3D multi-scale modelling of plasma-based seeded soft X-ray lasers

Modelling plasma-based seeded soft X-ray lasers from the creation of the plasma to the propagation of a femtosecond high-order harmonic (HOH) seed throughout several millimetres of inhomogeneous plasma is a complex challenge. Different spatio-temporal scales from the hydrodynamic evolution of the plasma (hundreds of micrometres and nanoseconds) to the propagation of pulses through the plasma (millimetres and tens of picoseconds), electron collisions (picoseconds or even shorter) and the evolution of the envelope of the seeded HOH (tens of femtoseconds) must be tackled in order to fully understand these systems. In this paper, we will present the multi-scale computational paradigm that we have used to perform a full ab initio simulation of a dense, Ni-like Krypton plasma amplifier of soft X-rays. Results of the modelling and expected future applications will also be shown.

A Clustered Approach for Load Balancing in Distributed Systems

Distributed systems are increasingly becoming the dominant and rapidly expanding computational paradigm of tomorrow. A cluster is really a form of parallel or distributed processing system that consists of a set of intertwined stand-alone machines that function together like truly coherent computing and storage resources with a single system image (SSI) which means that perhaps the clusters are viewed as a single platform by the consumers. Global resource management, on the other hand, poses several concerns due to the sheer complexity and range of tools, as well as the need for user accountability. The possible advantages of load balancing in addressing the occasional congestion faced by some nodes when everyone else is idle or congested are widely agreed on a level of performance. This is also widely acknowledged that neither specific load balancing algorithm can adequately address evolving device characteristics and complex capacity management in a distributed ecosystem. To have a systematic approach and also in distributed systems, a proposed approach is created for a holistic view of element load balancing and also the qualities features of load balancing algorithms. The nomenclature has been expanded. In order for adaptive algorithms to understand the problem and manner of prefixing resilience along with different components in distributed systems, they must first recognize the concerns. In addition, a proposed approach is specified. The much more effective load balancing techniques and the modeling hypotheses used in prior load balancing experiments are established through a study of related research. We consider the most appropriate load balancing algorithm and optimum metrics for parameter estimation of the algorithm as a consequence of and output of this assessment for a range of formulations of resulting goals, distributed system features, and workload balancing framework.

A vision to compute like nature

Advocating a new, physically grounded, computational paradigm centered on thermodynamics and an emerging understanding of using thermodynamics to solve problems.

Haplotype-aware pantranscriptome analyses using spliced pangenome graphs

Pangenomics is emerging as a powerful computational paradigm in bioinformatics. This field uses population-level genome reference structures, typically consisting of a sequence graph, to mitigate reference bias and facilitate analyses that were challenging with previous reference-based methods. In this work, we extend these methods into transcriptomics to analyze sequencing data using the pantranscriptome: a population-level transcriptomic reference. Our novel toolchain can construct spliced pangenome graphs, map RNA-seq data to these graphs, and perform haplotype-aware expression quantification of transcripts in a pantranscriptome. This workflow improves accuracy over state-of-the-art RNA-seq mapping methods, and it can efficiently quantify haplotype-specific transcript expression without needing to characterize a sample’s haplotypes beforehand.

On the Self-Repair Role of Astrocytes in STDP Enabled Unsupervised SNNs

Neuromorphic computing is emerging to be a disruptive computational paradigm that attempts to emulate various facets of the underlying structure and functionalities of the brain in the algorithm and hardware design of next-generation machine learning platforms. This work goes beyond the focus of current neuromorphic computing architectures on computational models for neuron and synapse to examine other computational units of the biological brain that might contribute to cognition and especially self-repair. We draw inspiration and insights from computational neuroscience regarding functionalities of glial cells and explore their role in the fault-tolerant capacity of Spiking Neural Networks (SNNs) trained in an unsupervised fashion using Spike-Timing Dependent Plasticity (STDP). We characterize the degree of self-repair that can be enabled in such networks with varying degree of faults ranging from 50 to 90% and evaluate our proposal on the MNIST and Fashion-MNIST datasets.

An Efficient Design of QCA Full-Adder-Subtractor with Low Power Dissipation

The continuous market demands for high performance and energy-efficient computing systems have steered the computational paradigm and technologies towards nanoscale quantum-dot cellular automata (QCA). In this paper, novel energy- and area-efficient QCA-based adder/subtractor designs have been proposed. First, a QCA-based 3-input XOR gate is designed and then a full adder and a full subtractor are realized. The power consumption of the proposed design was tested via the QCAPro estimator tool with different kind of energy (γ = 0.5 Ek, γ = 1.0 Ek, and γ = 1.5 Ek) at temperature T = 2 in Kelvin. QCADesigner 2.0.03 software was applied to evaluate the simulation results of the proposed designs. The proposed design has better complexity than the conventional designs in terms of cell count, area, and power dissipation.

GIS and Computational Notebooks

Researchers and practitioners across many disciplines have recently adopted computational notebooks to develop, document, and share their scientific workflows - and the GIS community is no exception. This chapter introduces computational notebooks in the geographical context. It begins by explaining the computational paradigm and philosophy that underlie notebooks. Next it unpacks their architecture to illustrate a notebook user's typical workflow. Then it discusses the main benefits notebooks offer GIS researchers and practitioners, including better integration with modern software, more natural access to new forms of data, and better alignment with the principles and benefits of open science. In this context, it identifies notebooks as the glue that binds together a broader ecosystem of open source packages and transferable platforms for computational geography. The chapter concludes with a brief illustration of using notebooks for a set of basic GIS operations. Compared to traditional desktop GIS, notebooks can make spatial analysis more nimble, extensible, and reproducible and have thus evolved into an important component of the geospatial science toolkit.