Optical–microphysical properties of smoke haze from Siberian forest fires in summer 2012

A comprehensive experiment on the study of dynamics of optical and microphysical characteristics of submicron aerosols in the near-ground air layer was carried out in June–August of 2012 under conditions of extremely strong smoke haze originating from vast forest fires in Siberia. Variations of the spectral angular scattering coefficients and the degree of linear polarization in the visible wavelength range are studied, as well as the mass concentrations of dry fractions of aerosol and black carbon, relative content of black carbon (BC fraction), single scattering albedo, particle size distribution function, complex refractive index of the particulate matter, and parameter of condensation activity of aerosol. Peculiarities of differences between optical and microphysical characteristics of near-ground aerosol in smoke haze and under smokeless conditions are revealed. This study is important for validation of results of remote sensing of the composition of smoke haze and background atmosphere obtained from satellite data and sun photometric measurements at the Aerosol Robotic Network (AERONET).     

Feature selection for k-means clustering stability: theoretical analysis and an algorithm

Stability of a learning algorithm with respect to small input perturbations is an important property, as it implies that the derived models are robust with respect to the presence of noisy features and/or data sample fluctuations. The qualitative nature of the stability property enhardens the development of practical, stability optimizing, data mining algorithms as several issues naturally arise, such as: how “much” stability is enough, or how can stability be effectively associated with intrinsic data properties. In the context of this work we take into account these issues and explore the effect of stability maximization in the continuous (PCA-based) k-means clustering problem. Our analysis is based on both mathematical optimization and statistical arguments that complement each other and allow for the solid interpretation of the algorithm’s stability properties. Interestingly, we derive that stability maximization naturally introduces a tradeoff between cluster separation and variance, leading to the selection of features that have a high cluster separation index that is not artificially inflated by the features variance. The proposed algorithmic setup is based on a Sparse PCA approach, that selects the features that maximize stability in a greedy fashion. In our study, we also analyze several properties of Sparse PCA relevant to stability that promote Sparse PCA as a viable feature selection mechanism for clustering. The practical relevance of the proposed method is demonstrated in the context of cancer research, where we consider the problem of detecting potential tumor biomarkers using microarray gene expression data. The application of our method to a leukemia dataset shows that the tradeoff between cluster separation and variance leads to the selection of features corresponding to important biomarker genes. Some of them have relative low variance and are not detected without the direct optimization of stability in Sparse PCA based k-means. Apart from the qualitative evaluation, we have also verified our approach as a feature selection method for $k$ -means clustering using four cancer research datasets. The quantitative empirical results illustrate the practical utility of our framework as a feature selection mechanism for clustering.     

Role of cognitive and functional performance in the interactions between elderly people with cognitive decline and an avatar on TV

The complexity of new information technologies (IT) may limit the access of elderly people to the information society, exacerbating what is known as “the digital divide,” as they appear to be too challenging for elderly citizens regardless of the integrity of their cognitive status. This study is an attempt to clarify how some cognitive functions (such as attention or verbal memory) may determine the interaction of cognitively impaired elderly people with technology. Twenty participants ranging from mild cognitive impairment to moderate Alzheimer’s disease were assessed by means of a neuropsychological and functional battery and were asked to follow simple commands from an avatar appearing on a TV by means of a remote control, such as asking the participant to confirm their presence or to respond Yes/No to a proposal to see a TV program. The number of correct answers and command repetitions required for the user to respond were registered. The results show that participants with a better cognitive and functional state in specific tests show a significantly better performance in the TV task. The derived conclusion is that neuropsychological assessment may be used as a useful complementary tool for assistive technology developers in the adaptation of IT to the elderly with different cognitive and functional profiles. Further studies with larger samples are required to determine to what extent cognitive functions can actually predict older users’ interaction with technology.     

Robustness of ISS systems to inputs with limited moving average: Application to spacecraft formations

We provide a theoretical framework that fits realistic challenges related to spacecraft formation with disturbances. We show that the input‐to‐state stability of such systems guarantees some robustness with respect to a class of signals with bounded average‐energy, which encompasses the typical disturbances acting on spacecraft formations. Solutions are shown to converge to the desired formation, up to an offset, which is somewhat proportional to the considered moving average of disturbances. In the presence of fast peaking perturbations, the approach provides a tighter evaluation of the disturbances' influence, which allows for the use of more parsimonious control gains. Copyright © 2015 John Wiley & Sons, Ltd.     

2-Transitivity is Insufficient for Local Testability

A basic goal in property testing is to identify a minimal set of features that make a property testable. For the case when the property to be tested is membership in a binary linear error-correcting code, Alon et al. (Trans Inf Theory, 51(11):4032–4039, 2005) had conjectured that the presence of a single low-weight codeword in the dual, and “2-transitivity” of the code (i.e., the code being invariant under a 2-transitive group of permutations on the coordinates of the code) suffice to get local testability. We refute this conjecture by giving a family of error-correcting codes where the coordinates of the codewords form a large field of characteristic two, and the code is invariant under affine transformations of the domain. This class of properties was introduced by Kaufman & Sudan (STOC, 2008) as a setting where many results in algebraic property testing generalize. Our result shows a complementary virtue: This family also can be useful in producing counterexamples to natural conjectures.     

NetCluster: A clustering-based framework to analyze internet passive measurements data

Internet measured data collected via passive measurement are analyzed to obtain localization information on nodes by clustering (i.e., grouping together) nodes that exhibit similar network path properties. Since traditional clustering algorithms fail to correctly identify clusters of homogeneous nodes, we propose the NetCluster novel framework, suited to analyze Internet measurement datasets. We show that the proposed framework correctly analyzes synthetically generated traces. Finally, we apply it to real traces collected at the access link of Politecnico di Torino campus LAN and discuss the network characteristics as seen at the vantage point.     

GraftFast Surface Engineering to Improve MOF Nanoparticles Furtiveness

Controlling the outer surface of nanometric metal–organic frameworks (nanoMOFs) and further understanding the in vivo effect of the coated material are crucial for the convenient biomedical applications of MOFs. However, in most studies, the surface modification protocol is often associated with significant toxicity and/or lack of selectivity. As an alternative, how the highly selective and general grafting GraftFast method leads, through a green and simple process, to the successful attachment of multifunctional biopolymers (polyethylene glycol (PEG) and hyaluronic acid) on the external surface of nanoMOFs is reported. In particular, effectively PEGylated iron trimesate MIL‐100(Fe) nanoparticles (NPs) exhibit suitable grafting stability and superior chemical and colloidal stability in different biofluids, while conserving full porosity and allowing the adsorption of bioactive molecules (cosmetic and antitumor agents). Furthermore, the nature of the MOF–PEG interaction is deeply investigated using high‐resolution soft X‐ray spectroscopy. Finally, a cell penetration study using the radio‐labeled antitumor agent gemcitabine monophosphate (³H‐GMP)‐loaded MIL‐100(Fe)@PEG NPs shows reduced macrophage phagocytosis, confirming a significant in vitro PEG furtiveness.     

Support environment for active rule design

The lack of tools for rule generation, analysis, and run-time monitoring appears one of the main obstacles to the widespreading of active database applications. This paper describes a complete tool environment for assisting the design of active rules applications; the tools were developed at Politecnico di Milano in the context of the IDEA Project, a 4-years Esprit project sponsored by the European Commission which was launched in June 1992. We describe tools for active rule generation, analysis, debugging, and browsing; rules are defined in Chimera, a conceptual design model and language for the specification of active rules applications. We also introduce a tool for mapping from Chimera into Oracle, a relational product supporting triggers.Most of the tools described in this paper are fully implemented and currently in operation (beta-testing) within the companies participating to the IDEA Project, with the exception of two of them (called Argonaut-V and Pandora), which will be completed by the end of 1996.Research presented in this paper is supported by Esprit project P6333 IDEA, and by ENEL contract VDS 1/94: Integrity Constraint Management     

Real-time hierarchical stereo Visual SLAM in large-scale environments

In this paper we present a new real-time hierarchical (topological/metric) Visual SLAM system focusing on the localization of a vehicle in large-scale outdoor urban environments. It is exclusively based on the visual information provided by a cheap wide-angle stereo camera. Our approach divides the whole map into local sub-maps identified by the so-called fingerprints (vehicle poses). At the sub-map level (low level SLAM), 3D sequential mapping of natural landmarks and the robot location/orientation are obtained using a top-down Bayesian method to model the dynamic behavior. A higher topological level (high level SLAM) based on fingerprints has been added to reduce the global accumulated drift, keeping real-time constraints. Using this hierarchical strategy, we keep the local consistency of the metric sub-maps, by mean of the EKF, and global consistency by using the topological map and the MultiLevel Relaxation (MLR) algorithm. Some experimental results for different large-scale outdoor environments are presented, showing an almost constant processing time.