What factors contributed to changes in employment during and after the Great Recession?

Unemployment increased drastically over the course of the Great Recession from 4.5 percent prior to the recession to 10 percent at its peak in October 2009. Since then, the unemployment rate has come down steadily, and it stood at 5.8 percent in November 2014. Based on existing analyses and some new evidence, this paper establishes that much of the change in unemployment during the Great Recession and during the recovery can be attributed to cyclical factors rather than structural factors. The paper then presents new suggestive evidence to quantify the employment impacts of various counter-cyclical policies introduced during this time. We conduct a counter-factual and find that employment would have been between 4.2 percent and 4.5 percent lower had it not been because of the spending in Medicaid injected in local economies by the Recovery Act. In addition, we conduct a differences-in-differences and triple difference analysis, which suggests that the Work Opportunity Tax Credits increased the likelihood of employment by about 4.7 percent for disconnected youth but had no effect on disabled and unemployed veterans. Finally, we also find evidence that suggests that the Hiring Incentive to Restore Employment (HIRE) Act increased employment of the unemployed by 2.6 percent and that the reemployment reforms introduced in 2012 as part of the UI extensions increased employment by 6 percent for the long-term unemployed

Cohomology of osp(1∣2)\frak {osp}(1|2) acting on the space of bilinear differential operators on the superspace R1∣1\mathbb{R}^{1|1}

We compute the first cohomology of the ortosymplectic Lie superalgebra $\mathfrak{osp}(1|2)$ on the (1,1)-dimensional real superspace with coefficients in the superspace $\frak{D}_{\lambda,\nu;\mu}$ of bilinear differential operators acting on weighted densities. This work is the simplest superization of a result by Bouarroudj [Cohomology of the vector fields Lie algebras on $\mathbb{R}\mathbb{P}^1$ acting on bilinear differential operators, International Journal of Geometric Methods in Modern Physics (2005), {\bf 2}; N 1, 23-40]

Half Gaussian-based wavelet transform for pooling layer for convolution neural network

Pooling methods are used to select most significant features to be aggregated to small region. In this paper, anew pooling method is proposed based on probability function. Depending on the fact that, most information is concentrated from mean of the signal to its maximum values, upper half of Gaussian function is used to determine weights of the basic signal statistics, which is used to determine the transform of the original signal into more concise formula, which can represent signal features, this method named half gaussian transform (HGT). Based on strategy of transform computation, Three methods are proposed, the first method (HGT1) is used basic statistics after normalized it as weights to be multiplied by original signal, second method (HGT2) is used determined statistics as features of the original signal and multiply it with constant weights based on half Gaussian, while the third method (HGT3) is worked in similar to (HGT1) except, it depend on entire signal. The proposed methods are applied on three databases, which are (MNIST, CIFAR10 and MIT-BIH ECG) database. The experimental results show that, our methods are achieved good improvement, which is outperformed standard pooling methods such as max pooling and average pooling

HIV/AIDS prisoners: a case study on quality of life in Roumieh, Lebanon

Prisons often lack the basic health services required by HIV/AIDS patients. As with many other chronic illnesses, the treatment of HIV is expensive in terms of medication, hygiene, testing and staff training. Strategies to combat the disease have been thoroughly developed, particularly in Europe (WHO/UNAIDS, 2006). The purpose of this study was to assess quality of life (QOL) of the only 5 reported cases of HIV/AIDS patients in Roumieh prison (the country’s largest male top-security prison) using the WHOQOL and the WHO guidelines on HIV infection and AIDS in prison. Virtually all prisoners reported that their rights had been violated. Isolation measures were taken to prevent the spread of the disease. According to UNAIDS, this particular measure has been proven ineffective. In conclusion, other approaches should be implemented to respect inmates’ rights and reduce transmission of the virus.Keywords: HIV/AIDS, quality of life, health, prisoners, human rights.Les prisons n’ont pas souvent les services de santé de base requises pour le traitement du VIH/SIDA. C’est le cas aussi d’autres maladies chroniques, le traitement du VIH coute cher en terme de médicaments, d’hygiène, de test, et la formation du personnel. Les stratégies pour combattre la maladie ont été bien développées, particulièrement en Europe (OMS/ONUSIDA, 2006). L’objectif de cette étude était d’évalué la qualité de vie (QDV) de 5 prisoniers malades du VIH/SIDA qui ont été signalés dans la prison de Roumieh (la plus grande prison du pays pour les hommes de haute sécurité) utilisant les guides de l’OMSQDV sur l’infection du VIH et le SIDA. Pratiquement tous les prisonniers signalaient que leurs droits ont été violés. Des mesures d’isolement ont été prises pour prévenir la propagation des maladies. Selon l’ONUSIDA cette mesure particulière s’est avérée inefficace. En conclusion, d’autres approches devraient être mises en oeuvre pour respecter les droits des détenus et réduire la transmission du virus

Specialized Deep Residual Policy Safe Reinforcement Learning-Based Controller for Complex and Continuous State-Action Spaces

Traditional controllers have limitations as they rely on prior knowledge about the physics of the problem, require modeling of dynamics, and struggle to adapt to abnormal situations. Deep reinforcement learning has the potential to address these problems by learning optimal control policies through exploration in an environment. For safety-critical environments, it is impractical to explore randomly, and replacing conventional controllers with black-box models is also undesirable. Also, it is expensive in continuous state and action spaces, unless the search space is constrained. To address these challenges we propose a specialized deep residual policy safe reinforcement learning with a cycle of learning approach adapted for complex and continuous state-action spaces. Residual policy learning allows learning a hybrid control architecture where the reinforcement learning agent acts in synchronous collaboration with the conventional controller. The cycle of learning initiates the policy through the expert trajectory and guides the exploration around it. Further, the specialization through the input-output hidden Markov model helps to optimize policy that lies within the region of interest (such as abnormality), where the reinforcement learning agent is required and is activated. The proposed solution is validated on the Tennessee Eastman process control

Design and Implementation of an Embedded System for Software Defined Radio

In this paper, developing high performance software for demanding real-time embedded systems is proposed. This software-based design will enable the software engineers and system architects in emerging technology areas like 5G Wireless and Software Defined Networking (SDN) to build their algorithms. An ADSP-21364 floating point SHARC Digital Signal Processor (DSP) running at 333 MHz is adopted as a platform for an embedded system. To evaluate the proposed embedded system, an implementation of frame, symbol and carrier phase synchronization is presented as an application. Its performance is investigated with an on line Quadrature Phase Shift keying (QPSK) receiver. Obtained results show that the designed software is implemented successfully based on the SHARC DSP which can utilized efficiently for such algorithms. In addition, it is proven that the proposed embedded system is pragmatic and capable of dealing with the memory constraints and critical time issue due to a long length interleaved coded data utilized for channel coding

Pearson coefficient matrix for studying the correlation of community detection scores in multi-objective evolutionary algorithm

Assessing a community detection algorithm is a difficult task due to the absence of finding a standard definition for objective functions to accurately identify the structure of communities in complex networks. Traditional methods generally consider the detecting of community structure as a single objective issue while its optimization generally leads to restrict the solution to a specific property in the community structure. In the last decade, new community detection models have been developed. These are based on multi-objective formulation for the problem, while ensuring that more than one objective (normally two) can be simultaneously optimized to generate a set of non-dominated solutions. However the issue of which objectives should be co-optimized to enhance the efficiency of the algorithm is still an open area of research. In this paper, first we generate a candidate set of partitions by saving the last population that has been generated using single objective evolutionary algorithm (SOEA) and random partitions based on the true partition for a given complex network. We investigate the features of the structure of communities which found by fifteen existing objectives that have been used in literature for discovering communities. Then, we found the correlation between any two objectives using the pearson coefficient matrix. Extensive experiments on four real networks show that some objective functions have a strong correlation and others either neutral or weak correlations

Mathematical simulation of memristive for classification in machine learning

Over the last few years, neuromorphic computation has been a widely researched topic. One of the neuromorphic computation elements is the memristor. The memristor is a high density, analogue memory storage, and compliance with Ohm's law for minor potential changes. Memristive behaviour imitates synaptic behaviour. It is a nanotechnology that can reduce power consumption, improve synaptic modeling, and reduce data transmission processes. The purpose of this paper is to investigate a customized mathematical model for machine learning algorithms. This model uses a computing paradigm that differs from standard Von-Neumann architectures, and it has the potential to reduce power consumption and increasing performance while doing specialized jobs when compared to regular computers. Classification is one of the most interesting fields in machine learning to classify features patterns by using a specific algorithm. In this study, a classifier based memristive is used with an adaptive spike encoder for input data. We run this algorithm based on Anti-Hebbian and Hebbian learning rules. These investigations employed two of datasets, including breast cancer Wisconsin and Gaussian mixture model datasets. The results indicate that the performance of our algorithm that has been used based on memristive is reasonably close to the optimal solution