Differential Analysis Algorithm Research Articles

FVDB : A Deep-Learning Framework for Sparse, Large Scale, and High Performance Spatial Intelligence

We present f VDB, a novel GPU-optimized framework for deep learning on large-scale 3D data. f VDB provides a complete set of differentiable primitives to build deep learning architectures for common tasks in 3D learning such as convolution, pooling, attention, ray-tracing, meshing, etc. f VDB simultaneously provides a much larger feature set (primitives and operators) than established frameworks with no loss in efficiency: our operators match or exceed the performance of other frameworks with narrower scope. Furthermore, f VDB can process datasets with much larger footprint and spatial resolution than prior works, while providing a competitive memory footprint on small inputs. To achieve this combination of versatility and performance, f VDB relies on a single novel VDB index grid acceleration structure paired with several key innovations including GPU accelerated sparse grid construction, convolution using tensorcores, fast ray tracing kernels using a Hierarchical Digital Differential Analyzer algorithm (HDDA), and jagged tensors. Our framework is fully integrated with PyTorch enabling interoperability with existing pipelines, and we demonstrate its effectiveness on a number of representative tasks such as large-scale point-cloud segmentation, high resolution 3D generative modeling, unbounded scale Neural Radiance Fields, and large-scale point cloud reconstruction.

Skin cancer detection and classification based on differential analyzer algorithm

Skin cancer detection and classification based on differential analyzer algorithm

The implementation of digital differential analyzer algorithm for route determination’s topographic maps based on android

The route is direction that must be taken from the starting point to the destination point. Determination of the route is usually done for a reference trip that will be taken include the distance, time or slope of the terrain to be passed. In the implementation, the determination of the route is often used by agencies and organizations operating in the open nature where the activities require special expertise that can process geographic information systems. Although the tools and technology for route determination are sufficient, they are still less effective and efficient because to know the distance, time and slope of a terrain on topographic maps are still using conventional calculations, therefore the application of route determination’s topographic maps based android and applying Digital Differential Analyzer algorithm. The algorithm can be used as a line maker to display a slope picture of the field being traversed. This application was developed using the Rational Unified Process (RUP) method which has 4 stages: Inception, Elaboration, Construction, and Transition. This application can be run at least on Android platform version 4.1.

Differential analysis of gene regulatory networks modeled with structural equation models

A gene regulatory networks (GRN) is composed of a set of genes as well as the regulatory relationships between them. Under different conditions, GRNs with the same gene set usually have similar but not exactly the same structure. The differential analysis of GRNs under different conditions is of great significance to understand gene functions and biological mechanisms. In a naive approach, GRNs under different conditions can be separately inferred via existing GRN inference algorithms, and then the difference between them can be identified by comparing the estimated structures. However, such an approach does not consider the commonality of the pairwise GRNs, which may decrease the predictive accuracy of the differential GRN. In this paper, we model GRNs with the structural equation models (SEMs) to integrate gene expression data and genetic perturbations, and propose a joint differential analysis algorithm named differential sparse SEM (DiffSSEM) to identify the topology structures of GRNs under different conditions as well as the difference between them. The DiffSSEM algorithm simultaneously consider the sparsity of separate GRNs and differential GRN. Simulations were run on synthetic data and the results demonstrated that the DiffSSEM algorithm outperforms the naive approach based on a state-of-the-art sparse SEM inference algorithm. Finally, a real data set of 15 lung genes from 42 tumor tissues and normal tissues was analyzed with the DiffSSEM algorithm to identify the differential GRN.

IODA: An integrated tool for analysis of cancer pathway consistency from heterogeneous multi-omics data

The latest advances in the next generation sequencing technology have greatly facilitated the extensive research of genomics and transcriptomics, thereby promoting the decoding of carcinogenesis with unprecedented resolution. Considering the contribution of analyzing high-throughput multi-omics data to the exploration of cancer molecular mechanisms, an integrated tool for heterogeneous multi-omics data analysis (iODA) is proposed for the systems-level interpretation of multi-omics data, i.e., transcriptomic profiles (mRNA or miRNA expression data) and protein-DNA interactions (ChIP-Seq data). Considering the data heterogeneity, iODA can compare six statistical algorithms in differential analysis for the selected sample data and assist users in choosing the globally optimal one for dysfunctional mRNA or miRNA identification. Since molecular signatures are more consistent at the pathway level than at the gene level, the tool is able to enrich the identified dysfunctional molecules onto the KEGG pathways and extracted the consistent items as key components for further pathogenesis investigation. Compared with other tools, iODA is multi-functional for the systematic analysis of different level of omics data, and its analytical power was demonstrated through case studies of single and cross-level prostate cancer omics data. iODA is open source under GNU GPL and can be downloaded from http://www.sysbio.org.cn/iODA.

A Re-Parametrization-Based Bayesian Differential Analysis Algorithm for Gene Regulatory Networks Modeled with Structural Equation Models

A Re-Parametrization-Based Bayesian Differential Analysis Algorithm for Gene Regulatory Networks Modeled with Structural Equation Models

Review and accuracy comparison of various permittivity-averaging schemes for material discontinuities in the two-dimensional FDFD method: implementation using efficient computer graphics techniques.

Several known and widely used averaging techniques aiming to improve the accuracy of the two-dimensional finite-difference frequency-domain (FDFD) method, in the presence of material discontinuities, are reviewed, numerically tested, and compared with respect to their accuracies. Furthermore, all averaging techniques are rigorously and efficiently implemented using the Supercover Digital Differential Analyzer algorithm and a modified Liang-Barsky algorithm suitably adapted from computer graphics applications. The FDFD with Gaussian blurring; the FDFD with volume-polarized effective permittivity; the FDFD with volume-polarized effective permittivity on shifted cells; and the FDFD with anisotropic smoothing [FDFD (AS)] are compared with respect to their accuracies (for both TE and TM polarization), in the case of scattering by an infinite homogeneous cylinder (for which analytical solution exists) comprising a lossless dielectric, a high-index, low-loss dielectric, or a metal. Sample plots of the relative errors are presented for various field components. Absolute error norms (L2 and L∞) are also presented for both polarizations and for two grid-cell sizes for quantitative comparisons. The results show that the FDFD (AS) prevails in accuracy mainly because it satisfies the boundary conditions at the cylinder's boundary the best. However, for the high-index dielectrics and metals, even the FDFD without any averaging gives very good results for the field components parallel to the uniformity direction. However, the FDFD (AS) is always more accurate when the in-plane field components are sought.

Linear and Differential Cryptanalysis of Russian GOST

Abstract In this paper the linear cryptanalysis and the differential cryptanalysis of the Russian GOST encryption algorithm are carried out. It is shown that GOST is secure against the linear cryptanalysis after five rounds and against the differential cryptanalysis after seven rounds. The differential analysis algorithm of the three round GOST is given. Also criteria for selection of the substitution boxes with provable security against linear cryptanalysis are given.