Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Unsupervised Subject-Independent Speech-Based Relapse Detection in Patients with Psychosis using Variational Autoencoders

Generative models, such as Variational Autoen-coders, are being increasingly utilized for various acoustic modeling tasks, such as anomaly detection from audio signals. Motivated by this, in this work we propose a Convolutional Variational Autoencoder (CVAE), in order to detect and predict the appearance of relapses in patients with psychotic disorders, such as schizophrenia and bipolar disorder. The proposed system utilizes speech segments of patients, isolated from interviews conducted with their clinicians containing spontaneous speech, and represented as log-mel spectrograms. The results from the analysis of each segment are then aggregated in a per-interview basis. We explore the performance of our system in both a personalized and a universal (patient-independent) setup. Evaluation of our method in data from 13 patients and 375 interviews, with a total duration of 30509 sec of isolated speech, indicate that the CVAE achieves similar results to a Convolutional Autoencoder (CAE) baseline in a personalized setup. Further-more, the proposed model significantly outperforms the CAE baseline when considering a universal relapse detection setup.
A Giant Metrewave Radio Telescope search for associated H i 21 cm absorption in high-redshift flat-spectrum sources

We report results from a Giant Metrewave Radio Telescope search for "associated" redshifted HI 21cm absorption from 24 active galactic nuclei (AGNs), at $1.1 < z < 3.6$, selected from the Caltech-Jodrell Bank Flat-spectrum (CJF) sample. 22 out of 23 sources with usable data showed no evidence of absorption, with typical $3\sigma$ optical depth detection limits of $\approx 0.01$ at a velocity resolution of $\approx 30$~km~s$^{-1}$. A single tentative absorption detection was obtained at $z \approx 3.530$ towards TXS0604+728. If confirmed, this would be the highest redshift at which HI 21cm absorption has ever been detected. Including 29 CJF sources with searches for redshifted HI 21cm absorption in the literature, mostly at $z < 1$, we construct a sample of 52 uniformly-selected flat-spectrum sources. A Peto-Prentice two-sample test for censored data finds (at $\approx 3\sigma$ significance) that the strength of HI 21cm absorption is weaker in the high-$z$ sample than in the low-$z$ sample, this is the first statistically significant evidence for redshift evolution in the strength of HI 21cm absorption in a uniformly selected AGN sample. However, the two-sample test also finds that the HI 21cm absorption strength is higher in AGNs with low ultraviolet or radio luminosities, at $\approx 3.4 \sigma$ significance. The fact that the higher-luminosity AGNs of the sample typically lie at high redshifts implies that it is currently not possible to break the degeneracy between AGN luminosity and redshift evolution as the primary cause of the low HI 21cm opacities in high-redshift, high-luminosity active galactic nuclei.
Image sensor based steering signal for a digital actuator system

Autonomous driving is an emerging field of research. The related industry is one of the most expensive areas nowadays. The core of these complex controlling systems is the perception of the environment and the usage of actuators for changing supported by sensors to give obvious feedback about the change of state. Steering controlling is such a subsystem. In the real-sized modern car, there are several methods for implementing feedback loops for it, such as torque and angle sensors. In this paper, we concentrate on extending our model-based research and development autonomous vehicle platform – DAVE to be able to study the hard conditions safely and cost-effectively. This work presents a method for a new sensor signal integrated into our CAN-BUS system to give feedback about the steering movement of the wheels to a digital steering controller using a rear-view camera. The advantage of using a rear-view blind-spot camera is that it is already in place, and no additional hardware is needed to use it as a pseudo angle sensor.
Adaptive Weights-based Dynamic Resource Provisioning in Space Division Multiplexed-Elastic Optical Networks (SDM-EONs)

One of the core research issues in Space Division Multiplexed-Elastic Optical Networks (SDM-EON) is the Routing, Space, and Spectrum assignment (RSSA) to support maximum possible traffic. Due to the addition of spatial di-mension, additional constraints and design issues crop up for the resource provisioning in SDM-EON. There could be severe fragmentation in the network spectrum along with the crosstalk impairment among cores leading to unavailability of the resources following RSSA constraints. It limits the efficient utilization of the available capacity in SDM-EON. We propose an Adaptive Weights-based Dynamic Resource Assignment (AW-DRA) technique using SDM-EON link cost parameters and their respective weights. Each link periodically adapts the weights, conforming with the link spectral state, using Teaching Learning-based Optimization (TLBO) approach. We compare the performance of the proposed technique with two other benchmark techniques using simulations. The results demonstrate that the proposed AW-DRA technique performs significantly better in connection blocking and spectrum utilization.
Improvement and Characterization of Small Cross-Piece Ionization Chambers at the Tritium Laboratory Karlsruhe

Abstract At the Tritium Laboratory Karlsruhe stainless steel cross-piece ionization chambers have been used to measure the activity concentration of tritiated gases in experiments and processes for more than 10 years. New chambers with an optimized design in terms of the effective chamber volume were produced. Furthermore, they were gold and copper plated to determine the influence of the coatings on the signal and on the memory effect. A new chamber of the old design was built for direct comparison of the signals. The chambers were characterized with different helium-tritium mixtures in 8 runs and the ionisation current as a function of the static gas pressure was measured. When comparing the three new chambers, the gold chamber always showed the highest current, followed by the copper chamber. After exposing the chambers to ~13,100 TBq/m3, the memory effect was investigated by using a similar gas mixture of the earlier runs with ~1,500 TBq/m3. The gold chamber showed the highest memory effect, the copper chamber the lowest. This paper describes the design and the testing procedure of the chambers. It presents the first experimental results on the chamber performance, on the memory effects as well as calibration curves.
A Neural-Driven Musculoskeletal Model for Continuous Estimation of Hand and Wrist Movements

Electromyography (EMG)-driven musculoskeletal models (MMs) have been developed for continuous control with multiple degrees of freedom. With the development of high-density surface EMG signal decomposition, extracted neural drive signals were widely used as inputs of the human-machine interface (HMI). This study proposed a neural-driven MM by using the neural drive signals as the inputs of the MM. The decomposed high-density EMG signals were used to extract motor unit (MU) discharge events and calculate the average discharge frequency to estimate the neural drive. The feasibility of the neural-driven MM in predicting the joint angles of the wrist and metacarpophalangeal (MCP) was tested on one ablebodied subject. The performance of the MM was evaluated by the Pearson's correlation coefficient (r) and the normalized root mean square error (NRMSE) between the estimated and measured joint angles. Compared with the EMG-driven MM, the neural-driven MM had higher r values and lower NRMSE values. The results demonstrated that the performance of the neural-driven MM was more accurate and robust with respect to that of the EMG-driven MM. The proposed neural-driven MM has great potential for the robust continuous control of wearable robots.
Study on the Impact of Aviation Electrification on Voltage Deviation of the GB Transmission System

With the increasing demand for clean energy and rapid development of battery technology, there has been a notable trend in developing aviation electrification, particularly for small or regional electric aircraft (EA). Nevertheless, large penetration of EA with high charging power could also influence the stable operation of the upstream grid and even cause severe problems, particularly causing voltage-drop issue. In this paper, the impact on voltage deviation of the Great Britain (GB) electrical power system with the electrification of commercial aviation in the UK is highlighted and analysed. A reduced model of the GB transmission system developed in DigSILENT PowerFactory with 36 zones is modified by incorporating the load curves of the EA charging demand into the model. Quasi-Dynamic simulation is used to obtain 24-hour voltage profiles of each zone. Meanwhile, the Granger causality test is utilised to analyse the voltage profiles obtained from the Quasi-Dynamic simulation and determine the causal effects of the voltage variation between different areas of GB. The results reveal the significant impact of EA charging on the system. When the EA charging demand reached nearly IOOOOMW at20:00, the system voltage dropped to 0.826 p.u. The locations of the most influential zones for voltage variation in the system are also illustrated and analysed in this paper.
A Practical Approach For Malware Identification Based On Anti-Emulation Techniques And Feature To Image Translation

Cyber-Security environment has always been characterized by a continuous process, in which every technique used to detect threats was shortly followed by a malware adjustment meant to evade that detection approach. While machine learning and emulators have been an important part of this process for several years now, translating features extracted from malware samples into images and feeding them as input to convolutional neural networks is a more recent endeavor in this area. This paper presents our research on several methods that can be used in order to translate features extracted from malware emulation during static analysis (with a special focus on features that reflect anti-emulation techniques) for threat detection.
Reducing Hovering and Motion Energy Consumptions for UAV-enabled Collaborative Beamforming

Unmanned aerial vehicles (UAVs) Communications and networks are of paramount importance in the 5G/6G networks. However, how to deploy the limited on-board energy and restricted transmit power of the UAV so that enhancing the communication efficiency is still a key issue. In this work, we consider a UAV-enabled communication scenario that a set of UAVs perform a virtual antenna array (VAA) to communicate with different remote base stations (BSs) by using collaborative beamforming (CB). For achieving a better energy efficiency, we formulate a hovering and motion energy consumption multiobjective optimization problem (HMECMOP) of UAV-enable CB to simultaneously minimize the total hovering and motion energy consumptions of UAVs by jointly optimizing the positions, excitation current weights of UAVs and the order of communicating with different BSs. Then, we propose an improved multiobjective multi-verse optimizer (IMOMVO) to solve the formulated HMECMOP. IMOMVO uses the vertical and horizontal renewal strategy and nearest neighbor procedure (NNP) to deal with the complex solution space which contains continuous and discrete solutions, so that making the algorithm more suitable for solving the formulated optimization problem. Simulation results demonstrate that the proposed algorithm is effective for solving the HMECMOP and it has better performance than some other comparison algorithms.
Subband Teager Energy Representations for Infant Cry Analysis and Classification

Due to quasi-periodic sampling of vocal tract system, spectrum formed by high pitch-source harmonics results in extremely poor spectral resolution for commonly used spectral features, such as Mel Frequency Cepstral Coefficients (MFCC). Therefore, classifying normal vs. pathological infant cry became technologically challenging signal processing problem. In that context, this study investigates the effectiveness of the spec-tral representations over cepstral representations for infant cry analysis. Furthermore, we show the spectrographic analysis for various spectral representations, such as Short-Time Fourier Transform (STFT), Mel Filterbank (MeIFB) coefficients, Linear Filterbank (LinFB) coefficients, and proposed subband Teager Energies (subband- TE). Experiments are performed on standard Baby Chilanto dataset with Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) as classifiers. It is observed that spectral representations performs slightly better over the cepstral representations. Moreover, subband- TE representations with GMM classifier achieves 99.47% classification accuracy for normal vs. pathology cry and outperforms other architectures. This is due to the capability of TEO to accurately estimate the energies, especially in low frequency regions which consists of fundamental frequency $(F_{0})$ and its harmonics.
Online Decentralized Task Allocation Optimization for Edge Collaborative Networks

In centralized task allocation strategies, real-time status information needs to be collected from distributed edge nodes. Therefore, the overloaded transmission on backbone network appears and leads to devastating decrease in the per-formance of centralized strategies. To address this issue, this paper proposes a multi-agent deep reinforcement learning based online decentralized task allocation mechanism, where each edge node makes task allocation decisions based on local network-state information. A centralized-training distributed-execution method is adopted to decrease data transmission load, and a value decomposition-based technique is applied at training stage for improving long-term performance of task allocation in edge col-laborative networks. Extensive experiments are conducted, and evaluation results demonstrate that our mechanism outperforms other three baseline algorithms in reducing the long-term average system delay and improving request completion rate.
Design of static human posture recognition algorithm based on CNN

As an important part of human behavior recognition, human posture recognition is one of the important research hotspots in the field of computer artificial intelligence. Convolutional neural network (CNN) is one of the representative algorithms of deep learning, this paper used Kinect’s infrared camera to collect depth images and established the depth image database needed for network training. The convolutional neural network model was built through the Pytorch deep learning framework, the model had 8 layers. In the sampling image data,determined the five stationary human postures, including raising hand, bending, forking, raising leg and standing. We divided the data sets of five human postures into training set and test set according to the ratio of 7:3 by split function. Then, according to the steps of convolution neural network training, the five kinds of human posture for convolution and pooling operation, finally, it entered the full connection layer for classification, obtained the model classifier, and adjust the network model until it converged. Finally, input the test image to verify the accuracy of recognition through experiments and data.
Deadlock Detection for MPI Programs Based on Refined Match-sets

Deadlock is one of the critical problems in the message passing interface. At present, most techniques for detecting the MPI deadlock issue rely on exhausting all execution paths of a program, which is extremely inefficient. In addition, with the increasing number of wildcards that receive events and processes, the number of execution paths raises exponentially, further worsening the situation. To alleviate the problem, we propose a deadlock detection approach called SAMPI based on match-sets to avoid exploring execution paths. In this approach, a match detection rule is employed to form the rough match-sets based on Lazy Lamport Clocks Protocol. Then we design three refining algorithms based on the non-overtaking rule and MPI communication mechanism to refine the match-sets. Finally, deadlocks are detected by analyzing the refined match-sets. We performed the experimental evaluation on 15 various programs, and the experimental results show that SAMPI is really efficient in detecting deadlocks in MPI programs, especially in handling programs with many interleavings.
Degradation Behavior and Analysis of GaN HEMTs Under High Power Microwave Pulse

In this work, the effect of electromagnetic pulses on the performance of AlGaN/GaN HEMTs was studied, by building a high-power microwave pulse (HPM) experimental platform. Repeated experiments with multiple devices show that the electrical characteristics of the devices changed after HPM stress. In order to reduce the impact of device recovery, the data of 4 hours after stress are selected for analysis. The experimental results show that the gate leakage current is reduced by an order of magnitude, and the transfer and output characteristic curve changes, the threshold voltage has a 0.25V positive shift after stress. Moreover, in the process of measuring the S-parameters of the devices, TRL calibration kits were designed to de-embed, and the measurement results show that the S-parameters of device have changed significantly. In addition, it is found that defects are a major factor in degradation. As current high-integration chips are susceptible to electromagnetic pulses, this experiment may provide useful reference for RF device design.
Supervised Image Retrieval and Ranking Technique for Lock-in Thermography Images

Lock-in Thermography (LIT) is a non-destructive technique in the failure analysis (FA) of integrated circuits (ICs). In diagnosing the cause of failure, a FA specialist spends a long time searching through a repository of historical images. In this paper, a supervised image retrieval and ranking algorithm incorporating image similarity and classification has been developed. Features are extracted from the images by passing them through the pre-trained VGG16 network. Principal component analysis (PCA) is then performed to identify 100 significant components that serve as signatures for each image and for computing Euclidean distance as the similarity metric. Next, a two-layer classifier replicating the human judgment process has been developed. The first layer of the classifier differentiates whether the query image is taken at the package or die level, whereas the second layer identifies the package or device class of the image. By analyzing the query image through the classifier, its classes in the two layers are determined. The distances of database images belonging to the same classes as the query image are reduced, shifting them ahead. The images thus sorted and ranked are recommended. The algorithm was tested on a dataset of 372 images of which 298 images were used for database construction, and 74 images were used as queried images. The incorporation of class classification improved the precision rate by recommending more images belonging to the same classes as the query.
A Unified Class of DC-type Convexity-Preserving Regularizers for Improved Sparse Regularization

Recently, a novel class of nonconvex sparse regularizers which can preserve the convexity of the cost function has gained considerable attention. These regularizers are expressed as difference of two convex functions, where the parameterized subtrahend function can be adjusted flexibly to maintain the overall convexity. In this paper we propose a unified class of such DC-type (Difference-of-Convex) convexity-preserving regularizers. By selecting proper kernel functions, the proposed regularizer reproduces existing convexity-preserving models and opens the way to a large number of promising new regularizers. In order to solve the convex but involved regularization model, we propose a novel iterative algorithm based on DC programming. Unlike normal DC algorithms, the proposed method is guaranteed to converge to a global minimizer of the cost function. In addition, compared to algorithms for existing convexity-preserving models, the proposed algorithm makes less stringent assumptions on the kernel functions, thus is more general. Moreover, the proposed algorithm can be interpreted as a nested forward-backward splitting method with overrelaxed step size, which leads to its fast convergence. Numerical experiments are conducted to verify the efficiency of our algorithm with comparisons to existing methods.
Transductive Inversion via Deep Transform Learning

This work addresses the problem of solving a linear inverse problem. Conventional inversion techniques are model based (transductive). The advent of deep learning led the way for data-driven (inductive) inversion techniques. The main issue with inductive inversion is that unless the unseen signal (to be inverted) is similar to the training data, the learnt model fails to generalize rendering poor inversion results. A recent study on deep dictionary learning has shown how it can combine the best of both worlds – deep learning with transductive inversion. In this work, we show how the analysis counterpart of dictionary learning, called transform learning, can be extended deeper for transductive inversion. Results on dynamic MRI reconstruction, show that the proposed technique improves over the state-of-the-art.
Analysis of Ship RCS Interpolation Data

Radar cross section (RCS) is a physical quantity that measures the echo intensity of a target under the illumination of radar waves, namely, the virtual area of the target. The acquisition methods of RCS are generally divided into two categories which are actual measurement and simulation, respectively. But it is time-consuming to get the data of any angle of the ship target RCS, whether to use electromagnetic simulation or actual measurement. So if part of the data of RCS is known, it should be possible to estimate the other RCS data based on them. So, the performance of different interpolation methods are compared by the statistical characteristics and the degree of similarity between raw data and interpolated data; then, the most suitable interpolation method is selected to fulfill the requirement of generating the RCS of the ship target at any angle.
On Interpretability of CNNs for Multimodal Medical Image Segmentation

Despite their huge potential, deep learning-based models are still not trustful enough to warrant their adoption in clinical practice. The research on the interpretability and explainability of deep learning is currently attracting huge attention. Multilayer Convolutional Sparse Coding (ML-CSC) data model, provides a model-based explanation of convolutional neural networks (CNNs). In this article, we extend the ML-CSC framework towards multimodal data for medical image segmentation, and propose a merged joint feature extraction ML-CSC model. This work generalizes and improves upon our previous model, by deriving a more elegant approach that merges feature extraction and convolutional sparse coding in a unified framework. A segmentation study on a multimodal magnetic resonance imaging (MRI) dataset confirms the effectiveness of the proposed approach. We also supply an interpretability study regarding the involved model parameters.