Projection X-Space Magnetic Particle Imaging Abstract-Projection In this paper, we have analytically described, designed, constructed, and tested a projection MPI imager. The system reconstructs using our recently developed x-space systems theory, which enabled reconstruction without precharacterization of the imaging tracer, harmonics, or matrix inversion techniques. X-space MPI further ensures that the resulting image is Linear and Space-invariant, which is apparent when imaging a complex "Cal" phantom.. We believe this paper is the first to present experimental projection MPI images in phantoms as well as in mice. Moreover, we believe the image of the "Cal" phantom is an advance to the state of the art, both in image quality and the large field of view. These results are encouraging as we develop sub-millimeter (native resolution) x-space MPI systems, as well as wider-bore x-space MPI scanners.. Projection MPI is a promising new imaging format for MPI that could improve imaging speed by two orders of magnitude with no loss of SNR. The improvement in speed would be useful in real-time applications such as MPI "fluoroscopy," dynamic contrast enhancement, and interventional MPI. MPI "fluoroscopy" holds particular promise as a safe rapid angiography technique because the safety profile of SPIO nanoparticle tracers [27] , [28] . Alternatively, we could improve 2-D SNR by an order of magnitude over 3-D MPI by using the faster acquisition rate to increase averaging time. This mode of acquisition would be particularly well suited for cell tracking with labeled SPIO reporters where sensitivity is paramount.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Memory-access-aware data structure transformations for embedded software with dynamic data accesses Abstract-Embedded systems are evolving from traditional, stand-alone devices to devices that participate in Internet activity. The days of simple, manifest embedded software [e.g. a simple finite-impulse response (FIR) algorithm on a digital signal processor DSP)] are over. Complex, nonmanifest code, executed on a variety of embedded platforms in a distributed manner, characterizes next generation embedded software. One dominant niche, which we concentrate on, is embedded, multimedia software. The need is present to map large scale, dynamic, multimedia software onto an embedded system in a systematic and highly optimized manner. The objective of this paper is to introduce high-level, systematically applicable, data structure transformations and to show in detail the practical feasibility of our optimizations on three real-life multimedia case studies. We derive Pareto tradeoff points in terms of accesses versus memory footprint and obtain significant gains in execution time and power consumption with respect to the initial implementation choices. Our approach is a first step to systematically applying high-level data structure transformations in the context of memory-efficient and low-power multimedia systems. The work presented in this paper is inspired by [39] and [43] - [45] . There are, however, two main differences.. First of all, we concentrate on data-structure optimizations in the context of multimedia applications as opposed to focusing on heavy data-oriented network routers. This implies that several of the data-structure transformations that we present are new with respect to the earlier work.. Second, we analyze the software implementation of powerefficient data structures as opposed to explicitly designing and using specific configured, physical memories. Redesigning the available memory organization is not feasible in our context. We assume that the embedded platform is already designed and fully functional on the Internet. Our final research goal is to transform the critical data structures, of software that migrates onto an embedded system, in conformance with the physical predefined memory architecture of the target platform.. Since the related work on data-structure management is immense and interdisciplinary, we compare our work to different research communities. By no means do we wish to categorize a research contribution into one specific community. For instance, Palem's work [32] is mentioned in the embedded system engineering domain, but it can just as well be cited in the dynamic memory management domain.. In the embedded system engineering community, low power is a main objective in the dynamic-power management domain (see [3] for more references) and in the dynamic voltage scheduling domain [23] . However, data management related issues are not or hardly covered.. Data management for traditional (i.e., nondynamic) embedded applications is covered in detail in the data transfer and storage exploration (DTSE) methodology [5] . However, dynamic applications, i.e., interactive, multimedia applications, are not dealt with directly in that context. Ideally, the work we present in this paper should be followed by transformations which are useful for actual platform mapping, for example, those advocated by the DTSE methodology.. Work related to dynamic applications in the context of low-cost design is presented in [25] , [29] , [32] , and [45] . This work is situated at a much lower abstraction level (e.g. platform dependent optimizations) than the one we present in this paper. McKee [29] designs a stream-memory controller and demonstrates its applicability for fast-page mode and Rambus DRAM memory systems. Zhang [45] presents compression techniques in order to reduce heap allocated storage, execution time, and power consumption. Palem [32] remaps data based on metrics such as locality, prefetching, and regularity of memory accesses and others. He lowers the memory needed without compromising execution time. Kistler [25] captures, by using dynamic profiling, which paths through the program are taken with what frequency. This allows spatial locality to be increased and load latency to be minimized. This is done by striving to cluster and order data members that are accessed closely after one another onto the same cache line.. Our own previous work [11] , [12] deals with energy-friendly data-structure transformations. Both platform-dependent and platform-independent transformations are briefly covered. The main emphasis of the referenced work is to show the practical feasibility of the optimizations for a specific computer game scenario. In this paper, however, we extensively cover several additional platform independent optimizations and we present three different case studies to prove the practical feasibility of our methodology. We do not discuss any platform dependent optimizations.. In the general-purpose domain, dynamic memory management (DMM) has a long history. In [41] , an extensive survey on how efficient memory allocator design has evolved over the past decades is presented. Often, in DMM for embedded systems [31] , the dynamic memory is partitioned into blocks which store the data structures of the application. Current approaches include managing all the free blocks in a single linked list [31] and simulating the application with different general-purpose memory managers [4] . In addition, Haggander [20] defines and exploits the object-oriented characteristics of an application in order to obtain less physical memory accesses by using application specific structure pools. Furthermore, part of Chilimbi's work in [7] is devoted to analyzing garbage collection based programming languages in terms of memory performance. Moreover, he presents three data placement design principles: clustering, coloring, and compression. These can be used to improve the spatial and temporal locality of pointer-centric applications.. More attention is paid to power consumption and other embedded systems criteria in [6] , [10] , and [28] . A fast, stepwise, cost-driven, and automated exploration and refinement is proposed by Leeman [28] at the system level for multimedia applications, which operate on large and irregular data structures. Also, in the telecom network domain, da Silva [10] presents a methodology which allows a system specification to be systematically mapped onto an optimized embedded single-chip hardware/software implementation. Chang [6] designs a high-performance memory allocator in hardware for object-oriented systems for full-customized architectures.. Dynamic memory allocation addresses the problem of when and how to allocate extra (or deallocate "garbage") memory. DMM schemes deal with multiple data structures of various shapes and access patterns in order to obtain an overall good mapping of the data onto physical memory. Extra, but necessary management, is added in order to cope with malloc requests from different parts of the application. Clearly, this problem is complementary to the problem we focus on, namely choosing a good representation of a specific data structure. Some of our driver implementations contain malloc requests but we have respected this as is and focused on the problem of data-structure representation instead. Potentially, more gains can be made, once the complementary problem of DMM has been addressed and the corresponding optimizations are applied to our drivers as well.. In the software performance engineering community, mostly unified modelling language (UML) based designs [16] are analyzed in terms of performance. Application specific scenarios are investigated and transformed into more economical solutions. Patterns [14] and antipatterns [36] are developed. For instance, one of the antipatterns presented in [36] is directly related to data-structure management. However, power consumption in particular and embedded software in general are not considered. The reason why this community is relevant to consider for our own work, is because the applications we want to embed are of the nature presented in this community, e.g. distributed gaming, web browsers, etc. (see [9] ). Dealing with such large scale and dynamic software implies that higher abstraction levels than pure C code are considered (e.g. UML or equivalent software design languages) during optimization. These high-level abstractions are situated on top of the transformations that we propose in this paper. Our proposed method chooses an efficient application-specific implementation of an 1-to-n relationship described in UML.. In the theoretical computer science community a lot of effort has been spent on formalizing abstract data types [2] , [13] , corresponding optimizations [40] , and verification [21] . Also, data structure and algorithmic design for specific problems have been a main research topic in the past [8] , [26] . Attempts to formalizing data-structure transformations have been around for quite some time [15] , but obtaining success outside academic institutes remains a challenge.. In the database community [17] - [19] , [35] , people have systematically explored various data structures expressed in formal (e.g. query) languages. Using a well-designed language for a specific problem domain (e.g., querying a spatial database) results in explicit information being expressed by the user. This information can then easily be exploited by optimization tools. In our multimedia application context, such explicit information is not present and hence our problem is quite different and requires other approaches to solve.

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


A distributed debugger for Amoeba We describe a debugger that is being developed for distributed programs in Amoeba. A major goal in our work is to make the debugger independent of the Amoeba kernel. Our design integrates many facilities found in other debuggers, such as execution replay, breakpointing, and an event-based view of the execution of the target program.This paper discusses the influence of Amoeba's architecture on the attainability of our goals and the desired functionality of the debugger.We also consider such problems as how to deal with timeouts and interactions between the target program and its environment + Amoeba is a capability-based, distributed operating system being developed at the Centre for Mathematics and Computer Science (CWI) and the Free University (VU) in Amsterdam [8, 9] . It is far enough along to need tools such as a debugger so that Amoeba programmers have some help when things go wrong. This paper describes such a debugger; however, many of the lessons can be applied to distributed debugging in general.. One of the primary goals for the debugger is that it is totally independent of the implementation of the Amoeba kernel. The Amoeba kernel is a small, monolithic part of the system with a well-defined user interface. Adding hooks for a debugger would complicate the kernel and make the two components heavily dependent on each other. It is too tempting to build in intimate knowledge about the implementation of the kernel if the debugger has access to its internal data structures. In addition, since Amoeba is still in the development stage, we would like to be able to completely reimplement the kernel without having to rewrite support tools such as the debugger (provided, of course, that the semantics and interface of the kernel do not change).. The Amoeba debugger takes an event-based approach [2, 10] and views the user's (target) program as a generator of a stream of events. Events correspond to things like sending a message, creating a task, hitting a breakpoint, and dividing by zero. Since the number of events generated can become quite large, the debugger provides filtering and abstraction mechanisms to make the event stream more manageable.. However, a purely event-based approach is not enough; some access to the state of the program must also be provided.. To this end the Amoeba debugger provides many of the facilities found in sequential debuggers: the ability to examine and alter memory, obtain a stack trace of procedure calls, set breakpoints, and to deal with the program on a source-level basis (i.e., refer to statements, userdefined types, variables, etc.). Nonetheless, the interesting aspects of the debugger are those that deal with the distributed nature of the target program; in this paper we will mostly ignore the sequential aspects.. The debugger attempts to provide an execution replay facility along the lines of Instant Replay [7] . Instant Replay is a program monitoring facility that faithfully reproduces a distributed program's execution sequence. It does this by remembering the program's interactions with its environment and the order in which significant events occur. Unfortunately, the existence of an asynchronous primitive in Amoeba -cluster signaling -spoils the chance to use their techniques to construct a comprehensive replay facility. At best we can only replay the execution sequence of the target program if cluster signaling is not used.. While the user may not always be able to replay the exact execution sequence, he can manually control the execution order by choosing which process to run next when doing a context switch. This gives him fine grain control over scheduling, but unfortunately does not scale well to large numbers of processes (doing frequent context switches).. It is our philosophy that debugging is a specific phase of the program development. cycle. We do not support the constant background monitoring of believed-to-be-correct ("production") programs. If such a program aborts during its execution, a separate postmortem analysis tool can be used to shed enough light on the nature of the problem so that the user can go back with the debugger and explore the conditions that caused the failure. This tool can analyze the state of the program at the time of the crash, but does not provide any event history information.. Even the best monitoring mechanisms incur some overhead.. We would like production programs to be as fast as possible, so ~to debugger support is added (this is like turning off run-time subscript checking in languages that support it).. The debugger must know what state a target program is in at all times. There are two approaches to this: it can cooperate with the operating system to share this information, or it can maintain its own idea of the state of a target program. Since the debugger is kernel independent we must use the latter approach.. There is a performance penalty to be paid for this decision but it is not really important. The whole point of a debugger is to take things slowly and at a human pace so that the execution can be scrutinized; lightning fast execution is not really the goal. (This does not mean that the debugger should be inefficient; as with any utility, a reasonable amount of effort should go into making it as efficient as possible given the available system services and desired functionality.) 2

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 B 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 B 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 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 B 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 O O O O O O O O O O O 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 B O O O O B O O B 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 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 O O O O O O O O O O B 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 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 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 B 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 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 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


Optimal transport and integer partitions We link the theory of optimal transportation to the theory of integer partitions. Let P(n) denote the set of integer partitions of n ∈ N and write partitions π ∈ P(n) as (n 1 , . . . , n k(π) ). Using terminology from optimal transport, we characterize certain classes of partitions like symmetric partitions and those in Euler's identity |{π ∈ P(n) | all n i distinct}| = |{π ∈ P(n) | all n i odd}|.Then we sketch how optimal transport might help to understand higher dimensional partitions. In this paper, we apply the theory of optimal transport to the classical topic of integer partitions. Both fields have been studied independently since the 18th century, but have -up to our knowledge -not yet been linked.

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


MORPHOLOGICAL COLOR IMAGE SIMPLIFICATION BY SATURATION-CONTROLLED REGIONAL LEVELINGS This paper deals with color image simplification using levelings. This class of connected filters suppresses details but preserves the contours of the remaining structures or objects. As the notion of "color structure" is not trivial, the formulation of morphological operators for color images involves many open issues. The principle choice of a welldefined color space is crucial and it is proposed to work on a luminance/saturation/hue representation defined by the norm L 1 . A family of morphological color operators is then introduced using the classical formulation with total orderings by means of lexicographic cascades. In this framework, a methodology for color image simplification is introduced, which takes advantage of a saturation-controlled combination of the chromatic and the achromatic (or the spectral and the spatio-geometric) components. More precisely, it is based on the application of a color leveling to each significant region, specifically adapted to the nature (chromatic/achromatic) of the region and which needs an initial image partition into the homogenous regions. Experimental results illustrate the performance of the new developed algorithms. Descriptions of human visual cortex suggest that the color (chromatic or spectral variation) and the form (achromatic or intensity variations) of the object contours in a real scene are analyzed by independent subsystems (anatomically segregated neural populations). Recent results, moreover, show that cortical neurons can simultaneously code for both attributes, adaptively combining the different sources. 16 In computer vision it now seems certain that the color and the geometry must be used in a combined way, following a conceptual approach which copies the human image perception. Most image processing techniques are defined for gray-level functions in order to exploit the geometric information of the digital images. Classically, in the practice of color image analysis, the easiest solution consists in applying the (scalar) gray-level algorithm to the (vectorial) color images independently to each channel.. More recent works attempt to extend adaptively the spatio-geometrical transformations for the color images by defining new specific algorithms. See, for instance, the studies in the scale-space theory, 11 for the partial differential equations or in the level sets approach. 9, 30 In this paper we focus on the application to color images of a mathematical morphology operator, the leveling, for filtering the "color structures". The aim is to simplify their complexity, that is, to remove the insignificant details, preserving the largest homogenous regions and in addition, keeping the original colors. This low-level task is fundamental for other image processing issues: object segmentation, feature extraction and indexing, coding and compression, etc. However the notion of "color structure" is not trivial, and hence the formulation of morphological operators for color images involves many open issues. Thus while the introduction of new colors in the filtered image can be critical for certain applications, it can also be unimportant for others. It should not be forgotten that working in a vectorial way with color images involves a computational complexity burden. Moreover, there is a multitude of choices to make for the definition of a specific color operator: color space, vectorial ordering, etc. And finally, another important question is the control of the required simplification degree.. The principle choice of a well-defined color space is crucial and here we deal with a recently proposed luminance/saturation/hue representation defined by the norm L 1 , and more particularly, we take advantage of the function of the saturation variable. For human vision, the most significant parameter is the hue in high saturated areas, and it turns to luminance when saturation decreases. This variable enables to locate regular areas, for segmenting partitions, and to define the nature of a region as chromatic (hue dominant) or achromatic (luminance dominant). The saturation, as the main variable to define image processing algorithms, has already been the subject of several works. 8, 10, 29 Mathematical morphology is the application of lattice theory to spatial structures, 15, 25 i.e. the definition of morphological operators needs a totally ordered complete lattice structure. Therefore, the application of mathematical morphology to color images is difficult due to the vectorial nature of the color data. Fundamental references to works which have formalized the vector morphology theory are in Refs. 13 and 26.. The morphological connected filters have the comfortable property to suppress details but preserve the contours of the remaining objects. 23 Levelings are a subclass of symmetric connected operators that have originally been defined and studied for gray-level images by Meyer. 19, 21 Several extensions to vector spaces have been proposed. In Ref. 12, Gomila and Meyer proposed two alternatives to the problem: (1) pseudo-scalar levelings, projecting the vector space onto a fixed vector and, after scalar leveling, back projection into the vector space; (2) The paper is organized as follows. A brief analysis of some basic concepts concerning the application of mathematical morphology to color images are included in Sec. 2 -mainly the equations of the luminance/hue/saturation color space in norm L 1 and on the other hand, the extension of morphological color operators using the classical formulation by means of lexicographic cascades (in particular, the color levelings). In Sec. 3, a methodology for color image simplification is proposed, which takes advantage of a saturation-controlled combination of the chromatic and the achromatic (or the spectral and the spatio-geometric) components. More precisely, it is based on the application of a color leveling to each significant region, adapted to the nature (chromatic/achromatic) of the region and which needs an image partition into the homogenous regions. A discussion based on the experimental results illustrates the performance of the newly developed algorithms in Sec. 4. Finally, in Sec. 5, some conclusions and perspectives are given.

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


Self-Organized Translational Wheeling Motion in Stochastic Self-Assembling Modules Abstract Self-organization is a phenomenon found in biomolecular self-assembly by which proteins are spontaneously driven to assemble and attain various functionalities. This study reports on self-organized behavior in which distributed centimeter-sized modules stochastically aggregate and exhibit a translational wheeling motion. The system consists of two types of centimeter-sized water-floating modules: a triangular-shaped module that is equipped with a vibration motor and a permanent magnet (termed the active module), which can quasirandomly rove around; and circular modules that are equipped with permanent magnets (termed passive modules). In its quasi-random movement in water, the active module picks up passive modules through magnetic attraction. The contacts between the modules induce a torque transfer from the active module to the passive modules. This results in rotational motion of the passive modules. As a consequence of the shape difference between the triangular module and the circular module, the passive modules rotate like wheels, being kept on the same edges as the active module. The motion of the active module is examined, as well as the characteristics and behavior of the self-organization process. Self-assembling robotics is a research field that mainly examines spontaneous manufacturing techniques at unreachable domains. The main aim of self-assembling robotics is to achieve a low-cost and efficient fabrication method, high fault tolerance, and high adaptability, regardless of environmental changes. The approach that this system employs is a so-called bottom-up approach. In this approach, components spontaneously aggregate through local interactions and eventually organize global structures/functionalities. In this way, the systems guarantee high robustness against incidental turbulence and a capability to tolerantly cope with massive parallelism in their aggregation processes. In molecular synthesis, which is arguably the most successful example of self-assembly, some physical conditions are necessary, such as weak interaction, thermal agitation, and nucleation. The mechanisms behind self-assembly are numerous trial-and-error attempts in collisions until the right connection is established. This right connectionʼs strength reaches a sustainable level and hence extends beyond the proofreading bias from the turbulent environment. The system utilizes environmental diffusion as a traveling aid and achieves highly efficient and fast assembly matching. Through such a process, the system gradually shifts to a more energetically stable state. The whole process is fundamentally different from the pick-and-place style, also known as deterministic, of engineering assembly.. In order to provide an overview of the current state and trends of self-assembly research, we investigate related research fields, as well as the approach of this study, in Figure 1 . For classification, we took the level of the systemʼs homogeneity or heterogeneity on the x axis, the way modules represent their states on the y axis, and the systemʼs environmental stochastic level on the z axis. Historically, the first advance was made in the field of modular robotics, where robots were made from homogeneous sets of modules and, as a result, featured failure tolerance, such as self-repair capability [7, 23] . Self-assembly (SA) robotics used a different approach from modular robotics. SA robotics often incorporated stochasticity in its assembly processes, and modules morphed into targeted configurations under that condition. One of the earliest prototypes was proposed by Murata et al. [21] . Their system consisted of a homogeneous set of units, the ability to develop the assembly patterns, and the ability to self-repair the formation. A unique approach realizing amoebic locomotion with many weakly coupled modules can be found in [16] . The system featured resilient body motions, and showed high adaptability to the environmental changes. The introduction of various forms of environmental stochasticity, such as adding mechanical turbulence to the ground [34] , air-jet turbulence [1, 10, 17] , and fluid turbulence [24, 32, 35] , has often been tried. Furthermore, the advancement of hardware allows for the possibility of a derivative field that focuses more on practical implementation, such as the approach that is used for medical diagnosis and surgery [25] . A unique approach to self-disassembly using novel electropermanent magnets is presented in [8] . It is important to note that most of the work subscribes to the so-called bottom-up (stochastic) approach. This Figure 1 . Related research fields are described as follows: homogeneity of components (x axis), module mechanism of internal states (y axis), and approaches (z axis).. approach has successfully mimicked biomolecular activities at the macro scale, proving engineered artifactsʼ self-replication capability, although, due to the frequent use of electronics in the mechanism for internal states, validated capabilities sometimes lack connection to nature.. On the other hand, there is another approach that employs mechanical components for the internal states, and that keeps the environment and assembly process stochastic. An early mechanical model for a self-replicating machine in a stochastic environment was made by Penrose, which simulated natural self-replication [26] . This research was followed by speculation about the clustering patterns of passive elements, focusing on the role of shape in template and component matching [6] , as well as on their time evolution [15] . A group led by Whitesides used a chemistryinspired approach, where components feature simple (mechanical/chemical) mechanisms. A series of studies was conducted and showed remarkable achievements in the realization of positional coordinates for molecule-mimetic chemistry [4, 36] , circuit functionality [3, 9] , reversible aggregation [19] , folding structures [2] , rotation of magnets [12] , and rotation of rotors [11] . Numerous similar research efforts have been devoted to the investigation of morphology [31] . Artificial chemicals that can form in several ways depending on the temperature of the system, such as polymers and dimers, were examined by Breivik et al. [5] . Different aggregation patterns with various sizes of components were shown by Yamaki et al. [37] . An intelligent self-assembling block, which can represent multiple states of the unitsʼ rotational angle, was designed by Tsutsumi and Murata [33] . The system can physically express multiple states, conducting exclusive or (XOR) calculation on a 2D plane.. Morphological computation is a notion that reveals the role of playersʼ morphology in various phenomena, regarding them as computation [28] . The focus can be the elucidation of how a particular body works as computation [13, 27] , or developing a design principle for designing a new body for a purpose. Self-assembly has had a close connection to this field, especially when the employed modules show strong mechanical features. For instance, differences in segregation patterns in macroscopic modules are reported in [22] .. Having considered the mechanisms of biochemical assembly systems, we derive the prerequisite criteria for our approach as follows.. 1. Bottom-up. The system can potentially handle stochasticity and multi-degree parallelism in the assembly processes, where distributed components autonomously assemble without human intervention. This article reports a self-organizing translational motion that is performed by a set of water-floating self-assembly modules. The system features a minimal hardware implementation for the operationnamely, one motor but no sensors-and shows reliable assembly and a stable wheeling behavior. During the experimental run, a floating module with a vibration motor (active module) roves around the area, and eventually picks up passive modules through magnetic attraction. By retaining contacts with multiple passive modules, an active triangular module transmits torque to the passive modules, inducing rotation of the passive modules, and consequently utilizes them as wheels. We analyze the entire self-organizing behavior as follows: (1) self-assembly phase, (2) rotational motion phase, and (3) wall-following phase. We also carefully investigated the associated characteristics. We further studied the influence of morphology, which, in this research, means the size of passive modules that act as wheels. This study provides insight into how morphology in assembled structures augments the aggregated functionalities of individual components of the structure. The experimental platform was designed to enable the systematic investigation of a complex selfassembly phenomenon, which guarantees dissipation and distribution of the system based on [20] . Figure 2 shows (a) the modules and (b) the experimental environment. The modules floated on water. There were two different types of modules: an active module and a passive module. The active module was equipped with a vibration motor on the top of the base plate, which allowed the module to jiggle and move around in its environment, and a permanent magnet at the bottom for attractive or repulsive interactions (see detailed descriptions in Table 1 ). For the power supply, we opted to supply electricity through a pantograph, which drew current from a metallic ceiling. When a voltage was applied to the ceiling plate, current flowed from the pantograph to the vibration motor and then returned to ground via electrodes that were immersed in the conductive water. During the experiments, an NaCl solution was used as conductive water. This water enabled sufficient current flow through the chemical reaction 2NaCl + 2H 2 O → H 2 ↑ + Cl 2 ↑ + 2NaOH. Due to this setup, all modules received constant power and were lightweight. Generally, the voltage applied to the ceiling determined the level of perturbation introduced into the system. Thus, it can be regarded as a kind of temperature on a macro scale, which can be used as a regulable parameter. We set up a camera below the water container and observed the modules through the transparent bottom. Table 1 summarizes the components that were installed on each module.

O B 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 B 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Identification of yeast cell cycle regulated genes based on genomic features Background: Time-course microarray experiments have been widely used to identify cell cycle regulated genes. However, the method is not effective for lowly expressed genes and is sensitive to experimental conditions. To complement microarray experiments, we propose a computational method to predict cell cycle regulated genes based on their genomic featurestranscription factor binding and motif profiles. Results: Through integrating gene-expression data with ChIP-chip binding and putative binding sites of transcription factors, our method shows high accuracy in discriminating yeast cell cycle regulated genes from non-cell cycle regulated ones. We predict 211 novel cell cycle regulated genes. Our model rediscovers the main cell cycle transcription factors and provides new insights into the regulatory mechanisms. The model also reveals a regulatory circuit mediated by a number of key cell cycle regulators. Conclusions: Our model suggests that the periodical pattern of cell cycle genes is largely coded in their promoter regions, which can be captured by motif and transcription factor binding data. Cell cycle is controlled by a relatively small number of master transcription factors. The concept of genomic feature based method can be readily extended to human cell cycle process and other transcriptionally regulated processes, such as tissue-specific expression. We model the cell cycle regulated genes using their genomic features (transcription factor binding and motif profiles), especially in the promoter regions. It suggests that cell division, to a large extent, is controlled by a relatively small number of master transcription factors. These master TFs form a self-regulatory circuit and then regulate downstream TFs and genes. Genomic feature based method exempts us from the drawbacks of microarray and helps us to reveal the regulatory machinery. This concept can be applied to other organisms and transcriptionally regulated processes. The idea of modeling gene expression based on regulatory information has been applied to predict gene expression levels [14, 44] . In this work, we used similar idea to quantify gene expression patterns assuming common mechanisms underlying cell cycle regulated genes. Genomic features used in our model include trans-regulators and cis-elements. They possess complementary effect in predicting cell cycle regulated genes. Trans-regulators' binding gives the direct measure of involvement of transcription factors, whereas cis-elements give overall binding potentials of regulatory proteins, such as transcription factors and other proteins. Our method suggests cell cycle progression is possibly dominated by only a few key transcription factors and these transcription factors themselves form a regulatory circuit. Other cell cycle transcription factors may function downstream of these key regulators. Compared to previous protein feature based method, which focused on post-transcriptional level regulation, genomic feature based method demonstrates increased prediction performance. The improvement is not surprising as transcriptional level regulation is the first and the most critical step that controls gene expression. Even though our method could separate periodical genes from nonperiodical ones with fair accuracy, cell cycle progression control is a much more complex system than could be simply explained by proximal transcription regulation alone. Other levels of regulation, such as distal transcription regulation, protein modification, activation, localization, and degradation are also important components. These regulations may account for false positives and false negative predictions in our method. Future works could focus on combining all kind of regulations together to explain cell cycle control.. One of the advantages of our method is the independence of microarray experiments in identifying novel periodical genes. As we known, microarray analysis is quite sensitive to de-synchronization and experimental condition influence. Also cell cycle process induced by different chemicals might harbor biases towards certain categories of cell cycle regulated genes. Thus, our method could provide an alternative way to complement and verify known list of cell cycle regulated genes. After quantifying cell cycle genes by general model, we also applied phase specific model to further classify genes. Phase specific prediction model is affected by the stage separation quality in Cyclebase.org. For example, several genes are classified having some ambiguity, as their expression peak lying in the conjunction of two stages. However, it could provide clues to stage specificity of genes and is easy to adapt to other predefined data.. Wu and Li proposed a simple method to predict cell cycle genes based on ChIP-chip, TF binding sites and other data [45] . They predicted 178 novel yeast cell cycles genes that were regulated by at least two of 17 cell cycle TFs. Here we applied a supervised statistical model to improve their method. Out of the 178 cell cycle genes predicted by Wu et al., 8 are in our positive training set. In the remaining 170 genes, 49 are also predicted to be cell cycle genes by our model (i.e. in the 211 gene list). Out of the 170 genes, 69% (118) have physical interactions with known cell cycle genes, and 31% (52) have at least one of the 9 supporting evidences (see Methods), compared with 63% (132) and 40% (85) for the 211 cell cycle genes predicted by our model. GO analysis indicates that the 170 genes were enriched in various metabolic processes (e.g. glucose metabolic process), while the 211 genes we predicted are more involved in cell cycle related processes (e.g. cell wall and chromosome condensation).. There are also certain drawbacks of our model. (1) Different transcription factors or motifs are weighted the same in our model, without considering their position and orientation relative to the gene. This information may further influence the expression peak time of genes. (2) As we shown, different cell cycle stages possess intrinsic varied regulatory mechanisms, but the general model we used assumes all cell cycle regulated genes harbor common genomic features. Thus the model could be further modified to accommodate both common features and stage specific features. Our genomic-based method can also be modified and extended to predict genes in other periodical processes, such as human cell cycle process and circadian phenomenon, or generally to transcriptionally regulated processes, such as tissue-specific expression. Cell division is under precise regulation in eukaryotic organisms. Many genes functioning in cell division are regulated and expressed right before they are needed [1] . These genes show periodical expression patterns with peaks at certain mitotic stages. The most effective way for identifying these genes is to analyze gene expression profiles using time course microarray. For example, in Saccharomyces cerevisiae, several hundreds of periodically expressed genes have been identified based on microarray [2] [3] [4] [5] [6] . Despite the high throughput, microarray has its limitations in terms of identifying cell cycle regulated gene. First, genes identified from different studies are poorly overlapped, presumably due to varied experimental conditions or de-synchronization problems [7] . Second, it is often difficult to identify lowly expressed or weakly regulated genes due to technical issues [4, 7] .. To overcome the limitations of microarray experiments, several computational methods have been proposed as compensatory methods to find cell cycle regulated genes in yeast. Streib et al. [8] predicted cell cycle regulated genes based on causal interaction using regulatory networks. Wang et al. [9] combined genetic interactions and co-expression data to infer candidate cell cycle regulated genes. These two methods are based on the assumption that these genes tend to possess interacting relationship with each other. Alternatively, de Lichtenberg et al. found that many cell cycle regulated genes shared common protein features, thus proposed a method to predict novel ones based on protein features [7] . These protein-level features mainly reflect cell cycle regulation at the posttranslational level, such as protein stability. However, the DNA sequence features, which contribute at the transcriptional level, are completely ignored by this method.. A subset of transcription factors responsible for the coordinated regulation of cell cycle genes has been uncovered [10] . For example, Mbp1 is a crucial transcription factor involved in cell cycle progression from G1 to S phase [11] ; Mcm1/Fkh1/Ndd1 cooperatively regulate G2/M genes through binding to their promoters [12] . Since cell cycle regulated genes tend to be regulated by a common set of regulators, we hypothesize that whether a gene is cell cycle regulated can be inferred based on genomic features -specifically, the regulatory transcriptional factors (TFs) binding (trans-factors) and motifs profiles in their promoters (cis-elements). Fortunately, in yeast the genomic occupations of most TFs have been identified with ChIP-chip experiment [13] . Meanwhile, the potential regulatory cis-elements have been systematically investigated by computational analysis [14] . Thus, we are motivated to construct statistical models to predict yeast cell cycle regulated genes based on these features.. In this study, we combined the large-scale TF binding data and motif profiles to predict cell cycle regulated genes in budding yeast. The predictive model achieved high accuracy evaluated by 10-fold cross-validations. We predicted 211 novel cell cycle genes and determined their potential phases (G1, S, G2, or M phase). These genes are enriched for cell cycle related processes according to Gene Ontology (GO) analysis and tend to be lowly expressed. The model also provided us a set of TFs significantly contributed to cell cycle gene regulation. Our analysis in this work suggests that genomic features are informative for predicting periodically expressed genes. The statistical model proposed in this paper can be applied to identify cell cycle regulated genes in other organisms or broadly to genes under transcriptional level regulation.

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


Nonsmooth Optimization Techniques for Semisupervised Classification Abstract-We apply nonsmooth optimization techniques to classification problems, with particular reference to the Transductive Support Vector Machine (TSVM) approach, where the considered decision function is nonconvex and nondifferentiable, hence difficult to minimize. We present some numerical results obtained by running the proposed method on some standard test problems drawn from the binary classification literature. In this paper, we have applied a nonconvex bundle-type method for semisupervised classification problems.. Our algorithm has performed well on all data sets except for the Ionosphere test problem (see Tables 2 and 4) , where, however, the performance appears comparable.. In our opinion, the main advantage of the proposed method is the possibility to train the classifier on the basis of a large number of labeled and unlabeled points, because it seems that the CPU time is more affected by the number of features than by the number of samples.. The results of our experimentation suggest that sophisticated nonsmooth optimization techniques can be fruitfully adopted for classification problems.. Possible object of feature research would be kernelization of our method. We solve the TSVM problem in its primal form; a possible choice could be to compute the coordinates of each point in the kernel Principal Component Analysis (PCA) basis by using an approach similar to the one described in [8] .

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 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


Improving the Efficiency of Image Clustering using Modified Non Euclidean Distance Measures in Data Mining Abstract: The Image is very important for the real world to transfer the messages from any source to destination. So, these images are converted in to useful information using data mining techniques. In existing all the research papers using kmeans and fuzzy k means with euclidean distance for image clustering. Here, each cluster needs its own centric for cluster calculation and the euclidean distance calculate the distance between the points. In clustering this process of distance calculation did not give efficient result. For make this in to efficient, this research paper proposes the non Euclidean distance measures for distance calculation. Here, the logical points are used to find the cluster. The result shows that image clustering based on the modified non Euclidean distance and the performance shows the efficiency of non euclidean distance measures. In real world, the image plays an important role in the entire field. Normally, all the images having some information related to any application [1] [2] . So, these images are converted in to some useful information using data mining techniques. This process of mining the image is called as image mining. Here, the clustering technique is applied to these images. The process of grouping the similar data or pixels is called as clustering [5] .. The data mining having two types of learning is supervised learning and unsupervised learning. In supervised learning, the training dataset was provided to train the system and got the output based on these training data. This type of system is called as classifiers. In classification, group the same item based on the physical behavior. In unsupervised learning, the training data set is not provided to the learning system. In this paper proposes the clustering for the Image. The clustering is one of the techniques in unsupervised learning. The clustering is having many algorithms are partitioned based clustering, Hierarchical based clustering, dense based clustering and distribution based clustering. In partitioned based clustering the cluster based on the centre point [6] [4] . Here, the initialization of centre is very difficult and this process takes more time to execute. In hierarchical clustering the clusters are formed by using distance connectivity [3] . In density based clustering the cluster formed using the dense value. The distance between the points is calculated by using the distance measures. Data mining having the two types of measures called Euclidean distance and non Euclidean distance measure [7] . In euclidean distance measure, the distance is calculated based on the physical points of the cluster. But this measure is not efficient for the clustering to produce the efficient result.

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


Automatic Generation of FFT for Translations of Multipole Expansions in Spherical Harmonics The fast multipole method (FMM) is an efficient algorithm for calculating electrostatic interactions in molecular simulations and a promising alternative to Ewald summation methods. Translation of multipole expansion in spherical harmonics is the most important operation of the fast multipole method and the fast Fourier transform (FFT) acceleration of this operation is among the fastest methods of improving its performance. The technique relies on highly optimized implementation of fast Fourier transform routines for the desired expansion sizes, which need to incorporate the knowledge of symmetries and zero elements in the input arrays. Here a method is presented for automatic generation of such, highly optimized, routines. The fast multipole method (FMM) in two dimensions was introduced by Greengard (1987) and Greengard and Rokhlin (1987) and its adaptive formulation by Carrier, Greengard, and Rokhlin (1988) . Later on it was extended to three dimensions in the work by Greengard and Rokhlin (1988) , where notably the fast Fourier transform acceleration was proposed for the first time. Being the most computationally expensive part of the FMM, the translation of multipole expansions in spherical harmonics has been the subject of the most extensive optimization efforts. Greengard and Rokhlin (1988) proposed the fast Fourier transform acceleration technique, also pointing out problems with numerical stability of their method. Elliot and Board (1996) proposed a solution to these problems by using the fast Fourier transforms with block decomposition of the input arrays. At the same time White and Head-Gordon (1996) proposed an algorithm utilizing rotations of spherical harmonics. The most recent acceleration mechanism using the plane wave representation was introduced in two dimensions by Hrycak and Rokhlin (1998) , and in three dimensions by Greengard and Rokhlin (1997) .. Translation implemented as a convolution, in the original approach, has quadratic complexity, the method proposed by White and Head-Gordon yields cubic complexity and the methods based on the Fourier transforms and the method based on the plane wave representation are of square complexity.. Much effort has been invested in improving numerical stability of the original FFT acceleration scheme. However, numerical properties of the original solution turn out to be satisfactory for classic molecular dynamics simulations. At the same time, the performance advantages of the Fourier transform make the method attractive for implementation on high performance computers.. This work focuses on the fast Fourier transform acceleration of translation. There exist a number of techniques that accelerate the fast multipole algorithm in other ways than by speeding up the translation operation, which will not be discussed here. An extensive overview of these techniques is provided by Kurzak and Pettitt (2006) . In this publication a methodology was shown for crafting fast Fourier transform routines for a specific problemtranslation of multipole expansions in spherical harmonics. A special purpose compiler was utilized to produce FFT routines, which take advantage of symmetries and zero elements in the input arrays and pruned elements of the output arrays, decreasing the number of floating point instructions and memory accesses. The resulting routines are at least twice as fast as a generic FFT library routines and an order of magnitude faster in some cases. Tables 2 and 3 show numbers of floating point operations (additions, multiplications, sign changes and assigns) for individual codelets associated with multipole expansions of sizes p from 6 to 16. Figure 4 presents performance, in terms of execution time and Gflops, of the individual codelets for those expansion sizes. Figure 5 shows performance, in terms of execution time and Gflops, of the entire two-dimensional Fourier transform in comparison with other operations common in the FMM algorithm.. The FMM operations presented in Figure 5 are the following:. • M2L conv -multipole to local translation implemented as a convolution of an O(p 4 ) complexity; • Rotation -rotation of a multipole expansion by about the y axis of an O(p 3 ) complexity, using a Wigner matrix; • FFT2L -inverse Fourier transform of a local expansion; • M2FFT -forward Fourier transform of a multipole expansion; • M2L FFT -multipole to local translation implemented as an element-wise product in Fourier space of an O(p 2 ) complexity; • Scaling -scaling of multipole expansion coefficients of an O(p 2 ) complexity.. π 2 --- It is important to notice, again, the better performance of the forward routines, which stems from the fact that more operations can be eliminated from the routines implementing these transforms. Figure 5 compares the performance of the entire two-dimensional Fourier transform when using the optimized codelets and a generic FFT library. The specific library used for this comparison is the FFTW (Frigo 1999; Frigo and Johnsson 2005) .. In the FFT community it is common to use the nlog n scale for the transform sizes. Here, however, transform sizes are related to multipole expansion sizes for which nlog n scale is never used. For that reason it was decided to use linear scale for Fourier transform sizes.. The benchmarking was performed on an Itanium2 1.4 GHz processor and an Opteron 2.0 GHz processor, which are both high performance processors, yet with different performance characteristics. It is not a goal here to make comparisons between those processors. The benchmarked routines have to be considered micro-kernels and their performance is not representative of the overall processor performance. Also, it should be pointed out that the results represent performance when both the code and the data resides in cache and are not representative of performance when run as a part of the entire FMM algorithm.. One of the most important observations is that, when using the techniques presented in this paper, there is no severe penalty for using uncommon sizes of FFTs, including sizes containing large prime numbers in their factorizations (Figure 4) . In particular sizes 22 and 26, which correspond to multipole expansion sizes 11 and 13, perform very well, and interestingly, achieve the best Gflops values, which is because they have very close numbers of floating point additions and multiplications (Tables 2 and 3) . Those instructions can be performed in parallel on most modern processors including those used for benchmarking here. It has been proposed to perform redundant FFTs in a parallel implementation of the FMM in order to decrease network traffic Pettitt 2005a, 2005b) . In such cases the routine performed redundantly is the forward FFT. It turns out that the biggest improvements can be achieved when optimizing forward routines, and as a result, they perform faster than the inverse routines.. Although the routines perform quite well for all the sizes, some sizes are noticeably better than others. Size p = 12 performs particularly well, which is important since it gives a very good trade-off between accuracy and performance in molecular dynamics simulations.. As expected, being an O(p 2 log 2 p) operation, the FFT executes faster than the O(p 3 ) operation and slower than the O(p 2 ) operations. Not surprisingly, the most efficient operation, in terms of Gflops number, is the M2L translation in Fourier space, which is an element-wise product of two complex vectors, implemented by a single loop with exactly the same number of additions and multiplications. It may be noticed that on the Itanium2 processor the Gflops number of the 2-D forward FFT matches the Gflops number of the M2L translation for expansion sizes of 11 and 13, because of their good ratio of additions and multiplications. It may seem counterintuitive that higher complexity operations achieve lower Gflops numbers than lower complexity operations. It would not be the case if large data sets were used and memory access times would come into play. Figure 6 shows the impact of the solution proposed in this article. For expansion sizes of 6, 7 and 8 the forward transform is roughly three times faster than a generic FFT routine and the inverse transform is roughly two times faster, which is the result of the reduced number of floating point instructions and memory accesses when symmetries and zero elements are addressed.. The big performance difference for sizes of 9 and bigger is because the standard setting the FFTW library only contains codelets for transform sizes up to 16 (p = 8) and has to combine different codelets for bigger sizes, whereas in the proposed solution one-dimensional routines for each size are implemented as single codelets (single blocks of straight-line code) end eliminate the penalty of function calls.

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


afterParty: turning raw transcriptomes into permanent resources Background: Next-generation DNA sequencing technologies have made it possible to generate transcriptome data for novel organisms quickly and cheaply, to the extent that the effort required to annotate and publish a new transcriptome is greater than the effort required to sequence it. Often, following publication, details of the annotation effort are only available in summary form, hindering subsequent exploitation of the data. To promote best-practice in annotation and to ensure that data remain accessible, we have written afterParty, a web application that allows users to assemble, annotate and publish novel transcriptomes using only a web browser. afterParty is an open-source tool for turning raw transcriptome sequencing reads and assembled contigs into searchable, browsable transcriptome resources with powerful visualization tools. In contrast to existing solutions, afterParty integrates all steps of the transcriptome annotation workflow and presents an intuitive user interface for non-expert users, while being flexible enough to accommodate assemblies and annotations produced by more experienced users. It implements best-practise assembly and annotation methods, and facilitates data sharing and visualization. It is our hope that, by easing the process of annotation, publication, and stable archiving, afterParty will facilitate the distribution and exploration of richly-decorated transcriptome data that would otherwise remain inaccessible. The afterParty web application functions as an interface to two sets of tools -one for creating datasets, and one for searching, browsing and visualizing them. To create a new dataset, the user uploads either a set of raw sequencing reads which afterParty assembles into contigs, or a collection of pre-assembled contigs (generated using any appropriate combination of sequencing technology and assembly software) and, optionally, coverage and quality data. Contigs are then annotated and the annotations indexed for rapid searching. To investigate an existing dataset, a user can browse individual contigs or search within datasets for contigs of interest. Searches can interrogate the annotations or contig properties (coverage, GC content, etc.) and can be performed across multiple assemblies in a dataset (e.g. for different species or different RNA libraries). afterParty is implemented as a web application and is written in Groovy [27] using the Grails [28] web framework and the PostgreSQL Relational Database Management Server [29] for data storage. It is offered as a publicly-available server at afterparty.bio.ed.ac.uk, but can also be downloaded and run locally.. Dataset structure afterParty datasets are organized into a structure with two overlapping hierarchies -one for raw sequence data, and one for assembled sequence data ( Figure 1 ). The raw sequence data hierarchy has been designed to be congruent with the The International Nucleotide Sequence Database Collaboration (INSDC) BioProject [30] schema to ease integration with raw sequence archives, and is described here from the bottom-up for clarity. A run contains read data for a single sequencing run, and an experiment may contain reads from several independent runs. Each experiment in a dataset may have different RNA pre paration and sequencing technologies. Experiments are grouped together into samples, which reflect separate biological sources of RNA material -for example, different tissue types, life stages, or environmental conditions. A compound sample represents a collection of samples and usually corresponds to a single species or strain of source organism. Finally, a study is a collection of related compound samples, such as a group of closely-related species.. Putative transcripts are represented in afterParty by contigs, which are grouped into assemblies. A compound sample can have multiple assemblies. Using this mechanism it is possible to have multiple versions of an assembly for a single set of reads. A contig may be decorated with multiple pieces of information, each of which is represented by an annotation. Each individual input sequence that makes up a contig is represented as a read. Arbitrary collections of contigs are stored as contig sets. A contig can belong to any number of contig sets.

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 B 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 B O O O O O O O O O O O O O O O O B 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 B 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Formal-language-constrained path problems Abstract. Given an alphabet Σ, a (directed) graph G whose edges are weighted and Σ-labeled, and a formal language L ⊆ Σ * , the formal-language-constrained shortest/simple path problem consists of finding a shortest (simple) path p in G complying with the additional constraint that l(p) ∈ L. Here l(p) denotes the unique word obtained by concatenating the Σ-labels of the edges along the path p. The main contributions of this paper include the following:(1) We show that the formal-language-constrained shortest path problem is solvable efficiently in polynomial time when L is restricted to be a context-free language (CFL). When L is specified as a regular language we provide algorithms with improved space and time bounds.(2) In contrast, we show that the problem of finding a simple path between a source and a given destination is NP-hard, even when L is restricted to fixed simple regular languages and to very simple classes of graphs (e.g., complete grids).(3) For the class of treewidth-bounded graphs, we show that (i) the problem of finding a regular-language-constrained simple path between source and destination is solvable in polynomial time and (ii) the extension to finding CFL-constrained simple paths is NP-complete. Our results extend the previous results in [SIAM

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 O O O


Intelligent Healthcare Service Provisioning Using Ontology with Low-Level Sensory Data Ubiquitous Healthcare (u-Healthcare) is the intelligent delivery of healthcare services to users anytime and anywhere. To provide robust healthcare services, recognition of patient daily life activities is required. Context information in combination with user real-time daily life activities can help in the provision of more personalized services, service suggestions, and changes in system behavior based on user profile for better healthcare services. In this paper, we focus on the intelligent manipulation of activities using the Context-aware Activity Manipulation Engine (CAME) core of the Human Activity Recognition Engine (HARE). The activities are recognized using video-based, wearable sensor-based, and location-based activity recognition engines. An ontology-based activity fusion with subject profile information for personalized system response is achieved. CAME receives real-time low level activities and infers higher level activities, situation analysis, personalized service suggestions, and makes appropriate decisions. A two-phase filtering technique is applied for intelligent processing of information (represented in ontology) and making appropriate decisions based on rules (incorporating expert knowledge). The experimental results for intelligent processing of activity information showed relatively better accuracy. Moreover, CAME is extended with activity filters and T-Box inference that resulted in better accuracy and response time in comparison to initial results of CAME. As the human lifespan increases, people are becoming more interested in living a healthy life, which results in high-cost healthcare systems and services. Maintaining good quality and widely availabile healthcare services at a minimal cost is challenging [1] . Home healthcare systems are becoming a more important form of healthcare service delivery. The management, maintenance, and coordination of healthcare services, educating users, and empowerment of individuals to manage their own health are the main focus. To support this, a powerful, flexible, and cost-effective infrastructure is required for healthcare services that can fulfill the vision of ubiquitous healthcare (u-healthcare) . Cloud Computing can potentially provide a huge cost savings, flexiblility, high-throughput, and ease of use for different services [2] as well as for healthcare services. For this reason, we have developed an architecture, called Secured Wireless Sensor Network (WSN)-integrated Cloud Computing for u-LifeCare (SC 3 ) [3] . Different wireless sensors are deployed that collect real-time data that is transmitted to a Cloud Server through a Cloud Gateway. Based on this real-time data collected by different sensors, SC 3 provides real-time home care and safety monitoring services, an information sharing and exchange facility, emergency connection services, and patient monitoring and care services.. One of the main components of SC 3 is the Human Activity Recognition Engine (HARE) [1] (see Fig. 1 ). This engine is necessary to provide improved daily medical care and real-time reaction to medical emergencies, and identifying patient activities (i.e., Activity Recognition (AR)) is a prerequisite. Low level activities are defined as simple motions or actions detected by the sensing device that are very general and unclear in meaning such as hand movement. High level activities are the linkage of low level activities in a sequence with reference to context to make it more understandable. For example, in the process of making tea, picking up the cup, boiling water, using sugar, and using a spoon are all low level activities and are unclear when interpreted separately. However, when these activities are sequenced together and are used in context with the kitchen location, then they comprise "making tea" as a high level activity.. The existing systems are based on a simple condition and action model [4] , not using context information including time, location, and user profile. In some cases, the existing systems use imperfect low level context information [5] , which makes the system results unpredictable. Their focus is more on environmental sensors (e.g., smoke detector, infrared control, and GPRS modem) rather than on real-time human activity. Traditional systems are moving from location or time based reminder systems toward context-aware activity recognition. However, humans rely on several modalities including the five classical senses and other senses such as thermoception (temperature) and equilibrioception (balance and acceleration) together with context information such as location, and time for everyday tasks. Currently, to the best of our knowledge, there is no systematic way to integrate multi-modalities such as profile information, vision with motion, environment, location, and time to infer human intentions, whereas the traditional systems are based on simple activity with a condition and action model. Our focus in this paper is on CAME [6] as a component of our proposed HARE [1] (see Fig. 1 ) that is beyond the limitations of traditional systems. We propose the integration of all the activities detected using different types of sensors together along with context and profile information of the subject. We model the activities in domain ontology within the explicit context of the activity and execution pattern. In addition, we apply semantic reasoning to infer high level activities (user intention for performed activity) and use it for decision making. This will help in enhancing capabilities of healthcare systems to facilitate more personalized recommendations and decision making, and it has tremendous value for intelligent/efficient service provisioning.. For the CAME implementation, we use different sources of information to avoid the possibility of missing information or imperfect context information [5] . For context representation and profile information, we use an ontology and have developed a semantic structure for information representation. Ontology is formally defined as an explicit and formal specification of a shared conceptualization [7] .. Sensors are deployed to collect real-time data about a person's activities and environmental information. Then with the use of an ontology (containing expert knowledge of the medical domain and user profile information) these detected activities are intelligently manipulated to infer higher level activities and also to make a situational analysis. The experimental results of the match-making process of CAME yielded good results. Rule-based filtering for situation analysis and decision making has verified our claims, and the results of activity recognition and manipulation are very encouraging in terms of accuracy. We have also extended our proposed CAME [6] to the analysis and decision making process. The extended CAME uses both A-Box (instance level) and T-Box (structure level) inference techniques that confer better accuracy compared to the traditional CAME. A filter is also implemented in extended CAME to filter out an "unknown activity" during the match making phase. This not only improves the accuracy of the extended CAME compared to the conventional CAME, but also results in better system response time.. This paper is arranged as follows. Section 2 is the background study of existing systems. Section 3 discusses activity recognition techniques used for the detection of low level activity. In Section 4 we discuss our proposed CAME architecture that takes the output of activity recognition engines and uses it for decision making. Section 5 presents our results and a discussion of these results. We conclude our discussion in Section 6. The Context-aware Activity Manipulation Engine (CAME) has been presented using a knowledge-driven approach to recognize Activities of Daily Livings (ADLs). The objective of this system is to infer high-level activities from low level real-time ADLs detected by sensors and to facilitate the provision of better healthcare services. The nature and characteristics of ADLs were analyzed. Based on the analyses, ontology was used to model the ADLs (including activity, location, time, profile, and environmental information), domain knowledge, and expert knowledge. Using ontology with the knowledge engineering practice, a context model for personalized service provisioning and intelligent healthcare facilities has been developed. Ontological modeling of the context and using it for recommendation is the compelling feature of the proposed system. An integrated framework architecture has been developed in addition to the modeled knowledge in order to use the sensed activity information for generating reminders, alerts, and emergency situation analysis for decision making. To achieve better results and provide caregivers an interface for rich interaction, description logic rules have been incorporated. The description logic rules filter out the unnecessary information during decision making. The proposed system was more accurate and had shorter response time for a given situation. To view the detailed operation of the proposed system, please see the video demonstration.. We are planning to test the system on more activities with an extensive set of rules in future studies. We are also planning to work on and provide more services for different kinds of diseases such as stroke and Parkinson's disease. The system in its current state produces some conflicting results when there is more than one subject in the environment. We are trying to overcome this issue as there are some cases in which the appropriate system response is unclear. For example, two subjects are in same room and one is reading a book while the other wants to watch TV. We are developing different heuristics, rules, and scheduling to overcome these issues. Currently planned solutions are: 1) set priorities and levels for different subjects; 2) a subject who initiated activity first will be served in cases of conflict; 3) the location of the conflict will be considered for resolution, i.e., if its subject A's room then the system will respond in favor of subject A; and 4) the subject's schedule will play an important role in handling conflicts, i.e., if a subject's planned activity is relaxing or entertainment then that subject will be served in a common room location.

O O 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 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 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 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 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 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 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 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 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 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 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 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 O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Construction of the child as a user We describe thoughts on three different parties, the child, the designer and the researcher, involved in child computer interaction and how they perceive the child as a user. Their perception is part of their construction of the child. The project's goal is to study how these constructions relate to and affect each other. Today most research that involves children talks loudly about the importance of a constructivistic approach to children and childhood. Well-known theorists such as Piaget and Vygotsky have influenced much of the work that has been done in the area [1] . The child is perceived to actively participate in the construction of both childhood and society at large. They are perceived as autonomous from adults and adult interests.. The research project described here aims to study how three different parties construct the child as a user. Foremost it is the child itself who is interesting. How does it perceive itself within the context of everyday use of computers? The everyday context is planned to be studied since construction is thought to take place all the time . The focus is on the home where both social and individual interaction appears and where the everyday is continuously present and constructed .. The second party of interest is the designer. How and with what kind of information does the designer formulate a mental model of the child as a user? A lot of the educational and edutainment applications have based their design on constructivistic theories . How does that apply to how they perceive the child? How about other designs that are based on other theories? What is the result from different constructions of the child? How does it relate to the child's own self-perception?. Finally, the third party of interest is the researcher that takes interest in child computer interaction. How do researchers construct their mental representation of the child? How does that construction influence the researcher's own theories? Well-known researchers such as Drum and Papert Copyright is held by the author/owner(s) .. IDC2003 7/03 Preston, UK To study the constructions presented above the following studies are planned to be performed:

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 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 O O O O O O O O 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 O O O O O O O O O O O O O O O O O O O O