Improving Spatial and Elemental Associations in Analytical Field Ion Microscopy.

Chemically resolved atomic resolution imaging can give fundamental information about material properties. However, even today, a technique capable of such achievement is still only an ambition. Here, we take further steps in developing the analytical field ion microscopy (aFIM), which combines the atomic spatial resolution of field ion microscopy (FIM) with the time-of-flight spectrometry of atom probe tomography (APT). To improve the performance of aFIM that are limited in part by a high level of background, we implement bespoke flight path time-of-flight corrections normalized by the ion flight distances traversed in electrostatic simulations modeled explicitly for an atom probe chamber. We demonstrate effective filtering in the field evaporation events upon spatially and temporally correlated multiples, increasing the mass spectrum's signal-to-background. In an analysis of pure tungsten, mass peaks pertaining to individual W isotopes can be distinguished and identified, with the signal-to-background improving by three orders of magnitude over the raw data. We also use these algorithms for the analysis of a CoTaB amorphous film to demonstrate application of aFIM beyond pure metals and binary alloys. These approaches facilitate elemental identification of the FIM-imaged surface atoms, making analytical FIM more precise and reliable.

Multi-Feature Single Target Robust Tracking Fused with Particle Filter.

Aiming at the problems of target model drift or loss of target tracking caused by serious deformation, occlusion, fast motion, and out of view of the target in long-term moving target tracking in complex scenes, this paper presents a robust multi-feature single-target tracking algorithm based on a particle filter. The algorithm is based on the correlation filtering framework. First, to extract more accurate target appearance features, in addition to the manual features histogram of oriented gradient features and color histogram features, the depth features from the conv3-4, conv4-4 and conv5-4 convolutional layer outputs in VGGNet-19 are also fused. Secondly, this paper designs a re-detection module of a fusion particle filter for the problem of how to return to accurate tracking after the target tracking fails, so that the algorithm in this paper can maintain high robustness during long-term tracking. Finally, in the adaptive model update stage, the adaptive learning rate update and adaptive filter update are performed to improve the accuracy of target tracking. Extensive experiments are conducted on dataset OTB-2015, dataset OTB-2013, and dataset UAV123. The experimental results show that the proposed multi-feature single-target robust tracking algorithm with fused particle filtering can effectively solve the long-time target tracking problem in complex scenes, while showing more stable and accurate tracking performance.

A Non-Destructive Health Evaluation Method for Wooden Utility Poles with Frequency-Modulated Empirical Mode Decomposition and Laplace Wavelet Correlation Filtering.

Wooden utility poles are one of the most commonly used utility carriers in North America. Even though they are given different protection treatments, wooden utility poles are prone to have defects that are mainly caused by temperature, oxygen, moisture, and high potential hydrogen levels after decades of being exposed in open-air areas. In order to meet the growing demand regarding their maintenance and replacement, an effective health evaluation technology for wooden utility poles is essential to ensure normal power supply and safety. However, the commonly used hole-drilling inspection method always causes extra damage to wooden utility poles and the precision of health evaluation highly relies on technician experience at present. Therefore, a non-destructive health evaluation method with frequency-modulated empirical mode decomposition (FM-EMD) and Laplace wavelet correlation filtering based on dynamic responses of wooden utility poles was proposed in this work. Specifically, FM-EMD was used to separate multiple confusing closely-spaced vibration modes due to nonlinear properties of wooden utility poles into several single modes. The instantaneous frequency and damping factor of the decomposed signal of each single mode of the dynamic response of a wooden utility pole could be determined using Laplace wavelet correlation filtering with high precision. The health status of a wooden utility pole could then be estimated according to the extracted instantaneous frequency and damping factor of the decomposed signal of each single mode. The proposed non-destructive health evaluation method for wooden utility poles was tested in the field and achieved successful results.

A Convolutional Neural Network Combining Discriminative Dictionary Learning and Sequence Tracking for Left Ventricular Detection.

Cardiac MRI left ventricular (LV) detection is frequently employed to assist cardiac registration or segmentation in computer-aided diagnosis of heart diseases. Focusing on the challenging problems in LV detection, such as the large span and varying size of LV areas in MRI, as well as the heterogeneous myocardial and blood pool parts in LV areas, a convolutional neural network (CNN) detection method combining discriminative dictionary learning and sequence tracking is proposed in this paper. To efficiently represent the different sub-objects in LV area, the method deploys discriminant dictionary to classify the superpixel oversegmented regions, then the target LV region is constructed by label merging and multi-scale adaptive anchors are generated in the target region for handling the varying sizes. Combining with non-differential anchors in regional proposal network, the left ventricle object is localized by the CNN based regression and classification strategy. In order to solve the problem of slow classification speed of discriminative dictionary, a fast generation module of left ventricular scale adaptive anchors based on sequence tracking is also proposed on the same individual. The method and its variants were tested on the heart atlas data set. Experimental results verified the effectiveness of the proposed method and according to some evaluation indicators, it obtained 92.95% in AP50 metric and it was the most competitive result compared to typical related methods. The combination of discriminative dictionary learning and scale adaptive anchor improves adaptability of the proposed algorithm to the varying left ventricular areas. This study would be beneficial in some cardiac image processing such as region-of-interest cropping and left ventricle volume measurement.

Understanding Changes in the Topology and Geometry of Financial Market Correlations during a Market Crash.

In econophysics, the achievements of information filtering methods over the past 20 years, such as the minimal spanning tree (MST) by Mantegna and the planar maximally filtered graph (PMFG) by Tumminello et al., should be celebrated. Here, we show how one can systematically improve upon this paradigm along two separate directions. First, we used topological data analysis (TDA) to extend the notions of nodes and links in networks to faces, tetrahedrons, or k-simplices in simplicial complexes. Second, we used the Ollivier-Ricci curvature (ORC) to acquire geometric information that cannot be provided by simple information filtering. In this sense, MSTs and PMFGs are but first steps to revealing the topological backbones of financial networks. This is something that TDA can elucidate more fully, following which the ORC can help us flesh out the geometry of financial networks. We applied these two approaches to a recent stock market crash in Taiwan and found that, beyond fusions and fissions, other non-fusion/fission processes such as cavitation, annihilation, rupture, healing, and puncture might also be important. We also successfully identified neck regions that emerged during the crash, based on their negative ORCs, and performed a case study on one such neck region.

A Fast Image Deformity Correction Algorithm for Underwater Turbulent Image Distortion.

An algorithm correcting distortion based on estimating the pixel shift is proposed for the degradation caused by underwater turbulence. The distorted image is restored and reconstructed by reference frame selection and two-dimensional pixel registration. A support vector machine-based kernel correlation filtering algorithm is proposed and applied to improve the speed and efficiency of the correction algorithm. In order to validate the algorithm, laboratory experiments on a controlled simulation system of turbulent water and field experiments in rivers and oceans are carried out, and the experimental results are compared with traditional, theoretical model-based and particle image velocimetry-based restoration and reconstruction algorithms. Using subjective visual evaluation, image distortion has been effectively suppressed; based on an objective performance statistical analysis, the measured values are better than the traditional and formerly studied restoration and reconstruction algorithms. The method proposed in this paper is also much faster than the other algorithms. It can be concluded that the proposed algorithm can effectively improve the de-distortion effect of the underwater turbulence degraded image, and provide potential techniques for the accurate operation of underwater target detection in real time.

Breast Cancer Prediction Based on Multiple Machine Learning Algorithms.

INTRODUCTION: The incidence of breast cancer has steadily risen over the years owing to changes in lifestyle and environment. Presently, breast cancer is one of the primary causes of cancer-related deaths among women, making it a crucial global public health concern. Thus, the creation of an automated diagnostic system for breast cancer bears great importance in the medical community. OBJECTIVES: This study analyses the Wisconsin breast cancer dataset and develops a machine learning algorithm for accurately classifying breast cancer as benign or malignant. METHODS: Our research is a retrospective study, and the main purpose is to develop a high-precision classification algorithm for benign and malignant breast cancer. To achieve this, we first preprocessed the dataset using standard techniques such as feature scaling and handling missing values. We assessed the normality of the data distribution initially, after which we opted for Spearman correlation analysis to examine the relationship between the feature subset data and the labeled data, considering the normality test results. We subsequently employed the Wilcoxon rank sum test to investigate the dissimilarities in distribution among various breast cancer feature data. We constructed the feature subset based on statistical results and trained 7 machine learning algorithms, specifically the decision tree, stochastic gradient descent algorithm, random forest algorithm, support vector machine algorithm, logistics algorithm, and AdaBoost algorithm. RESULTS: The results of the evaluation indicated that the AdaBoost-Logistic algorithm achieved an accuracy of 99.12%, outperforming the other 6 algorithms and previous techniques. CONCLUSION: The constructed AdaBoost-Logistic algorithm exhibits significant precision with the Wisconsin breast cancer dataset, achieving commendable classification performance for both benign and malignant breast cancer cases.

Room-temperature serial synchrotron crystallography of Drosophila cryptochrome.

Fixed-target serial crystallography allows the high-throughput collection of diffraction data from small crystals at room temperature. This methodology is particularly useful for difficult samples that have sensitivity to radiation damage or intolerance to cryoprotection measures; fixed-target methods also have the added benefit of low sample consumption. Here, this method is applied to the structure determination of the circadian photoreceptor cryptochrome (CRY), previous structures of which have been determined at cryogenic temperature. In determining the structure, several data-filtering strategies were tested for combining observations from the hundreds of crystals that contributed to the final data set. Removing data sets based on the average correlation coefficient among equivalent reflection intensities between a given data set and all others was most effective at improving the data quality and maintaining overall completeness. CRYs are light sensors that undergo conformational photoactivation. Comparisons between the cryogenic and room-temperature CRY structures reveal regions of enhanced mobility at room temperature in loops that have functional importance within the CRY family of proteins. The B factors of the room-temperature structure correlate well with those predicted from molecular-dynamics simulations.