Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Single-Stage Pedestrian Detector by Asymptotic Localization Fitting and Multi-Scale Context Encoding

Though Faster R-CNN based two-stage detectors have witnessed significant boost in pedestrian detection accuracy, they are still slow for practical applications. One solution is to simplify this working flow as a single-stage detector. However, current single-stage detectors (e.g. SSD) have not presented competitive accuracy on common pedestrian detection benchmarks. Accordingly, a structurally simple but effective module called Asymptotic Localization Fitting (ALF) is proposed, which stacks a series of predictors to directly evolve the default anchor boxes of SSD step by step to improve detection results. Additionally, combining the advantages from residual learning and multi-scale context encoding, a bottleneck block is proposed to enhance the predictors’ discriminative power. On top of the above designs, an efficient single-stage detection architecture is designed, resulting in an attractive pedestrian detector in both accuracy and speed. A comprehensive set of experiments on two of the largest pedestrian detection datasets (i.e. CityPersons and Caltech) demonstrate the superiority of the proposed method, comparing to the state of the arts on both the benchmarks.
Identifying Trajectories and Predictors of Response to Psychotherapy for Post-Traumatic Stress Disorder in Adults: A Systematic Review of Literature

Objective: There exists considerable individual variability in the development and progression of pathological stress reactions after experiencing trauma, as well as in individuals’ response to psychological interventions. Yet until recently, such individual differences had not been considered when evaluating the efficacy of therapeutic interventions for post-traumatic stress disorder (PTSD). This systematic review aims to examine the emerging literature on this subject and, specifically, to identify trajectories and predictors of psychotherapeutic response in adults with PTSD. Method: Four databases were searched using specific keywords without date or language restrictions. For each study, independent reviewers systematically evaluated whether it met eligibility criteria and assessed risk of bias. For included studies, reviewers completed data extraction using standard formats. Those examining how subgroups of adults respond to therapy for clinical PTSD using trajectory modeling were deemed eligible. Demographic, PTSD, clinical, and trauma-related factors associated to particular trajectories were also examined. Results: Of the 1,727 papers identified, 11 were included in this analysis. Of these studies, six focused on military-related traumas and five on civilian ones. Although studies found between two and five trajectories, most supported a three-trajectory model of response categorized as responders, nonresponders, and subclinical participants. Over 22 predictors of treatment trajectories were examined. Comorbid depression, anxiety, and alcohol abuse were the strongest predictors of poor therapeutic response. Age, combat exposure, social support, and hyperarousal were moderate predictors. Conclusion: This review provides valuable insight into the treatment of PTSD, as it supports the heterogeneous trajectories of psychotherapeutic responses and provides avenues for the development of interventions that consider individual-level factors in treatment response.
Gene dysregulation in peripheral blood of moyamoya disease and comparison with other vascular disorders

Objective Moyamoya disease (MMD) is a chronic occlusive cerebrovascular disease with unknown etiology, sharing many similar clinical symptoms with other vascular disorders. This study aimed to investigate gene dysregulation in peripheral blood of MMD and compare it with other vascular disorders. Methods Transcriptomic profiles of 12 MMD patients and 8 healthy controls were obtained using RNA sequencing. Differentially expressed genes (DEGs) were identified and several were validated by quantitative real-time PCR in independent samples. Biological pathway enrichment analysis of DEGs and deconvolution of leukocyte subsets in peripheral blood were performed. Expression profiles for other vascular diseases were downloaded from public database and consistent DEGs were calculated. Gene set enrichment analysis (GSEA) was conducted to compare gene dysregulation pattern between MMD and other vascular diseases. Results A total of 533 DEGs were identified for MMD. Up-regulated genes were mainly involved in extracellular matrix (ECM) organization, whereas down-regulated genes were primarily associated with inflammatory and immune responses. As for cell populations, significantly increased naïve B cells and naïve CD4 cells as well as obviously decreased resting natural killer cells were observed in peripheral blood of MMD patients. GSEA analysis indicated that only up-regulated genes of ischemic stroke and down-regulated genes of coronary artery disease and myocardial infarction were enriched in up-regulated and down-regulated genes of MMD, respectively. Conclusion Dysregulated genes in peripheral blood of MMD mainly played key roles in ECM organization, inflammatory and immune responses. This gene dysregulation pattern was specific compared with other vascular diseases. Besides, naïve B cells, naïve CD4 cells and resting natural killer cells were aberrantly disrupted in peripheral blood of MMD patients. These results will help elucidate the complicated pathogenic mechanism of MMD.
An Update on African Swine Fever Virology

Animal diseases constitute a continuing threat to animal health, food safety, national economy, and the environment. Among those, African swine fever (ASF) is one of the most devastating viruses affecting pigs and wild suids due to the lack of vaccine or effective treatment. ASF is endemic in countries in sub-Saharan Africa, but since its introduction to the Caucasus region in 2007, a highly virulent strain of ASF virus (ASFV) has continued to circulate and spread into Eastern Europe and Russia, and most recently into Western Europe, China, and various countries of Southeast Asia. Given the importance of this disease, this review will highlight recent discoveries in basic virology with special focus on proteomic analysis, replication cycle, and some recent data on genes involved in cycle progression and viral–host interactions, such as I215L (E2 ubiquitin-conjugating enzyme), EP402R (CD2v), A104R (histone-like protein), QP509L, and Q706L (RNA helicases) or P1192R (Topoisomerase II). Taking into consideration the large DNA genome of ASFV and its complex interactions with the host, more studies and new approaches are to be taken to understand the basic virus–host interaction for ASFV. Proteomic studies are just paving the way for future research.
Examining parent use of specific intervention techniques during a 12-week training program based on the Early Start Denver Model

Contemporary parent-implemented early intervention programs for children with autism spectrum disorder usually incorporate a range of techniques with different theoretical underpinnings. While research suggests that parents often learn to implement interventions with an acceptable degree of overall fidelity, there is limited research into parent use of individual intervention techniques. This study included five mothers of young children with autism spectrum disorder who participated in a 12-week parent training program based on the Early Start Denver Model. Ten-minute play samples were coded for the mothers’ use of 18 specific Early Start Denver Model techniques before, during, and 1 month after the training program. The correlation between the mothers’ use of each Early Start Denver Model technique and their child’s level of engagement and expressive language was also calculated. Results suggest that all mothers increased the number of techniques that they were using from baseline to parent training. Some Early Start Denver Model techniques were moderately or strongly correlated with both child engagement and expressive language. There was considerable variation in outcomes across all mother–child dyads. These preliminary results have implications for how parents are trained/coached to implement interventions for young children with autism spectrum disorder. Lay abstract Parents of young children with autism are often taught to deliver interventions which involve several different types of strategies. Research suggests that parents can usually learn to deliver these interventions but not much is known about their use of each specific intervention strategy. This study included five mothers of young children with autism who participated in a 12-week parent training program based on the Early Start Denver Model. We measured their use of 18 different ESDM strategies before, during, and 1 month after the training program. We found that parents increased the number of strategies that they used during the training program. There were differences between mothers in terms of the ESDM strategies that they used the most during the training. We also found that some of the strategies were more closely related to children’s levels of engagement and language than others. This suggests that parent training should be adapted to suit each parent’s needs.
Drugs used in the treatment of bipolar disorder and their effects on cholesterol biosynthesis – A possible therapeutic mechanism

Abstract Objectives: To understand the therapeutic mechanisms of bipolar disorder (BD) drugs at molecular and cellular levels. Methods: Next generation sequencing was used to determine the transcriptional effects of a combination of four commonly prescribed BD drugs (lithium, valproate, lamotrigine and quetiapine) or vehicle (0.2% DMSO) in NT2-N (human neuronal) cells and rats. Differential expression of genes and pathway analysis were performed using edgeR in R and Gene Set Enrichment Analysis software respectively. Free cholesterol levels and neurite outgrowth were quantified in NT2-N cells following combination and individual BD drug treatments. Results: Pathway analysis showed up-regulation of many elements of the cholesterol biosynthesis pathway in NT2-N cells and oxidative phosphorylation in rat brains. Intracellular cholesterol transport genes were upregulated (NPC1, NPC2 and APOE), while the cholesterol efflux gene (ABCA1) was downregulated. BD drug combination tended to increase intracellular cholesterol levels and neurite outgrowth, but these effects were not seen for the drugs when used individually. Conclusions: These data suggest that BD drug combination is increasing cholesterol biosynthesis and the newly synthesised cholesterol is being utilised within the cells, possibly for synthesis of new membranes to facilitate neurite outgrowth. This mechanism possibly underpins clinical efficacy in individuals with BD treated with polypharmacy.
XIST-Promoter Demethylation as Tissue Biomarker for Testicular Germ Cell Tumors and Spermatogenesis Quality

Background: The event of X chromosome inactivation induced by XIST, which is physiologically observed in females, is retained in testicular germ cell tumors (TGCTs), as a result of a supernumerary X chromosome constitution. X chromosome inactivation also occurs in male germline, specifically during spermatogenesis. We aimed to analyze the promoter methylation status of XIST in a series of TGCT tissues, representative cell lines, and testicular parenchyma. Methods: Two independent cohorts were included, comprising a total of 413 TGCT samples, four (T)GCT cell lines, and 86 testicular parenchyma samples. The relative amount of methylated and demethylated XIST promoter fragments was assessed by quantitative methylation-specific PCR (qMSP) and more sensitive high-resolution melting (HRM) methylation analyses. Results: Seminomas showed a lower amount of methylated XIST fragments as compared to non-seminomas or normal testis (p < 0.0001), allowing for a good discrimination among these groups (area under the curve 0.83 and 0.81, respectively). Seminomas showed a significantly higher content of demethylated XIST as compared to non-seminomas. The percentage of demethylated XIST fragment in cell lines reflected their chromosomal constitution (number of extra X chromosomes). A novel and strong positive correlation between the Johnsen’s score and XIST demethylation was identified (r = 0.75, p < 0.0001). Conclusions: The X chromosome inactivation event and demethylated XIST promoter are promising biomarkers for TGCTs and for assessing spermatogenesis quality.
A Robust Multi-Subject fMRI Analysis Method Using Dimensional Optimization

In blind source separation (BSS) for multisubject functional magnetic resonance imaging (fMRI) data, dimensionality reduction is generally performed for multiple times. This leads to the challenge of determining the number of the retained dimensionality, i.e. the order of BSS models, which dramatically influences the validity and performance of BSS models. In this study, a multisubject analysis method robust to order selection is developed. This approach remains effective for slight dimensionality reduction and thus utilizes more information from original data. Inspired by the idea of signal-intensity-maximization technology, which can suppress the overfitting that occurs during insufficient dimensionality reduction, we rotate the reduced dimensions to the optimized direction so that rotated components have the most significant intensity and smoothness. Because the optimized dimensions contain more useful information, involving dimensional optimization stage can reduce the negative impact of dimensionality reduction in multisubject data analysis. The experiments on simulated data and real fMRI data showed that involving dimensional optimization improves the validity and performance of the BSS model in analyzing multisubject data. The proposed method works better across a wide range of dimensionality reduction levels, allows inaccurate order selection, maintains more useful information, and is suitable for multisubject fMRI analysis, which requires multiple dimensionality reduction.
Design and Evaluation of a Low-Cost Passive Radar Receiver Based on IoT Hardware

Recent years saw an increase in computation power on Internet of things devices such as the Raspberry Pi. It is now common for such platforms to boast multiple CPU-cores with clock rates of 1 gigahertz and higher. We have taken this evolution as a motivator to see how far we can push the limit in performing complex operations on a large amount of data by implementing a passive radar system on the Raspberry Pi. To keep the costs of our system further down, we evaluated the use of low-cost RTL-SDR receivers. Our work shows that today's IoT devices allow real-time processing for passive radar applications for both, FM and DAB signals. With our low-cost receiver, we were able to receive echos of aircraft several kilometers away.
Deep Learning Approach to LPI Radar Recognition

In this study, an advanced automatic low probability of intercept (LPI) radar recognition technique (LWRT) that includes both LPI radar signal classification and parameter extraction is proposed. It is shown with Monte Carlo simulation that, even without the unrealistic assumptions used in the previous studies, the proposed LWRT achieves classification performance similar to that of the state-of-the-art LWRT for pulse wave (PW) LPI radar waveforms. And by the combination of the ‘single shot multi-box detector’ (SSD) or ‘you only look once version 3’ (YOLOv3) and a supplementary classifier, the proposed LWRT achieves an extraordinary classification performance for continuous (CW) LPI radar waveforms for all the twelve modulation schemes considered in the literature (i.e., BPSK, Costas, LFM, Frank, P1, P2, P3, P4, T1, T2, T3, and T4). Moreover, the proposed LWRT summarizes the existing and proposed new parameter extraction functions, which can help to design the countermeasure in electronic warfare.
Vision based Studies of Electrohydrodynamic Ejection Process

Sample deposition based on micro-droplet ejection has broad application prospects in the field of biomedicine. Ejection of RPMI-1640 culture medium (without cells) is investigated experimentally using a home-build electrohydrodynamic (EHD) ejection system, consisting of a liquid supplier and a nozzle, a voltage source, a droplet collector, and a supplemented photoelectric monitoring module. Electric voltage is applied between the nozzle and the droplet collector. The liquid surface is electrically charged and the ejection takes place when electric force overcomes the surface tension. At appropriate voltages, uniform ejection states are established with ejection frequency ranging from a few to a few tens of Hertz. Monitoring of the ejection process is performed by two methods. The ejection process can be recorded by a high speed camera, and then analysed by image processing. Using output signal of a charge amplifying circuit as a reference, a single shot camera is controlled to capture images at certain delays, and the entire process of the droplet ejection can be recorded. Experiments have shown that when the liquid meniscus breaks up, the output signal of the charge amplifier reaches the maximum.
On the Capacity of Full-Duplex Diamond Relay Networks Using NOMA

A full-duplex diamond relay network using non-orthogonal multiple access (NOMA) is proposed in this paper wherein a BS communicates with a user through two dedicated decode-and-forward relays. Based on the channel conditions of relays, the BS transmits a superposed NOMA signal by following the downlink NOMA principle. After decoding intended symbols, both relays concurrently transmit their symbols to the user with a slight processing delay by exploiting the uplink NOMA principle. Considering the practical scenario of imperfect interference cancellations, the ergodic sum capacity and ergodic capacities of the proposed system are analyzed over Rayleigh flat-fading channels. Monte-Carlo simulations are provided to substantiate the analysis. The outcomes show the usefulness of the proposed protocol over the corresponding counterparts half-duplex NOMA and half-duplex orthogonal multiple access protocols.
Quality of Service (QoS) Comparison Analysis of Snort IDS and Bro IDS Application in Software Define Network (SDN) Architecture

Intrusion Detection system (IDS) was an application which was aimed to monitor network activity or system and it could find if there was a dangerous operation. Implementation of IDS on Software Define Network architecture (SDN) has drawbacks. IDS on SDN architecture might decreasing network Quality of Service (QoS). So the network could not provide services to the existing network traffic. Throughput, delay and packet loss were important parameters of QoS measurement. Snort IDS and bro IDS were tools in the application of IDS on the network. Both had differences, one of which was found in the detection method. Snort IDS used a signature based detection method while bro IDS used an anomaly based detection method. The difference between them had effects in handling the network traffic through it. In this research, we compared both tools. This comparison are done with testing parameters such as throughput, delay, packet loss, CPU usage, and memory usage. From this test, it was found that bro outperform snort IDS for throughput, delay , and packet loss parameters. However, CPU usage and memory usage on bro requires higher resource than snort.
Price Prediction of Chili in Bandung Regency Using Support Vector Machine (SVM) Optimized with an Adaptive Neuro-Fuzzy Inference System (ANFIS)

The price fluctuation of chili is one of the economic problems faced by every chili-producing country in the world, including in Indonesia. Chili is a vegetable that is consumed almost every day by the people of Indonesia. In Bandung district area, chili has been experiencing price fluctuations in the last four years, according to data obtained from the Bandung Regency Area Trade and Industry Service. Many factors cause the price of chili to fluctuate—one of them being the weather. This is because chili is a plant that is easily damaged if exposed to too much water. This research predicts chili prices in Bandung Regency using the Support Vector Machine (SVM) algorithm, which is optimized by an Adaptive Neuro-Fuzzy Inference System (ANFIS) and based on weather factors. The average accuracy of training and testing data was 94.07%; the training and testing data using the SVM algorithm produced 89.90% average accuracy and the average accuracy of training and testing data using the ANFIS algorithm was 92.68%.
A Motion Intent Recognition Method for Lower Limbs Based on CNN-RF Combined Model

Motion intent recognition as a key technology to help wearers control the exoskeleton of lower extremities has received extensive attention in recent years. The surface electromyography (sEMG) signal of the lower limbs is the most commonly used identification signal source. The traditional recognition method is to extract the feature manually and then use the machine learning method to train the model. The recognition accuracy depends on the prior knowledge, and the manual extraction feature is more troublesome. This paper uses a CNN-RF combined model to recognize five movements (stand, sit, walk, up and down stairs). Convolutional neural network (CNN) has autonomous learning ability automatically extracts features and can combines with traditional random forest (RF) model for training. Firstly, real-time experimental data was extracted by four-channel sEMG sensor and gyroscope, then the convolutional neural network automatically extracted features, finally the feature vector was fed to the random forest model for training. The experiment achieved a high accuracy on the test set and the training speed also meets real-time requirements, which proved the superiority of the method.
Micro-Doppler and Micro-Range Detection via Doppler-resilient 802.11ad-Based Vehicle-to-Pedestrian Radar

We present a vehicle-to-pedestrian (V2P) link based on IEEE 802.11ad protocol for enhanced micro-Doppler and micro-range detection of targets such as pedestrians and cars. The 802.11ad preamble encapsulates complementary Golay sequences whose perfect autocorrelation property has been exploited recently for target localization in joint radar-communications systems. However, this property is perturbed by non-zero Doppler phase shifts introduced by moving targets. In this work, we propose embedding the 802.11ad packets with the Doppler-resilient waveforms based on the Prouhet-Thue-Morse sequence to improve the V2P target detection and recognition. We use analytical and animation models to generate the range-Doppler, range-time and Doppler-time radar signatures and compare them for standard and Doppler-resilient 802.11ad waveforms. Our numerical experiments for a pedestrian and a car show very detailed features with 20 dB improvement in sidelobe levels for a Doppler-tolerant link when compared with a standard 802.11ad protocol.