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2022 ◽  
Vol 271 ◽  
pp. 32-40
David Dugue ◽  
George A. Taylor ◽  
Jenna Maroney ◽  
Joseph R. Spaniol ◽  
Frederick V. Ramsey ◽  

2022 ◽  
Vol 13 (1) ◽  
pp. 1-18
Meng Chen ◽  
Qingjie Liu ◽  
Weiming Huang ◽  
Teng Zhang ◽  
Yixuan Zuo ◽  

Next location prediction is of great importance for many location-based applications and provides essential intelligence to various businesses. In previous studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Nevertheless, due to the time and space complexity, these methods (e.g., Markov models) only utilize the just passed locations to predict next locations, neglecting earlier passed locations in the trajectory. In this work, we seek to enhance the prediction performance by incorporating the travel time from all the passed locations in the query trajectory to each candidate next location. To this end, we propose a novel prediction method, namely the Travel Time Difference Model, which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Moreover, we integrate the Travel Time Difference Model with a Sequential and Temporal Predictor to yield a joint model. The joint prediction model integrates local sequential transitions, temporal regularity, and global travel time information in the trajectory for the next location prediction problem. We have conducted extensive experiments on two real-world datasets: the vehicle passage record data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over baseline methods.

2022 ◽  
Vol 40 (4) ◽  
pp. 1-35
Tetsuya Sakai ◽  
Sijie Tao ◽  
Zhaohao Zeng

In the context of depth- k pooling for constructing web search test collections, we compare two approaches to ordering pooled documents for relevance assessors: The prioritisation strategy (PRI) used widely at NTCIR, and the simple randomisation strategy (RND). In order to address research questions regarding PRI and RND, we have constructed and released the WWW3E8 dataset, which contains eight independent relevance labels for 32,375 topic-document pairs, i.e., a total of 259,000 labels. Four of the eight relevance labels were obtained from PRI-based pools; the other four were obtained from RND-based pools. Using WWW3E8, we compare PRI and RND in terms of inter-assessor agreement, system ranking agreement, and robustness to new systems that did not contribute to the pools. We also utilise an assessor activity log we obtained as a byproduct of WWW3E8 to compare the two strategies in terms of assessment efficiency. Our main findings are: (a) The presentation order has no substantial impact on assessment efficiency; (b) While the presentation order substantially affects which documents are judged (highly) relevant, the difference between the inter-assessor agreement under the PRI condition and that under the RND condition is of no practical significance; (c) Different system rankings under the PRI condition are substantially more similar to one another than those under the RND condition; and (d) PRI-based relevance assessment files (qrels) are substantially and statistically significantly more robust to new systems than RND-based ones. Finding (d) suggests that PRI helps the assessors identify relevant documents that affect the evaluation of many existing systems, including those that did not contribute to the pools. Hence, if researchers need to evaluate their current IR systems using legacy IR test collections, we recommend the use of those constructed using the PRI approach unless they have a good reason to believe that their systems retrieve relevant documents that are vastly different from the pooled documents. While this robustness of PRI may also mean that the PRI-based pools are biased against future systems that retrieve highly novel relevant documents, one should note that there is no evidence that RND is any better in this respect.

2022 ◽  
Vol 18 (2) ◽  
pp. 1-24
Sourabh Kulkarni ◽  
Mario Michael Krell ◽  
Seth Nabarro ◽  
Csaba Andras Moritz

Epidemiology models are central to understanding and controlling large-scale pandemics. Several epidemiology models require simulation-based inference such as Approximate Bayesian Computation (ABC) to fit their parameters to observations. ABC inference is highly amenable to efficient hardware acceleration. In this work, we develop parallel ABC inference of a stochastic epidemiology model for COVID-19. The statistical inference framework is implemented and compared on Intel’s Xeon CPU, NVIDIA’s Tesla V100 GPU, Google’s V2 Tensor Processing Unit (TPU), and the Graphcore’s Mk1 Intelligence Processing Unit (IPU), and the results are discussed in the context of their computational architectures. Results show that TPUs are 3×, GPUs are 4×, and IPUs are 30× faster than Xeon CPUs. Extensive performance analysis indicates that the difference between IPU and GPU can be attributed to higher communication bandwidth, closeness of memory to compute, and higher compute power in the IPU. The proposed framework scales across 16 IPUs, with scaling overhead not exceeding 8% for the experiments performed. We present an example of our framework in practice, performing inference on the epidemiology model across three countries and giving a brief overview of the results.

2022 ◽  
Vol 12 (5) ◽  
pp. 933-938
Xuejian Zhang ◽  
Yue Deng ◽  
Yan Wang ◽  
Chuanrong Yin ◽  
Junzhao Gao

Objective: To investigate the effect of insertion torque on implant osseointegration in an animal model. Methods: First, the first to fourth premolars of nine healthy adult beagles’ mandibular were extracted to form an edentulous area, and then the beagles were equally divided into three groups with different torques (low torque: 10–30 Ncm; medium torque: 30–50 Ncm; high torque: > 70 Ncm). Three implants were placed on each side of the edentulous area of the beagles (54 total), and the dogs were observed for 8 weeks. Implant performance and removal torque values (RTV) were determined at 1, 4, and 8 weeks after surgery. In addition, the expression ratios of OPG and RANKL mRNAs in the surrounding bone tissue were determined. Results: None of the 54 implants showed loosening or loss, and no significant bone resorption was observed. The removing torques and the expression ratios of OPG and RANKL mRNAs showed differences at 1 and 4 weeks after surgery, while they converged at 8 weeks after the surgery (p > 0.05). Conclusion: The osteointegration process lasted approximately 8 weeks depending on the difference in parameters, and all parameters showed the same values even though the insertion torques at the beginning were different.

2022 ◽  
Vol 28 (1) ◽  
pp. 53-55
Hongqiang Chen

ABSTRACT Introduction: High-intensity Intermittent Training (HIIT) ranked first in the ACSM “2013 Global Training Methodology Survey”. Objective: To explore the influence of different speed training intervals on athlete reaction speed. Methods: Sixteen male bicycle athletes were randomly divided into two groups. The two groups then completed a six-week training routine (NT). The two groups then completed a six-week training routine , started 6 weeks of Sprint Interval Training (SIT) (a total of 12 lessons), with SIT instead of Normal Training (NT) live endurance training, and another training remains unchanged. Results: After 6 weeks of NT, Pmax GXT in the CG and DG groups decreased by 0.7% and 1.7%, respectively,as compared to the pre-training numbers. The difference was not statistically significant (P>0.05). And after 6 weeks of SIT, Pmax GXT increased significantly (P<0.05) in both experimental groupss,with increases of 9.2% and 10.2% for the CG and DG groups, respectively. Conclusions: The results show that intermittent training can effectively improve the aerobic metabolism of short-haul cyclists. As the power bicycle load and the training intensity and volume of the deceleration intermittent training program increase, the more significant the changes in aerobic capacity that can result in adaptability. Level of evidence II; Therapeutic studies - investigation of treatment results.

2022 ◽  
Vol 31 (2) ◽  
pp. 1-37
Jiachi Chen ◽  
Xin Xia ◽  
David Lo ◽  
John Grundy

The selfdestruct function is provided by Ethereum smart contracts to destroy a contract on the blockchain system. However, it is a double-edged sword for developers. On the one hand, using the selfdestruct function enables developers to remove smart contracts ( SCs ) from Ethereum and transfers Ethers when emergency situations happen, e.g., being attacked. On the other hand, this function can increase the complexity for the development and open an attack vector for attackers. To better understand the reasons why SC developers include or exclude the selfdestruct function in their contracts, we conducted an online survey to collect feedback from them and summarize the key reasons. Their feedback shows that 66.67% of the developers will deploy an updated contract to the Ethereum after destructing the old contract. According to this information, we propose a method to find the self-destructed contracts (also called predecessor contracts) and their updated version (successor contracts) by computing the code similarity. By analyzing the difference between the predecessor contracts and their successor contracts, we found five reasons that led to the death of the contracts; two of them (i.e., Unmatched ERC20 Token and Limits of Permission ) might affect the life span of contracts. We developed a tool named LifeScope to detect these problems. LifeScope reports 0 false positives or negatives in detecting Unmatched ERC20 Token . In terms of Limits of Permission , LifeScope achieves 77.89% of F-measure and 0.8673 of AUC in average. According to the feedback of developers who exclude selfdestruct functions, we propose suggestions to help developers use selfdestruct functions in Ethereum smart contracts better.

2022 ◽  
Vol 65 ◽  
pp. 103068
Xuemei Bai ◽  
Yong Chen ◽  
Gangpeng Duan ◽  
Chao Feng ◽  
Wanli Zhang

2022 ◽  
Vol 1 (3) ◽  
pp. 1-7
Dr. Smitha Sambrani ◽  

Massive open online courses (MOOCs) is created greater prominence as a modern learning system mainly due to the advanced progress made in the area of Learning and Teaching Technology and. Covid pandemic also had open opportunities for Online Learning Platforms. Present study has focused on learners’ experience with various MOOCs platforms through online reviews and ratings, which were collected from Google play store and appbot application. Seven MOOCs platforms namely Coursera, edX, Udemy, Swayam, LinkedIn , Khan Academy and Upgrad are reviewed in this paper. The main objective is to compare the select MOOCs platforms in the area of users’ experience. Total number of reviews and rating has been taken for the study is 63, 652. The time frame of sample data was taken for last one year that is from 5th April, 2020 to 5th April, 2021. Sentiment analysis and chi-square test is applied to analyze the difference among the different MOOCs platforms. The major outcomes were the reviews and ratings of different platform found with very good uses experience.

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