Query: Localization and map-building of mobile robot based on RFID sensor fusion system

Relevant passages:
Monte Carlo Localization for Mobile Robots <=SEP=> To navigate reliably in indoor environments, a mobile robot must know where it is. Thus, reliable position estimation is a key problem in mobile robotics. We believe that probabilistic approaches are among the most promising candidates to providing a comprehensive and real-time solution to the robot localization problem. However, current methods still face considerable hurdles. In particular, the problems encountered are closely related to the type of representation used to represent probability densities over the robot’s state space. Recent work on Bayesian filtering with particle-based density representations opens up a new approach for mobile robot localization, based on these principles. In this paper we introduce the Monte Carlo Localization method, where we represent the probability density involved by maintaining a set of samples that are randomly drawn from it. By using a sampling-based representation we obtain a localization method that can represent arbitrary distributions. We show experimentally that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location. It is faster, more accurate and less memory-intensive than earlier grid-based methods.

Monte Carlo Localization: Efficient Position Estimation for Mobile Robots <=SEP=> This paper presents a new algorithm for mobile robot localization, called Monte Carlo Localization (MCL). MCL is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success. However, previous approaches were either computationally cumbersome (such as grid-based approaches that represent the state space by high-resolution 3D grids), or had to resort to extremely coarse-grained resolutions. Our approach is computationally efficient while retaining the ability to represent (almost) arbitrary distributions. MCL applies sampling-based methods for approximating probability distributions, in a way that places computation “where needed.” The number of samples is adapted on-line, thereby invoking large sample sets only when necessary. Empirical results illustrate that MCL yields improved accuracy while requiring an order of magnitude less computation when compared to previous approaches. It is also much easier to implement.

CONDENSATION—Conditional Density Propagation for Visual Tracking <=SEP=> The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimo dal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together with visual observations, to propagate the random set over time. The result is highly robust tracking of agile motion. Notwithstanding the use of stochastic methods, the algorithm runs in near real-time.

Bayesian Landmark Learning for Mobile Robot Localization <=SEP=> To operate successfully in indoor environments, mobile robots must be able to localize themselves. Most current localization algorithms lack flexibility, autonomy, and often optimality, since they rely on a human to determine what aspects of the sensor data to use in localization (e.g., what landmarks to use). This paper describes a learning algorithm, called BaLL, that enables mobile robots to learn what features/landmarks are best suited for localization, and also to train artificial neural networks for extracting them from the sensor data. A rigorous Bayesian analysis of probabilistic localization is presented, which produces a rational argument for evaluating features, for selecting them optimally, and for training the networks that approximate the optimal solution. In a systematic experimental study, BaLL outperforms two other recent approaches to mobile robot localization.

A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking <=SEP=> Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or “particle”) representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example.

PowerMatcher: multiagent control in the electricity infrastructure <=SEP=> Different driving forces push the electricity production towards decentralization. As a result, the current electricity infrastructure is expected to evolve into a network of networks, in which all system parts communicate with each other and influence each other. Multi-agent systems and electronic markets form an appropriate technology needed for control and coordination tasks in the future electricity network. We present the PowerMatcher, a market-based control concept for supply and demand matching (SDM) in electricity networks. In a presented simulation study is shown that the simultaneousness of electricity production and consumption can be raised substantially using this concept. Further, we present a field test with medium-sized electricity producing and consuming installations controlled via this concept, currently in preparation.

Investigating performance of students: a longitudinal study <=SEP=> This paper, investigates how academic performance of students evolves over the years in their studies during a study programme. To determine typical progression patterns over the years, students are described by a 4 tuple (e.g. x1, x2, x3, x4), these being the clusters' mean to which a student belongs to in each year of the degree. For this purpose, two consecutive cohorts have been analyzed using X-means clustering. Interestingly the patterns found in both cohorts show that a substantial number of students stay in the same kind of groups during their studies.

Kernel-Based Sensor Fusion With Application to Audio-Visual Voice Activity Detection <=SEP=> In this paper, we address the problem of multiple view data fusion in the presence of noise and interferences. Recent studies have approached this problem using kernel methods, by relying particularly on a product of kernels constructed separately for each view. From a graph theory point of view, we analyze this fusion approach in a discrete setting. More specifically, based on a statistical model for the connectivity between data points, we propose an algorithm for the selection of the kernel bandwidth, a parameter that, as we show, has important implications on the robustness of this fusion approach to interferences. Then, we consider the fusion of audio-visual speech signals measured by a single microphone and by a video camera pointed to the face of the speaker. Specifically, we address the task of voice activity detection, i.e., the detection of speech and nonspeech segments, in the presence of structured interferences such as keyboard taps and office noise. We propose an algorithm for voice activity detection based on the audio-visual signal. Simulation results show that the proposed algorithm outperforms competing fusion and voice activity detection approaches. In addition, we demonstrate that a proper selection of the kernel bandwidth indeed leads to improved performance.

Does Competition Destroy Ethical Behavior? <=SEP=> This paper shows that conduct described as unethical and blamed on “greed” is sometimes a consequence of market competition. I illustrate this observation using examples of five censured activities: employment of children, corruption, excessive executive pay, corporate earnings manipulation, and involvement of universities in commercial activities. In all these examples, I show how competitive pressures lead to the spread of the censured behavior. My point is not to excuse or condemn any or all of these practices, but merely to pinpoint the crucial role of competition, as opposed to greed, in their spread. I focus here on ethics, not efficiency. The relationship between the two is complex. In many cases, ethical norms evolve to sustain cooperative behavior and thus to promote successful functioning of social institutions. For example, ethical condemnation of corruption is based on the idea that officials should not selfishly abuse public trust, and that a society functions better when its government works fairly. Likewise, the ethical norm against the employment of children is driven in part by the more general concern with abuse of the weak by the strong. When ethics promote social cooperation, ethical behavior and efficient behavior typically go together. In other instances, there is a mismatch between ethics and efficiency, either because ethical standards which might have had efficiency justifications long ago no longer do or because the behavior that is ethical in some idealized society makes matters worse in the real world. For example, the ethical norm against debt or interest, which might have been justifiable a millennium ago, is clearly no longer efficient. And while child labor might be a bad idea in a world with good access to capital markets and educational opportunities, for many families in the developing world the alternative to child labor is malnutrition and disease. These examples of a mismatch suggest that behavior condemned as unethical is not always inefficient. In still other instances, the conduct that is advertised as ethical is the result of political indoctrination by parochial interests or of simple confusion. For example, the ethical exhortations to “Buy American!” or to pay the “living wage” are underwritten by labor unions serving their members, not the public. In various times and places, tribalism, racism, anti-Semitism, the hatred of the rich, and other unsavory sentiments reflected the dominant ethic. In these instances, what is considered ethical is very far from what economists would consider efficient. In some of the examples I discuss below, a credible case can be made that the conduct perceived to be unethical is also inefficient; in other examples, there is more ambiguity. My interest, however, is not to evaluate efficiency, but only to bring the crucial role of competition into the explanation of why activities morally sanctioned by the society spread. In four of the examples I discuss, censured behavior reduces costs (in the last one, it raises revenues). I assume that the proprietor of the firm values ethical behavior, but that such behavior is a normal good. When sanctioned behavior by competitors reduces their costs, it also reduces prices in the market, and as a result the proprietor’s income falls. When his income falls, so does his own demand for ethical behavior, leading to the spread of censured practices. The analysis I present is closely related to the ideas in Gary Becker’s (1957) classic study of discrimination and reveals a broad range of circumstances where competition promotes censured conduct.

The draft genome of the transgenic tropical fruit tree papaya (Carica papaya Linnaeus) <=SEP=> Papaya, a fruit crop cultivated in tropical and subtropical regions, is known for its nutritional benefits and medicinal applications. Here we report a 3× draft genome sequence of ‘SunUp’ papaya, the first commercial virus-resistant transgenic fruit tree to be sequenced. The papaya genome is three times the size of the Arabidopsis genome, but contains fewer genes, including significantly fewer disease-resistance gene analogues. Comparison of the five sequenced genomes suggests a minimal angiosperm gene set of 13,311. A lack of recent genome duplication, atypical of other angiosperm genomes sequenced so far, may account for the smaller papaya gene number in most functional groups. Nonetheless, striking amplifications in gene number within particular functional groups suggest roles in the evolution of tree-like habit, deposition and remobilization of starch reserves, attraction of seed dispersal agents, and adaptation to tropical daylengths. Transgenesis at three locations is closely associated with chloroplast insertions into the nuclear genome, and with topoisomerase I recognition sites. Papaya offers numerous advantages as a system for fruit-tree functional genomics, and this draft genome sequence provides the foundation for revealing the basis of Carica’s distinguishing morpho-physiological, medicinal and nutritional properties.

A Video Representation Using Temporal Superpixels <=SEP=> We develop a generative probabilistic model for temporally consistent super pixels in video sequences. In contrast to supermodel methods, object parts in different frames are tracked by the same temporal super pixel. We explicitly model flow between frames with a bilateral Gaussian process and use this information to propagate super pixels in an online fashion. We consider four novel metrics to quantify performance of a temporal super pixel representation and demonstrate superior performance when compared to supermodel methods.

Multi-Modal Learning: Study on A Large-Scale Micro-Video Data Collection <=SEP=> Micro-video sharing social services, as a new phenomenon in social media, enable users to share micro-videos and thus gain increasing enthusiasm among people. One distinct characteristic of micro-videos is the multi-modality, as these videos always have visual signals, audio tracks, textual descriptions as well as social clues. Such multi-modality data makes it possible to obtain a comprehensive understanding of videos and hence provides new opportunities for researchers. However, limited efforts thus far have been dedicated to this new emerging user-generated contents (UGCs) due to the lack of large-scale benchmark dataset. Towards this end, in this paper, we construct a large-scale micro-video dataset, which can support many research domains, such as popularity prediction and venue estimation. Based upon this dataset, we conduct an initial study in popularity prediction of micro-videos. Finally, we identify our future work.

BLEU Evaluation of Machine-Translated English-Croatian Legislation <=SEP=> This paper presents work on the evaluation of online available machine translation (MT) service, i.e. Google Translate, for English-Croatian language pair in the domain of legislation. The total set of 200 sentences, for which three reference translations are provided, is divided into short and long sentences. Human evaluation is performed by native speakers, using the criteria of adequacy and fluency. For measuring the reliability of agreement among raters, Fleiss' kappa metric is used. Human evaluation is enriched by error analysis, in order to examine the influence of error types on fluency and adequacy, and to use it in further research. Translation errors are divided into several categories: non-translated words, word omissions, unnecessarily translated words, morphological errors, lexical errors, syntactic errors and incorrect punctuation. The automatic evaluation metric BLEU is calculated with regard to a single and multiple reference translations. System level Pearson’s correlation between BLEU scores based on a single and multiple reference translations is given, as well as correlation between short and long sentences BLEU scores, and correlation between the criteria of fluency and adequacy and each error category.

Handover Performance in 3GPP Long Term Evolution (LTE) Systems <=SEP=> The specification of the long term evolution (LTE) of 3G systems is currently ongoing in 3GPP with a target date of ready specification at the end of 2007. The evolved radio access network (RAN) involves a new radio interface based on OFDM technology and a radically different RAN architecture, where radio functionality is distributed into the base stations. The distributed nature of the RAN architecture calls for new radio control algorithms and procedures that operate in a distributed manner, including a distributed handover scheme as well. The most important aspects of the handover procedure in LTE has been already settled in 3GPP except a few details. In this paper we give an overview of the LTE intra-access handover procedure and evaluate its performance focusing on the user perceived performance aspects of it. We investigate the necessity of packet forwarding from a TCP throughput point of view, we analyse the problem of out of order packet delivery during handover and propose a simple solution for it. Finally, we investigate the impact of HARQ/ARQ state discard at handover on the radio efficiency. The results show that neither the user perceived performance nor the radio efficiency are compromised by the relocation based handover procedure of LTE.

Adaptive removal of azimuth ambiguities in SAR images <=SEP=> We introduce an innovative algorithm that is capable of suppression of strong azimuth ambiguities in single-look complex (SLC) synthetic aperture radar images, to be used both for detected products and for interferometric surveys. The algorithm exploits a band-pass filter to select that portion of the azimuth spectrum that is less influenced by aliasing, the one that corresponds to the nulls in the replicated azimuth antenna pattern. The selectivity of this filter adapts locally depending on the estimated ambiguity intensity: the filter is more selective in the presence of strong ambiguities and becomes all-pass in absence of ambiguities. The design of such filter frames in the Wiener approach with two different normalization options, depending on the use of the SLC image, either for getting multilook averaged detected products or for interferometric applications. Preliminary results achieved by processing ENVISAT Advanced Synthetic Aperture Radar sensor data are provided.

ITIL as common practice reference model for IT service management: formal assessment and implications for practice <=SEP=> Due to enhanced focus on the customer in the planning, development and delivery of information services, IT service management has become increasingly important. These days IT management is focussing particularly on the de facto standard ITIL (IT Infrastructure Library) for implementing IT service management. In doing this, deficits of the ITIL reference model are often being overlooked, the benefits are merely assumed and misunderstandings spread. This results in uncertainties, unfulfilled expectations and problems in the execution of ITIL transformation projects. Against this background the article critically assesses the ITIL reference model. A formal analysis of the model on the basis of established criteria according to the principles for proper modelling is undertaken and implications for IT management are deduced from this. Four case studies of ITIL transformation projects conducted for this purpose, as well as an analysis of the ITIL documents, serve as basis for the assessment.

Prevalence of kidney stones in the United States. <=SEP=> BACKGROUND
The last nationally representative assessment of kidney stone prevalence in the United States occurred in 1994. After a 13-yr hiatus, the National Health and Nutrition Examination Survey (NHANES) reinitiated data collection regarding kidney stone history.


OBJECTIVE
Describe the current prevalence of stone disease in the United States, and identify factors associated with a history of kidney stones.


DESIGN, SETTING, AND PARTICIPANTS
A cross-sectional analysis of responses to the 2007-2010 NHANES (n=12 110).


OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS
Self-reported history of kidney stones. Percent prevalence was calculated and multivariable models were used to identify factors associated with a history of kidney stones.


RESULTS AND LIMITATIONS
The prevalence of kidney stones was 8.8% (95% confidence interval [CI], 8.1-9.5). Among men, the prevalence of stones was 10.6% (95% CI, 9.4-11.9), compared with 7.1% (95% CI, 6.4-7.8) among women. Kidney stones were more common among obese than normal-weight individuals (11.2% [95% CI, 10.0-12.3] compared with 6.1% [95% CI, 4.8-7.4], respectively; p<0.001). Black, non-Hispanic and Hispanic individuals were less likely to report a history of stone disease than were white, non-Hispanic individuals (black, non-Hispanic: odds ratio [OR]: 0.37 [95% CI, 0.28-0.49], p<0.001; Hispanic: OR: 0.60 [95% CI, 0.49-0.73], p<0.001). Obesity and diabetes were strongly associated with a history of kidney stones in multivariable models. The cross-sectional survey design limits causal inference regarding potential risk factors for kidney stones.


CONCLUSIONS
Kidney stones affect approximately 1 in 11 people in the United States. These data represent a marked increase in stone disease compared with the NHANES III cohort, particularly in black, non-Hispanic and Hispanic individuals. Diet and lifestyle factors likely play an important role in the changing epidemiology of kidney stones.

End to End Automation on Cloud with Build Pipeline: The Case for DevOps in Insurance Industry, Continuous Integration, Continuous Testing, and Continuous Delivery <=SEP=> In modern environment, delivering innovative idea in a fast and reliable manner is extremely significant for any organizations. In the existing scenario, Insurance industry need to better respond to dynamic market requirements, faster time to market for new initiatives and services, and support innovative ways of customer interaction. In past few years, the transition to cloud platforms has given benefits such as agility, scalability, and lower capital costs but the application lifecycle management practices are slow with this disruptive change. DevOps culture extends the agile methodology to rapidly create applications and deliver them across environment in automated manner to improve performance and quality assurance. Continuous Integration (CI) and Continuous delivery (CD) has emerged as a boon for traditional application development and release management practices to provide the capability to release quality artifacts continuously to customers with continuously integrated feedback. The objective of the paper is to create a proof of concept for designing an effective framework for continuous integration, continuous testing, and continuous delivery to automate the source code compilation, code analysis, test execution, packaging, infrastructure provisioning, deployment, and notifications using build pipeline concept.

Improving Neural Knowledge Base Completion with Cross-Lingual Projections <=SEP=> In this paper we present a cross-lingual extension of a neural tensor network model for knowledge base completion. We exploit multilingual synsets from BabelNet to translate English triples to other languages and then augment the reference knowledge base with cross-lingual triples. We project monolingual embeddings of different languages to a shared multilingual space and use them for network initialization (i.e., as initial concept embeddings). We then train the network with triples from the cross-lingually augmented knowledge base. Results on WordNet link prediction show that leveraging cross-lingual information yields significant gains over exploiting only monolingual triples.

Simulating Urban Growth Using the SLEUTH Model in a Coastal Peri-Urban District in China <=SEP=> China’s southeast coastal areas have witnessed rapid growth in the last two decades, owing mostly to their economic and social attractions. In this paper, we chose Jimei, a coastal peri-urban district of Xiamen city on the southeast coast of China, as a study area to explore the district’s growth dynamics, to predict future sprawl during the next decade and to provide a basis for urban planning. The SLEUTH urban growth model was calibrated against historical data derived from a series of Landsat TM 5 satellite images taken between 1992 and 2007. A Lee-Sallee value of 0.48 was calculated for the district, which is a satisfactory result compared with related studies. Five coefficients of urban growth, diffusion, spread, breed, slope resistance and road gravity had values of 25, 68, 86, 24 and 23, respectively, in 2007. The growth coefficients (i.e., urban character) can capture urban growth characteristics in Jimei district. The urban DNA revealed that, over the study period, urban growth in the district was dominated both by urbanization through establishment of new urban centers, and by expansion outward from existing urban centers. In contrast to interior cities, in which expansions are dramatically shaped by actual road patterns, urban expansion in the district was likely constrained by the nearby coastline. Future urban growth patterns were predicted to 2020 assuming three different development scenarios. The first scenario simulated a continuation of historical urban growth without changing current conditions. The second scenario projected managed growth in which OPEN ACCESS Sustainability 2014, 6 3900 urban growth is limited by a layer with areas excluded from urbanization, which is the future development plan for Jimei district and Xiamen city. The third scenario depicted a growth with maximum protection in which growth was allowed to continue, similar to the second scenario, but with lower diffusion and spread coefficients applied to the growth pattern. The third scenario demonstrated that valuable land could be saved, which is the most desirable outcome for Jimei urban development. The study showed that SLEUTH can be an extremely useful tool for coastal city managers to explore the likely outcomes of their city development plans.

How does EMDR work ? <=SEP=> Eye movement desensitisation and reprocessing (EMDR) is an effective treatment for alleviating trauma symptoms, and the positive effects of this treatment have been scientifically confirmed under wellcontrolled conditions. This has provided an opportunity to explore how EMDR works. The present paper reports on the findings of a long series of experiments that disproved the hypothesis that eye movements or other ‘dual tasks’ are unnecessary. These experiments also disproved the idea that ‘bilateral stimulation’ is needed; moving the eyes up and down produces the same effect as horizontal eye movement, and so do tasks that require no eye movement at all. However, it is important that the dual task taxes working memory. Several predictions can be derived from the working memory explanation for eye movements in EMDR. These seem to hold up extremely well in critical experimental tests, and create a solid explanation on how eye movements work. This paper discusses the implications that this theory and the empirical findings may have for the EMDR technique. © Copyright 2012 Textrum Ltd. All rights reserved.

An Empirical Analysis of Bug Reports and Bug Fixing in Open Source Android Apps <=SEP=> Smartphone platforms and applications (apps) have gained tremendous popularity recently. Due to the novelty of the smartphone platform and tools, and the low barrier to entry for app distribution, apps are prone to errors, which affects user experience and requires frequent bug fixes. An essential step towards correcting this situation is understanding the nature of the bugs and bug-fixing processes associated with smartphone platforms and apps. However, prior empirical bug studies have focused mostly on desktop and server applications. Therefore, in this paper, we perform an in-depth empirical study on bugs in the Google Android smartphone platform and 24 widely-used open-source Android apps from diverse categories such as communication, tools, and media. Our analysis has three main thrusts. First, we define several metrics to understand the quality of bug reports and analyze the bug-fix process, including developer involvement. Second, we show how differences in bug life-cycles can affect the bug-fix process. Third, as Android devices carry significant amounts of security-sensitive information, we perform a study of Android security bugs. We found that, although contributor activity in these projects is generally high, developer involvement decreases in some projects, similarly, while bug-report quality is high, bug triaging is still a problem. Finally, we observe that in Android apps, security bug reports are of higher quality but get fixed slower than non-security bugs. We believe that the findings of our study could potentially benefit both developers and users of Android apps.

Dynamic Tangible User Interface Palettes <=SEP=> Graphics editors often suffer from a large number of tool palettes that compete with valuable document space. To address this problem and to bring back physical affordances similar to a painter’s palette, we propose to augment a digital tabletop with spatially tracked handheld displays. These displays are dynamically updated depending on their spatial location. We introduce the concept of spatial Work Zones that take up distinct 3D regions above the table surface and serve as physical containers for digital content that is organized as stacks of horizontal layers. Spatial Work Zones are represented either by physical objects or on-screen on the tabletop. Associated layers can be explored fluently by entering a spatial Work Zone with a handheld display. This provides quick access and seamless changes between tools and parts of the document that are instantly functional, i.e., ready to be used by a digital pen. We discuss several use cases illustrating our techniques and setting them into context with previous systems. Early user feedback indicates that combining dynamic GUI functionality with the physicality of spatially tracked handheld displays is promising and can be generalized beyond graphics editing.

User-click modeling for understanding and predicting search-behavior <=SEP=> Recent advances in search users' click modeling consider both users' search queries and click/skip behavior on documents to infer the user's perceived relevance. Most of these models, including dynamic Bayesian networks (DBN) and user browsing models (UBM), use probabilistic models to understand user click behavior based on individual queries. The user behavior is more complex when her actions to satisfy her information needs form a search session, which may include multiple queries and subsequent click behaviors on various items on search result pages. Previous research is limited to treating each query within a search session in isolation, without paying attention to their dynamic interactions with other queries in a search session.
 Investigating this problem, we consider the sequence of queries and their clicks in a search session as a task and propose a task-centric click model~(TCM). TCM characterizes user behavior related to a task as a collective whole. Specifically, we identify and consider two new biases in TCM as the basis for user modeling. The first indicates that users tend to express their information needs incrementally in a task, and thus perform more clicks as their needs become clearer. The other illustrates that users tend to click fresh documents that are not included in the results of previous queries. Using these biases, TCM is more accurately able to capture user search behavior. Extensive experimental results demonstrate that by considering all the task information collectively, TCM can better interpret user click behavior and achieve significant improvements in terms of ranking metrics of NDCG and perplexity.

Generative Modeling with Conditional Autoencoders: Building an Integrated Cell <=SEP=> We present a conditional generative model to learn variation in cell and nuclear morphology and the location of subcellular structures from microscopy images. Our model generalizes to a wide range of subcellular localization and allows for a probabilistic interpretation of cell and nuclear morphology and structure localization from fluorescence images. We demonstrate the effectiveness of our approach by producing photorealistic cell images using our generative model. The conditional nature of the model provides the ability to predict the localization of unobserved structures given cell and nuclear morphology.

Prevalence of Vitamin D Insufficiency in an Adult Normal Population <=SEP=> The vitamin D status of a general adult urban population was estimated between November and April in 1569 subjects selected from 20 French cities grouped in nine geographical regions (between latitude 43° and 51° N). Major differences in 25-hydroxyvitamin D (25(OH)D) concentration were found between regions, the lowest values being seen in the North and the greatest in the South, with a significant ‘sun’ effect (r = 0.72; p = 0.03) and latitude effect (r = -0.79; p = 0.01). In this healthy adult population, 14% of subjects exhibited 25(OH)D values ≤ 30 nmol/l (12 ng/ml), which represents the lower limit (< 2 SD) for a normal adult population measured in winter with the same method (RIA Incstar). A significant negative correlation was found between serum intact parathyroid hormone (iPTH) and serum 25(OH)D values (p < 0.01). Serum iPTH held a stable plateau level at 36 pg/ml as long as serum 25(OH)D values were higher than 78 nmol/l (31 ng/ml), but increased when the serum 25(OH)D value fell below this. When the 25(OH)D concentration became equal to or lower than 11.3 nmol/l (4.6 ng/ml), the PTH values reached the upper limit of normal values (55 pg/ml) found in vitamin D replete subjects. These results showed that in French normal adults living in an urban environment with a lack of direct exposure to sunshine, diet failed to provide an adequate amount of vitamin D. It is important to pay attention to this rather high prevalence of vitamin D insufficiency in the general adult population and to discuss the clinical utility of winter supplementation with low doses of vitamin D.

Marital processes predictive of later dissolution: behavior, physiology, and health. <=SEP=> Seventy-three married couples were studied in 1983 and 1987. To identify marital processes associated with dissolution, a balance theory of marriage was used to generate 1 variable for dividing couples into regulated and nonregulated groups. For studying the precursors of divorce, a "cascade" model of marital dissolution, which forms a Guttman-like scale, received preliminary support. Compared with regulated couples, nonregulated couples had (a) marital problems rated as more severe (Time 1); (b) lower marital satisfaction (Time 1 and Time 2); (c) poorer health (Time 2); (d) smaller finger pulse amplitudes (wives); (e) more negative ratings for interactions; (f) more negative emotional expression; (g) less positive emotional expression; (h) more stubbornness and withdrawal from interaction; (i) greater defensiveness; and (j) greater risk for marital dissolution (lower marital satisfaction and higher incidence of consideration of dissolution and of actual separation).

A Tutorial on Machine Learning and Data Science Tools with Python <=SEP=> In this tutorial, we will provide an introduction to the main Python software tools used for applying machine learning techniques to medical data. The focus will be on open-source software that is freely available and is cross platform. To aid the learning experience, a companion GitHub repository is available so that you can follow the examples contained in this paper interactively using Jupyter notebooks. The notebooks will be more exhaustive than what is contained in this chapter, and will focus on medical datasets and healthcare problems. Briefly, this tutorial will first introduce Python as a language, and then describe some of the lower level, general matrix and data structure packages that are popular in the machine learning and data science communities, such as NumPy and Pandas. From there, we will move to dedicated machine learning software, such as SciKit-Learn. Finally we will introduce the Keras deep learning and neural networks library. The emphasis of this paper is readability, with as little jargon used as possible. No previous experience with machine learning is assumed. We will use openly available medical datasets throughout.

High frequency ultrasound imaging of surface and subsurface structures of fingerprints <=SEP=> High frequency ultrasound is suitable for non-invasive evaluation of skin because it can obtain both morphological and biomechanical information. A specially developed acoustic microscope system with the central frequency of 100 MHz was developed. The system was capable of (1) conventional C-mode acoustic microscope imaging of thinly sliced tissue, (2) ultrasound impedance imaging of the surface of in vivo thick tissue and (3) 3D ultrasound imaging of inside of the in vivo tissue. In the present study, ultrasound impedance imaging and 3D ultrasound imaging of in vivo fingerprints were obtained. The impedance image showed pores of the sweat glands in the surface of fingerprint and 3D ultrasound imaging showed glands of the rear surface of fingerprint. Both findings were not visualized by normal optical imaging, thus the system can be applied to pathological diagnosis of skin lesions and assessment of aging of the skin in cosmetic point of view.

A privacy-preserving approach to policy-based content dissemination <=SEP=> We propose a novel scheme for selective distribution of content, encoded as documents, that preserves the privacy of the users to whom the documents are delivered and is based on an efficient and novel group key management scheme. Our document broadcasting approach is based on access control policies specifying which users can access which documents, or subdocuments. Based on such policies, a broadcast document is segmented into multiple subdocuments, each encrypted with a different key. In line with modern attribute-based access control, policies are specified against identity attributes of users. However our broadcasting approach is privacy-preserving in that users are granted access to a specific document, or subdocument, according to the policies without the need of providing in clear information about their identity attributes to the document publisher. Under our approach, not only does the document publisher not learn the values of the identity attributes of users, but it also does not learn which policy conditions are verified by which users, thus inferences about the values of identity attributes are prevented. Moreover, our key management scheme on which the proposed broadcasting approach is based is efficient in that it does not require to send the decryption keys to the users along with the encrypted document. Users are able to reconstruct the keys to decrypt the authorized portions of a document based on subscription information they have received from the document publisher. The scheme also efficiently handles new subscription of users and revocation of subscriptions.