BVR photometry of a newly identified RS CVn binary star HD 61396

BVR photometry of a recently identified RS CVn binary star HD61396, carried out during 2001, is presented. The new photometry reveal significant evolution in the shape and amplitude of light curve when compared with those reported earlier by Padmakar etal (2000). The traditional two-starspot model has been used to obtain the spot parameters from the observed light curve. Changes in the spot area and their location on the stellar surface are discernible from the extracted parameters from the new photometry.Comment: 9 pages including 2 figures and 2 tables. New Astronomy in pres

Impact of photometric variability on age and mass determination of Young Stellar Objects: A case study on Orion Nebula Cluster

In case of pre-main sequence objects, the only way to determine age and mass is by fitting theoretical isochrones on color-magnitude (alternatively luminosity-temperature) diagrams. Since young stellar objects exhibit photometric variability over wide range in magnitude and colors, the age and mass determined by fitting isochrones is expected to be inaccurate, if not erroneous. These in turn will badly affect any study carried out on age spread and process of star formation. Since we have carried out very extensive photometric observations of the Orion Nebula Cluster (ONC), we decided to use our multi-band data to explore the influence of variability in determining mass and age of cluster members. In this study, we get the amplitudes of the photometric variability in V, R, and I optical bands of a sample of 346 ONC members and use it to investigate how the variability affects the inferred masses and ages and if it alone can take account for the age spread among the ONC members reported by earlier studies. We find that members that show periodic and smooth photometric rotational modulation have their masses and ages unaffected by variability. On other hand, we found that members with periodic but very scattered photometric rotational modulation and members with irregular variability have their masses and ages significantly affected. Moreover, using Hertzsprung-Russell (HR) diagrams we find that the observed I band photometric variability can take account of only a fraction (about 50%) of the inferred age spread, whereas the V band photometric variability is large enough to mask any age spread.Comment: Accepted by MNRAS; 17 pages, 4 Tables, 15 Figure

Identification of RAPD marker for the White Backed Plant Hopper (WBPH) resistant gene in rice

The experimental material consisted of two parents Gurjari (white backed plant hopper resistant) and Jaya (white backed plant hopper susceptible) and their F2 progeny. The purpose of the study was the identification of RAPD (Random Amplified Polymorphic DNA) marker for white backed plant hopper (WBPH) resistant gene. The RAPD analysis was done group wise as well as combined for the bulk segregant analysis (BSA). For the BSA, of the total 50 random primers surveyed, a single linked primer, OPA 08, was identified. This primer generated 8-bands, one of which, OPA08-7, was putatively linked to resistant gene as was evident by its presence in almost all the resistant bulks and vice-versa. This band had molecular weight equal to 1219.38 bp and was found in resistant parent, Gurjari, and in almost all the resistant bulks (the four susceptible bulks revealed absence of the same band) indicating the band OPA08-7 as a marker for WBPH resistance among the screened rice genotypes

Stochastic IMT (insulator-metal-transition) neurons: An interplay of thermal and threshold noise at bifurcation

Artificial neural networks can harness stochasticity in multiple ways to enable a vast class of computationally powerful models. Electronic implementation of such stochastic networks is currently limited to addition of algorithmic noise to digital machines which is inherently inefficient; albeit recent efforts to harness physical noise in devices for stochasticity have shown promise. To succeed in fabricating electronic neuromorphic networks we need experimental evidence of devices with measurable and controllable stochasticity which is complemented with the development of reliable statistical models of such observed stochasticity. Current research literature has sparse evidence of the former and a complete lack of the latter. This motivates the current article where we demonstrate a stochastic neuron using an insulator-metal-transition (IMT) device, based on electrically induced phase-transition, in series with a tunable resistance. We show that an IMT neuron has dynamics similar to a piecewise linear FitzHugh-Nagumo (FHN) neuron and incorporates all characteristics of a spiking neuron in the device phenomena. We experimentally demonstrate spontaneous stochastic spiking along with electrically controllable firing probabilities using Vanadium Dioxide (VO$_2$) based IMT neurons which show a sigmoid-like transfer function. The stochastic spiking is explained by two noise sources - thermal noise and threshold fluctuations, which act as precursors of bifurcation. As such, the IMT neuron is modeled as an Ornstein-Uhlenbeck (OU) process with a fluctuating boundary resulting in transfer curves that closely match experiments. As one of the first comprehensive studies of a stochastic neuron hardware and its statistical properties, this article would enable efficient implementation of a large class of neuro-mimetic networks and algorithms.Comment: Added sectioning, Figure 6, Table 1, and Section II.E Updated abstract, discussion and corrected typo

Direct Feedback Alignment with Sparse Connections for Local Learning

Recent advances in deep neural networks (DNNs) owe their success to training algorithms that use backpropagation and gradient-descent. Backpropagation, while highly effective on von Neumann architectures, becomes inefficient when scaling to large networks. Commonly referred to as the weight transport problem, each neuron's dependence on the weights and errors located deeper in the network require exhaustive data movement which presents a key problem in enhancing the performance and energy-efficiency of machine-learning hardware. In this work, we propose a bio-plausible alternative to backpropagation drawing from advances in feedback alignment algorithms in which the error computation at a single synapse reduces to the product of three scalar values. Using a sparse feedback matrix, we show that a neuron needs only a fraction of the information previously used by the feedback alignment algorithms. Consequently, memory and compute can be partitioned and distributed whichever way produces the most efficient forward pass so long as a single error can be delivered to each neuron. Our results show orders of magnitude improvement in data movement and $2\times$ improvement in multiply-and-accumulate operations over backpropagation. Like previous work, we observe that any variant of feedback alignment suffers significant losses in classification accuracy on deep convolutional neural networks. By transferring trained convolutional layers and training the fully connected layers using direct feedback alignment, we demonstrate that direct feedback alignment can obtain results competitive with backpropagation. Furthermore, we observe that using an extremely sparse feedback matrix, rather than a dense one, results in a small accuracy drop while yielding hardware advantages. All the code and results are available under https://github.com/bcrafton/ssdfa.Comment: 15 pages, 8 figure