Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlated Tensor Factorization for Audio Source Separation

This paper presents an ultimate extension of nonnegative matrix factorization (NMF) for audio source separation based on full covariance modeling over all the time-frequency (TF) bins of the complex spectrogram of an observed mixture signal. Although NMF has been widely used for decomposing an observed power spectrogram in a TF-wise manner, it has a critical limitation that the phase values of interdependent TF bins cannot be dealt with. This problem has been solved only partially by several phase-aware extensions of NMF that decompose an observed complex spectrogram in an time-and/or frequency-wise manner. In this paper, we propose correlated tensor factorization (CTF) that approximates the full covariance matrix over all TF bins as the sum of the Kronecker products between basis covariance matrices over frequency bands and the corresponding ones over time frames. All the TF bins of the complex spectrogram of each source signal are estimated jointly in an interdependent manner via Wiener filtering. We discuss how to reduce the computational cost of CTF and report the results of comparative evaluation of CTF with its special cases such as NMF and positive semidefinite tensor factorization (PSDTF).
Stiffness Variability in Jamming of Compliant Granules and a Case Study Application in Climbing Vertical Shafts

Jamming of granular media has been shown to possess the property of stiffness variation, transitioning from a soft to a quasi-solid state depending on the packing density of the granules. Recently, a gradual stiffness change for bending has been reported by using compliant, cubic shaped granules. Here we demonstrate that the same method and material also exhibits a gradual stiffness change for compression. As a potential application of “compliant jamming”, a bio-inspired robotic platform with jamming membranes as end effectors is designed and tasked with climbing straight vertical shafts. First, the benefit of varying the bending stiffness is investigated in the climbing task by measuring the friction force that the end effectors are applying to the shaft walls, especially if the walls are irregularly shaped. Then the role of compressive stiffness variation is explored by analyzing the performance of the robot in the climbing task, showing multi-modal properties of jamming membranes: (i) enabling a pressure sensor to detect the shaft walls, acting (ii) as grippers that actively use the irregularities of the walls to climb up by state-switching the granular material and (iii) as force dissipators that can dissipate internal forces caused by closed kinematic chains.
Knowledge Transfer in Permutation Invariant Training for Single-Channel Multi-Talker Speech Recognition

This paper proposes a framework that combines teacher-student training and permutation invariant training (PIT) for single-channel multi-talker speech recognition. In contrast to most of conventional teacher-student training methods that aim at compressing the model, the proposed method distills knowledge from the single-talker model to improve the multi-talker model in the PIT framework. The inputs to the teacher and student networks are the single-talker clean speech and the multi-talker mixed speech, respectively. The knowledge is transferred to the student through the soft labels generated by the teacher. Furthermore, the ensemble of multiple teachers is exploited with a progressive training scheme to further improve the system. In this framework it is easy to take advantage of data augmentation and perform domain adaptation for multi-talker speech recognition using only untranscribed data. The proposed techniques were evaluated on artificially mixed two-talker AMI speech data. The experimental results show that the teacher-student training can cut the word error rate (WER) by relative 20% against the baseline PIT model. We also evaluated our unsupervised domain adaptation method on an artificially mixed WSJO corpus and achieved relative 30% WER reduction against the AMI PIT model.
Speech Prediction Using an Adaptive Recurrent Neural Network with Application to Packet Loss Concealment

This paper proposes a novel approach for speech signal prediction based on a recurrent neural network (RNN). Unlike existing RNN-based predictors, which operate on parametric features and are trained offline on a large collection of such features, the proposed predictor operates directly on speech samples and is trained online on the recent past of the speech signal. Optionally, the network can be pre-trained offline to speed-up convergence at start-up. The proposed predictor is a single end-to-end network that captures all sorts of dependencies between samples, and therefore has the potential to outperform classicallinear/non-linear and short-termllong-term speech predictor structures. We apply it to the packet loss concealment (PLC) problem and show that it outperforms the standard ITU G.711 Appendix I PLC technique.
D3R-Net: Dynamic Routing Residue Recurrent Network for Video Rain Removal

In this paper, we address the problem of video rain removal by considering rain occlusion regions, i.e., very low light transmittance for rain streaks. Different from additive rain streaks, in such occlusion regions, the details of backgrounds are completely lost. Therefore, we propose a hybrid rain model to depict both rain streaks and occlusions. Integrating the hybrid model and useful motion segmentation context information, we present a Dynamic Routing Residue Recurrent Network (D3R-Net). D3R-Net first extracts the spatial features by a residual network. Then, the spatial features are aggregated by recurrent units along the temporal axis. In the temporal fusion, the context information is embedded into the network in a “dynamic routing” way. A heap of recurrent units takes responsibility for handling the temporal fusion in given contexts, e.g., rain or non-rain regions. In the certain forward and backward processes, one of these recurrent units is mainly activated. Then, a context selection gate is employed to detect the context and select one of these temporally fused features generated by these recurrent units as the final fused feature. Finally, this last feature plays a role of “residual feature. ” It is combined with the spatial feature and then used to reconstruct the negative rain streaks. In such a D3R-Net, we incorporate motion segmentation, which denotes whether a pixel belongs to fast moving edges or not, and rain type indicator, indicating whether a pixel belongs to rain streaks, rain occlusions, and non-rain regions, as the context variables. Extensive experiments on a series of synthetic and real videos with rain streaks verify not only the superiority of the proposed method over state of the art but also the effectiveness of our network design and its each component.
Novel Hydroxy- and Epoxy-cis-Jasmone and Dihydrojasmone Derivatives Affect the Foraging Activity of the Peach Potato Aphid Myzus persicae (Sulzer) (Homoptera: Aphididae)

Jasmonates show great potential in sustainable agriculture due to their various roles in natural mechanisms of plant defense, and because they are non-toxic, non-mutagenic, and easily metabolized. The aim of the study was to explore structure–activity relationships of dihydrojasmone, cis-jasmone, and their derivatives at the plant–aphid interface. We focused on the behavioral responses of aphids, following the exogenous application of natural jasmonates and their derivatives to the host plants. Aphid probing behavior was examined using an electrical penetration graph technique (EPG). The chemoenzymatic transformation of cis-jasmone and the activity of two new derivatives are described. The application of cis-jasmone, dihydrojasmone, the hydroxyderivatives, epoxyderivatives, and alkyl-substituted δ-lactones hindered the foraging activity of Myzus persicae (Sulz.) (Hemiptera: Aphididae) during early stages of probing at the level of non-phloem tissues. The application of saturated bicyclic epoxy-δ-lactone enhanced plant acceptance by M. persicae. Jasmonate derivatives containing a hydroxy group, especially in correlation with a lactone ring, were more active than natural compounds and other derivatives studied. Jasmonates of the present study are worth considering as elements of sustainable aphid control as components of the “push–pull” strategy.
Building Prior Knowledge: A Markov Based Pedestrian Prediction Model Using Urban Environmental Data

Autonomous Vehicles navigating in urban areas have a need to understand and predict future pedestrian behavior for safer navigation. This high level of situational awareness requires observing pedestrian behavior and extrapolating their positions to know future positions. While some work has been done in this field using Hidden Markov Models (HMMs), one of the few observed drawbacks of the method is the need for informed priors for learning behavior. In this work, an extension to the Growing Hidden Markov Model (GHMM) method is proposed to solve some of these drawbacks. This is achieved by building on existing work using potential cost maps and the principle of Natural Vision. As a consequence, the proposed model is able to predict pedestrian positions more precisely over a longer horizon compared to the state of the art. The method is tested over “legal” and “illegal” behavior of pedestrians, having trained the model with sparse observations and partial trajectories. The method, with no training data, is compared against a trained state of the art model. It is observed that the proposed method is robust even in new, previously unseen areas.
Robust Object-Aware Sample Consensus with Application to Lidar Odometry

Random sample consensus (RANSAC) is a popular paradigm for parameter estimation with outlier detection, which plays an essential role in 3D robot vision, especially for LiDAR odometry. The success of RANSAC strongly depends on the probability of selecting a subset of pure inliers, which sets barriers to robust and fast parameter estimation. Although significant efforts have been made to improve RANSAC in various scenarios, its strong dependency on inlier selection is still a problem. In this paper, we propose to address such dependency in the context of LiDAR odometry by robust object-aware sample consensus (ROSAC). In the proposed ROSAC, the sampling strategy is adjusted to preserve object shapes and a new consensus method is developed based on robust low-dimensional subspace analysis. It is demonstrated in extensive experiments that the proposed paradigm works well in LiDAR odometry, achieving estimation of 3D pose with superior accuracy compared to RANSAC. Even for the case of RANSAC failure, ROSAC still achieves up to 67% of improvement in accuracy compared to baseline LiDAR odometry. Since a partially parallel implementation of ROSAC already leads to a significant speedup, we believe it can be extended to other problems of parameter estimation with both higher accuracy and efficiency.
Learning Radio Resource Management in RANs: Framework, Opportunities, and Challenges

In the fifth generation (5G) of mobile broadband systems, radio resource management (RRM) will reach unprecedented levels of complexity. To cope with the ever more sophisticated RRM functionalities and the growing variety of scenarios, while carrying out the prompt decisions required in 5G, this manuscript presents a lean RRM architecture that capitalizes on recent advances in the field of machine learning in combination with the large amount of data readily available in the network from measurements and system observations. The architecture consists of a learner (or a few), which learns RRM policies directly from the data gathered in the network using a single general-purpose learning framework, and a set of distributed actors, which execute RRM policies issued by the learner and repeatedly generate samples of experience. Thus, the complexity of RRM is shifted to the design of the learning framework, while the RRM algorithms derived from this framework are executed in a computationally efficient distributed manner at the radio access nodes. The potential of this approach is verified in a pair of pertinent scenarios, and future directions on applications of machine learning to RRM are discussed. Although we focus on a mobile broadband context, the concepts proposed hereafter extend to any radio access network technology where one can conceive the idea of a central learning unit gathering data from distributed actors.
Online Action Recognition based on Skeleton Motion Distribution

Online action recognition which aims to jointly detect and recognize actions from video streams, is an essential step towards a comprehensive understanding of human behavior. However, it is challenging to accurately locate and recognize the occurrence of actions from noisy data streams. This paper proposes a skeleton motion distribution based method for effective online action recognition. Speciﬁcally, an adaptive density estimation function is built to calculate the density distribution of skeleton movements. Observing that each action has a unique motion distribution, we detect the occurrence of actions by identifying the transition of the motion distribution in a video stream. Once the starting point of an action is detected, a snippet-based classiﬁer is proposed for online action recognition, which continuously identiﬁes the most likely action class. Experimental results demonstrate that our method outperforms the state-of-the-art methods in terms of both detection accuracy and recognition precision.
Weighted and Multi-Task Loss for Rare Audio Event Detection

We present in this paper two loss functions tailored for rare audio event detection in audio streams. The weighted loss is designed to tackle the common issue of imbalanced data in background/foreground classification while the multi-task loss enables the networks to simultaneously model the class distribution and the temporal structures of the target events for recognition. We study the proposed loss functions with deep neural networks (DNNs) and convolutional neural networks (CNNs) coupled with state-of-the-art phase-aware signal enhancement. Experiments on the DCASE 2017 challenge's data show that our system with the proposed losses significantly outperforms not only the DCASE 2017 baseline but also our baseline which has a similar network architecture and a standard loss function.
Bounded Fuzzy Possibilistic Method Reveals Information about Lung Cancer through Analysis of Metabolomics

Learning methods, such as conventional clustering and classification, have been applied in diagnosing diseases to categorize samples based on their features. Going beyond clustering samples, membership degrees represent to what degree each sample belongs to a cluster. Variation of membership degrees in each cluster provides information about the cluster as a whole and each sample individually which enables us to have insights toward precision medicine. Membership degrees are measured more accurately through removing restrictions from clustering samples. Bounded Fuzzy Possibilistic Method (BFPM) introduces a membership function that keeps the search space flexible to cluster samples with higher accuracy. The method evaluates samples for their movement from one cluster to another. This technique allows us to find critical samples in advance those with the potential ability to belong to other clusters in the near future. BFPM was applied on metabolomics of individuals in a lung cancer case-control study. Metabolomics as proximal molecular signals to the actual disease processes may serve as strong biomarkers of current disease process. The goal is to know whether serum metabolites of a healthy human can be differentiated from those with lung cancer. Using BFPM, some differences were observed, the pathology data were evaluated, and critical samples were recognized.
Know your Game: A Bottom-Up Approach for Gaming Research

Recent advancements of network architecture such as 5G networks, promise cloud services with strict network constrains a bright future. Cloud gaming as an interactive service has strict end-to-end delay constraints. Therefore, many studies investigated the impact of network parameters such as delay or packet loss on gaming QoE. However, they mostly compared games or genres with each other and neglected the fact even two levels of the same game may have different sensitivity toward delay. In order to understand the game characteristics that cause this difference in delay sensitivity, a bottom-up approach by means of modifiable open source games can be of high value. In this paper we present a game designed to tackle this issue. The game allows to artificially change characteristics of the game, such as the pace and size of objects, and also simulate influences like delay, packet loss or a reduced frame rate. This allows the usage of the game also for crowdsourcing studies, where it is not possible to control the different network conditions of the participants, and to investigate the impact of spatial and temporal accuracy in respect to the sensitivity towards impairments.
GSC-Based Binaural Speaker Separation Preserving Spatial Cues

In this paper we investigate two methods for the preservation of spatial cues in binaural speaker separation. We develop these methods as extensions of our previously proposed model-based generalized sidelobe canceller (GSC) which utilizes a maximum likelihood technique for speaker localization. In the proposed implementation the adaptive GSC provides an estimate of the target signal as well as an estimation of target presence probability (TPP). Binaural outputs are generated in two different ways: In the first approach the binaural signals are rendered using the GSC output signal combined with the HRTF hypotheses which are adapted by the broadband localization. The second approach uses the GSC output and the TPP to determine a common spectral postfilter. We find that the adaptive beamformer combined with the binaural rendering technique leads to larger improvements of the quality of the desired signal and delivers less unnatural fluctuations as compared to the common spectral postfilter. Informal subjective tests as well as instrumental measurements in the presence of the listener head movements reveals, however, the benefit of the spatially motivated spectral postfilter for the preservation of binaural cues of both target and interferer signals.
Anscombe Meets Hough: Noise Variance Stablization Via Parametric Model Estimation

In this work we pose the parameter estimation of the Poisson-Gaussian noise model as a parametric model estimation problem. We first take patches from the image/video to analyze and treat variance stabilization transforms, e.g., the classical Generalized Anscombe transform, as a parametric model, which we fit to the patches using the Hough transform. This algorithm allows to successfully estimate the noise parameters, is computationally efficient, and is fully parallelizable. We present an application to calcium imaging data, where the estimated parameters are used to enhance state-of-the-art processing pipelines.
Single Channel Target Speaker Extraction and Recognition with Speaker Beam

This paper addresses the problem of single channel speech recognition of a target speaker in a mixture of speech signals. We propose to exploit auxiliary speaker information provided by an adaptation utterance from the target speaker to extract and recognize only that speaker. Using such auxiliary information, we can build a speaker extraction neural network (NN) that is independent of the number of sources in the mixture, and that can track speakers across different utterances, which are two challenging issues occurring with conventional approaches for speech recognition of mixtures. We call such an informed speaker extraction scheme “SpeakerBeam”. SpeakerBeam exploits a recently developed context adaptive deep NN (CADNN) that allows tracking speech from a target speaker using a speaker adaptation layer, whose parameters are adjusted depending on auxiliary features representing the target speaker characteristics. SpeakerBeam was previously investigated for speaker extraction using a microphone array. In this paper, we demonstrate that it is also efficient for single channel speaker extraction. The speaker adaptation layer can be employed either to build a speaker adaptive acoustic model that recognizes only the target speaker or a mask-based speaker extraction network that extracts the target speech from the speech mixture signal prior to recognition. We also show that the latter speaker extraction network can be optimized jointly with an acoustic model to further improve ASR performance.
Targeting Fallible Men: Communication Strategies and Moral Issues in a Pre-exposure Prophylaxis Trial

Abstract: Based on the analysis of a French pre-exposure prophylaxis trial (Ipergay), and focusing on the communication strategies used to recruit volunteers, this article explores the figure who serves to justify the trial and who shapes the way in which populations concerned by this prevention tool are targeted. We show that this figure is that of the fallible man, a classic in moral philosophy: while aware of what is good or right for him, he is unable to put this knowledge into practice. The targeting of fallible men makes sense in the context of a resurgence of high-risk behaviors objectified in the late 1990s: qualifying gays who take risks as fallible individuals create a distance with respect to the “barebacker” who risks his life deliberately and has no intention of changing his behavior. Recognizing that certain gays are vulnerable to risk also provides justification for a preventive strategy that acknowledges the inadequacy of behavioral prevention, without giving up on prevention altogether. All in all, this analysis shows that the technological and epidemiological realism often highlighted in pre-exposure prophylaxis interventions is not without a moral dimension, attentive to individuals' contradictions and singularities, doubts and uncertainties, and to the risk of stigmatization inherent to the acknowledgement of risk-taking.
A Shuffled-Based Iterative Demodulation and Decoding Scheme for Ldpc Coded Flash Memory

In previous studies, a very sparse low-density parity-check (LDPC) code was designed for triple-level cell (TLC) NAND flash using non-Gray mapping, which is able to achieve comparable error-rate performance to the conventional Gray mapping-based scheme. Although a sparse LDPC code can be of benefit to hardware implementations of an iterative demodulation and decoding (IDD) scheme, difficulties emerge, such as latency issue between the decoder and demodulator, when compared to non-IDD schemes. In this paper, a hardware-friendly structure interleaver is used such that a shuffle-based IDD scheme can be realized efficiently. Compared to the conventional Gray-based non-IDD scheme and layered-based IDD scheme, the proposed shuffled-based IDD scheme can provide a better hardware efficiency and better error-rate performance.
A Natural Shape-Preserving Stereoscopic Image Stitching

This paper presents a method for stereoscopic image stitching, which can make stereoscopic images look as natural as possible. Our method combines a constrained projective warp and a shape-preserving warp to reduce the projective distortion and the vertical disparity of the stitched image. In addition to provide a good alignment accuracy and maintain the consistency of input stereoscopic images, we add a specific restriction into the projective warp, which establishes the connection between target left and right images. To optimize the whole warp, a energy term is designed. It can constrain the shape of straight line and vertical disparity. Experimental results on a variety of stereoscopic images can ensure the efficiency of the proposed method.