Enhancement of wireless positioning in outdoor suburban NLOS environment using hybrid-network-GPS systems This article will introduce a method for locating mobile stations (MSs) in outdoor suburban non-line-of-sight (NLOS) environment. The measurements used to locate the MS are taken from three base stations and a satellite. Such a setup of measurements is named the hybrid-network-GPS system. The proposed method uses constraint nonlinear optimization to minimize the NLOS error. The problem is simplified to three independent nonlinear equations of three unknowns, then it is solved to find the MS location. Numerical simulations are introduced to assess the performance of the proposed method compared with other positioning algorithms. The well-known non-line-of-sight (NLOS) problem in wireless location has gained a great attention in the last decade. An important aspect of this problem is the huge error it induces in locating wireless devices. Thus, it is of interest to develop wireless positioning algorithms that will minimize the NLOS error.. In the literature, many articles addressed the NLOS problem. These articles differed in their approaches to solve the problem. The articles in [1] [2] [3] [4] [5] [6] used nonstatistical methods to locate the mobile station (MS) using network based measurements. In [1] , the authors propose using linear-programming method to locate the MS in NLOS environments. The authors in [2] , linearize the inequality of range models corrupted with NLOS errors for wireless positioning. In [3] , the authors propose a constrained-optimization algorithm to locate the MS using the sequential quadratic programming algorithm. The authors in [4] propose a geometry-assisted location estimation algorithm utilizing the different geometric layouts between the MS and the base stations (BSs). In [5] , the authors propose a constrained optimization technique to locate the MS. The method in [6] is a constrained nonlinear optimization approach, with constraints derived from the geometry of the cell layout and range measurements. Satellite assisted techniques were also proposed in [7, 8] for wireless positioning but they did not assume NLOS environment and measurements were taken at more than one satellite.. This article uses the hybrid-network-GPS system to minimize the NLOS error when locating the MS. The proposed method is named the hybrid-network-GPS constrained algorithm (HNGPS-CA) which depends on minimizing a constraint objective function to locate the MS. The proposed objective function used in the HNGPS-CA method has the advantage of guaranteed convexity. This advantage is not guaranteed in the regularly used least square (LS) objective function which might be non-convex in some cases as will be clarified in Section 4. The environment considered in this article is the outdoor suburban NLOS environment. We will consider that there are three time of arrival (TOA) measurements available from three BSs and only one TOA measurement from the satellite.. The rest of the article is organized as follows: Section 2 presents the problem formulation. Section 3 presents the HNGPS-CA algorithm. In Section 4, some insight on the convexity of the objective function will be provided. Section 5 shows simulation results for the proposed method. Conclusions are presented in Section 6. An algorithm, named HNGPS-CA, is proposed and investigated via simulations for mitigating the effect of NLOS error in outdoor suburban NLOS environment. The HNGPS-CA algorithm locates the MS by minimizing an objective function formed from TOA measurements at three BSs and utilizes the equality constraint provided by the GPS TOA measurement. This proposed objective function of the HNGP-CA algorithm has the guaranteed convexity advantage over the regularly used LS objective function. Simulations results showed that the proposed HNGPS-CA algorithm gave better performance than other positioning algorithms.

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Geosheet: A Distributed Visualization Tool for Geometric Algorithms GeoSheet (version 1.0) is an interactive visualization tool for visualizing geometric algorithms in distributed environments. It provides features such as interactive visualization of program states for debugging, high-level graphical input/output manipulation facilities for geometric objects, reuse of existing data structures and algorithms implementation, and more importantly distributed executions on heterogeneous machines at di erent sites.To minimize development e ort of the tool we make use of existing software packages available in public domain. Speci cally we extend X g with a message-driven interface and a socket-based interprocess communication (IPC) mechanism. This extended-X g is the backbone of this version of the tool.Object-oriented programming methodology is used to construct the visualization interface. By deriving from traditional data type and algorithm libraries, our abstract GeoObject representation super-classes are easy to use, easy to construct, and highly portable. Although GeoSheet is not restricted to a particular application domain or any programming language, this release only contains geometric algorithm implementations in C++ and LEDA.We hope that the geometric algorithm designers will nd it useful when they develop their algorithms. Using conventional approaches it is di cult to understand the structures of geometric objects represented in numerical format. Perception of objects is better obtained from visualization, as graphical form is most natural to human eyes, and conveys more information than numerical values or texts. By the same token to better understand the behavior of an algorithm, be it geometric or not, we would make use of visualization. Techniques and tools for algorithm visualization are thus developed in hopes that they not only help describe the algorithm behavior after they are fully implemented, but also help algorithm developers debug the programs when they are being implemented. The tools described in this article were developed primarily for geometric algorithm designers who often deal with both geometric and graph objects, and for educators who teach algorithms to non-experts such as students and application engineers. In this release, GeoSheet supports 2-dimensional geometric algorithm visualization. It is written in C++ running under UNIX and X windows environment.. To produce graphical output, the user is usually required to have special and non-trivial domain knowledge on visualization programming, such as computer graphics and animation. Such domain knowledge cannot be quickly acquired. Although many systems are now available for algorithm visualization 23, 18, 12] , users of most systems still face problems in using the visualization tools when implementing geometric algorithms. To invoke visualization functions provided in these systems, the user oftentimes has to follow speci c instructions to place special statements within programs. These instructions are usually restricted, and tedious to follow, rendering the tools hard to use. Furthermore, additions of these special visualization commands in conventional Pascal or C programs will clutter the program ow and reduce its readability.. In view of the above shortcomings we adopt object-oriented approach to develop a tool, called GeoSheet, that can be used with minimal knowledge of the visualization programming, and in which the invocation of the visualization functions is done in a manner similar to the conventional printf and scanf commands of C.. GeoSheet is an interactive visualization tool designed to simplify geometric algorithms visualization procedures in a distributed environment. It is display device independent, although in the current release, the display is based on X g (Facility for Interactive Generation of Figures under X11) and is primarily for 2-dimensional objects. This tool is developed as part of a bigger project, GeoMAMOS (for Geometric object MAnipulation and MOnitoring System), supported by the O ce of Naval Research. 1 To visualize geometric algorithms with GeoSheet, a geometric algorithm is divided into two concurrent but coordinated subtasks. One subtask is a conventional procedural program that concentrates on the aspect of computation, and is referred to simply as program in this article. The other takes charge of detailed graphical display operations and is referred to as visualization subsystem. To visualize an algorithm, the program uses a set of objectoriented geometric classes to interact with the visualization subsystem. These classes are called visualization classes. Visualization classes give the speci cations of generic GeoSheet visualization operations and can be derived from existing non-visualization algorithm and data structure classes such as LEDA 17, 19] in a straightforward manner by following objectoriented approach. When created, the instances of visualization classes are provided with inherited operations and states for both algorithmic and visualization support. The program can use member functions of the visualization base class for graphical operations, or just invoke member functions of the algorithmic base class for ordinary algorithm execution. The visualization requests from the program are passed to the visualization subsystem through a remote procedure call (RPC) for graphical data communication and synchronization. Through RPC, the current state of visualized objects is sent, and copied to GeoSheet 2 for display. The user can then use direct manipulation operations to view and graphically input the data required for algorithm execution.. As the display is inherited from X g, GeoSheet supports a wide spectrum of graphical objects manipulation mechanism. Both the input and output data can be visualized, printed or exported for other purposes. Its direct visualization manipulation style is useful for algorithm designers for testing their ideas in the development stage, and helpful for debugging the code during the implementation stage. It is also useful for practitioners who would like to visualize how an algorithm performs on various input data after it is implemented.. Having eliminated the need to handle actual visualization operations, such as when to process interactive mouse messages or how to draw on the canvas using z-bu er or display context of X windows, the program need not maintain complex visualization speci cation and runtime structures. Therefore the programmers are free from the task of tedious graphical manipulations and the time needed for algorithm implementation can be signi cantly reduced. Furthermore, the message invocation mechanism allows transfer of data and control messages between processes in disjoint memory space (or name space) and is therefore capable of supporting computation and visualization activities among machines in a computer network. Since all process communication and synchronization can be done via message invocation, the visualization subsystem and the program are not necessarily restricted to be in the same program name space and can therefore be implemented with di erent programming languages, or on heterogeneous machine architectures. The program only needs to issue invocation messages and need not concern about how messages are translated into actual input/output operations on graphical display devices. By this mechanism GeoSheet achieves its independence of the display device.. Another major advantage is the use of object-oriented visualization classes which can improve reusability. We derive GeoObject classes from reliable existing geometric classes by adding a set of RPC style GeoSheet invocation operations. The usage of original geometric classes will be the same and all the inherited, available data structures and algorithmic operations can be reused. Therefore, to reuse existing programs for visualization, the user only needs to modify geometric class declarations and replace the original Input/Output statements with graphical Input/Output operations. The remaining parts of the programs, including program control ow and geometric member function invocations, will remain the same as the non-visualized version. An example illustrating this point will be given later. Compared to window based visualization programming scheme, this approach can reduce program development e ort for most algorithm developers. For geometric class developers, the implementation of graphical Input/Output interface routines is also straightforward since the sequences of GeoSheet invocation messages are xed and regular. The parameter marshaling operations are in regular order and are almost identical for all geometric objects. The only di erence among various kinds of geometric objects is the retrieval of the contents of these objects. Visualization classes can also e ectively hide the complex RPC invocation style from users and spare visualization implementation e orts. Our experience has indicated that modi cation of LEDA's graph algorithms for visualization is quite straightforward and can be nished within days by deriving GeoGraph visualization class from LEDA graph class.. In the remainder of this article, we rst examine some of the previous work in this area and review major geometric algorithm visualization concepts in Section 2. The algorithm visualization interface and methodology are described in great details in Section 3. Section 4 discusses the usage of GeoSheet for algorithm visualization, followed by an example given in Section 4.4. Section 5 explores the design of our visualization subsystem. Our distributed support mechanism, geoIPC, is discussed in Section 6. The implementation details and its status are presented in Section 7, and the conclusion and future work is given in Section 8. GeoSheet is an interactive visualization tool for visualizing geometric algorithms in distributed environments. It provides features such as interactive visualization of program states for debugging, high-level graphical input/output manipulation facilities for geometric objects, reuse of existing data structures and algorithms implementation, and more importantly distributed executions for heterogeneous machines at di erent sites.. Several advantages of using this tools are as follows. First, the almost identical X g graphical interface supports a widely used graphical objects manipulation mechanism. Both Figure 6 : Snapshots of Algorithm Executions on GeoSheet the input and output data can be visualized, printed or exported for other purposes. Since the le can be saved and reloaded, it can reduce time for testing and veri cation of the correctness of the implementation of an algorithm. Second, the introduction of message driven interface allows separation of the execution of an algorithm from the visualization subsystem. With such separation, the algorithm implementor can concentrate on algorithmic operations and neglect the complex portion of visualization procedures. Furthermore visualization can be done at remote site and possibly on machines di erent from the one that executes the algorithm. In other words, the tool is distributed, and device independent. By controlling display messages, one can interactively execute an algorithm and have its intermediate output displayed on GeoSheet. This feature is useful in interactive debugging.. We use object-oriented programming methodology to construct the visualization interface. By deriving from traditional data type and algorithm libraries, our abstract geo-object representation super-classes have been proven to be easy to use, easy to construct, and highly portable. There are several existing visualization systems that were aimed to help geometric algorithm design. These systems provide the visualization support at various stages in the geometric algorithm design (abbreviated as GAD) life cycle, and therefore have di erent features. Uni cation of these systems is di cult since the Input/Output interface, le formats, and graphical display environment are di erent. Systems, such as \XYZ GeoBench" and \Work-bench" do consider most GAD phases, but provide primitive visualization support 14]. We brie y describe some of these systems below:. 1. Balsa. Brown and Sedgewick 9, 10], designers of Balsa (for Brown University Algorithm Simulator and Animator), were pioneers in using algorithm animation to teach algorithm design. Balsa and Zeus, which was implemented by Brown 5] , capture meaningful state changes (signi cant events) in the program by adding procedure calls to the program in a process called program annotation for visualization purpose. It emphasizes demonstration of algorithm execution.

O O O B O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O B O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O


Optimised robot-based system for the exploration of elastic joint properties Numerous publications provide measured biomechanical data relating to synovial joints. However, in general, they do not reflect the non-linear elastic joint properties in detail or do not consider all degrees of freedom (DOF), or the quantity of data is sparse. To perform more comprehensive, extended measurements of elastic joint properties, an optimised robot-based approach was developed. The basis was an industrial, high-precision robot that was capable of applying loads to the joint and measuring the joint displacement in 6 DOF. The system was equipped with novel, custom-made control hardware. In contrast to the commonly used sampling rates that are below 100 Hz, a rate of 4 kHz was realised for each DOF. This made it possible to implement advanced, highly dynamic, quasi-continuous closed-loop controllers. Thus oscillations of the robot were avoided, and measurements were speeded up. The stiffness of the entire system was greater than 44 kNm(-1) and 22 Nm deg(-1), and the maximum difference between two successive measurements was less than 0.5 deg. A sophisticated CT-based referencing routine facilitated the matching of kinematic data with the individual anatomy of the tested joint. The detailed detection of the elastic varus-valgus properties of a human knee joint is described, and the need for high spatial resolution is demonstrated. ONE METHOD to determine the elastic properties of human joints as a function of displacement is to apply load while the resulting displacement is recorded. Several authors have measured such data in vivo (MOORE et al., 1976; RIENER and EDRICH, 1999; TORZILLI et al., 1981) . The main problem has been the moving soft tissue between the fixing of the measurement apparatus and the bones, which made precise measurements difficult to obtain. Furthermore, measurements of both an intact and an injured knee joint is only possible by accident for a single subject, as it is not justifiable to cause injury to a healthy person for research reasons. Hence, many groups have used human cadaver knee joints for biomechanical data acquisition.. Early measurement systems used different devices to apply load in different degrees of freedom (DOF) in a hand-operated manner (MARKOLF et al., 1976) or with weights (BLANKEVOORT et al., 1988) . The load was measured with strain gauges, and the position was measured with externally fixed potentiometers (MA~KOLV et al., 1976) or with a camera system (BLACHARSKI et al., 1975 The following outstanding aspects of the presented robotbased set-up for the detection of passive elastic synovial joint properties should be emphasised. After testing and optimising of the experimental set-up with five animal knee joints, the elastic characteristics of three fresh frozen human knee joints were analysed. Therefore individual properties of the intact, as well as the pathological, knee joint were determined in 6 DOF and with high spatial resolution, to be applicable for the Munich knee joint simtflator (RIENER et al., 2004; FREY et al., 2003a) .. As an example, the detection of the passive elastic varusvalgus properties as a function of the knee flexion angle is described. Approximately 30 min were required to run the basic protocol. Both the displacement and load data were recorded in 6 DOF at flexion angles of -5 °, 0 °, 10 °, 20 °, 30 °, 40 °, 60 °, 80 °, 100 ° and 120 °. At each flexion angle, the varus load was increased up to 10 Nm, then decreased to -10 Nm and again increased up to 0 Nm in steps of 1 Nm. The setting of the protocol and two cycles of preconditioning at each flexion angle were also included within this time frame.. Regardless of the relative accuracy of the used robot, the point of interest is the repeatability of the measurements performed. To analyse this aspect, the protocol mentioned above was completed twice with varus load increments of 0.25 Nm and 0.5 Nm (Fig. 7) . in the first measurement, the robot EE was mounted medially of the tibia, and, in the second measurement, it was mounted frontally. The maximum deflection between the two measurements was 0.25 ° at the maximum load of 10Nm.

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FRACTAL DIMENSION AS A DESCRIPTOR OF URBAN GROWTH DYNAMICS The objective of this paper is to examine the development of the urban form of the city of Olomouc since the 1920s in terms of fractal dimension, and to link the observation with two other descriptors of shape -area and perimeter. The fractal dimension of built-up areas and fractal dimension of the boundary of the city are calculated employing the box-counting method; the possibilities of their interpretation and usage in urban planning are discussed. The process of urban growth is observed with respect to its fractality and perspectives of this approach are discussed. An interesting dependence between area and its fractal dimension is derived. Since Mandelbrot's famous paper entitled by How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension [10] , a fractal dimension is widely recognized as a valuable tool for description of shape. It has gained a large popularity in the geosciences [1, 9] (among other disciplines), where measures of object shape are essential. The year 1994 can be considered as the beginning of the exploration of cities in terms of fractal geometry; in this year, the book by Batty & Longley called Fractal Cities: A Geometry of Form and Function was published [1] , being currently regarded as a classic. They presented a number of ways in which fractal geometry (and, especially, fractal dimension) can be used for examining the form of a city; here, we will follow their work in a certain way.. Indeed, urban patterns display a fractal behavior. Similar structures emerging in different scales (compare the global network of roads and highways with a city and its transportation network) can be observed [3] , however, it is obvious that patterns observed on a local scale are not exact copies of the patterns observed on a global scale. Cities can be viewed as a complex system consisting of smaller parts, each of them embodying a pattern of its own. We may observe a self-similar structure in both suburbs and blocks of prefab buildings, however, they are not the same in principle.. Based on this observation, Myint warns that the usage of fractal geometry for description of cities as a whole may lead to considerable confusion [14] . Another problem emerges from the calculation of fractal dimension. Since real-world objects can not be described in a mathematical way, there is no exact formula for calculating the fractal dimension. There are many approaches how to estimate fractal dimension, each giving, more or less, different results. Thus, one must pick the appropriate technique carefully [16] .. In this paper, we focus on the issue of change in the urban form of the city of Olomouc, Czech Republic. Detailed aerial imagery covering almost 80 years are available. The fractal dimension of both boundary and area occupied by the city has been observed and the underlying causes of such behavior have been studied in detail. Up to this point, Batty [1] , as well as other researchers, are followed. Relations between fractal dimension, area and perimeter of the urban form are further examined and possibilities of using those relations for future predictions of urban growth and for urban planning are then discussed.. The structure of the paper is as follows. In Section 2, we expand the description of the concept of fractality and how fractal dimension is estimated. In Section 3, we describe data assembly and processing and in Section 4, we present results obtained. Various aspects regarding quality of data, their interpretation and other issues are discussed in Section 5. We conclude with final remarks and prospects of future work in Section 6. Changes of fractal dimension in the urban form of the city of Olomouc was observed. The relations between built-up area, perimeter and their fractal dimension were examined. A dependence between built-up area and its fractal dimension emerged, which may lead to interesting assumptions on future development of the city. Consequently, the fractal dimension of the boundary of the city seems to cluster around the value of 1.27, which is close to the value of the Koch curve.. Possible interpretations of the found dependencies were presented and these interpretations were briefly discussed from the viewpoint of urban planning. The readers are encouraged to view presented results in a critical way. Although these results are consistent in their logic, their application is limited, because the growth of a city is always influenced by external processes and development that would lead to an equilibrium, as presented here is not possible.. In the future, we would like to focus on the functional aspect of cities and their parts, and how they are related. Some studies suggest that fractal dimension can be used as a classificator of urban land use [20] ; we plan to explore this approach in our future research. The fact that fractal dimension is a valid descriptor of urban growth is well known [1] . The presented study shows that the fractal dimension can be used not only to examine the growth of the city in the past, but may also be a tool to help predict the future development of the city. The relation between built-up area and fractal dimension of an urban form is suggested as linear. This is important as in the last 80 years, there have been different strategies employed in urban planning; however, the relation between area and fractal dimension has remained the same. The predicted area of the city of Olomouc was also calculated. This area should correspond to the fractal dimension of 2 to satisfy the area/fractal dimension relation. However, no city has been reported to have a fractal dimension equal to 2. The highest reported value is the fractal dimension of Beijing -1.96 [8] .. Moreover, the urban growth is realized not only in a plane, but also in a vertical dimension, which is difficult to incorporate into current analyses.. Fractal dimension could be a helpful tool for urban planners, since it gives an easily interpretable descriptor of complexity of an urban form. However, the possibilities for prediction are limited. Even if it would be possible to predict the fractal dimension and area corresponding with a certain time, it would be impossible to predict the exact areas of the city where the urban growth would be realized.. One option would be to run several simulations (using, for example, cellular automata), and to choose the one which gives the best results with respect to predicted area and fractal dimension.. We are aware that our results were obtained by examining only one city and may not be applicable to other cities, especially to cities with different geographical conditions and historical development. On the other hand, growing fractal dimension through time was observed all over the world, and we believe that the area/fractal dimension relation we presented is of universal nature.. Since the urbanization process is still in progress, the question is whether the study area should not be larger. There are other settlements on the edge of the city, which are certainly influenced by the urban form of Olomouc. However, it is difficult to distinguish between areas which have an active influence on the urban form of Olomouc and those which are only influenced by it. Only those areas, which eventually became parts of the city, are taken into account in the analysis.. If study area would be enlarged, the fractal dimension of built-up areas would be smaller, because the city would not fill the space as well as it does now, and new settlements would cover up the study area, resulting thus in a more shattered pattern. The fractal dimension of the city boundary should, on the other hand, remain the same.. Our results and conclusions on maximum area which can be occupied by the city is, therefore, applicable only in an idealized world, where there are no other settlements around the city. Since there are some in our case study, the city of Olomouc spatially develops in their direction and they are eventually connected to it. Nevertheless, our results are a good example of how spatial processes can be described in terms of the fractal dimension and how those descriptors can be used to view the city not as a series of different states in time, but as a changing system with its own dynamics. Perhaps in geographical areas with sparse settlement, our results could be applied in a better way, since the city would not be influenced by its surroundings to a larger extent. Reaching a state where the city would completely fill the space (for example, circle-shaped) would also be logical with respect to minimization of costs. The fractal dimension of both built-up areas and their boundary were measured together with the area and perimeter. The results are presented in Tab

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An Algebraic Approach to Guarantee Harmonic Balance Method Using Grobner Base Harmonic balance (HB) method is well known principle for analyzing periodic oscillations on nonlinear networks and systems. Because the HB method has a truncation error, approximated solutions have been guaranteed by error bounds. However, its numerical computation is very time-consuming compared with solving the HB equation. This paper proposes an algebraic representation of the error bound using Gröbner base. The algebraic representation enables to decrease the computational cost of the error bound considerably. Moreover, using singular points of the algebraic representation, we can obtain accurate break points of the error bound by collisions. key words: harmonic balance method, error bound, Gröbner base, algebraic representation, quadratic approximation, singular point Harmonic balance (HB) method is well known principle for analyzing periodic oscillations of nonlinear networks and systems [1] , [2] . Using this method, we express circuit equations as simultaneous algebraic equations called HB equation due to an approximation by truncated Fourier series of variables. Although the HB method ignores high frequency components, the HB method enables to clarify essential relations among the system parameters. Recently, some techniques by the HB method for bifurcation analysis have been proposed [3] - [6] .. Because the HB method has a truncation error, approximated solutions of the HB equation have been guaranteed by bounded regions, called error bounds, within which the solution must reside [7] - [9] . In particular, Swern presented a method to obtain the error bound for a feedback system with a polynomial-type nonlinear element [9] . However, the numerical computation of the error bound is very timeconsuming because we have to express the high dimensional error bound using a set of the numerical values.. In order to overcome the difficulty, we propose an algebraic representation of the error bound using Gröbner base [10] , [11] . Several reports have proposed methods to apply Gröbner base to nonlinear circuit systems [12] - [15] . Gröbner base enables to eliminate variables from polynoManuscript received November 27, 2007. Manuscript revised March 12, 2008 . † The authors are with the Department of Electrical Engineering, Kyoto University, Kyoto-shi, 615-8510 Japan.. † † The author is with the Department of Computer Science, Hiroshima Institute of Technology, Hiroshima-shi, 731-5193 Japan.. a) E-mail: yagi@circuit.kuee.kyoto-u.ac.jp b) E-mail: hisakado@kuee.kyoto-u.ac.jp c) E-mail: kohshi@iis.it-hiroshima.ac.jp DOI: 10.1093/ietfec/e91-a. 9 .2442 mial simultaneous equations containing parameters [16] , [17] . By Gröbner base, the algebraic representation of the error bound is described as only one algebraic equation with system parameters. The proposed method does not depend on the number of specific frequency components. Further, linear elements of the system are contained as parameters. Thus, when we fix the nonlinear elements, the algebraic representation can be uniquely obtained.. In order to visualize the error bound, we project the error bound to a complex plane of a target frequency component using the algebraic representation. However, the computation of its projection is time-consuming. Thus, we propose an approximated error bound using the algebraic representation. Although the proposed error bound approximately guarantees solutions, the approximated error bound reduces the computational cost of the projection considerably [11] .. When we set the system parameters close to bifurcation parameters, there exist two error bounds in a neighborhood. In such cases, the error bounds are broken by a collision of each other. Hence, we cannot guarantee the solutions near the bifurcation point. The collision point is a break point which generates a singular point of the error bound. Since the singular point can be calculated by the algebraic representation, we propose a method to obtain the accurate break point of the error bound. We proposed an algebraic representation of an error bound for HB method using Gröbner base. Further, we proposed an efficient method to calculate the algebraic representation using transformations of variables. The proposed method does not depend on linear elements of the system and the number of specific frequency components. Next, we proposed a fast computational method of an approximated error bound by a quadratic approximation using the algebraic representation. We confirmed that the quadratic approximation guarantees approximately the solutions as fast as solving the HB equation. Moreover, we proposed a method to obtain accurate break points of the error bound near bifurcation points. In this way, algebraic approach is very powerful for high dimensional varieties such as error bounds.

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Triangulations from topologically correct digital Voronoi diagrams We prove that the dual of the digital Voronoi diagram constructed by flooding the plane from the data points gives a geometrically and topologically correct dual triangulation. This provides the proof of correctness for recently developed GPU algorithms that outperform traditional CPU algorithms for constructing two-dimensional Delaunay triangulations. In recent years, the computational power of graphics processing units (GPUs) has surpassed that of central processing units (CPUs) . Continuing the trend, the gap between the two is expected to widen in the foreseeable future. With the introduction of programming models, such as CUDA [8] and OpenCL [7] , there are now more application areas that benefit from the computational power of GPUs. These areas include scientific computing, games, data mining, and computational finance [9] . In computational geometry, GPUs have been used to solve problems in discrete as well as in continuous space. An example is the digital Voronoi diagram approximating the corresponding Euclidean structure, which has a wide range of applications in image processing, computer vision, and graphics [2] . Another is the Delaunay triangulation, which has applications in mesh generation and scientific computing [5] .. In Euclidean space, the Voronoi diagram and the Delaunay triangulation are but different geometric expressions of the same combinatorial structure. In digital geometry, the translation from one to the other is made difficult by the need to approximate. Indeed, it is easy to construct a digital Voronoi diagram, just by coloring the pixels or their higher-dimensional analogues. Early work in this direction uses graphics hardware [6] and the texture unit of the GPU [15] . More recent work takes the vector propagation approach [3] , which leads to algorithms whose running time depends solely on the image resolution and not on the number of data points [1, 11, 13] . In contrast, computing the Delaunay triangulation with GPUs has been more challenging. While Hoff et al. [6] mention the possibility to dualize the digital Voronoi diagram, it was not until recently that a complete GPU algorithm for the Delaunay triangulation has been described [12] . With the tremendous power of GPUs, this algorithm outperforms all traditional CPU algorithms, including the optimized Triangle software of Shewchuk [14] . This work has also been improved and further extended to handle constraints [10] . The reason for the difficulty is the approximate character of digital Voronoi diagrams, which may lead to unwanted artifacts when dualized, such as crossing edges and missing triangles. However, there is experimental evidence suggesting that a careful implementation can avoid such artifacts.. In this paper, we present a detailed proof that dualizing the digital Voronoi diagram gives a topologically and geometrically correct triangulation. Leaving the correction of non-locally Delaunay diagonals to a postprocessing step, we call the result of the dualization the digital Delaunay triangulation. We base our proof on the recent improvement of the GPU Delaunay triangulation algorithm described in [10] . The proof has been used in the latest implementation of that algorithm. 1 After presenting a conceptual version of this algorithm in Section 2, we prove its correctness in Section 3. Specifically, we show that the digital Voronoi diagram obtained by flooding the pixel array can be dualized to give a topologically as well as geometrically valid triangulation in the plane. Our proof takes three steps to establish the validity of the digital Delaunay triangulation. The first step rationalizes the flooding algorithm by proving that the pixels are colored in the order of distance from the data points. The second step exploits this ordering to prove a technical topological result about loops. The third step uses this result to establish the desired properties of the digital Delaunay triangulation. We believe that our approach to proving the correctness of digital geometry algorithms by progressive abstraction is of independent interest. The main contribution of this paper is a proof that the digital Delaunay triangulation has the geometric and topological properties we usually expect from a triangulation in the plane: its edges do not cross and after connecting the boundary to a dummy vertex at infinity, we get a triangulation of the 2-dimensional sphere. We get these properties if we dualize the collection of digital Voronoi regions colored by flooding. In contrast to coloring by Euclidean distance to the seed points, flooding forms regions that are connected. We can therefore think of flooding as a method to remove the topological noise caused by the digital approximation of the real plane. The most interesting next question is the extension of our correctness proof to 3-dimensional voxel arrays. box is a lemma. The most important are the Ordered Coloring Lemma, which encapsulates most of the digital geometry reasoning, and the Lasso Lemma, which forms the primary topological tool used to prove the rest. Table 1 provides a list of notation used in this paper.

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Autonomous Coordination Technology through Community Organization for Resource Utilization The challenge in resource utilization under dynamic environment is how to utilize appropriate resources to the right users at the right time and the right location. In conventional system, centralized management system is applied but it tends to congest when user requests increase or resources rapidly move. Therefore, this paper proposes Autonomous Coordination Technology (ACT) through community organization for resource utilization. In ACT, a node which has surplus resources autonomously constructs community with a surplus-level based size and distributes resources to members which are deficient in resources. ACT consists of autonomous coordination within community and among communities. According to community organization, online property and flexibility can be satisfied. However, it is difficult to achieve service provision timeliness and resource allocation operatability in the mean time. Thus, ACT includes successive transportation method, and autonomous resource allocation which dynamic decision is made by a tradeoff between timeliness and operatability. As a result, the service assurance in terms of timeliness and operatability can be assured. The effectiveness of proposed technology is affirmed through the simulation of taxi dispatching application in terms of response time and standard deviation versus user rates. In innovative world, the knowledge of wireless communication infrastructures have been rapidly developed [1] , [2] . Nonetheless, creating new technologies is not the only challenge. For service provider, searching for the precise service and allocate appropriate resources to satisfy the right users at right time and right place is a significant mission.. Referred to public institute, MLIT [3] , Intelligent Transport System (ITS) has been promoted. In transportation system [4] - [6] , promptly providing appropriate resources to users and effective utilizing resources are significant. One direction to manage this system is the conventional system which based on centralized management [5] , [6] . Yet, there is a limitation in conventional system; it is that management center must know the whole information in the system then it can allocate the most appropriate resource to provide to user. However, if management center fails, the whole system will also be corrupted. Because of its own traits, centralized system is effective in a smallscale system but tends to congest in a large-scale system. Moreover, its efficiency also deteriorates due to dynamic situations, i.e. unpredictable user requests, random resource mobility, etc. Hence, an approach on decentralized system aimed to achieve system requirements becomes a promising alternative way. Based on the concept of Autonomous Decentralized System (ADS) [7] , Autonomous Decentralized Community System (ADCS) has been proposed [8] - [13] . Community is autonomously formed with local majority information sharing among same-preference members and each member coordinates to one another autonomously. However, resource utilization system differs from information service system. Community constructs not only for information sharing but also for physical resource utilization. Therefore, this paper proposes Autonomous Coordination Technology (ACT) through community organization for resource utilization. A node which has surplus resources autonomously constructs a community (defined as a group of base stations and community size is based on surplus amount). The merit of community is successive transportation method which resources can be transported from surplus to deficient node within one physical hop. Base on ADCS architecture, the limitations in conventional system (i.e. congestion at the center, dynamic situation, etc.) can be overcome. However, to equally manipulate in resource allocation, there is a tradeoff between timeliness and operatability so dynamic setting for distribution rate which depends on the congestion is proposed. In addition, when collision occurs during community construction, the community will not expand in the collided direction but continue to expand in other directions further from original distance instead in order to substitute the collided area. So the community size will still be equivalent to original expected community size. This paper is organized as follows: Section 2 presents application; Section 3 presents ADCS; Section 4 presents ACT, proposed technology; Section 5 shows the simulation evaluation; and Sect. 6 concludes this paper. The resource utilization under dynamic changing environment requires a system which satisfies online property, flexibility, timeliness and operatability. Based on community approach, online property and flexibility can be satisfied but it is difficult to assure timeliness and operatability at the same time. Thus, this paper proposes Autonomous Coordination Technology (ACT) through community organization which based on Autonomous Decentralized Community System (ADCS). ACT consists of the coordination within community and among communities. ACT within community presents autonomous community construction, and resource allocation according to a tradeoff between timeliness and operatability. Because of community, the resources can be transported by successive transportation method in one physical hop. On the other hand, ACT among communities presents the coordination when community collision occurs. Finally, the simulation is made to evaluate the proposed technology in terms of timeliness and operatability assurance. Timeliness and operatability results show the effectiveness of proposed technology is improved at high user rate (> 59 users/min in timeliness and > 45 users/min in operatability). The stability of the system is evaluated by S D in a node. S D results show a linear increasing in conventional system at any user rate but a steady state in proposed technology at high user rate (> 24 users/min). In conclusion, the proposed technology improves timeliness and operatability in a large-scale resource utilization system.

B I O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O


Joint Modeling of Anatomical and Functional Connectivity for Population Studies Abstract-We propose a novel probabilistic framework to merge information from diffusion weighted imaging tractography and resting-state functional magnetic resonance imaging correlations to identify connectivity patterns in the brain. In particular, we model the interaction between latent anatomical and functional connectivity and present an intuitive extension to population studies. We employ the EM algorithm to estimate the model parameters by maximizing the data likelihood. The method simultaneously infers the templates of latent connectivity for each population and the differences in connectivity between the groups. We demonstrate our method on a schizophrenia study. Our model identifies significant increases in functional connectivity between the parietal/posterior cingulate region and the frontal lobe and reduced functional connectivity between the parietal/posterior cingulate region and the temporal lobe in schizophrenia. We further establish that our model learns predictive differences between the control and clinical populations, and that combining the two modalities yields better results than considering each one in isolation.Index Terms-Biomedical imaging, brain modeling, magnetic resonance imaging (MRI), population analysis. T HE INTERACTION between anatomical and functional imaging modalities offers a rich framework for understanding the effects of neuropsychiatric disorders. Specifically, correlations present in resting-state functional magnetic resonance imaging (fMRI) data are believed to reflect the intrinsic functional connectivity of the brain [1] , [2] . Similarly, diffusion weighted imaging (DWI) tractography is used to estimate the underlying white matter fibers and provides valuable information about anatomical connectivity within the brain [3] . To date, relatively little progress has been made on combining informa-tion from these imaging modalities. Prior analysis has focused on correspondences between independently computed statistics of fMRI and DWI data [4] - [7] . Clinical studies typically identify significant population differences separately within each modality and compare them a posteriori [8] , [9] .. In this work, we propose and demonstrate a novel probabilistic framework to infer the relationship between resting-state fMRI and DWI tractography. In particular, we introduce the notion of latent anatomical and functional connectivity between brain regions. These variables represent an underlying process in the brain which cannot be observed directly from the data. The resulting model describes how the latent connectivity differs between two populations and makes intuitive assumptions about the fMRI and DWI image generation process to construct the data likelihood. Our fMRI/DWI observation model is shared across subjects. Hence, we assume that the effects of a disorder can be explained via changes in latent anatomical and functional connectivity. To the best of our knowledge, ours is the first stochastic model to combine resting-state fMRI and DWI data in order to infer changes induced by a neurological disease.. We employ the EM algorithm to efficiently estimate templates of latent connectivity for each population and to identify group differences. The EM algorithm optimizes the model parameters by maximizing the data likelihood. We employ permutation tests and cross validation to verify the robustness of our method. We perform an extensive evaluation of the model on synthetic data. In addition, we learn stable patterns of interaction in a population study of schizophrenia.. Schizophrenia is a poorly-understood disorder marked by widespread cognitive difficulties affecting intelligence, memory, and executive attention. These impairments are not localized to a particular cortical region, but rather, they reflect abnormalities in widely-distributed functional and anatomical networks [10] , [11] . Accordingly, our model identifies connectivity differences dispersed throughout the brain. This paper extends our prior work presented at the International Conference on Medical Image Computing and Computer Assisted Intervention [12] by providing more detailed derivations of the model and estimation procedure and by including more experimental evaluation of the methods.. The remainder of this paper is organized as follows. Section II summarizes prior research on joint modeling of fMRI and DWI data. We also review clinical findings of schizophrenia in this section. We introduce our generative model in Section III and develop the corresponding inference algorithm in Section IV. Section V presents the framework used for the empirical validation of our approach. Sections VI and VII report experimental results based on synthetic and clinical data, respectively.. 0278-0062/$26.00 © 2011 IEEE Section VIII discusses the behavior of our model, its advantages and drawbacks, and future directions of research. We conclude with a summary of contributions in Section IX. We proposed a novel probabilistic framework that fuses information from resting-state fMRI correlations and DWI tractography to infer the differences between two populations. We show that the model captures variations in functional and anatomical connectivity induced by schizophrenia. In particular, we detect increased functional connectivity from the parietal lobe to the frontal lobe and decreased functional connectivity from the parietal lobe to the temporal lobe. Finally, we demonstrate the predictive power of our joint model through cross validation. These results establish the promise of our approach for combining multiple imaging modalities to better understand the brain.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B I I I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


The dynamics of robbery and violence hot spots This paper examines how hot spots shift by hour of day and day of week. Hot spot analysis is more likely to have a substantial impact on crime patterns if spatiotemporal shifts are incorporated into the crime analysis process. Even in some of the highest crime neighborhoods in Bronx County (NY), not all micro-level geographies (e.g. street segments and property lots) contain substantial (if any) amounts of crime over the 5-year study period. Moreover, while there are 168 h in a week, even the hottest hot spots do not contain crime 24 h a day, 7 days a week, and 52 weeks a year. Hot spots shift by both space and time and it is important to illustrate these dual shifts when researching and analyzing different levels of geographies and/or hot spots. Spatiotemporal crime analyses are appearing much more frequently in our academic literature in recent years and have become a principal contributor to the progression of routine activities, crime pattern theory, place-management and situational crime prevention. In addition, spatiotemporal hot spots provide important subject and opportunity context and can help answer some of the questions about what specific types of crime opportunities are available inside of hot spots based on land-use and people's movement patterns. When studying geographical hot spots, it becomes important to measure and illustrate the interrelated temporal shifts within each of the specific hot spots (i.e. not all hot spots are the same) that are generated. Similarly, when studying temporal hot spots, it is important to measure and illustrate the interrelated spatial shifts within each of the temporal frame(s) that are examined. Examples of space-time and time-space hot spot analysis are provided using violent crime data from the New York City Police Department. Key findings of this research include significant shifting of hot spots from weekday to weekend and afternoon to evening, as well as decisive spatiotemporal pattern variations between school-day robberies vs. non-school day robberies. In New York City, previous analyses conducted with NYPD (Herrmann, 2003 (Herrmann, -2012 indicate that not all violent crime hot spots act the same and almost all hot spots have significant internal spatiotemporal variance. Not only do hot spots shift over time, but if crime scientists and analysts conduct temporal analysis on large scale time periods (i.e. months and years), they will notice that crime hot spots have important temporal shifts within each hot spot. This intra-hotspot temporal variance is usually much more concentrated at more micro-levels (Ratcliffe 2004 (Ratcliffe , 2006 Herrmann 2012) . According to the routine activities theory (Cohen and Felson 1979) , crime scientists would expect to see more daytime violence patterns in geographical areas where large groups of people congregate (e.g. commercial, recreational, transportation areas) or where groups of people are intermingling (e.g. transportation hubs, restaurants/ bars). Nighttime violence patterns in geographical areas may be dominated by areas with higher percentages of vacant land, public transportation hubs near high population density residential areas, or commercial areas (with late-night/24-h businesses, especially those serving alcohol) that lack effective place managers. At more microlevels (e.g. hot spots, hot streets, 'risky facilities'), the Pareto Principle still remains relevant and more research should focus on micro-level variations, since these can be very beneficial when developing and implementing crime prevention and control initiatives, especially resource allocation(s) and place-management approaches.. More complex temporal analyses, such as the aforementioned school-day vs. non-school day robbery analysis, can have immediate and significant operational and administrative impacts for both crime prevention and law enforcement control programs. While we assume that there would be differences in crime patterns for these Fig. 9 Temporal analysis of robbery cluster #2 (n = 182 robberies) shows a 5 pm-7 pm Friday and Saturday peak, as well as a weekday Wednesday-Friday afternoon peak, between 2 pm-5 pm. Two distinct temporal patterns usually indicated two separate land-use/business establishment type robbery relationships or two separate groups of robbery offenders and/or targets. Friday are the longest crime trending days of the week, with stable trends form 1 pm-10 pm, with a prominent 5 pm-7 pm 'rush hour' peak Fig. 10 Temporal analysis of robbery cluster #3 (n = 165 robberies) is different from previous robbery clusters, since it shows that weekday afternoon robberies are the primary problem in this cluster. There is no significant weekend or evening/nighttime shift pattern, with the exception of a very small Saturday/Sunday, Midnight-2 am pattern two distinct time periods (school-day afternoons vs. nonschool day nights) based upon routine activity and crime pattern theory, the time-space hot spot method clearly illustrates the essential component that temporal analysis plays in our understanding of crime hot spots, especially how hot spots shift (spatially) over time and how victims, targets, and offenders also vary according to these distinct time periods.. There has been a renewed interest in this type of focus of crime at micro-level places, especially the role of place management within crime places, such as 'risky facilities' . In their place management research, Madensen and 12 Temporal analysis of assault cluster #1 (n = 158 assaults) where gray is zero/very little robbery, yellow/orange is medium amounts of robbery, and dark red is high counts of assaults. This cluster indicates a significant weekend and nighttime temporal pattern that is much different than assault cluster #3. There is a notable Saturday night/Sunday morning 2 am-4:30 am assault peak, as well as scattered Saturday and Sunday afternoon times. This cluster varies somewhat from #2 and #3, which have more weekday, afternoon trends (yellow areas) Eck (2012) , suggest three new descriptions of micro-level places that take into account the ownership (i.e. 'proprietary' nature of the business), the spatial clustering of places (i.e. 'proximal' relationships to other businesses), and the outermost boundaries or 'pooled' nature of the surrounding facility neighborhoods. Much of the current place management research suggests that effective place managers can have a considerable impact on the criminogenic nature of risky facilities and that several prevention and control efforts can have a direct impact on these 'convergent settings' (Felson 2003 ) that bring people together in both time and space. By utilizing time-space hot spot techniques, place management researchers can have a much more comprehensive understanding of the different targets/victims that are at risk and how they intersect/converge with potential offenders in both time and space. Some considerations for future research include examining the strength of the temporal patterns using circular statistics, which takes into consideration the 'circular nature' of the 24-h time periods that are utilized in micro-level place-based research (Brunsdon and Corcoran 2006; Wuschke et al. 2013) . The process of examining spatiotemporal variations and stabilities of micro-level crime clusters can also assist crime scientists in understanding how these micro-level place-based opportunities are more generally linked to theory, which in turn can promote more effective crime prevention and control strategies and better implementation configurations.. Many of the limitations of shifting hot spots research are similar to the more generalized limitations of hot spots research; namely, the geoprocessing of crime data, the various methods of constructing crime hot spots, and the generalizability of the 'findings' from within the Fig. 13 Temporal analysis of assault cluster #2 (n = 145 assaults). This cluster indicates a significant weekend and nighttime temporal pattern that is similar to assault cluster #1. There are several weekday afternoon and early evening trends (between 3 pm-8 pm) trends that occur Fig. 14 Temporal analysis of assault cluster #3 (n = 129 assaults) indicates two separate patterns, a weekday afternoon 3 pm-5 pm and a weekend nighttime 10 pm-2 am pattern hot spots. Geoprocessing of crime data typically includes selecting the type(s) of crime to include in the analyses, selecting the appropriate time period(s) to query and analyze the crime data, and defining the appropriate level(s) of geographic aggregation for geocoding the crime data. While the nearest neighbor hierarchical clustering method provides distinct geographical boundaries, this method can also exclude relevant crime data that falls marginally outside of the crime hot spot boundary, but is still considerably related to the phenomenon being studied. Lastly, a hot spot is not a hot spot is not a hot spot… not all crime hot spots behave the same way and generalizing the spatiotemporal shifts in hot spots should be crime specific, as well as location and time specific to each respective hot spot location.. Shifting hot spots research is an important issue that merits further research in crime science and crime analysis. Intra-hot spot variance is good news and bad news to crime scientists and crime analysts. The good news is that many hot spots have very specific temporal 'trends' or definitive patterns within them, usually based on routine activities, land-uses, facility types (especially 'risky facilities'), and victim/offender schedules that are active within each hot spot. When temporal analysis is conducted within each hot spot, a temporal trend can normally be identified and then an appropriate opportunity prevention framework, place management strategy, and/ or police response can be developed and applied. However, the bad news is that if temporal analysis is not conducted on each hot spot, prevention resources and patrol efforts may be ineffective at best and 'wasted' at worst. The crime science and crime analysis communities have become proficient in creating, tracking, and handling hot spots of crime (Chainey and Ratcliffe 2005) . Current research (Weisburd et al. 2012; Bernasco and Block 2011; Brantingham et al. 2009; Weisburd et al. 2009 ) indicates that as crime scientists drill down into the micro-levels of geography (e.g., streets, tax lots, buildings), crime hot spots start to form new shapes (e.g. lines, points, building outlines), sizes, and spatiotemporal patterns.. By developing a more comprehensive understanding of the spatial and temporal variations within violent crime hot spots at the micro-level, crime prevention and crime control specialists can have a greater impact on apprehending criminals, police resource allocation and planning, crime modeling and forecasting, and evaluation of crime prevention and crime control programs (Boba 2001; Townsley et al. 2003; Ratcliffe 2004; Johnson et al. 2007 ). In our continuing state of shrinking government operating budgets, crime scientists and crime analysts need to consider the interrelatedness of spatial and temporal shifts in crime patterns when creating, tracking, and handling crime hot spots.. Many studies indicate that crimes are clustered at the neighborhood level, but the entire neighborhood is rarely (if ever) criminogenic and only specific parts of neighborhoods contain high concentrations of crime (Taylor 1997; Fagan and Davies 2000; . Prior studies Open Access *Correspondence: cherrmann@jjay.cuny.edu New York, NY, USA incorrectly assume that the relationships between crime, population, land-use, and business establishment types are both homogenous and spatially stationary (Shaw and McKay 1969; Bursik and Grasmick 1993; Sampson et al. 1997; Morenoff and Sampson 1997) . Even at the neighborhood level, crime is highly clustered and the highest crime neighborhoods have significant percentages of low or zero crime streets within the neighborhood(s).. A substantial body of research has identified that a small percentage of crime locations (i.e. hot spots) contain a significant percentage of crime (Sherman et al. 1989; Block and Block 1995; Eck and Weisburd 1995; Brantingham and Brantingham 1999) . One of the current trends in environmental criminology and crime analysis is the study of crime at more 'micro' level places (i.e., buildings, properties, block faces, street segments), a geographic scale/level well below the neighborhood level. Part of this concept of micro-level analysis is known as the 80/20 rule or Pareto's Principle (Eck et al. 2007; Weisburd et al. 2012) , and reigns true not only in crime prevention and crime control, but other areas of the criminal justice field as well [this concept has also been branded as the 'law of crime concentrations' by Weisburd et al. (2012) ].. The 80/20 rule suggests that by targeting or focusing on the highest 20 % of crime hot spots can have a dramatic influence on the majority (approximately 80 %) of total crime in the study area. According to the 80/20 rule, the net impact of crime prevention and crime control strategies targeting the 20 % would be much higher than attempting to target an entire neighborhood (or whatever the larger study area is that is being examined).. Environmental criminologists using Pareto's 80/20 rule have pointed out that not all parks are full of drug users/dealers (Wilcox et al. 2004; Eck et al. 2007; Groff and McCord 2012) , not all high schools have high rates of delinquency (Wikstrom et al. 2012; Glover 2002; LaGrange 1999) , not all bars contain high rates of assault (Ratcliffe 2012; Newton and Hirschfield 2009; Eck et al. 2007; Gorman et al. 2001) , and not all parking lots have high rates of auto theft (Rengert 1997; Clarke and Goldstein 2003) . In fact, even 'high crime' neighborhoods contain hot spots (high density crime areas) and cold spots (zero/low crime areas), high crime streets and zero/low crime streets, and both 'good' (i.e. crime protectors) and 'bad' (i.e. crime generators) streets and businesses.. Violent crime (i.e., murder, rape, robbery, assault, and shootings) has declined 73 % in the Bronx since 1990 (NYPD Compstat 2010). In his book, Zimring (2011) suggests that the historic crime drop in New York City is a result of better policing (i.e. CompStat, crime analysis, hot spot policing, zero tolerance, stop and frisk) and community crime interventions (including 'gun buyback' and drug violence reduction programs). Shifting hotspots research hopes to advance these trends of increasing success for law enforcement crime control strategies, advancement of current environmental criminology theories, and expansion upon existing crime prevention frameworks. Integration of theory and new analyses at micro-levels (e.g. street segments and risky facilities) will help crime scientists, police departments, and policy makers better understand the spatial and temporal processes in the 'magma' that fuels today's crime hot spots.

O O O B O B B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I B O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O B O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I


Memory reconsolidation for natural language processing Abstract We propose a model of memory reconsolidation that can output new sentences with additional meaning after refining information from input sentences and integrating them with related prior experience. Our model uses available technology to first disambiguate the meanings of words and extracts information from the sentences into a structure that is an extension to semantic networks. Within our long-term memory we introduce an action relationships database reminiscent of the way symbols are associated in brain, and propose an adaptive mechanism for linking these actions with the different scenarios. The model then fills in the implicit context of the input and predicts relevant activities that could occur in the context based on a statistical action relationship database. The new data both of the more complete scenario and of the statistical relationships of the activities are reconsolidated into memory. Experiments show that our model improves upon the existing reasoning tool suggested by MIT Media lab, known as ConceptNet. One task of natural language processing (NLP) is to understand and represent the implicit meaning of a sentence. The implicit meaning can be obtained by analyzing the explicit meanings and information stored in memory. This may trigger a memory recall. Memory reconsolidation is the process where a recall of information leads to update or strengthen the already stored memory according newer existing information (Charniak 1991; Friedman 1998; Heckerman 1999) . As semantic roles of verbs have been characterized with nouns (Kipper et al. 2006; Liu and Singh 2004a) , they are reported to be used to predict the brain activity associated with the meanings of nouns by computational model on fMRI data (Loftus 1975) . This suggests that the predicates help to understand the role of the associated entities. For example, an antelope chased by a tiger could be described with the following states of running away, being caught, get injured, etc., and this helps to understand the scenario of the antelope as a prey.. Inspired by this biological evidence where entities are internally understood by a set of actions, and by the evidence for modularity in brain memory system (Miller 1998 ) a scenario-adaptive memory system is built for inferring implicit meaning and prediction by combining the entering sentences with a Long-term memory Action Relationship Database (ARDB).. The basic data structure within our memory system builds upon semantic networks, introduced by Richens (Miller et al. 1990 ). These networks are useful for representing the semantic relationships between objects or concepts. They were shown to be powerful tools for knowledge representation and language processing, as well as logical description of events (Mitchell et al. 2008; Mizraji et al. 2009 ). Yet, their structure is rigid and less expressive than the options given in a natural language, so an extended form of semantic networks will be required.. Computers commonly make sentence understanding errors due to the multiple meanings of individual words. To avoid incorrect inferences, our system disambiguates words robustly by utilizing two knowledge bases WordNet (Kipper et al. 2006) and VerbNet (Liu and Singh 2004b) . WordNet is an English lexical database that categorizes synonyms into groups, records word group relationships, and labels topic domains of some words; it can provide some word meanings for our system. VerbNet is another technology that describes constraints on the subject and the object of a verb and thus constitutes another way to disambiguate the word meaning. Input is matched with words in WordNet and VerbNet in order to find the unique word meanings and common topics; this correctly disambiguates the input words.. To obtain the implicit or full meaning of input sentences, we build a memory association system that is based on our Extended Semantic Network, described in ''Information representation''. We group the input words to topics and disambiguate them and then update the input via the Action Database (ARDB) from the long-term memory storage. We then update the scenario as well as the statistics in the ARDB in a reconsolidation-like process for future use.. We compare our new memory-inference model with the MIT Media Lab's ConceptNet (Murphy 2002; Niculescumizil 2007) . ConceptNet is an open source knowledge base that is built in the semantic network structures. There are a given number of edge types such as ''prerequisite of,'' ''subset of,'' and ''do;'' the data can be thought of pairs of concepts connected with one of the allowed types of edges. For example (''is capable of'' ''cook-food'' ''be fun'') is a pair with an edge where the name of the edge is written first. As this database is open to the public, many people have added information to it and created a large commonsense knowledge of pairs, presented in a semantic network format. ConceptNet has been suggested to be used to understand scenarios better than current natural language technologies due to the wide data it contains. While our focus is on Action Relations common-sense which is a subset of the information from ConceptNet we do propose particular ways to infer information in a Bayesian manner and this together with the topic-based disambiguation seems to provide a better tool for scenario understanding.. The following text is organized as follows. In ''Information representation'', we introduce the Extended Semantic Network (ESN). In ''Working versus long term memory'', we discuss how the working memory is constructed and updated by both input and the long-term memory. Section ''The action relationship database'' describes the action relationship database (ARDB) and the process of using it. Section ''Demonstration'' demonstrates our system on ball game scenarios and compares the results to those inferred by ConceptNet. We close in ''Conclusion and predictions'' with conclusions and predictions to be tested in future studies.

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