N.C. Rana: The Life of a `Comet' in the Astrophysical World

Narayan Chandra Rana, a person with extraordinary potential from a remote village of Bengal, India, came into the limelight of the international scientific world through his exceptional talent, zeal and courage. In his very short life-span, he excelled not only into various branches of astrophysics, but also took a leading role in science popularization, text book writing etc. In this paper, life and works of that budding scientist of India have been discussed from multifarious viewpoints.Comment: 24 pages, 18 figure

Searching for a Solution to the Age Problem of the Universe

We present here a phenomenological cosmological model under perfect fluid distribution with a stiff equation of state $p=\rho$. The erstwhile cosmological constant is assumed to be a time dependent variable, i.e., $\Lambda = \Lambda(t)$ in our study. It has been shown that the estimates of different cosmological parameters from this model are in good agreement with the experimental results, especially 13.79 Gyr as the age of the universe is quite satisfactory. The behavior and relation of $\Lambda$-stiff fluid model with dust, viscous fluid and variable $G$ have also been investigated in detail.Comment: 5 LaTex pages, considerable changes in the text and also addition of new references. Accepted for publication in Gravitation and Cosmolog

Dark Energy Models With Variable Equation Of State Parameter

Two variable $\Lambda$ models, viz. $\Lambda \sim (\dot a/a)^2$ and $\Lambda \sim \rho$ have been studied under the assumption that the equation of state parameter $\omega$ is a function of time. The selected $\Lambda$ models are found to be equivalent both in four and five dimensions. The possibility of signature flip of the deceleration parameter is also shown.Comment: 15 Latex pages, a few changes in the text. Accepted in IJMP

Memory Slices: A Modular Building Block for Scalable, Intelligent Memory Systems

While reduction in feature size makes computation cheaper in terms of latency, area, and power consumption, performance of emerging data-intensive applications is determined by data movement. These trends have introduced the concept of scalability as reaching a desirable performance per unit cost by using as few number of units as possible. Many proposals have moved compute closer to the memory. However, these efforts ignored maintaining a balance between bandwidth and compute rate of an architecture, with those of applications, which is a key principle in designing scalable large systems. This paper proposes the use of memory slices, a modular building block for scalable memory systems integrated with compute, in which performance scales with memory size (and volume of data). The slice architecture utilizes a programmable memory interface feeding a systolic compute engine with high reuse rate. The modularity feature of slice-based systems is exploited with a partitioning and data mapping strategy across allocated memory slices where training performance scales with the data size. These features enable shifting the most pressure to cheap compute units rather than expensive memory accesses or transfers via interconnection network. An application of the memory slices to a scale-out memory system is accelerating the training of recurrent, convolutional, and hybrid neural networks (RNNs and RNNs+CNN) that are forming cloud workloads. The results of our cycle-level simulations show that memory slices exhibits a superlinear speedup when the number of slices increases. Furthermore, memory slices improve power efficiency to 747 GFLOPs/J for training LSTMs. While our current evaluation uses memory slices with 3D packaging, a major value is that slices can also be constructed with a variety of packaging options, for example with DDR-based memory units

Modeling and Analysis of Loading Effect in Leakage of Nano-Scaled Bulk-CMOS Logic Circuits

In nanometer scaled CMOS devices significant increase in the subthreshold, the gate and the reverse biased junction band-to-band-tunneling (BTBT) leakage, results in the large increase of total leakage power in a logic circuit. Leakage components interact with each other in device level (through device geometry, doping profile) and also in the circuit level (through node voltages). Due to the circuit level interaction of the different leakage components, the leakage of a logic gate strongly depends on the circuit topology i.e. number and nature of the other logic gates connected to its input and output. In this paper, for the first time, we have analyzed loading effect on leakage and proposed a method to accurately estimate the total leakage in a logic circuit, from its logic level description considering the impact of loading and transistor stacking.Comment: Submitted on behalf of EDAA (http://www.edaa.com/

Indian Amateur Astronomer R. G. Chandra: A unique AAVSO member

In this Note, which is a sequel on the Indian amateur astronomer R.G. Chandra [Biswas2011a, Biswas2011b], we have presented some documents to reveal his three-decades relationship with the American Association for Variable Stars Observers and other connection to American astronomical societies. We have given a short account of his observations on different category, viz. (i) Variable stars, (ii) Nova, (iii) Meteors and (iv) Andromedae. His responsibilities and recognitions are also discussed.Comment: 16 pages, 1 figure and 5 table

Large Number Hypothesis: A Review

Large dimensionless numbers, arising out of ratios of various physical constants, intrigued many scientists, especially Dirac. Relying on the coincidence of large numbers, Dirac arrived at the revolutionary hypothesis that the gravitational constant $G$ should vary inversely as the cosmic time $t$. This hypothesis of Dirac, known as Large Number Hypothesis (LNH), sparked off many speculations, arguments and new ideas in terms of applications. Works done by several authors with LNH as their basic platform are reviewed in this work. Relationship between some of those works are pointed out here. Possibility of time-variations of physical constants other than $G$ are also discussed.Comment: 21 pages Latex, 0 figures, submitted to GR

Cascade Adversarial Machine Learning Regularized with a Unified Embedding

Injecting adversarial examples during training, known as adversarial training, can improve robustness against one-step attacks, but not for unknown iterative attacks. To address this challenge, we first show iteratively generated adversarial images easily transfer between networks trained with the same strategy. Inspired by this observation, we propose cascade adversarial training, which transfers the knowledge of the end results of adversarial training. We train a network from scratch by injecting iteratively generated adversarial images crafted from already defended networks in addition to one-step adversarial images from the network being trained. We also propose to utilize embedding space for both classification and low-level (pixel-level) similarity learning to ignore unknown pixel level perturbation. During training, we inject adversarial images without replacing their corresponding clean images and penalize the distance between the two embeddings (clean and adversarial). Experimental results show that cascade adversarial training together with our proposed low-level similarity learning efficiently enhances the robustness against iterative attacks, but at the expense of decreased robustness against one-step attacks. We show that combining those two techniques can also improve robustness under the worst case black box attack scenario.Comment: 16 pages, 9 figures, International Conference on Learning Representations (ICLR) 201

A Note On Astronomer R. G. Chandra and British Astronomical Association

In the present Note we have presented some documents to reveal the longstanding relationship of Indian amateur astronomer R. G. Chandra with British Astronomical Association.Comment: 8 Latex page

A Village Astronomer: Life and Works of R. G. Chandra

In the present article we provide a brief introduction to the Life and Works of Indian astronomer R. G. Chandra.Comment: 29 Latex page