Preventing Vaccine Failure in Poultry Flocks

Poultry sector is very useful for humans in terms of production of food items like meat and eggs. Pakistan has a developing poultry sector and is the second important sector after the textile industry. The poultry sector is encountered with many challenges; among them is the high incidence of disease outbreaks that result in colossal economic losses. The diseases of commercial and rural poultry include Newcastle disease (ND), infectious bursal disease (IBD), fowl pox, Marek’s disease, infectious bronchitis (IB), avian influenza, hydropericardium syndrome, etc. The disease outbreaks have also occurred in vaccinated flocks. Better understanding of the causes of vaccine failure will result in identifying prophylactic measures regarding disease outbreaks in poultry flocks. This chapter overviews the common causes of vaccine failure and further highlights the procedures for successful immunization

Synthetic Data-based Detection of Zebras in Drone Imagery

Nowadays, there is a wide availability of datasets that enable the training of common object detectors or human detectors. These come in the form of labelled real-world images and require either a significant amount of human effort, with a high probability of errors such as missing labels, or very constrained scenarios, e.g. VICON systems. On the other hand, uncommon scenarios, like aerial views, animals, like wild zebras, or difficult-to-obtain information, such as human shapes, are hardly available. To overcome this, synthetic data generation with realistic rendering technologies has recently gained traction and advanced research areas such as target tracking and human pose estimation. However, subjects such as wild animals are still usually not well represented in such datasets. In this work, we first show that a pre-trained YOLO detector can not identify zebras in real images recorded from aerial viewpoints. To solve this, we present an approach for training an animal detector using only synthetic data. We start by generating a novel synthetic zebra dataset using GRADE, a state-of-the-art framework for data generation. The dataset includes RGB, depth, skeletal joint locations, pose, shape and instance segmentations for each subject. We use this to train a YOLO detector from scratch. Through extensive evaluations of our model with real-world data from i) limited datasets available on the internet and ii) a new one collected and manually labelled by us, we show that we can detect zebras by using only synthetic data during training. The code, results, trained models, and both the generated and training data are provided as open-source at https://eliabntt.github.io/grade-rr.Comment: 8 pages, 7 figures, 3 tables. Published in IEEE ECMR 202

Perceived Transformational Leadership Style and Organizational Citizenship Behavior: A Case Study of Administrative Staff of University of the Punjab

In today’s dynamic organizations, leadership plays a central role in achieving organizational goals. Leaders are recommended to adopt transformational leadership style to influence the subordinates so that they may involve in an extra role behavior like organizational citizenship behavior beneficial to organizational growth. This study aims to measure the level of perceived transformational leadership (TL) style, the level of organizational citizenship behavior (OCB), and to investigate the impact of perceived transformational leadership on employees’ organizational citizenship behavior. In transformational leadership style, an innovation or transformation is introduced to both within organization and among individuals. Organizational citizenship behavior is cooperative and constructive gestures that are neither mandated by the formal job role prescriptions nor directly or contractually compensated for by the formal organizational reward system. Based on previous research, it had hypothesized that the relationship observed between transformational leadership and various dimensions of OCB was positive. In this study, population comprised of all the administrative staff of the Punjab University consisted of 15 faculties. Multi-stage probability Sampling was used. Sample was drawn at two stages. Firstly, stratified random sampling was used during which 48 departments/institutes/centers were selected randomly from the available population. Secondly, simple random sampling was done during which 13 employees from each department/institute/center, working in B Class BS 10-16 from administrative staff of the Punjab University, Lahore were approached. Finally a sample of 480 employees with response rate 76%  working in B Class BS 10-16 served as participants in this study, representing all the 15 faculties of the Punjab University, Lahore. The results suggest that transformational leadership and OCB are positively correlated. This study shows that transformational leaders encourage altruism, courtesy and conscientiousness in subordinates. So, organizations by using this can enhance the extra role behavior of the employees for their advantage. Key words: leadership, organizational behavior, employee motivation, organizational loyalt

Accelerated Video Annotation driven by Deep Detector and Tracker

Annotating object ground truth in videos is vital for several downstream tasks in robot perception and machine learning, such as for evaluating the performance of an object tracker or training an image-based object detector. The accuracy of the annotated instances of the moving objects on every image frame in a video is crucially important. Achieving that through manual annotations is not only very time consuming and labor intensive, but is also prone to high error rate. State-of-the-art annotation methods depend on manually initializing the object bounding boxes only in the first frame and then use classical tracking methods, e.g., adaboost, or kernelized correlation filters, to keep track of those bounding boxes. These can quickly drift, thereby requiring tedious manual supervision. In this paper, we propose a new annotation method which leverages a combination of a learning-based detector (SSD) and a learning-based tracker (RE$^3$). Through this, we significantly reduce annotation drifts, and, consequently, the required manual supervision. We validate our approach through annotation experiments using our proposed annotation method and existing baselines on a set of drone video frames. Source code and detailed information on how to run the annotation program can be found at https://github.com/robot-perception-group/smarter-labelmeComment: 8 pages, 5 figure

Qualitative analysis of Autonomization of teaching hospitals of Punjab: A case study of Jinnah Hospital Lahore

This study aims at evaluating the nature and extent of autonomy in the areas of hospital financial management and procurement. Autonomy in financial management refers to the freedom to generate resources for the running of the hospital, and the proper planning, accounting, and allocation of these resources; and autonomy in procurement refers to making all the decisions regarding purchase of drugs, medical and non-medical hospital supplies, and hospital equipment. The population of this study consists of all the medical and non-medical staff of Jinnah Hospital, Lahore (JHL). In this research design, JHL is selected as a single case and then multiple sub units of analysis are selected from the top level management of Jinnah Hospital Lahore to get information about the nature and extent of autonomy granted to hospital in the said areas. In this way, 14 top level medical and non-medical staff members of JHL were interviewed. The findings of this study show that autonomous hospital is not independent in making its decisions in the matters of financial management and procurement. Most of the decisions concerning financial management and procurement of the autonomous hospital are taken after the approval from the finance and health department. Key Words: autonomous public hospital, health reforms, nature of autonomy, extent of autonomy, Jinnah Hospital Lahore, Punja

Collective Asynchronous Remote Invocation (CARI): A High-Level and Effcient Communication API for Irregular Applications

The Message Passing Interface (MPI) standard continues to dominate the landscape of parallel computing as the de facto API for writing large-scale scientific applications. But the critics argue that it is a low-level API and harder to practice than shared memory approaches. This paper addresses the issue of programming productivity by proposing a high-level, easy-to-use, and effcient programming API that hides and segregates complex low-level message passing code from the application specific code. Our proposed API is inspired by communication patterns found in Gadget-2, which is an MPI-based parallel production code for cosmological N-body and hydrodynamic simulations. In this paper—we analyze Gadget-2 with a view to understanding what high-level Single Program Multiple Data (SPMD) communication abstractions might be developed to replace the intricate use of MPI in such an irregular application—and do so without compromising the effciency. Our analysis revealed that the use of low-level MPI primitives—bundled with the computation code—makes Gadget-2 diffcult to understand and probably hard to maintain. In addition, we found out that the original Gadget-2 code contains a small handful of—complex and recurring—patterns of message passing. We also noted that these complex patterns can be reorganized into a higherlevel communication library with some modifications to the Gadget-2 code. We present the implementation and evaluation of one such message passing pattern (or schedule) that we term Collective Asynchronous Remote Invocation (CARI). As the name suggests, CARI is a collective variant of Remote Method Invocation (RMI), which is an attractive, high-level, and established paradigm in distributed systems programming. The CARI API might be implemented in several ways—we develop and evaluate two versions of this API on a compute cluster. The performance evaluation reveals that CARI versions of the Gadget-2 code perform as well as the original Gadget-2 code but the level of abstraction is raised considerably

Evaluating the Nature and Extent of Autonomy in Public Teaching Hospitals: A Case study of Jinnah Hospital Lahore

This study is aimed at evaluating the nature and the extent of Autonomy in Hospital Administration and Medical Care. Autonomy in administration means the freedom in deciding all the matters involved in the day-to-day running of the hospital and the discharge of the functions defined by the mission statement. Autonomy in Medical Care was meant to provide efficient services to patients at autonomous hospitals. The population of this study consists of top level administrative medical and non-medical staff of Jinnah Hospital, Lahore. In this research design, Jinnah Hospital Lahore is selected as a single case and then multiple sub units of analysis are selected from top management of Jinnah Hospital Lahore to get information about nature and extent of autonomy. In this way with the support and cooperation of respondents, 14 top level administrative medical and non-medical staff members of Jinnah Hospital Lahore were interviewed. This study shows that autonomous hospital is not independent in making its decisions due to the increased role of health and finance department. Most of the matters concerning hospital administration and developing medical care tools are managed with the final approval of health department. Key Words: autonomous hospital, health reforms, nature of autonomy, extent of autonomy, public hospitals

Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios

In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these non-linear potential field functions as constraints within a convex optimization framework. Our method convexifies non-convex constraints and dependencies, by replacing them as pre-computed external input forces in robot dynamics. The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning. The motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking. We perform simulation studies in different environmental scenarios to showcase the convergence and efficacy of the proposed algorithm. Video of simulation studies: \url{https://youtu.be/umkdm82Tt0M

Are Karachi Stock Exchange and Bombay Stock Exchange

The research paper was aimed to find the integration between Karachi Stock Exchange and Bombay Stock Exchange. A daily data of stock prices from Nov 2009 to Dec 2012 has been obtained and their geometric returns are calculated as mentioned in Ahmad and Husain (2007). Stock returns were found integrated of order zero. Johanson Cointegration test is applied over historical returns of both the stock market returns in order to capture existence of cointegration. Result shows that, Trace-statistics and Eigen-values, fall within the critical region, i.e. their p-value is less than 0.05, and hence it is concluded on the bases of stochastic process and econometric evidence that Karachi Stock Exchange and Bombay Stock Exchange are not cointegrated. Keywords: KSE, BSE, Cointegratio

Collaborative Mapping of Archaeological Sites using multiple UAVs

UAVs have found an important application in archaeological mapping. Majority of the existing methods employ an offline method to process the data collected from an archaeological site. They are time-consuming and computationally expensive. In this paper, we present a multi-UAV approach for faster mapping of archaeological sites. Employing a team of UAVs not only reduces the mapping time by distribution of coverage area, but also improves the map accuracy by exchange of information. Through extensive experiments in a realistic simulation (AirSim), we demonstrate the advantages of using a collaborative mapping approach. We then create the first 3D map of the Sadra Fort, a 15th Century Fort located in Gujarat, India using our proposed method. Additionally, we present two novel archaeological datasets recorded in both simulation and real-world to facilitate research on collaborative archaeological mapping. For the benefit of the community, we make the AirSim simulation environment, as well as the datasets publicly available