Learning Read-Constant Polynomials of Constant Degree Modulo Composites

Boolean functions that have constant degree polynomial representation over a fixed finite ring form a natural and strict subclass of the complexity class ACC⁰. They are also precisely the functions computable efficiently by programs over fixed and finite nilpotent groups. This class is not known to be learnable in any reasonable learning model. In this paper, we provide a deterministic polynomial time algorithm for learning Boolean functions represented by polynomials of constant degree over arbitrary finite rings from membership queries, with the additional constraint that each variable in the target polynomial appears in a constant number of monomials. Our algorithm extends to superconstant but low degree polynomials and still runs in quasipolynomial time.     

A DISTANCE MEASURE FOR CLASSIFYING ARIMA MODELS

Abstract. In a number of practical problems where clustering or choosing from a set of dynamic structures is needed, the introduction of a distance between the data is an early step in the application of multivariate statistical methods. In this paper a parametric approach is proposed in order to introduce a well-defined metric on the class of autoregressive integrated moving-average (ARIMA) invertible models as the Euclidean distance between their autoregressive expansions. Two case studies for clustering economic time series and for assessing the consistency of seasonal adjustment procedures are discussed. Finally, some related proposals are surveyed and some suggestions for further research are made.     

Dynamic light scattering measurements on the polystyrene/ethyl acetate system at semi-dilute concentrations as a function of temperature

Summary Measurements of autocorrelation functions extending over a broad time range are reported for a sample of polystyrene in ethyl acetate as a function of temperature between –44°C (-temperature) and 70°C. The corresponding spectra of decay times are obtained by two mathematical methods. The existence of three dynamic processes is shown and their temperature and angular behaviour is studied.     

Microstructure of a nitrided steel previously decarburized

In this study the effects of a surface-controlled decarburization on the structure of a nitrided steel are analyzed. Samples of a quenched and tempered 42CrMo4 steel were decarburized by heating in air at different depths and submitted to gaseous nitriding. After decarburization and nitriding, the microstructure of surface layers was investigated by optical microscopy (OM) and scanning electron microscopy (SEM). The nitrogen and carbon profiles in the diffusion layers were determined by SEM equipped with a wavelength dispersive spectrometer (EPMA-WDS) and by glow discharge optical spectrometry (GDOS). The effect of nitriding was determined by microhardness measurements. Our results indicate that a previous decarburization only slightly affects the surface hardness, but reduces the conventional nitriding depth. The decarburization also favors the nitrogen take-up and produces increased nitrogen concentrations in the compound layer and in the narrow zone beneath it.     

3DStreaming: an open-source flexible framework for real-time 3D streaming services

The progress of three-dimensional 3D technologies, together with the wide diffusion of both Internet and broadband technologies, is paving the way to emerging live streaming services which have been conceived for delivering 3D video contents in real-time fashion to end users. Nowadays, the only available tools supporting stereoscopic 3D video services cannot be freely downloaded and require the adoption of owner stereoscopic players. Motivated by the lack of an effective solution, we developed a freeware and open source 3D live streaming framework, namely 3DStreaming. It provides stereoscopic 3D live streaming services over the Internet. In particular, it realizes a complete server implementation, offering the support for any transmission protocol and encoding scheme, as well as the full compatibility with any network architecture (i.e., LAN, MAN, Internet, and so on). At the same time, it allows users to use the preferable stereoscopic player and to render the video through any technique available for the chosen player. The overall performances of the proposed tool have been presented by testing its behavior in several network configurations (i.e., by varying network topology, coding technique, 3D representation format, and average encoding rate). All the measured metrics, which include the number of RTP segments that are transmitted and received, the frame loss ratio, and the PSNR, fully demonstrate the right behavior of the implemented tool in all the considered scenarios. We believe that, thanks to its high flexibility, this tool can be exploited by researchers working on stereoscopic-3D related issues to design, test, and evaluate novel and innovative algorithms, protocols, and network architectures.     

Coreference resolution: an empirical study based on SemEval-2010 shared Task 1

This paper presents an empirical evaluation of coreference resolution that covers several interrelated dimensions. The main goal is to complete the comparative analysis from the SemEval-2010 task on Coreference Resolution in Multiple Languages. To do so, the study restricts the number of languages and systems involved, but extends and deepens the analysis of the system outputs, including a more qualitative discussion. The paper compares three automatic coreference resolution systems for three languages (English, Catalan and Spanish) in four evaluation settings, and using four evaluation measures. Given that our main goal is not to provide a comparison between resolution algorithms, these are merely used as tools to shed light on the different conditions under which coreference resolution is evaluated. Although the dimensions are strongly interdependent, making it very difficult to extract general principles, the study reveals a series of interesting issues in relation to coreference resolution: the portability of systems across languages, the influence of the type and quality of input annotations, and the behavior of the scoring measures.     

Time series clustering and classification by the autoregressive metric

The statistical properties of the autoregressive (AR) distance between ARIMA processes are investigated. In particular, the asymptotic distribution of the squared AR distance and an approximation which is computationally efficient are derived. Moreover, the problem of time series clustering and classification is discussed and the performance of the AR distance is illustrated by means of some empirical applications.     

Experimental characterization of electrostatically actuated in-plane bending of microcantilevers

Experimental validation of numerical models developed by the authors to predict the static behaviour of microelectrostatic actuators is described. Cantilever microbeams, currently used in connection with RF-MEMS and micro-scale material testing were analysed. A set of microcantilevers, bending in the plane of the wafer, i.e. in the same plane as the profiling system’s target, was tested. This differs from the popular case of out-of-plane microbeams, usually studied in the literature. Geometry nonlinearity caused by large deflection of the microbeam was investigated and nonlinear coupled formulation of electromechanical equilibrium was performed. Coupled-field analysis was implemented using the Finite Element Method (FEM), to predict displacements and pull-in voltage measured by Fogale Zoomsurf 3D, subsequently plotting the displacement-versus-voltage curve to complete model validation. FEM nonlinear analysis, based on iterative approach with mesh morphing, and FEM non-incremental approach, including a special element proposed by the authors, are compared to the linear solution and to experimental results. Geometry nonlinearity appears relevant in microbeam modelling and requires a nonlinear solution of the coupled problem. Investigative work, which compared the results of 2D and 3D models to experimental data, revealed that some three dimensional effects are significant in model validation, but the 2D approach may be effective in predicting static behaviour provided that at least a microbeam thickness equivalent is adopted.     

Robust network monitoring in the presence of non-cooperative traffic queries

We present the design of a predictive load shedding scheme for a network monitoring platform that supports multiple and competing traffic queries. The proposed scheme can anticipate overload situations and minimize their impact on the accuracy of the traffic queries. The main novelty of our approach is that it considers queries as black boxes, with arbitrary (and highly variable) input traffic and processing cost. Our system only requires a high-level specification of the accuracy requirements of each query to guide the load shedding procedure and assures a fair allocation of computing resources to queries in a non-cooperative environment. We present an implementation of our load shedding scheme in an existing network monitoring system and evaluate it with a diverse set of traffic queries. Our results show that, with the load shedding mechanism in place, the monitoring system can preserve the accuracy of the queries within predefined error bounds even during extreme overload conditions.     

Self-adaptive utility-based web session management

In the Internet, where millions of users are a click away from your site, being able to dynamically classify the workload in real time, and predict its short term behavior, is crucial for proper self-management and business efficiency. As workloads vary significantly according to current time of day, season, promotions and linking, it becomes impractical for some ecommerce sites to keep over-dimensioned infrastructures to accommodate the whole load. When server resources are exceeded, session-based admission control systems allow maintaining a high throughput in terms of properly finished sessions and QoS for a limited number of sessions; however, by denying access to excess users, the website looses potential customers.In the present study we describe the architecture of AUGURES, a system that learns to predict Web user’s intentions for visiting the site as well its resource usage. Predictions are made from information known at the time of their first request and later from navigational clicks. For this purpose we use machine learning techniques and Markov-chain models. The system uses these predictions to automatically shape QoS for the most profitable sessions, predict short-term resource needs, and dynamically provision servers according to the expected revenue and the cost to serve it. We test the AUGURES prototype on access logs from a high-traffic, online travel agency, obtaining promising results.