Global uniqueness in an inverse problem for a class of damped stochastic plate equations

This paper deals with the global uniqueness of an inverse problem for the stochastic plate with structural damping. The key point is the Carleman estimate for the fourth order stochastic plate operators dyt − ρ∆ytdt + ∆2ydt. To this aim, a weighted point- wise identity for a fourth order stochastic plate operator is established, via which we obtained the desired Carleman estimate for the corresponding stochastic plate equation with structural damping.

Unsupervised weights selection for optimal transport based dataset integration

A formulation of the dataset integration problem describes the task of aligning two or more empirical distributions sampled from sources of the same kind, so that records of similar object end up close to one another. We propose a variant of the optimal transport- and Gromov-Wasserstein-based dataset integration algorithm introduced in SCOT. We formulate a constrained quadratic program to adjust sample weights before OT or GW so that weighted point density is close to be uniform over the point cloud, for a given kernel. We test this method with one synthetic and two real-life datasets from single-cell biology. Weights adjustment allows distributions with similar effective supports but different local densities to be reliably integrated, which is not always the case with the original method. This approach is entirely unsupervised, scales well to thousands of samples and does not depend on dimensionality of the ambient space, which makes it efficient for the analysis of single-cell datasets in biology. We provide an open-source implementation of this method in a Python package, woti.

Fast 6D Object Pose Estimation of Shell Parts for Robotic Assembly

Shell parts which have similar and close inner and outer surfaces are common in industrial manufacturing applications. In view of the 6D pose error compensation of parts in high-precision robotic assembly tasks, this work proposes a fast 6D pose estimation approach tailored for shell parts. With a binocular structured light camera, the proposed approach consists of two phases, namely initial pose estimation phase and local pose estimation phase. In the former one, an initial pose correction and translation offset methods serve to solve the local optimal estimation problem of the iterative closest point (ICP) algorithm. This problem is caused by the poorly assigned initial pose and the similar inner and outer surfaces of shell parts. In the latter one, the voxel sampling and the weighted point-to-plane ICP algorithms are applied to boost the efficiency of the pose estimation approach. With two typical shell parts, a simulation and an experiment of pose estimation are conducted to verify the effectiveness of the proposed approach. Experiment results prove that the accuracy of the pose estimation approach is 0:27mm/0:38°, and the runtime is 680ms.

Efficient Algorithms for Max-Weighted Point Sweep Coverage on Lines

As an important application of wireless sensor networks (WSNs), deployment of mobile sensors to periodically monitor (sweep cover) a set of points of interest (PoIs) arises in various applications, such as environmental monitoring and data collection. For a set of PoIs in an Eulerian graph, the point sweep coverage problem of deploying the fewest sensors to periodically cover a set of PoIs is known to be Non-deterministic Polynomial Hard (NP-hard), even if all sensors have the same velocity. In this paper, we consider the problem of finding the set of PoIs on a line periodically covered by a given set of mobile sensors that has the maximum sum of weight. The problem is first proven NP-hard when sensors are with different velocities in this paper. Optimal and approximate solutions are also presented for sensors with the same and different velocities, respectively. For M sensors and N PoIs, the optimal algorithm for the case when sensors are with the same velocity runs in O(MN) time; our polynomial-time approximation algorithm for the case when sensors have a constant number of velocities achieves approximation ratio 12; for the general case of arbitrary velocities, 12α and 12(1−1/e) approximation algorithms are presented, respectively, where integer α≥2 is the tradeoff factor between time complexity and approximation ratio.

Health behaviors and health services accessibility factors associated with diabetes: a propensity score matching analysis

Introduction: This study aims to explore diabetes prevalence and to identify the associated health behaviors and accessibility factors. Despite increasing burden of diabetes in Turkey, there is a lack of information regarding associated factors with diabetes.Methods: Data gathered from TurkStat-Health Survey for the year 2014. 1996 individuals who had reported diabetes were matched to similar non-diabetes participants in terms of socio-demographic characteristics and comorbidities by using 1:1 nearest matching based on estimated propensity scores.Results: The weighted point prevalence of diabetes among adults was 8.98%. Compared with smokers, non-smokers were less likely to develop diabetes (OR:0.96; 95% CI:0.95–0.97). Individuals engaging in regular physical activity were less likely to have experienced diabetes than individuals not engaging in any physical activity (OR:0.51; 95% CI:0.41–0.52). Conclusions: Improving the health behaviors of individuals and promoting a culture of exercise and healthy food consumption are necessary strategies to fight against diabetes in Turkey. Collaborations between health professionals will provide many lights for effective clinical decision making and preparations of diabetes self-management programs. Crisper understanding of associated health behaviors and health services accessibility factors associated with diabetes is beneficial to develop better nursing plans for patients.

Academic success as estimated by locus of control and motivation

Dropping out from undergraduate education, an indicator of academic success, is costly for students, parents, and society in general. Therefore, the early identification of potential dropout students is important. The contribution of personal features to dropout rates has merited exploration. However, there is a paucity of research on psychological factors that may lead to dropping out. In addition, the country’s adoption of K to 12 curriculum may have an impact on the academic success of students. This paper examines the influence of locus of control and motivation on academic success, as measured by dropout intention and academic performance, of first batch of freshmen under the K to 12 curriculum in the Philippines. A descriptive-correlational study using online survey questionnaires was employed to 21,012 respondents who were chosen through simple random sampling and Slovin’s formula. Standard questionnaires were used to gather data on locus of control, motivation, and dropout intention, while academic performance was measured using the Weighted Point Average (WPA) of the students. Results showed that majority of the respondents had very strong external locus of control but had low level of motivation. Results also showed that majority of the respondents had very high level of dropout intention and WPA. Correlation revealed a weak positive association between locus of control and WPA. Multiple regression analysis revealed that locus of control significantly influenced WPA while locus of control and motivation did not influence dropout intention.

Adjustment to college and academic performance: Insights from Filipino college freshmen in an allied health science course

This paper aims to know the relationship between the level of adjustment to college and academic performance of first year Radiologic Technology students of a higher education institution in the Philippines. A descriptive-correlational study using survey questionnaire was employed to 132 respondents who were chosen through stratified random sampling and Slovin’s formula. Standard questionnaires were used to gather data on the demographic profile and level of adjustment of the respondents while the academic performance was measured through the Weighted Point Average (WPA) requested from the school’s Registrar. Results showed that the majority of the respondents are female (53.8%), belong to middle income class (34.8%), were from STEM (59.1%) and travel between one kilometer and 10 kilometers to school (34.1%). The study reported a moderate level of adjustment and a 2.63 overall WPA of students. Test of difference showed that there is significant difference in the academic adjustment and academic strand taken during SHS (p<0.05); and in the institutional attachment and proximity of house to school (p<0.05). Bivariate correlation among variables revealed that there is no significant relationship between the level of adjustment to college and academic performance of first year Radiologic Technology students College (p>0.05).

Evaluation of variable pitot inlet concepts for transonic and supersonic civil aviation

Purpose This paper aims to describe the selection of the ideal variable inlet concept group by using results of aerodynamic investigations, system safety analyses and integration studies. Design/methodology/approach Aerodynamic and functional inlet requirements are explained and variable inlet concept groups are introduced. The concept evaluation by means of a weighted point rating is presented. The respective concept groups are analysed and evaluated regarding economic, functional and safety requirements. Findings By means of this evaluation, the concept group that adjusts the inlet geometry by rigid segment repositioning is identified as most suitable concept group. Originality/value The early selection of the most suitable concept group enables more detailed subsequent concept investigations, potentially enabling the technology of variable inlets for future commercial aircraft.

WEIGHTED POINT CLOUD AUGMENTATION FOR NEURAL NETWORK TRAINING DATA CLASS-IMBALANCE

<p><strong>Abstract.</strong> Recent developments in the field of deep learning for 3D data have demonstrated promising potential for end-to-end learning directly from point clouds. However, many real-world point clouds contain a large class im-balance due to the natural class im-balance observed in nature. For example, a 3D scan of an urban environment will consist mostly of road and façade, whereas other objects such as poles will be under-represented. In this paper we address this issue by employing a weighted augmentation to increase classes that contain fewer points. By mitigating the class im-balance present in the data we demonstrate that a standard PointNet++ deep neural network can achieve higher performance at inference on validation data. This was observed as an increase of F1 score of 19% and 25% on two test benchmark datasets; ScanNet and Semantic3D respectively where no class im-balance pre-processing had been performed. Our networks performed better on both highly-represented and under-represented classes, which indicates that the network is learning more robust and meaningful features when the loss function is not overly exposed to only a few classes.</p>