Practical fast matrix multiplication algorithms

Matrix multiplication is a core building block for numerous scientific computing and, more recently, machine learning applications. Strassen's algorithm, the original Fast Matrix Multiplication (FMM) algorithm, has long fascinated computer scientists due to its startling property of reducing the number of computations required for multiplying n x n matrices from O(n³) to O(n [superscript 2.807]). Over the last half century, this has fueled many theoretical improvements such as other variations of Strassen-like FMM algorithms. Previous implementations of these FMM algorithms led to the "street wisdom" that they are only practical for large, relatively square matrices, that they require considerable workspace, and that they are difficult to achieve thread-level parallelism. The thesis of this work dispels these notions by demonstrating significant benefits for small and non-square matrices, requiring no workspace beyond what is already incorporated in high-performance implementations of matrix multiplication, and achieving performance benefits on multi-core, many-core, and distributed memory architectures.Computer Science

A deep deformable residual learning network for SAR images segmentation

Reliable automatic target segmentation in Synthetic Aperture Radar (SAR) imagery has played an important role in the SAR fields. Different from the traditional methods, Spectral Residual (SR) and CFAR detector, with the recent adavance in machine learning theory, there has emerged a novel method for SAR target segmentation, based on the deep learning networks. In this paper, we proposed a deep deformable residual learning network for target segmentation that attempts to preserve the precise contour of the target. For this, the deformable convolutional layers and residual learning block are applied, which could extract and preserve the geometric information of the targets as much as possible. Based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set, experimental results have shown the superiority of the proposed network for the precise targets segmentation

Solvothermal Synthesis, Structure and Optical Property of Nanosized CoSb3 Skutterudite

Binary skutterudite CoSb3 nanoparticles were synthesized by solvothermal method. The nanostructuring of CoSb3 material was achieved by the inclusion of various kinds of additives. X-ray diffraction examination indicated the formation of the cubic phase of CoSb3. Structural analysis by transmission electron microscopy analysis further confirmed the formation of crystalline CoSb3 nanoparticles with high purity. With the assistance of additives, CoSb3 nanoparticles with size as small as 10 nm were obtained. The effect of the nanostructure of CoSb3 on the UV–visible absorption and luminescence was studied. The nanosized CoSb3 skutterudite may find application in developing thermoelectric devices with better efficiency

In situ atomic-scale oscillation sublimation of magnesium under CO₂ conditions.

Understanding the interactive role between Mg and CO₂ is crucial for many technological applications, including CO₂ storage, melting protection, corrosion resistance, and ceramic welding. Here we report observations of rapid oscillation sublimation of Mg at room temperature in the presence of both CO₂ gas and electron irradiation using environmental transmission electron microscopy. The sublimation is mainly related to phase transformation of amorphous MgCO₃. Differing from the direct formation of gas-state MgCO₃, which attributes to the sublimation of pure Mg under a mild electron beam dose, a unique oscillation process is detected during the process of Mg sublimation under a harsh electron beam dose. The main reason stems from the first-order reaction of a reversible decomposition-formation of amorphous MgCO₃. These atomic-level results provide some interesting insights into the interactive role between Mg and CO₂ under electron beam irradiation

Computational identification of putative cytochrome P450 genes in soybean (Glycine max) using expressed sequence tags (ESTs)

Cytochrome P450 is a group of monooxygenase that exists as a gene superfamily and plays an important role in metabolizing physiologically important compounds in plants. However, to date only a limited number of P450s have been identified and characterized in soybean (Glycine max.). In this work, a computational study of expressed sequence tags (ESTs) of soybean was performed by data mining methods and bio-informatics tools and as a result 78 putative P450 genes were identified, including 57 new ones. These genes were classified into five clans and 20 families by sequence similarities and among those 57 new families, 18 new subfamilies were found which have not been observed previously in soybean. This work may provide a basis for further functional dissection of P450 genes in soybean and other legumes.Key words: Expressed sequence tags (ESTs), in silico, soybean (Glycine max.), P450

Analysis of Nakamoto Consensus, Revisited

In the Bitcoin white paper, Nakamoto proposed a very simple Byzantine fault tolerant consensus algorithm that is also known as Nakamoto consensus. Despite its simplicity, some existing analysis of Nakamoto consensus appears to be long and involved. In this technical report, we aim to make such analysis simple and transparent so that we can teach senior undergraduate students and graduate students in our institutions. This report is largely based on a 3-hour tutorial given by one of the authors in June 2019.Comment: 8 page