Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis

Even with impressive advances in automated formal methods, certain problems in system verification and synthesis remain challenging. Examples include the verification of quantitative properties of software involving constraints on timing and energy consumption, and the automatic synthesis of systems from specifications. The major challenges include environment modeling, incompleteness in specifications, and the complexity of underlying decision problems. This position paper proposes sciduction, an approach to tackle these challenges by integrating inductive inference, deductive reasoning, and structure hypotheses. Deductive reasoning, which leads from general rules or concepts to conclusions about specific problem instances, includes techniques such as logical inference and constraint solving. Inductive inference, which generalizes from specific instances to yield a concept, includes algorithmic learning from examples. Structure hypotheses are used to define the class of artifacts, such as invariants or program fragments, generated during verification or synthesis. Sciduction constrains inductive and deductive reasoning using structure hypotheses, and actively combines inductive and deductive reasoning: for instance, deductive techniques generate examples for learning, and inductive reasoning is used to guide the deductive engines. We illustrate this approach with three applications: (i) timing analysis of software; (ii) synthesis of loop-free programs, and (iii) controller synthesis for hybrid systems. Some future applications are also discussed

Automatically Learning Formal Models from Autonomous Driving Software

The correctness of autonomous driving software is of utmost importance, as incorrect behavior may have catastrophic consequences. Formal model-based engineering techniques can help guarantee correctness and thereby allow the safe deployment of autonomous vehicles. However, challenges exist for widespread industrial adoption of formal methods. One of these challenges is the model construction problem. Manual construction of formal models is time-consuming, error-prone, and intractable for large systems. Automating model construction would be a big step towards widespread industrial adoption of formal methods for system development, re-engineering, and reverse engineering. This article applies active learning techniques to obtain formal models of an existing (under development) autonomous driving software module implemented in MATLAB. This demonstrates the feasibility of automated learning for automotive industrial use. Additionally, practical challenges in applying automata learning, and possible directions for integrating automata learning into the automotive software development workflow, are discussed

In situ characterisation and manipulation of biological systems with Chi.Bio

The precision and repeatability of in vivo biological studies is predicated upon methods for isolating a targeted subsystem from external sources of noise and variability. However, in many experimental frameworks, this is made challenging by nonstatic environments during host cell growth, as well as variability introduced by manual sampling and measurement protocols. To address these challenges, we developed Chi.Bio, a parallelised open-source platform that represents a new experimental paradigm in which all measurement and control actions can be applied to a bulk culture in situ. In addition to continuous-culturing capabilities, it incorporates tunable light outputs, spectrometry, and advanced automation features. We demonstrate its application to studies of cell growth and biofilm formation, automated in silico control of optogenetic systems, and readout of multiple orthogonal fluorescent proteins in situ. By integrating precise measurement and actuation hardware into a single low-cost platform, Chi.Bio facilitates novel experimental methods for synthetic, systems, and evolutionary biology and broadens access to cutting-edge research capabilities

Automation from lean perspective-potentials and challenges

The competitive climate of production and high labour cost, motivate western companies to use technologies like automation as a mean to increase manufacturing competitiveness. On the other hand companies are aware about cost reductive policies like lean production which has shown noticeable achievement, consequently some manufacturers tend to follow such system. In this situation, in order to have lean enterprise, it is vital to find a clear picture of challenges and potentials of implementing automation within a lean environment. So, finding the right level and type of automation becomes vital for companies, and achieving this is not possible without a lean development of automation. The paper presents an overview of automation development from a lean perspective. The focus is on manufacturing and a case study in the automotive industry is presented. Challenges and potentials of automation are pinpointed and some suggestions regarding automation development is given

Final report: Workshop on: Integrating electric mobility systems with the grid infrastructure

EXECUTIVE SUMMARY: This document is a report on the workshop entitled “Integrating Electric Mobility Systems with the Grid Infrastructure” which was held at Boston University on November 6-7 with the sponsorship of the Sloan Foundation. Its objective was to bring together researchers and technical leaders from academia, industry, and government in order to set a short and longterm research agenda regarding the future of mobility and the ability of electric utilities to meet the needs of a highway transportation system powered primarily by electricity. The report is a summary of their insights based on workshop presentations and discussions. The list of participants and detailed Workshop program are provided in Appendices 1 and 2. Public and private decisions made in the coming decade will direct profound changes in the way people and goods are moved and the ability of clean energy sources – primarily delivered in the form of electricity – to power these new systems. Decisions need to be made quickly because of rapid advances in technology, and the growing recognition that meeting climate goals requires rapid and dramatic action. The blunt fact is, however, that the pace of innovation, and the range of business models that can be built around these innovations, has grown at a rate that has outstripped our ability to clearly understand the choices that must be made or estimate the consequences of these choices. The group of people assembled for this Workshop are uniquely qualified to understand the options that are opening both in the future of mobility and the ability of electric utilities to meet the needs of a highway transportation system powered primarily by electricity. They were asked both to explain what is known about the choices we face and to define the research issues most urgently needed to help public and private decision-makers choose wisely. This report is a summary of their insights based on workshop presentations and discussions. New communication and data analysis tools have profoundly changed the definition of what is technologically possible. Cell phones have put powerful computers, communication devices, and position locators into the pockets and purses of most Americans making it possible for Uber, Lyft and other Transportation Network Companies to deliver on-demand mobility services. But these technologies, as well as technologies for pricing access to congested roads, also open many other possibilities for shared mobility services – both public and private – that could cut costs and travel time by reducing congestion. Options would be greatly expanded if fully autonomous vehicles become available. These new business models would also affect options for charging electric vehicles. It is unclear, however, how to optimize charging (minimizing congestion on the electric grid) without increasing congestion on the roads or creating significant problems for the power system that supports such charging capacity. With so much in flux, many uncertainties cloud our vision of the future. The way new mobility services will reshape the number, length of trips, and the choice of electric vehicle charging systems and constraints on charging, and many other important behavioral issues are critical to this future but remain largely unknown. The challenge at hand is to define plausible future structures of electric grids and mobility systems, and anticipate the direct and indirect impacts of the changes involved. These insights can provide tools essential for effective private ... [TRUNCATED]Workshop funded by the Alfred P. Sloan Foundatio

Advances in Microfluidics and Lab-on-a-Chip Technologies

Advances in molecular biology are enabling rapid and efficient analyses for effective intervention in domains such as biology research, infectious disease management, food safety, and biodefense. The emergence of microfluidics and nanotechnologies has enabled both new capabilities and instrument sizes practical for point-of-care. It has also introduced new functionality, enhanced sensitivity, and reduced the time and cost involved in conventional molecular diagnostic techniques. This chapter reviews the application of microfluidics for molecular diagnostics methods such as nucleic acid amplification, next-generation sequencing, high resolution melting analysis, cytogenetics, protein detection and analysis, and cell sorting. We also review microfluidic sample preparation platforms applied to molecular diagnostics and targeted to sample-in, answer-out capabilities

A pattern-based approach to a cell tracking ontology

Time-lapse microscopy has thoroughly transformed our understanding of biological motion and developmental dynamics from single cells to entire organisms. The increasing amount of cell tracking data demands the creation of tools to make extracted data searchable and interoperable between experiment and data types. In order to address that problem, the current paper reports on the progress in building the Cell Tracking Ontology (CTO): An ontology framework for describing, querying and integrating data from complementary experimental techniques in the domain of cell tracking experiments. CTO is based on a basic knowledge structure: the cellular genealogy serving as a backbone model to integrate specific biological ontologies into tracking data. As a first step we integrate the Phenotype and Trait Ontology (PATO) as one of the most relevant ontologies to annotate cell tracking experiments. The CTO requires both the integration of data on various levels of generality as well as the proper structuring of collected information. Therefore, in order to provide a sound foundation of the ontology, we have built on the rich body of work on top-level ontologies and established three generic ontology design patterns addressing three modeling challenges for properly representing cellular genealogies, i.e. representing entities existing in time, undergoing changes over time and their organization into more complex structures such as situations

Development of rapid, automated diagnostics for infectious disease: advances and challenges

The last 2 years has seen an exponential rise in the amount of research funding made available for the development of rapid diagnostic devices for infectious agents of medical importance. This review reports on several such projects. These highlight the development of fully automated devices for rapid diagnostics, ranging from fully automated real-time PCR-based detection methods to fully automated PCR- and array-based machines for the detection and typing of influenza. This review will also highlight the importance of refocusing work on classical immunoassay techniques, showing how biosensor-based immunoassays can greatly enhance existing assays and at a much reduced cost to molecular-based methods

Dynamic QoS/QoE-aware reliable service composition framework for edge intelligence

Edge intelligence has become popular recently since it brings smartness and copes with some shortcomings of conventional technologies such as cloud computing, Internet of Things (IoT), and centralized AI adoptions. However, although utilizing edge intelligence contributes to providing smart systems such as automated driving systems, smart cities, and connected healthcare systems, it is not free from limitations. There exist various challenges in integrating AI and edge computing, one of which is addressed in this paper. Our main focus is to handle the adoption of AI methods on resource-constrained edge devices. In this regard, we introduce the concept of Edge devices as a Service (EdaaS) and propose a quality of service (QoS) and quality of experience (QoE)-aware dynamic and reliable framework for AI subtasks composition. The proposed framework is evaluated utilizing three well-known meta-heuristics in terms of various metrics for a connected healthcare application scenario. The experimental results confirm the applicability of the proposed framework. Moreover, the results reveal that black widow optimization (BWO) can handle the issue more efficiently compared to particle swarm optimization (PSO) and simulated annealing (SA). The overall efficiency of BWO over PSO is 95%, and BWO outperforms SA with 100% efficiency. It means that BWO prevails SA and PSO in all and 95% of the experiments, respectively

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu