Query: Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach

Relevant passages:
Segmentation of Dermoscopy Images Using Wavelet Networks <=SEP=> This paper introduces a new approach for the segmentation of skin lesions in dermoscopic images based on wavelet network (WN). The WN presented here is a member of fixed-grid WNs that is formed with no need of training. In this WN, after formation of wavelet lattice, determining shift and scale parameters of wavelets with two screening stage and selecting effective wavelets, orthogonal least squares algorithm is used to calculate the network weights and to optimize the network structure. The existence of two stages of screening increases globality of the wavelet lattice and provides a better estimation of the function especially for larger scales. R, G, and B values of a dermoscopy image are considered as the network inputs and the network structure formation. Then, the image is segmented and the skin lesions exact boundary is determined accordingly. The segmentation algorithm were applied to 30 dermoscopic images and evaluated with 11 different metrics, using the segmentation result obtained by a skilled pathologist as the ground truth. Experimental results show that our method acts more effectively in comparison with some modern techniques that have been successfully used in many medical imaging problems.

Global cancer statistics. <=SEP=> The global burden of cancer continues to increase largely because of the aging and growth of the world population alongside an increasing adoption of cancer-causing behaviors, particularly smoking, in economically developing countries. Based on the GLOBOCAN 2008 estimates, about 12.7 million cancer cases and 7.6 million cancer deaths are estimated to have occurred in 2008; of these, 56% of the cases and 64% of the deaths occurred in the economically developing world. Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death among females, accounting for 23% of the total cancer cases and 14% of the cancer deaths. Lung cancer is the leading cancer site in males, comprising 17% of the total new cancer cases and 23% of the total cancer deaths. Breast cancer is now also the leading cause of cancer death among females in economically developing countries, a shift from the previous decade during which the most common cause of cancer death was cervical cancer. Further, the mortality burden for lung cancer among females in developing countries is as high as the burden for cervical cancer, with each accounting for 11% of the total female cancer deaths. Although overall cancer incidence rates in the developing world are half those seen in the developed world in both sexes, the overall cancer mortality rates are generally similar. Cancer survival tends to be poorer in developing countries, most likely because of a combination of a late stage at diagnosis and limited access to timely and standard treatment. A substantial proportion of the worldwide burden of cancer could be prevented through the application of existing cancer control knowledge and by implementing programs for tobacco control, vaccination (for liver and cervical cancers), and early detection and treatment, as well as public health campaigns promoting physical activity and a healthier dietary intake. Clinicians, public health professionals, and policy makers can play an active role in accelerating the application of such interventions globally.

Border detection in dermoscopy images using statistical region merging. <=SEP=> BACKGROUND
As a result of advances in skin imaging technology and the development of suitable image processing techniques, during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure, because the accuracy of the subsequent steps crucially depends on it.


METHODS
In this article, we present a fast and unsupervised approach to border detection in dermoscopy images of pigmented skin lesions based on the statistical region merging algorithm.


RESULTS
The method is tested on a set of 90 dermoscopy images. The border detection error is quantified by a metric in which three sets of dermatologist-determined borders are used as the ground-truth. The proposed method is compared with four state-of-the-art automated methods (orientation-sensitive fuzzy c-means, dermatologist-like tumor extraction algorithm, meanshift clustering, and the modified JSEG method).


CONCLUSION
The results demonstrate that the method presented here achieves both fast and accurate border detection in dermoscopy images.

Lesion border detection in dermoscopy images <=SEP=> BACKGROUND
Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders.


METHODS
In this article, we present a systematic overview of the recent border detection methods in the literature paying particular attention to computational issues and evaluation aspects.


CONCLUSION
Common problems with the existing approaches include the acquisition, size, and diagnostic distribution of the test image set, the evaluation of the results, and the inadequate description of the employed methods. Border determination by dermatologists appears to depend upon higher-level knowledge, therefore it is likely that the incorporation of domain knowledge in automated methods will enable them to perform better, especially in sets of images with a variety of diagnoses.

Segmentation of digitized dermatoscopic images by two-dimensional color clustering <=SEP=> A color-based segmentation scheme applied to dermatoscopic images is proposed. The RGB image is processed in the L*u*v* color space. A 2D histogram is computed with the two principal components and then smoothed with a Gaussian low-pass filter. The maxima location and a set of features are computed from the histogram contour lines. These features are the number of enclosed pixels, the surface of the base and the height of the maximum. They allow for the selection of valid clusters which determine the number of classes. The image is then segmented using a modified version of the fuzzy c-means (FCM) clustering technique that takes into account the cluster orientation. Finally, the segmented image is cleaned using mathematical morphology, the region borders are smoothed and small components are removed.

NICBPM: Non-invasive cuff-less blood pressure monitor <=SEP=> An ultra-low power, ultra portable, wearable integrated bio-medical device, the Non-Invasive Cuff-less Blood Pressure Monitor (NICBPM) is proposed to measure the Pulse Transit Time (PTT). The relationship between PTT and Blood Pressure (BP) is investigated using linear and non linear regression models. Simultaneous reading obtained from an FDA approved, commercially available Blood Pressure monitor and the (Electrocardiogram) ECG and (photoplethsymogram) PPG signal from the NICBPM are used to calibrate the device for 30 subjects during cardiopulmonary exercise test. Bland-Altman plots for the linear regression model revealed limits of agreement of −10.6 to 10.6 mmHg for systolic BP (sBP) and −10.5 to 10.5 mmHg for distolic BP (dBP). Bland-Altman plots for the non linear regression model revealed marginally better limits of agreement between −8.5 to 8.5 mmHg for sBP and −6.6 to 6.6 mmHg for dBP. sBP can be estimated from the NICBPM with 99% accuracy and 99% sensitivity.

A Dolev-Yao Model for Zero Knowledge <=SEP=> In cryptographic protocols, zero knowledge proofs are employed for a principal A to communicate some non-trivial information t to B while at the same time ensuring that B cannot derive any information “stronger” than t . Oen this implies that B can verify that some property holds without being able to produce a proof of this. While a rich theory of zero knowledge proofs exists, there are few formal models addressing verification questions.We propose an extension of the standard Dolev-Yao model of cryptographic protocols which involves not only constructibility of terms but also a form of verifiability. We present a proof system for term derivability, which is employed to yield a decision procedure for checking whether a given protocol meets its zero knowledge specification.

Tools for the Automatic Generation of Ontology Documentation: A Task-Based Evaluation <=SEP=> Ontologies are knowledge constructs essential for creation of the Web of Data. Good documentation is required to permit people to understand ontologies and thus employ them correctly, but this is costly to create by tradition authorship methods, and is thus inefficient to create in this way until an ontology has matured into a stable structure. The authors describe three tools, LODE, Parrot and the OWLDoc-based Ontology Browser, that can be used automatically to create documentation from a well-formed OWL ontology at any stage of its development. They contrast their properties and then report on the authors’ evaluation of their effectiveness and usability, determined by two task-based user testing sessions.

Digital Mosaic Frameworks - An Overview <=SEP=> Art often provides valuable hints for technological innovations especially in the field of Image Processing and Computer Graphics. In this paper we survey in a unified framework several methods to transform raster input images into good quality mosaics. For each of the major different approaches in literature the paper reports a short description and a discussion of the most relevant issues. To complete the survey comparisons among the different techniques both in terms of visual quality and computational complexity are provided.

Human movement analysis using stereophotogrammetry. Part 1: theoretical background. <=SEP=> This paper sets the stage for a series of reviews dealing with the problems associated with the reconstruction and analysis of in vivo skeletal system kinematics using optoelectronic stereophotogrammetric data. Instantaneous bone position and orientation and joint kinematic variable estimations are addressed in the framework of rigid body mechanics. The conceptual background to these exercises is discussed. Focus is placed on the experimental and analytical problem of merging the information relative to movement and that relative to the morphology of the anatomical body parts of interest. The various global and local frames that may be used in this context are defined. Common anatomical and mathematical conventions that can be used to describe joint kinematics are illustrated in a comparative fashion. The authors believe that an effort to systematize the different theoretical and experimental approaches to the problems involved and related nomenclatures, as currently reported in the literature, is needed to facilitate data and knowledge sharing, and to provide renewed momentum for the advancement of human movement analysis.

Differentially Private Smart Metering with Battery Recharging <=SEP=> The energy industry has recently begun using smart meters to take fine-grained readings of energy usage. These smart meters enable flexible timeof-use billing, forecasting, and demand response, but they also raise serious user privacy concerns. We propose a novel technique for provably hiding sensitive power consumption information in the overall power consumption stream. Our technique relies on a rechargeable battery that is connected to the household’s power supply. This battery is used to modify the household’s power consumption by adding or subtracting noise (i.e., increasing or decreasing power consumption), in order to establish strong privacy guarantees in the sense of differential privacy. To achieve these privacy guarantees in realistic settings, we first investigate the influence of, and the interplay between, capacity and throughput bounds that batteries face in reality. We then propose an integrated method based on noise cascading that allows for recharging the battery on-the-fly so that differential privacy is retained, while adhering to capacity and throughput constraints, and while keeping the additional consumption of energy induced by our technique to a minimum.

Huffman Coding-Based Adaptive Spatial Modulation <=SEP=> Antenna switch enables multiple antennas to share a common RF chain. It also offers an additional spatial dimension, i.e., antenna index, that can be utilized for data transmission via both signal space and spatial dimension. In this paper, we propose a Huffman coding-based adaptive spatial modulation that generalizes both conventional spatial modulation and transmit antenna selection. Through the Huffman coding, i.e., designing variable length prefix codes, the transmit antennas can be activated with different probabilities. When the input signal is Gaussian distributed, the optimal antenna activation probability is derived through optimizing channel capacity. To make the optimization tractable, closed form upper bound and lower bound are derived as the effective approximations of channel capacity. When the input is discrete QAM signal, the optimal antenna activation probability is derived through minimizing symbol error rate. Numerical results show that the proposed adaptive transmission offers considerable performance improvement over the conventional spatial modulation and transmit antenna selection.

Diagnosing performance overheads in the xen virtual machine environment <=SEP=> Virtual Machine (VM) environments (e.g., VMware and Xen) are experiencing a resurgence of interest for diverse uses including server consolidation and shared hosting. An application's performance in a virtual machine environment can differ markedly from its performance in a non-virtualized environment because of interactions with the underlying virtual machine monitor and other virtual machines. However, few tools are currently available to help debug performance problems in virtual machine environments.In this paper, we present Xenoprof, a system-wide statistical profiling toolkit implemented for the Xen virtual machine environment. The toolkit enables coordinated profiling of multiple VMs in a system to obtain the distribution of hardware events such as clock cycles and cache and TLB misses. The toolkit will facilitate a better understanding of performance characteristics of Xen's mechanisms allowing the community to optimize the Xen implementation.We use our toolkit to analyze performance overheads incurred by networking applications running in Xen VMs. We focus on networking applications since virtualizing network I/O devices is relatively expensive. Our experimental results quantify Xen's performance overheads for network I/O device virtualization in uni- and multi-processor systems. With certain Xen configurations, networking workloads in the Xen environment can suffer significant performance degradation. Our results identify the main sources of this overhead which should be the focus of Xen optimization efforts. We also show how our profiling toolkit was used to uncover and resolve performance bugs that we encountered in our experiments which caused unexpected application behavior.

Adaptive Courseware: A Literature Review <=SEP=> This paper gives a detailed description of how adaptivity is realized in e-learning systems, with a focus on adaptivity of courseware. The review aims to answer three basic questions on adaptive courseware: what has been adapted, how and why. Researchers have tried to adapt according to these different elements: student knowledge, behaviour, interests, preferences, learning style, cognitive style, goals, courseware presentation, navigation, annotation, sequencing and knowledge testing. An historical background of adaptivity is described: from word definition, through adaptive instructions and adaptive systems, all the way to adaptive courseware. The biggest challenge was to find the most prominent representatives of adaptive systems in courseware generation that were selected after an exhaustive search through relevant scientific paper databases. Each prevailing system is briefly described and the student features, to which the system adapts, are highlighted, together with the level of adaptation. This paper aims to provide important information to researchers, educators and software developers of computer-based educational software ranging from elearning systems in general to intelligent tutoring systems in particular. Such an analysis has not been done so far, especially in a way that adaptive systems are described uniformly. Finally, a comparative analysis of those systems and conclusions and demands about the ultimate adaptive system is given. This paper can be used as a guide for making decisions about what to adapt, how and why, while designing an adaptive e-learning system.

Influence of Rectifiers on High-Speed Permanent Magnet Generator Electromagnetic and Temperature Fields in Distributed Power Generation Systems <=SEP=> A power converter is very necessary in the system of high-speed permanent magnet generators (HSPMG), since the frequency of the output electrical energy is generally hundreds of Hertz or even more than 1000 Hz. However, the converter with switches in fast on-off transitions will have some effects on the generator and, therewith, some current harmonics will be also induced in the device. Taking a 117 kW 60 000 r/min HSPMG as an example, the harmonics in this generator were analyzed. And then, the voltage harmonic content, the current harmonic content, and the current total harmonic distortion could be obtained. In addition, stator core losses and rotor eddy current losses were analyzed comparatively, and the influence of the converter on the generator power factor was further studied. Based on the 3-D coupling field between fluid and temperature, the temperature distributions were obtained when the generator was connected to different loads, and the influence of the rectifier on the generator temperature field could be obtained, which was also compared with the test data.

A Cross-Type Thermal Wind Sensor With Self-Testing Function <=SEP=> A 2-D smart flow sensor with self-testing function was designed, fabricated and tested in this paper. It is composed of cross-structure heaters and four symmetrically located sensing parts. When the flow angle changes in the clockwise direction, the temperature differences among the four parts, namely Mode 1 given by T13(T24) and Model 2 given by T12(T14) will be Sine(Cosine) functions of wind direction. Further study shows that the magnitude of mode 1 is 1.414 times that of Mode 2, and the phase of Mode 2 leads Mode 1 by 45°. By using the fixed phase gap between the two modes, the self-test function can be realized without additional components. In order to achieve a high sensitivity and robust sensor, thermal insulated substrate was introduced to fabricate calorimetric flow sensor instead of silicon wafers. The proposed sensor was fabricated on the Pyrex7740 substrate using single liftoff process. Finally, a wind tunnel test was carried out in constant power (CP) mode, and the test results show reasonable agreement with the simulation curves.

Modeling the ownership of source code topics <=SEP=> Exploring linguistic topics in source code is a program comprehension activity that shows promise in helping a developer to become familiar with an unfamiliar software system. Examining ownership in source code can reveal complementary information, such as who to contact with questions regarding a source code entity, but the relationship between linguistic topics and ownership is an unexplored area. In this paper we combine software repository mining and topic modeling to measure the ownership of linguistic topics in source code. We conduct an exploratory study of the relationship between linguistic topics and ownership in source code using 10 open source Java systems. We find that classes that belong to the same linguistic topic tend to have similar ownership characteristics, which suggests that conceptually related classes often share the same owner(s). We also find that similar topics tend to share the same ownership characteristics, which suggests that the same developers own related topics.

The role of prefrontal cortex and posterior parietal cortex in task switching. <=SEP=> Human ability to switch from one cognitive task to another involves both endogenous preparation without an external stimulus and exogenous adjustment in response to the external stimulus. In an event-related functional MRI study, participants performed pairs of two tasks that are either the same (task repetition) or different (task switch) from each other. On half of the trials, foreknowledge about task repetition or task switch was available. On the other half, it was not. Endogenous preparation seems to involve lateral prefrontal cortex (BA 46/45) and posterior parietal cortex (BA 40). During preparation, higher activation increases in inferior lateral prefrontal cortex and superior posterior parietal cortex were associated with foreknowledge than with no foreknowledge. Exogenous adjustment seems to involve superior prefrontal cortex (BA 8) and posterior parietal cortex (BA 39/40) in general. During a task switch with no foreknowledge, activations in these areas were relatively higher than during a task repetition with no foreknowledge. These results suggest that endogenous preparation and exogenous adjustment for a task switch may be independent processes involving different brain areas.

Behavioural Finance : A Review and Synthesis <=SEP=> I provide a synthesis of the Behavioural finance literature over the past two decades. I review the literature in three parts, namely, (i) empirical and theoretical analyses of patterns in the cross-section of average stock returns, (ii) studies on trading activity, and (iii) research in corporate finance. Behavioural finance is an exciting new field because it presents a number of normative implications for both individual investors and CEOs. The papers reviewed here allow us to learn more about these specific implications.

PATTERNS OF INFORMATION SEEKING ON THE WEB: A QUALITATIVE STUDY OF DOMAIN EXPERTISE AND WEB EXPERTISE <=SEP=> This research examines the pattern of Web information seeking in four groups of nurses with different combinations of domain expertise and Web expertise. Protocols were gathered as the nurses carried out information-seeking tasks in the domain of osteoporosis. Domain and Web novices searched breadth-first and did little or no evaluation of the results. Domain expert/Web novices also searched breadth-first but evaluated information more thoroughly using osteoporosis knowledge. Domain novice/Web experts searched in a mixed, breadth-first/depth-first pattern and attempted to evaluate information using general criteria. Domain expert/Web experts carried out depth-first searches, following deep trails of information and evaluated information based on the most varied and sophisticated criteria. The results suggest that there are distinct differences in searching patterns related to expertise. Implications of these findings and suggestions for future research are provided. _______________________ Christine A. Jenkins is a Solution Architect with Sprint Corporation. christine_jenkin@hotmail.com. Cynthia L. Corritore is a professor at the College of Business Administration, Creighton University, Omaha, NE 68178 USA. cindy@creighton.edu. Susan Wiedenbeck is a professor at the College of Information Science and Technology, Drexel University, Philadelphia, PA 19104 USA. susan.wiedenbeck@cis.drexel.edu.

A Softwarization Architecture for UAVs and WSNs as Part of the Cloud Environment <=SEP=> The development of Unmanned Aerial Vehicles (UAV) and wireless sensor network (WSN) applications depends on the availability of resources in UAVs and sensor nodes. In traditional applications, UAVs obtain the required data from specific sensors. However, this tightly coupled architecture restricts the usage of the infrastructure for specific applications. This paper proposes a softwarization architecture for UAVs and WSNs. In addition, the higher layers are proposed to be part of the cloud to benefit from the various cloud opportunities. The proposed architecture is based on the network softwarization concept and the Software Defined Networks (SDN) concept along with the Network Functional Virtualization (NFV) technologies. These concepts are associated with decoupling the hardware infrastructure from the control layer that virtualizes the infrastructure resources for the higher layers. The architecture is illustrated with an agricultural example for a cooperative system of a WSN and UAVs. We implement a prototype system that consists of a sensing node, UAV, WSN controller, UAV controller, and orchestration layer to provide a proof of concept for the proposed architecture.

A Comprehensive Study for Software Testing and Test Cases Generation Paradigms <=SEP=> Software testing is accounted to be an essential part in software development life cycle in terms of cost and manpower, where its total cost is considerable high. Consequently, many studies [48] have been conducted to minimize the associated cost and human effort to fix bugs and errors, and to improve the quality of testing process by automatically generating test cases. Test cases can be generated from different phases (requirement phase, design phase and after development phase). Though, test case generation at early stages is more effective rather than that after development, where time and effort used for finding and fixing errors and bugs is less than that after development. At later stage, fixing errors results in enormous code correction, consuming a lot of time and effort. In this paper, we study the different paradigms of testing techniques for generating test cases, where we investigate their coverage and associated capabilities. We finally propose a preliminary model for a generic automated test cases generation.

Violent Crime Rate Studies in Philosophical Context : A Destructive Testing Approach to Heat and Southern Culture ofViolence Effects <=SEP=> The logic behind the translation of conceptual hypotheses into testable propositions was illustrated with the heat hypothesis, The destructive testing philosophy was introduced and applied, This consists of first showing that a predicted empirical relation exists, then attempting to break that relation. by adding competitor variables. The key question in destructive testing is "How difficult was it to break the relation?'~ This approach was used to analyze the heat effect on violent crime rates (Study I ) and on White violent crime arrest rates (Study 2) in U.S. cities. One competitor variable was the particular focus of analysis: southern culture of violence. The heat hypothesis was supported by highly significant correlations between the warmth of a city and its violence rate. This heat effect survived multiple destructive tests. Some support for the southern culture effect was also found, but this effect was more easily broken.

2D Color Barcodes for Mobile Phones <=SEP=> We propose a new high capacity color barcode, named HCC2D (High Capacity Colored 2-Dimensional), which use colors to increase the barcode data density. The introduction and recognition of colored modules poses some new and non-trivial computer vision challenges, such as handling the color distortions introduced by the hardware equipment that realizes the Print&Scan process. We developed a prototype for generating and reading the HCC2D code format, both on desktops (Linux and Windows platforms) and on mobile phones (Android platforms). We tested this prototype in many experiments considering different operating scenarios and data densities, and compared it to known 2-dimensional barcodes.

Fast Computation of Graph Kernels <=SEP=> Using extensions of linear algebra concepts to Reproducing Kernel Hilbert Spaces (RKHS), we define a unifying framework for random walk kernels on graphs. Reduction to a Sylvester equation allows us to compute many of these kernels in O(n) worst-case time. This includes kernels whose previous worst-case time complexity was O(n), such as the geometric kernels of Gärtner et al. [1] and the marginal graph kernels of Kashima et al. [2]. Our algebra in RKHS allow us to exploit sparsity in directed and undirected graphs more effectively than previous methods, yielding sub-cubic computational complexity when combined with conjugate gradient solvers or fixed-point iterations. Experiments on graphs from bioinformatics and other application domains show that our algorithms are often more than 1000 times faster than existing approaches.

Parallel and Distributed Data Pipelining with KNIME <=SEP=> In recent years a new category of data analysis applications have evolved, known as data pipelining tools, which enable even nonexperts to perform complex analysis tasks on potentially huge amounts of data. Due to the complex and computing intensive analysis processes and methods used, it is often neither sufficient nor possible to simply rely on the increase of performance of single processors. Promising solutions to this problem are parallel and distributed approaches that can accelerate the analysis process. In this paper we discuss the parallel and distribution potential of pipelining tools by demonstrating several parallel and distributed implementations in the open source pipelining platform KNIME. We verify the practical applicability in a number of real world experiments.

A Neural Representation of Sketch Drawings <=SEP=> We present sketch-rnn, a recurrent neural network (RNN) able to construct stroke-based drawings of common objects. The model is trained on thousands of crude human-drawn images representing hundreds of classes. We outline a framework for conditional and unconditional sketch generation, and describe new robust training methods for generating coherent sketch drawings in a vector format.

Multi-Document Summarization with GIST EXTER <=SEP=> This paperpresentsthe architectureandthe multidocument summarizationtechniquesimplementedin the GISTEXTER system.The paperpresentsanalgorithmfor producing incrementalmulti-document summariesif extractiontemplatesof goodquality areavailable. An empirical methodof generatingad-hoctemplatesthat can be populated with information extractedfrom texts by automatically acquiredextractionpatternsis alsopresented . The resultsof GISTEXTER in the DUC-2001evaluations account for the advantagesof usingthetechniquespresentedin this paper .

Robust Matrix Elastic Net based Canonical Correlation Analysis: An Effective Algorithm for Multi-View Unsupervised Learning <=SEP=> This paper presents a robust matrix elastic net based canonical correlation analysis (RMEN-CCA) for multiple view unsupervised learning problems, which emphasizes the combination of CCA and the robust matrix elastic net (RMEN) used as coupled feature selection. The RMEN-CCA leverages the strength of the RMEN to distill naturally meaningful features without any prior assumption and to measure effectively correlations between different ’views’. We can further employ directly the kernel trick to extend the RMEN-CCA to the kernel scenario with theoretical guarantees, which takes advantage of the kernel trick for highly complicated nonlinear feature learning. Rather than simply incorporating existing regularization minimization terms into CCA, this paper provides a new learning paradigm for CCA and is the first to derive a coupled feature selection based CCA algorithm that guarantees convergence. More significantly, for CCA, the newly-derived RMEN-CCA bridges the gap between measurement of relevance and coupled feature selection. Moreover, it is nontrivial to tackle directly the RMEN-CCA by previous optimization approaches derived from its sophisticated model architecture. Therefore, this paper further offers a bridge between a new optimization problem and an existing efficient iterative approach. As a consequence, the RMEN-CCA can overcome the limitation of CCA and address large-scale and streaming data problems. Experimental results on four popular competing datasets illustrate that the RMEN-CCA performs more effectively and efficiently than do state-of-the-art approaches.

Mars Sample Return campaign status <=SEP=> The proposed Mars Sample Return (MSR) Campaign would be the next big step in the Mars Exploration Program (MEP). Identified by the Planetary Decadal Survey of 2011 as the highest science priority for large missions, the Mars Program has been working in partnership with the European Space Agency (ESA) to define a campaign to return a surface sample of martian surface material to Earth for analysis. As currently envisioned, the MSR campaign concept consists of a series of flight missions to collect and return the sample, and a ground based project to develop a sample receiving facility. The first mission in this proposed campaign would be baselined for launch in 2018, and would consist of a rover capable of scientifically selecting and caching the sample for retrieval by a later mission. Subsequent missions would retrieve the cache, launch it into orbit around Mars, where it would be captured by an orbiter and returned to the Earth. This paper discusses the current status of the MSR Campaign architecture definition and requirements development and near term plans for technical definition and review. Also discussed are selected design drivers and proposed key requirements which would have to be addressed in the definition of the campaign.1, 2