Type Inference for Place-Oblivious Objects

In a distributed system, access to local data is much faster than access to remote data. As a help to programmers, some languages require every access to be local. A program in those languages can access remote data via first a shift of the place of computation and then a local access. To enforce this discipline, researchers have presented type systems that determine whether every access is local and every place shift is appropriate. However, those type systems fall short of handling a common programming pattern that we call place-oblivious objects. Such objects safely access other objects without knowledge of their place. In response, we present the first type system for place-oblivious objects along with an efficient inference algorithm and a proof that inference is P-complete. Our example language extends the Abadi-Cardelli object calculus with place shift and existential types, and our implementation has inferred types for some microbenchmarks

Modelling the Semantic Web using a Type System

We present an approach for modeling the Semantic Web as a type system. By using a type system, we can use symbolic representation for representing linked data. Objects with only data properties and references to external resources are represented as terms in the type system. Triples are represented symbolically using type constructors as the predicates. In our type system, we allow users to add analytics that utilize machine learning or knowledge discovery to perform inductive reasoning over data. These analytics can be used by the inference engine when performing reasoning to answer a query. Furthermore, our type system defines a means to resolve semantic heterogeneity on-the-fly

Recognizing point clouds using conditional random fields

Detecting objects in cluttered scenes is a necessary step for many robotic tasks and facilitates the interaction of the robot with its environment. Because of the availability of efficient 3D sensing devices as the Kinect, methods for the recognition of objects in 3D point clouds have gained importance during the last years. In this paper, we propose a new supervised learning approach for the recognition of objects from 3D point clouds using Conditional Random Fields, a type of discriminative, undirected probabilistic graphical model. The various features and contextual relations of the objects are described by the potential functions in the graph. Our method allows for learning and inference from unorganized point clouds of arbitrary sizes and shows significant benefit in terms of computational speed during prediction when compared to a state-of-the-art approach based on constrained optimization.Peer ReviewedPostprint (author’s final draft

The Coolest Isolated Brown Dwarf Candidate Member of TWA

We present two new late-type brown dwarf candidate members of the TW Hydrae association (TWA) : 2MASS J12074836-3900043 and 2MASS J12474428-3816464, which were found as part of the BANYAN all-sky survey (BASS) for brown dwarf members to nearby young associations. We obtained near-infrared (NIR) spectroscopy for both objects (NIR spectral types are respectively L1 and M9), as well as optical spectroscopy for J1207-3900 (optical spectral type is L0{\gamma}), and show that both display clear signs of low-gravity, and thus youth. We use the BANYAN II Bayesian inference tool to show that both objects are candidate members to TWA with a very low probability of being field contaminants, although the kinematics of J1247-3816 seem slightly at odds with that of other TWA members. J1207-3900 is currently the latest-type and the only isolated L-type candidate member of TWA. Measuring the distance and radial velocity of both objects is still required to claim them as bona fide members. Such late-type objects are predicted to have masses down to 11-15 MJup at the age of TWA, which makes them compelling targets to study atmospheric properties in a regime similar to that of currently known imaged extrasolar planets.Comment: 8 pages, 4 figures, accepted for publication in the ApJ Letter

Extensible records in the System E Framework and a new approach to object-oriented type inference

University of Technology, Sydney. Faculty of Engineering and Information Technology.Extensible records were proposed by Wand as a foundation for studying object-oriented type inference. One of their key benefits is that they allow for an elegant encoding of object-oriented inheritance, where one class of objects may be defined as an extension of another class of objects. However, every system of type inference designed for extensible records to date has been developed in the Hindley/Milner-style, a consequence being that polymorphism in these systems is not first-class and analysis is not strictly compositional. We argue that both of these features are necessary to retain the modelling and engineering benefits of traditional object-oriented languages such as Java: 1. Object-oriented modelling depends on the treatment of objects as first-class citizens, and this demands a type inference system capable of handling first-class polymorphism. 2. Object-oriented engineering encourages the separate development of software modules, and this should be supported by the type inference system with compositional analysis. Both of these features are present in a type system for the λ-calculus called System E, which supports first-class polymorphism via intersection types, and compositional type inference via expansion variables. However, research into System E has so far focused on refining and simplifying the formulation of expansion variables and exploring type inference algorithms with various properties. Meanwhile, the system has not yet been extended beyond the terms of the pure λ-calculus and it lacks many features that would be needed in a practical object-oriented language. In this dissertation, we combine System E with Wand’s extensible records resulting in a new approach to type inference for extensible records that better preserves the modelling and engineering benefits of object orientation stated above. The resulting system, called System Evcr, is significant because previous type inference systems, both for extensible records in particular, and also for object orientation in general, have at best preserved only one or the other of these two benefits, but never both of them simultaneously. System Evcr also makes a significant contribution to the work on System E, since it demonstrates for the first time that the System E’s expansion variables can be adapted to analyse programs whose term language extends beyond the pure λ-calculus. To demonstrate the potential use of System Evcr in object-oriented type inference, an implementation of our type inference algorithm was created and is shown to succeed on problem examples that previous systems either fail to analyse, or else fail to analyse compositionally

Properties of ultra-cool dwarfs with Gaia. An assessment of the accuracy for the temperature determination

We aimed to assess the accuracy of the Gaia teff and logg estimates as derived with current models and observations. We assessed the validity of several inference techniques for deriving the physical parameters of ultra-cool dwarf stars. We used synthetic spectra derived from ultra-cool dwarf models to construct (train) the regression models. We derived the intrinsic uncertainties of the best inference models and assessed their validity by comparing the estimated parameters with the values derived in the bibliography for a sample of ultra-cool dwarf stars observed from the ground. We estimated the total number of ultra-cool dwarfs per spectral subtype, and obtained values that can be summarised (in orders of magnitude) as 400000 objects in the M5-L0 range, 600 objects between L0 and L5, 30 objects between L5 and T0, and 10 objects between T0 and T8. A bright ultra-cool dwarf (with teff=2500 K and \logg=3.5 will be detected by Gaia out to approximately 220 pc, while for teff=1500 K (spectral type L5) and the same surface gravity, this maximum distance reduces to 10-20 pc. The RMSE of the prediction deduced from ground-based spectra of ultra-cool dwarfs simulated at the Gaia spectral range and resolution, and for a Gaia magnitude G=20 is 213 K and 266 K for the models based on k-nearest neighbours and Gaussian process regression, respectively. These are total errors in the sense that they include the internal and external errors, with the latter caused by the inability of the synthetic spectral models (used for the construction of the regression models) to exactly reproduce the observed spectra, and by the large uncertainties in the current calibrations of spectral types and effective temperatures.Comment: 18 pages, 17 figures, accepted by Astronomy & Astrophysic