Abstract
The problem of joint estimation of parameters and state of continuous time systems using discrete time observations is addressed. The plant parameters are assumed to be modeled by a Wiener process. The a priori probability density function (pdf) of an extended state that comprises the plant state variables and the parameters is propagated in time using an approximate solution of the Fokker-Planck equation that relies on Trotter's formula for semigroup decomposition. The a posteriori (i. e., given the observations) pdf is then computed at the observation instants using Bayes law.

Abstract
Social media are becoming an increasingly important source of information about the public mood regarding issues such as elections, Brexit, stock market, etc. In this paper we focus on sentiment classification of Twitter data. Construction of sentiment classifiers is a standard text mining task, but here we address the question of how to properly evaluate them as there is no settled way to do so. Sentiment classes are ordered and unbalanced, and Twitter produces a stream of time-ordered data. The problem we address concerns the procedures used to obtain reliable estimates of performance measures, and whether the temporal ordering of the training and test data matters. We collected a large set of 1.5 million tweets in 13 European languages. We created 138 sentiment models and out-of-sample datasets, which are used as a gold standard for evaluations. The corresponding 138 in-sample data-sets are used to empirically compare six different estimation procedures: three variants of cross-validation, and three variants of sequential validation (where test set always follows the training set). We find no significant difference between the best cross-validation and sequential validation. However, we observe that all cross-validation variants tend to overestimate the performance, while the sequential methods tend to underestimate it. Standard cross-validation with random selection of examples is significantly worse than the blocked cross-validation, and should not be used to evaluate classifiers in time-ordered data scenarios.

Abstract
Floating car data (FCD) denotes the type of data (location, speed, and destination) produced and broadcasted periodically by running vehicles. Increasingly, intelligent transportation systems take advantage of such data for prediction purposes as input to road and transit control and to discover useful mobility patterns with applications to transport service design and planning, to name just a few applications. However, there are considerable quality issues that affect the usefulness and efficacy of FCD in these many applications. In this paper, we propose a methodology to compute such quality indicators automatically for large FCD sets. It leverages on a set of statistical indicators (named Yuki-san) covering multiple dimensions of FCD such as spatio-temporal coverage, accuracy, and reliability. As such, the Yuki-san indicators provide a quick and intuitive means to assess the potential "value" and "veracity" characteristics of the data. Experimental results with two mobility-related data mining and supervised learning tasks on the basis of two real-world FCD sources show that the Yuki-san indicators are indeed consistent with how well the applications perform using the data. With a wider variety of FCD (e.g., from navigation systems and CAN buses) becoming available, further research and validation into the dimensions covered and the efficacy of the Yuki-San indicators is needed.

Abstract
Autoregressive Moving Average (ARMA) time series model fitting is a procedure often based on aggregate data, where parameter estimation plays a key role. Therefore, we analyze the effect of temporal aggregation on the accuracy of parameter estimation of mixed ARMA and MA models. We derive the expressions required to compute the parameter values of the aggregate models as functions of the basic model parameters in order to compare their estimation accuracy. To this end, a simulation experiment shows that aggregation causes a severe accuracy loss that increases with the order of aggregation, leading to poor accuracy.

Abstract
Dysfunction of arteriovenous access for hemodialysis is a challenge for the vascular surgeon. Some patients have complex vascular access with problems that are difficult to solve. Careful analysis of the vascular network with ultrasound and dissection of the veins during surgery can help to identify the best option for each access. We introduce and discuss the case of creation of a radio-cephalic fistula with outflow into the brachial vein in 64-year-old hemodialysis male patient. This technique enables extending fistula patency, arterializing the brachial vein, and improves cost efficiency.

Abstract
Vascular access dysfunction is a serious problem in dialysis units. Some patients have complex dysfunctions that are difficult to resolve. In this article, we report the case a of two patients with radiocephalic arteriovenous fistulae (RC-AVF) who had stenosis/occlusion of the forearm median vein and where we used the basilic vein of the forearm as a solution. We reviewed the use of this surgical solution in RC-AVF. Two male patients on hemodialysis exhibited stenosis/occlusion of the forearm median vein. The forearm basilic vein was isolated and rotated toward the forearm median vein in order to solve RC-AVF problems. One patient had fistula thrombosis 5 months after the procedure, while for the other patient, the fistula continues to work without problems. Literature describes only a few cases using the forearm basilic vein or the brachial vein for fistula recovery. This procedure increased the patency of fistulas. This approach has been proven to be a good solution for solving outflow problems using the superficial or deep veins, increasing fistula patency and avoiding the need to place a central venous catheter and all the related complications.