Advection‐Based Function Matching on Surfaces

A tangent vector field on a surface is the generator of a smooth family of maps from the surface to itself, known as the flow. Given a scalar function on the surface, it can be transported, or advected, by composing it with a vector field's flow. Such transport is exhibited by many physical phenomena, e.g., in fluid dynamics. In this paper, we are interested in the inverse problem: given source and target functions, compute a vector field whose flow advects the source to the target. We propose a method for addressing this problem, by minimizing an energy given by the advection constraint together with a regularizing term for the vector field. Our approach is inspired by a similar method in computational anatomy, known as LDDMM, yet leverages the recent framework of functional vector fields for discretizing the advection and the flow as operators on scalar functions. The latter allows us to efficiently generalize LDDMM to curved surfaces, without explicitly computing the flow lines of the vector field we are optimizing for. We show two approaches for the solution: using linear advection with multiple vector fields, and using non‐linear advection with a single vector field. We additionally derive an approximated gradient of the corresponding energy, which is based on a novel vector field transport operator. Finally, we demonstrate applications of our machinery to intrinsic symmetry analysis, function interpolation and map improvement.     

Discovering recurring activity in temporal networks

Recent advances in data-acquisition technologies have equipped team coaches and sports analysts with the capability of collecting and analyzing detailed data of team activity in the field. It is now possible to monitor a sports event and record information regarding the position of the players in the field, passing the ball, coordinated moves, and so on. In this paper we propose a new method to analyze such team activity data. Our goal is to segment the overall activity stream into a sequence of potentially recurrent modes, which reflect different strategies adopted by a team, and thus, help to analyze and understand team tactics. We model team activity data as a temporal network, that is, a sequence of time-stamped edges that capture interactions between players. We then formulate the problem of identifying a small number of team modes and segmenting the overall timespan so that each segment can be mapped to one of the team modes; hence the set of modes summarizes the overall team activity. We prove that the resulting optimization problem is \(\mathrm {NP}\)-hard, and we discuss its properties. We then present a number of different algorithms for solving the problem, including an approximation algorithm that is practical only for one mode, as well as heuristic methods based on iterative and greedy approaches. We benchmark the performance of our algorithms on real and synthetic datasets. Of all methods, the iterative algorithm provides the best combination of performance and running time. We demonstrate practical examples of the insights provided by our algorithms when mining real sports-activity data. In addition, we show the applicability of our algorithms on other types of data, such as social networks.     

A reconstruction algorithm of electrical impedance tomography with optimal configuration of the driven electrodes

A reconstruction algorithm for electrical impedance tomography (EIT) is presented. The least-squares (LS) method is applied and a formulation similar to that of the perturbation method is found. The main difference from perturbation lies with the sensitivity matrix, which here is replaced by the Jacobian matrix, defined in terms of the partial derivatives of every sensing electrode pair voltage difference with respect to every element's conductivity. The mutual position between the active electrodes is chosen to give optimum sensitivity. The results shown that the algorithm presented here has a better convergence and needs fewer iterations than the perturbation method.     

Effects of rind removal on physicochemical quality characteristics of fresh‐cut watermelon [Citrullus lanatus (Thunb) Matsum & Nakai] during cold storage

The impact of removing the rind from fresh‐cut watermelon slices was assessed on the quality of the product during storage at 4 °C for 9 days. Flesh lycopene declined from 55.4 to 47.9 mg kg^?1 f.w. and colour lightness (L*) increased from 43.2 to 45.8 after 2 days of storage. Initial heart and placental flesh firmness increased from 7.3 and 9.8 N, respectively, to 9.5 and 12.8 N after 9 days, but were unaffected by rind processing. Electrolyte leakage from placental tissue was unaffected by storage and rind. Rind presence limited juice run‐off by 47.2% and maintained mean total soluble sugar concentration in the slices at 86.0 mg mL^?1 as opposed to 76.8 mg mL^?1 in rind‐less slices. Change in the quality was most pronounced between 0 and 2 day of storage. Removing the rind accelerated senescence and off‐flavour production, while the presence of rind improved the overall storage stability of fresh‐cut watermelon slices.     

Data-Driven Multithreading Using Conventional Microprocessors

This paper describes the Data-Driven Multithreading (DDM) model and how it may be implemented using off-the-shelf microprocessors. Data-Driven Multithreading is a nonblocking multithreading execution model that tolerates internode latency by scheduling threads for execution based on data availability. Scheduling based on data availability can be used to exploit cache management policies that reduce significantly cache misses. Such policies include firing a thread for execution only if its data is already placed in the cache. We call this cache management policy the CacheFlow policy. The core of the DDM implementation presented is a memory mapped hardware module that is attached directly to the processor's bus. This module is responsible for thread scheduling and is known as the Thread Synchronization Unit (TSU). The evaluation of DDM was performed using simulation of the Data-Driven Network of Workstations ({rm{D}}^2{rm{NOW}}). {rm{D}}^2{rm{NOW}} is a DDM implementation built out of regular workstations augmented with the TSU. The simulation was performed for nine scientific applications, seven of which belong to the SPLASH-2 suite. The results show that DDM can tolerate well both the communication and synchronization latency. Overall, for 16 and 32-node {rm{D}}^2{rm{NOW}} machines the speedup observed was 14.4 and 26.0, respectively.     

A framework for predictive modeling of anatomical deformations

A framework for modeling and predicting anatomical deformations is presented, and tested on simulated images. Although a variety of deformations can be modeled in this framework, emphasis is placed on surgical planning, and particularly on modeling and predicting changes of anatomy between preoperative and intraoperative positions, as well as on deformations induced by tumor growth. Two methods are examined. The first is purely shape-based and utilizes the principal modes of co-variation between anatomy and deformation in order to statistically represent deformability. When a patient's anatomy is available, it is used in conjunction with the statistical model to predict the way in which the anatomy will/can deform. The second method is related, and it uses the statistical model in conjunction with a biomechanical model of anatomical deformation. It examines the principal modes of co-variation between shape and forces, with the latter driving the biomechanical model, and thus predicting deformation. Results are shown on simulated images, demonstrating that systematic deformations, such as those resulting from change in position or from tumor growth, can be estimated very well using these models. Estimation accuracy will depend on the application, and particularly on how systematic a deformation of interest is.     

IT-Compliance und IT-Governance

Ohne Zusammenfassung     

A mode-matching technique for the study of circular and coaxialwaveguide discontinuities based on closed-form coupling integrals

A mode-matching scheme for the analysis of concentric circular and/or coaxial waveguide step discontinuities is proposed in this paper. An analytical evaluation of the involved coupling integrals for all possible discontinuities formed by any combination of a circular and a coaxial waveguide is performed. The originality mainly concerns the multimode excitation and scattering at a circular-to-coaxial waveguide step junction. Numerical results for a number of applications are compared against those of other techniques and/or measurements and are found to be in good agreement