Sparse Bayesian Learning with Diagonal Quasi-Newton Method for Large Scale Classification

Sparse Bayesian Learning (SBL) constructs an extremely sparse probabilistic model with very competitive generalization. However, SBL needs to invert a big covariance matrix with complexity O(M^3 ) (M: feature size) for updating the regularization priors, making it difficult for practical use. There are three issues in SBL: 1) Inverting the covariance matrix may obtain singular solutions in some cases, which hinders SBL from convergence; 2) Poor scalability to problems with high dimensional feature space or large data size; 3) SBL easily suffers from memory overflow for large-scale data. This paper addresses these issues with a newly proposed diagonal Quasi-Newton (DQN) method for SBL called DQN-SBL where the inversion of big covariance matrix is ignored so that the complexity and memory storage are reduced to O(M). The DQN-SBL is thoroughly evaluated on non-linear classifiers and linear feature selection using various benchmark datasets of different sizes. Experimental results verify that DQN-SBL receives competitive generalization with a very sparse model and scales well to large-scale problems.Comment: 11 pages,5 figure

D3P : Data-driven demand prediction for fast expanding electric vehicle sharing systems

The future of urban mobility is expected to be shared and electric. It is not only a more sustainable paradigm that can reduce emissions, but can also bring societal benefits by offering a more affordable on-demand mobility option to the general public. Many car sharing service providers as well as automobile manufacturers are entering the competition by expanding both their EV fleets and renting/returning station networks, aiming to seize a share of the market and to bring car sharing to the zero emissions level. During their fast expansion, one determinant for success is the ability of predicting the demand of stations as the entire system is growing continuously. There are several challenges in this demand prediction problem: First, unlike most of the existing work which predicts demand only for static systems or at few stages of expansion, in the real world we often need to predict the demand as or even before stations are being deployed or closed, to provide information and decision support. Second, for the new stations to be deployed, there is no historical data available to help the prediction of their demand. Finally, the impact of deploying/closing stations on the other stations in the system can be complex. To address these challenges, we formulate the demand prediction problem in the context of fast expanding electric vehicle sharing systems, and propose a data-driven demand prediction approach which aims to model the expansion dynamics directly from the data. We use a local temporal encoding process to handle the historical data for each existing station, and a dynamic spatial encoding process to take correlations between stations into account with Graph Convolutional Neural Networks (GCN). The encoded features are fed to a multi-scale predictor, which forecasts both the long-term expected demand of the stations and their instant demand in the near future. We evaluate the proposed approach with real-world data collected from a major EV sharing platform for one year. Experimental results demonstrate that our approach significantly outperforms the state of the art, showing up to three-fold performance gain in predicting demand for the expanding EV sharing systems

AN INVESTIGATION ON THE ERROR OF CALIBRATING EXTERIOR POINTS WITH INTERIOR POINTS

This study was to investigate the accuracy of calibrating exterior points with interior points using a frame with the same structure as Peak frame. Two cameras were used. Some points of the frame were used as control points to calibrate others using the DLT method. When we calibrated exterior points with interior points, the minimal and maximal errors were 0.171 cm and 1.797 cm respectively in the horizontal direction (X), 0.213 cm and 4.856 cm in the horizontal direction (Y), 0.103 cm and 1.608 cm in the vertical direction (Z). When we calibrated the interior points with exterior points, almost all errors were less than 1cm. It was concluded that to get the most accurate 3D reconstruction of human movement, it is necessary to make sure that the space formed by control points contains the objects to be calibrated

The Association between Meteorological Parameters and Aneurysmal Subarachnoid Hemorrhage: A Nationwide Analysis

Prior research has suggested that regional weather patterns impact the risk of rupture of cerebral aneurysms, but the findings in the literature have been inconsistent. Furthermore, no nationwide analysis to date has examined the association between meteorological factors and the post-procedural outcomes of patients after the treatment for ruptured cerebral aneurysms. The purpose of this study was to use a nationwide sample to analyze the association between specific meteorological parameters—temperature, precipitation, sunlight, and humidity—and hospital admission rate for and outcome after aneurysmal subarachnoid hemorrhage. Patients were identified using the Nationwide Inpatient Sample (2001–2010): Those with an ICD-9 diagnosis code for subarachnoid hemorrhage and a procedural code for aneurysm repair were included. Climate data were obtained from the State of the Climate Report 2010 released by the National Climatic Data Center. Multivariate regression models were constructed to analyze the association between average state monthly temperature, precipitation, and percent possible sunlight, as well as relative morning humidity and both monthly hospital admission rate, adjusted for annual state population in millions, and in-hospital mortality. 16,970 admissions were included from 723 hospitals across 41 states. Decreased daily sunlight and lower relative humidity were associated with an increased rate of admission for ruptured cerebral aneurysms (p<0.001), but had no association with differential inpatient mortality. No significant changes in these observed associations were seen when multivariate analyses were constructed. This is the first nationwide study to suggest that decreased sunlight and lower relative humidity are associated with admission for ruptured cerebral aneurysms. While it has been postulated that external atmospheric factors may cause hormonal and homeostatic changes that impact the risk of rupture of cerebral aneurysms, additional research is needed to confirm and further understand these relationships

Proposal for optically realizing quantum game

We present a proposal for optically implementing the quantum game of the two-player quantum prisoner's dilemma involving nonmaximally entangled states by using beam splitters, phase shifters, cross-Kerr medium, photon detector and the single-photon representation of quantum bits.Comment: 4 pages, 4 figure

DYNAMICS OF PREDATOR-PREY POPULATION WITH MODIFIED LESLIE-GOWER AND HOLLING-TYPE II SCHEMES

Joint Research on Environmental Science and Technology for the Eart

The Impact of Insurance Status on the Outcomes after Aneurysmal Subarachnoid Hemorrhage

Investigation into the association of insurance status with the outcomes of patients undergoing neurosurgical intervention has been limited: this is the first nationwide study to analyze the impact of primary payer on the outcomes of patients with aneurysmal subarachnoid hemorrhage who underwent endovascular coiling or microsurgical clipping. The Nationwide Inpatient Sample (2001–2010) was utilized to identify patients; those with both an ICD-9 diagnosis codes for subarachnoid hemorrhage and a procedure code for aneurysm repair (either via an endovascular or surgical approach) were included. Hierarchical multivariate regression analyses were utilized to evaluate the impact of primary payer on in-hospital mortality, hospital discharge disposition, and length of hospital stay with hospital as the random effects variable. Models were adjusted for patient age, sex, race, comorbidities, socioeconomic status, hospital region, location (urban versus rural), and teaching status, procedural volume, year of admission, and the proportion of patients who underwent ventriculostomy. Subsequent models were also adjusted for time to aneurysm repair and time to ventriculostomy; subgroup analyses evaluated for those who underwent endovascular and surgical procedures separately. 15,557 hospitalizations were included. In the initial model, the adjusted odds of in-hospital mortality were higher for Medicare (OR 1.23, p<0.001), Medicaid (OR 1.23, p<0.001), and uninsured patients (OR 1.49, p<0.001) compared to those with private insurance. After also adjusting for timing of intervention, Medicaid and uninsured patients had a reduced odds of non-routine discharge (OR 0.75, p<0.001 and OR 0.42, p<0.001) despite longer hospital stays (by 8.35 days, p<0.001 and 2.45 days, p = 0.005). Variations in outcomes by primary payer–including in-hospital post-procedural mortality–were more pronounced for patients of all insurance types who underwent microsurgical clipping. The observed differences by primary payer are likely multifactorial, attributable to varied socioeconomic factors and the complexities of the American healthcare delivery system