Miracle-dz/UniFAR-FTUs huggingface dataset:
Vset: A Multimodal Transformer for Visual Speech Enhancement

The transformer architecture has shown great capability in learning long-term dependency and works well in multiple domains. However, transformer has been less considered in audio-visual speech enhancement (AVSE) research, partly due to the convention that treats speech enhancement as a short-time signal processing task. In this paper, we challenge this common belief and show that an audio-visual transformer can significantly improve AVSE performance, by learning the long-term dependency of both intra-modality and inter-modality. We test this new transformer-based AVSE model on the GRID and AVSpeech datasets, and show that it beats several state-of-the-art models by a large margin.
A Time-Domain Convolutional Recurrent Network for Packet Loss Concealment

Packet loss may affect a wide range of applications that use voice over IP (VoIP), e.g. video conferencing. In this paper, we investigate a time-domain convolutional recurrent network (CRN) for online packet loss concealment. The CRN comprises a convolutional encoder-decoder structure and long short-term memory (LSTM) layers, which have been shown to be suitable for real-time speech enhancement applications. Moreover, we propose lookahead and masked training to further improve the performance of the CRN framework. Experimental results show that the proposed system outperforms a baseline system using only LSTM layers in terms of two objective metrics – perceptual evaluation of speech quality (PESQ) and short-term objective intelligibility (STOI); it also reduces the word error rate (WER) more than the baseline when used as a frontend for speech recognition. The advantage of the proposed system is also verified in a subjective evaluation by the mean opinion score (MOS).
A Global Cayley Parametrization of Stiefel Manifold for Direct Utilization of Optimization Mechanisms Over Vector Spaces

Optimization problem with orthogonality constraints, whose feasible region is called the Stiefel manifold, has rich applications in data sciences. The severe non-linearity of the Stiefel manifold has hindered the utilization of optimization mechanisms developed specially over a vector space for the problem. In this paper, we present a global parametrization of the Stiefel manifold entirely by a single fixed vector space with the Cayley transform, say Global Cayley Parametrization (G-CP), to solve the problem through optimization over a vector space. The G-CP has key properties for solving the problem with G-CP and for applications to orthogonality constraint stochastic/distributed optimization problems. A numerical experiment shows that G-CP strategy outperforms the standard strategy with a retraction [Absil-Mahony-Sepulchre, 08].
Multi-Branch Tomlinson-Harashima Precoding for Rate Splitting Based Systems with Multiple Antennas

Rate splitting (RS) has emerged as a valuable technology for wireless communications systems due to its capability to deal with uncertainties in the channel state information at the transmitter (CSIT). RS with linear and non-linear precoders, such as the Tomlinson- Harashima (THP) precoder, have been explored in the downlink (DL) of multiuser multi antenna systems. In this work, we propose a multi-branch (MB) scheme for a RS-based multiple-antenna system, which creates patterns to order the transmitted symbols and enhances the overall sum rate performance compared to existing approaches. Analytical expressions to describe the signal-to-interference-plus- noise ratio (SINR) and compute the sum rate are derived. Simulation results show that the proposed MB-THP for RS outperforms conventional THP and MB-THP schemes.
Subject-Invariant Eeg Representation Learning For Emotion Recognition

The discrepancies between the distributions of the train and test data, a.k. a. , domain shift, result in lower generalization for emotion recognition methods. One of the main factors contributing to these discrepancies is human variability. Domain adaptation methods are developed to alleviate the problem of domain shift, however, these techniques while reducing between database variations fail to reduce between-subject variability. In this paper, we propose an adversarial deep domain adaptation approach for emotion recognition from electroencephalogram (EEG) signals. The method jointly learns a new representation that minimizes emotion recognition loss and maximizes subject confusion loss. We demonstrate that the proposed representation can improve emotion recognition performance within and across databases.
Joint Optimization for Bandwidth Utilization and Delay Based on Particle Swarm Optimization

One of the most significant problems in large-scale applications is how to allocate requests from end-users to datacenters. Most existing studies focus only on high bandwidth utilization of datacenters or low request delay of end-users, lacking the consideration of both at the same time. To achieve the joint optimization for bandwidth utilization of datacenter providers and delay of end-users, this paper proposes a novel Particle Swarm Optimization (PSO) based request allocation algorithm, based on the construction of a mathematical model for joint optimization. The algorithm adapts the coding scheme and redefines the operator to make PSO suitable for discrete optimization problems and use fitness function to get a global optimal solution. Through extensive simulations, it is demonstrated that the PSO based request allocation algorithm proposed in this study can improve the bandwidth utilization and keep the average request delay within acceptable limits or even better.
A Progressive Learning Approach to Adaptive Noise and Speech Estimation for Speech Enhancement and Noisy Speech Recognition

In this paper, we propose a progressive learning-based adaptive noise and speech estimation (PL-ANSE) method for speech preprocessing in noisy speech recognition, leveraging upon a frame-level noise tracking capability of improved minima controlled recursive averaging (IMCRA) and an utterance-level deep progressive learning of nonlinear interactions between speech and noise. First, a bi-directional long short-term memory model is adopted at each network layer to learn progressive ratio masks (PRMs) as targets with progressively increasing signal-to-noise ratios. Then, the estimated PRMs at the utterance level are combined within a conventional speech enhancement algorithm at the frame level for speech enhancement. Finally, the enhanced speech based on multi-level information fusion is directly fed into a speech recognition system to improve the recognition performance. Experiments show that our proposed approach can achieve a relative word error rate (WER) reduction of 22.1% when compared to results attained with unprocessed noisy speech (from 23.84% to 18.57%) on the CHiME-4 single-channel real test data.
Enabling User Grouping and Fixed Power Allocation Scheme for Reconfigurable Intelligent Surfaces-Aided Wireless Systems

This paper considers a two-user downlink transmission in reconfigurable intelligent surface (RIS)-aided network over fading channels. Particularly, by employing user grouping and fixed power allocation scheme to enable non-orthogonal multiple access (NOMA) approach, RIS-aided network can significant benefit from NOMA. To highlight the advantages of RIS, we compare the system performance of NOMA-RIS and traditional orthogonal multiple access (OMA) based RIS. Main practical circumstances are carefully analyzed, such as, RIS with direct link, system without RIS, and imperfect phase shifts. More specifically, we consider a RIS-aided downlink network, where the base station communicates with a group of two users under assistance of RIS, which acts equivalently as relay. As key expectation, the RIS is efficiently designed to improve the performance of users. To evaluate the system performance, two main system performance metrics including outage probability and average capacity are studied by deriving new closed-form expressions. The goal is to find out which system parameters need to be adjusted to achieve the expected performance. The numerical results reveal that: i) the outage probability and average capacity of considered NOMA-RIS aided wireless system outperforms the conventional NOMA network over fading channels; ii) with different power allocation factors assigned to users, the performance gap among two users can be adjusted to guarantee the fairness characteristic; iii) the number of reflecting elements in RIS has significant impact on the system performance of the considered NOMA-RIS system, which shows advantage of both RIS and NOMA compared with conventional OMA system.
Constraint Satisfaction Problems over Finite Structures

We initiate a systematic study of the computational complexity of the Constraint Satisfaction Problem (CSP) over finite structures that may contain both relations and operations. We show the close connection between this problem and a natural algebraic question: which finite algebras admit only polynomially many homomorphisms into them? We give some sufficient and some necessary conditions for a finite algebra to have this property. In particular, we show that every finite equationally nontrivial algebra has this property which gives us, as a simple consequence, a complete complexity classification of CSPs over two-element structures, thus extending the classification for two-element relational structures by Schaefer (STOC’78). We also present examples of two-element structures that have bounded width but do not have relational width (2,3), thus demonstrating that, from a descriptive complexity perspective, allowing operations leads to a richer theory.
Message Transmission Over Rapidly Time-Varying Channels

This paper presents a new method on transmitting data over linear time-variant (LTV) channels when no channel state estimation (CSI) is accessible. The considered receiver is equipped with multiple antennas and observes a superposition of delay-Doppler shifted versions of the transmitted signal. The messages are coded with known waveforms and are estimated by the receiver using a modified version of the multiple signal classification (MUSIC) algorithm. Although, the transmission scheme is designed for a multi-antenna receiver, the suggested method does not utilize the spatial signatures of multipath scatterers. Hence, the array response of the antenna setup is not required to be known in order to obtain a reliable estimate of the message. The effectiveness of the proposed transmission scheme is illustrated by numerical simulations.
Deep Weighted MMSE Downlink Beamforming

The weighted minimum mean square error (WMMSE) algorithm was proposed to provide a locally optimum solution to the otherwise NP-hard weighted sum rate maximization beamforming problem, but it can still be prohibitively complex for real-time implementation. With the success of deep unfolding in trading off complexity and performance, we propose to apply deep unfolding to the WMMSE algorithm. With respect to traditional end-to-end learning, deep unfolding incorporates expert knowledge, with the benefits of immediate and well-grounded architecture selection, fewer trainable parameters, and better explainability. However, the classical formulation of the WMMSE algorithm given by Shi et al. is not amenable for deep unfolding due to matrix inversions, eigendecompositions, and bisection searches. Therefore, we present an alternative formulation that circumvents these operations. By means of simulations, we show that the deep unfolded WMMSE algorithm performs on par with the original WMMSE algorithm, at a lower computational load.
Real-Time Interaural Time Delay Estimation via Onset Detection

Reliable real-time estimation of the interaural time delay of a sound source is difficult in the presence of noise and reverberation. However, the psychoacoustical precedence effect suggests that accurate estimation is possible by concentrating on the first-arriving sound. This paper introduces a novel real-time estimation method inspired by the precedence effect. First, the arrival of first-arriving sound is detected by performing a hypothesis test based on local approximation of the binaural signal with exponentially decaying sinusoids, which effectively model the shape of a sound onset. After detection, the interaural time delay is directly retrieved from the phase shift of the approximating sinusoids. The local model approximation is done with efficient recursions by parameterization of the model with autonomous linear state-space models, making the algorithm implementable in real-time.
Rate 1 Quasi Orthogonal Universal Transmission and Combining for MIMO Systems Achieving Full Diversity

This work addresses general multiple-input multiple-output systems and develops combined diversity transmission and combining schemes that achieve rate one and full diversity with reduced decoding complexity, while being universal in the sense that the operations performed at both transmission ends are channel independent. Such schemes may be useful in a scenario where a multiple-antenna source node communicates with the cloud via a multiple-antenna "dumb relay" that forwards the received vector over a rate-constrained digital front-haul link or serves as relay performing an amplify-forward operation over the air. The proposed schemes are derived by establishing an operational equivalence relation between the true channel and an associated multiple-input single-output channel.
Teacher-Assisted Mini-Batch Sampling for Blind Distillation Using Metric Learning

This paper addresses the problem of blind distillation, which aims to train a student model with unlabeled data under the supervision of a pre-trained teacher model. The proposed framework introduces metric learning to blind distillation. Specifically, teacher-assisted mini-batch (TAM) sampling is proposed, which makes triplets of anchor, positive and negative samples on unlabeled data by using the teacher's knowledge. In addition, we propose a metric-based loss, namely Contrastive Additive Margin (CAM) Softmax loss, which efficiently uses all combinations of triplets on each mini-batch obtained by TAM sampling. In experiments, we show the effectiveness of the proposed framework on face and speaker verification tasks, where student models are trained on unlabeled VoxCeleb videos with a teacher model pre-trained on VGGFace2 images.
LCBPA: An Enhanced Deep Neural Network-Oriented Bug Prioritization and Assignment Technique Using Content-Based Filtering

Software maintenance is an important phase of a development life cycle that needs to be essentially performed in order to avoid the software failure. To systematically handle the bugs (defects), the software development organization develops a bug report that demonstrates the vulnerabilities from the software under test. However, manually handling the bug reports is a laborious, tedious, and time-consuming task. Moreover, the bug repository receives large numbers of bug reports on daily basis, which demands to timely fix the found and received bugs. Motivated by this, current work proposes an automated bug prioritization and assignment technique, called LCBPA (Long short-term memory, Content-based filtering for Bug Prioritization and Assignment). To perform the bug prioritization, we employed Long Short-Term Memory (LSTM) to predict the priority of the bug report. In contrast, for bug assignment, we used content-based filtering, where the prioritized bug reports are automatically assigned to the developers based on their previous knowledge. The performance of the proposed bug prioritization model is determined by comparing with the state-of-the-art bug prioritization techniques, and measured using the evaluation metrics including Precision, Recall and F1-score. Similarly, the effectiveness of the bug assignment model is evaluated by defining various case scenarios. The results show that the proposed LCBPA technique outperforms the current state-of-the-art bug prioritization techniques (with a 22% increase in F1-score), and also efficiently handles the bug assignment problem compared to the existing bug assignment techniques.
An F-Test for Polynomial Frequency Modulation

We propose a semi-parametric multitaper F -test for the detection of line components which have been modulated by a polynomial of a given degree. The test is based on one proposed by Thomson in his 2009 paper, "Polymomial Phase Demodulation in Multitaper Analysis. " We give the asymptotic null distribution of our test statistic as F with degrees of freedom depending on the modulating polynomial degree, p, and the number of tapers used, K. We demonstrate the performance of our F -test via simulation, and we apply our F -test to optical solar data obtained by the Global Oscillations for Low Frequencies (GOLF) instrument onboard the SOHO solar observatory satellite.
A Comparison of Trajectory Compression Algorithms Over AIS Data

Today’s industry is flooded with tracking data originating from vessels across the globe that transmit their position at frequent intervals. These voluminous and high-speed streams of data has led researchers to develop novel ways to compress them in order to speed-up processing without losing valuable information. To this end, several algorithms have been developed that try to compress streams of vessel tracking data without compromising their spatio-temporal and kinematic features. In this paper, we present a wide range of several well-known trajectory compression algorithms and evaluate their performance on data originating from vessel trajectories. Trajectory compression algorithms included in this research are suitable for either historical data (offline compression) or real-time data streams (online compression). The performance evaluation is three-fold and each algorithm is evaluated in terms of compression ratio, execution speed and information loss. Experiments demonstrated that each algorithm has its own benefits and limitations and that the choice of a suitable compression algorithm is application-dependent. Finally, considering all assessed aspects, the Dead-Reckoning algorithm not only presented the best performance, but it also works over streaming data, which constitutes an important criterion in maritime surveillance.
Stochastic Successive Weighted Sum-Rate Maximization for Multiuser MIMO Systems with Finite-Alphabet Inputs

Weighted sum-rate maximization (WSRM) is a fundamental problem for multiuser multiple-input-multiple-output (MU- MIMO) systems with finite-alphabet inputs. However, solving this problem is challenging because of the intractable expectation involved in rate functions. The state-of-art WSRM methods for the case of finite-alphabet inputs suffer from high computational complexity due to the issue of complicated numerical integrals for expectation calculation. Inspired by the stochastic successive upper-bound minimization (SSUM) method [1], this paper proposes a stochastic successive inexact lower-bound maximization (SSILM) algorithm for the WSRM problem with finite-alphabet inputs. Our algorithm significantly differs from SSUM in that we use an inexact lower bound of the objective function which is skillfully devised based on an exact but extremely loose lower bound of the objective function. Simulation results show that the proposed algorithm exhibits much faster convergence than state-of-art algorithms.
On Fixation of Phosphate

Phosphate containing P32 was mixed with albumin and spread on an object glass. Then, before and after the action of histological fixatives the proportion of PO4 in the spread was determined with a GEIGER-MULLER's counter. It was found that alcohol and acetone can prevent P32 from going out, however after washing a large amount of P32 was removed. Lead salts made insoluble precipitation with PO4 and fixed P32 was not dissolved even by washing.Under several fixatives which were made of formalin and metal salts one with lead salts, especially with 1% basic lead acetate had the best fixative effect in fixing PO4.The precipitation made of phosphate and lead acetate in a test tube was found to be very fine. The reason why a very concentrated solution of basic lead acetate had less fixative effect may possibly due to the fact that it made a finer precipitation.
Injury incidence is higher on artificial turf compared with natural grass in high school athletes: a retrospective cohort study

Background: Prior investigations have demonstrated increased injury risk on artificial turf at the collegiate and professional levels. However, no prior study has examined this risk among high school athletics beyond football. The purpose of this study was to compare injury incidences on artificial versus natural playing surfaces among high school athletes. Methods: Data collected from 26 high schools was analyzed to compare injury incidences on artificial turf versus natural grass based on sport. Analyses were also performed to compare injury incidence by injury location (upper extremity, lower extremity, torso), sport, level of competitive play (freshman, junior varsity, varsity), and practice versus competition. Results: We identified 953 injuries, with 61% (n=585) occurring on turf and 39% (n=368) on grass. Athletes were 58% more likely to sustain injuries on artificial turf than natural grass relative risk ratio [RR] 1.5897, confidence interval [CI]: 1.4062 to 1.7971, P<0.0001). Lower extremity (RR 1.9597, CI: 1.6169 to 2.3752, P<0.0001), torso (RR 1.8636, CI: 1.1123 to 3.1225, P=0.0181), and upper extremity (RR 1.4494, CI: 1.1104 to 1.8919, P=0.0063) injuries were significantly more likely to occur on artificial turf. Football (RR 1.4572, CI: 1.2726 to 1.6687, P<0.0001), girls soccer (RR 1.7073, CI: 1.1857 to 2.4583, P=0.0040), boys soccer (RR 1.8286, CI: 1.2296 to 2.7194, P=0.0029), and rugby (RR 23.0000, CI: 3.1998 to 165.3244, P=0.0018) had higher injury incidences on artificial turf. Conclusions: Athletes were 58% more likely to sustain an injury on artificial turf. Football, soccer, and rugby athletes were at a significantly greater injury risk on artificial turf. Upper and lower extremity and torso injuries also occurred with higher incidence on artificial turf. Level of Evidence: Level IV.