Sequential Learning-based IaaS Composition

We propose a novel Infrastructure-as-a-Service composition framework that selects an optimal set of consumer requests according to the provider’s qualitative preferences on long-term service provisions. Decision variables are included in the temporal conditional preference networks to represent qualitative preferences for both short-term and long-term consumers. The global preference ranking of a set of requests is computed using a k -d tree indexing-based temporal similarity measure approach. We propose an extended three-dimensional Q-learning approach to maximize the global preference ranking. We design the on-policy-based sequential selection learning approach that applies the length of request to accept or reject requests in a composition. The proposed on-policy-based learning method reuses historical experiences or policies of sequential optimization using an agglomerative clustering approach. Experimental results prove the feasibility of the proposed framework.

Temporal Patterns Of User Preferences In The Tasks Of Forming Explanations In The Recommender System

The problem of taking into account changes in the user’s behavior of the recommendation system whenconstructing explanations for recommendations is considered. This problem occurs as a result of cyclical changes in userrequirements. Its solution is associated with the construction of an explanation comparing the alternative choices of theuser of the recommendation system. The developed models of temporal patterns consist of a set of temporal relationshipsbetween the events of users’ choice of goods and services. The first pattern contains an alternative in the form of sequential selection in time of several objects or the selection of only a pair - the first and the last object. The second pattern,sequential-alternative choice, consists of a sequence of choices over time, which ends with the first pattern. The proposedapproach to the formation of patterns is based on the construction of data sets containing temporal dependencies betweena group of user choices for a given level of time detail. The temporal dataset is used to construct a temporal graph of therecommender system user selection process. The latter includes a set of temporal patterns with an indication of the timeof their beginning and end, which makes it possible to determine the duration of the implementation of these patterns.On the basis of the patterns, subsets of temporal relationships are formed to build explanations for the recommendedlist of goods and services. Experimental verification of the developed approach using the “Online Retail” sales data sethas shown the possibility of identifying temporal patterns even on short initial samples.

Single Inductor Multiple Output Auto-Buck-Boost DC–DC Converter with Error-Driven Randomized Control

We propose a single inductor multiple output (SIMO) auto-buck-boost DC–DC converter with error-driven randomized control (EDRC). The conventional controls in a SIMO DC–DC converter supply power to outputs that have been selected in a sequential order. Furthermore, they control the inductor current levels at either edge of a switching period in a steady state to be at the same level to alleviate cross-regulation. However, this limits the flexibility of the converter to respond to changes in load requirements. A sequential selection of light loads results in these loads being selected more often than a load demand, degrading the efficiency for light loads. In addition, limited flexibility leads to delayed responses. This paper introduces an auto-buck-boost topology that selects outputs based on output errors, and instantaneously adjusts the inductor current level. Moreover, we propose a technique for allowing any output to avoid selection when all outputs are fully supplied. The proposed EDRC scheme achieves improvements in efficiency in regards to light loads, cross-regulation, and output driving capability.

Polygenic Patterns of Adaptive Introgression in Modern Humans Are Mainly Shaped by Response to Pathogens

Anatomically modern humans carry many introgressed variants from other hominins in their genomes. Some of them affect their phenotype and can thus be negatively or positively selected. Several individual genes have been proposed to be the subject of adaptive introgression, but the possibility of polygenic adaptive introgression has not been extensively investigated yet. In this study, we analyze archaic introgression maps with refined functional enrichment methods to find signals of polygenic adaptation of introgressed variants. We first apply a method to detect sets of connected genes (subnetworks) within biological pathways that present higher-than-expected levels of archaic introgression. We then introduce and apply a new statistical test to distinguish between epistatic and independent selection in gene sets of present-day humans. We identify several known targets of adaptive introgression, and we show that they belong to larger networks of introgressed genes. After correction for genetic linkage, we find that signals of polygenic adaptation are mostly explained by independent and potentially sequential selection episodes. However, we also find some gene sets where introgressed variants present significant signals of epistatic selection. Our results confirm that archaic introgression has facilitated local adaptation, especially in immunity related and metabolic functions and highlight its involvement in a coordinated response to pathogens out of Africa.

The efficiency of pig breeding by maturity and bacon thickness at different selection intensities

Scientific and economic experience in the study of the comparative effectiveness of using simultaneous and sequential selection at its various intensities (75, 50 and 30% in boars and 30% in breeding sows) by early maturity and thickness of bacon, aimed at improving feeding and meat qualities, established that simultaneous selection by a set of characters is possible, because connections between them in either the positive or the negative direction have not been identified. However, when one of the traits during prolonged selection improves by an excessive amount, other non-breeding traits deteriorate. At the same time, simultaneous and sequential selection by the early maturity and thickness of the bacon makes it possible to select the same animals for further breeding, regardless of the intensity of the rejection. A different level of producers’ selection intensity (75, 50, 30%) and the same breeding sows (30%) in terms of early maturity and bacon thickness ensure an improvement in these characteristics by an unequal value during one generation.