REST API Composition for Effective Testing the Cloud

Cloud offers many ready-made REST services for the end users. This offer realizes the service composition through implementation somewhere on internet based on Service Level Agreement (SLA). For ensuring this SLA, a software testing is a useful means for attesting a non-functional requirement that guarantees quality assurance from end user's perspective. However, test engineer experiences only what goes in and out through an interface that contains a high level behaviors separated from its underlying details. Testing with these behaviors become an issue for classical testing procedures. So, REST API through composition is an alternative new promising approach for modeling behaviors with parameters against the cloud. This new approach helps to devise test effectiveness in terms of REST based behavior-driven implementation. It aims to understand functional behaviors through API methods based on input domain modeling (IDM) on the standard keyboard pattern. By making an effective REST design the test engineer sends complete test inputs to its API directly on application, and gets test responses from the infrastructure. We consider NEMo mobility API specification to design an IDM, which represents pattern match of mobility search URL API path scope. With this scope, sample mobility REST API service compositions are used. Then, the test assertions are implemented to validate each path resource to test the components and the end-to-end integration on the specified service.

Regime Inference for Sound Floating-Point Optimizations

Efficient numerical programs are required for proper functioning of many systems. Today’s tools offer a variety of optimizations to generate efficient floating-point implementations that are specific to a program’s input domain. However, sound optimizations are of an “all or nothing” fashion with respect to this input domain—if an optimizer cannot improve a program on the specified input domain, it will conclude that no optimization is possible. In general, though, different parts of the input domain exhibit different rounding errors and thus have different optimization potential. We present the first regime inference technique for sound optimizations that automatically infers an effective subdivision of a program’s input domain such that individual sub-domains can be optimized more aggressively. Our algorithm is general; we have instantiated it with mixed-precision tuning and rewriting optimizations to improve performance and accuracy, respectively. Our evaluation on a standard benchmark set shows that with our inferred regimes, we can, on average, improve performance by 65% and accuracy by 54% with respect to whole-domain optimizations.

MPDB: a unified multi-domain protein structure database integrating structural analogue detection

With the development of protein structure prediction methods and biological experimental determination techniques, the structure of single-domain proteins can be relatively easier to be modeled or experimentally solved. However, more than 80% of eukaryotic proteins and 67% of prokaryotic proteins contain multiple domains. Constructing a unified multi-domain protein structure database will promote the research of multi-domain proteins, especially in the modeling of multi-domain protein structures. In this work, we develop a unified multi-domain protein structure database (MPDB). Based on MPDB, we also develop a server with two functional modules: (1) the culling module, which filters the whole MPDB according to input criteria; (2) the detection module, which identifies structural analogues of the full-chain according to the structural similarity between input domain models and the protein in MPDB. The module can discover the potential analogue structures, which will contribute to high-quality multi-domain protein structure modeling.

Engineering A Fluorescent Protein Color Switch Using Entropy-driven Beta Strand Exchange

Protein conformational switches are widely used in biosensing. They are typically composed of an input domain (which binds a target ligand) fused to an output domain (which generates an optical readout). A central challenge in designing such switches is to develop mechanisms for coupling the input and output signals via conformational change. Here, we create a biosensor in which binding-induced folding of the input domain drives a conformational shift in the output domain that results in a 6-fold green-to-yellow ratiometric fluorescence change in vitro, and a 35-fold intensiometric fluorescence increase in cultured cells. The input domain consists of circularly permuted FK506 binding protein (cpFKBP) that folds upon binding its target ligand (FK506 or rapamycin). cpFKBP folding induces the output domain, an engineered GFP variant, to replace one of its β-strands (containing T203 and specifying green fluorescence) with a duplicate β-strand (containing Y203 and specifying yellow fluorescence) in an intramolecular exchange reaction. This mechanism employs the loop-closure entropy principle, embodied by folding of the partially disordered cpFKBP domain, to couple ligand binding to the GFP color shift. This proof-of-concept design has the advantages of full genetic encodability, ratiometric or intensiometric response, and potential for modularity. The latter attribute is enabled by circular permutation of the input domain.

The pitfalls of using Gaussian Process Regression for normative modeling

Normative modeling, a group of methods used to quantify an individual’s deviation from some expected trajectory relative to observed variability around that trajectory, has been used to characterize subject heterogeneity. Gaussian Processes Regression includes an estimate of variable uncertainty across the input domain, which at face value makes it an attractive method to normalize the cohort heterogeneity where the deviation between predicted value and true observation is divided by the derived uncertainty directly from Gaussian Processes Regression. However, we show that the uncertainty directly from Gaussian Processes Regression is irrelevant to the cohort heterogeneity in general.

Deviations from classical droplet evaporation theory

In this article, we show that significant deviations from the classical quasi-steady models of droplet evaporation can arise solely due to transient effects in the gas phase. The problem of fully transient evaporation of a single droplet in an infinite atmosphere is solved in a generalized, dimensionless framework with explicitly stated assumptions. The differences between the classical quasi-steady and fully transient models are quantified for a wide range of the 10-dimensional input domain and a robust predictive tool to rapidly quantify this difference is reported. In extreme cases, the classical quasi-steady model can overpredict the droplet lifetime by 80%. This overprediction increases when the energy required to bring the droplet into equilibrium with its environment becomes small compared with the energy required to cool the space around the droplet and therefore establish the quasi-steady temperature field. In the general case, it is shown that two transient regimes emerge when a droplet is suddenly immersed into an atmosphere. Initially, the droplet vaporizes faster than classical models predict since the surrounding gas takes time to cool and to saturate with vapour. Towards the end of its life, the droplet vaporizes slower than expected since the region of cold vapour established in the early stages of evaporation remains and insulates the droplet.

The pitfalls of using Gaussian Process Regression for normative modeling

Normative modeling, a group of methods used to quantify an individual's deviation from some expected trajectory relative to observed variability around that trajectory, has been used to characterize subject heterogeneity. Gaussian Processes Regression includes an estimate of variable uncertainty across the input domain, which at face value makes it an attractive method to normalize the cohort heterogeneity where the deviation between predicted value and true observation is divided by the derived uncertainty directly from Gaussian Processes Regression. However, we show that the uncertainty directly from Gaussian Processes Regression is irrelevant to the cohort heterogeneity in general.