!Non-binary low density parity check (NB-LDPC) codes are known to have higher correction capability than their binary counterparts, low density parity check (LDPC) codes. The latest technique for analyzing NB-LDPC codes is to measure generalized girth. The generalized girth cannot be measured in a binary LDPC code, and can be considered an indicator for measuring the performance of NB-LDPC code. In this paper, we propose a new analysis method to represent NB-LDPC code as a graph. This graph is called a permutation graph. In this paper, we present and prove a reasoning for analyzing generalized girth using the permutation graph. This is a faster and simpler method than the existing generalized girth analysis method. The proposed code construction algorithm constructs NB-LDPC codes to have an arbitrary generalized girth length. Because the permutation graph-based analysis is applied, which is more effective than existing methods, the complexity of code construction is significantly reduced. This enables more efficient code construction. The decoding simulations show that the proposed method improves the correction performance of NB-LDPC codes compared to the existing state-of-the-art.

In this paper, a new ECC (error correcting code) solution for DRAM (dynamic random access memory) in computing systems is proposed. Existing papers on ECC for DRAM systems do not consider storage space for metadata. The methodology proposed in this paper considers storing metadata attached to a cacheline data in DRAM. We infer the maximum number of single-chip error correction cases that a linear code can support while considering metadata storage space. This can be said to be the maximum theoretical correction probability for a single chip error. A methodology to construct a code with maximum single-chip error correction is presented. A decoding methodology for the code is proposed. The proposed ECC solution can correct not only single chip failure but also additional small bit errors. We calculate the correction capability of the proposed methodology and verified it through simulation. The encoder and decoder hardware were synthesized and compared with existing methodologies.

Decision forests, including Random Forests and Gradient Boosting Trees, have recently demonstrated state-of-the-art performance in a variety of machine learning settings. Decision forests are typically ensembles of axis-aligned decision trees; that is, trees that split only along feature dimensions. In contrast, many recent extensions to decision forests are based on axis-oblique splits. Unfortunately, these extensions forfeit one or more of the favorable properties of decision forests based on axis-aligned splits, such as robustness to many noise dimensions, interpretability, or computational efficiency. We introduce yet another decision forest, called “Sparse Projection Oblique Randomer Forests” (SPORF). SPORF trees recursively split along very sparse random projections. Our method significantly improves accuracy over existing state-of-the-art algorithms on a standard benchmark suite for classification with $>100$ problems of varying dimension, sample size, and number of classes. To illustrate how SPORF addresses the limitations of both axis-aligned and existing oblique decision forest methods, we conduct extensive simulated experiments. SPORF typically yields improved performance over existing decision forest methods, while mitigating computational efficiency and scalability and maintaining interpretability. Very sparse random projections can be incorporated into gradient boosted trees to obtain potentially similar gains.

We introduce graspy, a Python library devoted to statistical inference, machine learning, and visualization of random graphs and graph populations. This package provides flexible and easy-to-use algorithms for analyzing and understanding graphs with a sklearn compliant API. graspy can be downloaded from Python Package Index (PyPi), and is released under the Apache 2.0 open-source license. The documentation and all releases are available at https://neurodata.io/graspy.