69 research outputs found
Waveform-Domain Adaptive Matched Filtering: A Novel Approach to Suppressing Interrupted-Sampling Repeater Jamming
The inadequate adaptability to flexible interference scenarios remains an
unresolved challenge in the majority of techniques utilized for mitigating
interrupted-sampling repeater jamming (ISRJ). Matched filtering system based
methods is desirable to incorporate anti-ISRJ measures based on prior ISRJ
modeling, either preceding or succeeding the matched filtering. Due to the
partial matching nature of ISRJ, its characteristics are revealed during the
process of matched filtering. Therefore, this paper introduces an extended
domain called the waveform domain within the matched filtering process. On this
domain, a novel matched filtering model, known as the waveform-domain adaptive
matched filtering (WD-AMF), is established to tackle the problem of ISRJ
suppression without relying on a pre-existing ISRJ model. The output of the
WD-AMF encompasses an adaptive filtering term and a compensation term. The
adaptive filtering term encompasses the adaptive integration outcomes in the
waveform domain, which are determined by an adaptive weighted function. This
function, akin to a collection of bandpass filters, decomposes the integrated
function into multiple components, some of which contain interference while
others do not. The compensation term adheres to an integrated guideline for
discerning the presence of signal components or noise within the integrated
function. The integration results are then concatenated to reconstruct a
compensated matched filter signal output. Simulations are conducted to showcase
the exceptional capability of the proposed method in suppressing ISRJ in
diverse interference scenarios, even in the absence of a pre-existing ISRJ
model
Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History
A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on K-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH) is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional), 10 CEC2005 benchmark functions (30-dimensional), and a real-world problem (multilevel image segmentation problems). Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests
Retrieve-then-extract Based Knowledge Graph Querying Using Graph Neural Networks
The abstract of Retrieve-then-extract Based Knowledge Graph Querying Using
Graph Neural Networks will be updated here
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction
Click-Through Rate (CTR) prediction is one of the most important machine
learning tasks in recommender systems, driving personalized experience for
billions of consumers. Neural architecture search (NAS), as an emerging field,
has demonstrated its capabilities in discovering powerful neural network
architectures, which motivates us to explore its potential for CTR predictions.
Due to 1) diverse unstructured feature interactions, 2) heterogeneous feature
space, and 3) high data volume and intrinsic data randomness, it is challenging
to construct, search, and compare different architectures effectively for
recommendation models. To address these challenges, we propose an automated
interaction architecture discovering framework for CTR prediction named
AutoCTR. Via modularizing simple yet representative interactions as virtual
building blocks and wiring them into a space of direct acyclic graphs, AutoCTR
performs evolutionary architecture exploration with learning-to-rank guidance
at the architecture level and achieves acceleration using low-fidelity model.
Empirical analysis demonstrates the effectiveness of AutoCTR on different
datasets comparing to human-crafted architectures. The discovered architecture
also enjoys generalizability and transferability among different datasets
A Novel Plant Root Foraging Algorithm for Image Segmentation Problems
This paper presents a new type of biologically-inspired global optimization methodology for image segmentation based on plant root foraging behavior, namely, artificial root foraging algorithm (ARFO). The essential motive of ARFO is to imitate the significant characteristics of plant root foraging behavior including branching, regrowing, and tropisms for constructing a heuristic algorithm for multidimensional and multimodal problems. A mathematical model is firstly designed to abstract various plant root foraging patterns. Then, the basic process of ARFO algorithm derived in the model is described in details. When tested against ten benchmark functions, ARFO shows the superiority to other state-of-the-art algorithms on several benchmark functions. Further, we employed the ARFO algorithm to deal with multilevel threshold image segmentation problem. Experimental results of the new algorithm on a variety of images demonstrated the suitability of the proposed method for solving such problem
Discrete and Continuous Optimization Based on Hierarchical Artificial Bee Colony Optimizer
This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC), to tackle complex high-dimensional problems. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operator is applied to enhance the global search ability between species. Experiments are conducted on a set of 20 continuous and discrete benchmark problems. The experimental results demonstrate remarkable performance of the HABC algorithm when compared with other six evolutionary algorithms
HPV16 oncogene expression levels during early cervical carcinogenesis are determined by the balance of epigenetic chromatin modifications at the integrated virus genome.
In cervical squamous cell carcinomas, high-risk human papillomavirus (HRHPV) DNA is usually integrated into host chromosomes. Multiple integration events are thought to be present within the cells of a polyclonal premalignant lesion and the features that underpin clonal selection of one particular integrant remain poorly understood. We previously used the W12 model system to generate a panel of cervical keratinocyte clones, derived from cells of a low-grade premalignant lesion naturally infected with the major HRHPV type, HPV16. The cells were isolated regardless of their selective advantage and differed only by the site of HPV16 integration into the host genome. We used this resource to test the hypothesis that levels of HPV16 E6/E7 oncogene expression in premalignant cells are regulated epigenetically. We performed a comprehensive analysis of the epigenetic landscape of the integrated HPV16 DNA in selected clones, in which levels of virus oncogene expression per DNA template varied ~6.6-fold. Across the cells examined, higher levels of virus expression per template were associated with more open chromatin at the HPV16 long control region, together with greater loading of chromatin remodelling enzymes and lower nucleosome occupancy. There were higher levels of histone post-translational modification hallmarks of transcriptionally active chromatin and lower levels of repressive hallmarks. There was greater abundance of the active/elongating form of the RNA polymerase-II enzyme (RNAPII-Ser2P), together with CDK9, the component of positive transcription elongation factor b complex responsible for Ser2 phosphorylation. The changes observed were functionally significant, as cells with higher HPV16 expression per template showed greater sensitivity to depletion and/or inhibition of histone acetyltransferases and CDK9 and less sensitivity to histone deacetylase inhibition. We conclude that virus gene expression per template following HPV16 integration is determined through multiple layers of epigenetic regulation, which are likely to contribute to selection of individual cells during cervical carcinogenesis.This work was supported by Cancer Research UK (Programme Grant A13080); the Medical Research Council; The Pathological Society of Great Britain and Ireland (E.L.A.K.); and the Agency for Science, Technology and Research, Singapore (Q.Y.A).This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/onc.2016.
Robust estimation of bacterial cell count from optical density
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data
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