Miracle-dz/UniFAR-FTUs huggingface dataset:
abnormalities of localized connectivity in schizophrenia patients and their unaffected relatives: a meta-analysis of resting-state functional magnetic resonance imaging studies

Objective: The localized dysfunction of specialized brain regions in schizophrenia patients and their unaffected relatives has been identified in a large-scale brain network; however, evidence is inconsistent. We aimed to identify abnormalities in the localized connectivity in schizophrenia patients and their relatives by conducting a meta-analysis of regional homogeneity (ReHo) studies. Methods: Fourteen studies on resting-state functional magnetic resonance imaging, with 316 schizophrenia patients, 342 healthy controls, and 66 unaffected relatives, were included in the meta-analysis. This analysis was performed using anisotropic effect-size-based signed differential mapping software. Results: Schizophrenia patients showed increased ReHo in right superior frontal gyrus and right superior temporal gyrus, as well as decreased ReHo in left fusiform gyrus, left superior temporal gyrus, left postcentral gyrus, and right precentral gyrus. Unaffected relatives showed decreased ReHo in right insula and right superior temporal gyrus. These results remained widely unchanged in both sensitivity and subgroup analyses. Conclusion: Schizophrenia patients and their unaffected relatives had extensive abnormal localized connectivity in cerebrum, especially in superior temporal gyrus, which were the potential diagnostic markers and expounded the pathophysiological hypothesis for the disorder.
Research on Eye Movement Classification Based on the Deep Learning Model

To improve the recognition accuracy of eye movement direction based on EOGM, the Emotiv Epoc, and EEG acquisition headset was used to collect the EEG signals of the subjects in real time. After the 14-channel signals were filtered by wavelet, the short-time energy calculation method was used to select the two channels most sensitive to eye movement. The long and short-term memory network (LSTM) was then used to classify the eye movement signals. On the same data set, the experimental results show that the classification rates in the upper, lower, left, and right directions are up to 91%, 90%, 83%, and 75%, respectively, and the average classification rate is up to 84%. Compared with the existing classification methods, the classification accuracy of the proposed method is higher and the implementation process of the classification algorithm is simpler, which further verifies the feasibility and effectiveness of using EEG to identify the direction of eye movement.
3D Human Pose Estimation Based on Transformer

Currently, 3D human pose estimation has gradually been a well-liked subject. Although various models based on the deep neural network have produced an excellent performance, they still suffer from the ignorance of multiple feasible pose solutions and the problem of the relatively-fixed input length. To solve these issues, a coordinate transformer encoder based on a 2D pose is constructed to generate multiple feasible pose solutions, and multi-to-one pose mapping is employed to generate a reliable pose. A temporal transformer encoder is used to exploit the temporal dependencies of consecutive pose sequences, which avoids the issue of relatively-fixed input length caused by temporal dilated convolution. Adequate experiments indicate that our model achieves a promising performance.
Daily flow profiles of urban traffic

Estimation of average annual daily traffic (AADT) on the basis of short-term traffic counts provides some insight into seasonal and day-to-day variability of urban traffic flows. However, insights into the differences in daily flow profiles between weekdays are limited. In this paper, daily flow profiles on a main route in a city in the Netherlands are analyzed. The total amount of traffic as the shapes of the daily profiles are also compared for various weekdays. The differences are examined both qualitatively and quantitatively using ANOVA analysis. Findings, indicating that the daily flow profile varies throughout the week, are presented.
Natural scene text detection algorithm based on the regional proposal

Text detection in natural scenes is widely used in various fields. The combination of deep learning models and text detection improves the accuracy and efficiency of detection. Its algorithm is also endless, with continuous optimization and improvement. On this basis, some algorithms for text detection in natural scenes are summarized from the two aspects of the Faster R-CNN method and SSD method based on regional suggestion. These algorithms and experimental data are analyzed and compared to further summarize the challenges faced by text detection in natural scenes and the direction of improvement.
Water-quality assessment of the eastern Iowa basins- nitrogen, phosphorus, suspended sediment, and organic carbon in surface water, 1996-98

1 Water-Quality Assessment of the Eastern Iowa Basins—Nitrogen, Phosphorus, Suspended Sediment, and Organic Carbon in Surface Water, 1996–98 By Kent D. Becher, Stephen J. Kalkhoff, Douglas J. Schnoebelen, Kimberlee K. Barnes, and Von E. Miller
MIXO: Mixture Of Experts-Based Visual Odometry for Multicamera Autonomous Systems

Visual-inertial odometry is a fundamental technology exploited by autonomous vehicles and mobile robots to determine their position in unknown environments. Indeed, many techniques have been proposed for monocular and stereo cameras. State-of-the-art autonomous vehicles, however, are equipped with multiple cameras covering the entire vehicle environment. We present here MIxture of eXperts Odometry (MIXO), a data-driven, machine learning-based technique that loosely combines odometry outputs from multiple cameras to obtain a more accurate and robust global estimate. It stems from the intuition that each camera provides an optimal vantage point in specific driving scenarios. In MIXO, each camera (or expert) is individually processed by a state-of-the-art visual odometry algorithm (e.g., ORB-SLAM2). Then, the odometry estimates are mixed by a gating network, which selects the locally optimal experts in the current operational conditions and weights their contributions accordingly. MIXO is a lightweight module that can be easily implemented on top of any visual odometry algorithm. Experimental results on real-life data from autonomous vehicles show that MIXO achieves more robust and accurate results than any single camera, reducing the absolute rotational and translation error by 38% and 15%, respectively.
Application of Tracking Signals to Detect Time Series Pattern Changes in Crime Mapping Systems

Tracking signals are widely used in industry to monitor inventory and sales demand. These signals automatically and quickly detect departures in product demand, such as step jumps and outliers, from “business-as-usual”. This paper explores the application of tracking signals for use in crime mapping to automatically identify areas that are experiencing changes in crime patterns and thus may need police intervention. Detecting such changes through visual examination of time series plots, while effective, creates too large a work load for crime analysts, easily on the order of 1,000 time series per month for mediumsized cities. We demonstrate the so-called smoothed-error-term tracking signal and carry out an exploratory validation on 10 grid cells for Pittsburgh, Pennsylvania. Underlying the tracking signal is an extrapolative forecast that serves as the counterfactual basis of comparison. The approach to validation is based on the assumption that we wish tracking signal behavior to match decisions made by crime analysts on identifying crime pattern changes. We present tracking signals in the context of crime early warning systems that provide wide area scanning for crime pattern changes and detailed drill-down maps for crime analysis. Based on preliminary results, the tracking signal is a promising tool for crime analysts.
Improved MobileViT: A More Efficient Light-weight Convolution and Vision Transformer Hybrid Model

Convolutional neural networks (CNNs) utilize spatial induction bias to learn local features of images, while vision transformers (ViTs) can learn global representations of visual information through self-attention. However, due to ViT’s large number of parameters, it is challenging to apply them to mobile vision tasks. Based on Conv-ViT light-weight hybrid model MobileViT, this paper proposed the improved backbone network and transformer block to improve the applicability. Firstly, the MV2 block in MobileViT is substituted with an improved depth separable convolution and linear activation function. Secondly, we proposed a novel self-attention to replace the original self-attention of the transformer block in MobileViT, which reduces computational load and enhances global representation by enabling information interaction between different windows. Experimental results demonstrate that the improved model’s accuracy in image classification is 2.2% higher than that of MobileViT on the CIFAR-100 dataset.
Design and Implementation of Handwritten Digit Recognition Accelerator Based on Systolic Array

Due to the rapid development in artificial intelligence, the classic Von Neumann architecture is no longer able to meet the demands of high-computing, high-storage, and high-bandwidth artificial intelligence applications. To address this issue, this paper proposes a handwritten digit recognition accelerator based on a systolic array. First, a 5 convolutional layer handwritten digit recognition network is built. Besides, The FPGA-based convolution accelerator employs im2col technology to convert convolution calculations into matrix multiplications and uses a systolic array to efficiently perform these multiplications. Furthermore, the four convolution processes are pipelined to improve the throughput. Finally, a handwritten digit recognition system is implemented on the PYNQ-Z2. Compared with the software implementation on Arm, the speedup of the accelerator is 598x, the delay is 1.24 s, and the power consumption is 2.51 w.
A hybrid multi-objective optimization method for nuclear essential service water system design

Evolutionary algorithms have proven to be very successful in solving multi-objective optimization problems (MOPs). However, their performance often deteriorates when constraints are introduced. This paper proposes a hybrid heuristic intelligence (HHI) approach to remedy this issue. First, a classification operator is designed to sort individuals by their relative constraint values, which enlarges the size of feasible solutions in the early process. Then, by combining constraints with the non-dominated sorting method, we define a new selection operator to increase the probability of selecting individuals who have low constraint values but high objective values. Finally, we use hybrid optimized operations to simultaneously enhance the convergence and diversity performance by performing different operators in a certain domain. The proposed method achieves state-of-the-art performance on test functions ZDT1, Binh2, and OSY, with a remarkable decrease of 24% in the inverted generational distance (IGD) on OSY. For further applications, our method is also tested with an engineering model established for the design of the nuclear essential service water system (SEC). The final result shows that without taking hundreds of hours, the final solution of HHI has met all constraints and reduced the total cost by 8.9%© YEAR The Authors. Published by Elsevier Ltd.
Phylogeography pattern of Lutjanus kasmira in the Bengal Strait and the Arabian Sea

Lutjanus kasmira is one of the snappers occupying and commonly found in the Arabian Sea and the Bengal Strait. The purpose of this study was to examine the comparative phylogeography pattern of Lutjanus kasmira in the Bengal Strait and the Arabian Sea using the mitochondrial COI gene as a marker. The population sampled of Indonesia was taken from fish landing in Banda Aceh, Indonesia, while the population of India represented Bengal Strait, Oman, Yemen, Djibouti and Somalia respresented Arabian sea was retrieved from the NCBI Genbank. The extraction method was carried out using a modified C-TAB protocol. In this study at total of 47 sequences were analysed that produced 22 haplotypes where 18 haplotypes distributed in the Arabian Sea and 4 haplotypes from Bengal Strait. 10 samples of Oman population produced 6 haplotypes, 10 samples from Djibouti produced 5 haplotypes, 5 haplotypes from the population of Yemen, Somalia 2 haplotypes and India 1 haplotypes, respectively. Meanwhile, the Indonesian population, from 6 samples, produced 3 haplotypes. L. kasmira population from Arabian Sea and Bengal strait had no connectivity. The highest haplotype diversity (Hd) value was found in Oman population of 0.956 and the lowest found in Somalia population, while Indonesia population has a moderate genetic diversity.
Automatic Bridge Detection of SAR Images Based on Interpretable Deep Learning Algorithm

As a typical feature target in SAR images, the accurate detection and localization of bridge targets are of important research value for the management of transportation facilities and battlefield reconnaissance. Since most of the current bridge target detection algorithms use horizontal frames, they cannot achieve accurate localization of bridges. There is a large amount of background information in the data set annotation, which is not conducive to the training of the network. In this paper, an in-depth analysis of the interpretable deep learning algorithm is presented to show that the use of rotating detection frames is more beneficial to the automatic detection of targets with large aspect ratios such as bridges. The algorithm framework first performs feature extraction of bridge targets through the Yolov5 network, and then passes the feature maps containing target information into the proposed regression prediction visualization module to obtain detection result maps and heat maps. In the experiments, the SAR images of the Foshan area are used to verify the results, and the advantages of the rotating detection frame in terms of localization and inspection performance are analysed by comparing the IoU and the heat map.
A flexible omnidirectional rotating magnetic array for MRI-safe transdermal wireless energy harvesting through flexible electronics

Magnetic resonance imaging (MRI)–safe implantable wireless energy harvester offers substantial benefits to patients suffering from brain disorders, hearing impairment, and arrhythmias. However, rigid magnets in cutting-edge systems with limited numbers of rotation axis impose high risk of device dislodgement and magnet failure. Here, a flexible omnidirectional rotating magnetic array (FORMA) and a flexible MRI-safe implantable wireless energy-harvesting system have been developed. Miniaturized flexible magnetic balls 1 millimeter in diameter achieved by molding three-dimensional printed templates can rotate freely in elastomer cavities and supply a magnetic force of 2.14 Newtons at a distance of 1 millimeter between an implantable receiver and a wearable transceiver. The system can work stably under an acceleration of 9g and obtain a power output of 15.62 decibel milliwatts at a transmission frequency of 8 megahertz. The development of the FORMA may lead to life-long flexible and batteryless implantable systems and offers the potential to promote techniques for monitoring and treating acute and chronic diseases.
Identification of common sequence motifs shared exclusively among selectively packed exosomal pathogenic microRNAs during rickettsial infections

We previously reported that microRNA (miR)23a and miR30b are selectively sorted into rickettsia-infected, endothelial cell-derived exosomes (R-ECExos). Yet, the mechanism remains unknown. The number of cases of spotted fever rickettsioses has been increasing in recent years, and infections with these bacteria cause life-threatening diseases by targeting brain and lung tissues. Therefore, the aim of the present study is to continue to dissect the molecular mechanism underlying R-ECExos-induced barrier dysfunction of normal recipient microvascular endothelial cells (MECs), depending on their exosomal RNA cargos. Rickettsiae are transmitted to human hosts by the bite of an infected tick into the skin. In the present study we demonstrate that treatment with R-ECExos, which were derived from spotted fever group R parkeri infected human dermal MECs, induced disruptions of the paracellular adherens junctional protein VE-cadherin and breached the paracellular barrier function in recipient pulmonary MECs (PMECs) in an exosomal RNA-dependent manner. Similarly, we did not detect different levels of miRs in parent dermal MECs following rickettsial infections. However, we demonstrated that the microvasculopathy-relevant miR23a-27a-24 cluster and miR30b are selectively enriched in R-ECExos. Bioinformatic analysis revealed that common sequence motifs are shared exclusively among the exosomal, selectively-enriched miR23a cluster and miR30b at different levels. Taken together, these data warrant further functional identification and characterization of a single, bipartition, or tripartition among ACA, UCA, and CAG motifs that guide recognition of microvasculopathy-relevant miR23a-27a-24 and miR30b, and subsequently results in their selective enrichments in R-ECExos.
Analysis of chlorophyll-a, sea surface temperature, and sea surface salinity in the Bay of Bengal

The waters of the Bay of Bengal (BoB) are influenced by the northeast monsoon (November-February) and the southwest monsoon (June-September). This study aims to determine the relationship between sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll-a (Chl-a) during January 2021 in the BoB. The method used in this study is the method of correlation analysis, hypothesis testing, and analysis of variance using monthly average data for January 2021. The data comes from the Copernicus Marine Environment Monitoring Service (CMEMS) portal. Our analysis showed that high Chl-a was inversely related to low SST and SSS conditions and vice versa. This finding confirms the results of other previous studies. Medium and negative correlations occurred for the Chl-a - SST and Chl-a - SSS pairs. Meanwhile, the SST- SSS pair shows a strong and positive correlation. The hypothesis test shows that the conclusions drawn from these correlation relationships are acceptable. Furthermore, from the analysis of variance obtained, it was found that Chl-a had a significant effect on variations in SST and SSS, while SST had a significant effect on variations in SSS.
Spectral-domain Spatial-temporal Convolution Graph Neural Network for Industrial Fault Diagnosis

In modern industrial production, once a fault occurs in the production process, it will affect the whole production and bring huge economic losses. There are a large number of device sensors in modern production, and there are complex interactions between them. But the existing methods can not efficiently and accurately mine the depth of information from mass production data. We proposed the Spectral-domain Spatial-Temporal Convolution Graph Neural Network model, which comprehensively considers the information of spatial and temporal dimensions in production data. The nodes and edges of the heterogeneous graph represent the sensor signals and their interactions. Each sensor data is converted to the spectral domain to extract the time domain features, and graph convolution is used to carry out the spatial relationship between input sequences. Finally, the proposed model is verified using the Three-Phase Flow Facility (TFF) dataset, and the overall accuracy rate reached 96.28%, showing better performance than other baseline models.
Comparative Study of Furuta Pendulum Based on LQR and PID Control

Furuta pendulum is an underdriven and unstable controlled object with one input and two outputs, and has an open-loop zero point at the origin of the complex plane. In this paper, a nonlinear dynamic model of the Furuta pendulum is established by the Eulerian-Lagrangian method, and a series-connected PID control strategy is designed by using the root locus method and the frequency domain analysis method based on the local linearization model at the steady-state operating point. Besides, an LQR control strategy is designed based on the discrete state-space model. In addition, a comparative analysis of the PID control strategy and the LQR control strategy is conducted, and it is found that the LQR control strategy can achieve stable control of the outer loop in the case of unstable inner loop. Finally, the effectiveness of the two control schemes is verified by real-time control experiments.
A Rotation-Invariance Face Detector Based on RetinaNet

The use of deep convolutional neural networks has greatly improved the performance of general face detection. For detecting rotated faces, the mainstream approach is to use multi-stage detectors to gradually adjust the rotated face to a vertical orientation for detection, which increases the complexity of training as multiple networks are involved. In this study, we propose a new method for rotation-invariant face detection, which abandons the previously used cascaded architecture with multiple stages and instead uses a single-stage detector to achieve end-to-end detection of face classification, face box regression, and facial landmark regression. Extensive experiments on FDDB in multiple orientations have shown the effectiveness of our method. The results demonstrate that our method achieves good detection performance and the detection accuracy of our method even exceeds that of other rotated face detectors on the front-facing FDDB dataset.
Variational Auto-Encoder Reconstruction Networks for Classification of Hyperspectral and LiDAR Data

The classification of remote sensing scene objects has been the subject of extensive studies in recent years due to the quick advancement of earth observation and remote sensing technology. More concentration, Hyperspectral images, and LiDAR data are complementary, and their combined use for data fusion can better mine the multi-dimensional features of ground objects in remote sensing scenes, which can effectively improve the classification accuracy and reliability of ground objects in remote sensing scenes. Single-modal remote sensing data frequently cannot fully meet the needs of ground feature classification due to the increasingly complex types of ground features. In order to solve this problem, we have developed two distinct multi-source fusion classification approaches using LiDAR and hyperspectral data and deep learning techniques. They are the reconstructed multi-layer perceptron network based on the variational auto-Encoder (encoding-decoding form) and the two-stream input convolutional neural network based on the cross-channel reconstruction mechanism. These two approaches can help us find more effective and deeper feature extraction methods and feature fusion methods in this research direction and design training. We adopted the network architecture model used in this area of research and used experimental data to demonstrate the effectiveness and superiority of the suggested network model.