Distinguish Coding And Noncoding Sequences In A Complete Genome Using Fourier Transform

A Fourier transform method is proposed to distinguish coding and non-coding sequences in a complete genome based on a number sequence representation of the DNA sequence proposed in our previous paper (Zhou et al., J. Theor. Biol. 2005) and the imperfect periodicity of 3 in protein coding sequences. The three parameters P_x(S) (1), P_x(S) (1/3) and P_x(S) (1/36) in the Fourier transform of the number sequence representation of DNA sequences are selected to form a three-dimensional parameter space. Each DNA sequence is then represented by a point in this space. The points corresponding to coding and non-coding sequences in the complete genome of prokaryotes are seen to be divided into different regions. If the point (P_x(�ar S) (1), Px(�ar S) (1/3), P_x(�ar S) (1/36)) for a DNA sequence is situated in the region corresponding to coding sequences, the sequence is distinguished as a coding sequence; otherwise, the sequence is classified as a noncoding one. Fisher's discriminant algorithm is used to study the discriminant accuracy. The average discriminant accuracies pc, pnc, qc and qnc of all 51 prokaryotes obtained by the present method reach 81.02%, 92.27%, 80.77% and 92.24% respectively

A Mutual Information Based Sequence Distance For Vertebrate Phylogeny Using Complete Mitochondrial Genomes

Traditional sequence distances require alignment. A new mutual information based sequence distance without alignment is defined in this paper. This distance is based on compositional vectors of DNA sequences or protein sequences from complete genomes. First we establish the mathematical foundation of this distance. Then this distance is applied to analyze the phylogenetic relationship of 64 vertebrates using complete mitochondrial genomes. The phylogenetic tree shows that the mitochondrial genomes are separated into three major groups. One group corresponds to mammals; one group corresponds to fish; and the last one is Archosauria (including birds and reptiles). The structure of the tree based on our new distance is roughly in agreement in topology with the current known phylogenies of vertebrates

DCRNN: A Deep Cross approach based on RNN for Partial Parameter Sharing in Multi-task Learning

In recent years, DL has developed rapidly, and personalized services are exploring using DL algorithms to improve the performance of the recommendation system. For personalized services, a successful recommendation consists of two parts: attracting users to click the item and users being willing to consume the item. If both tasks need to be predicted at the same time, traditional recommendation systems generally train two independent models. This approach is cumbersome and does not effectively model the relationship between the two subtasks of "click-consumption". Therefore, in order to improve the success rate of recommendation and reduce computational costs, researchers are trying to model multi-task learning. At present, existing multi-task learning models generally adopt hard parameter sharing or soft parameter sharing architecture, but these two architectures each have certain problems. Therefore, in this work, we propose a novel recommendation model based on real recommendation scenarios, Deep Cross network based on RNN for partial parameter sharing (DCRNN). The model has three innovations: 1) It adopts the idea of cross network and uses RNN network to cross-process the features, thereby effectively improves the expressive ability of the model; 2) It innovatively proposes the structure of partial parameter sharing; 3) It can effectively capture the potential correlation between different tasks to optimize the efficiency and methods for learning different tasks.Comment: Work done while the first author was an algorithm engineer at Xiaomi In

Domain Adaptive Dialog Generation via Meta Learning

Domain adaptation is an essential task in dialog system building because there are so many new dialog tasks created for different needs every day. Collecting and annotating training data for these new tasks is costly since it involves real user interactions. We propose a domain adaptive dialog generation method based on meta-learning (DAML). DAML is an end-to-end trainable dialog system model that learns from multiple rich-resource tasks and then adapts to new domains with minimal training samples. We train a dialog system model using multiple rich-resource single-domain dialog data by applying the model-agnostic meta-learning algorithm to dialog domain. The model is capable of learning a competitive dialog system on a new domain with only a few training examples in an efficient manner. The two-step gradient updates in DAML enable the model to learn general features across multiple tasks. We evaluate our method on a simulated dialog dataset and achieve state-of-the-art performance, which is generalizable to new tasks.Comment: Accepted as a long paper in ACL 201

Large magneto-optical Kerr effect in noncollinear antiferromagnets Mn3X_{3}X (XX = Rh, Ir, or Pt)

Magneto-optical Kerr effect, normally found in magnetic materials with nonzero magnetization such as ferromagnets and ferrimagnets, has been known for more than a century. Here, using first-principles density functional theory, we demonstrate large magneto-optical Kerr effect in high temperature noncollinear antiferromagnets Mn$_{3}X$ ($X$ = Rh, Ir, or Pt), in contrast to usual wisdom. The calculated Kerr rotation angles are large, being comparable to that of transition metal magnets such as bcc Fe. The large Kerr rotation angles and ellipticities are found to originate from the lifting of the band double-degeneracy due to the absence of spatial symmetry in the Mn$_{3}X$ noncollinear antiferromagnets which together with the time-reversal symmetry would preserve the Kramers theorem. Our results indicate that Mn$_{3}X$ would provide a rare material platform for exploration of subtle magneto-optical phenomena in noncollinear magnetic materials without net magnetization

Agent Design of SmArt License Management System Using Gaia Methodology

Modern software services and data centers require a license management system to regulate the agreements that have been reached between subscriber and provider. License management helps to track usage and protect service from abuse. License agreements provide the basis for enforcement and regulation. The automation of license agreements is desired by providers and subscribers to improve transaction efficiency, give flexibility, and minimize unwanted cost. We have proposed a framework, called SmArt (Semantic Agreement) system, that enables agreement automation in the autonomic computing context using ontology and agent technologies. This paper applies the SmArt system to the domain of license management and presents its agent design with Gaia methodology

Policy Driven Licensing Model for Component Software

Today, it is almost inevitable that software is licensed, rather than sold outright. As a part of the licensing policy, some protection mechanisms, whether hardware, legal or code-based, are invariably built into the license. The application of such mechanisms has primarily been in the realm of off-the-shelf, packaged, consumer software. However, as component-based software gradually becomes mainstream in software development, new component-oriented licensing systems are required. This paper proposes an enterprise component licensing model for the management of software component licenses. The model provides a comprehensive license management framework allowing for extensibility and flexibility. Furthermore, we identify differences between stand-alone software and component software, describe a high level model for policy driven component licensing, and discuss both the benefits and drawbacks of the enterprise component licensing model for the management of software component licenses