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Many pathogenic mutations percolate to protein dysfunction by altering dynamics. Reconstructing protein energy landscapes promises to relate dynamics to function but is generally infeasible due to the disparate spatio-temporal scales involved. Recent algorithmic innovation allows reconstructing energy landscapes of medium-size proteins in the presence of sufficient prior wet-laboratory structure data...
Fingerprinting Localization Solutions (FPSs) enjoy huge popularity due to their good performance and minimal environment information requirement. Considered as a data-driven approach, many modern data analytics can be used to improve its performance. In this paper, we propose tow learning algorithms, namely a deep learning architecture for regression and Support Vector Machine (SVM) for classification,...
Steganography is a form of secret communication in which a secret message (text, image) is hidden inside a carrier message (text, image). While in cryptography, the goal is to make everything unreadable to the ones who do not know the password, in steganography, on the other hand, the goal is to camouflage a message inside an apparently innocent carrier. In other words, while cryptography conceals...
Clustering is an effective method for data analysis and can be exploited to unknown features of data samples, its applications range from data mining to bioinformatics analysis. Several clustering approaches have been proposed in order to obtain a better trade-off between accuracy and efficiency of the clustering process. It is well-known that no existing clustering algorithm completely satisfies...
Most state-of-the-art graph kernels only take local graph properties into account, i.e., the kernel is computed with regard to properties of the neighborhood of vertices or other small substructures. On the other hand, kernels that do take global graph properties into account may not scale well to large graph databases. Here we propose to start exploring the spacebetween local and global graph kernels,...
In functional genomics, small interfering RNA (siRNA) can be used to knockdown gene expression. Usually, a target gene has numerous potential siRNAs, but their efficiencies of gene silencing often varies. Thus, for a successful RNA interference (RNAi), selecting the most effective siRNA is a critical step. Despite various computational algorithms have been developed, the efficacy prediction accuracy...
Motion estimation is an essential procedure in video coding and object tracing, but it always has a high computational load. Some low bit-depth motion estimation methods, such as one/two-bit transform or gray coding based methods, have lower complexity. However, one-bit transform based methods are sensitive to noise, whereas two-bit transform and gray coding methods use many bit planes or operations...
Prefix Scan (or simply scan) is an operator that computes all the partial sums of a vector. A scan operation results in a vector where each element is the sum of the preceding elements in the original vector up to the corresponding position. Scan is a key operation in many relevant problems like sorting, lexical analysis, string comparison, image filtering among others. Although there are libraries...
At present, shallow characteristics are usually utilized to represent the distributed features of text for Chinese spam classification, causing the problem of inexact text vector representation and low classification performance. A novel Chinese spam classification method based on weighted distributed feature is proposed by combining the features of TF-IDF weighted algorithm with the distributed text-based...
Given a set of points P⊄ R^d and a kernel k, the Kernel Density Estimate at a point x∊R^d is defined as \mathrm{KDE}_{P}(x)=\frac{1}{|P|}\sum_{y\in P} k(x,y). We study the problem of designing a data structure that given a data set P and a kernel function, returns approximations to the kernel density} of a query point in sublinear time}. We introduce a class of unbiased estimators...
Presented paper explains general purpose approach to the parallel pixel processing on GPU. It presents essential dataset structuring, correct type assignment and kernel configuration for CUDA application interface. Paper also explains data movement and optimal computation saturation. Transfers are also analyzed in correlation with the computation especially for the embarrassingly parallel problem...
Interactive video streaming requires very low latency and high throughput. Traditional latency based congestion control algorithm performs poorly in fairness. This results in very poor video quality to adaptive video streaming. Software defined networks (SDN) enables us to solve the problem by designing a network controller in the routers. This paper presents a SDN-centric TCP where sending rate of...
In process monitoring of batch process, Fisher discriminant analysis is a very popular method and has be widely applied. In this paper, a new kernel local Fisher discriminant analysis (KLFDA) algorithm is proposed for fault diagnosis. The main contributions of the presented approach are as follows: 1) the proposed algorithm can simultaneously extract the global European distribution of data and local...
Recently, convolutional neural networks (CNNs) have achieved great success in fields such as computer vision, natural language processing, and artificial intelligence. Many of these applications utilize parallel processing in GPUs to achieve higher performance. However, it remains a daunting task to optimize for GPUs, and most researchers have to rely on vendor-provided libraries for such purposes...
The low noise amplifier (LNA) is a significant device in RF front-end. In this paper, a straight and efficient modeling method for LNA based on the Volterra series with recursive least squares (RLS) algorithm is proposed. Instead of calculating the high nonlinearity order of Volterra kernels, the proposed method extracts the first three order Volterra kernels characterizing the memory effect to construct...
The paper uses machine learning methods to deal with the problem of reducing the cost of applying mutation testing. A method of classifying mutants of a program using structural similarity is proposed. To calculate such a similarity each mutant is firstly converted into a hierarchical graph, which represents the mutant's control flow, variables and conditions. Then using such a graph form graph kernels...
In this paper we present MLTBiqCrunch, a hierarchically parallelized version of the open-source solver BiqCrunch [1]. More precisely, this version has two levels of parallelization: a coarse grain, assigning a thread to a node evaluation and a fine grain, parallelizing a node evaluation when some threads are not busy. We present experiments on some classical binary quadratic optimization problems...
Multi-/many-core CPU based architectures are seeing widespread adoption due to their unprecedented compute performance in a small power envelope. With the increasingly large number of cores on each node, applications spend a significant portion of their execution time in intra-node communication. While shared memory is commonly used for intra-node communication, it needs to copy each message once...
This paper presents our study on image reconstruction algorithms for THz near field scanning systems. Based on the principle of Physical Optics (PO) algorithm, we have proposed and investigated a novel transposed convolution image reconstruction algorithm (TC), in comparison with the back propagation algorithm (BP) in simulation and experiment.
We propose a design for a fine-grained lock-based skiplist optimized for Graphics Processing Units (GPUs). While GPUs are often used to accelerate streaming parallel computations, it remains a significant challenge to efficiently offload concurrent computations with more complicated data-irregular access and fine-grained synchronization. Natural building blocks for such computations would be concurrent...
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