Development of Computer Vision-Enhanced Smart Golf Ball Retriever

An automatic vehicle system was developed to assist golfers in collecting golf balls from a practice field. Computer vision methodology was utilized to enhance the detection of golf balls in shallow and/or deep grass regions. The free software OpenCV was used in this project because of its powerful features and supported repository. The homemade golf ball picker was built with a smart recognition function for golf balls and can lock onto targets by itself. A set of field tests was completed in which the rate of golf ball recognition was as high as 95%. We report that this homemade smart golf ball picker can reduce the tremendous amount of labor associated with having to gather golf balls scattered throughout a practice field

Criterion of Incipient Re-Suspension of Deposition by Density Currents

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

ANALYSIS OF FIELDER STARTS AND BENCH ABILITY ON AMERICAN PROFESSIONAL BASEBALL PLAYERS

The development of athletes or players depends on two aspects: nature and nurture. The former is the talent and qualification of the players themselves, while the latter is the training that consumes human, material and financial resources. Take professional baseball players as an example. Matching the talents of players and referring to the relevant starting rules of the professional baseball league, when the up-and-coming players are first discovered, focused training are used on them. By doing so, the value of the players would be effectively enhanced and the players are helped to seek a better way out. This can form a virtuous circle: the pellets get quality players, and the players get better results. That is to say, strengthening the training for the shortcomings of the players with the potential of the starting players can avoid unnecessary training and huge training expenses behind them, and greatly reduce the risk of career, so that the players have higher security in their short career, and get a win-win-win situation. This study is aimed at the schedule information of the American Baseball League teams. Through feature selection of data mining, this study analyzes the main relationships and key differences between starting player and bench player of second baseman and shortstop in League of Nations teams. It is found that the on base percentage and speed of the infielders is an important ability indicator for the starting position; whereas, the second baseman emphasizes on the attack and the shortstop focuses on fielding. This feature is verified by comparing the opinions of experts and commentators.  Article visualizations

Aggretriever: A Simple Approach to Aggregate Textual Representations for Robust Dense Passage Retrieval

AbstractPre-trained language models have been successful in many knowledge-intensive NLP tasks. However, recent work has shown that models such as BERT are not “structurally ready” to aggregate textual information into a [CLS] vector for dense passage retrieval (DPR). This “lack of readiness” results from the gap between language model pre-training and DPR fine-tuning. Previous solutions call for computationally expensive techniques such as hard negative mining, cross-encoder distillation, and further pre-training to learn a robust DPR model. In this work, we instead propose to fully exploit knowledge in a pre-trained language model for DPR by aggregating the contextualized token embeddings into a dense vector, which we call agg★. By concatenating vectors from the [CLS] token and agg★, our Aggretriever model substantially improves the effectiveness of dense retrieval models on both in-domain and zero-shot evaluations without introducing substantial training overhead. Code is available at https://github.com/castorini/dhr

Proactive measures of governmental debt guarantees to facilitate Public-Private Partnerships project

Governmental Debt Guarantees (GDGs) are often used to encourage involvement by promoters and financial institutions in Public-Private Partnerships (PPP) projects. However, even after demonstrating the bankability of a project and reducing debt cost, the success of the project may be prevented by the lack of long-term commitment from shareholders. Equity contributions by promoters in the project company may be recovered from earnings on short-term construction activities. Based on lesson learned from early PPP projects with GDG, the hold-up problem for government in the view of transaction cost economic (TCE) theory may worsen if the designed contractual structure does not adequately manage opportunistic behaviours from promoters. This study empirically examined the effects of a structured GDG mechanism with particular complementary measures applied in joint projects to develop the Taipei Mass Rapid Transit (MRT) stations. A GDG game model was then applied to bridge the theoretical gap based on the Taipei MRT experience. The analysis shows that requiring the promoter to provide sufficient equity and ensuring the commitment of the lender to provide the loan are the appropriate proactive measures. This study demonstrates its practical value for policy makers by combining case study, TCE and game theory in contractual issues

Improving Conversational Passage Re-ranking with View Ensemble

This paper presents ConvRerank, a conversational passage re-ranker that employs a newly developed pseudo-labeling approach. Our proposed view-ensemble method enhances the quality of pseudo-labeled data, thus improving the fine-tuning of ConvRerank. Our experimental evaluation on benchmark datasets shows that combining ConvRerank with a conversational dense retriever in a cascaded manner achieves a good balance between effectiveness and efficiency. Compared to baseline methods, our cascaded pipeline demonstrates lower latency and higher top-ranking effectiveness. Furthermore, the in-depth analysis confirms the potential of our approach to improving the effectiveness of conversational search.Comment: SIGIR 202