Property-Driven Black-Box Testing of Numeric Functions

In this work, we propose a property-driven testing mechanism to perform unit testing of functions performing numerical computations. Our approach, similar to the property-based testing technique, allows the tester to specify the requirements to check. Unlike property-based testing, the specification is then used to generate test cases in a targeted manner. Moreover, our approach works as a black-box testing tool, i.e. it does not require knowledge about the internals of the function under test. Therefore, besides on programmed numeric functions, we also apply our technique to machine-learned regression models. The experimental evaluation on a number of case studies shows the effectiveness of our testing approach

Modelling Pricing Behavior with Weak A‐Priori Information: Exploratory Approach

In the absence of reliable a priori information, choosing the appropriate theoretical model to describe an industry’s behavior is a critical issue for empirical studies about market power. A wrong choice may result in model misspecification and the conclusions of the empirical analysis may be driven by the wrong assumption about the behavioral model.This paper develops a methodology aimed to reduce the risk of misspecification bias. The approach is based on the sequential application of a sliced inverse regression (SIR) and a nonparametric Nadaraya/ Watson regression (NW). The SIR‐NW algorithm identifies the factors affecting pricing behavior in an industry and provides a nonparametric characterization of the function linking these variables to price. This information may be used to guide the choice of the model specification for a parametric estimation of market power.The SIR NW algorithm is designed to complement the estimation of structural models of market behavior, rather than to replace it. The value of this methodology for empirical industrial organization studies lies in its data driven approach that does not rely on prior knowledge of the industry. The method reverses the usual hypothesis testing approach. Instead of first choosing the model based on a priori information and then testing if it is compatible with the data, the econometrician selects a theoretical model based on the observed data. Thus, the methodology is particularly suited for those cases where the researcher has no a priori information about the behavioral model, or little confidence in the information that is available

A structural approach to handling endogeneity in strategic management: The case of RBV

In this paper we posit that the lack of consensus about empirical tests of resource based view (RBV) could be the result of endogenous resource picking on the part of firms. If resources are endogenously selected, regression based methods that examine their connection to firm performance will be mis-estimated. We show that traditional remedies for endogeneity do not resolve this problem when returns to resources are heterogeneous (as theorized under RBV) and when managers act with at least partial knowledge of the expected, idiosyncratic return (as theorized under the strategic factor market hypotheses). As such, we develop a Bayesian approach that solves this endogeneity problem by directly incorporating resource picking into the modeling framework. We illustrate the validity of our approach through the use of a comprehensive simulation study and show that our proposed approach outperforms traditional linear models (including traditional cures of endogeneity and unobserved heterogeneity) under a variety of conditions. Our findings suggest that: (1) research in strategy requires a more careful and deeper understanding of potential sources of endogeneity and (2) the use of Bayesian methods in management can help develop more theoretically motivated empirical approaches to hypothesis testing. © 2014 European Academy of Management

Cascaded 3D Full-body Pose Regression from Single Depth Image at 100 FPS

There are increasing real-time live applications in virtual reality, where it plays an important role in capturing and retargetting 3D human pose. But it is still challenging to estimate accurate 3D pose from consumer imaging devices such as depth camera. This paper presents a novel cascaded 3D full-body pose regression method to estimate accurate pose from a single depth image at 100 fps. The key idea is to train cascaded regressors based on Gradient Boosting algorithm from pre-recorded human motion capture database. By incorporating hierarchical kinematics model of human pose into the learning procedure, we can directly estimate accurate 3D joint angles instead of joint positions. The biggest advantage of this model is that the bone length can be preserved during the whole 3D pose estimation procedure, which leads to more effective features and higher pose estimation accuracy. Our method can be used as an initialization procedure when combining with tracking methods. We demonstrate the power of our method on a wide range of synthesized human motion data from CMU mocap database, Human3.6M dataset and real human movements data captured in real time. In our comparison against previous 3D pose estimation methods and commercial system such as Kinect 2017, we achieve the state-of-the-art accuracy