pwc-archive/papers-with-abstracts huggingface dataset:
Network Structure Identification from Corrupt Data Streams
Date: 2020-06-08 00:00:00
Tasks: Network Identification

Complex networked systems can be modeled as graphs with nodes representing the agents and links describing the dynamic coupling between them. Previous work on network identification has shown that the network structure of linear time-invariant (LTI) systems can be reconstructed from the joint power spectrum of the data streams. These results assumed that data is perfectly measured. However, real-world data is subject to many corruptions, such as inaccurate time-stamps, noise, and data loss. We show that identifying the structure of linear time-invariant systems using corrupt measurements results in the inference of erroneous links. We provide an exact characterization and prove that such erroneous links are restricted to the neighborhood of the perturbed node. We extend the analysis of LTI systems to the case of Markov random fields with corrupt measurements. We show that data corruption in Markov random fields results in spurious probabilistic relationships in precisely the locations where spurious links arise in LTI systems.
A Real-Time Automated Point-Process Method for the Detection and Correction of Erroneous and Ectopic Heartbeats
Authors: Luca Citi, Emery N. Brown, Riccardo Barbieri
Date: 2012-08-02 00:00:00
Tasks: Heartbeat Classification, Heart Rate Variability, Rhythm, Specificity

The presence of recurring arrhythmic events (also known as cardiac dysrhythmia or irregular heartbeats), as well as erroneous beat detection due to low signal quality, significantly affects estimation of both time and frequency domain indices of heart rate variability (HRV). A reliable, real-time classification and correction of ECG-derived heartbeats is a necessary prerequisite for an accurate online monitoring of HRV and cardiovascular control. We have developed a novel point-process-based method for real-time R-R interval error detection and correction. Given an R-wave event, we assume that the length of the next R-R interval follows a physiologically motivated, time-varying inverse Gaussian probability distribution. We then devise an instantaneous automated detection and correction procedure for erroneous and arrhythmic beats by using the information on the probability of occurrence of the observed beat provided by the model. We test our algorithm over two datasets from the PhysioNet archive. The Fantasia normal rhythm database is artificially corrupted with known erroneous beats to test both the detection procedure and correction procedure. The benchmark MIT-BIH Arrhythmia database is further considered to test the detection procedure of real arrhythmic events and compare it with results from previously published algorithms. Our automated algorithm represents an improvement over previous procedures, with best specificity for the detection of correct beats, as well as highest sensitivity to missed and extra beats, artificially misplaced beats, and for real arrhythmic events. A near-optimal heartbeat classification and correction, together with the ability to adapt to time-varying changes of heartbeat dynamics in an online fashion, may provide a solid base for building a more reliable real-time HRV monitoring device.
Towards better heartbeat segmentation with deep learning classification
Authors: Pedro Silva, Eduardo Luz, Guilherme Silva, Gladston Moreira
Date: 2020-11-26 00:00:00
Tasks: Classification, Deep Learning, General Classification

The confidence of medical equipment is intimately related to false alarms. The higher the number of false events occurs, the less truthful is the equipment. In this sense, reducing (or suppressing) false positive alarms is hugely desirable. In this work, we propose a feasible and real-time approach that works as a validation method for a heartbeat segmentation third-party algorithm. The approach is based on convolutional neural networks (CNNs), which may be embedded in dedicated hardware. Our proposal aims to detect the pattern of a single heartbeat and classifies them into two classes: a heartbeat and not a heartbeat. For this, a seven-layer convolution network is employed for both data representation and classification. We evaluate our approach in two well-settled databases in the literature on the raw heartbeat signal. The first database is a conventional on-the-person database called MIT-BIH, and the second is one less uncontrolled off-the-person type database known as CYBHi. To evaluate the feasibility and the performance of the proposed approach, we use as a baseline the Pam-Tompkins algorithm, which is a well-known method in the literature and still used in the industry. We compare the baseline against the proposed approach: a CNN model validating the heartbeats detected by a third-party algorithm. In this work, the third-party algorithm is the same as the baseline for comparison purposes. The results support the feasibility of our approach showing that our method can enhance the positive prediction of the Pan-Tompkins algorithm from 97.84%/90.28% to 100.00%/96.77% by slightly decreasing the sensitivity from 95.79%/96.95% to 92.98%/ 95.71% on the MIT-BIH/CYBHi databases.
Dense cellular segmentation for EM using 2D–3D neural network ensembles
Authors: Matthew D. Guay, Zeyad A. S. Emam, Adam B. Anderson, Maria A. Aronova, Irina D. Pokrovskaya, Brian Storrie & Richard D. Leapman
Date: 2021-01-28 00:00:00
Tasks: 3D Semantic Segmentation, Electron Microscopy Image Segmentation, Segmentation, Semantic Segmentation

Biologists who use electron microscopy (EM) images to build nanoscale 3D models of whole cells and their organelles have historically been limited to small numbers of cells and cellular features due to constraints in imaging and analysis. This has been a major factor limiting insight into the complex variability of cellular environments. Modern EM can produce gigavoxel image volumes containing large numbers of cells, but accurate manual segmentation of image features is slow and limits the creation of cell models. Segmentation algorithms based on convolutional neural networks can process large volumes quickly, but achieving EM task accuracy goals often challenges current techniques. Here, we define dense cellular segmentation as a multiclass semantic segmentation task for modeling cells and large numbers of their organelles, and give an example in human blood platelets. We present an algorithm using novel hybrid 2D–3D segmentation networks to produce dense cellular segmentations with accuracy levels that outperform baseline methods and approach those of human annotators. To our knowledge, this work represents the first published approach to automating the creation of cell models with this level of structural detail.
A Method for Detection of Small Moving Objects in UAV Videos
Authors: Vladan Stojnić, Vladimir Risojević, Mario Muštra, Vedran Jovanović, Janja Filipi, Nikola Kezić, and Zdenka Babić
Date: 2021-02-11 00:00:00
Tasks: object-detection, Object Detection, Segmentation Of Remote Sensing Imagery, Small Object Detection, Video Stabilization

Detection of small moving objects is an important research area with applications including monitoring of flying insects, studying their foraging behavior, using insect pollinators to monitor flowering and pollination of crops, surveillance of honeybee colonies, and tracking movement of honeybees. However, due to the lack of distinctive shape and textural details on small objects, direct application of modern object detection methods based on convolutional neural networks (CNNs) shows considerably lower performance. In this paper we propose a method for the detection of small moving objects in videos recorded using unmanned aerial vehicles equipped with standard video cameras. The main steps of the proposed method are video stabilization, background estimation and subtraction, frame segmentation using a CNN, and thresholding the segmented frame. However, for training a CNN it is required that a large labeled dataset is available. Manual labelling of small moving objects in videos is very difficult and time consuming, and such labeled datasets do not exist at the moment. To circumvent this problem, we propose training a CNN using synthetic videos generated by adding small blob-like objects to video sequences with real-world backgrounds. The experimental results on detection of flying honeybees show that by using a combination of classical computer vision techniques and CNNs, as well as synthetic training sets, the proposed approach overcomes the problems associated with direct application of CNNs to the given problem and achieves an average F1-score of 0.86 in tests on real-world videos.
Zero-Shot Audio Classification via Semantic Embeddings
Authors: Huang Xie, Tuomas Virtanen
Date: 2020-11-24 00:00:00
Tasks: Audio Classification, Classification, General Classification, Sentence, Sentence Embeddings, Zero-shot Audio Classification, Zero-Shot Learning

In this paper, we study zero-shot learning in audio classification via semantic embeddings extracted from textual labels and sentence descriptions of sound classes. Our goal is to obtain a classifier that is capable of recognizing audio instances of sound classes that have no available training samples, but only semantic side information. We employ a bilinear compatibility framework to learn an acoustic-semantic projection between intermediate-level representations of audio instances and sound classes, i.e., acoustic embeddings and semantic embeddings. We use VGGish to extract deep acoustic embeddings from audio clips, and pre-trained language models (Word2Vec, GloVe, BERT) to generate either label embeddings from textual labels or sentence embeddings from sentence descriptions of sound classes. Audio classification is performed by a linear compatibility function that measures how compatible an acoustic embedding and a semantic embedding are. We evaluate the proposed method on a small balanced dataset ESC-50 and a large-scale unbalanced audio subset of AudioSet. The experimental results show that classification performance is significantly improved by involving sound classes that are semantically close to the test classes in training. Meanwhile, we demonstrate that both label embeddings and sentence embeddings are useful for zero-shot learning. Classification performance is improved by concatenating label/sentence embeddings generated with different language models. With their hybrid concatenations, the results are improved further.
Automated Annotation of Cell Identities in Dense Cellular Images
Authors: Shivesh Chaudhary, Sol Ah Lee, Yueyi Li, Dhaval S. Patel, Hang Lu
Date: 2020-03-11 00:00:00

Assigning cell identities in dense image stacks is critical for many applications, for comparing data across animals and experiment conditions, and investigating properties of specific cells. Conventional methods are laborious, require experience, and could introduce bias. We present a generalizable framework based on Conditional Random Fields models for automatic cell identification. This approach searches for optimal arrangements of labels that maximally preserves prior knowledge such as geometrical relationships. The algorithm shows better accuracy and more robust handling of perturbations, e.g. missing cells and position variability, with both synthetic and experimental ground-truth data. The framework is generalizable across strains, imaging conditions, and easily builds and utilizes active data-driven atlases, which further improves accuracy. We demonstrate the utility in gene-expression pattern analysis, multi-cellular calcium imaging, and whole-brain imaging experiments. Thus, our framework is highly valuable to a wide variety of annotation scenarios including in zebrafish, Drosophila, hydra, and mouse brains.
Centimeter-scale single-crystal perovskite devices
Authors: Nano Convergence
Date: 2020-07-14 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.
Detecting atrial fibrillation by deep convolutional neural networks
Authors: Yong Xia, Naren Wulan, Kuanquan Wang, Henggui Zhang
Date: 2018-02-18 00:00:00
Tasks: Sensitivity, Specificity

Background
Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning.

Method
The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms.

Results
Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved.

Conclusion
The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection.
Blockage-aware Reliable mmWave Access via Coordinated Multi-point Connectivity
Authors: Dileep Kumar, Jarkko Kaleva, Antti Tölli
Date: 2019-10-14 00:00:00

The fundamental challenge of the millimeter-wave (mmWave) frequency band is the sensitivity of the radio channel to blockages, which gives rise to unstable connectivity and impacts the reliability of a system. To this end, multi-point connectivity is a promising approach for ensuring the desired rate and reliability requirements. A robust beamformer design is proposed to improve the communication reliability by exploiting the spatial macro-diversity and a pessimistic estimate of rates over potential link blockage combinations. Specifically, we provide a blockage-aware algorithm for the weighted sum-rate maximization (WSRM) problem with parallel beamformer processing across distributed remote radio units (RRUs). Combinations of non-convex and coupled constraints are handled via successive convex approximation (SCA) framework, which admits a closed-form solution for each SCA step, by solving a system of Karush-Kuhn-Tucker (KKT) optimality conditions. Unlike the conventional coordinated multi-point (CoMP) schemes, the proposed blockage-aware beamformer design has, per-iteration, computational complexity in the order of RRU antennas instead of system-wide joint transmit antennas. This leads to a practical and computationally efficient implementation that is scalable to any arbitrary multi-point configuration. In the presence of random blockages, the proposed schemes are shown to significantly outperform baseline scenarios and result in reliable mmWave communication.
Duality in dynamic discrete-choice models
Date: 2021-02-08 00:00:00
Tasks: Discrete Choice Models

Using results from convex analysis, we investigate a novel approach to identification and estimation of discrete choice models which we call the Mass Transport Approach (MTA). We show that the conditional choice probabilities and the choice-specific payoffs in these models are related in the sense of conjugate duality, and that the identification problem is a mass transport problem. Based on this, we propose a new two-step estimator for these models; interestingly, the first step of our estimator involves solving a linear program which is identical to the classic assignment (two-sided matching) game of Shapley and Shubik (1971). The application of convex-analytic tools to dynamic discrete choice models, and the connection with two-sided matching models, is new in the literature.
Fast Multiparty Threshold ECDSA with Fast Trustless Setup
Authors: R. Gennaro, S. Goldfeder
Date: 2018-02-14 00:00:00

A threshold signature scheme enables distributed signing among n players such
that any subgroup of size t + 1 can sign, whereas any group with t or fewer players cannot.
While there exist previous threshold schemes for the ECDSA signature scheme, we present
the first protocol that supports multiparty signatures for any t ≤ n with efficient, dealerless
key generation. Our protocol is faster than previous solutions and significantly reduces the
communication complexity as well. We prove our scheme secure against malicious adversaries
with a dishonest majority. We implemented our protocol, demonstrating its efficiency and
suitability to be deployed in practice.
Harnessing NLG to create Finnish poetry automatically
Authors: Mika Hämäläinen
Date: 2018-06-01 00:00:00

This paper presents a new, NLG based approach to poetry generation in Finnish for use as a part of a bigger Poem Machine system the objective of which is to provide a platform for human computer co-creativity. The approach divides generation into a linguistically solid system for producing grammatical Finnish and higher level systems for producing a poem structure and choosing the lexical items used in the poems. An automatically extracted open-access semantic repository tailored for poem generation is developed for the system. Finally , the resulting poems are evaluated and compared with the state of the art in Finnish poem generation.
A Lightweight Stemmer for Gujarati
Authors: Juhi Ameta, Nisheeth Joshi, Iti Mathur
Date: 2012-10-19 00:00:00

Gujarati is a resource poor language with almost no language processing tools being available. In this paper we have shown an implementation of a rule based stemmer of Gujarati. We have shown the creation of rules for stemming and the richness in morphology that Gujarati possesses. We have also evaluated our results by verifying it with a human expert.
Assessment of intra-tumor heterogeneity in a two-dimensional vascular tumor growth model
Authors: I. Lampropoulos, M. Kavousanakis
Date: 2021-02-11 00:00:00

We present a two-dimensional continuum model of tumor growth, which treats the tissue as a composition of six distinct fluid phases; their dynamics are governed by the equations of mass and momentum conservation. Our model divides the cancer cells phase into two sub-phases depending on their maturity state. The same approach is also applied for the vasculature phase, which is divided into young sprouts (products of angiogenesis), and fully formed-mature vessels. The remaining two phases correspond to healthy cells and extracellular material (ECM). Furthermore, the model foresees the existence of nutrient chemical species, which are transferred within the tissue through diffusion or supplied by the vasculature (blood vessels). The model is numerically solved with the Finite Elements Method and computations are performed with the commercial software Comsol Multiphysics. The numerical simulations predict that mature cancer cells are well separated from young cancer cells, which form a protective shield for the growing tumor. We study the effect of different mitosis and death rates for mature and young cancer cells on the tumor growth rate, and predict accelerated rates when the mitosis rate of young cancer cells is higher compared to mature cancer cells.
Weighted Contourlet Parametric (WCP) Feature Based Breast Tumor Classification from B-Mode Ultrasound Image
Authors: Shahriar Mahmud Kabir, Md. Sayed Tanveer, ASM Shihavuddin, Mohammed Imamul Hassan Bhuiyan
Date: 2021-02-11 00:00:00
Tasks: General Classification

Automated detection of breast tumor in early stages using B-Mode Ultrasound image is crucial for preventing widespread breast cancer specially among women. This paper is primarily focusing on the classification of breast tumors through statistical modelling such as Rician inverse Gaussian (RiIG) pdf of contourlet transformed B-Mode image of breast tumors which is not reported yet in other earlier works. The suitability of RiIG distribution in modeling the contourlet coefficients is illustrated and compared with that of Nakagami distribution. The proposed method consists of pre-processing to remove the speckle noise, segmentation of the lesion region, contourlet transform on the B-Mode Ultrasound image and using the corresponding contourlet sub-band coefficients and the RiIG parameters, production of contourlet parametric (CP) images and weighted contourlet parametric (WCP) images. A number of geometrical, statistical, and texture features are calculated from B-Mode and the contourlet parametric images. In order to classify the features, seven different classifiers are employed. The proposed approach is applied to two different datasets (Mendeley Data and Dataset B) those are available publicly. It is shown that with parametric images, accuracies in the range of 94-97% are achieved for different classifiers. Specifically, with the support vector machine and k-nearest-neighbor classifier, very high accuracies of 97.2% and 97.55% can be obtained for the Mendeley Data and Dataset B,respectively, using the weighted contourlet parametric images.The reported classification performance is also compared with that of other works using the datasets employed in this paper. It is seen that the proposed approach using weighted contourlet parametric images can provide a superior performance.