Inferring the function of genes from synthetic lethal mutations

Techniques for detecting synthetic lethal mutations in double gene deletion experiments are emerging as powerful tool for analysing genes in parallel or overlapping pathways with a shared function. This paper introduces a logic-based approach that uses synthetic lethal mutations for mapping genes of unknown function to enzymes in a known metabolic network. We show how such mappings can be automatically computed by a logical learning system called eXtended Hybrid Abductive Inductive Learning (XHAIL)

Centroids of Gamow-Teller transitions at finite temperature in fp-shell neutron-rich nuclei

The temperature dependence of the energy centroids and strength distributions for Gamow-Teller (GT) $1^+$ excitations in several fp-shell nuclei is studied. The quasiparticle random phase approximations (QRPA) is extended to describe GT states at finite temperature. A shift to lower energies of the GT$^+$ strength is found, as compared to values obtained at zero temperature.Comment: 12 pages, contains 3 tables. E-mail: [email protected], [email protected]

A study of the portability of an Ada system in the software engineering laboratory (SEL)

A particular porting effort is discussed, and various statistics on analyzing the portability of Ada and the total staff months (overall and by phase) required to accomplish the rehost, are given. This effort is compared to past experiments on the rehosting of FORTRAN systems. The discussion includes an analysis of the types of errors encountered during the rehosting, the changes required to rehost the system, experiences with the Alsys IBM Ada compiler, the impediments encountered, and the lessons learned during this study

Abductive reasoning in neural-symbolic learning systems

Abduction is or subsumes a process of inference. It entertains possible hypotheses and it chooses hypotheses for further scrutiny. There is a large literature on various aspects of non-symbolic, subconscious abduction. There is also a very active research community working on the symbolic (logical) characterisation of abduction, which typically treats it as a form of hypothetico-deductive reasoning. In this paper we start to bridge the gap between the symbolic and sub-symbolic approaches to abduction. We are interested in benefiting from developments made by each community. In particular, we are interested in the ability of non-symbolic systems (neural networks) to learn from experience using efficient algorithms and to perform massively parallel computations of alternative abductive explanations. At the same time, we would like to benefit from the rigour and semantic clarity of symbolic logic. We present two approaches to dealing with abduction in neural networks. One of them uses Connectionist Modal Logic and a translation of Horn clauses into modal clauses to come up with a neural network ensemble that computes abductive explanations in a top-down fashion. The other combines neural-symbolic systems and abductive logic programming and proposes a neural architecture which performs a more systematic, bottom-up computation of alternative abductive explanations. Both approaches employ standard neural network architectures which are already known to be highly effective in practical learning applications. Differently from previous work in the area, our aim is to promote the integration of reasoning and learning in a way that the neural network provides the machinery for cognitive computation, inductive learning and hypothetical reasoning, while logic provides the rigour and explanation capability to the systems, facilitating the interaction with the outside world. Although it is left as future work to determine whether the structure of one of the proposed approaches is more amenable to learning than the other, we hope to have contributed to the development of the area by approaching it from the perspective of symbolic and sub-symbolic integration

A rapidly convergent iteration method and Gâteaux differentiable operators

AbstractLet {Fr}0⩽r⩽p be a family of Banach spaces satisfying, if 0⩽r1⩽r2⩽p, (i)Fr1 ⊇ Fr2; (ii)¦f¦r1 ⩽ ¦f¦r2 (f ϵ Fr1); and (iii)ϑ(r) = ln(¦f¦r) is a convex function. Let G0 be a Banach space and. F be a Gâteaux differentiate mapping, and suppose that F′(x)(Fp) is dense in G0. Under appropriate assumptions, the equation F(x)=0 has a solution in Fr for 0⩽r⩽p. The results extend the Inverse Function Theorem of J. Moser to the class of Gâteaux differentiable operators

The Economics of the Joint Antitrust Dissents of Justices Harlan and Stewart

Professor Werner presents a chronological study of the antitrust dissents authored by Justices Harlan and Stewart in an attempt to identify the minority rationale which may guide the Court\u27s future antitrust decisions. Analyzing these dissenting opinions against the economic criteria of industry structure, conduct and performance, Professor Werner concludes that the dissenters focus primarily on industry performance while showing some concern for the structure of the industry. The author views the dissenters as strict constructionists and believes that their conservative economic orientation may emerge as the antitrust philosophy of the Nixon appointees to the United States Supreme Court

Alien Registration- Ray, Gilbert O. (Fort Kent, Aroostook County)

https://digitalmaine.com/alien_docs/36325/thumbnail.jp