Miracle-dz/UniFAR-FTUs huggingface dataset:
Audio-Visual Speech Enhancement Method Conditioned in the Lip Motion and Speaker-Discriminative Embeddings

We propose an audio-visual speech enhancement (AVSE) method conditioned both on the speaker’s lip motion and on speaker-discriminative embeddings. We particularly explore a method of extracting the embeddings directly from noisy audio in the AVSE setting without an enrollment procedure. We aim to improve speech-enhancement performance by conditioning the model with the embedding. To achieve this goal, we devise an AV voice activity detection (AV-VAD) module and a speaker identification module for the AVSE model. The AV-VAD module assesses reliable frames from which the identification module can extract a robust embedding for achieving an enhancement with the lip motion. To effectively train our modules, we propose multi-task learning between the AVSE, speaker identification, and VAD. Experimental results show that (1) our method directly extracted robust speaker embeddings from the noisy audio without an enrollment procedure and (2) improved the enhancement performance compared with the conventional AVSE methods.
A Plug-and-Play Deep Image Prior

Deep image priors (DIP) offer a novel approach for the regularization that leverages the inductive bias of a deep convolutional architecture in inverse problems. However, the quality of DIP approaches often degrades when the number of iterations exceeds a certain threshold due to overfitting. To mitigate this effect, this work incorporates a plug-and-play prior scheme which can accommodate additional regularization steps within a DIP framework. Our modification is achieved using an augmented Lagrangian formulation of the problem, and is solved using an Alternating Direction Method of Multipliers (ADMM) variant, which can capture existing DIP approaches as a special case. We show experimentally that our ADMM-based DIP pairing outperforms competitive baselines in PSNR while exhibiting less overfitting.
Use of ethephon and chlormequat chloride to manage plant height and lodging of irrigated barley (cv. Puma) when high rates of N-fertiliser are applied

The objective of this study was to evaluate the effects of the plant growth regulators (PGR's) chlormequat, ethephon and their combination on plant height, lodging, grain yield and yield components of ‘Puma’ barley with differing amounts of N (120, 150 & 180 kg N ha−1). The PGR treatments and a water control were applied at three times of application (TOA): the beginning of stem elongation, at the flag leaf stage, or as a split (double) application at elongation and the flag leaf stage at two localities (Bethlehem and Vaalharts). Field trials were planted as 4 × 32 factorials in RCBD's with 4 replications in the 2004 and 2005 seasons. Plant height, lodging (at Vaalharts) and grain yield (at both sites) were significantly reduced with applications of ethephon and the PGR combination at the flag leaf stage and as a split application. Chlormequat application alone did not produce a response. Yield reductions with ethephon and the PGR combination were attributed to reductions in grains spike−1 and mass grain−1. Higher N levels increased lodging and simultaneously reduced grain yields, however, applications of ethephon significantly controlled lodging at higher N levels. Ethephon and the PGR combination, applied at the flag leaf stage, can be utilized as lodging controlling tools with ‘Puma’ in environments conducive to lodging.
Hypothallin, Ein Weiterer Vertreter Eines Aminosäure-Aminoalkohol-Esters Aus Der Krustenflechte Schismatomma Hypothallinum

Hypothallin, a metabolite of the lichen Schismatomma hypothallinum (Lecanactidaceae, Opegraphales) has been structurally elucidated as (-)-N-benzoyl-ʟ-phenylalaninyl-N′- benzoyl-ʟ-leucinate by NMR and MS and by synthesisᴅ
Hierarchical Network Based on the Fusion of Static and Dynamic Features for Speech Emotion Recognition

Many studies on automatic speech emotion recognition (SER) have been devoted to extracting meaningful emotional features for generating emotion-relevant representations. However, they generally ignore the complementary learning of static and dynamic features, leading to limited performances. In this paper, we propose a novel hierarchical network called HNSD that can efficiently integrate the static and dynamic features for SER. Specifically, the proposed HNSD framework consists of three different modules. To capture the discriminative features, an effective encoding module is firstly designed to simultaneously encode both static and dynamic features. By taking the obtained features as inputs, the Gated Multi-features Unit (GMU) is conducted to explicitly determine the emotional intermediate representations for frame-level features fusion, instead of directly fusing these acoustic features. In this way, the learned static and dynamic features can jointly and comprehensively generate the unified feature representations. Benefiting from a well-designed attention mechanism, the last classification module is applied to predict the emotional states at the utterance level. Extensive experiments on the IEMOCAP benchmark dataset demonstrate the superiority of our method in comparison with state-of-the-art baselines.
Low-Complexity Parameter Learning for OTFS Modulation Based Automotive Radar

Orthogonal time frequency space (OTFS) as an emerging modulation technique in the 5G and beyond era exploits full time-frequency diversity and is robust against doubly-selective channels in high mobility scenarios. In this work, we consider an OTFS modulation based automotive joint radar-communication system and focus on the design of low-complexity parameter estimation algorithm for radar targets. It is well known that target parameter estimation in OTFS radar is computationally much more expensive than the orthogonal frequency division multiplex based platform, which hampers low-cost and real-time implementation. In this context, an efficient Bayesian learning scheme is proposed for OTFS automotive radars, which leverages the structural sparsity of radar channel in the delay-Doppler domain. We also reduce the dimension of the measurement matrix by incorporating the prior knowledge on the motion parameter limit of the true targets. Numerical simulation results are presented to demonstrate the superior performance of the proposed method in comparison with the state-of-the-art.
Event-Driven Delay-Induced Tasks: Model, Analysis, and Applications

Parallel execution and hardware acceleration involving specialized devices such as GPUs and FPGAs are becoming increasingly relevant in the domain of embedded systems. Communication between jobs dispatched on different cores and hardware accelerators is most often implemented using asynchronous events. Modeling the timing behavior of such systems requires to account for the delays incurred by each task due to the additional time spent waiting for events. This paper presents the event-driven delay-induced (EDD) task model to explicitly deal with complex computing workloads that incur such kinds of delays. The EDD task model generalizes several state-of-the-art models, such as the DAG task model and the segmented self-suspending task model, and is particularly suited to analyze parallel tasks that issue asynchronous hardware acceleration requests. Two analysis techniques for EDD tasks executing on single core platforms are first provided. We then extend those approaches to analyze parallel real-time tasks under partitioned multicore scheduling by means of a model transformation. Experimental results are presented to compare the two analysis techniques for EDD tasks proposed in the paper. Finally, we compare the analysis of partitioned parallel tasks modeled with EDD tasks against federated scheduling.
Complex s-Plane Modeling and 2D Characterization of the Stochastic Scattering Loss in Symmetric Dielectric Slab Waveguides Exhibiting Ergodic Surface-Roughness With an Exponential Autocorrelation Function

The symmetric dielectric slab waveguide serves as an important canonical structure for the high-speed high-bandwidth optical and opto-electronic silicon-on-insulator interconnects. A stochastic model of propagation loss, due to electromagnetic wave scattering with surface-roughness of the nano-scale waveguide excited at frequencies in the 100s of THz, provides useful insights for analysis and design optimization of optical interconnects. In this work, we propose an analytic solution for the stochastic scattering integral based on contour integration of the exponential autocorrelation function in the complex s-plane that improves performance at larger autocorrelation length values, compared to previous works. An expression is proposed for computing the upper-bound value of scattering loss. Results show that the proposed 2D formulation offers reasonable correlation against data from experimental measurements of loss by previous investigators, while several discrepancies are noted compared to previous 2D models. An expression is proposed for the transverse magnetic (TM) modal field amplitude, and several discrepancies in the transverse electric (TE) modal amplitudes are noted compared to previous works. A method is proposed for computation of the effective index (and propagation constant) for arbitrary cladding that improves convergence of the Newton search method, and it is compared to two other methods. Background discussions include the stochastic theory and assumptions that lead to the stationary and ergodic treatment of the surface-roughness, extension of the ensemble-average and time-average to the spatial domain, and effective thickness due to the Goos-Hänchen shift.
Deep Neural Network Embeddings for the Estimation of the Degree of Sleepiness

Estimating the degree of sleepiness from the human speech is an emerging research problem with straightforward applications. In this study, we employ the x-vector approach, currently the state-of-the-art in speaker recognition, as a neural network feature extractor to detect the level of sleepiness of a speaker. Besides using different corpora for fitting the x- vector DNN, we also experiment with adding noise and reverberation to the training samples. According to our experimental results for the publicly available Dusseldorf Sleepy Language Corpus, utilizing x-vector embeddings as features for Support Vector Regression consistently leads to competitive performance scores in sleepiness detection. In particular, we present the highest Spearman's correlation coefficient on the public corpus that was achieved by a single method.
Predicting the Preference for Sad Music: The Role of Gender, Personality, and Audio Features

The “tragedy paradox” of music, avoiding experiencing negative emotions but enjoying the sadness portrayed in music, has attracted a great deal of academic attention in recent decades. Combining experimental psychology research methods and machine learning techniques, this study (a) investigated the effects of gender and Big Five personality factors on the preference for sad music in the Chinese social environment and (b) constructed sad music preference prediction models using audio features and individual features as inputs. Statistical analysis found that males have a greater preference for sad music than females do, and that gender and the extraversion factor are involved in significant two-way interactions. The best-performing random forest regression shows a low predictive effect on the preference for sad music ( $R^{2} =0.138$ ), providing references for music recommendation systems. Finally, the importance-based model interpretation feature reveals that, in addition to the same music inputs (audio features), the perceived relaxation and happiness of music play an important role in the prediction of sad music preferences.
Multi-Terminal HVDC System With Offshore Wind Farms Under Anomalous Conditions: Stability Assessment

Droop control is widely adopted to control Multi-Terminal high-voltage Direct Current (MTDC) systems with offshore wind farms. During permanent faults, the faulty line should be isolated promptly to preserve a high reliability of the MTDC system. This paper examines the MTDC system performance following a faulty line outage. This study aims to identify the outage types that may lead to a complete loss of system voltage stability and the outages that may have a secondary effect on the system. Moreover, strategies for dealing with outages that may lead to a complete shutdown of the system are also presented. Furthermore, the ranges of droop gains’ values that can be employed following fault occurrence to preserve system transient stability are studied. Different scenarios are explored during faulty conditions such as surplus and sparsity of wind power, line overcurrent, outage of lines connected to wind farms, and outage of lines connected to AC grids to validate this study. MATLAB/Simulink platform has been employed to elucidate the presented concept.
Synthetic Data For Dnn-Based Doa Estimation of Indoor Speech

This paper investigates the use of different room impulse response (RIR) simulation methods for synthesizing training data for deep neural network-based direction of arrival (DOA) estimation of speech in reverberant rooms.Different sets of synthetic RIRs are obtained using the image source method (ISM) and more advanced methods including diffuse reflections and/or source directivity. Multi-layer perceptron (MLP) deep neural network (DNN) models are trained on generalized cross correlation (GCC) features extracted for each set. Finally, models are tested on features obtained from measured RIRs.This study shows the importance of training with RIRs from directive sources, as resultant DOA models achieved up to 51% error reduction compared to the steered response power with phase transform (SRP-PHAT) baseline (significant with p << .01), while models trained with RIRs from omnidirectional sources did worse than the baseline. The performance difference was specifically present when estimating the azimuth of speakers not facing the array directly.
Antecedents of Actual Usage of e-Learning System in High Education During COVID-19 Pandemic: Moderation Effect of Instructor Support

The rapid full utilization of e-learning system during the coronavirus disease 2019 pandemic rises multiple challenges to practitioners and instructors – e.g., how to attain students’ needs and to increase their engagement with the system. Therefore, this study aims to examine the factors that influence students’ actual usage of e-learning system during the pandemic from three different dimensions (i.e., usability, interaction, and quality) and also to explore the interaction effects of instructor support on the three main dimensions identified above. This study adopted a quantitative research method and collected the survey data from 160 undergraduate students enrolled in two courses at King Abdulaziz University in Saudi Arabia, reflecting their individual experience with the system usage. Regression analysis and model assessment were tested using SmartPLS with the partial least square-structural equation modeling. The findings confirm the positive effects of the three dimensions on the e-learning usage in both models (direct effects and interaction effects). The quality dimension stands as the most significant driving force among the three dimensions. Furthermore, the findings show that instructor support positively moderates the relationship between the quality dimension and students’ usage of e-learning, while it has insignificant negative interaction effects on both usability and interaction dimension. This research sheds light on relevant factors within different dimensions for effective use of e-learning system, also offers new insights to academicians and practitioners to further provide more appropriate mechanisms likely to drive students’ usage of e-learning. The theoretical and practical implications are also drawn.
Visual and data stationarity of texture images

Abstract. The stationarity of a texture can be considered a fundamental property of images, although the property of stationarity is difficult to define precisely. We propose a stationarity test based on multiscale, locally stationary, 2D wavelets. Three separate experiments were performed to evaluate the capabilities and the limitations of this test. The experiments comprised a chessboard stationarity analysis, two classification tasks, and a psychophysical experiment. The classification tasks were performed on 110 texture images from a texture database. In one subtask, five texture feature vectors were extracted from each image and the classification accuracy of two classical methods compared, whereas in the second subtask, the classification accuracy of several methods was compared to the descriptors defined for each image within the database. In the psychophysical experiment, the correlation between the classification results and observer judgements of texture similarity were determined. It was found that a combination of wavelet shrinkage and rotation-invariant local binary pattern best predicted the observer response. The results show that the proposed stationarity test is able to provide relevant information for texture analysis.
A Partially-Relaxed Robust DOA Estimator Under Non-Gaussian Low-Rank Interference and Noise

In practical applications, non-Gaussianity of the signal at the sensor array is detrimental to the performance of conventional Direction-of-Arrival (DOA) estimators developed under the Gaussian model. In this paper, we propose a novel robust DOA estimator from the data collected at the sensor array under the corruption of non-Gaussian interference and noise. Additionally, the Cramér-Rao bound for DOA parameters under the considered signal model is derived. Simulation results show that the proposed estimator exhibits near-optimal estimation performance under the assumed model while being robust to model mismatch and/or the presence of outliers.
An Asymptotically Pointwise Optimal Procedure For Sequential Joint Detection And Estimation

We investigate the problem of jointly testing two hypotheses and estimating a random parameter based on sequentially observed data whose distribution belongs to the exponential family. The aim is to design a scheme which minimizes the expected number of used samples while limiting the detection and estimation errors to pre-set lev-els. This constrained problem is first converted to an unconstrained problem which is then reduced to an optimal stopping problem. To solve the optimal stopping problem, we propose an asymptotically pointwise optimal (APO) stopping rule, i.e., a stopping rule that is optimal when the tolerated detection and estimation errors tend to zero. The policy parameterizing coefficients are then chosen such that the constraints on the detection and estimation errors are fulfilled. The proposed theory is illustrated with a numerical example.
A novel microfluidic device that integrates nucleic acid extraction, amplification, and detection to identify an EGFR mutation in lung cancer tissues

The ability to detect gene mutations is critical for the development of individual therapeutic strategies to improve lung cancer patient survival rate. We developed an easy-to-use, easy-to-take, low-cost, pump-free, loop-mediated isothermal amplification (LAMP)-based, glass powder-assisted point-of-care testing (POCT) microfluidic device that can extract DNA from samples in a minute and can detect gene mutations with high sensitivity and high specificity in 40 min. Incorporation of the fluorescent dye calcein allows the presence of the mutation to be detected by the naked eye. Using this device, we detected the L858R mutation in the epidermal growth factor receptor (EGFRL858R) in lung adenocarcinoma NCI-H1975 cells, but not in A549 cells that do not carry the mutation. Moreover, the EGFRL858R mutation was detected in five clinical tissue specimens from lung adenocarcinoma patients using this device.
Towards Parkinson’s Disease Prognosis Using Self-Supervised Learning and Anomaly Detection

Parkinson’s disease (PD) is a chronic disease with a high risk of incidence after the age of 60 and is a problem for many countries facing an aging population. Current works have mainly focused on supervised learning using data collected from various sensors to differentiate between PD and healthy subjects. However, such supervised methods are not ideal for prognosis where there are no labels (i.e., we do not know in advance which subjects will develop PD in the future). We propose to tackle the problem as a semi-supervised anomaly detection task, where we model the physiological patterns of healthy subjects instead. A self-supervised learning technique first learns a good representation of the sensor signals. The representations are then adapted to capture inter-class patterns for anomaly detection. Evaluation on a large-scale PD dataset shows that our approach can learn discriminative features.
Coughwatch: Real-World Cough Detection using Smartwatches

Continuous monitoring of cough may provide insights into the health of individuals as well as the effectiveness of treatments. Smart-watches, in particular, are highly promising for such monitoring: they are inexpensive, unobtrusive, programmable, and have a variety of sensors. However, current mobile cough detection systems are not designed for smartwatches, and perform poorly when applied to real-world smartwatch data since they are often evaluated on data collected in the lab.In this work we propose CoughWatch, a lightweight cough detector for smartwatches that uses audio and movement data for in-the-wild cough detection. On our in-the-wild data, CoughWatch achieves a precision of 82% and recall of 55%, compared to 6% precision and 19% recall achieved by the current state-of-the-art approach. Furthermore, by incorporating gyroscope and accelerometer data, CoughWatch improves precision by up to 15.5 percentage points compared to an audio-only model.
Non-Parallel Many-To-Many Voice Conversion by Knowledge Transfer from a Text-To-Speech Model

In this paper, we present a simple but novel framework to train a nonparallel many-to-many voice conversion (VC) model based on the encoder-decoder architecture. It is observed that an encoder-decoder text-to-speech (TTS) model and an encoder-decoder VC model have the same structure. Thus, we propose to pre-train a multi-speaker encoder-decoder TTS model and transfer knowledge from the TTS model to a VC model by (1) adopting the TTS acoustic decoder as the VC acoustic decoder, and (2) forcing the VC speech encoder to learn the same speaker-agnostic linguistic features from the TTS text encoder so as to achieve speaker disentanglement in the VC encoder output. We further control the conversion of the pitch contour from source speech to target speech, and condition the VC decoder on the converted pitch contour during inference. Subjective evaluation shows that our proposed model is able to handle VC between any speaker pairs in the training speech corpus of over 200 speakers with high naturalness and speaker similarity.