Finszter Géza: Rendészettan. Dialóg Campus Kiadó, 2018

The professional literature of law has been enriched with a stopgap new book: in the series Studia Universitatis Communa issued the publisher Dialóg Campus Kiadó the new work of Géza Finszter. The corpulent book of nearby 500 pages is even as a spectacle admirable. Its author designed it as a textbook but as he mentioned it can be handled as a handbook for law enforcement right, too. Although it includes relatively many legal rule texts that could harm the long-standing character of a handbook. The book consists of an introductory part and of ten chapters with detailed explications.A jogi szakirodalom hézagpótlónak nevezhető újszak könyvvel gazdagodott: a Dialóg Campus Kiadó a Studia Universitatis Communa sorozatban megjelentette Finszter Géza új művét. A testes – közel félezer oldal terjedelmű – kötet látványként is tiszteletet ébreszt. Műfaját tekintve a szerző tankönyvnek szánta ugyan, de maga is rámutat: a rendészeti jog kézi könyvének is megfelelne, bár viszonylag sok jogszabály szöveg található benne, ami egy kézi könyv időtállóságának kétségtelenül árt. A mű egy bevezető részből és az érdemi fejtegetéseket tartalmazó tíz fejezetből áll

Optimizing direct response in Internet display advertising

Internet display advertising has grown into a multi-billion dollar a year global industry and direct response campaigns account for about three-quarters of all Internet display advertising. In such campaigns, advertisers reach out to a target audience via some form of a visual advertisement (hereinafter also called "ad") to maximize short-term sales revenue. In this study, we formulate an advertiser's revenue maximization problem in direct response Internet display advertisement campaigns as a mixed integer program via piecewise linear approximation of the revenue function. A novelty of our approach is that ad location and content issues are explicitly incorporated in the optimization model. Computational experiments on a large-scale actual campaign indicate that adopting the optimal media schedule can significantly increase advertising revenues without any budget changes, and reasonably sized instances of the problem can be solved within short execution times

Penalty-based algorithms for the stochastic obstacle scene problem

We consider the stochastic obstacle scene problem wherein an agent needs to traverse a spatial arrangement of possible obstacles, and the status of the obstacles may be disambiguated en route at a cost. The goal is to find an algorithm that decides what and where to disambiguate en route so that the expected length of the traversal is minimized. We present a polynomial-time method for a graph-theoretical version of the problem when the associated graph is restricted to parallel avenues with fixed policies within the avenues. We show how previously proposed algorithms for the continuous space version can be adapted to a discrete setting. We propose a generalized framework encompassing these algorithms that uses penalty functions to guide the navigation in real time. Within this framework, we introduce a new algorithm that provides near-optimal results within very short execution times. Our algorithms are illustrated via computational experiments involving synthetic data as well as an actual naval minefield data set

Odontogenic tumours in Istanbul: 527 cases

We retrieved and analysed the records of 527 odontogenic tumours from a total of 62,565 cases in the department of tumour pathology in the Institute of Oncology, University of Istanbul, from 1971 to 2003. Of these 527 tumours, 521 were benign and 6 were malignant. The most common lesions were ameloblastomas (n = 133) followed by odontomas (n = 109), odontogenic myxomas (n = 83) and others

The use of spatial graphs for optimal obstacle placement: A study on impact of the clutter spatial distribution

Consider a situation where the goal is to place true obstacles in an environment cluttered with false obstacles in order to maximize the total traversal length of a navigating agent (NAVA). Prior to the traversal, NAVA is given location information and probabilistic estimates of each disk-shaped regions being a true obstacle. The NAVA can disambiguate a disk's status only when situated on its boundary. There exists an obstacle placing agent (OPA) that locates obstacles prior to NAVA's traversal. The goal of OPA is to place true obstacles in between the clutter in such a way that NAVA's traversal length is maximized in a game-theoretic sense. We call this the optimal obstacle placement with disambiguations problem. A particular variant we consider is the one where OPA knows the clutter spatial distribution type, but not the exact locations of clutter disks. In this study, we show how such a continuous obstacle field can be fruitfully discretized using spatial graphs. We discuss the impact of different clutter spatial distribution types on the optimal obstacle placement scheme including homogeneous and inhomogeneous Poisson, Matern, Thomas, Strauss and hardcore spatial distributions. Our methodology is based on utilization of repeated measures analysis of variance for analysis of traversal lengths for various obstacle placing schemes for identification of the optimal combination

An AO* based exact algorithm for the Canadian traveler problem

The Canadian traveler problem (CTP) is a simple, yet challenging, stochastic optimization problem wherein an agent is given a graph where some edges are blocked with certain probabilities and the status of these edges can be disambiguated dynamically upon reaching an incident vertex. The goal is to devise a traversal policy that results in the shortest expected walk length between a given starting vertex and a termination vertex. CTP has been shown to be intractable in many broad settings. In this paper, we introduce an optimal algorithm for the problem based on a Markov decision process formulation, which is a new improvement on AO∗ search that takes advantage of the special problem structure in CTP. We call our algorithm CAO∗, which stands for AO∗ with caching. CAO∗ uses a caching mechanism to avoid re-expansion of previously visited states and makes use of admissible upper bounds at a node level for dynamic state-space pruning. CAO∗ is not polynomial time, but it can dramatically shorten the execution time needed to find an exact solution for moderately sized instances. We present computational experiments on a realistic variant of the problem involving an actual maritime minefield data set

A quantitative analysis of turkey's 2011 elections

The changes in Turkey's political landscape over the past decade have been quite dramatic. In this study, we present a quantitative analysis of the 2011 national elections based on clustering techniques and we compare our results with those of the previous elections in 1999, 2002, and 2009. Our results suggest, once again, that Turkish citizens turn out to vote consistently since the1950s. We also investigate significant changes in voting trends of different regions and provinces. We conclude with a future-based qualitative outlook to indicate what the results could be if certain electoral changes are made, such as the law for political parties, a different national threshold for parties to be represented and elected to Parliament, and an eventual new constitution

The reset disambiguation policy for navigating stochastic obstacle fields

The problem we consider is a stochastic shortest path problem in the presence of a dynamic learning capability. Specifically, a spatial arrangement of possible obstacles needs to be traversed as swiftly as possible, and the status of the obstacles may be disambiguated (at a cost) en route. No efficiently computable optimal policy is known, and many similar problems have been proven intractable. In this article, we adapt a policy which is optimal for a related problem and prove that this policy is indeed also optimal for a restricted class of instances of our problem. Otherwise, this policy is generally suboptimal but, nonetheless, it is both effective and efficiently computable. Examples/simulations are provided in a mine countermeasures application. Of central use is the Tangent Arc Graph, a polynomially sized topological superimposition of exponentially many visibility graphs