Selection of Statistical Software for Solving Big Data Problems for Teaching

The need for analysts with expertise in big data software is becoming more apparent in 4 today’s society. Unfortunately, the demand for these analysts far exceeds the number 5 available. A potential way to combat this shortage is to identify the software sought by 6 employers and to align this with the software taught by universities. This paper will 7 examine multiple data analysis software – Excel add-ins, SPSS, SAS, Minitab, and R – and 8 it will outline the cost, training, statistical methods/tests/uses, and specific uses within 9 industry for each of these software. It will further explain implications for universities and 10 students (PDF

Recognising the Clothing Categories from Free-Configuration Using Gaussian-Process-Based Interactive Perception

In this paper, we propose a Gaussian Process- based interactive perception approach for recognising highly- wrinkled clothes. We have integrated this recognition method within a clothes sorting pipeline for the pre-washing stage of an autonomous laundering process. Our approach differs from reported clothing manipulation approaches by allowing the robot to update its perception confidence via numerous interactions with the garments. The classifiers predominantly reported in clothing perception (e.g. SVM, Random Forest) studies do not provide true classification probabilities, due to their inherent structure. In contrast, probabilistic classifiers (of which the Gaussian Process is a popular example) are able to provide predictive probabilities. In our approach, we employ a multi-class Gaussian Process classification using the Laplace approximation for posterior inference and optimising hyper-parameters via marginal likelihood maximisation. Our experimental results show that our approach is able to recognise unknown garments from highly-occluded and wrinkled con- figurations and demonstrates a substantial improvement over non-interactive perception approaches

Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting

This paper proposes a single-shot approach for recognising clothing categories from 2.5D features. We propose two visual features, BSP (B-Spline Patch) and TSD (Topology Spatial Distances) for this task. The local BSP features are encoded by LLC (Locality-constrained Linear Coding) and fused with three different global features. Our visual feature is robust to deformable shapes and our approach is able to recognise the category of unknown clothing in unconstrained and random configurations. We integrated the category recognition pipeline with a stereo vision system, clothing instance detection, and dual-arm manipulators to achieve an autonomous sorting system. To verify the performance of our proposed method, we build a high-resolution RGBD clothing dataset of 50 clothing items of 5 categories sampled in random configurations (a total of 2,100 clothing samples). Experimental results show that our approach is able to reach 83.2\% accuracy while classifying clothing items which were previously unseen during training. This advances beyond the previous state-of-the-art by 36.2\%. Finally, we evaluate the proposed approach in an autonomous robot sorting system, in which the robot recognises a clothing item from an unconstrained pile, grasps it, and sorts it into a box according to its category. Our proposed sorting system achieves reasonable sorting success rates with single-shot perception.Comment: 9 pages, accepted by IROS201

Besov class via heat semigroup on Dirichlet spaces III: BV functions and sub-Gaussian heat kernel estimates

With a view toward fractal spaces, by using a Korevaar-Schoen space approach, we introduce the class of bounded variation (BV) functions in a general framework of strongly local Dirichlet spaces with a heat kernel satisfying sub-Gaussian estimates. Under a weak Bakry-\'Emery curvature type condition, which is new in this setting, this BV class is identified with a heat semigroup based Besov class. As a consequence of this identification, properties of BV functions and associated BV measures are studied in detail. In particular, we prove co-area formulas, global $L^1$ Sobolev embeddings and isoperimetric inequalities. It is shown that for nested fractals or their direct products the BV class we define is dense in $L^1$. The examples of the unbounded Vicsek set, unbounded Sierpinski gasket and unbounded Sierpinski carpet are discussed.Comment: The notes arXiv:1806.03428 will be divided in a series of papers. This is the third paper. v2: Final versio

A re-examination of the BEST Trial using composite outcomes, including emergency department visits

Objectives: The influence of choice of endpoint on trial size, duration, and interpretation of results was examined in patients with heart failure who were enrolled in BEST (Beta-blocker Evaluation of Survival Trial). Background: The choice of endpoints in heart failure trials has evolved over the past 3 decades. Methods: In the BEST trial, we used Cox regression analysis to examine the effect of bucindolol on the current standard composite of cardiovascular death or heart failure hospitalization (CVD/HFH) compared with the original primary mortality endpoint and the expanded composite that included emergency department (ED) visits. We also undertook an analysis of recurrent events primarily using the Lin, Wei, Ying, and Yang model. Results: Overall, 448 (33%) patients on placebo and 411 (30%) patients on bucindolol died (hazard ratio [HR]: 0.90; 95% confidence interval [CI]: 0.78 to 1.02; p = 0.11). A total of 730 (54%) patients experienced CVD/HFH on placebo and 624 (46%) on bucindolol (HR: 0.80; 95% CI: 0.72 to 0.89; p < 0.001). Adding ED visits increased these numbers to 768 (57%) and 668 (49%), respectively (HR: 0.81; 95% CI: 0.73 to 0.90; p < 0.001). A total of 568 (42%) patients on placebo experienced HFH compared with 476 (35%) patients on bucindolol (HR: 0.78; 95% CI: 0.69 to 0.89; p < 0.001), with a total of 1,333 and 1,124 admissions, respectively. With the same statistical assumptions, using the composite endpoint instead of all-cause mortality would have reduced the trial size by 40% and follow-up duration by 69%. The rate ratio for recurrent events (CVD/HFH) was 0.83 (95% CI: 0.73 to 0.94; p = 0.003). Conclusions: Choice of endpoint has major implications for trial size and duration, as well as interpretation of results. The value of broader composite endpoints and inclusion of recurrent events needs further investigation. (Beta Blocker Evaluation in Survival Trial [BEST]; NCT00000560

Cracking behavior of thin gold strip deposited on polycarbonate plate under Cyclic and stepwisely-increased tension

Thin metal films deposited on polymers have been attracting much attention in flexible electronics. Various combinations of film and polymer substrate are expected to be developed. In this paper, deformation and fracture of thin metal film on polymer substrate was briefly analysed and the influencing factors were summarized. After that, focusing on the shape and thickness of thin metal film on polymer substrate, cracking behavior of small rounded-rectangular thin gold films called “strips” with a thickness less than one micrometer was observed under cyclic and stepwisely-increased trapezoidal tensions. The strips were deposited on a polycarbonate plate specimen, and wide and narrow strips with different thickness were prepared for the tests. The cracking behaviour was successfully observed and the increasing rate of crack number with the load or strain was examined. Since most cracks were initiated from the small defects, the crack initiation behavior was correlated with the distribution of defects