Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Organized Structuring of Recurrent Neuronal Networks for Reliable Information Transmission

Simple Summary Information processing in the brain takes places at multiple stages, each of which is a local network of neurons. The long-range connections between these network stages are sparse and do not change over time. Thus, within each stage information arrives at a sparse subset of input neurons and must be routed to a sparse subset of output neurons. In this theoretical work, we investigate how networks achieve this routing in a self-organized manner without losing information. We show that biologically inspired self-organization entails that input information is distributed to all neurons in the network by strengthening many synapses in the local networks. Thus, after successful self-organization, input information can be read out and decoded from a small number of outputs. We also show that this way of self-organization can still be more energy efficient than creating more long-range in- and output connections. Abstract Our brains process information using a layered hierarchical network architecture, with abundant connections within each layer and sparse long-range connections between layers. As these long-range connections are mostly unchanged after development, each layer has to locally self-organize in response to new inputs to enable information routing between the sparse in- and output connections. Here we demonstrate that this can be achieved by a well-established model of cortical self-organization based on a well-orchestrated interplay between several plasticity processes. After this self-organization, stimuli conveyed by sparse inputs can be rapidly read out from a layer using only very few long-range connections. To achieve this information routing, the neurons that are stimulated form feed-forward projections into the unstimulated parts of the same layer and get more neurons to represent the stimulus. Hereby, the plasticity processes ensure that each neuron only receives projections from and responds to only one stimulus such that the network is partitioned into parts with different preferred stimuli. Along this line, we show that the relation between the network activity and connectivity self-organizes into a biologically plausible regime. Finally, we argue how the emerging connectivity may minimize the metabolic cost for maintaining a network structure that rapidly transmits stimulus information despite sparse input and output connectivity.
Using Machine Learning to Map Western Australian Landscapes

Landscapes evolve due to climatic conditions, tectonic activity, geological features, biological activity, and sedimentary dynamics. These processes link geological processes at depth to surface features. Consequently, the study of landscapes can reveal essential information about the geochemical footprint of ore deposits at depth. Advances in satellite imaging and computing power have enabled the creation of large geospatial datasets, the sheer size of which necessitates automated processing. This study describes a methodology to enable the automated mapping of landscape pattern domains using machine learning (ML) algorithms. From a freely available Digital Elevation Model, derived data, and sample landclass boundaries provided by domain experts, our algorithm produces a dense map of the model region in Western Australia. Both random forest and support vector machine classification achieve about 98% classification accuracy with reasonable runtime of 48 minutes on a single core. We discuss computational resources and study the effect of grid resolution. Larger tiles result in a more contiguous map, while smaller tiles result in a more detailed, and at some point, noisy map. Diversity and distribution of landscapes mapped in this study support previous results. In addition, our results are consistent with the geological trends and main basement features in the region.
Input Image Adaption for Robust Direct SLAM using Deep Learning

Direct SLAM methods have drawn much attention in the recent years since they have achieved exceptional performance on visual odometry tasks. However, they are prone to suffer from lighting or weather changes. To overcome this, we employ an adapted U-Net that translates the colors of regular images into a high-dimensional feature space. The network is trained to be insensitive to lighting effects as a Siamese U-Net, using labels that are automatically generated from synthetic datasets, without any human intervention. To generate more consistent high-dimensional feature maps, we propose the Cross Triplet Loss utilizing cross information in two images under different domains, and a new sampling method which can generate a wider range of samples by adding weights while sampling. Experiments on different weather and sequences with different textures show that the proposed method outperforms classical feature extraction methods and state-of-art deep learned feature extraction methods.
Impact of Personality Types and Matching Messaging on Password Strength

People often create passwords for their accounts that are insecure. An insecure password is often easy to guess – thus, hackers can easily access their victims’ accounts. It is important for users to know how to create and manage secure passwords so they can better protect themselves from hackers. It is well-known that di ﬀ erent users have di ﬀ erent personality types, such as Big Five and True Colors. This research examines if there is any link between personality types and password security behavior. Each participant was shown either a matching or mismatching message based on their personality type, and it was measured whether their password security behavior changed a month later. Our results show that 66% of participants with a Green (knowledgeable and competent) personality type chose a strong password, compared to less than 50% of other personality types. Our results also demonstrate that messaging has a statistical impact on improving password security behavior.
The Theoretical Breakthrough of Self-Supervised Learning : Variational Autoencoders and Its Application In Big Data Analysis

In the study of big data statistics, a large number of unknown latent variables bring great difficulties to modeling. Variational autoencoders(VAE) can overcome the shortcomings of traditional variational methods such as low efficiency and poor generality, and provide an efficient and extensible framework for variational posteriori inference and approximate maximum likelihood learning based on gradient. On the basis of reviewing the development history of variational autoencoders and taking the deep latent variable model(DLVM) as an example, this paper introduces the basic principle of variational autoencoders and analyzes its application under the background of big data. The problems in theory and application of variational autoencoders are presented, as well as the topics to be further studied. The combination of variational autoencoders and other statistical modeling methods may become a new idea for big data research.
Research on the Control System and Risk Management Based on Internet Big Data and Cloud Computing

The advancement of Internet information technology and the strong demand for private finance have promoted the vigorous development of Internet financial netw ork lending. However, due to the complexity of the network environment and the volat ility of the financial market, Internet finance has a higher risk. Aiming at the particular ity of Chinese Internet financial big data platform, this article uses Internet lending as a case to screen out 15 financial risk indicators. The article uses the principle of game t heory to set the principal component factor as a variable, and the operation of the platf orm in 2018 as the dependent variable to establish a financial risk early warning syste m for the online lending platform. At the same time, the paper cited cloud computing t o optimize its performance, and designed a financial risk management and control syst em based on cloud computing. Construct system test links to test system performance. With the same amount of data and the same test platform, the computing performance of this system is better than that of the original system.
High-Precision Indoor Robot Dynamic Obstacle Detection with Laser and Camera

Indoor robot is a very common robot, and its main navigation method is laser SLAM. In the process of detecting dynamic obstacles with laser SLAM, the clustering effect is not obvious, the accuracy is low, and the real-time performance is poor. To solve this problem, a sensor fusion method of lidar and monocular camera is proposed, which improves the accuracy of detecting the position of moving obstacles. When processing lidar data, first preprocess the data, then use the BDSCAN algorithm to cluster the point cloud, and finally use the Kalman filter to predict and calculate the location of the moving obstacle. When processing the data of the monocular camera, the YOLOv3 algorithm is used to quickly detect the target and return the position of the monocular camera. After obtaining the camera and laser data, according to the installation position of the radar and camera, map the position calculated by the radar to the coordinates of the camera. If the coordinate difference after mapping is small, it can be considered that the two sensors have detected the same target. The result is returned to the display interface. Using this method, the efficiency and accuracy of indoor robots in detecting moving obstacles have been greatly improved.
Study on Image Classification Algorithm Based on Improved DenseNet

In order to perform fast analysis and recognition of power equipment images. A residual attention mechanism based image classification model is proposed. First, the power equipment dataset is preprocessed and the power equipment dataset is randomly divided into a training set and a test set. Based on the improved DenseNet, the image features of the training set are extracted. The training set images are used for training, and the classification test is implemented on the test set. The experimental results show that the accuracy of the improved DenseNet algorithm is improved by 8.89%. It can be seen that the classification accuracy of the improved DenseNet algorithm is effectively improved.
Research on Information Fusion Technology of MEMS Gyro Array

The measurement accuracy of MEMS gyroscope is relatively low. Starting from the software level, the multi-sensor information fusion technology of MEMS gyroscope array (MGA) is used to reduce the drift of MEMS gyroscope. Firstly, a signal acquisition and processing system for communicating with ADXRS810 gyroscope based on field-programmable gate array (FPGA) is designed. Secondly, the collected drift data of the gyroscope array is preprocessed. long-term drift trend terms are obtained by ensemble empirical mode decomposition (EEMD) and the linear function fitting, and the trend terms are filtered out to obtain a smooth and normal drift signal. Then, the data fusion model based on time series analysis is built for the gyroscope array and the error analysis is performed by Allan variance. Finally, based on the AR (1) model, the moving horizon optimization Kalman filter method is used to filter the drift data of the gyroscope array. The experiment shows that when the time domain length N=3, the bias instability of gyroscope 1, 2, 3 and 4 decreases from 9.716 o/h, 8.5682 o/h, 13.484 o/h and 26.414 o/h to 1.2922 o/h, 0.61147 o/h, 1.4184 o/h and 1.6964 o/h, respectively, and the average noise coefficient decreases by more than 85%. Compared with the ordinary Kalman filter method, the bias instability has been significantly improved.
Survey of Target Detection Based on Neural Network

Target detection is a hot topic in the field of artificial intelligence, which is widely used in robot, UAV, aerospace and other fields. In this paper, the research background and significance of target detection are summarized, and two categories of target detection algorithms based on deep learning, i.e., candidate region based and regression based, are described. For the first category, a series of region with convolutional neural network (r-cnn) algorithms are introduced, this paper introduces the researchers’ research on the basis of r-cnn algorithm: the improvement of feature extraction network, pooling layer of region of interest and non-maximum suppression algorithm. The second algorithm is divided into Yolo (you only look once) series, SSD (single shot multibox detector) algorithm and its improvement. According to the current target detection algorithm in the development of more efficient and reasonable development trend, the research hotspot of target detection algorithm in the future is prospected, including unsupervised and unknown class object detection.
A Study on Recruitment of Data Analyst Based on Text Mining and Visualization Technology

Nowadays, the demand for data analysts has increased dramatically. First the job requirements of data analyst are downloaded from online recruitment website ChinaHR.com and zhaopin.com, then the information of the recruitments are analyzed by text mining method such as word segmentation, word frequency statistics and visualization technology. At last we summarized the required skills for data analyst. We found that compared with Python, the company is inclined to use R for data analysis, the recruitment requirements of every type of company are different. Our findings can provide reference for schools and guide individual professionals to assess and advance their competencies.
Semicontact AFM Mode for Fast Determining the Subsurface Structure of Filled Elastomers

Using atomic force microscopy in the semicontact AFM mode, we examined the surface of the filled elastomer obtained by the rupture method. A feature of the material is that it consists of a soft binder and hard nanofiller particles. Filler particles are usually hidden by a binder layer. In our work, we have shown that the information on the phase shift obtained during scanning makes it possible to look into the subsurface layer and obtain more information about the geometry of the filler particles and their location in the nanocomposite. It is possible to make visible the fragments of particles immersed in the binder, which are almost invisible on the surface relief. This does not require the use of special modes of the atomic force microscope for analysis. It is enough to use the reliable and fast scanning method in semicontact mode.
A cross-domain authorization access control model based on security label attribute

This paper proposes a minimum diffusion and cross-domain security authorization strategy based on security label, which solves the security problems of minimum visible range and cross-domain authorization in access control mechanism. The strategy makes full use of security label attributes, introduces multi-level constraint mechanism in access control, and makes the scope of knowledge controllable in cross-domain authorization. Through the experimental validation of different security domain scenarios, compared with the traditional access control mechanism, the diffusion speed of sensitive files in the access control based on security label attributes is significantly reduced. If the number of security labels is set as Nlabel , and each label has Nscale values, the file diffusion speed of the network is only 1ΠNscale times that of the linear diffusion speed.
WGCNA revisited: Module indetification

WGCNA is a very popular R language software package used in biomedical field. It is mainly designed to deal with gene microarray samples to find clusters (modules), which include highly correlated genes, in biomedical studies. Theoretically, it results in the hierarchical modules, which are different from the results based on the edge density inside module and outside modules. In this study, we address this difference and indicate that caution should be exercised when using these two methods to interpret the implication of studies.
Research on High Precision of Projection Exposure Imaging of Large-Scale Integrated Circuit Lithography Machine

The precision and work efficiency of the distributed repeat projection lithography machine (DSW) directly affect the special requirements of the large-scale integrated circuit production process. In order to improve the exposure and imaging accuracy of the large-scale integrated circuit lithography machine projection, the paper proposes the lithography machine projection object image difference in-situ detection (AMF) technology based on particle swarm optimization. The article analyzes the basic principles of this technology using special test marks to detect spherical aberration, astigmatism, and coma of the projection objective, and discusses the method of using the alignment position coordinates to calculate the imaging position shift caused by aberration. The experimental results show that the AMF technology can achieve accurate measurement of aberration parameters such as spherical aberration, coma, and astigmatism.
A Research of Vespa Mandarinia through Visualization Technology and Convolution Neural Network

After being discovered in September 2019, Vespa mandarinia, a fierce alien invader, quickly got public’s attention. The inhabitants of Washington State flooded to report the suspected cases around them. However, most of them were proved to be false information. How to interpret the provided data and determine the priority of dealing with the problem is of vital importance. Based on this situation, this paper develops a model that robustly and accurately predicts the spread of Vespa mandarinia and the facticity of provided reports. By adopting the idea of mining common behavior patterns, this paper took full use of other wasps’ data to make up for our lacking data on Vespa mandarinia. Though few samples of Vespa mandarinia been provided, this paper took complete utilizing of other data. To evaluate our external model, this paper gave 2019 data of the Vespa mandarinia to propagate then the prediction of 2020 turned to be perfectly accurate. After this, this paper combined the image, location and time information together to predict the sample. This paper got 87.6% accuracy on positive samples and 98.7% accuracy on negative samples. The calculated renewal frequency was proved to be fit for Vespa madarinia’s actual living habits. And the MSE of our enhanced Logistic function was 10–4, which can simulate the growth of Vespa mandarinia well and also taken human eradication into consideration.
Research on the Statistical Model System of Each Balanced Game Based on Big Data

The article uses the holistic, comprehensive and dynamic diversified and complex thinking mode of big data system theory. The article creates a generalized linear model for the value of each technical and tactical performance index and the game result in each score-balanced game. To define the linear relationship between the technical and tactical performance indicators of the game and the probability of winning the game; finally, adopt the data series inference method to define the significance of the linear relationship between each technical and tactical performance indicator and the probability of winning the game. The research found that the information provided by the model created in the paper can be used in football practice such as game performance evaluation, opponent information detection, selection and modification of training preparation plans.
MLU-Net: Efficient Segmentation for Retinal Layers In Optical Coherence Tomography Images

The automatic segmentation of retinal images obtained by optical coherence tomography is increasingly important for ophthalmologists to diagnose and monitor many kinds of ophthalmic diseases. U-Net is the most widely used deep learning network in retinal segmentation, but the limited number of data-flow paths made it hard to capture complex features. We proposed here an optimized Mobile Ladder U-Net (MLU-Net), which consists of a Ladder Connection for increasing the network’s data-flow paths and a depthwise separable convolution for reducing the model’s parameters. Experiments on 100 B-scans from 10 human eyes demonstrated that the 9 retinal layer boundaries can be segmented accurately with the MLU-Net. In addition, compared with the original U-Net and LadderNet, the segmentation result of our method is closest to the expert label.
Link Prediction Algorithm Based on Node Structure Similarity Measured by Relative Entropy

To solve the problem that the link prediction method based on local information ignores the influence of neighbor structure information on the similarity measurement of nodes, a link prediction method based on relative entropy and local structure of nodes is proposed. Firstly, the second-order local network is introduced to describe the local structure of nodes; then, the structural similarity between nodes is described by redefining the relative entropy; finally, the structural similarity of nodes is measured based on relative entropy, and the structural similarity index of the node structure is proposed considering the structure information of the neighbor. Experimental results on 7 real network data sets show that the proposed method can achieve better results and can be applied to networks with a small average aggregation coefficient compared with other similarity indexes based on local and global information, and also have better performance on large-scale networks.
Training framework based on multi model competition for deep reinforcement learning

In this paper, we propose a simple, effective and universal deep reinforcement learning training framework, inspired by A3C algorithm and genetic algorithm. The framework uses multi-process technology to realize multiple model competition and optimal evolution to optimize the deep neural network during the training process. The experimental results show that the proposed training framework can improve the training effect of reinforcement learning algorithms to a certain extent.
Predicting the Unconfined Compressive Strength of Rubber Fiber Modified Cemented Paste Backfill Using Support Vector Machine

Machine learning based on the support vector machine algorithm was used for the prediction of the unconfined compressive strength of rubber fiber modified cemented paste backfill. Grid analysis was used to find the optimal hyper-parameters. The SVM model was well trained on the training set, and performed well on the testing set. Analysis results showed that the SVM model achieved a higher accuracy. The squared correlation coefficient values were 0.9914 and 0.9321 on the training set and testing set, respectively. It is recommended that laboratories around the world could share UCS tests results of CPB to expand the dataset for the training of prediction models.
Application of Electroimpedance Spirometry for Clinical Assessment of Respiratory Function Indices

A practical method of studying the dynamics of respiratory function associated with the registration of the electrical impedance of conductive biological fluids exhaled by the patient is described. In the model measuring cell, the physical causes and relationships in the conductivity of the air flow, saturated with an electrolytic fluid aerosol created by an ultrasonic nebulizer have been investigated experimentally. The design of a new electrical impedance spirometer in the form of a mouthpiece with electrical sensors, filled with a porous medium impregnated with electrolyte, through which the patient breathes, is developed and tested. To register fluctuations in the magnitude of the impedance modulus, an original hardware and software complex is used, made on the basis of a high-speed analog-to-digital converter. Based on electrical impedance analysis data, the methods are proposed for quantitative assessment of the basic spirometric medical indicators characterizing the state and functioning of the patient’s breathing apparatus, such as respiration rate, entry and expiration rate, exhaled air volume, amount and concentration of exhaled lung fluid.