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Position paper: the science of deep specification.

ABSTRACT: We introduce our efforts within the project 'The science of deep specification' to work out the key formal underpinnings of industrial-scale formal specifications of software and hardware components, anticipating a world where large verified systems are routinely built out of smaller verified components that are also used by many other projects. We identify an important class of specification that has already been used in a few experiments that connect strong component-correctness theorems across the work of different teams. To help popularize the unique advantages of that style, we dub it deep specification, and we say that it encompasses specifications that are rich, two-sided, formal and live (terms that we define in the article). Our core team is developing a proof-of-concept system (based on the Coq proof assistant) whose specification and verification work is divided across largely decoupled subteams at our four institutions, encompassing hardware microarchitecture, compilers, operating systems and applications, along with cross-cutting principles and tools for effective specification. We also aim to catalyse interest in the approach, not just by basic researchers but also by users in industry.This article is part of the themed issue 'Verified trustworthy software systems'.

SUBMITTER: Appel AW

PROVIDER: S-EPMC5597730 | biostudies-other | 2017 Oct

REPOSITORIES: biostudies-other

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Position paper: the science of deep specification.

Appel Andrew W AW Beringer Lennart L Chlipala Adam A Pierce Benjamin C BC Shao Zhong Z Weirich Stephanie S Zdancewic Steve S

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 20171001 2104

We introduce our efforts within the project 'The science of deep specification' to work out the key formal underpinnings of industrial-scale formal specifications of software and hardware components, anticipating a world where large verified systems are routinely built out of smaller verified components that are also used by many other projects. We identify an important class of specification that has already been used in a few experiments that connect strong component-correctness theorems acros ...[more]

PMID: 28871056

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Project description:BackgroundAdvances in biotechnology have created "big-data" situations in molecular and cellular biology. Several sophisticated algorithms have been developed that process big data to generate hundreds of biomedical hypotheses (or predictions). The bottleneck to translating this large number of biological hypotheses is that each of them needs to be studied by experimentation for interpreting its functional significance. Even when the predictions are estimated to be very accurate, from a biologist's perspective, the choice of which of these predictions is to be studied further is made based on factors like availability of reagents and resources and the possibility of formulating some reasonable hypothesis about its biological relevance. When viewed from a global perspective, say from that of a federal funding agency, ideally the choice of which prediction should be studied would be made based on which of them can make the most translational impact.ResultsWe propose that algorithms be developed to identify which of the computationally generated hypotheses have potential for high translational impact; this way, funding agencies and scientific community can invest resources and drive the research based on a global view of biomedical impact without being deterred by local view of feasibility. In short, data-analytic algorithms analyze big-data and generate hypotheses; in contrast, the proposed inference-analytic algorithms analyze these hypotheses and rank them by predicted biological impact. We demonstrate this through the development of an algorithm to predict biomedical impact of protein-protein interactions (PPIs) which is estimated by the number of future publications that cite the paper which originally reported the PPI.ConclusionsThis position paper describes a new computational problem that is relevant in the era of big-data and discusses the challenges that exist in studying this problem, highlighting the need for the scientific community to engage in this line of research. The proposed class of algorithms, namely inference-analytic algorithms, is necessary to ensure that resources are invested in translating those computational outcomes that promise maximum biological impact. Application of this concept to predict biomedical impact of PPIs illustrates not only the concept, but also the challenges in designing these algorithms.

Project description:INTRODUCTION:Nightmare disorder affects approximately 4% of adults, occurring in isolation or as part of other disorders such as posttraumatic stress disorder (PTSD), and can significantly impair quality of life. This paper provides the American Academy of Sleep Medicine (AASM) position regarding various treatments of nightmare disorder in adults. METHODS:A literature search was performed based upon the keywords and MeSH terms from the Best Practice Guide for the Treatment of Nightmare Disorder in Adults that was published in 2010 by the AASM. The search used the date range March 2009 to August of 2017, and sought to find available evidence pertaining to the use of behavioral, psychological, and pharmacologic therapies for the treatment of nightmares. A task force developed position statements based on a thorough review of these studies and their clinical expertise. The AASM Board of Directors approved the final position statements. DETERMINATION OF POSITION:Positions of "recommended" and "not recommended" indicate that a treatment option is determined to be clearly useful or ineffective/harmful for most patients, respectively, based on a qualitative assessment of the available evidence and clinical judgement of the task force. Positions of "may be used" indicate that the evidence or expert consensus is less clear, either in favor or against the use of a treatment option. The interventions listed below are in alphabetical order within the position statements rather than clinical preference: this is not meant to be instructive of the order in which interventions should be used. POSITION STATEMENTS:The following therapy is recommended for the treatment of PTSD-associated nightmares and nightmare disorder: image rehearsal therapy. The following therapies may be used for the treatment of PTSD-associated nightmares: cognitive behavioral therapy; cognitive behavioral therapy for insomnia; eye movement desensitization and reprocessing; exposure, relaxation, and rescripting therapy; the atypical antipsychotics olanzapine, risperidone and aripiprazole; clonidine; cyproheptadine; fluvoxamine; gabapentin; nabilone; phenelzine; prazosin; topiramate; trazodone; and tricyclic antidepressants. The following therapies may be used for the treatment of nightmare disorder: cognitive behavioral therapy; exposure, relaxation, and rescripting therapy; hypnosis; lucid dreaming therapy; progressive deep muscle relaxation; sleep dynamic therapy; self-exposure therapy; systematic desensitization; testimony method; nitrazepam; prazosin; and triazolam. The following are not recommended for the treatment of nightmare disorder: clonazepam and venlafaxine. The ultimate judgment regarding propriety of any specific care must be made by the clinician, in light of the individual circumstances presented by the patient, accessible treatment options, and resources.

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Position paper: the science of deep specification.

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Position paper: the science of deep specification.

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