2,113 research outputs found
Unitary Approximate Message Passing for Sparse Bayesian Learning and Bilinear Recovery
Over the past several years, the approximate message passing (AMP) algorithm has been applied to a broad range of problems, including compressed sensing (CS), robust regression, Bayesian estimation, etc. AMP was originally developed for compressed sensing based on the loopy belief propagation (BP). Compared to convex optimization based algorithms, AMP has low complexity and its performance can be rigorously characterized by a scalar state evolution (SE) in the case of a large independent and identically distributed (i.i.d.) (sub-) Gaussian matrix. AMP was then extended to solve general estimation problems with a generalized linear observation model. However, AMP performs poorly on a generic matrix such as non-zero mean, rank-deficient, correlated, or ill-conditioned matrix, resulting in divergence and degraded performance. It was discovered later that applying AMP to a unitary transform of the original model can remarkably enhance the robustness to difficult matrices. This variant is named unitary AMP (UAMP), or formally called UTAMP. In this thesis, leveraging UAMP, we propose UAMP-SBL for sparse signal recovery and Bi-UAMP for bilinear recovery, both of which inherit the low complexity and robustness of UAMP.
Sparse Bayesian learning (SBL) is a powerful tool for recovering a sparse signal from noisy measurements, which finds numerous applications in various areas. As a traditional implementation of SBL, e.g., Tipping’s method, involves matrix inversion in each iteration, the computational complexity can be prohibitive for large scale problems. To circumvent this, AMP and its variants have been used as low-complexity solutions. Unfortunately, they will diverge for ‘difficult’ measurement matrices as previously mentioned. In this thesis, leveraging UAMP, a novel SBL algorithm called UAMP-SBL is proposed where UAMP is incorporated into the structured variational message passing (SVMP) to handle the most computationally intensive part of message computations. It is shown that, compared to state-of-the-art AMP based SBL algorithms, the proposed UAMP-SBL is more robust and efficient, leading to remarkably better performance.
The bilinear recovery problem has many applications such as dictionary learning, selfcalibration, compressed sensing with matrix uncertainty, etc. Compared to existing nonmessage passing alternates, several AMP based algorithms have been developed to solve bilinear problems. By using UAMP, a more robust and faster approximate inference algorithm for bilinear recovery is proposed in this thesis, which is called Bi-UAMP. With the lifting approach, the original bilinear problem is reformulated as a linear one. Then, variational inference (VI), expectation propagation (EP) and BP are combined with UAMP to implement the approximate inference algorithm Bi-UAMP, where UAMP is adopted for the most computationally intensive part. It is shown that, compared to state-of-the-art bilinear recovery algorithms, the proposed Bi-UAMP is much more robust and faster, and delivers significantly better performance.
Recently, UAMP has also been employed for many other applications such as inverse synthetic aperture radar (ISAR) imaging, low-complexity direction of arrival (DOA) estimation, iterative detection for orthogonal time frequency space modulation (OTFS), channel estimation for RIS-Aided MIMO communications, etc. Promising performance was achieved in all of the applications, and more applications of UAMP are expected in the future
GPU-Accelerated BWT Construction for Large Collection of Short Reads
Advances in DNA sequencing technology have stimulated the development of
algorithms and tools for processing very large collections of short strings
(reads). Short-read alignment and assembly are among the most well-studied
problems. Many state-of-the-art aligners, at their core, have used the
Burrows-Wheeler transform (BWT) as a main-memory index of a reference genome
(typical example, NCBI human genome). Recently, BWT has also found its use in
string-graph assembly, for indexing the reads (i.e., raw data from DNA
sequencers). In a typical data set, the volume of reads is tens of times of the
sequenced genome and can be up to 100 Gigabases. Note that a reference genome
is relatively stable and computing the index is not a frequent task. For reads,
the index has to computed from scratch for each given input. The ability of
efficient BWT construction becomes a much bigger concern than before. In this
paper, we present a practical method called CX1 for constructing the BWT of
very large string collections. CX1 is the first tool that can take advantage of
the parallelism given by a graphics processing unit (GPU, a relative cheap
device providing a thousand or more primitive cores), as well as simultaneously
the parallelism from a multi-core CPU and more interestingly, from a cluster of
GPU-enabled nodes. Using CX1, the BWT of a short-read collection of up to 100
Gigabases can be constructed in less than 2 hours using a machine equipped with
a quad-core CPU and a GPU, or in about 43 minutes using a cluster with 4 such
machines (the speedup is almost linear after excluding the first 16 minutes for
loading the reads from the hard disk). The previously fastest tool BRC is
measured to take 12 hours to process 100 Gigabases on one machine; it is
non-trivial how BRC can be parallelized to take advantage a cluster of
machines, let alone GPUs.Comment: 11 page
MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph
MEGAHIT is a NGS de novo assembler for assembling large and complex
metagenomics data in a time- and cost-efficient manner. It finished assembling
a soil metagenomics dataset with 252Gbps in 44.1 hours and 99.6 hours on a
single computing node with and without a GPU, respectively. MEGAHIT assembles
the data as a whole, i.e., it avoids pre-processing like partitioning and
normalization, which might compromise on result integrity. MEGAHIT generates 3
times larger assembly, with longer contig N50 and average contig length than
the previous assembly. 55.8% of the reads were aligned to the assembly, which
is 4 times higher than the previous. The source code of MEGAHIT is freely
available at https://github.com/voutcn/megahit under GPLv3 license.Comment: 2 pages, 2 tables, 1 figure, submitted to Oxford Bioinformatics as an
Application Not
D3P : Data-driven demand prediction for fast expanding electric vehicle sharing systems
The future of urban mobility is expected to be shared and electric. It is not only a more sustainable paradigm that can reduce emissions, but can also bring societal benefits by offering a more affordable on-demand mobility option to the general public. Many car sharing service providers as well as automobile manufacturers are entering the competition by expanding both their EV fleets and renting/returning station networks, aiming to seize a share of the market and to bring car sharing to the zero emissions level. During their fast expansion, one determinant for success is the ability of predicting the demand of stations as the entire system is growing continuously. There are several challenges in this demand prediction problem: First, unlike most of the existing work which predicts demand only for static systems or at few stages of expansion, in the real world we often need to predict the demand as or even before stations are being deployed or closed, to provide information and decision support. Second, for the new stations to be deployed, there is no historical data available to help the prediction of their demand. Finally, the impact of deploying/closing stations on the other stations in the system can be complex. To address these challenges, we formulate the demand prediction problem in the context of fast expanding electric vehicle sharing systems, and propose a data-driven demand prediction approach which aims to model the expansion dynamics directly from the data. We use a local temporal encoding process to handle the historical data for each existing station, and a dynamic spatial encoding process to take correlations between stations into account with Graph Convolutional Neural Networks (GCN). The encoded features are fed to a multi-scale predictor, which forecasts both the long-term expected demand of the stations and their instant demand in the near future. We evaluate the proposed approach with real-world data collected from a major EV sharing platform for one year. Experimental results demonstrate that our approach significantly outperforms the state of the art, showing up to three-fold performance gain in predicting demand for the expanding EV sharing systems
Sparse Bayesian Learning with Diagonal Quasi-Newton Method for Large Scale Classification
Sparse Bayesian Learning (SBL) constructs an extremely sparse probabilistic
model with very competitive generalization. However, SBL needs to invert a big
covariance matrix with complexity O(M^3 ) (M: feature size) for updating the
regularization priors, making it difficult for practical use. There are three
issues in SBL: 1) Inverting the covariance matrix may obtain singular solutions
in some cases, which hinders SBL from convergence; 2) Poor scalability to
problems with high dimensional feature space or large data size; 3) SBL easily
suffers from memory overflow for large-scale data. This paper addresses these
issues with a newly proposed diagonal Quasi-Newton (DQN) method for SBL called
DQN-SBL where the inversion of big covariance matrix is ignored so that the
complexity and memory storage are reduced to O(M). The DQN-SBL is thoroughly
evaluated on non-linear classifiers and linear feature selection using various
benchmark datasets of different sizes. Experimental results verify that DQN-SBL
receives competitive generalization with a very sparse model and scales well to
large-scale problems.Comment: 11 pages,5 figure
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