Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Reinforcement Learning for Communication Flow Control in Wireless Mesh Networks

Wireless mesh network (WMN) is one of the most promising technologies for Internet of Things (IoT) applications because of its self-adaptive and self-organization nature. To meet different performance requirements on communications in WMNs, traditional approaches always have to program flow control strategies in an explicit way. In this case, the performance of WMNs will be significantly affected by the dynamic properties of underlying networks in real applications. With providing a more flexible solution in mind, in this article, for the first time, we present how we can apply emerging Deep Reinforcement Learning (DRL) on communication flow control in WMNs. Moreover, different from a general DRL based networking solution, in which the network properties are pre-defined, we leverage the adaptive nature of WMNs and propose a self-adaptive DRL approach. Specifically, our method can reconstruct a WMN during the training of a DRL model. In this way, the trained DRL model can capture more properties of WMNs and achieve better performance. As a proof of concept, we have implemented our method with a self-adap-tive Deep Q-learning Network (DQN) model. The evaluation results show that the presented solution can significantly improve the communication performance of data flows in WMNs, compared to a static benchmark solution.
Spatiotemporal Correlation of Epileptiform Activity and Gene Expression in vitro

Epileptiform activity alters gene expression in the central nervous system, a phenomenon that has been studied extensively in animal models. Here, we asked whether also in vitro models of seizures are in principle suitable to investigate changes in gene expression due to epileptiform activity and tested this hypothesis mainly in rodent and additionally in some human brain slices. We focused on three genes relevant for seizures and epilepsy: FOS proto-oncogene (c-Fos), inducible cAMP early repressor (Icer) and mammalian target of rapamycin (mTor). Seizure-like events (SLEs) were induced by 4-aminopyridine (4-AP) in rat entorhinal-hippocampal slices and by 4-AP/8 mM potassium in human temporal lobe slices obtained from surgical treatment of epilepsy. SLEs were monitored simultaneously by extracellular field potentials and intrinsic optical signals (IOS) for 1–4 h, mRNA expression was quantified by real time PCR. In rat slices, both duration of SLE exposure and SLE onset region were associated with increased expression of c-Fos and Icer while no such association was shown for mTor expression. Similar to rat slices, c-FOS induction in human tissue was increased in slices with epileptiform activity. Our results indicate that irrespective of limitations imposed by ex vivo conditions, in vitro models represent a suitable tool to investigate gene expression. Our finding is of relevance for the investigation of human tissue that can only be performed ex vivo. Specifically, it presents an important prerequisite for future studies on transcriptome-wide and cell-specific changes in human tissue with the goal to reveal novel candidates involved in the pathophysiology of epilepsy and possibly other CNS pathologies.
Gene Expression Signature of Traumatic Brain Injury

Background: Traumatic brain injury (TBI) is a brain function change caused by external forces, which is one of the main causes of death and disability worldwide. The aim of this study was to identify early diagnostic markers and potential therapeutic targets for TBI. Methods: Differences between TBI and controls in GSE89866 and GSE104687 were analyzed. The two groups of differentially expressed genes (DEGs) were combined for coexpression analysis, and the modules of interest were performed using enrichment analysis. Hub genes were identified by calculating area under curve (AUC) values of module genes, PPI network analysis, and functional similarity. Finally, the difference in immune cell infiltration between TBI and control was calculated by ssGSEA. Results: A total of 4,817 DEGs were identified in GSE89866 and 1,329 DEGs in GSE104687. They were clustered into nine modules. The genes of modules 1, 4, and 7 had the most crosstalk and were identified as important modules. Enrichment analysis revealed that they were mainly associated with neurodevelopment and immune inflammation. In the PPI network constructed by genes with top 50 AUC values in module genes, we identified the top 10 genes with the greatest connectivity. Among them, down-regulated RPL27, RPS4X, RPL23A, RPS15A, and RPL7A had similar functions and were identified as hub genes. In addition, DC and Tem were significantly up-regulated and down-regulated between TBI and control, respectively. Conclusion: We found that hub genes may have a diagnostic role for TBI. Molecular dysregulation mechanisms of TBI are associated with neurological and immune inflammation. These results may provide new ideas for the diagnosis and treatment of TBI.
Selection of Optimum Circuit Breaker for a Low-Voltage Industrial Power Distribution System

Low-voltage power distribution system in a commercial and industrial environment is controlled and protected mainly by air circuit breakers. It is essential that the breakers selected are fit for the duty they are required to perform. The strength and capability of the breakers selected should be such that they offer adequate protection to the associated equipment and do not give rise to any danger to the operating personnel. In order to comply with this requirement, the breakers selected should be able to interrupt the maximum prospective fault current and have adequate making current and short time current withstand capability. The determination of essential breaker current ratings is done by calculating maximum prospective making and breaking current at the point of breaker installation. The short time withstand current rating depends upon the number of breakers in series and their protection coordination. Hand calculations can be performed for simple system and power system analysis software tools may be used for complex networks. Zero-point quenching air circuit breaker is considered to determine the essential current parameters.
Altered Heart Rate Variability in Patients With Schizophrenia During an Autonomic Nervous Test

Reduced heart rate variability (HRV) and dysfunction of the autonomic nervous system (ANS) have been observed in schizophrenia patients. HRV parameters of schizophrenia patients in the resting state have been well-documented; however, these parameters of schizophrenia patients who experience continuous psychophysiological stress remain unclear. The objective of this study was to systematically explore the linear and nonlinear HRV parameters between schizophrenia patients and normal controls and to detect the adaptive capabilities of HRV of schizophrenia patients during the stimulation tests of autonomic nervous system. Forty-five schizophrenia patients and forty-five normal controls, matched for age, sex and body mass index, completed a 14 min ANS test. Thirteen linear and nonlinear HRV parameters of all subjects under the ANS test were computed and statistically analyzed between groups and between sessions. The STROBE checklist was adhered to in this study. All time-domain HRV features in the ANS test were significantly different between schizophrenia patients and normal controls (p < 0.01). The schizophrenia patients showed significantly low values in the Poincaré indices, which revealed significantly decreased heart rate fluctuation complexity compared with that of normal controls (p < 0.001). In addition, the normal controls, not schizophrenia patients, showed significant differences between the recovery and stress states in the parameters of low frequency, high frequency, and nonlinear dynamics. Schizophrenia patients showed autonomic dysfunction of the heart in a series of stimulation tests of the autonomic nervous system and could not regain normal physiological functions after stress cessation. Our findings revealed that the dynamic parameters of HRV in psychophysiological stress are sensitive and practical for a diagnosis of schizophrenia.
Innovative Bert-Based Reranking Language Models for Speech Recognition

More recently, Bidirectional Encoder Representations from Transformers (BERT) was proposed and has achieved impressive success on many natural language processing (NLP) tasks such as question answering and language understanding, due mainly to its effective pre-training then fine-tuning paradigm as well as strong local contextual modeling ability. In view of the above, this paper presents a novel instantiation of the BERT-based contextualized language models (LMs) for use in reranking of N-best hypotheses produced by automatic speech recognition (ASR). To this end, we frame N-best hypothesis reranking with BERT as a prediction problem, which aims to predict the oracle hypothesis that has the lowest word error rate (WER) given the N-best hypotheses (denoted by PBERT). In particular, we also explore to capitalize on task-specific global topic information in an unsupervised manner to assist PBERT in N-best hypothesis reranking (denoted by TPBERT). Extensive experiments conducted on the AMI benchmark corpus demonstrate the effectiveness and feasibility of our methods in comparison to the conventional autoregressive models like the recurrent neural network (RNN) and a recently proposed method that employed BERT to compute pseudo-log-likelihood (PLL) scores for N-best hypothesis reranking.
Towards Large-Scale Data Annotation of Audio from Wearables: Validating Zooniverse Annotations of Infant Vocalization Types

Recent developments allow the collection of audio data from lightweight wearable devices, potentially enabling us to study language use from everyday life samples. However, extracting useful information from these data is currently impossible with automatized routines, and overly expensive with trained human annotators. We explore a strategy fit to the 21st century, relying on the collaboration of citizen scientists. A large dataset of infant speech was uploaded on a citizen science platform. The same data were annotated in the laboratory by highly trained annotators. We investigate whether crowd-sourced annotations are qualitatively and quantitatively com-parable to those produced by expert annotators in a dataset of children at high- and low-risk for language disorders. Our results reveal that classification of individual vocalizations on Zooniverse was overall moderately accurate compared to the laboratory gold standard. The analysis of descriptors defined at the level of individual children found strong correlations between descriptors derived from Zooniverse versus laboratory annotations.
An Investigation into the Multi-channel Time Domain Speaker Extraction Network

This paper presents an investigation into the effectiveness of spatial features for improving time-domain speaker extraction systems. A two-dimensional Convolutional Neural Network (CNN) based encoder is proposed to capture the spatial information within the multichannel input, which are then combined with the spectral features of a single channel extraction network. Two variants of target speaker extraction methods were tested, one which employs a pre-trained i-vector system to compute a speaker embedding (System A), and one which employs a jointly trained neural network to extract the embeddings directly from time domain enrolment signals (System B). The evaluation was performed on the spatialized WSJ0-2mix dataset using the Signal-to-Distortion Ratio (SDR) metric, and ASR accuracy. In the anechoic condition, more than 10 dB and 7 dB absolute SDR gains were achieved when the 2-D CNN spatial encoder features were included with Systems A and B, respectively. The performance gains in reverberation were lower, however, we have demonstrated that retraining the systems by applying dereverberation preprocessing can significantly boost both the target speaker extraction and ASR performances.
Flow-Based Approach to Detect Abnormal Behavior in Neighbor Discovery Protocol (NDP)

Internet Protocol version six (IPv6) is equipped with new protocols, such as the Neighbor Discovery Protocol (NDP). NDP is a stateless protocol without authentication that makes it vulnerable to many types of attacks, such as Router Advertisement (RA) and Neighbour Solicitation (NS) DoS flooding attacks. In these types of attacks, attackers send an enormous volume of abnormal NDP traffic, which causes congestion that degrades network performance. The expected behavior among these attacks is the existence of NDP traffic abnormalities. Thus, this research aims to propose a flow-based approach to detect abnormal NDP traffic behavior, which is considered an indicator of the presence of NDP-based attacks, such as RA and NS DoS flooding attacks. Also, the proposed approach relies on flow-based network traffic representation and adoption of the Entropy algorithm to detect the randomness in the network traffic. The proposed approach is evaluated in terms of detection accuracy, precision, recall, and F1-Score using a simulated dataset. The experimental result shows that the proposed approach obtained 98.1%, 55%, 100%, and 70.96% for average accuracy, precision, recall, and F1-Score, respectively, in detecting abnormal NDP traffic behavior caused by the RA DoS flooding attack. Meanwhile, the proposed approach obtained 99%, 91.3%, 100%, and 95.45% for average accuracy, precision, recall, and F1-Score, respectively, in detecting the abnormal NDP traffic behavior caused by the NS DoS flooding attack. Also, the proposed approach shows better results compared to other existing approaches.
Dual-Path RNN for Long Recording Speech Separation

Continuous speech separation (CSS) is an arising task in speech separation aiming at separating overlap-free targets from a long, partially-overlapped recording. A straightforward extension of previously proposed sentence-level separation models to this task is to segment the long recording into fixed-length blocks and perform separation on them independently. However, such simple extension does not fully address the cross-block dependencies and the separation performance may not be satisfactory. In this paper, we focus on how the block-level separation performance can be improved by exploring methods to utilize the cross-block information. Based on the recently proposed dual-path RNN (DPRNN) architecture, we investigate how DPRNN can help the block-level separation by the interleaved intra- and inter-block modules. Experiment results show that DPRNN is able to significantly outperform the baseline block-level model in both offline and block-online configurations under certain settings.
A Contextual Hybrid Model for Vessel Movement Prediction

Predicting the movement of the vessels can significantly improve the management of safety. While the movement can be a function of geographic contexts, the current systems and methods rarely incorporate contextual information into the analysis. This paper initially proposes a novel context-aware trajectories’ simplification method to embed the effects of geographic context which guarantees the logical consistency of the compressed trajectories, and further suggests a hybrid method that is built upon a curvilinear model and deep neural networks. The proposed method employs contextual information to check the logical consistency of the curvilinear method and then, constructs a Context-aware Long Short-Term Memory (CLSTM) network that can take into account contextual variables, such as the vessel types. The proposed method can enhance the prediction accuracy while maintaining the logical consistency, through a recursive feedback loop. The implementations of the proposed approach on the Automatic Identification System (AIS) dataset, from the eastern coast of the United States of America which was collected, from November to December 2017, demonstrates the effectiveness and better compression, i.e. 80% compression ratio while maintaining the logical consistency. The estimated compressed trajectories are 23% more similar to their original trajectories compared to currently used simplification methods. Furthermore, the overall accuracy of the implemented hybrid method is 15.68% higher than the ordinary Long Short-Term Memory (LSTM) network which is currently used by various maritime systems and applications, including collision avoidance, vessel route planning, and anomaly detection systems.
A Droplet-Based Signal Reconstruction Approach to Channel Modeling in Molecular Communication

In this article, a novel droplet-based signal reconstruction (SR) approach to channel modeling, which considers liquid droplets as information carriers instead of molecules in the molecular communication (MC) channel, is proposed for practical sprayer-based macroscale MC systems. These practical MC systems are significant, since they can be used in order to investigate airborne pathogen transmission with biological sensors due to the similar mechanisms of sneezing/coughing and sprayer. Our proposed approach takes a two-phase flow which is generated by the interaction of droplets in liquid phase with air molecules in gas phase into account. Two-phase flow is combined with the SR of the receiver (RX) to propose a channel model. The SR part of the model quantifies how the accuracy of the sensed molecular signal in its reception volume depends on the sensitivity response of the RX and the adhesion/detachment process of droplets. The proposed channel model is validated by employing experimental data.
Optical Frequency Comb Generation Using Ultralong SOA and Different Amplification Methods in MZM-based Optical Fiber Loops

Optical frequency combs have been explored in different applications from optical frequency measurements to optical communications. OFCs can be formed using numerous techniques such as optical modulation, laser mode locking and optical fiber recirculating loops. An optical frequency comb generator based on an optical fiber recirculation loop operating with a Mach-Zehnder modulator was experimentally investigated using different types of amplifiers as gain media. The idea is to compare the use of semiconductor optical amplifiers (SOAs) and erbium-doped fiber amplifiers (EDFAs) and their efficiency in aiding or contributing on the generation of spectral lines. The process efficiency associated with nonlinear effects, specially four-wave mixing, is the main advantage of using SOAs over EDFAs. Under the best operational conditions, an optical comb with up to 30 lines and spacing around 20 GHz was obtained by a SOA-based loop, where an ultralong SOA was used for the first time in such an application.
Gradient-based Optimization for Unrepeatered Optical Systems

We propose a multi-parameter adaptive technique based on the gradient descent algorithm (MAT-GRAD) for optimizing the amplification scheme in unrepeatered wavelength-division multiplexed optical systems. The proposed methodology uses the gradient descent method to jointly optimize the transmitter (Tx) and receiver (Rx) remote optically pumped amplifiers (ROPAs), as well as the Tx forward first-order distributed Raman amplifier (DRA). The MAT-GRAD algorithm is evaluated in combination with the conventional gradient descent algorithm (GDA), and GDA with momentum (GDAM). The simulations indicate that the MAT-GRAD with GDAM achieves a suitable minimum at a higher convergence speed.
A Single-Trim Switched Capacitor CMOS Bandgap Reference With a 3σ Inaccuracy of +0.02%, −0.12% for Battery-Monitoring Applications

This article presents a single-trim switched capacitor (SC) CMOS bandgap reference (BGR) for battery monitoring applications. For a single-temperature trimming, <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-compensation and curvature correction techniques are employed to minimize non-proportional-to-absolute-temperature (PTAT) errors. In conjunction with these techniques, this article proposes dynamic element matching (DEM) techniques with low-pass filtering which employs the decimation filter of a delta-sigma analog-to-digital converter (ADC) in a digital domain. It achieves a further reduction of non-PTAT errors resulting from mismatches of the bias current, of the PNP transistor current gain (<inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>), and of the gain coefficient in the SC summing amplifier. The remaining PTAT errors are canceled out using a single room-temperature trimming. The bandgap circuit is implemented using vertical PNP transistors with a <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> of about 2.7 at 27 °C in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. The proposed SC BGR achieves a <inline-formula> <tex-math notation="LaTeX">$3\sigma $ </tex-math></inline-formula> inaccuracy of +0.02%, −0.12% from −40 °C to 125 °C. From a 1.8-V supply voltage, it consumes <inline-formula> <tex-math notation="LaTeX">$17~\mu \text{A}$ </tex-math></inline-formula> at 27 °C and occupies an active area of 0.38 mm<sup>2</sup>.
Towards Unsupervised Learning of Speech Features in the Wild

Recent work on unsupervised contrastive learning of speech representation has shown promising results, but so far has mostly been applied to clean, curated speech datasets. Can it also be used with unprepared audio data "in the wild"? Here, we explore three potential problems in this setting: (i) presence of non-speech data, (ii) noisy or low quality speech data, and (iii) imbalance in speaker distribution. We show that on the Libri-light train set, which is itself a relatively clean speech-only dataset, these problems combined can already have a performance cost of up to 30% relative for the ABX score. We show that the first two problems can be alleviated by data filtering, with voice activity detection selecting speech segments, while perplexity of a model trained with clean data helping to discard entire files. We show that the third problem can be alleviated by learning a speaker embedding in the predictive branch of the model. We show that these techniques build more robust speech features that can be transferred to an ASR task in the low resource setting.
Design of a Patch Antenna Array for Downlink Rotorcraft System

Rotorcraft are widely used in downlink missions in order to transmit and to capture images for base stations. Traditional dipoles antennas have been used, nevertheless the sitting process faces limitations, due to lack of location. Therefore, this paper presents a microstrip antenna array operating in 5 GHz frequency as an alternative solution to be used in rotorcraft downlink missions. The proposed antenna array, composed by four antennas, was manufactured using low profile printed one and a coaxial feed system. The analysis comprised theoretical calculations, simulations, and a validation through test measurements on a representative vertical tail reduced model, inside a semi anechoic camera. The design results obtained for antenna array constructed on a substrate with relative permittivity $\varepsilon_{r}=2.2$ presented a gain of 7.25 dBi and a better reflection coefficient of -18.5 dB at the design frequency. For a reflection coefficient better than -10 dB a 154.8 MHz bandwidth was obtained.