pwc-archive/papers-with-abstracts huggingface dataset:
Selective Listening by Synchronizing Speech with Lips
Authors: Zexu Pan, Ruijie Tao, Chenglin Xu, Haizhou Li
Date: 2021-06-14 00:00:00
Tasks: Lip Reading, Target Speaker Extraction

A speaker extraction algorithm seeks to extract the speech of a target speaker from a multi-talker speech mixture when given a cue that represents the target speaker, such as a pre-enrolled speech utterance, or an accompanying video track. Visual cues are particularly useful when a pre-enrolled speech is not available. In this work, we don't rely on the target speaker's pre-enrolled speech, but rather use the target speaker's face track as the speaker cue, that is referred to as the auxiliary reference, to form an attractor towards the target speaker. We advocate that the temporal synchronization between the speech and its accompanying lip movements is a direct and dominant audio-visual cue. Therefore, we propose a self-supervised pre-training strategy, to exploit the speech-lip synchronization cue for target speaker extraction, which allows us to leverage abundant unlabeled in-domain data. We transfer the knowledge from the pre-trained model to the attractor encoder of the speaker extraction network. We show that the proposed speaker extraction network outperforms various competitive baselines in terms of signal quality, perceptual quality, and intelligibility, achieving state-of-the-art performance.
Recurrent Inference Machines as inverse problem solvers for MR relaxometry
Authors: E. R. Sabidussi, S. Klein, M. W. A. Caan, S. Bazrafkan, A. J. den Dekker, J. Sijbers, W. J. Niessen, D. H. J. Poot
Date: 2021-06-08 00:00:00
Tasks: Quantitative MRI

In this paper, we propose the use of Recurrent Inference Machines (RIMs) to perform T1 and T2 mapping. The RIM is a neural network framework that learns an iterative inference process based on the signal model, similar to conventional statistical methods for quantitative MRI (QMRI), such as the Maximum Likelihood Estimator (MLE). This framework combines the advantages of both data-driven and model-based methods, and, we hypothesize, is a promising tool for QMRI. Previously, RIMs were used to solve linear inverse reconstruction problems. Here, we show that they can also be used to optimize non-linear problems and estimate relaxometry maps with high precision and accuracy. The developed RIM framework is evaluated in terms of accuracy and precision and compared to an MLE method and an implementation of the ResNet. The results show that the RIM improves the quality of estimates compared to the other techniques in Monte Carlo experiments with simulated data, test-retest analysis of a system phantom, and in-vivo scans. Additionally, inference with the RIM is 150 times faster than the MLE, and robustness to (slight) variations of scanning parameters is demonstrated. Hence, the RIM is a promising and flexible method for QMRI. Coupled with an open-source training data generation tool, it presents a compelling alternative to previous methods.
Dataset for eye-tracking tasks
Authors: R. Ildar
Date: 2021-06-01 00:00:00

In recent years many different deep neural networks were developed, but due to a large number of layers in deep networks, their training requires a long time and a large number of datasets. Today is popular to use trained deep neural networks for various tasks, even for simple ones in which such deep networks are not required. The well-known deep networks such as YoloV3, SSD, etc. are intended for tracking and monitoring various objects, therefore their weights are heavy and the overall accuracy for a specific task is low. Eye-tracking tasks need to detect only one object - an iris in a given area. Therefore, it is logical to use a neural network only for this task. But the problem is the lack of suitable datasets for training the model. In the manuscript, we presented a dataset that is suitable for training custom models of convolutional neural networks for eye-tracking tasks. Using data set data, each user can independently pre-train the convolutional neural network models for eye-tracking tasks. This dataset contains annotated 10,000 eye images in an extension of 416 by 416 pixels. The table with annotation information shows the coordinates and radius of the eye for each image. This manuscript can be considered as a guide for the preparation of datasets for eye-tracking devices
An optimized Capsule-LSTM model for facial expression recognition with video sequences
Authors: Siwei Liu, Yuanpeng Long, Gao Xu, Lijia Yang, Shimei Xu, Xiaoming Yao, Kunxian Shu
Date: 2021-05-27 00:00:00
Tasks: Decoder, Facial Expression Recognition, Facial Expression Recognition (FER)

To overcome the limitations of convolutional neural network in the process of facial expression recognition, a facial expression recognition model Capsule-LSTM based on video frame sequence is proposed. This model is composed of three networks includingcapsule encoders, capsule decoders and LSTM network. The capsule encoder extracts the spatial information of facial expressions in video frames. Capsule decoder reconstructs the images to optimize the network. LSTM extracts the temporal information between video frames and analyzes the differences in expression changes between frames. The experimental results from the MMI dataset show that the Capsule-LSTM model proposed in this paper can effectively improve the accuracy of video expression recognition.
BPLF: A Bi-Parallel Linear Flow Model for Facial Expression Generation from Emotion Set Images
Authors: Gao Xu, Yuanpeng Long, Siwei Liu, Lijia Yang, Shimei Xu, Xiaoming Yao, Kunxian Shu
Date: 2021-05-27 00:00:00
Tasks: Facial expression generation

The flow-based generative model is a deep learning generative model, which obtains the ability to generate data by explicitly learning the data distribution. Theoretically its ability to restore data is stronger than other generative models. However, its implementation has many limitations, including limited model design, too many model parameters and tedious calculation. In this paper, a bi-parallel linear flow model for facial emotion generation from emotion set images is constructed, and a series of improvements have been made in terms of the expression ability of the model and the convergence speed in training. The model is mainly composed of several coupling layers superimposed to form a multi-scale structure, in which each coupling layer contains 1*1 reversible convolution and linear operation modules. Furthermore, this paper sorted out the current public data set of facial emotion images, made a new emotion data, and verified the model through this data set. The experimental results show that, under the traditional convolutional neural network, the 3-layer 3*3 convolution kernel is more conducive to extracte the features of the face images. The introduction of principal component decomposition can improve the convergence speed of the model.
Grasping force estimation using state-space model and Kalman filter
Authors: Bruno Dutra, Antonio Silveira, Antônio Pereira
Date: 2021-06-14 00:00:00
Tasks: Friction

The grip force required to handle an object depends on the object's mass and the friction coefficient of its surface. The control of grip force in myoelectric prosthesis is crucial for handling objects adequately. In the current paper we propose a new method for improving the proportional and continuous grasping force estimation to improve control systems for myoelectric prosthesis based on surface electromyography (sEMG) recordings. For this purpose, we develop an approach based on multivariable system identification in the state-space (SS) and continuous force estimation with Kalman Filter (KF). The sEMG recordings of ten healthy individuals performing a grip task were used as data set for model identification. The root mean square (RMS), the mean absolute value (MAV), and the waveform length (WL) extracted from the sEMG signals were used at the model's input while the measured grasping force was the output. The performance of the method was evaluated with the normalized root-mean-squared-error (NRMSE) and the square of the Pearson's correlation coefficient ($ R^2 $). We found the $R^2$ and NRMSE values were 0.92$ \pm $ 0.0319 and 0.723$ \pm $ 0.0563, respectively. The performance of the proposed technique was superior to the results obtained with other regression models, such as the recurrent nonlinear autoregressive exogenous (NARX)-based neural network, the multi-layer perceptron (MLP) network and the linear discriminant analysis (LDA) with a quadratic polynomial fitting (QPF). The results confirm that the method is adequate for real-time applications with myoelectric hand prostheses.
Multi-RIS Discrete-Phase Encoding for Interpath-Interference-Free Channel Estimation
Authors: Kamran Keykhosravi, Henk Wymeersch
Date: 2021-06-13 00:00:00

Reconfigurable intelligent surfaces (RISs) are one of the foremost technological enablers of future wireless systems. They improve communication and localization by providing a strong non-line-of-sight path to the receiver. In this paper, we propose a pilot transmission method to enable the receiver to separate signals arriving from different RISs and from the uncontrolled multipath. This facilitates channel estimation and localization, as the channel or its geometric parameters can be estimated for each path separately. Our method is based on designing temporal phase profiles that are orthogonal across RISs without affecting the RIS beamforming capabilities. We take into consideration the limited resolution of the RIS phase shifters and show that in the presence of this practical limitation, orthogonal phase profiles can be designed based on Butson-type Hadamard matrices. For a localization scenario, we show that with our proposed method the estimator can attain the theoretical lower bound even with one-bit RIS phase resolution.
Ultrasound Classification of Breast Masses Using a Comprehensive Nakagami Imaging and Machine Learning Framework
Authors: Ahmad Chowdhury, Rezwana R. Razzaque, Ahmad Shafiullah, Sabiq Muhtadi, Brian S. Garra, S. Kaisar Alam
Date: 2021-06-13 00:00:00
Tasks: Classification, Diagnostic, feature selection, Image Generation

In this study we investigate the potential of parametric images formed from ultrasound B-mode scans using the Nakagami distribution for non-invasive classification of breast lesions. Through a sliding window technique, we generated seven types of parametric images from each patient scan in our dataset using basic and as well as derived parameters of the Nakagami distribution. To determine the most suitable window size for image generation, we conducted an empirical analysis using three windows, and selected the best one for our study. From the parametric images formed for each patient, we extracted a total of 72 features. Feature selection was performed to find the optimum subset of features for the best classification performance. Incorporating the selected subset of features with the Support Vector Machine (SVM) classifier, and by tuning the decision threshold, we obtained a maximum classification accuracy of 93.08%, an Area under the ROC Curve (AUC) of 0.9712, a False Negative Rate of 0%, and a very low False Positive Rate of 8.65%. Our results indicate that the high accuracy of such a procedure may assist in the diagnostic process associated with detection of breast cancer, as well as help to reduce false positive diagnosis.
Towards Fast Region Adaptive Ultrasound Beamformer for Plane Wave Imaging Using Convolutional Neural Networks
Authors: Roshan P Mathews, Mahesh Raveendranatha Panicker
Date: 2021-06-13 00:00:00
Tasks: Diagnostic

Automatic learning algorithms for improving the image quality of diagnostic B-mode ultrasound (US) images have been gaining popularity in the recent past. In this work, a novel convolutional neural network (CNN) is trained using time of flight corrected in-vivo receiver data of plane wave transmit to produce corresponding high-quality minimum variance distortion less response (MVDR) beamformed image. A comprehensive performance comparison in terms of qualitative and quantitative measures for fully connected neural network (FCNN), the proposed CNN architecture, MVDR and Delay and Sum (DAS) using the dataset from Plane wave Imaging Challenge in Ultrasound (PICMUS) is also reported in this work. The CNN architecture could leverage the spatial information and will be more region adaptive during the beamforming process. This is evident from the improvement seen over the baseline FCNN approach and conventional MVDR beamformer, both in resolution and contrast with an improvement of 6 dB in CNR using only zero-angle transmission over the baseline. With the observed reduction in the requirement of number of angles to produce similar image metrics would prove advantageous in providing a possibility for higher frame rates.
AI Enlightens Wireless Communication: Analyses, Solutions and Opportunities on CSI Feedback
Authors: Han Xiao, Zhiqin Wang, Wenqiang Tian, Xiaofeng Liu, Wendong Liu, Shi Jin, Jia Shen, Zhi Zhang, Ning Yang
Date: 2021-06-12 00:00:00
Tasks: Quantization

In this paper, we give a systematic description of the 1st Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AI Work Group. Firstly, the framework of full channel state information (F-CSI) feedback problem and its corresponding channel dataset are provided. Then the enhancing schemes for DL-based F-CSI feedback including i) channel data analysis and preprocessing, ii) neural network design and iii) quantization enhancement are elaborated. The final competition results composed of different enhancing schemes are presented. Based on the valuable experience of 1st WAIC, we also list some challenges and potential study areas for the design of AI-based wireless communication systems.
Interference Mitigation for FMCW Radar With Sparse and Low-Rank Hankel Matrix Decomposition
Authors: JianPing Wang, Min Ding, Alexander Yarovoy
Date: 2021-06-12 00:00:00

In this paper, the interference mitigation for Frequency Modulated Continuous Wave (FMCW) radar system with a dechirping receiver is investigated. After dechirping operation, the scattered signals from targets result in beat signals, i.e., the sum of complex exponentials while the interferences lead to chirp-like short pulses. Taking advantage of these different time and frequency features between the useful signals and the interferences, the interference mitigation is formulated as an optimization problem: a sparse and low-rank decomposition of a Hankel matrix constructed by lifting the measurements. Then, an iterative optimization algorithm is proposed to tackle it by exploiting the Alternating Direction of Multipliers (ADMM) scheme. Compared to the existing methods, the proposed approach does not need to detect the interference and also improves the estimation accuracy of the separated useful signals. Both numerical simulations with point-like targets and experiment results with distributed targets (i.e., raindrops) are presented to demonstrate and verify its performance. The results show that the proposed approach is generally applicable for interference mitigation in both stationary and moving target scenarios.
Analog Seizure Detection for Implanted Responsive Neurostimulation
Authors: Abbas A. Zaki, Noah C. Parker, Tae-Yoon Kim, Sam Ishak, Ty E. Stovall, Genchang Peng, Hina Dave, Jay Harvey, Mehrdad Nourani, Xuan Hu, Alexander J. Edwards, Joseph S. Friedman
Date: 2021-06-11 00:00:00
Tasks: Bayesian Inference, Seizure Detection

Epilepsy can be treated with medication, however, $30\%$ of epileptic patients are still drug resistive. Devices like responsive neurostimluation systems are implanted in select patients who may not be amenable to surgical resection. However, state-of-the-art devices suffer from low accuracy and high sensitivity. We propose a novel patient-specific seizure detection system based on na\"ive Bayesian inference using M\"uller C-elements. The system improves upon the current leading neurostimulation device, NeuroPace's RNS by implementing analog signal processing for feature extraction, minimizing the power consumption compared to the digital counterpart. Preliminary simulations were performed in MATLAB, demonstrating that through integrating multiple channels and features, up to $98\%$ detection accuracy for individual patients can be achieved. Similarly, power calculations were performed, demonstrating that the system uses $6.5 \mu W$ per channel, which when compared to the state-of-the-art NeuroPace system would increase battery life by up to $50 \%$.
Probabilistic Forecasting of Imbalance Prices in the Belgian Context
Authors: Jonathan Dumas, Ioannis Boukas, Miguel Manuel de Villena, Sébastien Mathieu, Bertrand Cornélusse
Date: 2021-06-09 00:00:00
Tasks: Gaussian Processes

Forecasting imbalance prices is essential for strategic participation in the short-term energy markets. A novel two-step probabilistic approach is proposed, with a particular focus on the Belgian case. The first step consists of computing the net regulation volume state transition probabilities. It is modeled as a matrix computed using historical data. This matrix is then used to infer the imbalance prices since the net regulation volume can be related to the level of reserves activated and the corresponding marginal prices for each activation level are published by the Belgian Transmission System Operator one day before electricity delivery. This approach is compared to a deterministic model, a multi-layer perceptron, and a widely used probabilistic technique, Gaussian Processes.
Comparing the topology of phylogenetic network generators
Authors: Remie Janssen, Pengyu Liu
Date: 2021-06-12 00:00:00

Phylogenetic networks represent evolutionary history of species and can record natural reticulate evolutionary processes such as horizontal gene transfer and gene recombination. This makes phylogenetic networks a more comprehensive representation of evolutionary history compared to phylogenetic trees. Stochastic processes for generating random trees or networks are important tools in evolutionary analysis, especially in phylogeny reconstruction where they can be utilized for validation or serve as priors for Bayesian methods. However, as more network generators are developed, there is a lack of discussion or comparison for different generators. To bridge this gap, we compare a set of phylogenetic network generators by profiling topological summary statistics of the generated networks over the number of reticulations and comparing the topological profiles.
GPA-Tree: Statistical Approach for Functional-Annotation-Tree-Guided Prioritization of GWAS Results
Authors: Aastha Khatiwada, Bethany J. Wolf, Ayse Selen Yilmaz, Paula S. Ramos, Maciej Pietrzak, Andrew Lawson, Kelly J. Hunt, Hang J. Kim, Dongjun Chung
Date: 2021-06-12 00:00:00

Motivation: In spite of great success of genome-wide association studies (GWAS), multiple challenges still remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), each with small or moderate effect sizes. Second, our understanding of the functional mechanisms through which genetic variants are associated with complex traits is still limited. To address these challenges, we propose GPA-Tree and it simultaneously implements association mapping and identifies key combinations of functional annotations related to risk-associated SNPs by combining a decision tree algorithm with a hierarchical modeling framework. Results: First, we implemented simulation studies to evaluate the proposed GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs and identifying the true combinations of functional annotations with high accuracy. Second, we applied GPA-Tree to a systemic lupus erythematosus (SLE) GWAS and functional annotation data including GenoSkyline and GenoSkylinePlus. The results from GPA-Tree highlight the dysregulation of blood immune cells, including but not limited to primary B, memory helper T, regulatory T, neutrophils and CD8+ memory T cells in SLE. These results demonstrate that GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.
DEMETER: Efficient simultaneous curation of genome-scale reconstructions guided by experimental data and refined gene annotations
Authors: Almut Heinken, Stefanía Magnúsdóttir, Ronan M. T. Fleming, Ines Thiele
Date: 2021-06-11 00:00:00

Motivation: Manual curation of genome-scale reconstructions is laborious, yet existing automated curation tools typically do not take species-specific experimental data and manually refined genome annotations into account. Results: We developed DEMETER, a COBRA Toolbox extension that enables the efficient simultaneous refinement of thousands of draft genome-scale reconstructions while ensuring adherence to the quality standards in the field, agreement with available experimental data, and refinement of pathways based on manually refined genome annotations. Availability: DEMETER and tutorials are available at https://github.com/opencobra/cobratoolbox.
Dynamics and Sensitivity of Signaling Pathways
Authors: Michael A. Kochen, Steven S. Andrews, H. Steven Wiley, Song Feng, Herbert M. Sauro
Date: 2021-06-13 00:00:00
Tasks: Sensitivity

Signaling pathways serve to communicate information about extracellular conditions into the cell, to both the nucleus and cytoplasmic processes to control cell responses. Genetic mutations in signaling network components are frequently associated with cancer and can result in cells acquiring an ability to divide and grow uncontrollably. Because signaling pathways play such a significant role in cancer initiation and advancement, their constituent proteins are attractive therapeutic targets. In this review, we discuss how signaling pathway modeling can assist with identifying effective drugs for treating diseases, such as cancer. An achievement that would facilitate the use of such models is their ability to identify controlling biochemical parameters in signaling pathways, such as molecular abundances and chemical reaction rates, because this would help determine effective points of attack by therapeutics.
Representative Functional Connectivity Learning for Multiple Clinical groups in Alzheimer's Disease
Authors: Lu Zhang, Xiaowei Yu, Yanjun Lyu, Li Wang, Dajiang Zhu
Date: 2021-06-14 00:00:00
Tasks: Functional Connectivity, Multi-class Classification

Mild cognitive impairment (MCI) is a high-risk dementia condition which progresses to probable Alzheimer's disease (AD) at approximately 10% to 15% per year. Characterization of group-level differences between two subtypes of MCI - stable MCI (sMCI) and progressive MCI (pMCI) is the key step to understand the mechanisms of MCI progression and enable possible delay of transition from MCI to AD. Functional connectivity (FC) is considered as a promising way to study MCI progression since which may show alterations even in preclinical stages and provide substrates for AD progression. However, the representative FC patterns during AD development for different clinical groups, especially for sMCI and pMCI, have been understudied. In this work, we integrated autoencoder and multi-class classification into a single deep model and successfully learned a set of clinical group related feature vectors. Specifically, we trained two non-linear mappings which realized the mutual transformations between original FC space and the feature space. By mapping the learned clinical group related feature vectors to the original FC space, representative FCs were constructed for each group. Moreover, based on these feature vectors, our model achieves a high classification accuracy - 68% for multi-class classification (NC vs SMC vs sMCI vs pMCI vs AD).