The economy of chromosomal distances in bacterial gene regulation

In the transcriptional regulatory network (TRN) of a bacterium, the nodes are genes and a directed edge represents the action of a transcription factor (TF), encoded by the source gene, on the target gene. It is a condensed representation of a large number of biological observations and facts. Nonrandom features of the network are structural evidence of requirements for a reliable systemic function. For the bacterium Escherichia coli we here investigate the (Euclidean) distances covered by the edges in the TRN when its nodes are embedded in the real space of the circular chromosome. Our work is motivated by ’wiring economy’ research in Computational Neuroscience and starts from two contradictory hypotheses: (1) TFs are predominantly employed for long-distance regulation, while local regulation is exerted by chromosomal structure, locally coordinated by the action of structural proteins. Hence long distances should often occur. (2) A large distance between the regulator gene and its target requires a higher expression level of the regulator gene due to longer reaching times and ensuing increased degradation (proteolysis) of the TF and hence will be evolutionarily reduced. Our analysis supports the latter hypothesis.

Generating a Condensed Representation for Positive and Negative Association Rules

Given a large collection of transactions containing items, a basic common association rules problem is the huge size of the extracted rule set. Pruning uninteresting and redundant association rules is a promising approach to solve this problem. In this paper, we propose a Condensed Representation for Positive and Negative Association Rules representing non-redundant rules for both exact and approximate association rules based on the sets of frequent generator itemsets, frequent closed itemsets, maximal frequent itemsets, and minimal infrequent itemsets in database B. Experiments on dense (highly-correlated) databases show a significant reduction of the size of extracted association rule set in database B.

The October Revolution as the Passion of Christ: Boris Pasternak’s Easter Narrative in Doctor Zhivago and Its Cultural Contexts

This article offers a new interpretation of Boris Pasternak’s novel Doctor Zhivago in the cultural and historical context of the first half of the 20th century, with an emphasis on the interrelationship between religion and philosophy of history in the text. Doctor Zhivago is analysed as a condensed representation of a religious conception of Russian history between 1901 and 1953 and as a cyclical repetition of the Easter narrative. This bipartite narrative consists of the Passion and Resurrection of Christ as symbols of violence and renewal (liberation). The novel cycles through this narrative several times, symbolically connecting the ‘Easter’ revolution (March 1917) and the Thaw (the spring of 1953). The sources of Pasternak’s Easter narrative include the Gospels, Leo Tolstoy’s philosophy of history and pre-Christian mythology. The model of cyclical time in the novel brings together the sacred, natural and historical cycles. This concept of a cyclical renewal of life differs from the linear temporality of the Apocalypse as an expectation of the end of history.

Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model

Non-Intrusive Load Monitoring (NILM) increases awareness on user energy usage patterns. In this paper, an efficient and highly accurate NILM method is proposed featuring condensed representation, super-state and fusion of two deep learning based models. Condensed representation helps the two models perform more efficiently and preserve longer-term information, while super-state helps the model to learn correlations between appliances. The first model is a deep user model that learns user appliances usage patterns to predict the next appliance usage behavior based on past behaviors by capturing the dynamics of user behaviors history and appliances usage habits. The second model is a deep appliance group model that learns the characteristics of appliances with temporal and electrical information. These two models are then fused to perform NILM. The case study based on REFIT datasets demonstrates that the proposed NILM method outperforms two state-of-the-art benchmark methods.

Deriving Frequent Itemsets from Lossless Condensed Representation

In data mining, major research topic is frequent itemset mining (FIM). Frequent Itemsets (FIs) usually generating a large amount of Itemsets from database it causing from high memory and long execution time usage. Frequent Closed Itemsets(FCI) and Frequent Maximal Itemsets(FMI) are a reduced lossless representation of frequent itemsets. The FCI allows to decreasing the memory usage and execution time while comparing to FMIs. The whole data of frequent Itemsets(FIs) may be derived from FCIs and FMIs with correct methods. While various study has presented several efficient approach for FCIs and FMIs mining. In sight of this, that we proposed an algorithm called DCFI-Mine for capably derive FIs from Closed FIs and RFMI algorithm derive FMIs to FIs. The advantages of DCFI-Mine algorithm has two features: First, efficiency, different existing algorithm that tends to develop an enormous quantity of Itemsets all through process, DCFI-Mine process the Itemsets straight without candidate generation. But in proposed RFMI multiple scan occurs due to search of item support so efficiency is less than proposed algorithm DCFI-Mine. Second, in terms of losslessness DCFI-Mine and RFMI can discover complete frequent itemset without lapse. Experimental result shows That DCFI-Mine is best deriving FIs in term of memory usage and executions time

PARASOL: a hybrid approximation approach for scalable frequent itemset mining in streaming data

Here, we present a novel algorithm for frequent itemset mining in streaming data (FIM-SD). For the past decade, various FIM-SD methods in one-pass approximation settings that allow to approximate the support of each itemset have been proposed. They can be categorized into two approximation types: parameter-constrained (PC) mining and resource-constrained (RC) mining. PC methods control the maximum error that can be included in the approximate support based on a pre-defined parameter. In contrast, RC methods limit the maximum memory consumption based on resource constraints. However, the existing PC methods can exponentially increase the memory consumption, while the existing RC methods can rapidly increase the maximum error. In this study, we address this problem by introducing a hybrid approach of PC-RC approximations, called PARASOL. For any streaming data, PARASOL ensures to provide a condensed representation, called a Δ-covered set, which is regarded as an extension of the closedness compression; when Δ = 0, the solution corresponds to the ordinary closed itemsets. PARASOL searches for such approximate closed itemsets that can restore the frequent itemsets and their supports while the maximum error is bounded by an integer, Δ. Then, we empirically demonstrate that the proposed algorithm significantly outperforms the state-of-the-art PC and RC methods for FIM-SD.