F2DNet: Fast Focal Detection Network for Pedestrian Detection

Two-stage detectors are state-of-the-art in object detection as well as pedestrian detection. However, the current two-stage detectors are inefficient as they do bounding box regression in multiple steps i.e. in region proposal networks and bounding box heads. Also, the anchor-based region proposal networks are computationally expensive to train. We propose F2DNet, a novel two-stage detection architecture which eliminates redundancy of current two-stage detectors by replacing the region proposal network with our focal detection network and bounding box head with our fast suppression head. We benchmark F2DNet on top pedestrian detection datasets, thoroughly compare it against the existing state-of-the-art detectors and conduct cross dataset evaluation to test the generalizability of our model to unseen data. Our F2DNet achieves 8.7\%, 2.2\%, and 6.1\% MR-2 on City Persons, Caltech Pedestrian, and Euro City Person datasets respectively when trained on a single dataset and reaches 20.4\% and 26.2\% MR-2 in heavy occlusion setting of Caltech Pedestrian and City Persons datasets when using progressive fine-tunning. Furthermore, F2DNet have significantly lesser inference time compared to the current state-of-the-art. Code and trained models will be available at https://github.com/AbdulHannanKhan/F2DNet.Comment: Accepted at ICPR 202

Prevalence of paternal postpartum depression

Background: Postpartum Depression is a type of mood disorder associated with childbirth, which can occur in both parents. It is a major public health concern because it produces insidious effects on the well-being of new born as well as on the whole family. Objective: This study aims to determine the prevalence of postpartum depression among fathers in Karachi, Pakistan. Methods: This cross sectional survey was conducted among fathers, purposive convenient and snowball sampling was used to approach 120 participants after obtaining permission from Al-Khidmat Hospital and Combined Military Hospital (CMH).Participants were aged between 20-50 years, had a newly born child between the past 6 weeks to 1 year. Each participant was required to fill an informed consent, demographic form and Edinburgh Postnatal Depression Scale’s (EPDS), translated Urdu version. The data was collected and statistically analyzed. Results: The study findings show that 28.3% fathers experienced depressive symptoms, out of which 25.8% experienced mild while 2.5% experienced severe depressive symptoms. Conclusion: Paternal postpartum depression is quite prevalent among new fathers in Karachi. Depression among fathers is an area of substantial significance as it may enhance the risk of mental health problems among children. Health services should support new fathers by providing information about this major transition. These results clearly show that there is a need to assess expectant and new fathers for depression. Prevention, early identification and intervention of paternal post- partum depression are immensely required

Association of fahr disease with Rhabdomyolysis and hypoparathyroidism

Fahr syndrome is an idiopathic deposition of calcium in the basal ganglia (most commonly in the Globus pallidus) of the brain; other most common sites may include striatum pallidum. It can have a wide variety of presentations like progressive psychosis, dementia, movement disorders, gait disturbances or it can also be asymptomatic. Department of Neuro-medicine Hyderabad admitted this 28 year old male first. And then was referred to us in the department of Neuro-medicine JPMC Karachi. He is a known case of epilepsy for 8 years, who now presented with the complaint of fever, fits, and altered sensorium for 3 days. The CT scan of his brain showed hyperdense areas, bilateral symmetrical extensive basal and cerebral calcifications. CSF examination of the patient showed unremarkable results. On further examinations, it was found he also had Rhabdomyolysis and Hypoparathyroidism. It is unusual to have an association of Fahr syndrome with Rhabdomyolysis and Hypoparathyroidism. Our case report describes the unique behavior of this syndrome

An energy management system of campus microgrids:State-of-the-art and future challenges

The multiple uncertainties in a microgrid, such as limited photovoltaic generations, ups and downs in the market price, and controlling different loads, are challenging points in managing campus energy with multiple microgrid systems and are a hot topic of research in the current era. Microgrids deployed at multiple campuses can be successfully operated with an exemplary energy management system (EMS) to address these challenges, offering several solutions to minimize the greenhouse gas (GHG) emissions, maintenance costs, and peak load demands of the microgrid infrastructure. This literature survey presents a comparative analysis of multiple campus microgrids’ energy management at different universities in different locations, and it also studies different approaches to managing their peak demand and achieving the maximum output power for campus microgrids. In this paper, the analysis is also focused on managing and addressing the uncertain nature of renewable energies, considering the storage technologies implemented on various campuses. A comparative analysis was also considered for the energy management of campus microgrids, which were investigated with multiple optimization techniques, simulation tools, and different types of energy storage technologies. Finally, the challenges for future research are highlighted, considering campus microgrids’ importance globally. Moreover, this paper is expected to open innovative paths in the future for new researchers working in the domain of campus microgrids

Simulation-optimization of Tarbela Reservoir operation to enhance multiple benefits and to achieve sustainable development goals

Pakistan’s agriculture and economy rely heavily on the Tarbela Reservoir. The present storage capacity of Tarbela is 8.2 BCM and it has been depleted by more than 40% due to sedimentation since 1976. It also has had a 0.94 percent (0.134 BCM) decrease in gross reservoir capacity every year. Historically, the amount of sediment trapped in the Tarbela Reservoir during the period 1976–2020 was 198.5 million tonnes annually. Based on the current operation by the Water and Power Development Authority (WAPDA), the delta is expected to extend to 2.41 km from the dam in 2035. The reservoir will become a run-of-the-river reservoir with a gross storage capacity of 2.87 BCM. This rapid loss of storage capacity will significantly impact reservoir benefits while also putting turbine performance at risk due to abrasion. Slowing the sediment deposition phenomena by a flexible operational strategy is a worthwhile aim from the dam manager’s viewpoint to achieve Sustainable Development Goals (i.e., poverty and hunger alleviation, clean affordable energy, protecting ecosystem etc.). Therefore, for the safe and long-term operation of the turbines, the existing Standard Operating Procedures (SOPs) adopted by WAPDA need to be appraised to delineate their impact on future optimized operations. The aspect of considering static SOPs on the whole period of reservoir operation has not been attempted earlier. The Tarbela Reservoir was selected as a case study to enhance the existing reservoir operation. The methodology relies upon the use of a 1-D sediment transport model in HEC-RAS to study the impact of the operational strategy on sedimentation. In conjunction, the existing reservoir operation of Tarbela was modelled in HEC-ResSim using its physical, operational, and 10-daily time-series data for simulation of releases and hydropower benefits based on a revised elevation-capacity curve for sedimentation. After calibration and validation, the model was applied to predict future reservoir operation impacts on a 5-year basis from 2025 to 2035 for determining storage capacity, irrigation releases, power production and energy generation. It was predicted that as the storage capacity of the reservoir is depleted (by application of the WAPDA current SOPs in future years), the irrigation releases would be increased in the Kharif season (April–September) by 7% and decreased by 50% in the Rabi season (October–March) with a corresponding increase in power generation by 4% and decrease by 37%, respectively, and the average annual energy generation would be decreased by 6.5%. The results showed that a gradual increase in the minimum operating level will slow down delta movement but it will reduce irrigation releases at times of high demand. The findings may assist water managers to improve the Tarbela Reservoir operation to achieve sustainable development goals and to attain societal future benefits

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving

A Hybrid Framework for Time-series Analysis

Data is the new gold and serves as a key to answer the five W’s (Who, What, Where, When, Why) and How’s of any business. Companies are now mining data more than ever and one of the most important aspects while analyzing this data is to detect anomalous patterns to identify critical patterns and points. To tackle the vital aspects of timeseries analysis, this thesis presents a novel hybrid framework that stands on three pillars: Anomaly Detection, Uncertainty Estimation, and Interpretability and Explainability. The first pillar is comprised of contributions in the area of time-series anomaly detection. Deep Anomaly Detection for Time-series (DeepAnT), a novel deep learning-based anomaly detection method, lies at the foundation of the proposed hybrid framework and addresses the inadequacy of traditional anomaly detection methods. To the best of the author’s knowledge, Convolutional Neural Network (CNN) was used for the first time in Deep Anomaly Detection for Time-series (DeepAnT) to robustly detect multiple types of anomalies in the tricky and continuously changing time-series data. To further improve the anomaly detection performance, a fusion-based method, Fusion of Statistical and Deep Learning for Anomaly Detection (FuseAD) is proposed. This method aims to combine the strengths of existing wellfounded statistical methods and powerful data-driven methods. In the second pillar of this framework, a hybrid approach that combines the high accuracy of the deterministic models with the posterior distribution approximation of Bayesian neural networks is proposed. In the third pillar of the proposed framework, mechanisms to enable both HOW and WHY parts are presented