Query: Semi-automated map creation for fast deployment of AGV fleets in modern logistics

Relevant passages:
Graph SLAM based mapping for AGV localization in large-scale warehouses <=SEP=> The operation of industrial Automated Guided Vehicles (AGV) today requires designated infrastructure and readily available maps for their localization. In logistics, high effort and investment is necessary to enable the introduction of AGVs. Within the SICK AG coordinated EU-funded research project PAN-Robots we aim to reduce the installation time and costs dramatically by semi-automated plant exploration and localization based on natural landmarks. In this paper, we present our current mapping and localization results based on measurement data acquired at the site of our project partner Coca-Cola Iberian Partners in Bilbao, Spain. We evaluate our solution in terms of accuracy of the map, i.e. comparing landmark position estimates with a ground truth map of millimeter accuracy. The localization results are shown based on artificial landmarks as well as natural landmarks (gridmaps) based on the same graph based optimization solution.

V-Clip: Fast and Robust Polyhedral Collision Detection <=SEP=> This article presents the Voronoi-clip, or V-Clip, collision detection alogrithm for polyhedral objects specified by a boundary representation. V-Clip tracks the closest pair of features between convex polyhedra, using an approach reminiscent of the Lin-Canny closest features algorithm. V-Clip is an improvement over the latter in several respects. Coding complexity is reduced, and robustness is significantly improved; the implementation has no numerical tolerances and does not exhibit cycling problems. The algorithm also handles penetrating polyhedra, and can therefore be used to detect collisions between nonvconvex polyhedra described as hierarchies of convex pieces. The article presents the theoretical principles of V-Clip, and gives a pseudocode description of the algorithm. It also documents various test that compare V-Clip, Lin-Canny, and the Enhanced GJK algorithm, a simplex-based algorithm that is widely used for the same application. The results show V-Clip to be a strong contender in this field, comparing favorably with the other algorithms in most of the tests, in term of both performance and robustness.

Vision for Mobile Robot Navigation: A Survey <=SEP=> This paper surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment.

SHORTEST PATHS FOR THE REEDS-SHEPP CAR : A WORKED OUT EXAMPLE OF THE USE OF GEOMETRIC TECHNIQUES IN NONLINEAR OPTIMAL CONTROL <=SEP=> We illustrate the use of the techniques of modern geometric optimal control theory by studying the shortest paths for a model of a car that can move forwards and backwards. This problem was discussed in recent work by Reeds and Shepp who showed, by special methods, (a) that shortest path motion could always be achieved by means of trajectories of a special kind, namely, concatenations of at most five pieces, each of which is either a straight line or a circle, and (b) that these concatenations can be classified into 48 three-parameter families. We show how these results fit in a much more general framework, and can be discovered and proved by applying in a systematic way the techniques of Optimal Control Theory. It turns out that the “classical” optimal control tools developed in the 1960’s, such as the Pontryagin Maximum Principle and theorems on the existence of optimal trajectories, are helpful to go part of the way and get some information on the shortest paths, but do not suffice to get the full result. On the other hand, when these classical techniques are combined with the use of a more recently developed body of theory, namely, geometric methods based on the Lie algebraic analysis of trajectories, then one can recover the full power of the Reeds-Shepp results, and in fact slightly improve upon them by lowering their 48 to a 46.

Autonomous robotic exploration using occupancy grid maps and graph SLAM based on Shannon and Rényi Entropy <=SEP=> In this paper we examine the problem of autonomously exploring and mapping an environment using a mobile robot. The robot uses a graph-based SLAM system to perform mapping and represents the map as an occupancy grid. In this setting, the robot must trade-off between exploring new area to complete the task and exploiting the existing information to maintain good localization. Selecting actions that decrease the map uncertainty while not significantly increasing the robot's localization uncertainty is challenging. We present a novel information-theoretic utility function that uses both Shannon's and Rényi's definitions of entropy to jointly consider the uncertainty of the robot and the map. This allows us to fuse both uncertainties without the use of manual tuning. We present simulations and experiments comparing the proposed utility function to state-of-the-art utility functions, which only use Shannon's entropy. We show that by using the proposed utility function, the robot and map uncertainties are smaller than using other existing methods.

Advances in Variational Inference <=SEP=> Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesian probabilistic models. These models are usually intractable and thus require approximate inference. Variational inference (VI) lets us approximate a high-dimensional Bayesian posterior with a simpler variational distribution by solving an optimization problem. This approach has been successfully used in various models and large-scale applications. In this review, we give an overview of recent trends in variational inference. We first introduce standard mean field variational inference, then review recent advances focusing on the following aspects: (a) scalable VI, which includes stochastic approximations, (b) generic VI, which extends the applicability of VI to a large class of otherwise intractable models, such as non-conjugate models, (c) accurate VI, which includes variational models beyond the mean field approximation or with atypical divergences, and (d) amortized VI, which implements the inference over local latent variables with inference networks. Finally, we provide a summary of promising future research directions.

Stacking-based deep neural network: Deep analytic network on convolutional spectral histogram features <=SEP=> Stacking-based deep neural network (S-DNN), in general, denotes a deep neural network (DNN) resemblance in terms of its very deep, feedforward network architecture. The typical S-DNN aggregates a variable number of individually learnable modules in series to assemble a DNN-alike alternative to the targeted object recognition tasks. This work likewise devises an S-DNN instantiation, dubbed deep analytic network (DAN), on top of the spectral histogram (SH) features. The DAN learning principle relies on ridge regression, and some key DNN constituents, specifically, rectified linear unit, fine-tuning, and normalization. The DAN aptitude is scrutinized on three repositories of varying domains, including FERET (faces), MNIST (handwritten digits), and CIFAR10 (natural objects). The empirical results unveil that DAN escalates the SH baseline performance over a sufficiently deep layer.

A Simple and Reliable PDMS and SU-8 Irreversible Bonding Method and Its Application on a Microfluidic-MEA Device for Neuroscience Research <=SEP=> Polydimethylsiloxane (PDMS) and SU-8 are currently two very commonly used polymeric materials in the microfluidics field for biological applications. However, there is a pressing need to find a simple, reliable, irreversible bonding method between these two materials for their combined use in innovative integrated microsystems. In this paper, we attempt to investigate the aminosilane-mediated irreversible bonding method for PDMS and SU-8 with X-Ray Photoelectron Spectroscopy (XPS) surface analysis and bonding strength tests. Additionally, the selected bonding method was applied in fabricating a microelectrode array (MEA) device, including microfluidic features, which allows electrophysiological observations on compartmentalized neuronal cultures. As there is a growing trend towards microfluidic devices for neuroscience research, this type of integrated microdevice, which can observe functional alterations on compartmentalized neuronal culture, can potentially be used for neurodegenerative disease research and pharmaceutical development.

A review of overview+detail, zooming, and focus+context interfaces <=SEP=> There are many interface schemes that allow users to work at, and move between, focused and contextual views of a dataset. We review and categorize these schemes according to the interface mechanisms used to separate and blend views. The four approaches are overview+detail, which uses a spatial separation between focused and contextual views; zooming, which uses a temporal separation; focus+context, which minimizes the seam between views by displaying the focus within the context; and cue-based techniques which selectively highlight or suppress items within the information space. Critical features of these categories, and empirical evidence of their success, are discussed. The aim is to provide a succinct summary of the state-of-the-art, to illuminate both successful and unsuccessful interface strategies, and to identify potentially fruitful areas for further work.

Measurements and Simulations of the Convective Heat Transfer Coefficients on the End Windings of an Electrical Machine <=SEP=> The prediction of the thermal behavior of an electrical machine in the design process basically depends on the used boundary conditions such as the convective heat transfer coefficient. Due to the complicated shape of the end windings, the heat transfer coefficient has to be identified empirically by using thermopiles as heat flux sensors placed at certain positions on the end windings, because an analytical derivation is not feasible. This paper presents the results of the measured values of the heat transfer on the end windings of an electrical machine. The gained coefficients are compared to the results obtained by numerical simulations using commercial software for computational fluid dynamics. The simulation results are discussed according to the validity of the gained heat transfer coefficient. The configuration of the simulation has been tested on a cylindrical shape in a cross-flow.

Modeling Credit Risk for SMEs : Evidence from the US Market <=SEP=> Considering the fundamental role played by small and medium sized enterprises (SMEs) in the economy of many countries and the considerable attention placed on SMEs in the new Basel Capital Accord, we develop a distress prediction model specifically for the SME sector and to analyze its effectiveness compared to a generic corporate model. The behaviour of financial measures for SMEs is analyzed and the most significant variables in predicting the entities’ credit worthiness are selected in order to construct a default prediction model. Using a logit regression technique on panel data of over 2,000 US firms (with sales less than $65 million) over the period 1994-2002, we develop a one-year default prediction model. This model has an out-of-sample prediction power which is almost 30 percent higher than a generic corporate model. An associated objective is to observe our model’s ability to lower bank capital requirements considering the new Basel Capital Accord’s rules for SMEs.

Query optimization using column statistics in hive <=SEP=> Hive is a data warehousing solution on top of the Hadoop MapReduce framework that has been designed to handle large amounts of data and store them in tables like a relational database management system or a conventional data warehouse while using the parallelization and batch processing functionalities of the Hadoop MapReduce framework to speed up the execution of queries. Data inserted into Hive is stored in the Hadoop FileSystem (HDFS), which is part of the Hadoop MapReduce framework. To make the data accessible to the user, Hive uses a query language similar to SQL, which is called HiveQL. When a query is issued in HiveQL, it is translated by a parser into a query execution plan that is optimized and then turned into a series of map and reduce iterations. These iterations are then executed on the data stored in the HDFS, writing the output to a file.
 The goal of this work is to to develop an approach for improving the performance of the HiveQL queries executed in the Hive framework. For that purpose, we introduce an extension to the Hive MetaStore which stores metadata that has been extracted on the column level of the user database. These column level statistics are then used for example in combination with join ordering algorithms which are adapted to the specific needs of the Hadoop MapReduce environment to improve the overall performance of the HiveQL query execution.

Perspectives of granular computing <=SEP=> Granular computing emerges as a new multi-disciplinary study and has received much attention in recent years. A conceptual framework is presented by extracting shared commonalities from many fields. The framework stresses multiple views and multiple levels of understanding in each view. It is argued that granular computing is more about a philosophical way of thinking and a practical methodology of problem solving. By effectively using levels of granularity, granular computing provides a systematic, natural way to analyze, understand, represent, and solve real world problems. With granular computing, one aims at structured thinking at the philosophical level, and structured problem solving at the practical level.

Learning Attribute-to-Feature Mappings for Cold-Start Recommendations <=SEP=> Cold-start scenarios in recommender systems are situations in which no prior events, like ratings or clicks, are known for certain users or items. To compute predictions in such cases, additional information about users (user attributes, e.g. gender, age, geographical location, occupation) and items (item attributes, e.g. genres, product categories, keywords) must be used. We describe a method that maps such entity (e.g. user or item) attributes to the latent features of a matrix (or higher-dimensional) factorization model. With such mappings, the factors of a MF model trained by standard techniques can be applied to the new-user and the new-item problem, while retaining its advantages, in particular speed and predictive accuracy. We use the mapping concept to construct an attribute-aware matrix factorization model for item recommendation from implicit, positive-only feedback. Experiments on the new-item problem show that this approach provides good predictive accuracy, while the prediction time only grows by a constant factor.

Language and the Newborn Brain: Does Prenatal Language Experience Shape the Neonate Neural Response to Speech? <=SEP=> Previous research has shown that by the time of birth, the neonate brain responds specially to the native language when compared to acoustically similar non-language stimuli. In the current study, we use near-infrared spectroscopy to ask how prenatal language experience might shape the brain response to language in newborn infants. To do so, we examine the neural response of neonates when listening to familiar versus unfamiliar language, as well as to non language stimuli. Twenty monolingual English-exposed neonates aged 0-3 days were tested. Each infant heard low-pass filtered sentences of forward English (familiar language), forward Tagalog (unfamiliar language), and backward English and Tagalog (non-language). During exposure, neural activation was measured across 12 channels on each hemisphere. Our results indicate a bilateral effect of language familiarity on neonates' brain response to language. Differential brain activation was seen when neonates listened to forward Tagalog (unfamiliar language) as compared to other types of language stimuli. We interpret these results as evidence that the prenatal experience with the native language gained in utero influences how the newborn brain responds to language across brain regions sensitive to speech processing.

Progress and prospects in plant genome editing <=SEP=> The emergence of sequence-specific nucleases that enable genome editing is revolutionizing basic and applied biology. Since the introduction of CRISPR–Cas9, genome editing has become widely used in transformable plants for characterizing gene function and improving traits, mainly by inducing mutations through non-homologous end joining of double-stranded breaks generated by CRISPR–Cas9. However, it would be highly desirable to perform precision gene editing in plants, especially in transformation-recalcitrant species. Recently developed Cas9 variants, novel RNA-guided nucleases and base-editing systems, and DNA-free CRISPR–Cas9 delivery methods now provide great opportunities for plant genome engineering. In this Review Article, we describe the current status of plant genome editing, focusing on newly developed genome editing tools and methods and their potential applications in plants. We also discuss the specific challenges facing plant genome editing, and future prospects.

Material-based Design Computation An Inquiry into Digital Simulation of Physical Material Properties as Design Generators <=SEP=> The paper demonstrates the association between geometry and material behavior, specifically the elastic properties of resin impregnated latex membranes, by means of homogenizing protocols which translate physical properties into geometrical functions. Resin-impregnation patterns are applied to 2-D pre-stretched form-active tension systems to induce 3-D curvature upon release.This method enables form-finding based on material properties, organization and behavior. Some theoretical foundations for material-computation are outlined. A digital tool developed in the Processing (JAVA coded) environment demonstrates the simulation of material behavior and its prediction under specific environmental conditions. Finally, conclusions are drawn from the physical and digital explorations which redefine generative material-based design computation, supporting a synergetic approach to design integrating form, structure, material and environment.

Cooperative Multi-Sensor Video Surveillance <=SEP=> Takeo Kanade, Robert T. Collins, and Alan J. Lipton Carnegie Mellon University, Pittsburgh, PA e-mail: fkanade,rcollins,ajlg@cs.cmu.edu homepage: http://www.cs.cmu.edu/ vsam P. Anandan, Peter Burt, and Lambert Wixson David Sarno Research Center, Princeton, NJ e-mail: fpanandan,pburt,lwixsong@sarno .com Abstract Carnegie Mellon University (CMU) and the David Sarno Research Center (Sarno ) have begun a joint, integrated feasibility demonstration in the area of Video Surveillance and Monitoring (VSAM). The objective is to develop a cooperative, multi-sensor video surveillance system that provides continuous coverage over large battle eld areas. Image Understanding (IU) technologies will be developed to: 1) coordinate multiple sensors to seamlessly track moving targets over an extended area, 2) actively control sensor and platform parameters to track multiple moving targets, 3) integrate multisensor output with collateral data to maintain an evolving, scene-level representation of all targets and platforms, and 4) monitor the scene for unusual \trigger" events and activities. These technologies will be integrated into an experimental testbed to support evaluation, data collection, and demonstration of other VSAM technologies developed within the DARPA IU community.

Test Case Selection and Prioritization Using Multiple Criteria <=SEP=> Regression testing activities such as test case selection and test case prioritization are ordinarily based on the criteria which focused around code coverage, code modifications and test execution costs. The approach mainly based on the multiple criteria of code coverage which performs efficient selection of test case. The method mainly aims to maximize the coverage size by executing the test cases effectively. The selected test cases are then prioritized based on the priority which depends on the code coverage to achieve the desired results. Keywords—Test case selection, Test case prioritization, Cod coverage, Jaccard distance, Greedy algorithm.

Studying weak central coherence at low levels: children with autism do not succumb to visual illusions. A research note. <=SEP=> While anecdotal reports of abnormal perceptual experiences in autism abound, there have been to date no experimental studies showing fundamental perceptual peculiarities. The present paper reports results from a first study of low-level visual integration in autism. Twenty-five subjects with autism, 21 normal 7- and 8-year-olds, and 26 children with learning difficulties were asked to make simple judgements about six well-known visual illusions. Two conditions were used, in an attempt to explore group differences; standard two-dimensional black and white line drawings, and the same figures augmented with raised coloured lines. The subjects with autism were less likely to succumb to the two-dimensional illusions than were the other groups, and were less aided by the three-dimensional 'disembedded' condition. These striking results are discussed with reference to the 'central coherence' account of autism.

On Throughput Maximization of Time Division Multiple Access With Energy Harvesting Users <=SEP=> In this paper, we consider a multiple-access channel, where multiple users equipped with energy harvesting batteries communicate to an access point. To avoid consuming extra energy on competition for the channel, the users are supposed to share the channel via time division multiple access (TDMA). In many existing works, it is commonly assumed that the users' energy harvesting processes and storage status are known to all the users before transmissions. In practice, such knowledge may not be readily available. To avoid excessive overhead for real-time information exchange, we consider the scenario where the users schedule their individual transmissions according to the users' statistical energy harvesting profiles. We first study the optimal transmission scheme in the case where each node has an infinite-capacity battery. By optimization theory, we show that, to maximize the average system throughput, all the users should transmit at an identical optimal power, which solely depends on the energy harvesting rate per time slot. We then study the equal-power TDMA scheme in the case where each node is equipped with a battery of finite capacity. The system is formulated as a polling system consisting of multiple energy queues and one server. By the Markov chain modeling method, we derive the performance of equal-power TDMA in this case, in terms of the energy loss ratio and average system throughput. In addition, we develop an algorithm to efficiently compute the optimal transmission power in the finite-capacity battery case. We also consider an equal-time TDMA scheme, which assigns equal-length subslots to each user, and analyze its system performance. It is found that equal-power TDMA always outperforms equal-time TDMA in the infinite-capacity battery case, whereas equal-time TDMA exhibits compatible or even slightly better performance in some scenarios when the batteries have finite capacities.

Predictive habitat distribution models in ecology <=SEP=> With the rise of new powerful statistical techniques and GIS tools, the development of predictive habitat distribution models has rapidly increased in ecology. Such models are static and probabilistic in nature, since they statistically relate the geographical distribution of species or communities to their present environment. A wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate change research, and habitat or species management. In this paper, we present a review of predictive habitat distribution modeling. The variety of statistical techniques used is growing. Ordinary multiple regression and its generalized form (GLM) are very popular and are often used for modeling species distributions. Other methods include neural networks, ordination and classification methods, Bayesian models, locally weighted approaches (e.g. GAM), environmental envelopes or even combinations of these models. The selection of an appropriate method should not depend solely on statistical considerations. Some models are better suited to reflect theoretical findings on the shape and nature of the species’ response (or realized niche). Conceptual considerations include e.g. the trade-off between optimizing accuracy versus optimizing generality. In the field of static distribution modeling, the latter is mostly related to selecting appropriate predictor variables and to designing an appropriate procedure for model selection. New methods, including threshold-independent measures (e.g. receiver operating characteristic (ROC)-plots) and resampling techniques (e.g. bootstrap, cross-validation) have been introduced in ecology for testing the accuracy of predictive models. The choice of an evaluation measure should be driven primarily by the goals of the study. This may possibly lead to the attribution of different weights to the various types of prediction errors (e.g. omission, commission or confusion). Testing the model in a wider range of situations (in space and time) will permit one to define the range of applications for which the model predictions are suitable. In turn, the qualification of the model depends primarily on the goals of the study that define the qualification criteria and on the usability of the model, rather than on statistics alone. © 2000 Elsevier Science B.V. All rights reserved.

MOBILE LEARNING FOR EDUCATION : BENEFITS AND CHALLENGES <=SEP=> Education and training is the process by which the wisdom, knowledge and skills of one generation are passed on to the next. Today there are two form s f education and training: conventional education and distance education. Mobile learning, or "M-Lear ning", offers modern ways to support learning process through mobile devices, such as handheld an d tablet computers, MP3 players, smart phones and mobile phones. This document introduces the sub ject of mobile learning for education purposes. It examines what impact mobile devices have had on teaching and learning practices and goes on to look at the opportunities presented by the use of d igital media on mobile devices. The main purpose of this paper is to describe the current state of mobi le learning, benefits, challenges, and it’s barrier s to support teaching and learning. Data for this paper w re collected through bibliographic and internet research from January to March 2013. Four key areas will be addressed in this paper: 1. An analysis of Mobile Learning. 2. Differentiating E-Learning from Mobile Learning 3. Value and Benefits of Mobile Learning 4. Challenges and Barriers of M obile Learning: Study showed that M-Learning as a Distance learning brought great benefits to socie ty include : Training when it is needed, Training a t any time; Training at any place; Learner-centred co ntent; Avoidance of re-entry to work problems; Training for taxpayers, and those fully occupied du ring university lectures and sessions at training centres; and The industrialisation of teaching and learning. And also, notebooks, mobile Tablets, iPod touch, and iPads are very popular devices for mobil e learning because of their cost and availability o f apps. --------------------------------

Gaming frequency and academic performance <=SEP=> There are numerous claims that playing computer and video games may be educationally beneficial, but there has been little formal investigation into whether or not the frequency of exposure to such games actually affects academic performance. This paper explores the issue by analysing the relationships between gaming frequency – measured as the amount of time undergraduate students spend playing games in their free time – and their academic performance as measured by their examination marks. Using a sample of 713 students, correlation analyses between gaming frequency and examination performance were conducted for students of varying gaming frequency, study discipline, gender, and general attitudes towards gaming and study. The results reveal that examination marks are in fact negatively correlated with gaming frequency – i.e. frequent gamers generally achieve lower marks than less frequent gamers.

Evaluation of mobile app paradigms <=SEP=> The explosion of mobile applications both in number and variety raises the need of shedding light on their architecture, composition and quality. Indeed, it is crucial to understand which mobile application paradigm fits better to what type of application and usage. Such understanding has direct consequences on the user experience, the development cost and sale revenues of mobile apps. In this paper, we identified four main mobile application paradigms and evaluated them from the developer, user and service provider viewpoints. To ensure objectivity and soundness we start by defining high level criteria and then breaking down in finer-grained criteria. After a theoretical evaluation an implementation was carried out as a practical verification. The selected application is object recognition app, which is both exciting and challenging to develop.

Spectral conversion based on maximum likelihood estimation considering global variance of converted parameter <=SEP=> The paper describes a novel spectral conversion method for voice transformation. We perform spectral conversion between speakers using a Gaussian mixture model (GMM) on the joint probability density of source and target features. A smooth spectral sequence can be estimated by applying maximum likelihood (ML) estimation to the GMM-based mapping using dynamic features. However, there is still degradation of the converted speech quality due to an over-smoothing of the converted spectra, which is inevitable in conventional ML-based parameter estimation. In order to alleviate the over-smoothing, we propose an ML-based conversion taking account of the global variance of the converted parameter in each utterance. Experimental results show that the performance of the voice conversion can be improved by using the global variance information. Moreover, it is demonstrated that the proposed algorithm is more effective than spectral enhancement by postfiltering.

Can Structural Models Price Default Risk ? Evidence from Bond and Credit Derivative Markets ∗ <=SEP=> Using a set of structural models, we evaluate the price of default protection for a sample of US corporations. In contrast to previous evidence from corporate bond data, CDS premia are not systematically underestimated. In fact, one of our studied models has little difficulty on average in predicting their level. For robustness, we perform the same exercise for bond spreads by the same issuers on the same trading date. As expected, bond spreads relative to the Treasury curve are systematically underestimated. This is not the case when the swap curve is used as a benchmark, suggesting that previously documented underestimation results may be sensitive to the choice of risk free rate. ∗We are indebted to seminar participants at Wilfrid Laurier University, Carnegie Mellon University, the Bank of Canada, McGill University and Queen’s University for helpful discussions. In addition, we are grateful to participants of the 2005 EFA in Moscow, the 2005 NFA in Vancouver, the 2006 CFP Vallendar conference and the 2006 Moody’s NYU Credit Risk Conference. We are particularly grateful to Stephen Schaefer for comments. †Faculty of Management, McGill University, 1001 Sherbrooke Street West, Montreal QC, H3A 1G5 Canada. Tel +1 514 398-3186, Fax +1 514 398-3876, Email jan.ericsson@mcgill.ca. ‡Stockholm School of Economics, Department of Finance, Box 6501, S-113 83 Stockholm, Sweden. Tel: +46 8 736 9143, fax +46 8 312327. 1

Transfer Learning Improves Supervised Image Segmentation Across Imaging Protocols <=SEP=> The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two magnetic resonance imaging brain-segmentation tasks with multi-site data: white matter, gray matter, and cerebrospinal fluid segmentation; and white-matter-/MS-lesion segmentation. The experiments showed that when there is only a small amount of representative training data available, transfer learning can greatly outperform common supervised-learning approaches, minimizing classification errors by up to 60%.

Learning to lip read words by watching videos <=SEP=> Our aim is to recognise the words being spoken by a talking face, given only the video but not the audio. Existing works in this area have focussed on trying to recognise a small number of utterances in controlled environments (e.g. digits and alphabets), partially due to the shortage of suitable datasets. We make three novel contributions: first, we develop a pipeline for fully automated data collection from TV broadcasts. With this we have generated a dataset with over a million word instances, spoken by over a thousand different people; second, we develop a two-stream convolutional neural network that learns a joint embedding between the sound and the mouth motions from unlabelled data. We apply this network to the tasks of audio-to-video synchronisation and active speaker detection; third, we train convolutional and recurrent networks that are able to effectively learn and recognize hundreds of words from this large-scale dataset. In lip reading and in speaker detection, we demonstrate results that exceed the current state-of-the-art on public benchmark datasets.

Older adults' views of "successful aging"--how do they compare with researchers' definitions? <=SEP=> OBJECTIVES
To determine whether older adults have thought about aging and aging successfully and to compare their perceptions of successful aging with attributes of successful aging identified in the published literature.


DESIGN
A cross-sectional, mailed survey.


SETTING
King County, Washington.


PARTICIPANTS
Nondemented participants from two cohorts. The first cohort, referred to as Kame, which means turtle, a symbol of longevity for Japanese, enrolled 1,985 Japanese Americans aged 65 and older and was established in 1992-94. The second cohort, Adult Changes in Thought, enrolled 2,581 white men and women aged 65 and older from a health maintenance organization and was established in 1994-96.


MEASUREMENTS
Respondents were asked whether they had ever thought about aging and aging successfully and whether these thoughts had changed over the previous 20 years and about how important specific attributes, originating from the published literature, were in characterizing successful aging.


RESULTS
Overall, 90% had previously thought about aging and aging successfully, and approximately 60% said their thoughts had changed over the previous 20 years. The Japanese-American group rated 13 attributes as important to successful aging; the white group rated the same 13 as important and added one additional attribute, learning new things.


CONCLUSION
Older adults' definition of successful aging is multidimensional, encompassing physical, functional, psychological, and social health. In contrast, none of the published work describing attributes of successful aging includes all four dimensions. Future work would benefit from an expanded definition to adequately reflect the perceptions of older adults.