Monitoring robot actions for error detection and recovery

Reliability is a serious problem in computer controlled robot systems. Although robots serve successfully in relatively simple applications such as painting and spot welding, their potential in areas such as automated assembly is hampered by programming problems. A program for assembling parts may be logically correct, execute correctly on a simulator, and even execute correctly on a robot most of the time, yet still fail unexpectedly in the face of real world uncertainties. Recovery from such errors is far more complicated than recovery from simple controller errors, since even expected errors can often manifest themselves in unexpected ways. Here, a novel approach is presented for improving robot reliability. Instead of anticipating errors, researchers use knowledge-based programming techniques so that the robot can autonomously exploit knowledge about its task and environment to detect and recover from failures. They describe preliminary experiment of a system that they designed and constructed

Two-dimensional imaging of the spin-orbit effective magnetic field

We report on spatially resolved measurements of the spin-orbit effective magnetic field in a GaAs/InGaAs quantum-well. Biased gate electrodes lead to an electric-field distribution in which the quantum-well electrons move according to the local orientation and magnitude of the electric field. This motion induces Rashba and Dresselhaus effective magnetic fields. The projection of the sum of these fields onto an external magnetic field is monitored locally by measuring the electron spin-precession frequency using time-resolved Faraday rotation. A comparison with simulations shows good agreement with the experimental data.Comment: 6 pages, 4 figure

Semanticizing syntactic patterns in NLP processing using SPARQL-DL queries

Some recent works on natural language semantic parsing make use of syntax and semantics together using different combination models. In our work we attempt to use SPARQL-DL as an interface between syntactic information given by the Stanford statistical parser (namely part-of-speech tagged text and typed dependency representation) and semantic information obtained from the FrameNet database. We use SPARQL-DL queries to check the presence of syntactic patterns within a sentence and identify their role as frame elements. The choice of SPARQL-DL is due to its usage as a common reference language for semantic applications and its high expressivity, which let rules to be generalized exploiting the inference capabilities of the underlying reasoner

Towards wafer-scale integration of high repetition rate passively mode-locked surface-emitting semiconductor lasers

One of the most application-relevant milestones that remain to be achieved in the field of passively mode-locked surface-emitting semiconductor lasers is the integration of the semiconductor absorber into the gain structure, enabling the realization of ultra-compact high-repetition-rate laser devices suitable for wafer-scale integration. We have recently succeeded in fabricating the key element in this concept, a quantum-dot-based saturable absorber with a very low saturation fluence, which for the first time allows stable mode locking of surface-emitting semiconductor lasers with the same mode areas on gain and absorber. Experimental results at high repetition rates of up to 30GHz are show

Individual scatterers as microscopic origin of equilibration between spin- polarized edge channels in the quantum Hall regime

The equilibration length between spin-polarized edge states in the Quantum Hall regime is measured as a function of a gate voltage applied to an electrode on top of the edge channels. Reproducible fluctuations in the coupling are observed and interpreted as a mesoscopic fingerprint of single spin-flip scatterers which are turned on and off. A model to analyze macroscopic edge state coupling in terms of individual scatterers is developed, and characteristic values for these scatterers in our samples are extracted. For all samples investigated, the distance between spin-flip scatterers lies between the Drude and the quantum scattering length.Comment: 4 pages, 2 figure

Price dispersion: the case of pasta

Scopo della ricerca è indagare la possibilità di utilizzare scanner data sugli acquisti di pasta per costruire indici dei prezzi spaziali bilaterali e multilaterali utilizzando un approccio binario nella loro costruzione.The aim of our research is to explore the possibility of utilizing scanner data on pasta purchases to build bilateral and multilateral spatial price indexes, taking a binary approach in the latter.1 Pasta plays a major role in the Italian diet. Historically, pasta consumption was mainly concentrated in the Southern regions of the country but today pasta is perhaps the product most representative of the eating habits of the Italians. The range of pasta producers runs from firms of longstanding tradition (some of them mainly directed towards local markets, such as Mastromauro in Puglia) to well known international brands (such as Barilla and De Cecco). The marked increase in pasta prices over the last two years has aroused great interest, but with little focus on spatial price diversity. This study stems from the availability of an extremely detailed panel dataset (Nielsen data) on values and quantities of pasta purchased. This data was produced by the use of bar-code scanning at retail outlets and thus includes information which provides weights at an elementary level. The use of scanner data to construct price indexes is not new in literature and there is a widespread consensus on the advantages of this approach in achieving more representative indexes. Average prices (unit values) show a marked spatial price variability: even when only considering the five bestselling products, regional prices vary greatly. The paper is set out as follows: Sect. 2 provides a description of the pasta scanner dataset and briefly looks for price variability; in Sect. 3 the requirements of comparability and representativity in the case of pasta are discussed; Sect. 4 deals with the methods and formulas chosen to obtain indexes for the regional comparisons of prices; Sect. 5 shows empirical results; in Sect. 6 a brief conclusion and suggestions for future work are given

Assessing Code Authorship: The Case of the Linux Kernel

Code authorship is a key information in large-scale open source systems. Among others, it allows maintainers to assess division of work and identify key collaborators. Interestingly, open-source communities lack guidelines on how to manage authorship. This could be mitigated by setting to build an empirical body of knowledge on how authorship-related measures evolve in successful open-source communities. Towards that direction, we perform a case study on the Linux kernel. Our results show that: (a) only a small portion of developers (26 %) makes significant contributions to the code base; (b) the distribution of the number of files per author is highly skewed --- a small group of top authors (3 %) is responsible for hundreds of files, while most authors (75 %) are responsible for at most 11 files; (c) most authors (62 %) have a specialist profile; (d) authors with a high number of co-authorship connections tend to collaborate with others with less connections.Comment: Accepted at 13th International Conference on Open Source Systems (OSS). 12 page

Tunable few electron quantum dots in InAs nanowires

Quantum dots realized in InAs are versatile systems to study the effect of spin-orbit interaction on the spin coherence, as well as the possibility to manipulate single spins using an electric field. We present transport measurements on quantum dots realized in InAs nanowires. Lithographically defined top-gates are used to locally deplete the nanowire and to form tunneling barriers. By using three gates, we can form either single quantum dots, or two quantum dots in series along the nanowire. Measurements of the stability diagrams for both cases show that this method is suitable for producing high quality quantum dots in InAs.Comment: 8 pages, 4 figure

Co-Evolutionary Learning for Cognitive Computer Generated Entities

In this paper, an approach is advocated to use a hybrid approach towards learning behaviour for computer generated entities (CGEs) in a serious gaming setting. Hereby, an agent equipped with cognitive model is used but this agent is enhanced with Machine Learning (ML) capabilities. This facilitates the agent to exhibit human like behaviour but avoid an expert having to define all parameters explicitly. More in particular, the ML approach utilizes co-evolution as a learning paradigm. An evaluation in the domain of one-versus-one air combat shows promising results