Rac GTPases:domain-specific functions in neuronal development

Understanding fundamental mechanisms governing axon outgrowth and guidance can inform the development of therapeutic strategies to restore neuronal function damaged though injury or disease. Axons navigate the extracellular environment by responding to guidance cues that bind to cell surface receptors to relay information intracellularly via Rho GTPase family members, including the Rac GTPases.

NNL:a domain-specific language for neural networks

Recent years,neural networks(NNs)have received increasing attention from both academia and industry.So far significant diversity among existing NNs as well as their hardware platforms makes NN programming a daunting task.In this paper,a domain-specific language(DSL)for NNs,neural network language(NNL)is proposed to deliver productivity of NN programming and portable performance of NN execution on different hardware platforms.The productivity and flexibility of NN programming are enabled by abstracting NNs as a directed graph of blocks.The language describes 4 representative and widely used NNs and runs them on 3 different hardware platforms(CPU,GPU and NN accelerator).Experimental results show that NNs written with the proposed language are,on average,14.5%better than the baseline implementations across these 3 platforms.Moreover,compared with the Caffe framework that specifically targets the GPU platform,the code can achieve similar performance.

Building a Productive Domain-Specific Cloud for Big Data Processing and Analytics Service

Cloud Computing as a disruptive technology, provides a dynamic, elastic and promising computing climate to tackle the challenges of big data processing and analytics. Hadoop and MapReduce are the widely used open source frameworks in Cloud Computing for storing and processing big data in the scalable fashion. Spark is the latest parallel computing engine working together with Hadoop that exceeds MapReduce performance via its in-memory computing and high level programming features. In this paper, we present our design and implementation of a productive, domain-specific big data analytics cloud platform on top of Hadoop and Spark. To increase user’s productivity, we created a variety of data processing templates to simplify the programming efforts. We have conducted experiments for its productivity and performance with a few basic but representative data processing algorithms in the petroleum industry. Geophysicists can use the platform to productively design and implement scalable seismic data processing algorithms without handling the details of data management and the complexity of parallelism. The Cloud platform generates a complete data processing application based on user’s kernel program and simple configurations, allocates resources and executes it in parallel on top of Spark and Hadoop.

Influence of Software Modeling and Design on Domain-Specific Abstract Thinking: Student’s Perspective

Software engineering has been taught at many institutions as individual course for many years. Recently, many higher education institutions offer a BSc degree in Software Engineering. Software engineers are required, especially at the small enterprises, to play many roles, and sometimes simultaneously. Beside the technical and managerial skills, software engineers should have additional intellectual skills such as domain-specific abstract thinking. Therefore, software engineering curriculum should help the students to build and improve their skills to meet the labor market needs. This study aims to explore the perceptions of software engineering students on the influence of learning software modeling and design on their domain-specific abstract thinking. Also, we explore the role of the course project in improving their domain-specific abstract thinking. The study results have shown that, most of the surveyed students believe that learning and practicing modeling and design concepts contribute to their ability to think abstractly on specific domain. However, this finding is influenced by the students’ lack of the comprehension of some modeling and design aspects (e.g., generalization). We believe that, such aspects should be introduced to the students at early levels of software engineering curriculum, which certainly will improve their ability to think abstractly on specific domain.

Domain-Specific Ontology of Botany

Domain-specific ontologies are greatly useful in knowledge acquisition,sharing and analysis. In this paper, botany-specific ontology for acquiring and analyzing botanicalknowledge is presented. The ontology is represented in a set of well-defined categories, and eachconcept is viewed as an instance of certain category. The authors also introduce botany-specificaxioms, an integral part of the ontology, for checking and reasoning with the acquired knowledge.Consistency, completeness and redundancy of the axioms are discussed.

Domain-Specific Formal Ontology of Archaeology and Its Application in Knowledge Acquisition and Analysis

Inherent heterogeneity and distribution of knowledge strongly prevent knowledge from sharing and reusing among different agents and software entities, and a formal ontology has been viewed as a promising means to tackle this problem. In this paper, a domain-specific formal ontology of archaeology is presented. The ontology mainly consists of three parts: archaeological categories, their relationships and axioms. The ontology not only captures the semantics of archaeological knowledge, but also provides archaeology with an explicit and formal specification of a shared conceptualization, thus making archaeological knowledge shareable and reusable across humans and machines in a structured fashion. Further, we propose a method to verify ontology. correctness based on the individuals of categories. As applications of the ontology,we have developed an ontology-driven approach to knowledge acquisition from archaeological text and a question answering system for archaeological knowledge.

A multi-paradigm decision modeling framework for combat system effectiveness measurement based on domain-specific modeling

Decision modeling is an essential part of the combat system effectiveness simulation (CoSES), which needs to cope with the cognitive quality, diversity, flexibility, and higher abstraction of decision making. In this paper, a multi-paradigm decision modeling framework is proposed to support decision modeling at three levels of abstraction based on domain-specific modeling (DSM). This framework designs a domain-specific modeling language (DSML) for decision modeling to raise the abstraction level of modeling, transforms the domain-specific models to formalism-based models to enable formal analysis and early verification and validation, and implements the semantics of the DSML based on a Python scripts framework which incorporates the decision model into the whole simulation system. The case study shows that the proposed approach incorporates domain expertise and facilitates domain modeler's participation in CoSES to formulate the problem using DSML in the problem domain, and enables formal analysis and automatic implementation of the decision model in the solution domain.

Research on Metamodels Consistency Verification Based on Formalization of Domain-Specific Metamodeling Language

Domain-specific metamodeling language(DSMML) defined by informal method cannot strictly represent its structural semantics,so its properties such as consistency cannot be holistically and systematically verified.In response,the paper proposes a formal representation of the structural semantics of DSMML named extensible markup language(XML) based metamodeling language(XMML) and its metamodels consistency verification method.Firstly,we describe our approach of formalization,based on this,the method of consistency verification of XMML and its metamodels based on first-order logical inference is presented;then,the formalization automatic mapping engine for metamodels is designed to show the feasibility of our formal method.

Theoretical Data Science: bridging the gap between domain-general and domain-specific studies

The entering into big data era gives rise to a novel discipline called Data Science.Data Science is interdisciplinary in its nature,and the existing relevant studies can be categorized into domain-independent studies and domain-dependent studies.The domain-dependent studies and domain-independent ones are evolving into Domain-general Data Science and Domain-specific Data Science.Domain-general Data Science emphasizes Data Science in a general sense,involving concepts,theories,methods,technologies,and tools.Domain-specific Data Science is a variant of Domain-general Data Science and varies from one domain to another.The most popular Domain-specific Data Science includes Data journalism,Industrial Data Science,Business Data Science,Health Data Science,Biological Data Science,Social Data Science,and Agile Data Science.The difference between Domain-general Data Science and Domain-specific Data Science roots in their thinking paradigms:DGDS conforms to data-centered thinking,while DSDS is in line with knowledge-centered thinking.As a result,DGDS focuses on the theoretical studies,while DSDS is centered on applied ones.However,DSDS and DGDS possess complementary advantages.Theoretical Data Science(TDS)is a new branch of Data Science that employs mathematical models and abstractions of data objects and systems to rationalize,explain and predict big data phenomena.TDS will bridge the gap between DGDS and DSDS.TDS contrasts with DSDS,which uses casual analysis,as well as DGDS,which employs data-centered thinking to deal with big data problems in that it balances the usability and the interpretability of Data Science practices.The main concerns of TDS are concentrated on integrating the data-centered thinking with the knowledge-centered thinking as well as transforming a correlation analysis into the casual analysis.Hence,TDS can bridge the gaps between DGDS and DSDS,and balance the usability and the interpretability of big data solutions.The studies of TDS should be focused on the following research purpose:to develop theoretical studies of TDS,to take advantages of active property of big data,to embrace design of experiments,to enhance causality analysis,and to develop data products.

Overview of Center for Domain-Specific Computing

In this short article, we would like to introduce the Center for Domain-Specific Computing （CDSC） established in 2009, primarily funded by the US National Science Foundation with an award from the 2009 Expeditions in Computing Program. In this project we look beyond parallelization and focus on customization as the next disruptive technology to bring orders-of-magnitude power-performance efficiency improvement for applications in a specific domain.

AocML： A Domain-Specific Language for Model-Driven Development of Activity-Oriented Context-Aware Applications

Activity-oriented context-aware （AOCA） applications are representative in pervasive computing. These appli- cations recognize daily-life human activities, perceive the environment status related to the activities, and react to ensure the smooth performance of the activities. Existing research proposed a specific light-weight, incremental method to support the development of such applications; however it is not easy to learn and use. This paper aims to facilitate the development of such applications and improve the productivity of developers. We propose AocML, a textual domain-specific language which provides a high-level abstraction of AOCA applications. Specifically, we first show the software model of AOCA applications and the abstract syntax of AocML. Then, we introduce the concrete syntax of AocML. We also implement the tools for AocML, including the development environment as well as the generation of Java code and ontology specification. Moreover, we use a case study and evaluation to demonstrate the advantages of AocML.

DSML ProcGraph: Overview and a Mid-Size Industrial Application Example

This paper presents model-based approach to process-control software development. The presented approach enables modelling of control software in a straightforward manner and, at the same time, on a high level of abstraction. The essence of the presented approach is a high-level, domain-specific modelling language ProcGraph, which is based on three types of diagrams that describe the modelled system using a domain-oriented hierarchical structure of interdependent procedural control entities and state-transition diagrams describing the behaviour of the procedural control entities. The presented concept is demonstrated by means of higher-level model segments of a real process-control application that deals with the micronisation process in the production of titanium dioxide. The presented industrial case shows that the application of ProcGraph provides adequate expressive power for an elegant preparation of graphic specifications in a transparent and easy way.

A Survey of Accelerator Architectures for Deep Neural Networks

Recently,due to the availability of big data and the rapid growth of computing power,artificial intelligence(AI)has regained tremendous attention and investment.Machine learning(ML)approaches have been successfully applied to solve many problems in academia and in industry.Although the explosion of big data applications is driving the development of ML,it also imposes severe challenges of data processing speed and scalability on conventional computer systems.Computing platforms that are dedicatedly designed for AI applications have been considered,ranging from a complement to von Neumann platforms to a“must-have”and stand-alone technical solution.These platforms,which belong to a larger category named“domain-specific computing,”focus on specific customization for AI.In this article,we focus on summarizing the recent advances in accelerator designs for deep neural networks(DNNs)-that is,DNN accelerators.We discuss various architectures that support DNN executions in terms of computing units,dataflow optimization,targeted network topologies,architectures on emerging technologies,and accelerators for emerging applications.We also provide our visions on the future trend of AI chip designs.

A Solution Based on Modeling and Code Generation for Embedded Control System

With the development of computer technology, embedded control system plays an important role in modern industry. For the embedded system, traditional development methods are time-consuming and system is not easy to maintain. Domain-specific modeling provides a solution for the problems. In this paper, we proposed development architecture for embedded control systems based on MIC. GME is used to construct meta-model and application model, model in-terpreter interprets model and stores model information in xml format document. The final cross-platform codes are automatically generated by different templates and xml format document. This development method can reduce time and cost in the lifecycle of system development.

Asurvey on design and application of open-channel solid-state drives

Compared with traditional solid-state drives(SSDs),open-channel SSDs(OCSSDs)expose their internal physical layout and provide a host-based flash translation layer(FTL)that allows host-side software to control the internal operations such as garbage collection(GC)and input/output(I/O)scheduling.In this paper,we comprehensively survey research works built on OCSSDs in recent years.We show how they leverage the features of OCSSDs to achieve high throughput,low latency,long lifetime,strong performance isolation,and high resource utilization.We categorize these efforts into five groups based on their optimization methods:adaptive interface customizing,rich FTL co-designing,internal parallelism exploiting,rational I/O scheduling,and efficient GC processing.We discuss the strengths and weaknesses of these efforts and find that almost all these efforts face a dilemma between performance effectiveness and management complexity.We hope that this survey can provide fundamental knowledge to researchers who want to enter this field and further inspire new ideas for the development of OCSSDs.

Cheap and secure metatransactions on the blockchain using hash-based authorisation and preferred batchers

Smart contracts are self-executing programs running in the blockchain allowing for decentralised storage and execution without a middleman.On-chain execution is expensive,with miners charging fees for distributed execution according to a cost model defined in the protocol.In particular,transactions have a high fixed cost.We present MultiCall,a transaction-batching interpreter for Ethereum that reduces the cost of smart contract executions by gathering multiple users’transactions into a batch.Our current implementation of MultiCall includes the following features:the ability to emulate Ethereum calls and create transactions,both from MultiCall itself and using an identity unique to the user;the ability to cheaply pay Ether to other MultiCall users;and the ability to authorise emulated transactions on behalf of multiple users in a single transaction using hash-based authorisation rather than more expensive signatures.This improves upon a previous version of MultiCall.Our experiments show that MultiCall provides a saving between 57%and 99%of the fixed transaction cost compared with the standard approach of sending Ethereum transactions directly.

A Survey on Graph Processing Accelerators:Challenges and Opportunities

Graph is a well known data structure to represent the associated relationships in a variety of applications,e.g.,data science and machine learning.Despite a wealth of existing efforts on developing graph processing systems for improving the performance and/or energy efficiency on traditional architectures,dedicated hardware solutions,also referred to as graph processing accelerators,are essential and emerging to provide the benefits significantly beyond what those pure software solutions can offer.In this paper,we conduct a systematical survey regarding the design and implementation of graph processing accelerators.Specifically,we review the relevant techniques in three core components toward a graph processing accelerator:preprocessing,parallel graph computation,and runtime scheduling.We also examine the benchmarks and results in existing studies for evaluating a graph processing accelerator.Interestingly,we find that there is not an absolute winner for all three aspects in graph acceleration due to the diverse characteristics of graph processing and the complexity of hardware configurations.We finally present and discuss several challenges in details,and further explore the opportunities for the future research.

A Modeling Language Based on UML for Modeling Simulation Testing System of Avionic Software

With direct expression of individual application domain patterns and ideas,domain-specific modeling language（DSML） is more and more frequently used to build models instead of using a combination of one or more general constructs.Based on the profile mechanism of unified modeling language（UML） 2.2,a kind of DSML is presented to model simulation testing systems of avionic software（STSAS）.To define the syntax,semantics and notions of the DSML,the domain model of the STSAS from which we generalize the domain concepts and relationships among these concepts is given,and then,the domain model is mapped into a UML meta-model,named UML-STSAS profile.Assuming a flight control system（FCS） as system under test（SUT）,we design the relevant STSAS.The results indicate that extending UML to the simulation testing domain can effectively and precisely model STSAS.

A rewriting system for convex optimization problems

We describe a modular rewriting system for translating optimization problems written in a domain-specific language(DSL)to forms compatible with low-level solver interfaces.Translation is facilitated by reductions,which accept a category of problems and transform instances of that category to equivalent instances of another category.Our system proceeds in two key phases:analysis,in which we attempt to find a suitable solver for a supplied problem,and canonicalization,in which we rewrite the problem in the selected solver’s standard form.We implement the described system in version 1.0 of CVXPY,a DSL for mathematical and especially convex optimization.By treating reductions as first-class objects,our method makes it easy to match problems to solvers well-suited for them and to support solvers with a wide variety of standard forms.

FlexPDA:A Flexible Programming Framework for Deep Learning Accelerators

There are a wide variety of intelligence accelerators with promising performance and energy efficiency,deployed in a broad range of applications such as computer vision and speech recognition.However,programming productivity hinders the deployment of deep learning accelerators.The low-level library invoked in the high-level deep learning framework which supports the end-to-end execution with a given model,is designed to reduce the programming burden on the intelligence accelerators.Unfortunately,it is inflexible for developers to build a network model for every deep learning application,which probably brings unnecessary repetitive implementation.In this paper,a flexible and efficient programming framework for deep learning accelerators,FlexPDA,is proposed,which provides more optimization opportunities than the low-level library and realizes quick transplantation of applications to intelligence accelerators for fast upgrades.We evaluate FlexPDA by using 10 representative operators selected from deep learning algorithms and an end-to-end network.The experimental results validate the effectiveness of FlexPDA,which achieves an end-to-end performance improvement of 1.620x over the low-level library.