Asynchronous progress

JavaScript programmers make extensive use of event-driven programming to help build responsive web applications. However, standard approaches to sequencing events are messy, and often lead to code that is difficult to understand and maintain. We have found that arrows, a generalization of monads, are an elegant solution to this problem. Arrows allow us to easily write asynchronous programs in small, modular units of code, and flexibly compose them in many different ways, while nicely abstracting the details of asynchronous program composition. In this paper, we present Arrowlets, a new JavaScript library that offers arrows to the everyday JavaScript programmer. We show how to use Arrowlets to construct a variety of state machines, including state machines that branch and loop. We also demonstrate how Arrowlets separate computation from composition with examples such as a drag-and-drop handler and a bubblesort animation.

An asynchronous message-passing program P is nondeterministic. Given the same input, multiple executions of P may exercise different send/receive event sequences (or SR-sequences) and may even produce different results. Such nondeterminacy makes it difficult to determine the correctness of P. Let X be an input of P. Assume that any execution of P with X terminates. Reachability testing of P with X is to execute, in a systematic manner, all possible SR-sequences of P with X such that the correctness of P with X can be determined. The basic idea of reachability testing is described as follows. We first execute P with X nondeterministically to collect one or more SR-sequences. For each collected SR-sequence, we analyze its race conditions and generate race variants, which are prefixes of other SR-sequences. We replay race variants to generate new SR-sequences. For each new SR-sequence, we repeat the same process until we eventually execute all possible SR-sequences of P with X. We describe an efficient implementation of reachability testing of asynchronous message-passing programs. Our technique deals with partially-ordered SR-sequences and reduces the complexity and redundancy caused by totally-ordered SR-sequences.

The determination of elastoplastic properties of composite materials is a rather complicated problem. Analytical approaches to the solution of the problem available in the literature [1–3] are based on a number of simplifying assumptions which allow the distribution of local fields of displacements, strains, stresses in a composite to be described in terms of a small number of parameters (degrees of freedom). For example, for the construction of a composite model under elastoplastic deformation, Dvorak and Bahei-El-Din [1] and Levitas [3] assumed a uniform distribution of local fields of strains and stresses in each phase of a composite (i.e. one parameter or degree of freedom was used to describe strains and stresses in each phase). Aboudi [2] obtained elastoplastic properties of a two-phase composite analytically using the method of cells with four subcells. For each subcell a linear variation of the displacements was accepted (i.e. three and twelve parameters or degrees of freedom were used to describe a field of each component of displacement vector for the subcell and the cell, respectively). These models can give overestimated values of macroscopic stresses. The question is open whether simplified models with a small number of degrees of freedom for description of local fields of displacements, strains and stresses in each phase can give results of high accuracy under elastic and elastoplastic straining.KeywordsLocal FieldTotal StrainNodular Cast IronElastoplastic DeformationMacroscopic StressThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Data Driven Framework with Graphical User Interface for Predicting Flexural Behavior of FRCM Strengthened RC Beams

Hot pressed zinc sulfide, dispersion hardened with diamond particles has turned out to be an ideal material in which to explore the optical complexity introduced by perhaps the simplest optical composite structure. Because the transverse vibrational mode of ZnS is IR active while that of diamond is IR inactive, the vibrational polarization properties of the resulting medium are extremely inhomogeneous at far infrared frequencies. This novel system has turned out to be extremely useful for identifying the important dynamical features underlying the optical properties of all transparent composites and complex dielectrics. One result of far ir measurements of the reststrahlen region of this system is the discovery of a generalized Lyddane-Sachs-Teller (LST) relation for solids and liquids. The characteristic dynamical frequencies are defined in terms of second moments of the relevant response functions, and very general causality arguments are used to obtain the generalized LST relation. The measured electronic excitation properties of silicon have been used to illustrate the generality of the resulting relation, which provides a useful connection between the long-wavelength dynamical behavior of nonmetallic condensed media and their static and high-frequency dielectric properties. When the characteristic vibrational frequencies of small disordered dielectric particles are described in terms of optical moments of the appropriate response functions, a generalized Fröhlich relation follows which connects the second moment of the small particle response function to the dc dielectric properties of the particle. This result is then used to obtain a new representation for the Clausius-Mossotti relation. Finally, the frequency dependent extinction cross section of an ellipsoidal particle of arbitrary size is examined from the same perspective of sum rules and optical moments. A number of general results can be found. It is demonstrated here that an extinction strength sum rule exists which directly relates the effective number of oscillators in the scattering particle to the integral over the extinction cross section spectrum, independent of particle shape. In addition, the characteristic frequency for the extinction cross section spectrum has the interesting property of being independent of particle size. It is also shown that the frequency expression characterizing the extinction properties of an ellipsoid of arbitrary size has the same form as the Fröhlich relation for small single crystal particles. The expression agrees with the generalized Fröhlich relation that relates the squared frequency characterizing the absorption behavior of an ellipsoid in the Rayleigh limit with the small particle dielectric constant. The end result is that there is a much stronger connection between the scattering and absorptive properties of a large disordered particle and the absorptive properties of a small single crystalline particle than previously recognized.KeywordsExtinction SpectrumEllipsoidal ParticleExtinction Cross SectionDiamond CompositeMoment FrequencyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The sensing properties of SnO2-graphene composites prepared by a one-pot non-aqueous procedure assisted by microwave treatment have been investigated. The combined characteristics of the microwave heating and the “benzyl alcohol route” allowed to selectively grow, at the temperature of 185 °C and in only few minutes, metal oxide nanoparticles on the surface of reduced graphene (RGO). Chemoresistive devices consisting of a thick SnO2/RGO layer on alumina substrates with Pt interdigitated electrodes were fabricated and their electrical and sensing characteristics were investigated.KeywordsGraphene OxideBenzyl AlcoholMicrowave HeatingAlumina SubstrateMicrowave TreatmentThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

When exposed to environmental conditions, fibre-reinforced polymer (FRP) composites are prone to material degradation. The environmental reduction factor in different structural codes reflects the significant effect of the long-term durability of FRPs in aggressive environments. Traditional prediction methods rely on oversimplified premises, which may result in erroneous errors. Due to its proficiency in dealing with complex non-linear structural problems, machine learning (ML) offers a unique potential to increase the predictability of structural engineering factors. This can be attributed to the recent advancements in ML techniques, which leverage their robustness when handling large datasets, as well as the increased processing power that facilitates more efficient data analysis. This article reviews the current implementation cases and capabilities of ML algorithms in overcoming the shortcomings of conventional models for predicting the durability performance of FRP systems. According to the literature, it was found that the efficiency of ML approach varies significantly depending on the quality and comprehensiveness of the database. While various researcher-employed algorithms generally yield accurate predictions for retaining mechanical properties in FRP composites with minor errors, sensitivity analysis highlights varied impacts of variables when using different datasets or machine learning algorithms. This variance may arise from factors like inadequate or low-quality datasets, insufficient training, overfitting, and other influences. More experimental data are needed to enhance the current database to effectively apply ML in more applications for FRPs under different loading and environmental conditions. The paper ends by suggesting future research directions in this field.

Composition driven machine learning for unearthing high-strength lightweight multi-principal element alloys

The purpose of the KTB is to drill a 10 – 14 km deep scientific well into the internal zone of the Central European Hercynides. As target areas the Schwarzwald and the Oberpfalz (Bavaria) were selected and have been investigated in the course of pre-site studies. The project is expected to provide fundamental contributions to the following major geoscientific problems: Basic research on the physical and chemical conditions, gradients and processes in a continuous vertical profile through the crystalline upper crust down into the deeper middle crust. The drilled section should define the specific physical, compositional and structural properties of the crustal type of an isostatically balanced Phanerozoic platform and exhibit the present crustal state. In order to collect information on time-depending parameters long-term measurements are planned to be carried out from a deep earth laboratory and observatory. Reconstruction of paleogradients and paleoprocesses in a vertical crustal section through one of the key areas for the understanding of the Paleozoic crustal dynamics with subsequent Mesozoic crustal rejuvenation in Central Europe. Special emphasis will be given to elucidating the significance of the middle crust as a possible geodynamic controlling element in orogenically active and isostatically non-balanced intracontinental crustal regions and to deciphering the effects of crustal fluids on tectonics, metamorphism, anatexis, and mineralization. Both target areas provide favorable conditions for finding answers to these questions because as a result of a post-Hercynian isostatic uplift the transitional portion between upper and middle crust can be penetrated. The KTB is thematically closely related with a number of current geoscientific research programs that are devoted to studying the deeper continental crust. Among these are the German Continental Seismic Reflection Program (DEKORP), the DFG priority program "Structure, Composition and Evolution of the Continental Lower Crust", the European Geotraverse (EGT) with a north-south transect across Germany and a DFG special research program "Stress and Strain in the Lithosphere". The borehole itself will be drilled in an extremely well-investigated basement region and by means of concomitant geological, petrological and geophysical surface studies and experiments (e.g. 3-D seismics) will be embedded into a 3-dimentionally thoroughly investigated basement. The decision on the drill site will be made in October 1986. In September 1987 a 3000–5000 m pilot hole will be sunk and in 1989 the main borehole will be started some 200 m away from the pilot hole.KeywordsContinental CrustLower CrustSuture ZoneBohemian MassifMetamorphic EventThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

High-throughput genome sequencing projects continue to churn out enormous amounts of raw sequence data. However, most of this raw sequence data is unannotated and, hence, not very useful. Among the various approaches to decipher the function of a protein, one is to determine its localization. Experimental approaches for proteome annotation including determination of a protein's subcellular localizations are very costly and labor intensive. Besides the available experimental methods, in silico methods present alternative approaches to accomplish this task. Here, we present two machine learning approaches for prediction of the subcellular localization of a protein from the primary sequence information. Two machine learning algorithms, k Nearest Neighbor (k-NN) and Probabilistic Neural Network (PNN) were used to classify an unknown protein into one of the 11 subcellular localizations. The final prediction is made on the basis of a consensus of the predictions made by two algorithms and a probability is assigned to it. The results indicate that the primary sequence derived features like amino acid composition, sequence order and physicochemical properties can be used to assign subcellular localization with a fair degree of accuracy. Moreover, with the enhanced accuracy of our approach and the definition of a prediction domain, this method can be used for proteome annotation in a high throughput manner. SubCellProt is available at www.databases.niper.ac.in/SubCellProt.

The high strain rate response of ultra high molecular weight polyethylene (UHMWPE) laminae and laminates is of interest to support computational modeling of applications where impact damage may occur. In this study, the efficacy of utilizing time-temperature superposition principles (tTSP) to determine the high strain rate response of UHMWPE composites is investigated. Constituent and composite tensile properties are measured at various temperatures and strain rates. Testing was completed at thermo-rheologically equivalent temperature-strain rate combinations to evaluate the effectiveness and limitations of the shifting approach. Analysis of modulus and failure data indicated that at the composite level, tTSP may not be applicable, while at the lamina level tTSP may be applicable, therefore high strain rate properties are estimated.KeywordsHigh Strain RateMaster CurveUltra High Molecular Weight PolyethyleneShift FactorCreep ComplianceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

There is a history of hybrid machine learning approaches where the result of an unsupervised learning algorithm is used to provide data annotation from which ILP can learn in the usual supervised manner [7, 8]. Here we consider the task of predicting the property of cointegration between the time series of stock price of two companies, which can be used to implement a robust pair-trading strategy that can remain profitable regardless of the overall direction in which the market evolves. We start with an original FinTech ontology of relations between companies and their managers, which we have previously extracted from SEC reports, quarterly filings that are mandatory for all US companies. When combined with stock price time series, these relations have been shown to help find pairs of companies suitable to pair trading [3]. Here we use node2vec embeddings to produce clusters of companies and managers, which are then used as background predicates in addition to the relations linking companies and staff present in the ontology, and the values of the target predicate for a given time period. Progol [10] is used to learn from this mixture of predicates combining numerical with structural relations of the entities represented in the data set to reveal rules with predictive power.KeywordsOntologiefinancial forecastingSEC financial reportsILPunsupervised learninggraph embedding

A dataflow network consists of nodes that communicate by passing data over unbounded FIFO channels. For dataflow networks containing only deterministic nodes, Kahn has presented a simple and elegant semantic model. However, the straight-forward generalization of this model to nondeterministic networks is not compositional. In the literature are two compositional models which in addition are fully abstract, i.e. they have added the least amount of extra information to attain compositionality. We relate these two model to each other, thereby showing that they are isomorphic. We give comments on the relation of the models to other compositional models, which are not fully abstract.KeywordsData ItemCommunication EventInfinite SequenceOutput ChannelInput ChannelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In these notes, we give an overview of the join calculus, its semantics, and its equational theory. The join calculus is a language that models distributed and mobile programming. It is characterized by an explicit notion of locality, a strict adherence to local synchronization, and a direct embedding of the ML programming language. The join calculus is used as the basis for several distributed languages and implementations, such as JoCaml and functional nets.Local synchronization means that messages always travel to a set destination, and can interact only after they reach that destination; this is required for an efficient implementation. Specifically, the join calculus uses ML’s function bindings and pattern-matching on messages to program these synchronizations in a declarative manner.Formally, the language owes much to concurrency theory, which provides a strong basis for stating and proving the properties of asynchronous programs. Because of several remarkable identities, the theory of process equivalences admits simplifications when applied to the join calculus. We prove several of these identities, and argue that equivalences for the join calculus can be rationally organized into a five-tiered hierarchy, with some trade-off between expressiveness and proof techniques.We describe the mobility extensions of the core calculus, which allow the programming of agent creation and migration. We briefly present how the calculus has been extended to model distributed failures on the one hand, and cryptographic protocols on the other.KeywordsMobile AgentOperational SemanticParallel CompositionProof TechniqueCouple SimulationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Predicting heat release properties of flammable fiber-polymer laminates using artificial neural networks