Semiconformal symmetry- A new symmetry of the spacetime manifold of the general relativity

In this paper we have introduced a new symmetry property of spacetime which is named as semiconformal curvature collineation, and its relationship with other known symmetry properties has been established. This new symmetry property of the spacetime has also been studied for non-null and null electromagnetic fields

Towards Autonomous and Efficient Machine Learning Systems

Computation-intensive machine learning (ML) applications are becoming some of the most popular workloads running atop cloud infrastructure. While training ML applications, practitioners face the challenge of tuning various system-level parameters, such as the number of training nodes, communication topology during training, instance type, and the number of serving nodes, to meet the SLO requirements for bursty workload during the inference. Similarly, efficient resource utilization is another key challenge in cloud computing. This dissertation proposes high-performing and efficient ML systems to speed up training time and inference tasks while enabling automated and robust system management.To train an ML model in a distributed fashion we focus on strategies to mitigate the resource provisioning overhead and improve the training speed without impacting the model accuracy. More specifically, a system for autonomic and adaptive scheduling is built atop serverless computing that dynamically optimizes deployment and resource scaling for ML training tasks for cost-effectiveness and fast training. Similarly, a dynamic client selection framework is developed to address the stragglers problem caused by resource heterogeneity, data quality, and data quantity in a privacy-preserving Federated Learning (FL) environment without impacting the model accuracy.For serving bursty ML workloads we focus on developing highly scalable and adaptive strategies to serve the dynamically changing workload in a cost-effective manner in an autonomic fashion. We develop a framework that optimizes batching parameters on the fly using a lightweight profiler and an analytical model. We also devise strategies for serving ML workloads of varying sizes, leading to non-deterministic service time in a cost-effective manner. More specifically, we develop an SLO-aware framework that first analyzes the request size variations and workload variation to estimate the number of serving functions and intelligently route requests to multiple serving functions. Finally, resource utilization of burstable instances is optimized to benefit the cloud provider and end-user through a careful orchestration of resources (i.e., CPU, network, and I/O) using an analytical model and lightweight profiling, while complying with a user-defined SLO

Bond behavior of lightweight steel fibre-reinforced concrete

This research was undertaken for studying the bond behaviour of Lightweight Fibre-reinforced Concrete (LWFC). Lightweight concrete is inherently weak in tension and has higher brittleness than the conventional concrete. To improve these and other properties, it is generally reinforced with deformed bars and fibres. There are number of studies that favour the use of Steel fibres, however such studies are mainly focused either on normal weight concrete or on the mechanical properties of different concretes. There are also different committee reports and in some cases specific sections of codes that specifically deal with the normal weight fibre-reinforced concrete. However, such is not the case with lightweight fibre-reinforced concrete; there is limited literature available especially on the Bond of lightweight fibre-reinforced concrete. In current research work effect of fibres is studied on the bond behaviour of the lightweight reinforced concrete. Since most of code provisions for bond are based on experimental work originally carried out on conventional concrete, effect of fibres on bond of conventional concrete was therefore also included in present research domain. Main bond tests were carried out using Pull-out test methodology. Test results indicate that the ultimate bond strength of conventional concrete when reinforced with steel fibres increased by 29%. However due to very low density and high porosity of lightweight aggregates, no significant improvement on bond strength of LWFC, as a result of fibres’ addition could be observed. Nevertheless, there is noteworthy improvement in the post-cracking bond strength of LWFC. Besides this, current bond-stress slip law as defined by Model Code 2010 does not reflect the positive effect of fibres, hence some modifications are suggested. It is also found that among the existing code expressions for estimation of bond strength, expression proposed by Model Code 2010 presents better results and its effectiveness can be further increased if fibre factor and factor for lightweight concrete are considered

Kesiapan Dosen dan Mahasiswa dalam Pembelajaran Berbasis E-Learning di IAIN Parepare

ABSTRAK Penelitian ini bertujuan untuk mengetahui kesiapan dosen dan mahasiswa dalam pembelajaran berbasis e-learning di IAIN Parepare. Penelitian ini merupakan penelitian evaluasi menggunakan model Context, Input, Process, Product (CIPP) dengan sampel sampel minimum dosen berjumlah 30 orang dan untuk sampel minimum mahasiswa berjumlah 204 orang Tahun Akademik 2018/2019. Metode pengumpulan data pada penelitian ini adalah metode angket. Hasil penelitin menunjukkan bahwa: (1) kesiapan dosen IAIN Parepare dalam melaksanakan program pembelajaran berbasis e-learning berdasarkan evaluasi model CIPP diperoleh hasil, yakni 2 aspek termasuk kategori tinggi, yaitu aspek context dan input dan 2 aspek termasuk kategori rendah, yaitu aspek process dan product dan (2) kesiapan mahasiswa IAIN Parepare dalam mengikuti pembelajaran berbasis e-learning berdasarkan evaluasi model CIPP diperoleh hasil, yakni 3 aspek termasuk kategori tinggi, yaitu aspek context, input dan process dan 1 aspek termasuk kategori rendah, yaitu aspek product