Active Collaborative Ensemble Tracking

A discriminative ensemble tracker employs multiple classifiers, each of which casts a vote on all of the obtained samples. The votes are then aggregated in an attempt to localize the target object. Such method relies on collective competence and the diversity of the ensemble to approach the target/non-target classification task from different views. However, by updating all of the ensemble using a shared set of samples and their final labels, such diversity is lost or reduced to the diversity provided by the underlying features or internal classifiers' dynamics. Additionally, the classifiers do not exchange information with each other while striving to serve the collective goal, i.e., better classification. In this study, we propose an active collaborative information exchange scheme for ensemble tracking. This, not only orchestrates different classifier towards a common goal but also provides an intelligent update mechanism to keep the diversity of classifiers and to mitigate the shortcomings of one with the others. The data exchange is optimized with regard to an ensemble uncertainty utility function, and the ensemble is updated via co-training. The evaluations demonstrate promising results realized by the proposed algorithm for the real-world online tracking.Comment: AVSS 2017 Submissio

Efficient Asymmetric Co-Tracking using Uncertainty Sampling

Adaptive tracking-by-detection approaches are popular for tracking arbitrary objects. They treat the tracking problem as a classification task and use online learning techniques to update the object model. However, these approaches are heavily invested in the efficiency and effectiveness of their detectors. Evaluating a massive number of samples for each frame (e.g., obtained by a sliding window) forces the detector to trade the accuracy in favor of speed. Furthermore, misclassification of borderline samples in the detector introduce accumulating errors in tracking. In this study, we propose a co-tracking based on the efficient cooperation of two detectors: a rapid adaptive exemplar-based detector and another more sophisticated but slower detector with a long-term memory. The sampling labeling and co-learning of the detectors are conducted by an uncertainty sampling unit, which improves the speed and accuracy of the system. We also introduce a budgeting mechanism which prevents the unbounded growth in the number of examples in the first detector to maintain its rapid response. Experiments demonstrate the efficiency and effectiveness of the proposed tracker against its baselines and its superior performance against state-of-the-art trackers on various benchmark videos.Comment: Submitted to IEEE ICSIPA'201

Effects of multiple doses of gonadotropin-releasing hormone agonist on the luteal-phase support in assisted reproductive cycles: A clinical trial study

Background: The effect of adding gonadotropin-releasing hormone (GnRH) agonist on the luteal phase support in assisted reproductive technique (ART) cycles is controversial. Objective: To determine the effects of adding multiple doses of GnRH agonist to the routine luteal phase support on ART cycle outcomes. Materials and Methods: This clinical trial study included 200 participants who underwent the antagonist protocol at the Research and Clinical Center for Infertility, Yazd, Iran, between January and March 2020. Of the 200, 168 cases who met the inclusion criteria were equally divided into two groups – the case and the control groups. Both groups received progesterone in the luteal phase, following which the case group received GnRH agonist subcutaneously (0/1 mg triptorelin) zero, three, and six days after the fresh embryo transfer, while the control group did not receive anything. Finally, chemical and clinical pregnancy rates, number of mature oocytes, fertilization rate, total dose of gonadotropin, and the estradiol level were determined. Results: The baseline characteristics were similar in both groups. No significant difference was observed between embryo transfer cycles. Clinical results showed that differences between the fertilization rate, chemical and clinical pregnancies were not significant. Conclusion: The results showed that receiving multiple doses of GnRH agonist in the luteal phase of ART cycles neither improves embryo implantation nor the pregnancy rates; therefore, further studies are required. Key words: Luteal phase, GnRH agonist, ART, Pregnancy rate

Investigating The Discourse of Trilingual Youth Identity; Nickname among Trilingual Youth in The Village of Dashkasan

‌This study explores the use of nicknames among trilingual youth, investigating the influence of identity, culture, language, and attitudes on their propensity to assign nicknames to others. This research is cross-sectional and uses survey research. Results reveal that nicknames mirror the intricacy of social relations in a trilingual society. Young people’s attitudes towards others’ titles are predominantly negative, while their views on their own titles are more positive. Physical attributes form the basis for the most common nicknames. In this trilingual village, nicknames are primarily given to incapacitated individuals, those with differing religious beliefs, and those who do not share commonalities with the dominant language (Georgian, the native language of the dominant group) and ethnicity. The dominant language group is more inclined to assign titles. Most titles are based on descriptive phrases rather than verbal, prepositional, or adverbial phrases

Corporate Default Prediction with Industry Effects : Evidence from Emerging Markets

The accurate prediction of corporate bankruptcy for the firms in different industries is of a great concern to investors and creditors. Firm-specific data accompany with industry and macroeconomic factors offer a potentially large number of candidate predictors of corporate default. We employ a predictor selection procedure based on non-parametric regression and classification tree method (CART) and test its performance within a standard logistic regression model. Overall entire analyses indicate that the orientation between firm-level determinants and the probability of default is affected by each industry’s characteristics. As well, our selection method represents an efficient way of introducing non-linear effects of predictor variables on the default probability