Miracle-dz/UniFAR-FTUs huggingface dataset:
TwoStep Cluster: Análise comparativa do algoritmo e proposta de melhoramento da medida de verossimilhança

In this paper we made comparisons of the algorithm TwoStep Cluster (TSC) with other clustering algorithms for large databases. The comparisons were made using simulated data, whose variations of the parameters were made in according to the Design of Experiments methodology. The results showed that TSC had better accuracy when the clusters had different variances; however it was shown to be slower than the traditional K-means algorithm. In this work we also propose an improvement in the measure of the log-likelihood considered in the algorithm. It allows incorporating information about the correlations between the variables. Resumo. Neste artigo foram feitas comparacoes do algoritmo TwoStep Cluster (TSC) com outros algoritmos de agrupamentos para grandes bases de dados. As comparacoes foram realizadas utilizando dados simulados, cujas variacoes dos parâmetros foram feitas de acordo com a metodologia de Planejamento de Experimentos. Os resultados mostraram que o TSC teve melhor acuracia quando os grupos tinham diferentes variâncias, no entanto se mostrou mais lento que o tradicional algoritmo K-medias. Neste artigo tambem foi proposto um melhoramento a medida de log da verossimilhanca utilizada no TSC, permitindo a incorporacao de informacao sobre as correlacoes entre as variaveis.
Match Selection and Refinement for Highly Accurate Two-View Structure from Motion — Supplementary Material —

This supplementary material further supports and illustrates some of the points mentioned in paper “Match Selection and Refinement for Highly Accurate Two-View Structure from Motion”, published at ECCV 2014. It is organized as follows. – Section 1 discusses possible biases for match selection. It first explains the reason for putting the RANSAC stage as the last step of match selection and not earlier. Second, it shows the impact of using different ranking functions in match selection. In particular, it shows that the distance to the epipolar line is not suitable. – Section 2 displays statistics on the proportion of matches that are selected by our algorithm. On our test dataset, it selects on average 61% of the matches (MS), or 78% if match refinement is applied first (MR+MS). – Section 3 provides some visual illustrations that the improvement of calibration accuracy with our algorithm leads to a reduction of the reconstruction error of 3D points. Please also note the following missing definition at line 182 of the paper: e3D(M) = e3D(M,RM , tM ).
A Review of 5G Technology Application in Digital Smart Grid

In the process of digital transformation of power grids, the 5th generation mobile communication technology (5G) plays a key role in supporting technological innovation in power systems and promoting industry reform due to its characteristics of high bandwidth, high capacity, and low latency. Based on this, this paper introduces the new features of digital smart grid, proposes the new requirements for communication in the digital smart grid, analyzes the application scenario power of 5G communication technology in various aspects of "generation-transmission-transformation-distribution-use" of digital smart grid, and finally presents the challenges and countermeasures for the integration of 5G and power grid.
Object-Specific Rapid Feature Adaption for One-Shot Contour Primitive of Interest Extraction

The one-shot contour primitive of interest (CPI) extraction method is meaningful for flexible machine vision, which can obtain the target CPI on a novel object under the guidance of a support image. The one-shot guidance is based on an object-agnostic feature extractor and the feature comparison in a metric space. Therefore, the discrimination and generalization abilities of the object-agnostic feature extractor are essential to the one-shot CPI extraction. In this paper, we propose an object-specific rapid feature adaption method to enhance a trained CPI extraction network. Three lightweight feature adaption modules are designed to adjust the features specifically for the present object type. A module can be flexibly plugged into a trained network at three different candidate positions, to influence the network’s final outputs. The parameters of the module are learned with the annotated support sample and only tens of optimization steps in several seconds. Thus, although the feature adaption module only works for a specific object, it can be deployed flexibly and rapidly. The effectiveness of the proposed method is validated with a series of experiments.
An Outlier-Robust Kalman Filter Based on the Generalized $t$ Distribution with a Mixed Gaussian Approximation

In this paper, a novel robust Kalman filter (KF) based on the generalized $t$ distribution is proposed to deal with measurement outliers. By ultilizing the relationship between the generalized $t$ distribution and the Gauss-Inverse-Wishart distribution, a latent random variable is introduced, which helps us derive the accurate analytical form of the posterior density. After that, a mixed Gaussian approximation method is proposed to obtain an accurate approximation of the posterior mean and covariance. We also establish the best Gauss distribution to approximate the posterior distribution under the Kullback-Leibler (KL) divergence. Compared with the current robust KFs, our method is more implementation-friendly, and can achieve a higher accuracy due to the adoption of the latent matrix random variable and the best Gaussian approximation for the posterior distribution under the KL divergence.
Analysis of U.S. Banking Industry Based on Fama-French Model Under COVID-19

The global epidemic of the COVID-19 has further aggravated the imbalance of the world’s economic order, and the global economic recession is inevitable. The purpose of this research is to test the performance of factors and the applicability of Fama-French Factor model for the banking industry during the epidemic. The data is used from Kenneth R. French-Data Library to conduct Multiple Linear Regression based on Fama-French 3 Factor model and Fama-French 5 Factor model. The timeline consists of 3 stages, using the outbreak of COVID-19 and the availability of COVID-19 vaccines as time point. The result shows that Fama-French Five Factor Model can better explain the performance of the industry, and all of the factors have an impact during the epidemic.
Data Augmentation vs. Synthetic Data Generation: An Empirical Evaluation for Enhancing Radiology Image Classification

Radiology is a field of medicine dealing with diagnostic images to detect diseases for further treatment. Collecting and annotating diagnostic images like Magnetic Resonance Imaging (MRI) and X-Ray is a rigorous and time-consuming process. Deep Learning methods are widely utilized for disease classification and prediction from diagnostic images, but they demand substantial amounts of training data. Additionally, certain diseases are uncommon in large patient cohorts, posing difficulties in obtaining sufficient imaging samples to construct accurate deep learning models. Data augmentation techniques are commonly used to overcome this challenge of limited data. These techniques involve applying geometric transformations such as rotation, cropping, zooming, flipping, and other similar operations to the images to enlarge the dataset artificially. Another possible way of expanding the dataset is by synthesizing data to generate artificial medical images by mimicking the original images. This study presents RAD-DCGAN: A Deep Convolutional Generative Adversarial Network to produce high-resolution synthetic radiology images from the X-ray and MRI images collected from a private medical hospital (KMC Hospital, India). We aim to determine the most effective technique for enhancing the performance of radiology image classifiers by comparing and evaluating the proposed RAD-DCGAN with the standard data augmentation strategy. Our empirical evaluation, which involved eight standard deep learning models, demonstrated that deep learning classifiers trained on synthetic radiology data outperformed those trained on standard augmented data. The utilization of the RAD-DCGAN model for training and testing deep learning models on synthetic data has demonstrated a notable improvement of 4-5% in accuracy compared to conventional augmentation techniques. This signifies the state-of-the-art performance achieved by the RAD-DCGAN model in enhancing the accuracy of deep learning models.
A Visual SLAM Algorithm Based on Multi-Feature Optimization for Indoor Environments

How to locate mobile robots, unmanned aerial vehicles and other carriers in indoor environment with GPS signal rejection is one of the key issues to determine the completion of their tasks. This paper uses the continuous image information obtained by the camera to design and implement a visual SLAM algorithm based on multi-feature optimization for indoor positioning scenes. In order to reduce the tracking failure caused by insufficient features, this paper adds line features, uses LSD segment extraction algorithm to extract and process line features from video frame images, uses line features to combine edge point features and corner points to initialize the algorithm, and then tracks in adjacent frames and local maps. In addition, a priori model of uniform motion is used, which takes into account both the dynamic and the stability. Finally, for indoor scenes, this paper will test the accuracy, robustness and real-time performance of the algorithm compared with existing algorithms in different datasets and real-time environments through indoor dataset experiments and camera experiments in real-time indoor scenes, and verify that the algorithm can be applied in real-time environments, and has good robustness, accuracy and real-time performance.
The use of PCR and T-RFLP as a means of identifying sources of fecal bacteria pollution in the tidal creeks of New Hanover County, North Carolina

Tidal creeks are deteriorating more each day as a consequence of increased pollution caused by a boom in urbanization, which has intensified the anthropogenic impact on these ecosystems. These systems are subject to anthropogenic pollutants such as nutrients, pesticides, and fecal bacteria, which can cause loss of habitat, anoxia, and shellfish bed closures. In 2001, 13,410 recreational area closings and advisories were issued nationwide. Nearly 90% of these were due to the level of fecal coliform bacteria in the water. The standard method for fecal coliform pollution measurement enumerates, but does not distinguish between sources. Using standard methods for fecal coliform bacteria enumeration, and molecular methods of PCR and T-RFLP for bacterial source tracking using the genera Bacteroides as a target, it was possible to identify areas with high level of fecal coliform bacteria pollution as well as distinguish between human, canine and ruminant sources. Of the 54 samples, less than 23% were positive for canine fecal bacteria DNA by PCR, indicating fairly successful domestic animal waste management. Ruminant sources were found in 12 of the 54 PCR samples, mainly in the upstream sampling areas and near a known horse farm. Human fecal bacteria DNA was found in 18% of the PCR samples. T-RFLP produced 40 peaks, each corresponding to a bacterial taxon. Using the MiCA T-RFLP PAT+, it was possible to identify 13 of the 40 peaks, 11 of which were human-specific The presence of human-specific DNA is of particular concern. New Hanover County has been plagued with sewer-system failures. The presence of human-borne Bacteroides DNA is indicative of either continued sewer-line problems, septic system failures, or a general persistence in the bacteria itself.
A New Harmonic Compensation Based Modified Field Oriented Control of Permanent Magnet BLDC Motor with Reduced Torque Pulsation

This paper presents a novel current harmonic compensation based modified Field-Oriented-Control (FOC) for permanent magnet BLDC motor to mitigate torque ripples. A brief analysis of conventional vector control of PMBLDC motor is presented. The proposed FOC for BLDC motor is based on harmonic compensation of stator current. To implement the current harmonic compensation the harmonic analysis of back emf in rotor reference frame has been carried out. To track harmonic components a Proportional-Resonant (P-R) based current controller is designed considering motor drive parameters and desired dynamic performance. The proposed method rejects all the torque ripples without compromising the dynamic response of motor drive. Validation of this modified control is validated using MATLAB/SIMULINK environment and experimental setup both.
Weighted Networked Evolutionary Games with Switched Topologies and Time-delay Threshold

In this paper, two kinds of threshold control with history information for the networked evolutionary games with switched topologies are studied. First, the concepts of threshold with history information are introduced. Next, efficient algorithms to design the threshold control with length-r information are proposed based on the graph theory. Finally, an numerical example is given to illustrate the correctness of the theoretical results.
Research on the nvshu character recognition algorithm based on deep learning

At present, people pay more and more attention to the protection and inheritance of Chinese intangible cultural heritage, and the Jiang Yong nvshu text in Yongzhou, Hunan Province, as a kind of female-specific text, embodies the real demand of women for spiritual freedom and cultural equality in traditional society. However, as fewer and fewer people learn and use the nvshu text, people know less and less about the culture of nvshu at present. Deep learning algorithms can be well used to identify and understand the nvshu character, so as to further protect and inherit nvshu culture. In this paper, we build the nvshu text classification dataset NTCD2023 (Nvshu Text Classification Dataset2023), which contains 137 common nvshu characters in 5 categories. Meanwhile, multiple deep learning models such as AlexNet, VGG16, GoogLeNet, MobileNetV3, and ResNet are used to train and test on the dataset, and two better-performing network models, AlexNet and GoogLeNet, were selected for improvement by comparison. The experimental results show that the accuracy of nvshu character recognition in the improved models reaches up to 94.3%, which obviously improves the recognition rate of nvshu character, provides new ideas for the preservation and inheritance of nvshu culture, and is important for solving the problem of nvshu character recognition in practical application scenarios.
Chest X-ray Image Segmentation Based on Improved Unet

In this paper, we use the Unet network to achieve automatic segmentation of chest X-ray images and develop a chest X-ray image segmentation application. To further improve segmentation performance, we have improved the original network structure by changing the network depth, adding attention mechanisms and changing the encoder convolution module. Our improved methods achieved a Dice coefficient of 0.973 and an accuracy of 0.983 on the Kaggle Chest X-ray Masks and Labels dataset.
A Job-Aware Decision Method for Hybrid HPC Cluster Scenarios

As an emerging science and technology industry, the development direction and strategic height of high-performance computing has become one of the important symbols of scientific and technological progress nowadays, and also a centralized embodiment of a country's comprehensive strength in science and technology. To cope with the development demand of high performance computing, meeting the resource utilization conditions under multi-dimensional constraints and forming reasonable scheduling are always the most urgent issues. Based on the current development status of hybrid HPC clusters and the problem of resource wastage due to unreasonable scheduling allocation, a job task-aware migration policy method applicable to hybrid HPC cluster environment is proposed in this paper: ECP - Perception-aware decision method, ECP - Perception-aware decision method can target HPC job tasks submitted by users The ECP - Perception-aware decision method can make reasonable load prediction for HPC job tasks submitted by users, and migrate the job tasks to a cluster environment with relatively lower resource consumption or monetary cost based on the prediction result and the perception policy. After comparing several different job task migration models in simulated production environments, the results show that the ECP - Perception-aware decision approach enables HPC job tasks to obtain lower resource consumption or lower monetary cost in a hybrid HPC cluster environment than when running in a single physical HPC cluster or Kubernetes cluster, and also enables the hybrid HPC cluster to resources as well as users' demands are better balanced before, which better solves the multi-objective optimization problem of HPC job task-aware scheduling.
Characterization and Steady State Analysis of Multiport Switched Boost Converter

Multiport DC-DC converters are extensively used in electric vehicles (EVs), charging applications, UPS systems, and hybrid energy storage systems. In this paper, a multiport DC-DC converter is presented with switched boost action topology. The detailed steady state behaviour of multiport switched boost converter (MSBC) is presented. The scale factor with respect to duty ratio is defined for the characterization of MSBC. The mathematical relations are developed for gain analysis of both boost and buck output side of the converter. The steady-state characteristics of input/output current and voltage waveforms are analysed. The analytical relations are developed for voltage and current ripple and accordingly switching sequence of switches are applied. Simulation results are presented for the comparative analysis of theoretical results.
Research on Link Prediction Algorithm Integrating High-order Information and Node Centrality in Network

Link prediction is an important branch in the field of complex network research. Traditional similarity-based link prediction algorithms mainly calculate the similarity between nodes based on single features such as common neighbors and degrees, without considering the impact of higher-order network structures on link formation. To address this issue, this paper proposes a link prediction algorithm called (H-CCNC) and its extended version (FH-CCNC), which integrates high-order information and node centrality by considering node degree, node centrality, and high-order clustering coefficient from both local and global perspectives in the network. Experiments were conducted on real datasets with different densities, using the area under the curve (AUC) and precision as evaluation metrics and comparing with traditional link prediction algorithms. The results show that H-CCNC and FH-CCNC can predict missing links in the network more accurately, with an AUC improvement ranging from 1% to 22% on different datasets.
Characteristics of m6A-related LncRNAs in breast cancer as prognostic biomarkers and immunotherapy

N6-methyladenosine (m6A) is a common RNA modification in coding and non-coding RNAs and plays an important role in the occurrence and development of breast cancer (BC). However, the role of m6A-related lncRNAs in breast cancer prognosis is unclear. This study aimed to help verify the biological function of m6A-related lncRNAs in breast cancer prognosis through bio-informatics techniques. First, we screened 18 m6A-related lncRNAs from the TCGA database: AL137847.1, AC137932.2, OTUD6B-AS1, MORF4L2-AS1, AC078846.1, AC012442.1, AL118556.1, AL138955.1, AC009754.1, AC024257.4, AL391095.1, AC024270.3, AC087392.1, LINC02649, AC090948.2, AL158212.1, ITGA6-AS1, AL133243.2 and constructed a risk-prognosis model based on this. Based on the model's median risk score, BC patients were divided into high-risk and low-risk groups. Then, the predictive value of the model was verified by Cox regression, Lasso regression, Kaplan-Meier curve and ROC curve analysis, and biological differences between the two groups were verified by GO enrichment analysis, tumor mutation burden, immune indications and in vitro tests. Importantly, the risk score of this prognostic model is an excellent independent prognostic factor, and m6A regulators are differentially expressed in patients with different risks. In addition, based on patients' different sensitivities to drugs, some drug candidates for different risk populations are screened to provide targets for breast cancer treatment. The difference in immune function between high-risk and low-risk patients also affected the sensitivity to immunotherapy. In the validation of clinical samples, we analyzed the expression of relevant lncRNAs in different risk groups and speculated the possible impact on the prognosis of breast cancer patients. The risk assessment tool built based on the full analysis of these m6A-related genes and m6A-related lncRNA libraries, as well as the m6A-related lncRNAs, has a high prognostic prediction ability, which may provide a supplementary screening method for accurately judging the prognosis of BC and a new perspective for personalized treatment of breast cancer patients.
RL-VIO: A Robust and Lightweight Visual-inertial Odometry in Dynamic Environments

The invariable location of the features in the world is the precondition of calculating poses and building maps in SLAM algorithms whose performance will descend in dynamic environments. In this paper, we present a novel visual-inertial odometry which is lightweight and robust enough to produce an accurate trajectory while working with moving objects, called RL-VIO. First of all, a sliding window filter scheme is used as the main estimator to fuse visual and inertial information. The efficiency of feature correspondence is maintained by a robust feature tracking method. Then, the proposed dynamic points removal strategy prevents the system from being influenced by non-static features. A comprehensive stationary detection function which employs both visual and inertial measurements is also presented to further improve the accuracy of the filter. Finally, we use the public VIODE dataset to evalute our algorithm and the results reveal that the RMSE ATE of RL-VIO is much lower than our predecessor Hyb VIO with time consumption being still at a low level.
Image classification of bone metastases from lung cancer based on attention mechanism

SPECT imaging is one of the main functional imaging methods in the field of medical imaging, playing an important role in the detection and diagnosis of bone metastasis caused by diverse primary tumors. Bone metastasis is a common pathological manifestation of lung cancer, having typical symptoms including bone pain, fractures, and hypercalcemia. To classify SPECT images of lung cancer bone metastasis into categories of concerns, in this work, we propsoe a ResNet-based image classification model. Specifically, an attention mechanism is incorporated into the ResNet-18 model, and hyperparameters are adjusted to build an improved model. Finally, a set of SPECT images are used to test the proposed model, obtaining scores of 0.7525, 0.7705, 0.7213, and 0.7442 for accuracy, precision, recall, and F-1, respectively.