Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid EEG-EMG system to detect steering actions in car driving settings

Understanding mental processes in complex human behaviour is a key issue in the context of driving, representing a milestone for developing user-centred assistive driving devices. Here we propose a hybrid method based on electroencephalographic (EEG) and electromyographic (EMG) signatures to distinguish left from right steering in driving scenarios. Twenty-four participants took part in the experiment consisting of recordings 128-channel EEG as well as EMG activity from deltoids and forearm extensors in non-ecological and ecological steering tasks. Specifically, we identified the EEG mu rhythm modulation correlates with motor preparation of self-paced steering actions in the non-ecological task, while the concurrent EMG activity of the left (right) deltoids correlates with right (left) steering. Consequently, we exploited the mu rhythm de-synchronization resulting from the non-ecological task to detect the steering side by means of a cross-correlation analysis with the ecological EMG signals. Results returned significant cross-correlation values showing the coupling between the non-ecological EEG feature and the muscular activity collected in ecological driving conditions. Moreover, such cross-correlation patterns discriminate left from right steering with an earlier dynamic with respect to the single EMG signal. This hybrid system overcomes the limitation of the EEG signals collected in ecological settings such as low reliability, accuracy and adaptability, thus adding to the EMG the characteristic predictive power of the cerebral data. These results are a proof of concept of how it is possible to complement different physiological signals to control the level of assistance needed by the driver.
Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities

Every T cell receptor (TCR) repertoire is shaped by a complex probabilistic tangle of genetically determined biases and immune exposures. T cells combine a random V(D)J recombination process with a selection process to generate highly diverse and functional TCRs. The extent to which an individual’s genetic background is associated with their resulting TCR repertoire diversity has yet to be fully explored. Using a previously published repertoire sequencing dataset paired with high-resolution genome-wide genotyping from a large human cohort, we infer specific genetic loci associated with V(D)J recombination probabilities using genome-wide association inference. We show that V(D)J gene usage profiles are associated with variation in the TCRB locus and, specifically for the functional TCR repertoire, variation in the major histocompatibility complex locus. Further, we identify specific variations in the genes encoding the Artemis protein and the TdT protein to be associated with biasing junctional nucleotide deletion and N-insertion, respectively. These results refine our understanding of genetically-determined TCR repertoire biases by confirming and extending previous studies on the genetic determinants of V(D)J gene usage and providing the first examples of trans genetic variants which are associated with modifying junctional diversity. Together, these insights lay the groundwork for further explorations into how immune responses vary between individuals.
Age-related differences in performance and fatigability during an isometric quadriceps intermittent fatigue test

The aim of the present study was to investigate age-related differences in fatigability induced by an isometric quadriceps intermittent fatiguing test in young (<35 years old), old (>60 years old) and very old (>80 years old) men and women. Maximal force loss, contractile function and voluntary activation of the knee extensors were evaluated throughout an isometric fatiguing test using femoral nerve magnetic stimulations. Older adults performed more contractions (index of relative performance) than young (P = 0.046) and very old adults (P = 0.007), without differences between young and very old adults. Total work (absolute performance) was greater for young and old adults compared to very old adults (P < 0.001), without differences between young and old adults. At exhaustion, force loss was greater for young (−28 ± 9%) compared to old adults (−19 ± 8%), but not very old adults (−23 ± 8%). The response to femoral nerve stimulation decreased similarly at exhaustion for the three age groups, indicating similar alteration in contractile function with age. No impairment in voluntary activation was observed. Impairments in neuromuscular parameters were similar for men and women. This study showed that older adults were less fatigable than young adults during an isometric intermittent fatiguing task of the knee extensors. This greater fatigue resistance was not maintained in very old adults independent of sex. Fatigability at exhaustion was likely due to impairments in contractile function for the three age groups.
EXAMINING THE EFFECTS OF FREE GAZE AND DYNAMIC VIDEO STIMULI ON ENGAGEMENT, EYE MOVEMENTS, AND EEG SIGNAL QUALITY IN A VISUAL AESTHETIC RATING TASK

Free gaze and the use of dynamically changing video stimuli are typically avoided in EEG experiments to avoid artefacts and confounds related to uncontrolled eye movements. Yet, often it is unclear whether these artificial secondary manipulations might have unwanted effects on the primary measures of interest and for a growing number of research questions removing them would be beneficial: Among those is the investigation of visual aesthetic experiences, which typically involve open-ended exploration of highly variable stimuli. Here we aimed to quantify the effect of fixation task and using still vs. movie stimuli on EEG signal quality and several behavioral and physiological measures of interest during an aesthetic rating task. Participants observed scenes from landscapes and dance performances and rated each stimulus for both aesthetic appeal and their state of boredom while watching it. The scenes were presented either as dynamic video clips or static pictures, and participants observed them either with unconstrained gaze or under attempted fixation We recorded EEG, ECG and eyetracking from 43 participants. An auditory stream of 40Hz amplitude modulated pink noise was played during each trial and signal-to-noise-ratio (SNR) of the auditory steady-state response measured at the scalp was extracted as a proxy measure for overall EEG signal quality. The study including hypotheses and a priori power analysis was preregistered. We found that both behavioral ratings were influenced by the experimental conditions: boredom and aesthetic ratings were positively affected by dynamic video stimuli, indicating that these are experienced as more engaging; both these effects were stronger in dance. As already reported before, landscape stimuli were experienced as more appealing. Fixaton task, on the other hand, had no significant effect on the ratings which is encouraging given how canonically it is applied. Eye movements were significantly affected not only by viewing task, but by stimulus dynamics and content as well: we observed fewer eyeblinks, saccades and microsaccades in video stimuli, and fewer saccades but more microsaccades in dance than in landscape stimuli, with several significant interactions. EEG SNR, to our surprise, was barely affected by fixation task despite only minimal preprocessing and no trial rejection. We nevertheless believe that the new metric is sensitive to capture noise: it was significantly correlated with the number of eye blinks, and after cleaning the dataset with an ICA based preprocessing pipeline the significant effect of fixation task and the correlation with blink rate vanished. We see these as promising results indicating that at least in the lab more liberal experimental conditions could be achieved without significant loss of signal quality. Specifically the use of dynamic video material bears a lot of potential for future investigations in human neurophysiological studies.
iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data

Despite the tremendous increase in omics data generated by modern sequencing technologies, their analysis can be tricky and often requires substantial expertise in bioinformatics. To address this concern, we have developed a user-friendly pipeline to analyze (cancer) genomic data that takes in raw sequencing data (FASTQ format) as input and outputs insightful statistics on the nature of the data. Our iCOMIC toolkit pipeline can analyze whole-genome and transcriptome data and is embedded in the popular Snakemake workflow management system. iCOMIC is characterized by a user-friendly GUI that offers several advantages, including executing analyses with minimal steps, eliminating the need for complex command-line arguments. The toolkit features many independent core workflows for both whole genomic and transcriptomic data analysis. Even though all the necessary, well-established tools are integrated into the pipeline to enable ‘out-of-the-box’ analysis, we provide the user with the means to replace modules or alter the pipeline as needed. Notably, we have integrated algorithms developed in-house for predicting driver and passenger mutations based on mutational context and tumor suppressor genes and oncogenes from somatic mutation data. We benchmarked our tool against Genome In A Bottle (GIAB) benchmark dataset (NA12878) and got the highest F1 score of 0.971 and 0.988 for indels and SNPs, respectively, using the BWA MEM - GATK HC DNA-Seq pipeline. Similarly, we achieved a correlation coefficient of r=0.85 using the HISAT2-StringTie-ballgown and STAR-StringTie-ballgown RNA-Seq pipelines on the human monocyte dataset (SRP082682). Overall, our tool enables easy analyses of omics datasets, with minimal steps, significantly ameliorating complex data analysis pipelines. Availability: https://github.com/RamanLab/iCOMIC
Analysis of the upper respiratory tract microbiota in mild and severe COVID-19 patients

The microbiota of the respiratory tract remains a relatively poorly studied subject. At the same time, like the intestinal microbiota, it is involved in modulating the immune response to infectious agents in the host organism. A causal relationship between the composition of the respiratory microbiota and the likelihood of development and the severity of COVID-19 may be hypothesized. We analyze biomaterial from nasopharyngeal smears from 336 patients with a confirmed diagnosis of COVID-19, selected during the first and second waves of the epidemic in Russia. Sequences from a similar study conducted in Spain were also included in the analysis. We investigated associations between disease severity and microbiota at the level of microbial community (community types) and individual microbes (differentially represented species). To search for associations, we performed multivariate analysis, taking into account comorbidities, type of community and lineage of the virus. We found that two out of six community types are associated with a more severe course of the disease, and one of the community types is characterized by high stability (very similar microbiota profiles in different patients) and low level of lung damage. Differential abundance analysis with respect to comorbidities and community type suggested association of Rothia and Streptococcus genera representatives with more severe lung damage, and Leptotrichia, unclassified Lachnospiraceae and Prevotella with milder forms of the disease.
A Compact Double-Sided Cooling 650V/30A GaN Power Module With Low Parasitic Parameters

Compared with silicon and silicon carbide devices, the unique electrical and structural characteristics of gallium nitride high electron mobility transistors (GaN HEMTs) make them have different requirements for power module integration. This article proposes a novel integration scheme for the high-voltage lateral GaN HEMT dies without bonding wires. Based on the proposed integration scheme, a compact 650 V/30 A GaN power module with low parasitic parameters and high thermal performance is designed. The GaN dies are sandwiched between two ceramic substrates to improve thermal performance and ensure consistent thermal expansion coefficients. The multiple copper layer structure is used to increase wiring flexibility to reduce parasitic parameters. The design of gate and power loop layouts is discussed, and the common-mode (CM) capacitance is optimized. A comprehensive reliability evaluation is also carried out for this integration scheme. Finally, a double-sided cooling 650 V/30 A full-bridge GaN power module with 2.4 cm×1.3 cm×0.17 cm is fabricated. The thermal resistance is reduced by 30%–48% compared with the conventional single-sided cooling module. The power loop and gate loop inductances are reduced to 0.94 nH and 2 nH, respectively, and the CM capacitance is limited to 2.5 pF. The maximum dv/dt of the drain–source voltage is high as 150 V/ns with only 10% overshoot. Based on the power module, a 3.3-kW two-phase interleaved buck converter is developed. It has 820 W/in3 power density and 98.85% peak efficiency.
Benchmark Datasets for Stochastic Petri Net Learning

The existing Stochastic Petri Net (SPN) analysis methods are based on a series of steps, including generating the reachable graph, solving the state equation, etc. Unfortunately, these methods cannot perform performance analysis when the state equation has no unique solution. The end-to-end deep learning methods can build a mapping relationship from SPN to performance indicators, which avoids solving the state equation. However, there is a lack of benchmark datasets for SPN learning and training. This paper proposes an automatic generation method of SPN datasets, including SPN random generation, data labeling, data enhancement, and filtering. To relieve the local aggregation problem of random-based organization, a grid-based data organization method is proposed to ensure the diversity of the datasets. In the experimental section, the generated datasets are trained and tested on three types of neural networks. The results show that the generated benchmark datasets are useful, and the increase of dataset size will significantly improve the learning performance.
Lightweight multi-dimensional memristive CapsNet

Capsule network (CapsNet) is a novel neural network architecture that overcame the drawback of loss of poses and position caused in convolutional neural networks and achieves better results than convolutional neural networks in some tasks. However, CapsNet computing efficiency needs to be improved. This paper uses the lightweight network method to design the structure of the capsule network reconstruction layer and proposes a lightweight capsule network, DSC-CapsNet, which can effectively improve network computing efficiency and keep network performance. Moreover, the memristor-based circuit structure corresponding to the main operation part of DSC-CapsNet was developed to enable a high-speed method of inference. It provides a modern approach to hardware deployment in terminal applications.
HCt-SNE: Hierarchical Constraints with t-SNE

Dimensionality reduction (DR) methods are useful when analyzing high dimensional data, in particular, if one wants to visualize them. t-distributed stochastic neighbor embedding (t-SNE), one of the most widely used DR methods, can preserve neighborhood information and reveal groups in embeddings. However, it may not preserve the global structure and fail to reveal the semantic information in the visualization. From a user point-of-view, a DR visualization is useful if it not only reveals hidden structures in the data but also corresponds to the user knowledge. This paper addresses these problems by proposing Hierarchical Constraint t-SNE (HC$t$-SNE), a method that allows users to integrate hierarchical constraints directly into $t$-SNE embeddings. The user constraints are encoded in an explicit tree. We transform the hierarchical information in this tree into a novel regularization term based on triplet constraints among the nodes at different levels in the tree. Our method takes advantage of semantic information provided in class labels and outperforms the original $t$-SNE and two other supervised DR methods in terms of both visual assessment and quality metrics on three classic image datasets: MNIST, Fashion-MNIST and CIFAR10.
Closed-Form Expressions For Symbol Error Rate of Two-User NOMA Systems

Ahstract- This paper presents the development of closed-form mathematical expressions for the exact calculation of the symbol error rate of two-User NOMA systems. These expressions are very useful to evaluate the performance of these systems without the need to perform expensive computational simulations. To obtain the expressions, the presence of white Gaussian additive noise and an imperfect process of successive interference cancellation were considered. The obtained results are validated by Monte Carlo simulations.
Wavelet-Temporal Neural Network for Multivariate Time Series Prediction

Multivariate time series prediction task has long been a crucial problem in machine learning since it includes applications in real life such as energy consumption, traffic congestion and economic trending. Challenge is that there are many factors affecting the accuracy of prediction, such as information in time domain and frequency domain, as well as interdependence among sequences. Therefore, effectively capturing and combining features in different domains becomes the focus of research. In this paper, a deep learning framework enhanced by wavelet transform is proposed to predict multivariate time series. Our model captures the long-term and short-term characteristics of time series in the time domain respectively through convolutional network. Further more, we use LSTM to achieve trending vector of short-term features, while for long-term features, we use the multi-head attention mechanism to get its representation. For frequency-domain features, our framework extracts from the scalo-gram generated by applying wavelet transform to the time sequence. Through the design of CNN convolution kernel, our model also ensures that the dependence between time series can be extracted. Experimental results show that our proposed model outperforms other baseline approaches on three of the four realworld datasets with sophisticated patterns.
A Comprehensive Review on Structural Topologies, Power Levels, Energy Storage Systems, and Standards for Electric Vehicle Charging Stations and Their Impacts on Grid

The penetration of electric vehicles (EVs) in the transportation sector is increasing but conventional internal combustion engine (ICE) based vehicles dominates. To accelerate the adoption of EVs and to achieve sustainable transportation, the bottlenecks need to be elevated that mainly include the high cost EVs, range anxiety, lack of EV charging infrastructure, and the pollution of the grid due to EV chargers. The high cost of EVs is due to costly energy storage systems (ESS) with high energy density. This paper provides a comprehensive review of EV technology that mainly includes electric vehicle supply equipment (EVSE), ESS, and EV chargers. A detailed discussion is presented on the state-of-the-art of EV chargers that include on-/off-board chargers. Different topologies are discussed with low-/high-frequency transformers. The different available power levels for charging are discussed. To reduce the range anxiety the EV chargers based on inductive power transfer (IPT) are discussed. The last part of the paper focuses on the negative impact of EV chargers along with the remedies that can be adopted. The international standards decided by different institutions and adopted universally are discussed in the latter part of this paper and finally, this paper concludes with the near to future advancement in EV technology.
A Compact EMI Filter Design by Reducing the Common-Mode Inductance With Chaotic PWM Technique

Passive electromagnetic interference filters (PEFs) are the most common way to solve electromagnetic interference (EMI) problems in power converters. However, PEFs bring additional volume, weight, and cost for power converters, especially common-mode (CM) inductors in PEFs, and it is a tricky issue for the high-power-density converters that must meet the electromagnetic compatibility specification. In order to design compact PEFs for power converters with pulsewidth modulation (PWM), a volume reduction method of CM inductors with chaotic PWM (CPWM) is proposed in this article. First, the mechanism that CPWM reduces the CM inductance by increasing the corner frequency is analyzed. Second, the relationship between the reduction of EMI spectrum magnitude by CPWM and the decrease of the CM inductance is quantitatively calculated. Third, utilization rate of the magnetic core η is defined to reasonably compare the size of the different inductors under traditional PWM and CPWM. Finally, the proposed design method of CM EMI filters is applied into a dc–dc converter and a dc–ac inverter, respectively, to verify its effectiveness and feasibility. In a dc–dc converter with the switching frequency 100 kHz, 275 W, the volume of the CM inductor and the volume of CM EMI filters are reduced by 63.5% and 48.3%, respectively, by using the proposed compact EMI filter.
Control of a Three-Phase Power Converter Connected to Unbalanced Power Grid in a Non-Cartesian Oblique Frame

The article presents a new approach to positive and negative sequence current vector control of a grid-connected three-phase three-wire power electronic converter operating under grid voltage imbalance conditions. The concept utilizes the representation of unbalanced converter current in the new coordinates frame in which the current vector components are constant. The nonlinear trigonometric transformation of two-dimensional current vector components from the stationary frame to the new frame is found online depending on the reference current asymmetry. The presented concept of new coordinates utilization allows implementation of proportional-integral terms as current regulators without the use of resonant terms and without the use of the measured current symmetrical sequences decomposition. The article presents the theoretical approach, simulation results, as well as laboratory tests results.
NOMA for Wireless-Powered Communication Networks With Buffered Sources

To increase the feasibility of wireless powered communication networks (WPCNs), the synergy of efficient multiple access protocols and diverse types of resources, e.g., multiple antennas, memories, and energy storage devices is of paramount importance. To this end, in this paper, we introduce the use of buffered sources in WPCNs with uplink non-orthogonal multiple access, in which a multi-antenna hybrid access point can both transmit energy and receive information. The proposed scheme aims at maximizing the long-term utility function, which is determined by the achieved average data rate and fairness among sources. The formulated long-term time-average optimization problem is converted into instantaneous ones by exploiting the Lyapunov optimization framework. The derived analytical solutions reveal the impact of data buffers’ length on the decoding order of sources’ messages. Finally, it is shown that the proposed scheme achieves superior long-term utility compared to the considered baseline schemes with non-buffered sources and buffered sources with orthogonal multiple access. Additionally, it is demonstrated that our proposed scheme can ensure fairness without reducing the average data rate.