status over time. To evaluate the performance of the proposed monitoring system, the status of railpads at four different locations along a railway turnout is monitored over a period of 18 months. It is shown that the monitoring system can successfully detect changes in railpad properties over the considered period.
Attention-based multi-task learning for speech-enhancement and speaker-identification in multi-speaker dialogue scenario
Authors: Chiang-Jen Peng, Yun-Ju Chan, Cheng Yu, Syu-Siang Wang, Yu Tsao, Tai-Shih Chi
Date: 2021-01-07 00:00:00
Tasks: Multi-Task Learning, Speaker Identification, Speech Enhancement

Multi-task learning (MTL) and attention mechanism have been proven to effectively extract robust acoustic features for various speech-related tasks in noisy environments. In this study, we propose an attention-based MTL (ATM) approach that integrates MTL and the attention-weighting mechanism to simultaneously realize a multi-model learning structure that performs speech enhancement (SE) and speaker identification (SI). The proposed ATM system consists of three parts: SE, SI, and attention-Net (AttNet). The SE part is composed of a long-short-term memory (LSTM) model, and a deep neural network (DNN) model is used to develop the SI and AttNet parts. The overall ATM system first extracts the representative features and then enhances the speech signals in LSTM-SE and specifies speaker identity in DNN-SI. The AttNet computes weights based on DNN-SI to prepare better representative features for LSTM-SE. We tested the proposed ATM system on Taiwan Mandarin hearing in noise test sentences. The evaluation results confirmed that the proposed system can effectively enhance speech quality and intelligibility of a given noisy input. Moreover, the accuracy of the SI can also be notably improved by using the proposed ATM system.
Heatmap-based 2D Landmark Detection with a Varying Number of Landmarks
Authors: Antonia Stern, Lalith Sharan, Gabriele Romano, Sven Koehler, Matthias Karck, Raffaele De Simone, Ivo Wolf, Sandy Engelhardt
Date: 2021-01-07 00:00:00

Mitral valve repair is a surgery to restore the function of the mitral valve. To achieve this, a prosthetic ring is sewed onto the mitral annulus. Analyzing the sutures, which are punctured through the annulus for ring implantation, can be useful in surgical skill assessment, for quantitative surgery and for positioning a virtual prosthetic ring model in the scene via augmented reality. This work presents a neural network approach which detects the sutures in endoscopic images of mitral valve repair and therefore solves a landmark detection problem with varying amount of landmarks, as opposed to most other existing deep learning-based landmark detection approaches. The neural network is trained separately on two data collections from different domains with the same architecture and hyperparameter settings. The datasets consist of more than 1,300 stereo frame pairs each, with a total over 60,000 annotated landmarks. The proposed heatmap-based neural network achieves a mean positive predictive value (PPV) of 66.68$\pm$4.67% and a mean true positive rate (TPR) of 24.45$\pm$5.06% on the intraoperative test dataset and a mean PPV of 81.50\pm5.77\% and a mean TPR of 61.60$\pm$6.11% on a dataset recorded during surgical simulation. The best detection results are achieved when the camera is positioned above the mitral valve with good illumination. A detection from a sideward view is also possible if the mitral valve is well perceptible.
Combining pretrained CNN feature extractors to enhance clustering of complex natural images
Authors: Joris Guerin, Stephane Thiery, Eric Nyiri, Olivier Gibaru, Byron Boots
Date: 2021-01-07 00:00:00
Tasks: Clustering, image-classification, Image Classification, Image Clustering, Unsupervised Image Classification

Recently, a common starting point for solving complex unsupervised image classification tasks is to use generic features, extracted with deep Convolutional Neural Networks (CNN) pretrained on a large and versatile dataset (ImageNet). However, in most research, the CNN architecture for feature extraction is chosen arbitrarily, without justification. This paper aims at providing insight on the use of pretrained CNN features for image clustering (IC). First, extensive experiments are conducted and show that, for a given dataset, the choice of the CNN architecture for feature extraction has a huge impact on the final clustering. These experiments also demonstrate that proper extractor selection for a given IC task is difficult. To solve this issue, we propose to rephrase the IC problem as a multi-view clustering (MVC) problem that considers features extracted from different architectures as different "views" of the same data. This approach is based on the assumption that information contained in the different CNN may be complementary, even when pretrained on the same data. We then propose a multi-input neural network architecture that is trained end-to-end to solve the MVC problem effectively. This approach is tested on nine natural image datasets, and produces state-of-the-art results for IC.
Learning Grammar of Complex Activities via Deep Neural Networks
Authors: Becky Mashaido
Date: 2021-01-07 00:00:00
Tasks: Autonomous Driving

Motivated by the growing amount of publicly available video data on online streaming services and an increased interest in applications that analyze continuous video streams such as autonomous driving, this technical report provides a theoretical insight into deep neural networks for video learning, under label constraints. I build upon previous work in video learning for computer vision, make observations on model performance and propose further mechanisms to help improve our observations.
Off-Line Arabic Handwritten Words Segmentation using Morphological Operators
Authors: Nisreen AbdAllah, Serestina Viriri
Date: 2021-01-07 00:00:00
Tasks: Segmentation

The main aim of this study is the assessment and discussion of a model for hand-written Arabic through segmentation. The framework is proposed based on three steps: pre-processing, segmentation, and evaluation. In the pre-processing step, morphological operators are applied for Connecting Gaps (CGs) in written words. Gaps happen when pen lifting-off during writing, scanning documents, or while converting images to binary type. In the segmentation step, first removed the small diacritics then bounded a connected component to segment offline words. Huge data was utilized in the proposed model for applying a variety of handwriting styles so that to be more compatible with real-life applications. Consequently, on the automatic evaluation stage, selected randomly 1,131 images from the IESK-ArDB database, and then segmented into sub-words. After small gaps been connected, the model performance evaluation had been reached 88% against the standard ground truth of the database. The proposed model achieved the highest accuracy when compared with the related works.
Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction
Authors: Dan Xu, Xavier Alameda-Pineda, Wanli Ouyang, Elisa Ricci, Xiaogang Wang, Nicu Sebe
Date: 2021-01-08 00:00:00
Tasks: Depth Estimation, Graph Attention, Monocular Depth Estimation, Prediction, Semantic Segmentation

Multi-scale representations deeply learned via convolutional neural networks have shown tremendous importance for various pixel-level prediction problems. In this paper we present a novel approach that advances the state of the art on pixel-level prediction in a fundamental aspect, i.e. structured multi-scale features learning and fusion. In contrast to previous works directly considering multi-scale feature maps obtained from the inner layers of a primary CNN architecture, and simply fusing the features with weighted averaging or concatenation, we propose a probabilistic graph attention network structure based on a novel Attention-Gated Conditional Random Fields (AG-CRFs) model for learning and fusing multi-scale representations in a principled manner. In order to further improve the learning capacity of the network structure, we propose to exploit feature dependant conditional kernels within the deep probabilistic framework. Extensive experiments are conducted on four publicly available datasets (i.e. BSDS500, NYUD-V2, KITTI, and Pascal-Context) and on three challenging pixel-wise prediction problems involving both discrete and continuous labels (i.e. monocular depth estimation, object contour prediction, and semantic segmentation). Quantitative and qualitative results demonstrate the effectiveness of the proposed latent AG-CRF model and the overall probabilistic graph attention network with feature conditional kernels for structured feature learning and pixel-wise prediction.
HIVE-Net: Centerline-Aware HIerarchical View-Ensemble Convolutional Network for Mitochondria Segmentation in EM Images
Authors: Zhimin Yuan, Xiaofen Ma, Jiajin Yi, Zhengrong Luo, Jialin Peng
Date: 2021-01-08 00:00:00
Tasks: Segmentation, Semantic Segmentation

Semantic segmentation of electron microscopy (EM) is an essential step to efficiently obtain reliable morphological statistics. Despite the great success achieved using deep convolutional neural networks (CNNs), they still produce coarse segmentations with lots of discontinuities and false positives for mitochondria segmentation. In this study, we introduce a centerline-aware multitask network by utilizing centerline as an intrinsic shape cue of mitochondria to regularize the segmentation. Since the application of 3D CNNs on large medical volumes is usually hindered by their substantial computational cost and storage overhead, we introduce a novel hierarchical view-ensemble convolution (HVEC), a simple alternative of 3D convolution to learn 3D spatial contexts using more efficient 2D convolutions. The HVEC enables both decomposing and sharing multi-view information, leading to increased learning capacity. Extensive validation results on two challenging benchmarks show that, the proposed method performs favorably against the state-of-the-art methods in accuracy and visual quality but with a greatly reduced model size. Moreover, the proposed model also shows significantly improved generalization ability, especially when training with quite limited amount of training data.
Octave Mix: Data augmentation using frequency decomposition for activity recognition
Authors: Tatsuhito Hasegawa
Date: 2021-01-08 00:00:00
Tasks: Activity Recognition, Data Augmentation

In the research field of activity recognition, although it is difficult to collect a large amount of measured sensor data, there has not been much discussion about data augmentation (DA). In this study, I propose Octave Mix as a new synthetic-style DA method for sensor-based activity recognition. Octave Mix is a simple DA method that combines two types of waveforms by intersecting low and high frequency waveforms using frequency decomposition. In addition, I propose a DA ensemble model and its training algorithm to acquire robustness to the original sensor data while remaining a wide variety of feature representation. I conducted experiments to evaluate the effectiveness of my proposed method using four different benchmark datasets of sensing-based activity recognition. As a result, my proposed method achieved the best estimation accuracy. Furthermore, I found that ensembling two DA strategies: Octave Mix with rotation and mixup with rotation, make it possible to achieve higher accuracy.
From Black-box to White-box: Examining Confidence Calibration under different Conditions
Authors: Franziska Schwaiger, Maximilian Henne, Fabian Küppers, Felippe Schmoeller Roza, Karsten Roscher, Anselm Haselhoff
Date: 2021-01-08 00:00:00
Tasks: Object, object-detection, Object Detection

Confidence calibration is a major concern when applying artificial neural networks in safety-critical applications. Since most research in this area has focused on classification in the past, confidence calibration in the scope of object detection has gained more attention only recently. Based on previous work, we study the miscalibration of object detection models with respect to image location and box scale. Our main contribution is to additionally consider the impact of box selection methods like non-maximum suppression to calibration. We investigate the default intrinsic calibration of object detection models and how it is affected by these post-processing techniques. For this purpose, we distinguish between black-box calibration with non-maximum suppression and white-box calibration with raw network outputs. Our experiments reveal that post-processing highly affects confidence calibration. We show that non-maximum suppression has the potential to degrade initially well-calibrated predictions, leading to overconfident and thus miscalibrated models.
Residual networks classify inputs based on their neural transient dynamics
Authors: Fereshteh Lagzi
Date: 2021-01-08 00:00:00
Proceeding: ICLR 2019 5

We analyze the input-output behavior of residual networks from a dynamical system point of view by disentangling the residual dynamics from the output activities before the classification stage. For a network with simple skip connections between every successive layer, and for logistic activation function, and shared weights between layers, we show analytically that there is a cooperation and competition dynamics between residuals corresponding to each input dimension. Interpreting these kind of networks as nonlinear filters, the steady state value of the residuals in the case of attractor networks are indicative of the common features between different input dimensions that the network has observed during training, and has encoded in those components. In cases where residuals do not converge to an attractor state, their internal dynamics are separable for each input class, and the network can reliably approximate the output. We bring analytical and empirical evidence that residual networks classify inputs based on the integration of the transient dynamics of the residuals, and will show how the network responds to input perturbations. We compare the network dynamics for a ResNet and a Multi-Layer Perceptron and show that the internal dynamics, and the noise evolution are fundamentally different in these networks, and ResNets are more robust to noisy inputs. Based on these findings, we also develop a new method to adjust the depth for residual networks during training. As it turns out, after pruning the depth of a ResNet using this algorithm, the network is still capable of classifying inputs with a high accuracy.
Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search
Authors: Chenyang Gao, Guanyu Cai, Xinyang Jiang, Feng Zheng, Jun Zhang, Yifei Gong, Pai Peng, Xiaowei Guo, Xing Sun
Date: 2021-01-08 00:00:00
Tasks: Descriptive, Sentence, Text based Person Retrieval, Text based Person Search

Text-based person search aims at retrieving target person in an image gallery using a descriptive sentence of that person. It is very challenging since modal gap makes effectively extracting discriminative features more difficult. Moreover, the inter-class variance of both pedestrian images and descriptions is small. So comprehensive information is needed to align visual and textual clues across all scales. Most existing methods merely consider the local alignment between images and texts within a single scale (e.g. only global scale or only partial scale) then simply construct alignment at each scale separately. To address this problem, we propose a method that is able to adaptively align image and textual features across all scales, called NAFS (i.e.Non-local Alignment over Full-Scale representations). Firstly, a novel staircase network structure is proposed to extract full-scale image features with better locality. Secondly, a BERT with locality-constrained attention is proposed to obtain representations of descriptions at different scales. Then, instead of separately aligning features at each scale, a novel contextual non-local attention mechanism is applied to simultaneously discover latent alignments across all scales. The experimental results show that our method outperforms the state-of-the-art methods by 5.53% in terms of top-1 and 5.35% in terms of top-5 on text-based person search dataset. The code is available at https://github.com/TencentYoutuResearch/PersonReID-NAFS