Core Selection Method Research Articles

Machine-learning-based impairment-aware dynamic RMSCA in multi-core elastic optical networks

This paper presents a routing, modulation, spectrum, and core assignment (RMSCA) algorithm for space-division-multiplexing-based elastic optical networks (SDM-EONs) comprising multi-core links. A network state-dependent route and core selection method is proposed using a deep neural network (DNN) classifier. The DNN is trained using a metaheuristic optimization algorithm to predict lightpath suitability, considering the quality of transmission and resource availability. Physical layer impairments, including inter-core crosstalk, amplified spontaneous emission, and Kerr fiber nonlinearities, are considered, and a random forest (RF)-based link noise estimator is proposed. A feature importance selection analysis is provided for all the features considered for the DNN classifier and the RF link noise estimator. The proposed machine-learning-enabled RMSCA approach is evaluated on three network topologies, USNET, NSFNET, and COST-239 with 7-core and 12-core fiber links. It is shown to be superior in terms of blocking probability, bandwidth blocking probability, and acceptable computational speed compared to the standard and published benchmarks at different traffic loads.

The SRG/eROSITA All-Sky Survey: X-ray beacons at late cosmic dawn

The Spektrum-Roentgen-Gamma (SRG)/extended Roentgen Survey with an Imaging Telescope Array (eROSITA) All-Sky Survey (eRASS) is expected to contain $ quasars that emitted their light when the universe was less than a billion years old, that is, at $z>5.6$. By selection, these quasars populate the bright end of the active galactic nuclei (AGN) X-ray luminosity function, and their space density offers a powerful demographic diagnostic of the parent super-massive black hole (SMBH) population. Of the $ 400$ quasars that have been discovered at $z>5.6$ to date, less than 15<!PCT!> have been X-ray detected. We present a pilot survey to uncover the elusive X-ray luminous end of the distant quasar population. We have designed a quasar selection pipeline based on optical, infrared and X-ray imaging data from DES DR2, VHS DR5, CatWISE2020 and the eRASS (up to its four-pass cumulative version, eRASS:4). The core selection method relies on SED template fitting. We performed optical follow-up spectroscopy with the Magellan /LDSS3 instrument for the redshift confirmation of a subset of candidates. We have further obtained a deeper X-ray image of one of our candidates with Chandra ACIS-S. We report the discovery of five new quasars in the redshift range $5.6 < z < 6.1$. Two of these quasars are detected in eRASS and are, therefore, X-ray ultra-luminous by selection. We also report the detection of these quasars at radio frequencies. The first one is a broad absorption line quasar, which shows significant, order-of-magnitude X-ray dimming over 3.5 years, corresponding to six months in the quasar rest frame. The second X-ray detected quasar is a jetted source with compact morphology. We show that a blazar configuration is likely for this source, making it one of the most distant blazars known to date. With our pilot study, we demonstrate the power of eROSITA as a discovery machine for luminous quasars in the epoch of reionization. The X-ray emission of the two eROSITA detected quasars are likely to be driven by different high-energetic emission mechanisms, a diversity which we will further explore in a future systematic full-hemisphere survey.

Impairment- and fragmentation-aware, energy-efficient dynamic RMSCA for SDM-EONs

This paper presents a routing, modulation, spectrum, and core allocation (RMSCA) algorithm for space-division multiplexing-based elastic optical networks (SDM-EONs). A network state-dependent route and core selection method is proposed using a multi-attribute decision-making method based on the analytic hierarchy process (AHP) and preference ranking organization method for enrichment evaluations (PROMETHEE) methods. This systematic resource allocation allows the network designer to choose which resources are most valuable. It is followed by a spectrum allocation algorithm using a weighted score function to rate and select the best spectrum blocks. Physical layer impairments, including inter-core cross talk, amplified spontaneous emission, and Kerr fiber nonlinearities, are considered alongside fragmentation and energy consumption. The proposed RMSCA approach is compared with published benchmarks incorporating quality of transmission constraints and evaluated on two network topologies, NSFNET (7- and 12-core multicore fiber links) and COST. It is shown to be superior in terms of blocking probability, bandwidth blocking probability, network fragmentation, and energy consumption compared to standard and published benchmarks.

Selecting Heterogeneous Cores for Diversity

Mobile devices with heterogeneous processors are becoming mainstream. With a heterogeneous processor, the runtime scheduler can pick the best CPU core for a given task based on program characteristics, performance requirements, and power limitations. For a heterogeneous processor to be effective, it must contain a diverse set of cores to match a range of runtime requirements and program behaviors. Selecting a diverse set of cores is, however, a non-trivial problem. Power and performance are dependent on both program features and the microarchitectural features of cores, and a selection of cores must satisfy the competing demands of different types of programs. We present a method of core selection that chooses cores at a range of power-performance points. Our algorithm is based on the observation that it is not necessary for a core to consistently have high performance or low power; one type of core can fulfill different roles for different types of programs. Given a power budget, cores selected with our method provide an average speedup of 6% on EEMBC mobile benchmarks and a 24% speedup on SPEC 2006 integer benchmarks over the state-of-the-art core selection method.

An Efficient Core Selection Method for Heterogeneous Trench-Assisted Multi-Core Fiber

We introduce an efficient core selection method for heterogeneous trench-assisted multi-core fiber. In order to make the design more flexible, the relative position and the width of one trench are usually assumed to have a proportional relation to core radius. However, it is hard to choose non-identical cores simultaneously, since cores are selected under the requirement of cable cutoff wavelength of higher order mode that is influenced by the surrounding trenches, whose relative position and width are also decided by core radii. Therefore, in this method, we first decide the cores at the outer layer and second obtain a core selection region for cores at the inner layer with the outer cores fixed. Both outer and inner cores are designed to achieve the target effective area and crosstalk.

Delay and Delay-Variation Constrained Multicast Routing Algorithm Based on Core Selection

With the development of multimedia network, more and more real-time multimedia applications require a network capable of satisfying QoS constraints such as delay and delay-variation bound so that the messages reach each destination node at almost the same time within a certain specified time limit, and certain application also demand that different streams for each different destination node should reach with minimum cost under the constraints. Therefore, the delay and delay-variation constraint multicast routing problem is studied and a new strategy in core selection methods is proposed. The algorithm we propose guarantees that the delay from the source to any destination does not exceed a real-time constraint satisfying the delay-variation constraint under cost minimization. In addition, this core selection methods achieve a balance of optimizing cost and delay of the multicast tree. Simulation results show that the algorithm has low complexity and balances between the computational complexity and performance, it can also meet the requirements of real-time network communication.

Maximizing genetic differentiation in core collections by PCA-based clustering of molecular marker data

Developing genetically diverse core sets is key to the effective management and use of crop genetic resources. Core selection increasingly uses molecular marker-based dissimilarity and clustering methods, under the implicit assumption that markers and genes of interest are genetically correlated. In practice, low marker densities mean that genome-wide correlations are mainly caused by genetic differentiation, rather than by physical linkage. Although of central concern, genetic differentiation per se is not specifically targeted by most commonly employed dissimilarity and clustering methods. Principal component analysis (PCA) on genotypic data is known to effectively describe the inter-locus correlations caused by differentiation, but to date there has been no evaluation of its application to core selection. Here, we explore PCA-based clustering of marker data as a basis for core selection, with the aim of demonstrating its use in capturing genetic differentiation in the data. Using simulated datasets, we show that replacing full-rank genotypic data by the subset of genetically significant PCs leads to better description of differentiation and improves assignment of genotypes to their population of origin. We test the effectiveness of differentiation as a criterion for the formation of core sets by applying a simple new PCA-based core selection method to simulated and actual data and comparing its performance to one of the best existing selection algorithms. We find that although gains in genetic diversity are generally modest, PCA-based core selection is equally effective at maximizing diversity at non-marker loci, while providing better representation of genetically differentiated groups.

Predictive Association between Trait Data and Ecogeographic Data for Nordic Barley Landraces

Focused Identification of Germplasm (FIGS) is a new method to select plant genetic resources for the improvement of food crops. Traditional cultivars (landraces) and crop wild relatives (CWR) provide a valuable source for novel alleles in crop improvement programs, but conserved landraces and CWR often lack important documentation. Genebank collections worldwide provide ready access to plant genetic resources including online documentation. However, incomplete documentation, and in particular the lack of relevant characterization and evaluation data (traits), often limit the efficient use of plant genetic resources. This current study demonstrates how trait mining with the new FIGS method can be used to predict missing trait information for landraces. Ecogeographic data from the location of origin for 14 Nordic landraces of barley (Hordeum vulgare L.) was successfully correlated to morphological traits using a modern multilinear data modeling method (multilinear partial least squares [N‐PLS]). This result suggests that trait mining can efficiently be used as a targeted germplasm selection method and complement or replace the current core selection method in situations when the requirements for the trait mining method are fulfilled.

96 shRNAs designed for maximal coverage of HIV-1 variants.

BackgroundThe RNA interference (RNAi) pathway is a mechanism of gene-suppression with potential gene therapy applications for treating viral disease such as HIV-1. The most suitable inducer of RNAi for this application is short hairpin RNA (shRNA) although it is limited to suppressing a single target. A successful anti-HIV-1 therapy will require combinations of multiple highly active, highly conserved shRNAs to adequately counter the emergence of resistant strains.ResultsWe calculated the percentage conservations of 8, 846 unique 19 nucleotide HIV-1 targets amongst 37, 949 HIV-1 gene sequence fragments containing 24.8 million 19 mers. We developed a novel method of determining conservation in 'profile' sets of 5 overlapping 19 mer sequences (covering 23 nucleotides in total) to ensure that the intended conservation of each shRNA would be unaffected by possible variations in shRNA processing. Ninety six of the top ranking targets from 22 regions were selected based on conservation profiles, predicted activities, targets and specific nucleotide inclusion/exclusion criteria. We constructed 53 shRNAs with 20 bp stems and 43 shRNAs with 21 bp stems which we tested and ranked using fluorescent reporter and HIV-1 expression assays. Average suppressive activities ranged from 71 – 75%, with 65 hairpins classed as highly active (> 75% activity). Overall we found little difference in activities from minor changes in stem length (20 cf. 21), or between neighboring targets differing by a single nucleotide in start position. However, there were several exceptions which suggest that all sequences, irrespective of similarities in target site or design, may be useful candidates. We encountered technical limitations with GFP reporter assays when the target domain was long and or when the distance between the target site and fusion junction was large. Assay performance was improved by dividing large targets into several shorter domains.ConclusionIn summary, our novel selection process resulted in a large panel of highly active shRNAs spanning the HIV-1 genome, representing excellent candidates for use in multiple shRNA gene therapies. Our core selection method ensuring maximal conservation in the processed product(s) is also widely applicable to other shRNA applications.

Core-based Shared Tree Multicast Routing Algorithms for LEO Satellite IP Networks

A new core-based shared tree algorithm, viz core-cluster combination-based shared tree (CCST) algorithm and the weighted version (i.e. w-CCST algorithm) are proposed in order to resolve the channel resources waste problem in typical source-based multicast routing algorithms in low earth orbit (LEO) satellite IP networks. The CCST algorithm includes the dynamic approximate center (DAC) core selection method and the core-cluster combination multicast route construction scheme. Without complicated onboard computation, the DAC method is uniquely developed for highly dynamic networks of periodical and regular movement. The core-cluster combination method takes core node as the initial core-cluster, and expands it stepwise to construct an entire multicast tree at the lowest tree cost by a shortest path scheme between the newly-generated core-cluster and surplus group members, which results in great bandwidth utilization. Moreover, the w-CCST algorithm is able to strike a balance between performance of tree cost and that of end-to-end propagation delay by adjusting the weighted factor to meet strict end-to-end delay requirements of some real-time multicast services at the expense of a slight increase in tree cost. Finally, performance comparison is conducted between the proposed algorithms and typical algorithms in LEO satellite IP networks. Simulation results show that the CCST algorithm significantly decreases the average tree cost against to the others, and also the average end-to-end propagation delay of w-CCST algorithm is lower than that of the CCST algorithm.

On the performance and feasibility of multicast core selection heuristics

A core-based forwarding multicast protocol uses a core router as a traffic transit center: All multicast packets are first sent to the core, then distributed to destinations on a multicast tree rooted at the core. The purpose of this paper was to evaluate, via simulation, the effect of various core selection methods on multicast performance. Performance metrics of interest include network resource usage, packet delay, the join time of multicast participants, and link congestion. In addition, we assess the feasibility of these heuristics in real-world environments. The main contribution of this work is the discovery of a simple yet effective core selection heuristic that can be implemented in a wide variety of networks. Specifically, our results show that the tree center heuristic (using the center of the existing multicast tree as the new core node) significantly outperforms heuristics based on random selection and performs as well as other heuristics that are computationally more expensive.