A Socioeconomic Well-Being Index

An annual well-being index constructed from thirteen socioeconomic factors is proposed in order to dynamically measure the mood of the US citizenry. Econometric models are fitted to the log-returns of the index in order to quantify its tail risk and perform option pricing and risk budgeting. By providing a statistically sound assessment of socioeconomic content, the index is consistent with rational finance theory, enabling the construction and valuation of insurance-type financial instruments to serve as contracts written against it. Endogenously, the VXO volatility measure of the stock market appears to be the greatest contributor to tail risk. Exogenously, "stress-testing" the index against the politically important factors of trade imbalance and legal immigration, quantify the systemic risk. For probability levels in the range of 5% to 10%, values of trade below these thresholds are associated with larger downward movements of the index than for immigration at the same level. The main intent of the index is to provide early-warning for negative changes in the mood of citizens, thus alerting policy makers and private agents to potential future market downturns

GASL: Guided Attention for Sparsity Learning in Deep Neural Networks

The main goal of network pruning is imposing sparsity on the neural network by increasing the number of parameters with zero value in order to reduce the architecture size and the computational speedup. In most of the previous research works, sparsity is imposed stochastically without considering any prior knowledge of the weights distribution or other internal network characteristics. Enforcing too much sparsity may induce accuracy drop due to the fact that a lot of important elements might have been eliminated. In this paper, we propose Guided Attention for Sparsity Learning (GASL) to achieve (1) model compression by having less number of elements and speed-up; (2) prevent the accuracy drop by supervising the sparsity operation via a guided attention mechanism and (3) introduce a generic mechanism that can be adapted for any type of architecture; Our work is aimed at providing a framework based on interpretable attention mechanisms for imposing structured and non-structured sparsity in deep neural networks. For Cifar-100 experiments, we achieved the state-of-the-art sparsity level and 2.91x speedup with competitive accuracy compared to the best method. For MNIST and LeNet architecture we also achieved the highest sparsity and speedup level

Adapting Stream Processing Framework for Video Analysis

AbstractStream processing (SP) became relevant mainly due to inexpensive and hence ubiquitous deployment of sensors in many domains (e.g., environmental monitoring, battle field monitoring). Other continuous data generators (surveillance, traffic data) have also prompted processing and analysis of these streams for applications such as traffic congestion/accidents and personalized marketing. Image processing has been researched for several decades. Recently there is emphasis on video stream analysis for situation monitoring due to the ubiquitous deployment of video cameras and unmanned aerial vehicles for security and other applications.This paper elaborates on the research and development issues that need to be addressed for extending the traditional stream processing framework for video analysis, especially for situation awareness. This entails extensions to: data model, operators and language for expressing complex situations, QoS (Quality of service) specifications and algorithms needed for their satisfaction. Specifically, this paper demonstrates inadequacy of current data representation (e.g., relation and arrable) and querying capabilities to infer long-term research and development issues

Development of a double-pole double-throw radio frequency micro electro-mechanical systems switch using an ‘S’ shaped pivot

This paper investigates the design of a novel pivot for a seesaw, RF MEMS, double-pole double-throw (DPDT) switch, which has been developed to operate within mobile communication systems and devices. The pivot employs a unique ‘S’ structure at the nano scale, in the form of a, which helps to keep von-Mises stresses below 21 MPa. The pivot requires less pulling force than similar designs due to its flexibility which allows the beam and contacts a greater space of separation while the switch is off. This in turn results in improved contact isolation of greater than −77 dB at 5 GHz. The RF MEMS switch is an improvement over the previously published paper (Al-Amin et al. in International symposium on microelectronics, vol 2013, no 1, pp 000831–000835, 2013. doi:10.1109/ECS.2014.6892558), since the pulling force of the electrostatic plates can be generated with a voltage which is greatly reduced from 14 to 8 V using the same electrostatic plate area size. The switch is a progression from SPST and DPDT seesaw switching since it provides improved flexibility over the previously described devices. With the redesign of the pivot the switch attains a greater ‘air-gap’ between the contacts when open-circuited which therefore allows for improved isolation during the off-state

Application of Queuing Analytic Theory to Decrease Waiting Times in Emergency Department: Does it Make Sense?

Background: Patients who receive care in an emergency department (ED), are usually unattended while waiting in queues. Objectives: This study was done to determine, whether the application of queuing theory analysis might shorten the waiting times of patients admitted to emergency wards. Patients and Methods: This was an operational study to use queuing theory analysis in the ED. In the first phase, a field study was conducted to delineate the performance of the ED and enter the data obtained into simulator software. In the second phase, "ARENA" software was used for modeling, analysis, creating a simulation and improving the movement of patients in the ED. Validity of the model was confirmed through comparison of the results with the real data using the same instrument. The third phase of the study concerned modeling in order to assess the effect of various operational strategies, on the queue waiting time of patients who were receiving care in the ED. Results: In the first phase, it was shown that 47.7% of the 3000 patient records were cases referred for trauma treatment, and the remaining 52.3% were referred for non-trauma services. A total of 56% of the cases were male and 44% female. Maximum input was 4.5 patients per hour and the minimum input was 0.5 per hour. The average length of stay for patients in the trauma section was three hours, while for the non-trauma section it was four hours. In the second phase, modeling was tested with common scenarios. In the third phase, the scenario with the addition of one or more senior emergency resident(s) on each shift resulted in a decreased length of stay from 4 to 3.75 hours. Moreover, the addition of one bed to the Intensive Care Unit (ICU) and/or Critical Care Unit (CCU) in the study hospital, reduced the occupancy rate of the nursing service from 76% to 67%. By adding another clerk to take electrocardiograms (ECG) in the ED, the average time from a request to performing the procedure is reduced from 26 to 18 minutes. Furthermore, the addition of 50% more staff to the laboratory and specialist consultations led to a 90 minute reduction in the length of stay. It was also shown that earlier consultations had no effect on the length of stay. Conclusions: Application of queuing theory analysis can improve movement and reduce the waiting times of patients in bottlenecks within the ED throughput