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Paper-based analytical devices (PADs) are simple, convenient, and portable devices that can detect selected substances and quickly display results. PADs are being adopted at increasing rates for use in an expanding array of fields including biochemistry, drug test assaying, and environmental analysis. Compared with traditional analytical methodology, PADs provide several advantages including the following: (1) the capacity for easy mass production; (2) portability; (3) low cost; and (4) small sample volume requirements. These features position PADs as significantly useful tools in rural areas and developing countries, where sophisticated laboratories are limited. In addition to these advantages, PADs offer great potential for integration with the Internet and new artificial intelligence technology. Integrating what may be called “lab-on-a-paper” technology with the Internet of Things offers new opportunities for individuals, in the lab, in the field, and even at home, to carry out advanced examinations and analyses of a variety of things including point-of-care health diagnostics, health status, and environmental quality. In this way, PADs can significantly improve quality of life for a great number of people.

[EN] In this work, we present a dataset which provides information on the scientific program of a set conferences of Machine Learning. Data were extracted from the IEEE Xplore Digital Library and the official web site of the International Conference on Machine Learning Applications (ICMLA). We include data of four different editions (from 2014 to 2017). Web scrapping techniques were used to mine the data contained in these web sites. The dataset covers 448 papers presented in the conference and every paper contains 6 attributes including information about the thematic session in which they were presented in the conference. The dataset is hosted in the Mendeley Dataset Repository., This work was partially supported by IDEIAGEOCA Research Group of the Universidad Politecnica Salesiana. This work also has been supported by the EU (FEDER) and the Spanish MINECO under grant TIN2015-69175-C4-1-R, and by Generalitat Valenciana PROMETE0112019/018.

The classification of science into disciplines is at the heart of bibliometric analyses. While most classifications systems are implemented at the journal level, their accuracy has been questioned, and paper-level classifications have been considered by many to be more precise. However, few studies investigated the difference between journal and the paper classification systems. This study addresses this gap by comparing the journal- and paper-level classifications for the same set of papers and journals. This isolates the effects of classification precision (i.e., journal- or paper-level) to reveal the extent of paper misclassification. Results show almost half of papers could be misclassified in journal classification systems. Given their importance in the construction and analysis of bibliometric indicators, more attention should be given to the robustness and accuracy of these disciplinary classifications schemes.

In this paper, we introduce a framework for processing genetics and genomics literature, based on ontologies and lexical resources from the biomedical domain. The main objective is to support the diagnosis process that is done by medical geneticists who extract knowledge from published works. We constructed a pipeline that gathers several genetics- and genomics-related resources and applies natural language processing techniques, which include named entity recognition and relation extraction. Working on a corpus created from PubMed abstracts, we built a knowledge database that can be used for processing medical records written in Spanish. Given a medical record from Uruguayan healthcare patients, we show how we can map it to the database and perform graph queries for relevant knowledge paths. The framework is not an end user application, but an extensible processing structure to be leveraged by external applications, enabling software developers to streamline incorporation of the extracted knowledge. Keywords: Controlled vocabulary, Natural language processing, Genomics, Automated pattern recognition, Publications, Medical records

This paper describes how SHARE (Sharing Hosted Autonomous Research Environments) satisfies the criteria of the Elsevier 2011 Executable Paper Grand Challenge. This challenge aims at disseminating the use of systems that provide reviewers and fellow scientists a convenient way to reproduce computational results of research papers. This can involve among others the calculation of a number, the plotting of a diagram, the automatic proof of a theorem or the interactive transformation of various inputs into a complex output document. Besides reproducing the literate results, readers of an executable paper should also be able to explore the result space by entering different input parameters than the ones reported in the original text. SHARE is a web portal that enables academics to create, share, and access remote virtual machines that can be cited from research papers. By deploying in SHARE a copy of the required operating system as well as all the relevant software and data, authors can make a conventional paper fully reproducible and interactive. Shared virtual machines can also contain the original paper text— when desirable even with embedded computations. This paper shows the concrete potential of SHARE-based articles by means of an example virtual machine that is based on a conventional research article published by Elsevier recently. More generally, it demonstrates how SHARE has supported the publication workflow of a journal special issue and various workshop proceedings. Finally, it clarifies how the SHARE architecture supports among others the Elsevier challenge's licensing and scalability requirements without domain specific restrictions.

Technology is improving day-by-day and so is the usage of mobile devices. Every activity that would involve manual and paper transactions can now be completed in seconds using your fingertips. On one hand, life has become fairly convenient with the help of mobile devices, whereas on the other hand security of the data and the transactions occurring in the process have been under continuous threat. This paper, re-evaluates the different policies and procedures used for preserving the privacy of sensitive data and device location. Policy languages have been very vital in the mobile environments as they can be extended/used significantly for sending/receiving any data. In the mobile environment users always go to service providers to access various services. Hence, communications between the service providers and mobile handsets needs to be secured. Also, the data access control needs to be in place. A section of this paper will review the communication paths and channels and their related access criteria. This paper is a contribution to the mobile domain, showing the possible attacks related to privacy and the various mechanisms used to preserve the end-user privacy. In addition, it also gives a comparison of the different privacy preserving methods in mobile environments to provide guidance to the readers. Finally, the paper summarizes future research challenges in the area of privacy preservation. This paper examines the 'where' problem and in particular, examines tradeoffs between enforcing location security at a device vs. enforcing location security at an edge location server. This paper also sketches an implementation of location security solution at both the device and the edge location server and presents detailed experiments using real mobility and user profile data sets collected from multiple data sources (taxicabs, Smartphones).

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We propose an intelligent sensor system based on a new sensing methodology, relying also on 3D map reconstruction techniques, for computing with high precision, in real-time and without human intervention the parameters needed for stream-flow computa- tion: water levels, morphology of the streams of all potentially flooded areas by each controlled stream. The collected data will be continuously transmitted, through a communication infrastructure, to software agents designed to compute the stream-flow and to quantify the spatial distribution of flood risk for each controlled watershed. The computed risks, together with other data coming from other sources (barometric sensors, camera operators of public organizations, emergency agencies, private citizens), will be analyzed by a diagnostic decision system implementing a risk-alert scheduling strategy. This system will be able to diagnose the health state of the controlled environment and to define specialized alarm levels for each potentially interested area that will be used to alert all citizens (ubiquity) locally present (alerting locality).

Sheet-break is a long standing problem in pulp and paper industry. If advance warnings can be issued ahead of time, corrective actions may be taken to prevent some of the breaks. In this paper, we describe the development and implementation of an online monitoring scheme. A data based approach was taken in this analysis. PCA was used to model the process, and a combined index based on the Hotelling’s T2 and Q-statistics was developed as a sheet-break predictor. As the process is subject to external disturbances, grade changes, and frequent interruptions, preprocessing of the data played an important role in getting consistent results. We used several novel techniques for data selection, scaling and modelling. The models were validated using a large validation data set with known fault condition.

Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data has become increasingly dicult. Information visualization and visual data mining can help to deal with the flood of information. The advan- tage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualiza- tion techniques that have been developed over the last two decades to support the exploration of large data sets. In this paper, we propose a classification of information visualization and visual data mining tech- niques based on the data type to be visualized, the visualization technique, and the interaction technique. We illustrate the classification using a few examples, and indicate some directions for future work.

P3P [23, 24] is a set of standards that allow corporations to declare their privacy policies. Hippocratic Databases [6] have been proposed to implement such policies within a corporation's datastore. From an end-user individual's point of view, both of these rest on an uncomfortable philosophy of trusting corporations to protect his/her privacy. Recent history chronicles several episodes when such trust has been willingly or accidentally violated by corporations facing bankruptcy courts, civil subpoenas or lucrative mergers. We contend that data management solutions for information privacy must restore controls in the individual's hands. We suggest that enabling such control will require a radical re-think on modeling, release, and management of personal data.

This paper presents the most recent results obtained in the frame of the ESA study on “Autonomous and Advanced Navigation Techniques”, part of the Technology and Research Programme of ESA. New guidance, navigation and control (GNC) concepts applicable to the near-comet phases of the ROSETTA mission have been investigated and detailed performance results obtained with a complete simulation software tool are described here. This paper is focused on the evaluation and performance assessment of GNC concepts applicable to the descent and landing phase of the mission, including the following points : - Attitude and Articulation Control System (AACS) concepts for a multibody spacecraft (Section 3); - autonomous spacecraft trajectory Guidance, Navigation and Control concepts for the descent phase, including the open-loop and the guided subphases (Section 4); - touch down control concepts (Section 5) The introduction presents the study assumptions, Section 2 is dedicated to the description of the software tool and the simulated models, and a synthesis with further recommendations are discussed in Section 6.

Today's real-time systems are characterized by managing large volumes of data. Efficient database management algorithms for accessing and manipulating data are required to satisfy timing constraints of supported applications. Real-time database systems involve a new research area investigating possible ways of applying database systems technology to real-time systems. Management of real-time information through a database system requires the integration of concepts from both real-time systems and database systems. Some new criteria need to be developed to involve timing constraints of real-time applications in many database systems design issues, such as transaction/query processing, data buffering, CPU, and IO scheduling. In this paper, a basic understanding of the issues in real-time database systems is provided and the research efforts in this area are introduced. Different approaches to various problems of real-time database systems are briefly described, and possible future research directions are discussed. © 1995.

Exploiting the performance potential of distributed memory machines requires a careful distribution of data across the processors. Vienna FORTRAN is a language extension of FORTRAN which provides the user with a wide range of facilities for such mapping of data structures. However, programs in Vienna FORTRAN are written using global data references. Thus, the user has the advantage of a shared memory programming paradigm while explicitly controlling the placement of data. The basic features of Vienna FORTRAN are presented along with a set of examples illustrating the use of these features.

In this paper we use a simple neural net to model the development of spelling in children, and we describe the results of an experiment which shows that the error rates shown by children in spelling different types of word closely mirror the error scores of the model after it has learned the same words. We conclude that connectionist modelling can inform research into spelling development by focussing attention on the statistical nature of the input-output mappings that children must learn.

I describe several applications of automata theory to the analysis of Datalog programs, ranging from the easy to the difficult, in the hope of convincing the reader that automata theory provides a powerful set of abstractions and tools to database theoreticians.

Outlined here are two methods which we believe will play an important role in any distributed memory compiler able to handle sparse and unstructured problems. We describe how to link runtime partitioners to distributed memory compilers. In our scheme, programmers can implicitly specify how data and loop iterations are to be distributed between processors. This insulates users from having to deal explicitly with potentially complex algorithms that carry out work and data partitioning. We also describe a viable mechanism for tracking and reusing copies of off-processor data. In many programs, several loops access the same off-processor memory locations. As long as it can be verified that the values assigned to off-processor memory locations remain unmodified, we show that we can effectively reuse stored off-processor data. We present experimental data from a 3-D unstructured Euler solver run on iPSC/860 to demonstrate the usefulness of our methods.

OOLP integrates the superior modeling capabilities of object-oriented paradigm in the declarative framework of logic programming. Method invocation in OOLP is given a precise model theoretic semantics which is consistent with that of logic programming. OOLP is extended to a practical object-oriented database language OOLP+ by adding some extra-logical features. OOLP+ allows object identity, multiple inheritance, method overriding and dynamic updating among other features. OOLP+ is implemented by translating it to Prolog. The translated programs executes without metainterpretation. This allows the use of all Prolog or Datalog optimization techniques. In this respect OOLP+ is unique among alternative proposals presented in the literature.

Background Hereditary hearing loss (HHL) is the most common sensory deficit, which highly afflicts humans. With gene sequencing technology development, more variants will be identified and support genetic diagnoses, which is difficult for human experts to diagnose. This study aims to develop a machine learning-based genetic diagnosis model of HHL-related variants of GJB2, SLC26A4 and MT-RNR1. Methods This case-control study included 1898 subjects, among which 1354 were HHL patients and 544 were carriers. Risk assessment models were established based on variants at 144 sites in three genes related to HHL by building six machine learning (ML) models. We compared the ML models with the genetic risk score (GRS) and expert interpretation (EI) to verify the clinical performance. Findings Among the six ML models, the support vector machine (SVM) showed the best performance. For the prediction of HHL-related gene sites in subjects with variants, the area under the receiver operating characteristic (AUC) of the SVM model was 0.803 (0.680–0.814) in the 10-fold stratified cross-validation and 0.751 (0.635–0.779) in external validation. The predicted results were better than both EI and GRS. Furthermore, 11 sites were identified as the smallest feature set that can be accurately predicted. Interpretation The developed SVM model has great potential to be an efficient and effective tool for HHL prediction when high throughput sequencing data are available. Funding This study was supported by the National Key Research and Development Program (2017YFC1001800).

After the presentation of various identification and parameter estimation methods in the previous papers, some selected practical aspects of process identification are discussed. This includes, for a given identification method, the steps from the design of experiments to the verification of the final model. Therefore a general procedure of process identification, the selection of input signals, the selection of the sampling time, off-line and on-line identification, comparison of parameter estimation methods, model order testing and model verification is presented. A short discussion on program packages for process identification follows.

This paper surveys the evolution of “high-lever” programming languages during the first decade of computer programming activity. We discuss the contributions of Zuse in 1945 (the “Plankalkul”), Goldstine and von Neumann in 1946 (“Flow Diagrams”), Curry in 1948 (“Composition”), Mauchly et al. in 1949 (“Short Code”), Burks in 1950 (“Intermediate PL”), Rutishauser in 1951 (“Klammerausdrucke”), Bohm in 1951 (“Formules”), Glennie in 1952 (“AUTOCODE”), Hopper et al. in 1953 (“A-2”), Laning and Zierler in 1953 (“Algebraic Interpreter”), Backus et al. in 1954–1957 (“FORTRAN”), Brooker in 1954 (“Mark I AUTOCODE”), Kamynin and Liubimskii in 1954 (“ΠΠ-2”), Ershov in 1955 (“ΠΠ”), Grems and Porter in 1955 (“BACAIC”), Elsworth et al. in 1955 (“Kompiler 2”), Blum in 1956 (“ADES”), Perlis et al. in 1956 (“IT”), Katz et al. in 1956–1958 (“MATH-MATIC”), Bauer and Samelson in 1956–1958 (U.S. Patent 3,047,228). The principal features of each contribution are illustrated and discussed. For purposes of comparison, a particular fixed algorithm has been encoded (as far as possible) in each of the languages. This research is based primarily on unpublished source materials, and the authors hope that they have been able to compile a fairly complete picture of the early developments in this area.

Despite appealing characteristics for the clinical trials setting, Bayesian inference methods remain scarcely used, especially in randomized controlled clinical trials (RCT). This is particularly true when dealing with a survival endpoint, likely due to the additional complexities to model specifications. We propose to use Bayesian inference to estimate the treatment effect in this setting, using a proportional hazards (PH) model for right-censored data. Implementation of such an estimation process is illustrated on two working examples from cancer RCTs, the ALLOZITHRO and the CLL7-SA trials, both originally analyzed using a frequentist approach. In these two different settings, we show that Bayesian sequential analyses can provide early insight on treatment effect in RCTs. Relying on posterior distributions and predictive posterior probabilities, we find that Bayesian sequential analyses of the ALLOZITHRO trial, which was terminated early due to an unanticipated deleterious effect of the intervention on survival, allow quantifying early that the treatment effect was opposite to what was expected. Then, incorporating historical data in the sequential analyses of the CLL7-SA trial would have allowed the treatment effect to be closer to the protocol hypothesis. These post-hoc results give grounds to advocate for a wider use of Bayesian approaches in RCTs, including those with right-censored endpoints, as informative decision tools.

Publisher Summary The quantification requires the selection of one of a set of special morphemes that designates certain semantic features of whatever is being enumerated. Changes in classifiers are interesting because they provide an occasion to investigate an idea of traditional concern in linguistics—the relationship between language learning and language change. One can use shape or process classifiers for most of the things that have their own taxonomy specific classifiers. The unmarked classifiers are used in place of the more specific marked forms and also true in situations where there are intergenerational differences in use of forms. Numeral classifiers differ from phonology as they appear infrequently in spontaneous conversation and classifier forms are not among the earliest part of the language mastered because they depend not only on the mastery of number concepts but also on having a set of lexemes representing concepts to which they can be applied.

Publisher Summary This chapter presents a comparison of biplot display and pencil-and-paper exploratory data analysis (EDA) methods. The chapter also presents an introduction to the biplot accompanied by an example in which the biplot is used for data summarization and description. On a point of terminology, the prefix “bi–” of biplot indicates that this is a joint display of rows and columns. It does not indicate the two-dimensionality of the biplot. The chapter discusses the uses of the biplot. These are mostly of two kinds: (1) inspection of data and (2) diagnostics. In using the biplot, a few displays suffice to reveal relevant patterns in a pretty striking manner. EDA may show more detail if one inspects fits and residuals carefully at each stage but it requires iterative cycles of modelling, fitting, residuals inspection, re-expression, and decisions. If one fit is inadequate, another is tried until a model is judged adequate. This is a search by trial and error rather than by a systematic method. Irregularities are provided by data, inspection takes time, but inspiration is something that may be difficult to come by. The EDA modeling procedure is in general not systematic. Biplot diagnostics are more systematic and direct. One does not start by guessing a model but rather displays data and inspects it, and the diagnosis is then often immediate.

R 2 D 2 is an object-oriented database system intended for engineering applications that is based on the nested relational data model. R 2 D 2 provides a two layer architecture for engineering application programming: a public database to globally store the engineering objects and a private database consisting of a local database and an object cache that are local to the application program. This paper is concerned with the transaction control mechanisms needed to control concurrent transactions that access the public database system. Objects are requested by a transaction to be transferred from the public database into the transaction's local database connected with an object cache, which together form the private data repository. Upon release of the objects from the cache they are checked back into the public database. The central implementation idea forms a modified intention locking scheme to provide for a high level of concurrency in accessing objects that are represented in nested relational structure, the underlying data model of R 2 D 2 . This scheme facilitates the (exclusive or shared) locking of subobjects within an abstraction hierarchy while the remainder of the hierarchical object is still accessible by other transactions.

In many robotic applications the need to represent and reason about spatial relationships is of great importance. However, our knowledge of particular spatial relationships is inherently uncertain. The most used method for handling the uncertainty is to "pre-engineer" the problem away, by structuring the working environment and using specially-suited high-precision equipment. In some advanced robotic research domains, however, such as automatic task planning, off-line robot programming, and autonomous vehicle operation, prior structuring will not be possible, because of dynamically changing environments, or because of the demand for greater reasoning flexibility. Spatial reasoning is further complicated because relationships are often not described explicitly, but are given by uncertain relative information. This is particularly true when many different frames of reference are used, producing a network of uncertain relationships. Rather than treat spatial uncertainty as a side issue in geometrical reasoning, we believe it must be an intrinsic part of spatial representations. In this paper, we describe a representation for spatial information, called the stochastic map, and associated procedures for building it, reading information from it, and revising it incrementally as new information is obtained. The map always contains the best estimates of relationships among objects in the map, and their uncertainties. The procedures provide a general solution to the problem of estimating uncertain relative spatial relationships. The estimates are probabilistic in nature, an advance over the previous, very conservative, worst-case approaches to the problem. Finally, the procedures are developed in the context of state-estimation and filtering theory, which provides a solid basis for numerous extensions.

Graphical Abstract The prediction model of oral disintegrating tablets formulations with direct compression process by artificial neural network (ANN) and deep neural network (DNN) techniques were established. 145 formulation data were extracted from Web of Science. All datasets were divided into three parts: training set (105 data), validation set (20) and testing set (20) to build prediction model.Image, graphical abstract, Oral disintegrating tablets (ODTs) are a novel dosage form that can be dissolved on the tongue within 3 min or less especially for geriatric and pediatric patients. Current ODT formulation studies usually rely on the personal experience of pharmaceutical experts and trial-and-error in the laboratory, which is inefficient and time-consuming. The aim of current research was to establish the prediction model of ODT formulations with direct compression process by artificial neural network (ANN) and deep neural network (DNN) techniques. 145 formulation data were extracted from Web of Science. All datasets were divided into three parts: training set (105 data), validation set (20) and testing set (20). ANN and DNN were compared for the prediction of the disintegrating time. The accuracy of the ANN model have reached 85.60%, 80.00% and 75.00% on the training set, validation set and testing set respectively, whereas that of the DNN model were 85.60%, 85.00% and 80.00%, respectively. Compared with the ANN, DNN showed the better prediction for ODT formulations. It is the first time that deep neural network with the improved dataset selection algorithm is applied to formulation prediction on small data. The proposed predictive approach could evaluate the critical parameters about quality control of formulation, and guide research and process development. The implementation of this prediction model could effectively reduce drug product development timeline and material usage, and proactively facilitate the development of a robust drug product.

We present a new methodology for the design and development of large-scale simulations. Such simulations have often been criticized for their lack of intelligibility, modifiability, credibility, and performance. They are costly to build, to run, to interpret, and to change. Recent advances in artificial intelligence, most notably in the areas of expert systems and object-oriented languages, suggest new approaches to overcoming these limitations. We have applied this technology to the design and implementation of a new simulation language and environment called ROSS (Rule-Oriented Simulation System). Within our research system, we have implemented two military battle simulations, SWIRL (a strategic simulation) and TWIRL (a tactical simulation), which simulate the interactions and outcomes of combat between two opposing military forces. A graphics facility has been developed to visually display simulation runs dynamically. written in Franzlisp running on a VAX. Our graphics routines are written in C and loaded directly into the Franzlisp environment. Our graphics processor is an AED 512. Also we are experimenting with a network of Xerox 1100s (Lisp Machines) for our work on distributed simulation (discussed elsewhere [4]).

Publisher Summary This chapter describes the applications of multilevel models of educational data. Multilevel models are powerful tools for the applied statistician involved in educational research. Several examples of multilevel analyses of data from Educational Testing Service is presented in the chapter, in which multilevel analysis using a Bayesian approach provides better answers than methods based on sampling theory ideas such as un-biasedness and minimum variance. Multilevel models lie between standard models—between the extremes associated with the significant/not-significant dichotomy arising from the testing of standard models. Multilevel models can be used when data are thin without invoking awkward rules regarding the minimum sample size required for some form of analysis. Multilevel models make the quantities to be estimated random variables. The best confirmation is honest validation: make predictions to new data sets and see how accurate the predictions are. The consideration of conceptual replications can then be useful by eliminating inappropriate models. Diagnostics, such as frequentist-based examination of residuals, or posterior predictive model monitoring are, thus, necessary adjuncts to a successful multilevel analysis.

Publisher Summary A programming paradigm is a supported style of programming with significant advantages for a domain of problems. Many programming paradigms have been added on top of LISP, but few have been tightly integrated. Common LISP Object System (CLOS) is a model of a good integration. CLOS blends the object-oriented programming paradigm smoothly and tightly with the usual procedure-oriented paradigm of LISP. Functions and methods are combined in a more general abstraction. Message passing is invoked via normal LISP function call and methods are viewed as partial descriptions of procedures. LISP data types are integrated with object classes. With these integrations, it is easy to incrementally move a program between the procedure and object-oriented styles. By integrating classes with the LISP type system and using a syntax for method invocation that is identical to the LISP function call, CLOS makes possible a smooth and incremental transition from using only the functional paradigm for user code to using the object paradigm. As a portable system implemented in a widely available base, it allows users the choice of hardware and environments. It allows them a road to the future.

Publisher Summary This chapter focuses on phonological analysis prepared for the third and final volume of the Linguistic Atlas of the Upper Midwest (LAUM), along with the treatment of the lexicon and the grammar in the first two volumes, adds measurably to the sociolinguistic data available not only to dialectologists but also to students and teachers of usage, to textbook writers, and to sociolinguists. Although within the 800-item corpus of the Upper Midwest worksheets more attention is given specifically to the vocabulary, the lexicon is not very significant in exhibiting social contrast. Several of the miscellaneous grammatical items recorded during the interviews provide more positive social markers than do the strong verbs. Greater inconsistency appears in the contrast between two locutions in which a numeral precedes a modified noun. Two syntactic constructions with relative clauses yield variants providing rather sharp social contrast.

The basic ideas for the design and the implementation of a direct (graphical) manipulation interface system are described. Architecture and structure are based on layered models. The system allows the inclusion of deictic / natural language references to objects represented on the screen. The system was developed and implemented on Sun workstations in ‘C’ and ‘Prolog’ under the operating system Unix, providing a set of modules and a communication layer for combined interfaces. Two applications, a direct manipulative interface to an expert system and a form query system allowing natural language/direct manipulation interaction, are described.

Publisher Summary This chapter focuses on an ALGOL 60 compiler and discusses the internal operation of an ALGOL translator. There are two aspects to the translation from problem-oriented algebraic languages to machine languages. These are referred to as formula translation and assembly. The operation of formula translation consists of scanning the statements of the source code and determining the constituent parts. On the basis of this analysis, it is determined which types of machine operations are to be compiled and in what order. The assembly process is the storage allocation and assigning of machine locations for both the compiled code and the program data. Basic to all translation and assembly schemes is the idea of a symbol table. The block structure of ALGOL, which permits the reuse in a different context of a given identifier, causes serious problems in the maintenance of the symbol and equivalence tables. The use of a stack for temporary storage during translation is basic to almost all modern translators. The stack provides for the convenient storage of information needed on a last-in-first-out basis.

The International Classification of Diseases (ICD) relies on clinical features and lags behind the current understanding of the molecular specificity of disease pathobiology, necessitating approaches that incorporate growing biomedical data for classifying diseases to meet the needs of precision medicine. Our analysis revealed that the heterogeneous molecular diversity of disease chapters and the blurred boundary between disease categories in ICD should be further investigated. Here, we propose a new classification of diseases (NCD) by developing an algorithm that predicts the additional categories of a disease by integrating multiple networks consisting of disease phenotypes and their molecular profiles. With statistical validations from phenotype-genotype associations and interactome networks, we demonstrate that NCD improves disease specificity owing to its overlapping categories and polyhierarchical structure. Furthermore, NCD captures the molecular diversity of diseases and defines clearer boundaries in terms of both phenotypic similarity and molecular associations, establishing a rational strategy to reform disease taxonomy., Highlights • The International Classification of Diseases (ICD) lags behind the current molecular characteristics of disease. • We quantified the limitations (specificity and blurred boundary) of ICD with integrated phenotypic and molecular profiles. • An integrative disease network integrating phenotypic and genotypic profiles proposes a refined disease category framework. Disease taxonomy is one of the foundations of medical science and healthcare solutions. The most widely used disease taxonomy in clinical settings is the International Classification of Diseases (ICD), a system established >100 years ago and maintained by the World Health Organization to track disease incidence. It is well recognized that ICD, which is based on clinical observations, largely lags behind the molecular achievements of this medical big data era. We quantified the limitations of ICD using integrated phenotypic and molecular profiles and proposed a refined disease taxonomy with possible applications for precision medicine.

Background Seizure prediction can increase independence and allow preventative treatment for patients with epilepsy. We present a proof-of-concept for a seizure prediction system that is accurate, fully automated, patient-specific, and tunable to an individual's needs. Methods Intracranial electroencephalography (iEEG) data of ten patients obtained from a seizure advisory system were analyzed as part of a pseudoprospective seizure prediction study. First, a deep learning classifier was trained to distinguish between preictal and interictal signals. Second, classifier performance was tested on held-out iEEG data from all patients and benchmarked against the performance of a random predictor. Third, the prediction system was tuned so sensitivity or time in warning could be prioritized by the patient. Finally, a demonstration of the feasibility of deployment of the prediction system onto an ultra-low power neuromorphic chip for autonomous operation on a wearable device is provided. Results The prediction system achieved mean sensitivity of 69% and mean time in warning of 27%, significantly surpassing an equivalent random predictor for all patients by 42%. Conclusion This study demonstrates that deep learning in combination with neuromorphic hardware can provide the basis for a wearable, real-time, always-on, patient-specific seizure warning system with low power consumption and reliable long-term performance., Highlights • We use deep learning and long-term neural data to develop an automated, patient-tunable epileptic seizure prediction system. • We deploy our prediction system on a low-power neuromorphic chip to form the basis of a wearable device. Predicting and treating the debilitating seizures suffered by epileptic patients has challenged medical researchers for over fifty years. A new way forward was opened when Cook and colleagues, in 2013, collected a large longitudinal and continuous dataset recorded directly from patients' brains for one to three years. Harnessing the recent breakthroughs in deep learning techniques and in building specialized processing chips, we have demonstrated that seizures can now be predicted by a portable device. Our system automatically learns patient-specific pre-seizure signatures, and, in real time, warns of oncoming seizures.

Digital PCR has become the emerging technique for the sequence-specific detection and quantification of nucleic acids for various applications. During the past years, numerous reports on the development of new digital PCR methods have been published. Maturation of these developments into reliable analytical methods suitable for diagnostic or other routine testing purposes requires their validation for the intended use.Here, the results of an in-house validation of a droplet digital PCR method are presented. This method is intended for the quantification of the absolute copy number concentration of a purified linearized plasmid in solution with a nucleic acid background. It has been investigated which factors within the measurement process have a significant effect on the measurement results, and the contribution to the overall measurement uncertainty has been estimated. A comprehensive overview is provided on all the aspects that should be investigated when performing an in-house method validation of a digital PCR method.

The earliest collections of computerized data began with wired boards, paper tape, and punched cards. Then magnetic tapes came quickly followed by disk storage. After disk storage came data base management systems, followed by online high-performance transaction systems. Then came 4GL technology, the personal computer, and spreadsheets. The first processors were uniprocessors. Then came tightly coupled multiplexed processors. Parallel processors came next, leading to Big Data. With data base management systems came data warehouses, where there was “a single version of the truth.” After data warehouse came data vault.

Publisher Summary Locks are serious business and lock technicians are true engineers, most backed with years of hands-on experience. But what happens when one takes the age-old respected profession of the locksmith and sprinkle it with hacker ingenuity? The locks individuals depended on fall victim to no-tech tools like fountain pens, electric flossers and toilet paper rolls; the combo locks that protected the stuff with tens of thousands of combinations fall in less than three minutes, with no tools at all; the locks the government mandates the use on the luggage surrender to tools made of soda cans shims and discarded shards of plastic; and most disturbing of all one finds that the gun locks that protect children from harm snap open after clever wielding of a drinking straw. Once the locks fall to these trivial attacks, individuals count on the cameras, motion sensors, and alarm systems, only to see them trivially bypassed as well.

Blank-model prototyping is closely related to paper prototyping. It is another form of physical prototyping that involves the representation of both hardware and software user interfaces. Blank-model artifacts are used during a participatory design process to elicit users' perceptions and mental models about innovative design concepts involving hardware devices in combination with software operating systems and user interfaces, especially portable ones. Examples of these devices include mobile phones, personal digital assistants (PDAs), digital music players, electronic tablets, and other similar portable devices. A blank-model prototype is a moderated participatory design process that involves a facilitator and a participant who attempts to construct a representation of an imagined technology artifact out of the provided materials and tools. In the blank-model activity, the facilitator engages the participant in a dialogue in which the participant thinks aloud about using an initially undefined technology to perform a task or an activity based on a scenario. The scenario details are progressively revealed by the facilitator; this progressive method allows the user to focus on particular aspects of the prototype at a given time rather than being distracted by too much information. A blank-modeling session is usually captured on videotape for later review and analysis by the design team. Blank-model sessions should include a representative sampling of individuals to engage in the study and to create artifacts.

This chapter presents various methods for prototyping that can be used to best achieve the prototyping goals. The chapter is devoted to brief presentations of the different methods that are covered in more depth in individual chapters in the book, including card sorting, wireframe prototyping, storyboard prototyping, paper prototyping, digital prototyping, blank-model prototyping, video prototyping, Wizard-of-Oz prototyping, and coded prototyping (including scripting and HTML). With the prototyping characteristics set and the appropriate method chosen to reach the desired goal, the next step is to choose the right tool. The tool one wants may not necessarily have a one-to-one mapping to the method one wants to use, and fortunately, more than one tool can be used for a particular prototyping method. One tool can be used with different methods to achieve various prototype results as well.

Publisher Summary This chapter provides an overview of Internet as the foundation of the information superhighway and e-commerce. The backbone of the information superhighway and e-commerce is the Internet. The Internet is a collection of millions of computers and network systems of all sizes. One can simply refer to the Internet as the “network of networks”. Navigational tools allow the user to surf the Web, and search engines provide access to various resources available on the Web, such as library searches for writing a term paper or making a reservation for an airline ticket. Directories use indexes of information based on keywords in the document. The lack of bandwidth or network capacity may soon become an issue with increasing number of users, high volume e-commerce activities, and large files such as multimedia applications. New multimedia capabilities such as transmitting live video consume extensive Internet resources. The chapter also provides a brief history of the Internet, reviews various access options such as domain name systems, navigational tools, directories, and search engines. The chapter concludes with an outlook for the future development of the Internet, a discussion of Internet 2, and highlights Netscape Corporation as the industry connection.