UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation

Multi-object tracking (MOT) in video sequences remains a challenging task, especially in scenarios with significant camera movements. This is because targets can drift considerably on the image plane, leading to erroneous tracking outcomes. Addressing such challenges typically requires supplementary appearance cues or Camera Motion Compensation (CMC). While these strategies are effective, they also introduce a considerable computational burden, posing challenges for real-time MOT. In response to this, we introduce UCMCTrack, a novel motion model-based tracker robust to camera movements. Unlike conventional CMC that computes compensation parameters frame-by-frame, UCMCTrack consistently applies the same compensation parameters throughout a video sequence. It employs a Kalman filter on the ground plane and introduces the Mapped Mahalanobis Distance (MMD) as an alternative to the traditional Intersection over Union (IoU) distance measure. By leveraging projected probability distributions on the ground plane, our approach efficiently captures motion patterns and adeptly manages uncertainties introduced by homography projections. Remarkably, UCMCTrack, relying solely on motion cues, achieves state-of-the-art performance across a variety of challenging datasets, including MOT17, MOT20, DanceTrack and KITTI. More details and code are available at https://github.com/corfyi/UCMCTrackComment: Accepted to AAAI 202

Genetic Polymorphisms in CYP2E1: Association with Schizophrenia Susceptibility and Risperidone Response in the Chinese Han Population

CYP2E1 is a member of the cytochrome P450 superfamily, which is involved in the metabolism and activation of both endobiotics and xenobiotics. The genetic polymorphisms of CYP2E1 gene (Chromosome 10q26.3, Accession Number NC_000010.10) are reported to be related to the development of several mental diseases and to be involved in the clinical efficacy of some psychiatric medications. We investigated the possible association of CYP2E1 polymorphisms with susceptibility to schizophrenia in the Chinese Han Population as well as the relationship with response to risperidone in schizophrenia patients.In a case-control study, we identified 11 polymorphisms in the 5' flanking region of CYP2E1 in 228 schizophrenia patients and 384 healthy controls of Chinese Han origin. From among the cases, we chose 130 patients who had undergone 8 weeks of risperidone monotherapy to examine the relationship between their response to risperidone and CYP2E1 polymorphisms. Clinical efficacy was assessed using the Brief Psychiatric Rating Scale (BPRS).Statistically significant differences in allele or genotype frequencies were found between cases and controls at rs8192766 (genotype p = 0.0048, permutation p = 0.0483) and rs2070673 (allele: p = 0.0018, permutation p = 0.0199, OR = 1.4528 95%CI = 1.1487-1.8374; genotype: p = 0.0020, permutation p = 0.0225). In addition, a GTCAC haplotype containing 5 SNPs (rs3813867, rs2031920, rs2031921, rs3813870 and rs2031922) was observed to be significantly associated with schizophrenia (p = 7.47E-12, permutation p<0.0001). However, no association was found between CYP2E1 polymorphisms/haplotypes and risperidone response.Our results suggest that CYP2E1 may be a potential risk gene for schizophrenia in the Chinese Han population. However, polymorphisms of the CYP2E1 gene may not contribute significantly to individual differences in the therapeutic efficacy of risperidone. Further studies in larger groups are warranted to confirm our results

The Temperature Dependence of Deformation Behaviors in High-Entropy Alloys: A Review

Over the past seventeen years, deformation behaviors of various types of high-entropy alloys (HEAs) have been investigated within a wide temperature range, from cryogenic to high temperatures, to demonstrate the excellent performance of HEAs under extreme conditions. It has been suggested that the dominated deformation mechanisms in HEAs would be varied with respect to the environmental temperatures, which significantly alters the mechanical properties. In this article, we systematically review the temperature-dependent mechanical behaviors, as well as the corresponding mechanisms of various types of HEAs, aiming to provide a comprehensive and up-to-date understanding of the recent progress achieved on this subject. More specifically, we summarize the deformation behaviors and microscale mechanisms of single-phase HEAs, metastable HEAs, precipitates-hardened HEAs and multiphase HEAs, at cryogenic, room and elevated temperatures. The possible strategies for strengthening and toughening HEAs at different temperatures are also discussed to provide new insights for further alloy development

Knowledge structure and global trends of machine learning in stroke over the past decade: A scientometric analysis

Objective: Machine learning (ML) models have been widely applied in stroke prediction, diagnosis, treatment, and prognosis assessment. We aimed to conduct a comprehensive scientometrics analysis of studies related to ML in stroke and reveal its current status, knowledge structure, and global trends. Methods: All documents related to ML in stroke were retrieved from the Web of Science database on March 15, 2023. We refined the documents by including only original articles and reviews in the English language. The literature published over the past decade was imported into scientometrics software for influence detection and collaborative network analysis. Results: 2389 related publications were included. The annual publication outputs demonstrated explosive growth, with an average growth rate of 63.99 %. Among the 90 countries/regions involved, the United States (729 articles) and China (636 articles) were the most productive countries. Frontiers in Neurology was the most prolific journal with 94 articles. 234 highly cited articles, each with more than 31 citations, were detected. Keyword analysis revealed a total of 5333 keywords, with a predominant focus on the application of ML models in the early diagnosis, classification, and prediction of “acute ischemic stroke” and “atrial fibrillation-related stroke”. The keyword “classification” had the first and longest burst, spanning from 2013 to 2018. ‘Upport vector machine’ got the strongest burst strength with 6.2. Keywords such as ‘mechanical thrombectomy’, ‘expression’, and 'prognosis' experienced bursts in 2022 and have continued to be prominent. Conclusion: The applications of ML in stroke are increasingly diverse and extensive, with researchers showing growing interest over the past decade. However, the clinical application of ML in stroke is still in its early stages, and several limitations and challenges need to be addressed for its widespread adoption in clinical practice

The Prescription trends and dosing appropriateness analysis of novel oral anticoagulants in ischemic stroke patients: a retrospective study of 9 cities in China

Background: Novel oral anticoagulants (NOACs) have been recommended by guidelines as the first-line drugs for preventing cardiogenic stroke. We aimed to provide an overview of the prescription trends and dosing appropriateness of NOACs in China.Methods: We conducted a retrospective analysis of NOAC prescriptions using the Hospital Prescription Analysis Cooperation Project data from 2016 to 2022. Various patient features, such as gender, age, city, year, source, department visited, original diagnosis, dosing, cost, and insurance type, were collected and analyzed to examine the trends and dosing appropriateness of NOAC usage in ischemic stroke patients.Results: 62,014 NOAC prescriptions were analyzed, including 16,602 for dabigatran, 45,253 for rivaroxaban, and 159 for apixaban. 85.14% of the patients were aged 65 or above, and tertiary hospitals accounted for 95.97% of NOAC prescriptions. NOAC prescriptions rose from 1828 in 2016 to 13,998 in 2021 but dropped to 13,166 in 2022. The percentage of annual prescriptions for NOACs among stroke patients has increased from 0.05% in 2016 to 0.37% in 2022. Total drug cost increased from ¥704541.18 in 2016 to ¥4128648.44 in 2021, then decreased to ¥1680109.14 in 2022. Prescriptions were divided into 48,321 appropriate and 11,262 inappropriate dosing groups, showing significant differences in medications, age, year, city type, hospital level, source, insurance type, and department visited (all p &lt; 0.001). The median drug cost for inappropriate dosing was higher than for appropriate dosing (¥55.20 VS ¥83.80). The top comorbidities in ischemic stroke patients were atrial fibrillation (35.30%), hypertension (32.75%), and coronary heart disease (16.48%).Conclusion: The application of NOACs in the Chinese population is increasing. Our findings highlight the frequent deviation from labeled dosing of NOACs in clinical practice. Continued efforts are necessary to promote the appropriate use of NOACs according to the standard dosage in the drug insert

UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation

Multi-object tracking (MOT) in video sequences remains a challenging task, especially in scenarios with significant camera movements. This is because targets can drift considerably on the image plane, leading to erroneous tracking outcomes. Addressing such challenges typically requires supplementary appearance cues or Camera Motion Compensation (CMC). While these strategies are effective, they also introduce a considerable computational burden, posing challenges for real-time MOT. In response to this, we introduce UCMCTrack, a novel motion model-based tracker robust to camera movements. Unlike conventional CMC that computes compensation parameters frame-by-frame, UCMCTrack consistently applies the same compensation parameters throughout a video sequence. It employs a Kalman filter on the ground plane and introduces the Mapped Mahalanobis Distance (MMD) as an alternative to the traditional Intersection over Union (IoU) distance measure. By leveraging projected probability distributions on the ground plane, our approach efficiently captures motion patterns and adeptly manages uncertainties introduced by homography projections. Remarkably, UCMCTrack, relying solely on motion cues, achieves state-of-the-art performance across a variety of challenging datasets, including MOT17, MOT20, DanceTrack and KITTI. More details and code are available at https://github.com/corfyi/UCMCTrack

A Method for SRTM DEM Elevation Error Correction in Forested Areas Using ICESat-2 Data and Vegetation Classification Data

The past decade has witnessed the rapid development of the SRTM (Shuttle Radar Topography Mission) DEM (digital elevation model) in engineering applications and scientific research. The near-global SRTM DEM was generated based on radar interference theory. The latest version of the SRTM DEM with a resolution of 1 arc-second has been widely used in various applications. However, many studies have shown the poor elevation accuracy of the SRTM DEM in forested areas. Recent developments in the field of spaceborne lidar have provided an additional chance to correct the elevation error of the SRTM DEM in forested areas. We developed an easy-to-use method to correct the elevation error of the SRTM DEM based on the spatial interpolation method using the recent Ice, Cloud and land Elevation Satellite-2 data. First, an ICESat-2 terrain control point selection criterion was proposed to reject some erroneous ICESat-2 terrains caused by many factors. Second, we derived the elevation correction surface based on the interpolation method using the refined ICESat-2 terrain. Finally, a corrected SRTM DEM of forested areas was generated through the obtained elevation correction surface. The proposed method was tested in the typical forested area located in Massachusetts, USA. The results show that the RMSE of the selected terrain control points in vegetation areas and non-vegetation areas are 1.03 and 0.68 m, respectively. The corrected SRTM DEM have an RMSE of 4.2 m which is significantly less than that of the original SRTM DEM with an RMSE of 9.8 m, which demonstrates the proposed method is feasible to correct the elevation error in forested areas. It can be concluded that the proposed method obviously decreases the elevation error of the original SRTM DEM

Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning

With the exponential growth of traffic data and the complexity of traffic conditions, in order to effectively store and analyse data to feed back valid information, this paper proposed an urban road traffic status prediction model based on the optimized deep recurrent Q-Learning method. The model is based on the optimized Long Short-Term Memory (LSTM) algorithm to handle the explosive growth of Q-table data, which not only avoids the gradient explosion and disappearance but also has the efficient storage and analysis. The continuous training and memory storage of the training sets are used to improve the system sensitivity, and then, the test sets are predicted based on the accumulated experience pool to obtain high-precision prediction results. The traffic flow data from Wanjiali Road to Shuangtang Road in Changsha City are tested as a case. The research results show that the prediction of the traffic delay index is within a reasonable interval, and it is significantly better than traditional prediction methods such as the LSTM, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), exponential smoothing method, and Back Propagation (BP) neural network, which shows that the model proposed in this paper has the feasibility of application

InSAR-DEM Block Adjustment Model for Upcoming BIOMASS Mission: Considering Atmospheric Effects

The unique P-band synthetic aperture radar (SAR) instrument, BIOMASS, is scheduled for launch in 2024. This satellite will enhance the estimation of subcanopy topography, owing to its strong penetration and fully polarimetric observation capability. In order to conduct global-scale mapping of the subcanopy topography, it is crucial to calibrate systematic errors of different strips through interferometric SAR (InSAR) DEM (digital elevation model) block adjustment. Furthermore, the BIOMASS mission will operate in repeat-pass interferometric mode, facing the atmospheric delay errors introduced by changes in atmospheric conditions. However, the existing block adjustment methods aim to calibrate systematic errors in bistatic mode, which can avoid possible errors from atmospheric effects through interferometry. Therefore, there is still a lack of systematic error calibration methods under the interference of atmospheric effects. To address this issue, we propose a block adjustment model considering atmospheric effects. Our model begins by employing the sub-aperture decomposition technique to form forward-looking and backward-looking interferograms, then multi-resolution weighted correlation analysis based on sub-aperture interferograms (SA-MRWCA) is utilized to detect atmospheric delay errors. Subsequently, the block adjustment model considering atmospheric effects can be established based on the SA-MRWCA. Finally, we use robust Helmert variance component estimation (RHVCE) to build the posterior stochastic model to improve parameter estimation accuracy. Due to the lack of spaceborne P-band data, this paper utilized L-band Advanced Land Observing Satellite (ALOS)-1 PALSAR data, which is also long-wavelength, to emulate systematic error calibration of the BIOMASS mission. We chose climatically diverse inland regions of Asia and the coastal regions of South America to assess the model’s effectiveness. The results show that the proposed block adjustment model considering atmospheric effects improved accuracy by 72.2% in the inland test site, with root mean square error (RMSE) decreasing from 10.85 m to 3.02 m. Moreover, the accuracy in the coastal test site improved by 80.2%, with RMSE decreasing from 16.19 m to 3.22 m