An analogue of the Narasimhan-Seshadri theorem and some applications

We prove an analogue in higher dimensions of the classical Narasimhan-Seshadri theorem for strongly stable vector bundles of degree 0 on a smooth projective variety $X$ with a fixed ample line bundle $\Theta$. As applications, over fields of characteristic zero, we give a new proof of the main theorem in a recent paper of Balaji and Koll\'ar and derive an effective version of this theorem; over uncountable fields of positive characteristics, if $G$ is a simple and simply connected algebraic group and the characteristic of the field is bigger than the Coxeter index of $G$, we prove the existence of strongly stable principal $G$ bundles on smooth projective surfaces whose holonomy group is the whole of $G$.Comment: 42 pages. Theorem 3 of this version is new. Typos have been corrected. To appear in Journal of Topolog

Tensor product theorem for Hitchin pairs -An algebraic approach

We give an algebraic approach to the study of Hitchin pairs and prove the tensor product theorem for Higgs semistable Hitchin pairs over smooth projective curves defined over algebraically closed fields $k$ of characteristic $0$ and characteristic $p$, with $p$ satisfying some natural bounds. We also prove the corresponding theorem for polystable bundles.Comment: To appear in Annales de l'Institut Fourier, Volume 61 (2011

PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network

We present PyCARL, a PyNN-based common Python programming interface for hardware-software co-simulation of spiking neural network (SNN). Through PyCARL, we make the following two key contributions. First, we provide an interface of PyNN to CARLsim, a computationally-efficient, GPU-accelerated and biophysically-detailed SNN simulator. PyCARL facilitates joint development of machine learning models and code sharing between CARLsim and PyNN users, promoting an integrated and larger neuromorphic community. Second, we integrate cycle-accurate models of state-of-the-art neuromorphic hardware such as TrueNorth, Loihi, and DynapSE in PyCARL, to accurately model hardware latencies that delay spikes between communicating neurons and degrade performance. PyCARL allows users to analyze and optimize the performance difference between software-only simulation and hardware-software co-simulation of their machine learning models. We show that system designers can also use PyCARL to perform design-space exploration early in the product development stage, facilitating faster time-to-deployment of neuromorphic products. We evaluate the memory usage and simulation time of PyCARL using functionality tests, synthetic SNNs, and realistic applications. Our results demonstrate that for large SNNs, PyCARL does not lead to any significant overhead compared to CARLsim. We also use PyCARL to analyze these SNNs for a state-of-the-art neuromorphic hardware and demonstrate a significant performance deviation from software-only simulations. PyCARL allows to evaluate and minimize such differences early during model development.Comment: 10 pages, 25 figures. Accepted for publication at International Joint Conference on Neural Networks (IJCNN) 202

Indigenous trawl operations during fishing ban period in Chennai

North Chennai is a major centre for mechanised fishing with approximately 1200 fishing units. Generally during the fishing ban period, the fishermen from these units either sit idle or enroll as labourers for fishing in permitted traditional fishing units. But during the mechanised fishing ban period in 2017, some of the fishers in North Chennai started mini trawl operations to tide over their lean period. The size of the trawl net was 15 m in length and cod end mesh size of 24 mm

A Review of Lightweight Thread Approaches for High Performance Computing

High-level, directive-based solutions are becoming the programming models (PMs) of the multi/many-core architectures. Several solutions relying on operating system (OS) threads perfectly work with a moderate number of cores. However, exascale systems will spawn hundreds of thousands of threads in order to exploit their massive parallel architectures and thus conventional OS threads are too heavy for that purpose. Several lightweight thread (LWT) libraries have recently appeared offering lighter mechanisms to tackle massive concurrency. In order to examine the suitability of LWTs in high-level runtimes, we develop a set of microbenchmarks consisting of commonly-found patterns in current parallel codes. Moreover, we study the semantics offered by some LWT libraries in order to expose the similarities between different LWT application programming interfaces. This study reveals that a reduced set of LWT functions can be sufficient to cover the common parallel code patterns andthat those LWT libraries perform better than OS threads-based solutions in cases where task and nested parallelism are becoming more popular with new architectures.The researchers from the Universitat Jaume I de Castelló were supported by project TIN2014-53495-R of the MINECO, the Generalitat Valenciana fellowship programme Vali+d 2015, and FEDER. This work was partially supported by the U.S. Dept. of Energy, Office of Science, Office of Advanced Scientific Computing Research (SC-21), under contract DEAC02-06CH11357. We gratefully acknowledge the computing resources provided and operated by the Joint Laboratory for System Evaluation (JLSE) at Argonne National Laboratory.Peer ReviewedPostprint (author's final draft

One pion events by atmospheric neutrinos: A three flavor analysis

We study the one-pion events produced via neutral current (NC) and charged current (CC) interactions by the atmospheric neutrinos. We analyze the ratios of these events in the framework of oscillations between three neutrino flavors. The ratios of the CC events induced by $\nu_e$ to that of the NC events and a similar ratio defined with $\nu_\mu$ help us in distinguishing the different regions of the neutrino parameter space.Comment: 14 pages, 4 figures (separate postscript files

Magnetic and humidity sensing properties of nanostructured Cu[x]Co[1-x]Fe2O4 synthesized by auto combustion technique

Magnetic nanomaterials (23-43 nm) of Cu$_x$Co$_{1-x}$Fe$_2$O$_4$\ (x = 0.0, 0.5 and 1.0) were synthesized by auto combustion method. The crystallite sizes of these materials were calculated from X-ray diffraction peaks. The band observed in Fourier transform infrared spectrum near 575 cm$^{-1}$ in these samples confirm the presence of ferrite phase. Conductivity measurement shows the thermal hysteresis and demonstrates the knee points at 475$^o$C, 525$^o$C and 500$^o$C for copper ferrite, cobalt ferrite and copper-cobalt mixed ferrite respectively. The hystersis M-H loops for these materials were traced using the Vibrating Sample Magnetometer (VSM) and indicate a significant increase in the saturation magnetization (M$_s$) and remanence (M$_r$) due to the substitution of Cu$^{2+}$ ions in cobalt ferrite, while the intrinsic coercivity (H$_c$) was decreasing. Among these ferrites, copper ferrite exhibits highest sensitivity for humidity.Comment: 12 pages, 7 figure

Experimental Analysis And Setup Of Gravity Assisted Shell And Tube Heat Exchanger

Heat transfer is one of the most important thing to be considered in thermal industry. There are several type of heat exchangers available for heat transferring purposes. But scientists are involved in finding new methodologies which would further increase the heat transfer rate and the effectiveness of heat transfer by conducting several experiments. Many researchers have found the different methodologies for increasing the heat transfer rate with the application of various research. In this paper we have proposed a new methodology for the heat exchanger in various aspects. In this paper we have proposed a new concept which can be uses in shell and tube heat exchangers. Here we have considered the angle of the heat exchanger to know whether the heat transfer rate increases or decreases with increase in inclination angles of the exchanger. Here we have used the heat exchanger in various angles from 0` to 90` to find at which angle the heat transfer rate is maximum. The experimental analysis shows the heat transfer rate is maximum at 45` and it increases further with increase in the mass flow rate of both the fluids. In this proposal we used water as both hot and cold fluid with varying mass flow rates of the liquids