Forecasting peak air pollution levels using NARX models

Air pollution has a negative impact on human health. For this reason, it is important to correctly forecast over-threshold events to give timely warnings to the population. Nonlinear models of the nonlinear autoregressive with exogenous variable (NARX) class have been extensively used to forecast air pollution time series, mainly using artificial neural networks (NNs) to model the nonlinearities. This work discusses the possible advantages of using polynomial NARX instead, in combination with suitable model structure selection methods. Furthermore, a suitably weighted mean square error (MSE) (one-step-ahead prediction) cost function is used in the identification/learning process to enhance the model performance in peak estimation, which is the final purpose of this application. The proposed approach is applied to ground-level ozone concentration time series. An extended simulation analysis is provided to compare the two classes of models on a selected case study (Milan metropolitan area) and to investigate the effect of different weighting functions in the identification performance index. Results show that polynomial NARX are able to correctly reconstruct ozone concentrations, with performances similar to NN-based NARX models, but providing additional information, as, e.g., the best set of regressors to describe the studied phenomena. The simulation analysis also demonstrates the potential benefits of using the weighted cost function, especially in increasing the reliability in peak estimation.     

ITEA—interactive trajectories and events analysis: exploring sequences of spatio-temporal events in movement data

Widespread use of GPS and similar technologies makes it possible to collect extensive amounts of trajectory data. These data sets are essential for reasonable decision making in various application domains. Additional information, such as events taking place along a trajectory, makes data analysis challenging, due to data size and complexity. We present an integrated solution for interactive visual analysis and exploration of events along trajectories data. Our approach supports analysis of event sequences at three different levels of abstraction, namely spatial, temporal, and events themselves. Customized views as well as standard views are combined to form a coordinated multiple views system. In addition to trajectories and events, we include on-the-fly derived data in the analysis. We evaluate our integrated solution using the IEEE VAST 2015 Challenge data set. A successful detection and characterization of malicious activity indicate the usefulness and efficiency of the presented approach.     

A study of co-occurrence based local features for camera model identification

Camera model identification has great relevance for many forensic applications, and is receiving growing attention in the literature. Virtually all techniques rely on the traces left in the image by the long sequence of in-camera processes which are specific of each model. They differ in the prior assumptions, if any, and in how such evidence is gathered in expressive features. In this work we study a class of blind features, based on the analysis of the image residuals of all color bands. They are extracted locally, based on co-occurrence matrices of selected neighbors, and then used to train a classifier. A number of experiments are carried out on the well-known Dresden Image Database. Besides the full-knowledge case, where all models of interest are known in advance, other scenarios with more limited knowledge and partially corrupted images are also investigated. Experimental results show these features to provide a state-of-the-art performance.     

Automatic selection of arterial input function on dynamic contrast-enhanced MR images

Dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) data analysis requires the knowledge of the arterial input function (AIF) to quantify the cerebral blood flow (CBF), volume (CBV) and the mean transit time (MTT). AIF can be obtained either manually or using automatic algorithms. We present a method to derive the AIF on the middle cerebral artery (MCA). The algorithm draws a region of interest (ROI) where the MCA is located. Then, it uses a recursive cluster analysis on the ROI to select the arterial voxels. The algorithm had been compared on simulated data to literature state of art automatic algorithms and on clinical data to the manual procedure. On in silico data, our method allows to reconstruct the true AIF and it is less affected by partial volume effect bias than the other methods. In clinical data, automatic AIF provides CBF and MTT maps with a greater contrast level compared to manual AIF ones. Therefore, AIF obtained with the proposed method improves the estimate reliability and provides a quantitatively reliable physiological picture.     

Template-based quadrilateral mesh generation from imaging data

This paper describes a novel template-based meshing approach for generating good quality quadrilateral meshes from 2D digital images. This approach builds upon an existing image-based mesh generation technique called Imeshp, which enables us to create a segmented triangle mesh from an image without the need for an image segmentation step. Our approach generates a quadrilateral mesh using an indirect scheme, which converts the segmented triangle mesh created by the initial steps of the Imesh technique into a quadrilateral one. The triangle-to-quadrilateral conversion makes use of template meshes of triangles. To ensure good element quality, the conversion step is followed by a smoothing step, which is based on a new optimization-based procedure. We show several examples of meshes generated by our approach, and present a thorough experimental evaluation of the quality of the meshes given as examples.     

Molecular basis of agonicity and antagonicity in the androgen receptor studied by molecular dynamics simulations

Treatment of prostate cancer patients with antiandrogens is initially successful, though the therapy often becomes refractory over the time. This mechanism is not fully understood, but the presence of androgen receptor (AR) mutant forms which are activated by antiandrogens and other endogenous ligands, and overexpression of the receptor have been suggested. In an attempt to explain the molecular basis for agonicity and antagonicity in the androgen receptor, and the changes on biological activity of subtle modifications at the ligand and receptor (mutations) level, molecular dynamics simulations were performed on the androgen receptor wild type (WT), and T877A and W741 mutant forms, complexed with several non-steroidal androgens. The stabilizing role of residues from helices 3, 5, 11 and 12 was observed in non-steroidal androgens R-3, S-1, and R-bicalutamide and hydroxyflutamide in resistant mutations. In the AR WT antiandrogen R-bicalutamide complex, destabilization of M895 by both W741 and the sulfonyl linkage of the ligand may be responsible for reported antagonism. Changes in the ligand or mutations alleviating this effect were observed to stabilize the receptor in the active conformation, thus developing resistance to R-bicalutamide. The results presented provide a plausible explanation for the molecular basis of agonicity and antagonicity in the androgen receptor, and complement previous studies using static crystal structures, incorporating for the first time protein dynamics into the analysis. Thus, our results provide a valuable framework for the structure-based design of improved antiandrogens.     

Shadow attacks on users’ anonymity in pervasive computing environments

Privacy preserving technologies are likely to become an essential component of adaptive services in pervasive and mobile computing. Although privacy issues have been studied for a long time in computer science as well as in other fields, most studies are focused on the release of data from large repositories. Mobile and pervasive computing pose new challenges, requiring specific formal models for attacks and new privacy preserving techniques. This paper considers a specific pervasive computing scenario, and shows that the application of state-of-the-art techniques for the anonymization of service requests is insufficient to protect the privacy of users. A specific class of attacks, called shadow attacks, is formally defined and a defense technique is proposed. This defense is formally proved to be correct, and its effectiveness is validated by extensive experiments in a simulated environment.     

A coarse-grain reconfigurable architecture for multimedia applications featuring subword computation capabilities

This paper presents the design and the implementation of a coarse-grain reconfigurable machine used as an accelerator for a programmable RISC core, to speed up the execution of computationally demanding tasks like multimedia applications. We created a VHDL model of the proposed architecture and implemented it on a FPGA board for prototyping purposes; then we mapped on our architecture some DSP and image processing algorithms as a benchmark. In particular, we provided the proposed architecture with subword computation capabilities, which turns out to be extremely effective especially when dealing with image processing algorithms, achieving significant benefits in terms of speed and efficiency in resource usage. To create the configuration bitstream (configware) we created a tool based on a graphical user interface (GUI) which provides a first step towards the automation of the programming flow of our design: the tool is meant to ease the life of the programmer, relieving him from the burden of calculating the configuration bits by hand. Synthesis results indicate that the area occupation and the operating frequency of our design are reasonable also when compared to other similar design. In addition to this, the amount of clock cycles taken by our machine to perform a given algorithm is orders of magnitude smaller than the one required by a corresponding software implementation on a RISC microprocessor.     

Atomic force microscopy of protein films and crystals

A customized atomic force microscopy (AFM) instrument optimized for imaging protein crystals in solution is described. The device was tested on crystals and Langmuir-Blodgett (LB) films of two proteins with quite different molecular weights. This approach enables the periodicity and morphology of crystals to be studied in their mother liquid, thereby preserving the native periodic protein crystal structure, which is typically destroyed by drying. Moreover, the instrument appears to distinguish protein crystals from salt crystals, which under the optical microscope are frequently quite similar, the difference between them often being revealed only during x-ray analysis. AFM estimates of the packing, order, and morphology of the given single proteins appear quite similar in the LB thin film and in the crystals, which means that routine crystal measurements can be performed at high resolution. The AFM consists of a custom-built measuring head and a homemade flexible SPM controller which can drive the head for contact, noncontact and spectroscopy modes, thus providing the user with a high degree of customization for crystal measurement.