Predicting dental student\u27s performance on national board dental examination (NBDE) part I: exploring demographic factors, dental admission test (DAT) factors, pre-program academic factors, and dental program academic performance.

Dental students need to successfully challenge a national licensure examination to be able to practice dentistry. Dental educators currently have difficulty in identifying candidates who are at risk of failing this examination. This non-experimental quantitative study examined existing dental student data from the 2017, 2018, and 2019 graduating classes, using a retrospective and correlational approach to identify possible markers for at risk students. Demographic factors, dental admission test (DAT) factors, pre-program academic factors, dental program academic performance, and National Board Dental Examination (NBDE) Part I performance. A series of independent t-tests and One-Way analysis of variances (ANOVAs) were used to examine the students’ performances regarding their gender and race. Logistic regression models were used to predict NBDE Part I performance at the first attempt from each categorical (demographic factor) and continuous predictor (pre-program academic performance, DAT performance, and dental program academic Performance). Gender and race were significantly associated with the student’s academic achievement at the undergraduate level and the DAT; however, the influence of these factors diminished in the dental program academic performance and the NBDE Part I. Student’s dental program performance were significantly associated with the NBDE Part I outcomes. Within the limitations of this study, dental students with different gender and race backgrounds all have the potential to successfully complete NBDE. Additional enrichment and bridge programs for the underrepresented minorities students may be used to maximize the future success of the enrolled diverse student body. The dental program can have performance benchmarks starting at program admission and continuing through the end of the second year to help identify at-risk students early and provide them with additional academic support

Practical Block-wise Neural Network Architecture Generation

Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy. The optimal network block is constructed by the learning agent which is trained sequentially to choose component layers. We stack the block to construct the whole auto-generated network. To accelerate the generation process, we also propose a distributed asynchronous framework and an early stop strategy. The block-wise generation brings unique advantages: (1) it performs competitive results in comparison to the hand-crafted state-of-the-art networks on image classification, additionally, the best network generated by BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of the search space in designing networks which only spends 3 days with 32 GPUs, and (3) moreover, it has strong generalizability that the network built on CIFAR also performs well on a larger-scale ImageNet dataset.Comment: Accepted to CVPR 201

Clinical performance of intentionally tilted implants versus axially positioned implants: A systematic review

Objectives The aim of this review was to determine the clinical performance of dental implants that are intentionally tilted when compared with implants that are placed following the long axis of the residual alveolar ridge. Materials and methods A systematic review of the scientific literature using a predefined research question (PICO) and search strategy was undertaken. This search included five electronic databases. Two independent reviewers examined electronic databases and performed a manual review following search strategy to accomplish the item generation and reduction. Included articles were evaluated to determine the level of evidence. Data were extracted only from level I and level II studies, based on the Oxford Centre for Evidence‐based Medicine—Levels of Evidence (March 2009). If included studies were homogeneous in nature, data were to be accumulated. However, if included studies were heterogeneous in nature, only descriptive data would be reviewed and analyzed. Results A total of 811 articles were identified through the PICO question and search strategy. Detailed review of the abstracts and articles resulted in further item reduction, and 46 articles were included for full‐text review. A total of 42 articles were then selected for inclusion in the systematic review. The identified articles included two level I and 20 level II studies. In addition, 15 level IV, one gray literature, and four previous systematic reviews with meta‐analyses were also used in the study. The extracted data from the included studies demonstrated heterogeneity that prevented quantitative assessment, and only one level II study directly compared tilted and axially placed implants. Assessment of the descriptive data demonstrated no differences in implant survival, marginal bone loss, prosthesis survival, or patient‐reported outcome measures (PROMs) whether implants are placed axially or with intentional inclination of the coronal aspect of the implant toward the distal aspect of edentulous jaws. Conclusions Based upon the systematic review of the literature, an analysis of the descriptive data suggested no differences in clinical performance between implants that are placed in an axial position relative to the residual alveolar ridge when compared with implants that are intentionally tilted toward the distal aspect of edentulous jaws

The asymptotic distribution and Berry--Esseen bound of a new test for independence in high dimension with an application to stochastic optimization

Let $\mathbf{X}_1,...,\mathbf{X}_n$ be a random sample from a $p$-dimensional population distribution. Assume that $c_1n^{\alpha}\leq p\leq c_2n^{\alpha}$ for some positive constants $c_1,c_2$ and $\alpha$. In this paper we introduce a new statistic for testing independence of the $p$-variates of the population and prove that the limiting distribution is the extreme distribution of type I with a rate of convergence $O((\log n)^{5/2}/\sqrt{n})$. This is much faster than $O(1/\log n)$, a typical convergence rate for this type of extreme distribution. A simulation study and application to stochastic optimization are discussed.Comment: Published in at http://dx.doi.org/10.1214/08-AAP527 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Active Authentication via Hiding Programs in Digital Contents

We propose a generic active authentication framework via hiding programs in digital contents, especially designed for H.264 / MPEG-4 AVC video formats. Besides using cryptography and steganography techniques, we bind a scripting language runtime as process virtual machine, giving the developer the possibility to design their own variant from passive authentication to active code execution