A Novel Model of Working Set Selection for SMO Decomposition Methods

In the process of training Support Vector Machines (SVMs) by decomposition methods, working set selection is an important technique, and some exciting schemes were employed into this field. To improve working set selection, we propose a new model for working set selection in sequential minimal optimization (SMO) decomposition methods. In this model, it selects B as working set without reselection. Some properties are given by simple proof, and experiments demonstrate that the proposed method is in general faster than existing methods.Comment: 8 pages, 12 figures, it was submitted to IEEE International conference of Tools on Artificial Intelligenc

Integration on acceleration signals by adjusting with envelopes

Direct integration of acceleration often causes unrealistic drifts in velocity and displacement. A method of integration on acceleration data to acquire realistic velocity and displacement is proposed. In this approach, drifts are estimated by using the mean of the upper and lower envelopes of signals after integration from acceleration into velocity and displacement. The experimental results obtained by using simulated data and real world signals are presented to demonstrate the effectiveness of the method

Real-time object detection method based on improved YOLOv4-tiny

The "You only look once v4"(YOLOv4) is one type of object detection methods in deep learning. YOLOv4-tiny is proposed based on YOLOv4 to simple the network structure and reduce parameters, which makes it be suitable for developing on the mobile and embedded devices. To improve the real-time of object detection, a fast object detection method is proposed based on YOLOv4-tiny. It firstly uses two ResBlock-D modules in ResNet-D network instead of two CSPBlock modules in Yolov4-tiny, which reduces the computation complexity. Secondly, it designs an auxiliary residual network block to extract more feature information of object to reduce detection error. In the design of auxiliary network, two consecutive 3x3 convolutions are used to obtain 5x5 receptive fields to extract global features, and channel attention and spatial attention are also used to extract more effective information. In the end, it merges the auxiliary network and backbone network to construct the whole network structure of improved YOLOv4-tiny. Simulation results show that the proposed method has faster object detection than YOLOv4-tiny and YOLOv3-tiny, and almost the same mean value of average precision as the YOLOv4-tiny. It is more suitable for real-time object detection.Comment: 14pages,7figures,2table

Characteristics of the O(1S) to O(1D) 557.7 nm green emission observed in an argon plasma jet

An extensive study on the green auroral emission characterization is presented based on a single dielectric barrier discharge geometry argon plasma jet driven by a kHz sine voltage. The plasma was generated by using 99.999% pure argon and the observed 557.7 nm green line resulted from the excited O(1S) state. An optical emission spectroscopy method using line ratios of argon was used to obtain the electron density and electron temperature under different conditions in the downstream region. The characteristics of discharge and green emission with variations in interelectrode distance, applied voltage (power) and flow rate are discussed. The spatially diffuse distribution of O(1S), owing to its long lifetime, is shown by the short exposure imaging. Two discharge regimes are presented, accompanied by two distinct branches of the green emission intensity, with a clear conclusion that the 557.7 nm emission is favored in the low electron temperature environment. In this work, the intense and diffuse green plume only forms when the downstream electron density is approximately lower than 1 × 1014 cm−3 and the electron temperature is lower than 1.1 eV. By charging the two electrodes in two opposite ways, it is shown that the green emission from oxygen is favored in the case where the electric field and the electron drift are not continuous

Class Prior-Free Positive-Unlabeled Learning with Taylor Variational Loss for Hyperspectral Remote Sensing Imagery

Positive-unlabeled learning (PU learning) in hyperspectral remote sensing imagery (HSI) is aimed at learning a binary classifier from positive and unlabeled data, which has broad prospects in various earth vision applications. However, when PU learning meets limited labeled HSI, the unlabeled data may dominate the optimization process, which makes the neural networks overfit the unlabeled data. In this paper, a Taylor variational loss is proposed for HSI PU learning, which reduces the weight of the gradient of the unlabeled data by Taylor series expansion to enable the network to find a balance between overfitting and underfitting. In addition, the self-calibrated optimization strategy is designed to stabilize the training process. Experiments on 7 benchmark datasets (21 tasks in total) validate the effectiveness of the proposed method. Code is at: https://github.com/Hengwei-Zhao96/T-HOneCls.Comment: Accepted to ICCV 202

Association between ALDH2 Glu504Lys polymorphism and colorectal cancer risk: a meta-analysis.

Background: The findings from studies on the relationship between aldehyde dehydrogenases(ALDH) gene Glu504Lys polymorphism and colorectal cancer(CRC) were inconsistent. Objectives: The aim of this meta-analysis was to assess ALDH gene Glu504Lys polymorphism and CRC risk. Methods: All of the relevant studies were identified from PubMed and Embase database. Statistical analyses were conducted with STATA 12.0 software.Odds ratio (OR) with 95% confidence interval (CI) values were applied to evaluate the strength of the association. Nine studies with 2779 cases and 4533 controls were included. Results: No significant variation in CRC risk was detected in any of the genetic models overall. To explore the sources of heterogeneity,we performed further sub-group analyses by ethnicity and quality assessment of these studies.In the sub-group analysis by race,significant associations between ALDH gene Glu504Lys polymorphism and CRC risk were found in China(Glu/ Lys vs Glu/Glu: OR = 0.70, 95%CI = 0.57\u20130.85; the dominant model: OR =0.69, 95%CI =0.48\u20130.98) and Japan(Lys/Lys vs Glu/Glu:OR =0.72, 95%CI =0.55\u20130.95). Conclusion: This meta-analysis suggests that the ALDH2 Glu504Lys polymorphism may be associated with susceptibility to CRC. Furthermore, large and well-designed studies are needed to confirm these conclusions