LINKING EMPOWERMENT AND CAPABILITY DEVELOPMENT WITH INNOVATIVE BEHAVIOR: TESTING A MODERATING MODEL OF EMPLOYEE’S CREATIVE SELF-EFFICACY

The purpose of this study is to investigate the effect ofempowerment and capability development on employee’s innovative behavior. We also examine the moderating effect of worker’s creative self-efficacy. The software developers working in IT firms have been selected for this study. The data were collected randomly from200 software developers by using questionnaires. The results indicate that empowerment and capability development is significantly and positively related with employee’s innovative behavior. Furthermore, the analysis revealed that employee’s creative self-efficacy moderatesthe empowerment and capability development relationship withemployee’s innovative behavior. The results of the study are novel and productive for the top management of the IT sector. The study provides insight into employee’s innovative behavior and suggests how organizations can boost innovations by empowerment is consistence with effective capability development programs

On scalable inference and learning in spike-and-slab sparse coding

Sparse coding is a widely applied latent variable analysis technique. The standard formulation of sparse coding assumes Laplace as a prior distribution for modeling the activations of latent components. In this work we study sparse coding with spike-and-slab distribution as a prior for latent activity. A spike-and-slab distribution has its probability mass distributed across a ’spike’ at zero and a ’slab’ spreading over a continuous range. For its capacity to induce exact zeros with a higher likelihood, a spike-and-slab prior distribution constitutes a more accurate model of sparse coding. The distribution as a prior also allows for the sparseness of latent activity to be directly inferred from observed data, which essentially makes spike-and-slab sparse coding more flexible and self-adaptive to a wide range of data distributions. By modeling the slab with a Gaussian distribution, we furthermore show that in contrast to the standard approach to sparse coding, we can indeed derive closed-form analytical expressions for exact inference and learning in linear spike-and-slab sparse coding. However, as the posterior landscape of a spike-and-slab prior turns out to be highly multi-modal with a prohibitive exploration cost, in addition to the exact method, we also develop subspace and Gibbs sampling based approximate inference techniques for scalable applications of the linear model. We contrast our approximation methods with variational approximation for scalable posterior inference in linear spike-and-slab sparse coding. We further combine the Gaussian spike-and-slab prior with a nonlinear generative model, which assumes a point-wise maximum combination rule for the generation of observed data. We analyze the model as a precise encoder of low-level features such as edges and their occlusions in visual data. We again combine subspace selection with Gibbs sampling to overcome the analytical intractability of performing exact inference in the model. We numerically analyze our methods on both synthetic and real data for their verification and comparison with other approaches. We assess the linear spike-and-slab approach on source separation and image denoising benchmarks. In most experiments we obtain competitive or state-of-the-art results, while we find that spike-and-slab sparse coding overall outperforms other comparable approaches. By extracting thousands of latent components from a large amount of training data we further demonstrate that our subspace Gibbs sampler is among the most scalable posterior inference methods for a linear sparse coding approach. For the nonlinear model we experiment with artificial and real images to demonstrate that the components learned by the model lie closer to the ground-truth and are easily interpretable as the underlying generative causes of the input. We find that in comparison to standard sparse coding, the nonlinear spike-and-slab approach can compressively encode images using naturally sparse and discernible compositions of latent components. We also demonstrate that the components inferred by the model from natural image patches are statistically more consistent with respect to their structure and distribution to the response patterns of simple cells in the primary visual cortex of the brain. This work thereby contributes novel methods for sophisticated inference and learning in spike-and-slab sparse coding, while it also empirically showcases their functional efficacy through a variety of applications.Sparse Coding ist eine weit verbreitete Technik der latenten Variablenanalyse. Die Standardformulierung von Sparse Coding setzt a priori eine Laplace-Verteilung zur Modellierung der Aktivierung von latenten Komponenten voraus. In dieser Arbeit untersuchen wir Sparse Coding mit einer a priori Spike-and-Slab-Verteilung für latente Aktivität. Eine Spike-and-Slab-Verteilung verteilt ihre Wahrscheinlichkeitsmasse um ein Aktionspotential (“Spike”) um Null und eine dicke Verteilung (“slab”) über einen kontinuierlichen Wertebereich. Durch die Induktion von exakten Nullen mit einer höheren Wahrscheinlichkeit erzeugt eine Apriori-Spike-and-Slab-Verteilung ein genaueres Modell von Sparse Coding. Als A-priori-Verteilung erlaubt sie es uns die Seltenheit von latenten Komponenten direkt von Daten abzuleiten, sodass ein Spike-and-Slab-getriebenes Modell von Sparse Coding sich besser verschiedensten Verteilungen von Daten anpasst. Durch das Modellieren des Slab mittels einer Gauß-Verteilung zeigen wir, dass – im Gegensatz zur Standardformulierung von Sparse Coding – wir in der Tat geschlossene analytische Ausdrücke ableiten können, um eine exakte Ableitung und das Lernen eines linearen Spike-and-Slab-Sparse-Coding-Modell durchzuführen. Weil eine Spike-and-Slab-A-priori-Verteilung zu einer hoch multimodalen A-posteriori-Landschaft mit viel zu hohen Suchkosten führt, entwickeln wir zusätzlich zur exakten Methode Näherungslösungen basierend auf einem Teilraum und Gibbs-Sampling für skalierbare Anwendungen des Modells. Wir vergleichen unseren Ansatz der näherungsweisen Inferenz mit näherungsweiser Variationsrechnung des linearen Spike-and-Slab-Sparse Coding. Des Weiteren kombinieren wir die Spike-and-Slab-A-priori-Verteilung mit einem nicht-linearen Sparse-Coding-Modell, das eine punktweise Maximum-Kombinationsregel zur Datengenerierung voraussetzt. Wir analysieren das Modell als genauen Kodierer von untergeordneten Merkmalen in Bildern wie z.B. Kanten und deren Okklusionen. Wir lösen die analytische Ausweglosigkeit, eine Ableitung von multimodalen A-posteriori-Verteilungen im Modell durchzuführen, durch die Kombination von Gibbs-Sampling und der Auswahl eines Teilraums, um eine skalierbare Prozedur für die approximative Inferenz des Modells zu entwickeln. Wir analysieren unsere Methode numerisch durch synthetische und wirkliche Daten zum Nachweis und Vergleich mit anderen Ansätzen. Wir bewerten den linearen Spike-and-Slab-Ansatz mittels Maßstäben für die Quellentrennung und zur Rauschunterdrückung in Bildern. In den meisten Experimenten erhalten wir vergleichsweise oder die beste Resultate. Gleichzeitig finden wir, dass Spike-and-Slab-Sparse-Coding insgesamt andere vergleichbare Ansätze übertrifft. Durch die Extraktion von Tausenden von latenten Komponenten aus einer riesigen Menge an Trainingsdaten zeigen wir des Weiteren, dass unserer Teilraum Gibbs-Sampler zu den skalierbarsten Inferenzmethoden der linearen Sparse-Coding-Modelle gehört. Für das nichtlineare Modell experimentieren wir mit künstlichen und echten Bildern zur Demonstration, dass die von dem Modell gelernten Komponenten näher an der “Ground Truth” liegen und leichter zu interpretieren sind als die zugrundeliegenden generierenden Einflüsse der Eingabe. Wir finden, dass – im Vergleich zu Standard-Sparse-Coding – der nichtlineare Spike-and-Slab-Ansatz Bilder komprimierend kodieren kann durch natürliche dünnbesetzte und klar erkennbare Kompositionen von latenten Komponenten. Wir zeigen auch, dass die vom Modell abgeleiteten Komponenten von natürlichen Bildern statistisch konsistenter sind in ihrer Struktur und Verteilung mit dem Antwortmuster von einfachen Zellen im primären visuellen Kortex. Diese Arbeit leistet durch neue Methoden zur komplexen Inferenz und zum Erlernen ivvon Spike-and-Slab-Sparse-Coding einen Beitrag und demonstriert deren praktikable Wirksamkeit durch einen Vielzahl von Anwendungen

Measuring Efficiency of Hospitals by DEA: An Empirical Evidence from Pakistan

ABSTRACT There has been increasing focus on efficiency measurement in health sector around the world. This empirical study aims at measuring efficiency level of non-profit private organization by using Data Envelopment Analysis (DEA) in the health sector of Pakistan. DEA is non-parametric linear programming based approach for homogeneous decision making units. Layton Rehmatullah Benevolent Trust (non-profit private organization) will be subject matter for investigational analysis constituting over 16 sub-units spreading across the country. Secondary data of number of patient beds, specialists and nurses in all the 16 branches of LRBT hospitals will be used applying quantitative specification tool, both scale and technical efficiency levels in an environment where multiple of inputs and outputs are in place, will be used for final evaluation. The outcomes so expected will help policy makers to formulate effective plans and programs in order to enhance the efficiency of health measures conducted by non- profit private organizations.

Probability of Line of Sight Evaluation in Urban Environments using 3D Simulator

The integration of Non-Terrestrial Networks (NTNs) into 6G networks is one of the most promising ways to achieve significant improvements in capacity, reliability, and global coverage. The design of NTN heavily relies on using channel models. In this paper, we propose two easy-to-use simulators for estimating the Line-of-Sight (LoS) probability $\mathbf{P_{LoS}}$ in a 3D urban environment. The first simulator is a 3D city simulator that employs simplified Ray Tracing (RT), while the second one is a lightweight geometry-based simulator that generates only the relevant buildings between users and Unmanned Aerial Vehicles (UAVs). Using these simulators, we assess the accuracy of existing models for $\mathbf{P_{LoS}}$ estimation and examine $\mathbf{P_{LoS}}$ for different UAV heights, user-UAV distances, and azimuth/elevation angles. We conclude that 1) existing models overestimate $\mathbf{P_{LoS}}$, resulting in overoptimistic NTN performance predictions, 2) nodes location (including azimuth and elevation angles) is an important factor influencing $\mathbf{P_{LoS}}$, however, this influence is not captured by the existing models.Comment: 6 pages, submitted to IEEE conferenc

Food Security in Pakistan and Need for Public Policy Adjustments

Sustainable food and nutrition security solutions demand integration and alignment in public policies, particularly in the post-COVID-19 scenarios. The introduction of integrated public policies to address the food and nutrition needs in Pakistan is an immediate requirement. This study has applied the Foster, Greer and Thorbecke (FGT) index to estimate food and nutrition security dimensions through primary and secondary data. This analysis reveals that food utilization and sustainability have destabilized and deteriorated in Pakistan in recent years. It shows that non-farmers are more food insecure (8 percent) than farmers (4 percent) and this ratio has increased from 2008 to 2018. Food insecurity in terms of food availability and food accessibility has decreased. A holistic approach in public policies toward food security is the clarion call of the time. Therefore, the paper recommends that more focus should be given to knowledge transmission about dietary diversity, provision of quality education, and health facilities in the formulation as well as execution of food security policies