Neutrino Interactions in Octet Baryon Matter

Neutrino processes caused by the neutral current are studied in octet baryon matter. Previous confusion about the baryonic matrix elements of the neutral current interaction is excluded, and a correct table for them improved by consideration of the proton spin problem is presented instead.Comment: 6 page

Protoneutron Stars with Kaon Condensation and their Delayed Collapse

Properties of protoneutron stars are discussed in the context of kaon condensation. Thermal and neutrino trapping effects are very important ingredients to study them. By solving the TOV equation, we discuss the static properties of protoneutron stars and the possibility of the delayed collapse during their evolution.Comment: 33pages,15 figures, accepted for publication in Nucl. Phys.

Residual stress development and evolution in two-phase crystalline material: a discrete dislocation study

Crystalline materials undergo heterogeneous deformation upon the application of external load, which results in the development of incompatible elastic strains in the material as soon as the load is removed. The presence of heterogeneous distribution of elastic strains in the absence of any form of external load results in the building up of stresses referred to as residual stresses. The heterogeneity of strain is attributed either to the presence of multiple phases or to the orientation gradients across the sample volume. This paper is an endeavour to model the presence of second phase in a two-dimensional discrete dislocation dynamics framework, which already contains constitutive rules to include three-dimensional mechanisms, such as line tension and dynamic junction formation. The model is used to investigate residual stress development in single crystals subjected to plane strain loading and then subsequently unloaded to study residual stresses. The dislocation accumulation around the second phase and its effect on the mechanical properties is studied. The orientation dependence of residual stresses as a function of the underlying defect substructure has also been explored. A variety of results are obtained. In particular, the development of stresses as a function of underlying defect substructure is also presented and found to depend upon the orientation of the crystal

Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers

A massive gap exists between current quantum computing (QC) prototypes, and the size and scale required for many proposed QC algorithms. Current QC implementations are prone to noise and variability which affect their reliability, and yet with less than 80 quantum bits (qubits) total, they are too resource-constrained to implement error correction. The term Noisy Intermediate-Scale Quantum (NISQ) refers to these current and near-term systems of 1000 qubits or less. Given NISQ's severe resource constraints, low reliability, and high variability in physical characteristics such as coherence time or error rates, it is of pressing importance to map computations onto them in ways that use resources efficiently and maximize the likelihood of successful runs. This paper proposes and evaluates backend compiler approaches to map and optimize high-level QC programs to execute with high reliability on NISQ systems with diverse hardware characteristics. Our techniques all start from an LLVM intermediate representation of the quantum program (such as would be generated from high-level QC languages like Scaffold) and generate QC executables runnable on the IBM Q public QC machine. We then use this framework to implement and evaluate several optimal and heuristic mapping methods. These methods vary in how they account for the availability of dynamic machine calibration data, the relative importance of various noise parameters, the different possible routing strategies, and the relative importance of compile-time scalability versus runtime success. Using real-system measurements, we show that fine grained spatial and temporal variations in hardware parameters can be exploited to obtain an average $2.9$x (and up to $18$x) improvement in program success rate over the industry standard IBM Qiskit compiler.Comment: To appear in ASPLOS'1

Power consumption prediction in cloud data center using machine learning

The flourishing development of the cloud computing paradigm provides several services in the industrial business world. Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. Pursuant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A diverse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased carbon footprints. Nearly a million physical machines (PM) are running all over the data centers, along with (5 – 6) million virtual machines (VM). In the next five years, the power needs of this domain are expected to spiral up to 5% of global power production. The virtual machine power consumption reduction impacts the diminishing of the PM’s power, however further changing in power consumption of data center year by year, to aid the cloud vendors using prediction methods. The sudden fluctuation in power utilization will cause power outage in the cloud data centers. This paper aims to forecast the VM power consumption with the help of regressive predictive analysis, one of the Machine Learning (ML) techniques. The potency of this approach to make better predictions of future value, using Multi-layer Perceptron (MLP) regressor which provides 91% of accuracy during the prediction process