The Topology of Foliations Formed by the Generic K-Orbits of a Subclass of the Indecomposable MD5-Groups

The present paper is a continuation of [13], [14] of the authors. Specifically, the paper considers the MD5-foliations associated to connected and simply connected MD5-groups such that their Lie algebras have 4-dimensional commutative derived ideal. In the paper, we give the topological classification of all considered MD5-foliations. A description of these foliations by certain fibrations or suitable actions of $\mathbb{R}^{2}$ and the Connes' C*-algebras of the foliations which come from fibrations are also given in the paper.Comment: 20 pages, no figur

Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Travel Time Data

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link

NeuCEPT: Locally Discover Neural Networks' Mechanism via Critical Neurons Identification with Precision Guarantee

Despite recent studies on understanding deep neural networks (DNNs), there exists numerous questions on how DNNs generate their predictions. Especially, given similar predictions on different input samples, are the underlying mechanisms generating those predictions the same? In this work, we propose NeuCEPT, a method to locally discover critical neurons that play a major role in the model's predictions and identify model's mechanisms in generating those predictions. We first formulate a critical neurons identification problem as maximizing a sequence of mutual-information objectives and provide a theoretical framework to efficiently solve for critical neurons while keeping the precision under control. NeuCEPT next heuristically learns different model's mechanisms in an unsupervised manner. Our experimental results show that neurons identified by NeuCEPT not only have strong influence on the model's predictions but also hold meaningful information about model's mechanisms.Comment: 6 main page

Spin dependent photoelectron tunnelling from GaAs into magnetic Cobalt

The spin dependence of the photoelectron tunnel current from free standing GaAs films into out-of- plane magnetized Cobalt films is demonstrated. The measured spin asymmetry (A) resulting from a change in light helicity, reaches +/- 6% around zero applied tunnel bias and drops to +/- 2% at a bias of -1.6 V applied to the GaAs. This decrease is a result of the drop in the photoelectron spin polarization that results from a reduction in the GaAs surface recombination velocity. The sign of A changes with that of the Cobalt magnetization direction. In contrast, on a (nonmagnetic) Gold film A ~ 0%

Neighbouring Link Travel Time Inference Method Using Artificial Neural Network

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper presents a method for modelling relationship between road segments using feed forward back-propagation neural networks. Unlike most previous papers that focus on travel time estimation of a road based on its traffic information, we proposed the Neighbouring Link Inference Method (NLIM) that can infer travel time of a road segment (link) from travel time its neighbouring segments. It is valuable for links which do not have recent traffic information. The proposed method learns the relationship between travel time of a link and traffic parameters of its nearby links based on sparse historical travel time data. A travel time data outlier detection based on Gaussian mixture model is also proposed in order to reduce the noise of data before they are applied to build NLIM. Results show that the proposed method is capable of estimating the travel time on all traffic link categories. 75% of models can produce travel time data with mean absolute percentage error less than 22%. The proposed method performs better on major than minor links. Performance of the proposed method always dominates performance of traditional methods such as statistic-based and linear least square estimate methods

Cloud condensation nuclei (CCN) activity of aliphatic amine secondary aerosol

Aliphatic amines can form secondary aerosol via oxidation with atmospheric radicals (e.g., hydroxyl radical and nitrate radical). The particle can contain both secondary organic aerosol (SOA) and inorganic salts. The ratio of organic to inorganic materials in the particulate phase influences aerosol hygroscopicity and cloud condensation nuclei (CCN) activity. SOA formed from trimethylamine (TMA) and butylamine (BA) reactions with hydroxyl radical (OH) is composed of organic material of low hygroscopicity (single hygroscopicity parameter, κ, ≤ 0.25). Secondary aerosol formed from the tertiary aliphatic amine (TMA) with N_2O_5 (source of nitrate radical, NO_3) contains less volatile compounds than the primary aliphatic amine (BA) aerosol. As relative humidity (RH) increases, inorganic amine salts are formed as a result of acid–base reactions. The CCN activity of the humid TMA–N_2O_5 aerosol obeys Zdanovskii, Stokes, and Robinson (ZSR) ideal mixing rules. The humid BA + N_2O_5 aerosol products were found to be very sensitive to the temperature at which the measurements were made within the streamwise continuous-flow thermal gradient CCN counter; κ ranges from 0.4 to 0.7 dependent on the instrument supersaturation (ss) settings. The variance of the measured aerosol κ values indicates that simple ZSR rules cannot be applied to the CCN results from the primary aliphatic amine system. Overall, aliphatic amine aerosol systems' κ ranges within 0.2 < κ < 0.7. This work indicates that aerosols formed via nighttime reactions with amines are likely to produce hygroscopic and volatile aerosol, whereas photochemical reactions with OH produce secondary organic aerosol of lower CCN activity. The contributions of semivolatile secondary organic and inorganic material from aliphatic amines must be considered for accurate hygroscopicity and CCN predictions from aliphatic amine systems