Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data

Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has become possible to crowdsource the collection of speed information to road users (e.g. through mobile applications or dedicated in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced speed data also brings very important challenges, such as the highly variable measurement noise in the data due to a variety of driving behaviors and sample sizes. When not properly accounted for, this noise can severely compromise any application that relies on accurate traffic data. In this article, we propose the use of heteroscedastic Gaussian processes (HGP) to model the time-varying uncertainty in large-scale crowdsourced traffic data. Furthermore, we develop a HGP conditioned on sample size and traffic regime (SRC-HGP), which makes use of sample size information (probe vehicles per minute) as well as previous observed speeds, in order to more accurately model the uncertainty in observed speeds. Using 6 months of crowdsourced traffic data from Copenhagen, we empirically show that the proposed heteroscedastic models produce significantly better predictive distributions when compared to current state-of-the-art methods for both speed imputation and short-term forecasting tasks.Comment: 22 pages, Transportation Research Part C: Emerging Technologies (Elsevier

Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation

Traffic speed data imputation is a fundamental challenge for data-driven transport analysis. In recent years, with the ubiquity of GPS-enabled devices and the widespread use of crowdsourcing alternatives for the collection of traffic data, transportation professionals increasingly look to such user-generated data for many analysis, planning, and decision support applications. However, due to the mechanics of the data collection process, crowdsourced traffic data such as probe-vehicle data is highly prone to missing observations, making accurate imputation crucial for the success of any application that makes use of that type of data. In this article, we propose the use of multi-output Gaussian processes (GPs) to model the complex spatial and temporal patterns in crowdsourced traffic data. While the Bayesian nonparametric formalism of GPs allows us to model observation uncertainty, the multi-output extension based on convolution processes effectively enables us to capture complex spatial dependencies between nearby road segments. Using 6 months of crowdsourced traffic speed data or "probe vehicle data" for several locations in Copenhagen, the proposed approach is empirically shown to significantly outperform popular state-of-the-art imputation methods.Comment: 10 pages, IEEE Transactions on Intelligent Transportation Systems, 201

Competências transversais dos recém-diplomados do ensino superior no mercado global

O mercado de trabalho está a mudar rapidamente e de muitas formas, nomeadamente, a revolução das tecnologias de informação e comunicação, a globalização, a flexibilização e as alterações da estrutura do trabalho, que conduzem a uma crescente imprevisibilidade e à necessidade de lidar com este contexto de incerteza. Estas mudanças, inevitavelmente, têm implicações no conjunto de competências necessárias para que os trabalhadores sejam capazes de funcionar adequadamente num mercado global cada vez mais competitivo e em constante mutação. Desta forma, nos dias de hoje, o mercado de trabalho requer mais do que conhecimentos teóricos e técnicos, reclama por um conjunto de competências transversais que servirão de suporte e ligação entre o conhecimento técnico e a prática profissional. É neste cenário que emergem as competências transversais que cada vez mais assumem uma importância maior. O objetivo deste estudo consiste em identificar as competências transversais requeridas aos recém-diplomados do ensino superior no mercado global. Inicialmente, analisa-se o conceito de competência transversal, apresentando algumas definições e assinalando as suas características comuns, destacando ainda a variedade de designações utilizadas para as nomear. No âmbito do estudo foi realizada revisão da literatura nacional e internacional sobre as competências transversais nos diplomados, quer na perspetiva dos próprios diplomados, quer dos empregadores. Através da análise de conteúdo, foram identificadas as competências transversais mais valorizadas por continente e, a partir da análise das mesmas, definiram-se as competências transversais exigidas no mercado global. Finalmente são discutidas as implicações teóricas e empíricas das conclusões deste trabalho

Extractability and mobility of mercury from agricultural soils surrounding industrial and mining contaminated areas

This study focussed on a comparison of the extractability of mercury in soils with two different contamination sources (a chlor-alkali plant and mining activities) and on the evaluation of the influence of specific soil properties on the behaviour of the contaminant. The method applied here did not target the identification of individual species, but instead provided information concerning the mobility of mercury species in soil. Mercury fractions were classified as mobile, semi-mobile and non-mobile. The fractionation study revealed that in all samples mercury was mainly present in the semi-mobile phase (between 63 and 97%). The highest mercury mobility (2.7 mg kg-1) was found in soils from the industrial area. Mining soils exhibited higher percentage of non-mobile mercury, up to 35%, due to their elevated sulfur content. Results of factor analysis indicate that the presence of mercury in the mobile phase could be related to manganese and aluminum soil contents. A positive relation between mercury in the semi-mobile fraction and the aluminium content was also observed. By contrary, organic matter and sulfur contents contributed to mercury retention in the soil matrix reducing the mobility of the metal. Despite known limitations of sequential extraction procedures, the methodology applied in this study for the fractionation of mercury in contaminated soil samples provided relevant information on mercury's relative mobility

Modeling Censored Mobility Demand through Quantile Regression Neural Networks

Shared mobility services require accurate demand models for effective service planning. On one hand, modeling the full probability distribution of demand is advantageous, because the full uncertainty structure preserves valuable information for decision making. On the other hand, demand is often observed through usage of the service itself, so that the observations are censored, as they are inherently limited by available supply. Since the 1980s, various works on Censored Quantile Regression models have shown them to perform well under such conditions, and in the last two decades, several works have proposed to implement them flexibly through Neural Networks (CQRNN). However, apparently no works have yet applied CQRNN in the Transport domain. We address this gap by applying CQRNN to datasets from two shared mobility providers in the Copenhagen metropolitan area in Denmark, as well as common synthetic baseline datasets. The results show that CQRNN can estimate the intended distributions better than both censorship-unaware models and parametric censored models.Comment: 13 pages, 7 figures, 4 table

Evaluation of lead i ECG features discriminant power for cardiac diseases identification

This work proposes to analyze the capacity of several ECG features ofLead I to discriminate 28 pairs of study groups, combining 7 patholog-ical groups and 1 control group, presented in the PTB Diagnostic ECGDatabase. For each pair, it was achieved an accuracy between 66.7% and96.9% using feature selection algorithm and SVM classifiers.info:eu-repo/semantics/publishedVersio

Black-bounce solution in kk-essence theories

In the present work, we construct black-bounce configurations in the context of $k$-essence theory. The solutions have a regular metric function at the origin. The area metric function is linked to the black-bounce area initially considered by Simpson-Visser, $\Sigma^2=x^2+a^2$. Subsequently, the expressions for the scalar field and scalar potential corresponding to the found solutions are determined, exhibiting phantom behavior everywhere due to violation of Null Energy Condition $(NEC^\phi)$. The Kretschmann scalar is regular throughout spacetime, and the geodesics are complete. The energy conditions are analyzed, verifying that the null $(NEC^\phi_1)$ and dominant energy conditions $(DEC^\phi_1)$ are violated inside and outside the event horizon. Finally, the extrinsic curvature method was applied to determine the sign of the mass on the junction surface.Comment: 13 pages, 15 figure

Photon-number-resolving segmented avalanche-photodiode detectors

We investigate the feasibility and performance of photon-number-resolved photodetection employing avalanche photodiodes (APDs) with low dark counts. The main idea is to split n photons over m modes such that every mode has no more than one photon, which is detected alongside propagation by an APD. We characterize performance by evaluating the purities of positive-operator-valued measurements (POVMs), in terms of APD number and photon loss.Comment: 5 pages, 7 figures, submitted for publicatio

Goat's milk allergy

BACKGROUND: Goat's milk (GM) allergy not associated with allergy to cow's milk (CM) is a rare disorder. Caseins have been implicated has the major allergens eliciting symptoms. METHODS: We report the case of a 27 years-old female patient that experienced two episodes of urticaria related to ingestion of goat's cheese (GC). She tolerated CM, dairy products and sheep cheese. Skin prick tests were performed with GM, CM, bovine casein and alpha -lactalbumin and fresh milk and GC. Serum specific IgE to GM, CM and its fractions, and GM and CM immunobloting assays with inhibition were also evaluated. RESULTS: Skin tests were positive to GM and GC and negative to CM. GM immunoblot showed an IgE-binding 14 kDa band that was totally inhibited after serum pre-incubation with GM. CONCLUSIONS: Allergens other than casein can be involved in allergy to GM. Even small quantities of protein can elicit symptoms