title: Acquisition of Vernacular Place Names from Web Sources

abstract: Vernacular place names are names that are commonly in use to refer to geographical places. For purposes of effective information retrieval, the spatial extent associated with these names should reflect peoples perception of the place, even though this may differ sometimes from the administrative definition of the same place name. Due to their informal nature, vernacular place names are hard to capture, but methods to acquire and define vernacular place names are of great benefit to search engines and all kinds of information services that deal with geographic data. This paper discusses the acquisition of vernacular use of place names from web sources and their representation as surface models derived by kernel density estimators. We show that various web sources containing user created geographic information and business data can be used to represent neighbourhoods in Cardiff, UK. The resulting representations can differ in their spatial extent from administrative definitions. The chapter closes with an outlook on future research questions.


title: Visual Positioning of a Delta Robot

abstract: An uncalibrated dynamic look and move visual servoing is successfully implemented on a light weight Delta robot to position it relative to an object which its position is sensed by a USB camera and is driven by Dynamixel motors. The online estimated Jacobian is proposed to map the deference in joint space to the difference of parameters in image space. The paper demonstrates that the estimated Jacobian is accurate and can be used for a dynamic look and move visual servoing in this case. The experiment demonstrates that the robot is three dimensional point to point position controlled in image space with its accuracy is defined in this space as well.


title: PSYCHOLOGICAL SCIENCE Research Article VISUAL SEARCH REMAINS EFFICIENT WHEN VISUAL WORKING MEMORY IS FULL

abstract: Many theories of attention have proposed that visual working memory plays an important role in visual search tasks. The present study examined the involvement of visual working memory in search using a dual-task paradigm in which participants performed a visual search task while maintaining no, two, or four objects in visual working memory. The presence of a working memory load added a constant delay to the visual search reaction times, irrespective of the number of items in the visual search array. That is, there was no change in the slope of the function relating reaction time to the num- ber of items in the search array, indicating that the search process it- self was not slowed by the memory load. Moreover, the search task did not substantially impair the maintenance of information in visual working memory. These results suggest that visual search requires minimal visual working memory resources, a conclusion that is incon- sistent with theories that propose a close link between attention and working memory.


MODELLING THE HUMAN BLINK : A COMPUTATIONAL MODEL FOR USE WITHIN HUMAN-ROBOT INTERACTION


title: When moderation is mediated and mediation is moderated

abstract: Procedures for examining whether treatment effects on an outcome are mediated and/or moderated have been well developed and are routinely applied. The mediation question focuses on the intervening mechanism that produces the treatment effect. The moderation question focuses on factors that affect the magnitude of the treatment effect. It is important to note that these two processes may be combined in informative ways, such that moderation is mediated or mediation is moderated. Although some prior literature has discussed these possibilities, their exact definitions and analytic procedures have not been completely articulated. The purpose of this article is to define precisely both mediated moderation and moderated mediation and provide analytic strategies for assessing each.


title: A hybrid medium access control protocol for underwater wireless networks

abstract: Underwater networks allow investigation of many areas of the world not easily accessed by humans, but offer interesting challenges to device and protocol designers due to the unique channel conditions present when using acoustic communications. The high transmit power of acoustic transceivers makes the medium access protocol a primary focus point for reducing energy consumption in energy limited underwater devices. Scheduled protocols use very little power by eliminating collisions, but cannot adapt to changing traffic conditions in the same way as random protocols. We attempt to bridge these two ideas by dividing time into scheduled and unscheduled access periods in order to yield the benefits of each protocol. We show that this technique increases the bits delivered per energy unit in many cases of interest. Additionally, the hybrid technique provides low latency for a wider range of traffic rates than either of the two protocols when considered individually. We also investigate some of the design tradeoffs to consider when using a hybrid protocol.


Pattern Matching on Encrypted Streams: Applications to DPI and searches on genomic data.


title: The Relationship between Brand Trust, Brand Affect, Attitudinal Loyalty and Behavioral Loyalty: A Field Study towards Sports Shoe Consumers in Turkey

abstract: The main purpose of this study is to examine the relations between brand trust, brand affect, attitudinal loyalty and behavioral loyalty. In this regard, an online survey was conducted on sports shoe consumers in Turkey. 428 consumers participated in the survey which has been open for two months on the relevant web page. Hypotheses, which were proposed within the framework of the research model, were tested with structural equation modeling. The results indicate that there is a positive relation between brand trust and brand affect. Brand trust is also positively related to both attitudinal loyalty and behavioral loyalty. Contrary to expectations, brand affect exerted no significant impact on behavioral loyalty.


title: Low-Power and Area-Efficient Carry Select Adder

abstract: Carry Select Adder (CSLA) is one of the fastest adders used in many data-processing processors to perform fast arithmetic functions. From the structure of the CSLA, it is clear that there is scope for reducing the area and power consumption in the CSLA. This work uses a simple and efficient gate-level modification to significantly reduce the area and power of the CSLA. Based on this modification 8-, 16-, 32-, and 64-b square-root CSLA (SQRT CSLA) architecture have been developed and compared with the regular SQRT CSLA architecture. The proposed design has reduced area and power as compared with the regular SQRT CSLA with only a slight increase in the delay. This work evaluates the performance of the proposed designs in terms of delay, area, power, and their products by hand with logical effort and through custom design and layout in 0.18-μm CMOS process technology. The results analysis shows that the proposed CSLA structure is better than the regular SQRT CSLA.


title: A Unified Approach to Back-EMF Detection for Brushless DC Motor Drives without Current and Hall Sensors

abstract: The main theme of this paper is to present a unified approach to back-EMF detection of brushless DC motor drives without using any current and Hall sensors. Theoretical analysis of back-EMF detection is presented, and followed by the relationship between PWM techniques and back-EMF detection. It will be shown that the back-EMF detection depends upon the PWM techniques and the method is required to be slightly modified as the PWM technique is changed. Experimental results derived from BLDCM drives without using any current and Hall sensors fully confirm the theoretical analysis.


title: An empirical study of dormant bugs

abstract: Over the past decade, several research efforts have studied the quality of software systems by looking at post-release bugs. However, these studies do not account for bugs that remain dormant (i.e., introduced in a version of the software system, but are not found until much later) for years and across many versions. Such dormant bugs skew our under- standing of the software quality. In this paper we study dormant bugs against non-dormant bugs using data from 20 different open-source Apache foundation software systems. We find that 33% of the bugs introduced in a version are not reported till much later (i.e., they are reported in future versions as dormant bugs). Moreover, we find that 18.9% of the reported bugs in a version are not even introduced in that version (i.e., they are dormant bugs from prior versions). In short, the use of reported bugs to judge the quality of a specific version might be misleading. Exploring the fix process for dormant bugs, we find that they are fixed faster (median fix time of 5 days) than non- dormant bugs (median fix time of 8 days), and are fixed by more experienced developers (median commit counts of developers who fix dormant bug is 169% higher). Our results highlight that dormant bugs are different from non-dormant bugs in many perspectives and that future research in software quality should carefully study and consider dormant bugs.


title: High Density TSV-Free Interposer (TFI) Packaging with Submicron Cu Damascene RDLs for Integration of CPU/GPU and HBM

abstract: TSV-Free Interposer (TFI) packaging technology was developed for central/graphics processing unit (CPU/GPU) and stacked memory system-in-package (SiP) applications. TFI package is targeting to accommodate thick module as High Bandwidth Memory (HBM) and wide-bus interconnections between CPU/GPU and HBM in a package with cost-effective process. The redistribution layer structure was studied based on inter-chip connectivity between CPU/GPU and HBM as well as signal integrity and power integrity. This study demonstrated capability of TFI package in view of electrical performance, cost effectiveness and process challenges. The process challenges were overcome by co-design modeling and process optimizations.


title: Configurable, Photorealistic Image Rendering and Ground Truth Synthesis by Sampling Stochastic Grammars Representing Indoor Scenes

abstract: We propose the configurable rendering of massive quantities of photorealistic images with ground truth for the purposes of training, benchmarking, and diagnosing computer vision models. In contrast to the conventional (crowd-sourced) manual labeling of ground truth for a relatively modest number of RGB-D images captured by Kinect-like sensors, we devise a non-trivial configurable pipeline of algorithms capable of generating a potentially infinite variety of indoor scenes using a stochastic grammar, specifically, one represented by an attributed spatial And-Or graph. We employ physics-based rendering to synthesize photorealistic RGB images while automatically synthesizing detailed, per-pixel ground truth data, including visible surface depth and normal, object identity and material information, as well as illumination. Our pipeline is configurable inasmuch as it enables the precise customization and control of important attributes of the generated scenes. We demonstrate that our generated scenes achieve a performance similar to the NYU v2 Dataset on pre-trained deep learning models. By modifying pipeline components in a controllable manner, we furthermore provide diagnostics on common scene understanding tasks; eg., depth and surface normal prediction, semantic segmentation, etc.


title: Passwords and Perceptions

abstract: The security of many computer systems hinges on the secrecy of a single word - if an adversary obtains knowledge of a password, they will gain access to the resources controlled by this password. Human users are the 'weakest link' in password control, due to our propensity to reuse passwords and to create weak ones. Policies which forbid such unsafe password practices are often violated, even if these policies are well-advertised. ::: ::: We have studied how users perceive their accounts and their passwords. Our participants mentally classified their accounts and passwords into a few groups, based on a small number of perceived similarities. Our participants used stronger passwords, and reused passwords less, in account groups which they considered more important. Our participants thus demonstrated awareness of the basic tenets of password safety, but they did not behave safely in all respects. Almost half of our participants reused at least one of the passwords in their high-importance accounts. Our findings add to the body of evidence that a typical computer user suffers from 'password overload'. Our concepts of password and account grouping point the way toward more intuitive user interfaces for password-and account-management systems.


title: Modeling relations and their mentions without labeled text

abstract: Several recent works on relation extraction have been applying the distant supervision paradigm: instead of relying on annotated text to learn how to predict relations, they employ existing knowledge bases (KBs) as source of supervision. Crucially, these approaches are trained based on the assumption that each sentence which mentions the two related entities is an expression of the given relation. Here we argue that this leads to noisy patterns that hurt precision, in particular if the knowledge base is not directly related to the text we are working with. We present a novel approach to distant supervision that can alleviate this problem based on the following two ideas: First, we use a factor graph to explicitly model the decision whether two entities are related, and the decision whether this relation is mentioned in a given sentence; second, we apply constraint-driven semi-supervision to train this model without any knowledge about which sentences express the relations in our training KB. We apply our approach to extract relations from the New York Times corpus and use Freebase as knowledge base. When compared to a state-of-the-art approach for relation extraction under distant supervision, we achieve 31% error reduction.


title: Behavior based software theft detection

abstract: Along with the burst of open source projects, software theft (or plagiarism) has become a very serious threat to the healthiness of software industry. Software birthmark, which represents the unique characteristics of a program, can be used for software theft detection. We propose a system call dependence graph based software birthmark called SCDG birthmark, and examine how well it reflects unique behavioral characteristics of a program. To our knowledge, our detection system based on SCDG birthmark is the first one that is capable of detecting software component theft where only partial code is stolen. We demonstrate the strength of our birthmark against various evasion techniques, including those based on different compilers and different compiler optimization levels as well as two state-of-the-art obfuscation tools. Unlike the existing work that were evaluated through small or toy software, we also evaluate our birthmark on a set of large software. Our results show that SCDG birthmark is very practical and effective in detecting software theft that even adopts advanced evasion techniques.


title: ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation

abstract: Directed graphs have been widely used in Community Question Answering services (CQAs) to model asymmetric relationships among different types of nodes in CQA graphs, e.g., question, answer, user. Asymmetric transitivity is an essential property of directed graphs, since it can play an important role in downstream graph inference and analysis. Question difficulty and user expertise follow the characteristic of asymmetric transitivity. Maintaining such properties, while reducing the graph to a lower dimensional vector embedding space, has been the focus of much recent research. In this paper, we tackle the challenge of directed graph embedding with asymmetric transitivity preservation and then leverage the proposed embedding method to solve a fundamental task in CQAs: how to appropriately route and assign newly posted questions to users with the suitable expertise and interest in CQAs. The technique incorporates graph hierarchy and reachability information naturally by relying on a nonlinear transformation that operates on the core reachability and implicit hierarchy within such graphs. Subsequently, the methodology levers a factorization-based approach to generate two embedding vectors for each node within the graph, to capture the asymmetric transitivity. Extensive experiments show that our framework consistently and significantly outperforms the state-of-the-art baselines on three diverse realworld tasks: link prediction, and question difficulty estimation and expert finding in online forums like Stack Exchange. Particularly, our framework can support inductive embedding learning for newly posted questions (unseen nodes during training), and therefore can properly route and assign these kinds of questions to experts in CQAs.


title: Predicting CT Image From MRI Data Through Feature Matching With Learned Nonlinear Local Descriptors

abstract: Attenuation correction for positron-emission tomography (PET)/magnetic resonance (MR) hybrid imaging systems and dose planning for MR-based radiation therapy remain challenging due to insufficient high-energy photon attenuation information. We present a novel approach that uses the learned nonlinear local descriptors and feature matching to predict pseudo computed tomography (pCT) images from T1-weighted and T2-weighted magnetic resonance imaging (MRI) data. The nonlinear local descriptors are obtained by projecting the linear descriptors into the nonlinear high-dimensional space using an explicit feature map and low-rank approximation with supervised manifold regularization. The nearest neighbors of each local descriptor in the input MR images are searched in a constrained spatial range of the MR images among the training dataset. Then the pCT patches are estimated through k-nearest neighbor regression. The proposed method for pCT prediction is quantitatively analyzed on a dataset consisting of paired brain MRI and CT images from 13 subjects. Our method generates pCT images with a mean absolute error (MAE) of 75.25 ± 18.05 Hounsfield units, a peak signal-to-noise ratio of 30.87 ± 1.15 dB, a relative MAE of 1.56 ± 0.5% in PET attenuation correction, and a dose relative structure volume difference of 0.055 ± 0.107% in ${D}_{98\%}$ , as compared with true CT. The experimental results also show that our method outperforms four state-of-the-art methods.


title: The disabled throwing shoulder: Spectrum of Pathology Part II: Evaluation and treatment of SLAP lesions in throwers

abstract: Abstract Arthroscopy: The Journal of Arthroscopic and Related Surgery, Vol 19, No 5 (May-June), 2003: pp 531–539


title: A New Low Cost Leaky Wave Coplanar Waveguide Continuous Transverse Stub Antenna Array Using Metamaterial-Based Phase Shifters for Beam Steering

abstract: In this paper, we have proposed a new leaky-wave coplanar waveguide (CPW) continuous transverse stub (CTS) antenna array with metamaterial-based phase shifters for beam steering applications. The array integrates three CTS elements and two 6-stage negative reflective index (NRI) phase shifters, and is fed by CPW transmission line. Beam steering capabilities are achieved by tuning the values of the NRI phase shifters. The proposed CPW-CTS array is fabricated for 2.4-GHz wireless local area network (WLAN). The measured data, including parameters, radiation patterns and gain, agree well with the simulation results. A scan-angle range from 58 to 124 of unidirectional radiation pattern is measured in the -plane with good impedance matching (-1 dB ). Designs incorporating continuous beam steering using tunable NRI metamaterial phase shifters are also discussed.


title: Wideband Offset Slot-Coupled Patch Antenna Array for X/Ku-Band Multimode Radars

abstract: A high-gain, wideband antenna element that covers most portions of X- and Ku-bands is essential for multimode radar applications. This letter presents an aperture-coupled stacked patch antenna for 8.75-12.25-GHz frequency band. Fixed beam tilt up to 30 ° in scan range can be obtained by offsetting the aperture relative to a driven patch. When there is no offset, a single antenna has broadside gain greater than 10 dBi. An antenna array (4 × 4) with fixed beam tilt is designed and validated with measurements. The prototype antenna exhibits 44% fractional bandwidth and maximum gain 20.6 dBi in the target frequency band.


title: Cluster Canonical Correlation Analysis

abstract: In this paper we present cluster canonical correlation analysis (cluster-CCA) for joint dimensionality reduction of two sets of data points. Unlike the standard pairwise correspondence between the data points, in our problem each set is partitioned into multiple clusters or classes, wheretheclass labelsdefinecorrespondencesbetween the sets. Cluster-CCA is able to learn discriminant low dimensional representations that maximize the correlation between the two sets while segregating the different classes on the learned space. Furthermore, we present a kernel extension, kernel cluster canonical correlation analysis (cluster-KCCA) that extends clusterCCA to account for non-linear relationships. Cluster-(K)CCA is shown to be computationally efficient, the complexity being similar to standard (K)CCA. By means of experimental evaluation on benchmark datasets, cluster-(K)CCA is shown to achieve state of the art performance for cross-modal retrieval tasks.


title: A fast, efficient and automated method to extract vessels from fundus images

abstract: We present a fast, efficient, and automatic method for extracting vessels from retinal images. The proposed method is based on the second local entropy and on the gray-level co-occurrence matrix (GLCM). The algorithm is designed to have flexibility in the definition of the blood vessel contours. Using information from the GLCM, a statistic feature is calculated to act as a threshold value. The performance of the proposed approach was evaluated in terms of its sensitivity, specificity, and accuracy. The results obtained for these metrics were 0.9648, 0.9480, and 0.9759, respectively. These results show the high performance and accuracy that the proposed method offers. Another aspect evaluated in this method is the elapsed time to carry out the segmentation. The average time required by the proposed method is 3 s for images of size 565 × 584 pixels. To assess the ability and speed of the proposed method, the experimental results are compared with those obtained using other existing methods.


title: Quantum Field Theory and Coalgebraic Logic in Theoretical Computer Science

abstract: In this paper we suggest that in the framework of the Category Theory it is possible to demonstrate the mathematical and logical \textit{dual equivalence} between the category of the $q$-deformed Hopf Coalgebras and the category of the $q$-deformed Hopf Algebras in QFT, interpreted as a thermal field theory. Each pair algebra-coalgebra characterizes, indeed, a QFT system and its mirroring thermal bath, respectively, so to model dissipative quantum systems persistently in far-from-equilibrium conditions, with an evident significance also for biological sciences. The $q$-deformed Hopf Coalgebras and the $q$-deformed Hopf Algebras constitute two dual categories because characterized by the same functor $T$, related with the Bogoliubov transform, and by its contravariant application $T^{op}$, respectively. The \textit{q}-deformation parameter, indeed, is related to the Bogoliubov angle, and it is effectively a thermal parameter. Therefore, the different values of $q$ identify univocally, and then label, the vacua appearing in the foliation process of the quantum vacuum. This means that, in the framework of Universal Coalgebra, as general theory of dynamic and computing systems ("labelled state-transition systems"), the so labelled infinitely many quantum vacua can be interpreted as the Final Coalgebra of an"Infinite State Black-Box Machine". All this opens the way to the possibility of designing a new class of universal quantum computing architectures based on this coalgebraic formulation of QFT, as its ability of naturally generating a Fibonacci progression demonstrates.


1 Kernels for graphs


title: Symbolic Representation of Neural Networks

abstract: Neural networks often surpass decision trees in predicting pattern classifications, but their predictions cannot be explained. This algorithm's symbolic representations make each prediction explicit and understandable. Our approach to understanding a neural network uses symbolic rules to represent the network decision process. The algorithm, NeuroRule, extracts these rules from a neural network. The network can be interpreted by the rules which, in general, preserve network accuracy and explain the prediction process. We based NeuroRule on a standard three layer feed forward network. NeuroRule consists of four phases. First, it builds a weight decay backpropagation network so that weights reflect the importance of the network's connections. Second, it prunes the network to remove irrelevant connections and units while maintaining the network's predictive accuracy. Third, it discretizes the hidden unit activation values by clustering. Finally, it extracts rules from the network with discretized hidden unit activation values.


title: The Effect of Brand Image and Product Knowledge on Purchase Intention Moderated by Price Discount

abstract: Most of previous studies either take product knowle dge as a moderator, or involve varying degree of product knowledge on the consumers’ perceived evalu ation as an influential factor. It is rare to come across research that discusses how both brand image and product knowledge affect purchase intention. Thus, this research has chosen both an intrinsic an d an extrinsic product cue─brand image and product knowledge ─as independent variables while using price discount as a moderator and conducted research on purchase intention.


title: Analysis of deep neural networks with the extended data Jacobian matrix

abstract: Deep neural networks have achieved great success on a variety of machine learning tasks. There are many fundamental and open questions yet to be answered, however. We introduce the Extended Data Jacobian Matrix (EDJM) as an architecture-independent tool to analyze neural networks at the manifold of interest. The spectrum of the EDJM is found to be highly correlated with the complexity of the learned functions. After studying the effect of dropout, ensembles, and model distillation using EDJM, we propose a novel spectral regularization method, which improves network performance.


title: Intense Focused Ultrasound: Evaluation of a New Treatment Modality for Precise Microcoagulation within the Skin

abstract: BACKGROUND AND OBJECTIVE Focused ultrasound can produce thermal and/or mechanical effects deep within tissue. We investigated the capability of intense focused ultrasound to induce precise and predictable subepidermal thermal damage in human skin. ::: ::: MATERIALS AND METHODS Postmortem human skin samples were exposed to a range of focused ultrasound pulses, using a prototype device (Ulthera Inc.) emitting up to 45 W at 7.5 MHz with a nominal focal distance of 4.2 mm from the transducer membrane. Exposure pulse duration ranged from 50 to 200 ms. Thermal damage was confirmed by light microscopy using a nitroblue tetrazolium chloride assay, as well as by loss of collagen birefringence in frozen sections. Results were compared with a computational model of intense ultrasound propagation and heating in tissue. ::: ::: RESULTS Depth and extent of thermal damage were determined by treatment exposure parameters (source power, exposure time, and focal depth). It was possible to create individual and highly confined lesions or thermal damage up to a depth of 4 mm within the dermis. Thermal lesions typically had an inverted cone shape. A precise pattern of individual lesions was achieved in the deep dermis by applying the probe sequentially at different exposure locations. ::: ::: DISCUSSION AND CONCLUSION Intense focused ultrasound can be used as a noninvasive method for spatially confined heating and coagulation within the skin or its underlying structures. These findings have a significant potential for the development of novel, noninvasive treatment devices in dermatology.


title: THE CONNOTATIONS OF ARABIC COLOUR TERMS (

abstract: Following Allan (2009), this paper investigates the X-phemistic uses of Arabic colour terms with especial reference to Jordanian Arabic. The study, adopting colour-based metaphors, explores the figurative uses of black, white, yellow, red, green, and blue; the most common colours that are used figuratively. The connotations of Arabic colour terms are judged on the basis of whether the phrases in which colour terms occur are orthophemistic, euphemistic, or dys- phemistic. It could be said, building on the colours explored in this study, that colours have many orthophemistic connotations, and that dysphemistic connota- tions of the colours under study are more common than euphemistic connotations. It was also found that the uses of black, yellow, red and blue are predominantly dysphemistic. In contrast, white mostly has positive connotations, but green is found to be associated with both euphemistic and dysphemistic connotations.


title: North-Holland Nonlinear total variation based noise removal algorithms*

abstract: A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using the gradient-projection method. This amounts to solving a time dependent partial differential equation on a manifold determined by the constraints. As t---~ 0o the solution converges to a steady state which is the denoised image. The numerical algorithm is simple and relatively fast. The results appear to be state-of-the-art for very noisy images. The method is noninvasive, yielding sharp edges in the image. The technique could be interpreted as a first step of moving each level set of the image normal to itself with velocity equal to the curvature of the level set divided by the magnitude of the gradient of the image, and a second step which projects the image back onto the constraint set.


title: Testing dynamical models of vision

abstract: Most models of vision focus either on the spatial or temporal aspects of visual processing and neglect the other component. A variety of studies have shown, however, that spatial and temporal processing cannot easily be separated. The shine-through effect has proven to be a sensitive tool to study spatio-temporal processing. Two very different dynamical models, the 3D-LAMINART and the WCTM model, have explained the key aspects of the shine-through effect. Based on computer simulations, Francis (2009 proposed a set of predictions based on stimulus variants of the shinethrough effect that are crucial for both models. Here, we tested these predictions psychophysically. Both models fail to correctly predict the outcome of these experiments.


title: Grasping unknown objects by exploiting shape adaptability and environmental constraints

abstract: In grasping, shape adaptation between hand and object has a major influence on grasp success. In this paper, we present an approach to grasping unknown objects that explicitly considers the effect of shape adaptability to simplify perception. Shape adaptation also occurs between the hand and the environment, for example, when fingers slide across the surface of the table to pick up a small object. Our approach to grasping also considers environmental shape adaptability to select grasps with high probability of success. We validate the proposed shape-adaptability-aware grasping approach in 880 real-world grasping trials with 30 objects. Our experiments show that the explicit consideration of shape adaptability of the hand leads to robust grasping of unknown objects. Simple perception suffices to achieve this robust grasping behavior.


title: Optimal Dynamic Assortment Planning with Demand Learning

abstract: We study a family of stylized assortment planning problems, where arriving customers make purchase decisions among offered products based on maximizing their utility. Given limited display capacity and no a priori information on consumers' utility, the retailer must select which subset of products to offer. By offering different assortments and observing the resulting purchase behavior, the retailer learns about consumer preferences, but this experimentation should be balanced with the goal of maximizing revenues. We develop a family of dynamic policies that judiciously balance the aforementioned trade-off between exploration and exploitation, and prove that their performance cannot be improved upon in a precise mathematical sense. One salient feature of these policies is that they “quickly” recognize, and hence limit experimentation on, strictly suboptimal products.


title: Combining Poisson singular integral and total variation prior models in image restoration

abstract: In this paper, a novel Bayesian image restoration method based on a combination of priors is presented. It is well known that the Total Variation (TV) image prior preserves edge structures while imposing smoothness on the solutions. However, it tends to oversmooth textured areas. To alleviate this problem we propose to combine the TV and the Poisson Singular Integral (PSI) models, which, as we will show, preserves the image textures. The PSI prior depends on a parameter that controls the shape of the filter. A study on the behavior of the filter as a function of this parameter is presented. Our restoration model utilizes a bound for the TV image model based on the majorization-minimization principle, and performs maximum a posteriori Bayesian inference. In order to assess the performance of the proposed approach, in the experimental section we compare it with other restoration methods. HighlightsWe present a novel image restoration method that combines TV and PSI models.Poisson singular integral (PSI) model is capable to preserve the textures.We study the behavior of the PSI filter as a function of its parameter.The combination TV + PSI provides better restorations than the TV or PSI alone.The proposed method is competitive with state-of-the-art restoration methods.


title: Biometric Systems under Morphing Attacks: Assessment of Morphing Techniques and Vulnerability Reporting

abstract: With the widespread deployment of biometric recognition systems, the interest in attacking these systems is increasing. One of the easiest ways to circumvent a biometric recognition system are so-called presentation attacks, in which artefacts are presented to the sensor to either impersonate another subject or avoid being recognised. In the recent past, the vulnerabilities of biometric systems to so-called morphing attacks have been unveiled. In such attacks, biometric samples of multiple subjects are merged in the signal or feature domain, in order to allow a successful verification of all contributing subjects against the morphed identity. Being a recent area of research, there is to date no standardised manner to evaluate the vulnerability of biometric systems to these attacks. Hence, it is not yet possible to establish a common benchmark between different morph detection algorithms. In this paper, we tackle this issue proposing new metrics for vulnerability reporting, which build upon our joint experience in researching this challenging attack scenario. In addition, recommendations on the assessment of morphing techniques and morphing detection metrics are given.


title: A multi‐centre evaluation of eleven clinically feasible brain PET/MRI attenuation correction techniques using a large cohort of patients

abstract: Aim: To accurately quantify the radioactivity concentration measured by PET, emission data need to be corrected for photon attenuation; however, the MRI signal cannot easily be converted into attenuation values, making attenuation correction (AC) in PET/MRI challenging. In order to further improve the current vendor‐implemented MR‐AC methods for absolute quantification, a number of prototype methods have been proposed in the literature. These can be categorized into three types: template/atlas‐based, segmentation‐based, and reconstruction‐based. These proposed methods in general demonstrated improvements compared to vendor‐implemented AC, and many studies report deviations in PET uptake after AC of only a few percent from a gold standard CT‐AC. Using a unified quantitative evaluation with identical metrics, subject cohort, and common CT‐based reference, the aims of this study were to evaluate a selection of novel methods proposed in the literature, and identify the ones suitable for clinical use. Methods: In total, 11 AC methods were evaluated: two vendor‐implemented (MR‐ACDIXON and MR‐ACUTE), five based on template/atlas information (MR‐ACSEGBONE (Koesters et al., 2016), MR‐ACONTARIO (Anazodo et al., 2014), MR‐ACBOSTON (Izquierdo‐Garcia et al., 2014), MR‐ACUCL (Burgos et al., 2014), and MR‐ACMAXPROB (Merida et al., 2015)), one based on simultaneous reconstruction of attenuation and emission (MR‐ACMLAA (Benoit et al., 2015)), and three based on image‐segmentation (MR‐ACMUNICH (Cabello et al., 2015), MR‐ACCAR‐RiDR (Juttukonda et al., 2015), and MR‐ACRESOLUTE (Ladefoged et al., 2015)). We selected 359 subjects who were scanned using one of the following radiotracers: [18F]FDG (210), [11C]PiB (51), and [18F]florbetapir (98). The comparison to AC with a gold standard CT was performed both globally and regionally, with a special focus on robustness and outlier analysis. Results: The average performance in PET tracer uptake was within ±5% of CT for all of the proposed methods, with the average±SD global percentage bias in PET FDG uptake for each method being: MR‐ACDIXON (−11.3±3.5)%, MR‐ACUTE (−5.7±2.0)%, MR‐ACONTARIO (−4.3±3.6)%, MR‐ACMUNICH (3.7±2.1)%, MR‐ACMLAA (−1.9±2.6)%, MR‐ACSEGBONE (−1.7±3.6)%, MR‐ACUCL (0.8±1.2)%, MR‐ACCAR‐RiDR (−0.4±1.9)%, MR‐ACMAXPROB (−0.4±1.6)%, MR‐ACBOSTON (−0.3±1.8)%, and MR‐ACRESOLUTE (0.3±1.7)%, ordered by average bias. The overall best performing methods (MR‐ACBOSTON, MR‐ACMAXPROB, MR‐ACRESOLUTE and MR‐ACUCL, ordered alphabetically) showed regional average errors within ±3% of PET with CT‐AC in all regions of the brain with FDG, and the same four methods, as well as MR‐ACCAR‐RiDR, showed that for 95% of the patients, 95% of brain voxels had an uptake that deviated by less than 15% from the reference. Comparable performance was obtained with PiB and florbetapir. Conclusions: All of the proposed novel methods have an average global performance within likely acceptable limits (±5% of CT‐based reference), and the main difference among the methods was found in the robustness, outlier analysis, and clinical feasibility. Overall, the best performing methods were MR‐ACBOSTON, MR‐ACMAXPROB, MR‐ACRESOLUTE and MR‐ACUCL, ordered alphabetically. These methods all minimized the number of outliers, standard deviation, and average global and local error. The methods MR‐ACMUNICH and MR‐ACCAR‐RiDR were both within acceptable quantitative limits, so these methods should be considered if processing time is a factor. The method MR‐ACSEGBONE also demonstrates promising results, and performs well within the likely acceptable quantitative limits. For clinical routine scans where processing time can be a key factor, this vendor‐provided solution currently outperforms most methods. With the performance of the methods presented here, it may be concluded that the challenge of improving the accuracy of MR‐AC in adult brains with normal anatomy has been solved to a quantitatively acceptable degree, which is smaller than the quantification reproducibility in PET imaging.


title: Fast restoration of natural images corrupted by high-density impulse noise

abstract: In this paper, we suggest a general model for the fixed-valued impulse noise and propose a two-stage method for high density noise suppression while preserving the image details. In the first stage, we apply an iterative impulse detector, exploiting the image entropy, to identify the corrupted pixels and then employ an Adaptive Iterative Mean filter to restore them. The filter is adaptive in terms of the number of iterations, which is different for each noisy pixel, according to the Euclidean distance from the nearest uncorrupted pixel. Experimental results show that the proposed filter is fast and outperforms the best existing techniques in both objective and subjective performance measures.


title: Efficacy of 800 nm diode laser to treat trichostasis spinulosa in Asian patients

abstract: Context: Trichostasis spinulosa (TS) is a common but underdiagnosed follicular disorder involving retention of successive telogen hair in the hair follicle. Laser hair removal is a newer treatment modality for TS with promising results. Aims: This study aims to evaluate the efficacy of 800 nm diode laser to treat TS in Asian patients. Subjects and Methods: We treated 50 Indian subjects (Fitzpatrick skin phototype IV–V) with untreated trichostasis spinulosa on the nose with 800 nm diode laser at fluence ranging from 22 to 30 J/cm2 and pulse width of 30 ms. The patients were given two sittings at 8 week intervals. The evaluation was done by blinded assessment of photographs by independent dermatologists. Results: Totally 45 (90%) patients had complete clearance of the lesions at the end of treatment. Five (10%) subjects needed one-third sitting for complete clearance. 45 patients had complete resolution and no recurrence even at 2 years follow-up visit. 5 patients had partial recurrence after 8–9 months and needed an extra laser session. Conclusions: Laser hair reduction in patients with TS targets and removes the hair follicles which are responsible for the plugged appearance. Due to permanent ablation of the hair bulb and bulge, the recurrence which is often seen with other modalities of treatment for TS is not observed here.


title: Using semantic networks for knowledge representation in an intelligent environment

abstract: When building intelligent spaces, the knowledge representation for encapsulating rooms, users, groups, roles, and other information is a fundamental design question. We present a semantic network as such a representation, and demonstrate its utility as a basis for ongoing work.


title: Nonword Repetition: A Comparison of Tests

abstract: Purpose This study compared performance of children on 2 tests of nonword repetition to investigate the factors that may contribute to the well-documented nonword repetition deficit in specific language impairment (SLI). Method Twelve children with SLI age 7 to 11 years, 12 age-matched control children, and 12 control children matched for language ability completed 2 tests of nonword repetition: the Children’s Test of Nonword Repetition (CNRep) and the Nonword Repetition Test (NRT). Results The children with SLI performed significantly more poorly on both tests than typically developing children of the same age. The SLI group was impaired on the CNRep but not the NRT relative to younger children with similar language abilities when adjustments were made for differences in general cognitive ability. The children with SLI repeated the lengthiest nonwords and the nonwords containing consonant clusters significantly less accurately than the control groups. Conclusion The evidence suggests that the nonword rep...


title: Communicating Hierarchical Neural Controllers for Learning Zero-shot Task Generalization

abstract: The ability to generalize from past experience to solve previously unseen tasks is a key research challenge in reinforcement learning (RL). In this paper, we consider RL tasks defined as a sequence of high-level instructions described by natural language and study two types of generalization: to unseen and longer sequences of previously seen instructions, and to sequences where the instructions themselves were previously not seen. ::: We present a novel hierarchical deep RL architecture that consists of two interacting neural controllers: a meta controller that reads instructions and repeatedly communicates subtasks to a subtask controller that in turn learns to perform such subtasks. To generalize better to unseen instructions, we propose a regularizer that encourages to learn subtask embeddings that capture correspondences between similar subtasks. We also propose a new differentiable neural network architecture in the meta controller that learns temporal abstractions which makes learning more stable under delayed reward. Our architecture is evaluated on a stochastic 2D grid world and a 3D visual environment where the agent should execute a list of instructions. We demonstrate that the proposed architecture is able to generalize well over unseen instructions as well as longer lists of instructions.


title: Spice-compatible modeling of high injection and propagation of minority carriers in the substrate of Smart Power ICs

abstract: Classical substrate noise analysis considers the silicon resistivity of an integrated circuit only as doping dependent besides neglecting diffusion currents as well. In power circuits minority carriers are injected into the substrate and propagate by drift diffusion. In this case the conductivity of the substrate is spatially modulated and this effect is particularly important in high injection regime. In this work a description of the coupling between majority and minority drift diffusion currents is presented. A distributed model of the substrate is then proposed to take into account the conductivity modulation and its feedback on diffusion processes. The model is expressed in terms of equivalent circuits in order to be fully compatible with circuit simulators. The simulation results are then discussed for diodes and bipolar transistors and compared to the ones obtained from physical device simulations and measurements. (C) 2014 Published by Elsevier Ltd.


title: Project management maturity: an assessment of project management capabilities among and between selected industries

abstract: The project management community is actively demonstrating substantial interest in the development of viable methods to assess and improve project management maturity. This interest also underscores the important need to assess project management maturity among industries to provide many organizations a means to benchmark their maturity relative to others. This research provides a snapshot of the current level of project management maturity-among industries based on 42 detailed components of maturity. A survey of 126 organizations reveals the median level of project management maturity is level 2 out of 5 with respect to 36 of the 42 components analyzed. Additionally, this research compares project management maturity between four major industries: professional, scientific and technical services; information; finance and insurance; and manufacturing. We conclude that with few exceptions, there is not a significant difference in project management maturity between industries. These results establish a baseline to support organizational assessments of project management maturity at a component level of analysis. Additionally, these results will support future longitudinal research to monitor the evolution of project management maturity.


title: Analysis of estimation of distribution algorithms and genetic algorithms on NK landscapes

abstract: This study analyzes performance of several genetic and evolutionary algorithms on randomly generated NK fitness landscapes with various values of n and k. A large number of NK problem instances are first generated for each n and k, and the global optimum of each instance is obtained using the branch-and-bound algorithm. Next, the hierarchical Bayesian optimization algorithm (hBOA), the univariate marginal distribution algorithm (UMDA), and the simple genetic algorithm (GA) with uniform and two-point crossover operators are applied to all generated instances. Performance of all algorithms is then analyzed and compared, and the results are discussed.


title: Ownership of the human genome

abstract: Despite wide interest in human genome patents, there has been little information about exactly who owns what. Europe would do well to ponder over the stake already held by Japan and the United States.


title: HGMF: Hierarchical Group Matrix Factorization for Collaborative Recommendation

abstract: Matrix factorization is one of the most powerful techniques in collaborative filtering, which models the (user, item) interactions behind historical explicit or implicit feedbacks. However, plain matrix factorization may not be able to uncover the structure correlations among users and items well such as communities and taxonomies. As a response, we design a novel algorithm, i.e., hierarchical group matrix factorization (HGMF), in order to explore and model the structure correlations among users and items in a principled way. Specifically, we first define four types of correlations, including (user, item), (user, item group), (user group, item) and (user group, item group); we then extend plain matrix factorization with a hierarchical group structure; finally, we design a novel clustering algorithm to mine the hidden structure correlations. In the experiments, we study the effectiveness of our HGMF for both rating prediction and item recommendation, and find that it is better than some state-of-the-art methods on several real-world data sets.


title: Variable frequency speed-regulation system of elevator using PLC technology

abstract: In this paper, the system of variable frequency speed-regulation based on PLC was proposed. Firstly, the overall structure of control system was determined. Inverter and programmable logic controller were chosen to complete variable frequency speed-regulation. Secondly, parameters of inverter were set by develop system hardware. PLC drive selection and I/0 points distribution were completed; rotary encoder was connected with PLC interface. Finally, the software flow charts and ladder programs were designed by analysis of elevator system on the basis of software design methods.


title: Automated seizure detection systems and their effectiveness for each type of seizure

abstract: Abstract Epilepsy affects almost 1% of the population and most of the approximately 20–30% of patients with refractory epilepsy have one or more seizures per month. Seizure detection devices allow an objective assessment of seizure frequency and a treatment tailored to the individual patient. A rapid recognition and treatment of seizures through closed-loop systems could potentially decrease morbidity and mortality in epilepsy. However, no single detection device can detect all seizure types. Therefore, the choice of a seizure detection device should consider the patient-specific seizure semiologies. This review of the literature evaluates seizure detection devices and their effectiveness for different seizure types. Our aim is to summarize current evidence, offer suggestions on how to select the most suitable seizure detection device for each patient and provide guidance to physicians, families and researchers when choosing or designing seizure detection devices. Further, this review will guide future prospective validation studies.


title: Interactive dynamic graphics for exploratory survival analysis

abstract: Abstract The availability of powerful and inexpensive hardware and software for graphical computing makes the use of interactive dynamic graphics feasible for the examination of survival data. The use of these approaches may help the clinical researcher formulate specific hypotheses and select appropriate models. This paper describes some simple techniques, which were implemented in the LISP-STAT environment.


title: Learning to recommend quotes for writing

abstract: In this paper, we propose and address a novel task of recommending quotes for writing. Quote is short for quotation, which is the repetition of someone else's statement or thoughts. It is a common case in our writing when we would like to cite someone's statement, like a proverb or a statement by some famous people, to make our composition more elegant or convincing. However, sometimes we are so eager to make a citation of quote somewhere, but have no idea about the relevant quote to express our idea. Because knowing or remembering so many quotes is not easy, it is exciting to have a system to recommend relevant quotes for us while writing. In this paper we tackle this appealing AI task, and build up a learning framework for quote recommendation. We collect abundant quotes from the Internet, and mine real contexts containing these quotes from large amount of electronic books, to build up a dataset for experiments. We explore the particular features of this task, and propose a few useful features to model the characteristics of quotes and the relevance of quotes to contexts. We apply a supervised learning to rank model to integrate multiple features. Experiment results show that, our proposed approach is appropriate for this task and it outperforms other recommendation methods.


title: Adaptive Memory Networks

abstract: We present Adaptive Memory Networks (AMN) that processes input-question pairs to dynamically construct a network architecture optimized for lower inference times for Question Answering (QA) tasks. AMN processes the input story to extract entities and stores them in memory banks. Starting from a single bank, as the number of input entities increases, AMN learns to create new banks as the entropy in a single bank becomes too high. Hence, after processing an input-question(s) pair, the resulting network represents a hierarchical structure where entities are stored in different banks, distanced by question relevance. At inference, one or few banks are used, creating a tradeoff between accuracy and performance. AMN is enabled by dynamic networks that allow input dependent network creation and efficiency in dynamic mini-batching as well as our novel bank controller that allows learning discrete decision making with high accuracy. In our results, we demonstrate that AMN learns to create variable depth networks depending on task complexity and reduces inference times for QA tasks.


title: Detecting integrity attacks on control systems using robust physical watermarking

abstract: Ensuring the security of control systems against integrity attacks is a major challenge. Due to the events of Stuxnet, replay attacks in particular have been considered by the research community. Replaying previous measurements of a system in steady state allows an adversary to generate statistically correct virtual outputs which can bypass traditional detectors. The adversary can then inject destabilizing inputs to cause damage to the plant. The method of injecting secret noisy control inputs, or physical watermarking, has recently been proposed to detect replay attacks. However, the proposed watermarking design methods assume that the adversary does not use his potential access to real time communication channels to create stealthy virtual outputs to send to the defender. In this paper, we formulate an attack model for an adversary who uses knowledge of the system as well as access to a subset of real time control inputs and sensor outputs to construct stealthy virtual outputs. A robust physical watermark and detector to counter such an adversary is proposed.


title: Shadow Removal Using Bilateral Filtering

abstract: In this paper, we propose a simple but effective shadow removal method using a single input image. We first derive a 2-D intrinsic image from a single RGB camera image based solely on colors, particularly chromaticity. We next present a method to recover a 3-D intrinsic image based on bilateral filtering and the 2-D intrinsic image. The luminance contrast in regions with similar surface reflectance due to geometry and illumination variances is effectively reduced in the derived 3-D intrinsic image, while the contrast in regions with different surface reflectance is preserved. However, the intrinsic image contains incorrect luminance values. To obtain the correct luminance, we decompose the input RGB image and the intrinsic image. Each image is decomposed into a base layer and a detail layer. We obtain a shadow-free image by combining the base layer from the input RGB image and the detail layer from the intrinsic image such that the details of the intrinsic image are transferred to the input RGB image from which the correct luminance values can be obtained. Unlike previous methods, the presented technique is fully automatic and does not require shadow detection.


title: PCD: A privacy-preserving predictive clinical decision scheme with E-health big data based on RNN

abstract: As large amount of e-health data is generated exponentially, recurrent neural networks (RNN) can be utilized to make predictive clinical decision which is helpful to improve diagnosis accuracy and reduce diagnosis time. However, it is still a challenging task to guarantee the information security and solve the privacy concerns. We design a new Privacy-preserving Predictive Clinical Decision scheme based on RNN, called PCD, that can predict and alert before diseases occur while preserving the privacy of patients. In PCD, we utilize a homomorphic encryption scheme, so no e-health data will be leaked. PCD could resist various security threats. We design a sequential and an averaged RNN model in real-time systems that is capable to improve prediction accuracy. The experimental results illustrate that our scheme achieves high disease prediction accuracy and time efficiency while protecting privacy of patients.


title: Privacy Against Statistical Inference

abstract: We propose a general statistical inference framework to capture the privacy threat incurred by a user that releases data to a passive but curious adversary, given utility constraints. We show that applying this general framework to the setting where the adversary uses the self-information cost function naturally leads to a non-asymptotic information-theoretic approach for characterizing the best achievable privacy subject to utility constraints. Based on these results we introduce two privacy metrics, namely average information leakage and maximum information leakage. We prove that under both metrics the resulting design problem of finding the optimal mapping from the user's data to a privacy-preserving output can be cast as a modified rate-distortion problem which, in turn, can be formulated as a convex program. Finally, we compare our framework with differential privacy.


title: A guide to convolution arithmetic for deep learning

abstract: We introduce a guide to help deep learning practitioners understand and manipulate convolutional neural network architectures. The guide clarifies the relationship between various properties (input shape, kernel shape, zero padding, strides and output shape) of convolutional, pooling and transposed convolutional layers, as well as the relationship between convolutional and transposed convolutional layers. Relationships are derived for various cases, and are illustrated in order to make them intuitive.


title: DCTCP: Efficient Packet Transport for the Commoditized Data Center

abstract: Cloud data centers host diverse applications, mixing in the same network a plethora of workflows that require small predictable latency with others requiring large sustained throughput. In this environment, today’s state-of-the-art TCP protocol falls short. We present measurements of a 6000 server production cluster and reveal network impairments, such as queue buildup, buffer pressure, and incast, that lead to high application latencies. Using these insights, propose a variant of TCP, DCTCP, for data center networks. DCTCP leverages Explicit Congestion Notification (ECN) and a simple multibit feedback mechanism at the host. We evaluate DCTCP at 1 and 10Gbps speeds, through benchmark experiments and analysis. In the data center, operating with commodity, shallow buffered switches, DCTCP delivers the same or better throughput than TCP, while using 90% less buffer space. Unlike TCP, it also provides hight burst tolerance and low latency for short flows. While TCP’s limitations cause our developers to restrict the traffic they send today, using DCTCP enables the applications to handle 10X the current background traffic, without impacting foreground traffic. Further, a 10X increase in foreground traffic does not cause any timeouts, thus largely eliminating incast problems.


title: An IMM Algorithm for Tracking Maneuvering Vehicles in an Adaptive Cruise Control Environment

abstract: In this paper, an unscented Kalman filter (UKF) for curvilinear motions in an interacting multiple model (IMM) algorithm to track a maneuvering vehicle on a road is investigated. Driving patterns of vehicles on a road are modeled as stochastic hybrid systems. In order to track the maneuvering vehicles, two kinematic models are derived: A constant velocity model for linear motions and a constant-speed turn model for curvilinear motions. For the constant-speed turn model, an UKF is used because of the drawbacks of the extended Kalman filter in nonlinear systems. The suggested algorithm reduces the root mean squares error for linear motions and rapidly detects possible turning motions. 1. INTRODUCTON Recently, the majority of automobile companies are developing various driver assistance systems to increase vehicle safety and alleviate driver workload. The driver assistance systems include adaptive cruise control (ACC), lane-keeping support, collision warning and collision avoidance, and assisted lane changes. The effectiveness of these driver assistant systems depends on the interpretation of the information arriving from sensors, which provide details of the surrounding vehicle environment and of the driver-assisted vehicle itself. In particular, all these systems rely on the detection and subsequent tracking of objects around the vehicle. Such detection information is provided by radar, lidar, and vision sensor. The assistance systems mentioned above have certain objectives that their controllers try to meet. Before a controller can make a decision that enables the driver to feel natural, the motion of the surrounding object must be properly interpreted from the available sensor information (2).


title: Sentinel-2A MSI and Landsat 8 OLI Provide Data Continuity for Geological Remote Sensing

abstract: Sentinel-2A MSI is the Landsat-like spatial resolution (10–60 m) super-spectral instrument of the European Space Agency (ESA), aimed at additional data continuity for global land surface monitoring with Landsat and Satellite Pour l’Observation de la Terre (SPOT) missions. Several simulation studies have been conducted in the last several years to show the potential of Sentinel-2A MSI (MultiSpectral Instrument). Now that real data are available, the first confirmations of this potential and comparisons with other operational systems are being made. This paper aims at evaluating Sentinel-2A MSI band ratio products that are relevant for geological remote sensing. A Sentinel-2A MSI and a Landsat 8 OLI (Operational Land Imager) scene were processed from their respective levels L1C and L1T to level L2A (bottom of atmosphere reflectance). Then, three band ratios originally defined for Landsat TM (Thematic Mapper) were used to map mineralogy associated with a hydrothermal alteration system in southeast Spain. The results obtained with Sentinel-2A MSI were compared with those obtained with Landsat 8 OLI and a simulated Sentinel-2A MSI dataset that was used before actual data were released. Results show that the images appear similar to the human eye having a correlation of approximately 0.8 and higher, but that the associated data ranges differ significantly. The resulting products are also compared to a published geologic map of the study area, and it is shown that the resulting maps correspond with the conceptual geologic model of the epithermal deposit.


title: CS principles: piloting a new course at national scale

abstract: Since 2008, NSF and The College Board, have been developing a "Computer Science: Principles" curriculum to "introduce students to the central ideas of computing and CS, to instill ideas and practices of computational thinking, and to have students engage in activities that show how computing and CS change the world". We report on the initial pilot of the CS Principles curriculum at 5 universities in 2010/11. The instructors from the pilot schools will describe their classes, the piloting experience (teaching under a microscope), and successes and failures. Emphasis will be on: mapping the CS Principles curriculum to a college's specific needs, and how others can use or modify the existing materials for pilots at their schools.


title: An Aging-Resistant RO-PUF for Reliable Key Generation

abstract: Physical unclonable functions (PUFs) have emerged as a promising security primitive for low-cost authentication and cryptographic key generation. However, PUF stability with respect to temporal variations still limits its utility and widespread acceptance. Previous techniques in the literature have focused on improving PUF robustness against voltage and temperature variations, but the issues associated with aging have been largely neglected. In this paper, we address aging in the popular ring oscillator (RO)-PUF. We propose a new aging-resistant design that reduces sensitivity to negative-bias temperature instability and hot-carrier injection stresses. Simulation results demonstrate that our aging-resistant RO-PUF (ARO-PUF) can produce unique, random, and more reliable keys. On an average, only 3.8% bits of an ARO-PUF flip over a ten-year operational period because of aging, compared with a 12.8% bit flip for a conventional RO-PUF. The proposed ARO-PUF allows us to eliminate the need for error correction by adding extra ROs. The result shows that an ARO-PUF saves ~ ::: 32x area overhead compared with a conventional RO-PUF with required error correction schemes for a reliable key.


title: Self-locking underactuated mechanism for robotic gripper

abstract: We describe the concept and first prototype of a novel mechatronic design of a robotic gripper, which aims at being mounted on a humanoid robot to achieve firm (i.e. locked) and robust grasps. Such grasps could ideally support complex multi-contact motions, such as ladder climbing, or manipulation of complex tools, with energy efficiency. For this purpose, we propose a solution by designing a smart self-locking underactuated mechanism mounted in parallel to actuators to be triggered automatically when the desired grasp is achieved. This design leverages adjustable power distribution between the gripper and the brake through a differential gear. The advantages of adaptive, firm grasping, and energy-saving capabilities of our gripper are experimentally demonstrated by a prototype gripper.


title: Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm

abstract: Learning rates for regularized least-squares algorithms are in most cases expressed with respect to the excess risk, or equivalently, the $L_2$-norm. For some applications, however, guarantees with respect to stronger norms such as the $L_\infty$-norm, are desirable. We address this problem by establishing learning rates for a continuous scale of norms between the $L_2$- and the RKHS norm. As a byproduct we derive $L_\infty$-norm learning rates, and in the case of Sobolev RKHSs we actually obtain Sobolev norm learning rates, which may also imply $L_\infty$-norm rates for some derivatives. In all cases, we do not need to assume the target function to be contained in the used RKHS. Finally, we show that in many cases the derived rates are minimax optimal.


title: Learning Features by Watching Objects Move

abstract: This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation. Specifically, we use unsupervised motion-based segmentation on videos to obtain segments, which we use as pseudo ground truth to train a convolutional network to segment objects from a single frame. Given the extensive evidence that motion plays a key role in the development of the human visual system, we hope that this straightforward approach to unsupervised learning will be more effective than cleverly designed pretext tasks studied in the literature. Indeed, our extensive experiments show that this is the case. When used for transfer learning on object detection, our representation significantly outperforms previous unsupervised approaches across multiple settings, especially when training data for the target task is scarce.


title: A practical approach to calculating luminance contrast on a CRT

abstract: Luminance contrast is the basis of text legibility, and maintaining luminance contrast is essential for any color selection algorithm. In principle, it can be calculated precisely on a sufficiently well-calibrated display surface, but calibration is very expensive. Consequently, most current systems deal with contrast using heuristics. However, the usual CRT setup puts the display surface into a state that is relatively predictable. Luminance values can be estimated based on this state, and these luminance values have been used to calculate contrast using the Michelson definition. This paper proposes a method for determining the contrast of colored areas displayed on a CRT. It uses a contrast metric that is in wide use in visual psychophysics and shows that the metric can be approximated reasonably without display measurement, as long as it is possible to assume that the CRT has been adjusted according to usual CRT setup standards.


title: An Origami Reconfigurable Axial-Mode Bifilar Helical Antenna

abstract: This communication presents a new reconfigurable origami bifilar helical antenna. This antenna can change its operating frequencies by changing its height. Also, analytical equations for the design of such antennas are derived based on an equivalent model of a standard helical antenna. An origami bifilar helical antenna is designed and its performance is verified using simulations and measurements.


title: Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees

abstract: Abstract Prominent applications of sentiment analysis are countless, covering areas such as marketing, customer service and communication. The conventional bag-of-words approach for measuring sentiment merely counts term frequencies; however, it neglects the position of the terms within the discourse. As a remedy, we develop a discourse-aware method that builds upon the discourse structure of documents. For this purpose, we utilize rhetorical structure theory to label (sub-)clauses according to their hierarchical relationships and then assign polarity scores to individual leaves. To learn from the resulting rhetorical structure, we propose a tensor-based, tree-structured deep neural network (named Discourse-LSTM) in order to process the complete discourse tree. The underlying tensors infer the salient passages of narrative materials. In addition, we suggest two algorithms for data augmentation (node reordering and artificial leaf insertion) that increase our training set and reduce overfitting. Our benchmarks demonstrate the superior performance of our approach. Moreover, our tensor structure reveals the salient text passages and thereby provides explanatory insights.


title: One Model To Learn Them All

abstract: Deep learning yields great results across many fields, from speech recognition, image classification, to translation. But for each problem, getting a deep model to work well involves research into the architecture and a long period of tuning. We present a single model that yields good results on a number of problems spanning multiple domains. In particular, this single model is trained concurrently on ImageNet, multiple translation tasks, image captioning (COCO dataset), a speech recognition corpus, and an English parsing task. Our model architecture incorporates building blocks from multiple domains. It contains convolutional layers, an attention mechanism, and sparsely-gated layers. Each of these computational blocks is crucial for a subset of the tasks we train on. Interestingly, even if a block is not crucial for a task, we observe that adding it never hurts performance and in most cases improves it on all tasks. We also show that tasks with less data benefit largely from joint training with other tasks, while performance on large tasks degrades only slightly if at all.


title: Validation, verification, and testing techniques throughout the life cycle of a simulation study

abstract: Life cycle validation, verification, and testing (VV&T) is extremely important for the success of a simulation study. This paper surveys current software VV&T techniques and current simulation model VV&T techniques and describes how they can all be applied throughout the life cycle of a simulation study. The processes and credibility assessment stages of the life cycle are described and the applicability of the VV&T techniques for each stage is stated.


title: Fabrication and modeling of temperature-controllable artificial muscle actuator

abstract: Research about the artificial muscle made of fishing lines or sewing threads, called the twisted and coiled polymer actuator (abbreviated as TCA in this paper) has collected many interests, recently. Since TCA has a specific power surpassing the human skeletal muscle theoretically, it is expected to be a new generation of the artificial muscle actuator. In order that the TCA is utilized as a useful actuator, this paper introduces the fabrication and the modeling of the temperature-controllable TCA. With an embedded micro thermistor, the TCA is able to measure temperature directly, and feedback control is realized. The safe range of the force and temperature for the continuous use of the TCA was identified through experiments, and the closed-loop temperature control is successfully performed without the breakage of TCA.


title: Fast efficient algorithm for enhancement of low lighting video

abstract: We describe a novel and effective video enhancement algorithm for low lighting video. The algorithm works by first inverting the input low-lighting video and then applying an image de-haze algorithm on the inverted input. To facilitate faster computation and improve temporal consistency, correlations between temporally neighboring frames are utilized. Simulations using naive implementations of the algorithm show good enhancement results and 2x speed-up as compared with frame-wise enhancement algorithms, with further improvements in both quality and speed possible.


title: Improving Question Retrieval in Community Question Answering Using World Knowledge

abstract: Community question answering (cQA), which provides a platform for people with diverse background to share information and knowledge, has become an increasingly popular research topic. In this paper, we focus on the task of question retrieval. The key problem of question retrieval is to measure the similarity between the queried questions and the historical questions which have been solved by other users. The traditional methods measure the similarity based on the bag-of-words (BOWs) representation. This representation neither captures dependencies between related words, nor handles synonyms or polysemous words. In this work, we first propose a way to build a concept thesaurus based on the semantic relations extracted from the world knowledge of Wikipedia. Then, we develop a unified framework to leverage these semantic relations in order to enhance the question similarity in the concept space. Experiments conducted on a real cQA data set show that with the help of Wikipedia thesaurus, the performance of question retrieval is improved as compared to the traditional methods.


title: Term-Structure Models: a Review

abstract: The topic of term-structure modelling for derivatives pricing has been covered in recent years, at least in book form (see, eg, [Rebonato (1998)], [James and Webber (2000)], [Hughston (2000)], [Brigo and Mercurio (2001)]). One obvious justification for updating these works is that the modelling of interest rates is still rapidly evolving. There are, however, deeper reasons why a fresh examination of the current state of research in this area deserves attention. The first is that recently a qualitatively new dimension has been added to the modelling complexity, because of the appearance of pronounced and complex smiles in the implied volatility surfaces of caplets and swaptions. Sure enough, smiles of sorts were produced by some of the early modelling approaches (eg, by the [Hull and White (1993)] model), but these were at the time obtained as an ’afterthought’, and by and large regarded as an unpleasant feature to be ignored or excused away. Currently, the modelling of smiles is possibly the most active area of research in interest-rate derivatives pricing, and a sufficiently substantial body of work has accumulated to warrant a review of its achievements and of the problems still to be tackled. The second reason why a review of term structure modelling is timely is connected with two related market developments, namely the compression in profit margins for the well-established complex products and the simultaneous increase in complexity of many of the new-generation interest-rate derivatives. These joint market developments are putting under severe strain some of the basic underpinning theoretical concepts of the classic pricing approach, which ultimately derives from the [Black and Scholes (1973)] (BS in the following) paradigm (although, often, via the [Harrison and Pliska (1981)] route). In particular, given the robustness of the BS model, market completeness and exact payoff replicability (both reasonable but inaccurate descriptions of financial reality) can constitute a valid framework when one is pricing, say, a simple cap


title: Distributed channel allocation scheme for wireless interference avoidance in smart grid

abstract: Herein, we propose a distributed channel allocation scheme for power distribution wireless communication systems in smart grid. By defining the priority for each user equipment(UE), the wireless channels are allocated for each interrupted UE efficiently. Since the UEs obtain their priorities based on the local information, the proposed channel allocation scheme is scalable, and therefore suitable to be deployed in large wireless networks. Furthermore, numerical simulations are conducted for performance analysis and good results are obtained using the proposed scheme.


title: Live Target Detection with Deep Learning Neural Network and Unmanned Aerial Vehicle on Android Mobile Device

abstract: This paper describes the stages faced during the development of an Android program which obtains and decodes live images from DJI Phantom 3 Professional Drone and implements certain features of the TensorFlow Android Camera Demo application. Test runs were made and outputs of the application were noted. A lake was classified as seashore, breakwater and pier with the proximities of 24.44%, 21.16% and 12.96% respectfully. The joystick of the UAV controller and laptop keyboard was classified with the proximities of 19.10% and 13.96% respectfully. The laptop monitor was classified as screen, monitor and television with the proximities of 18.77%, 14.76% and 14.00% respectfully. The computer used during the development of this study was classified as notebook and laptop with the proximities of 20.04% and 11.68% respectfully. A tractor parked at a parking lot was classified with the proximity of 12.88%. A group of cars in the same parking lot were classified as sports car, racer and convertible with the proximities of 31.75%, 18.64% and 13.45% respectfully at an inference time of 851ms.


title: Expectation and reality in ERP implementation: consultant and solution provider perspective

abstract: Purpose – This paper aims to analyse expectation and reality in enterprise resource planning implementation from the consultants' and software vendors' point of view and process these further as requirements of future IT systems.Design/methodology/approach – A small‐scale survey among Finnish enterprise resource planning system (ERP) software vendors and consultants on ERP implementation challenges is analysed (n=59). The results are connected to existing literature in the field of deploying ERP systems in the form of discussion.Findings – The consultants' opinions show similar results with studies conducted with companies using ERP systems. The implementer's point of view shows clearly the challenge of using standardized ERP packages for various requirements on different levels. Although the sales presentations tend to emphasize the general purpose and flexibility of software packages, the dilemma between customization and vanilla system remains. The implementer's viewpoint emphasises challenges in opera...


title: Discriminative Bi-Term Topic Model for Headline-Based Social News Clustering

abstract: Social news are becoming increasingly popular. News organizations and popular journalists are starting to use social media more and more heavily for broadcasting news. The major challenge in social news clustering lies in the fact that textual content is only a headline, which is much shorter than the fulltext. Previous works showed that the bi-term topic model (BTM) is effective in modeling short text such as tweets. However, the drawback is that all non-stop terms are considered equally in forming the bi-terms. In this paper, a discriminative bi-term topic model ($d$-BTM) is presented, which tries to exclude less indicative bi-terms by discriminating topical terms from general and document-specific ones. Experiments on {TDT4} and Reuter-21578 show that using merely headlines, the $d$-BTM model is able to induce latent topics that are nearly as good as that are generated by LDA using news fulltext as evidence. The major contribution of this work lies in the empirical study on the reliability of topic modeling using merely news headlines.


title: Caiipa: automated large-scale mobile app testing through contextual fuzzing

abstract: Scalable and comprehensive testing of mobile apps is extremely challenging. Every test input needs to be run with a variety of contexts, such as: device heterogeneity, wireless network speeds, locations, and unpredictable sensor inputs. The range of values for each context, e.g. location, can be very large. In this paper we present Caiipa, a cloud service for testing apps over an expanded mobile context space in a scalable way. It incorporates key techniques to make app testing more tractable, including a context test space prioritizer to quickly discover failure scenarios for each app. We have implemented Caiipa on a cluster of VMs and real devices that can each emulate various combinations of contexts for tablet and phone apps. We evaluate Caiipa by testing 265 commercially available mobile apps based on a comprehensive library of real-world conditions. Our results show that Caiipa leads to improvements of 11.1x and 8.4x in the number of crashes and performance bugs discovered compared to conventional UI-based automation (i.e., monkey-testing).


title: Local Descriptors encoded by Fisher Vectors for

abstract: This paper proposes a new descriptor for person re-identification building on the recent advances of Fisher Vectors. Specifically, a simple vector of attributes consisting in the pixel coordinates, its intensity as well as the first and second-order derivatives is computed for each pixel of the image. These local descriptors are turned into Fisher Vectors before being pooled to produce a global representation of the image. The so-obtained Local Descriptors encoded by Fisher Vector (LDFV) have been validated through experiments on two person re-identification benchmarks (VIPeR and ETHZ), achieving state-of-the-art performance on both datasets.


title: Bridging value modelling to ArchiMate via transaction modelling

abstract: The ArchiMate modelling language provides a coherent and a holistic view of an enterprise in terms of its products, services, business processes, actors, business units, software applications and more. Yet, ArchiMate currently lacks (1) expressivity in modelling an enterprise from a value exchange perspective, and (2) rigour and guidelines in modelling business processes that realize the transactions relevant from a value perspective. To address these issues, we show how to connect e $$^{3}$$ value, a technique for value modelling, to ArchiMate via transaction patterns from the DEMO methodology. Using ontology alignment techniques, we show a transformation between the meta models underlying e $$^{3}$$ value, DEMO and ArchiMate. Furthermore, we present a step-wise approach that shows how this model transformation is achieved and, in doing so, we also show the of such a transformation. We exemplify the transformation of DEMO and e $$^{3}$$ value into ArchiMate by means of a case study in the insurance industry. As a proof of concept, we present a software tool supporting our transformation approach. Finally, we discuss the functionalities and limitations of our approach; thereby, we analyze its and practical applicability.


title: Beyond Text QA: Multimedia Answer Generation by Harvesting Web Information

abstract: Community question answering (cQA) services have gained popularity over the past years. It not only allows community members to post and answer questions but also enables general users to seek information from a comprehensive set of well-answered questions. However, existing cQA forums usually provide only textual answers, which are not informative enough for many questions. In this paper, we propose a scheme that is able to enrich textual answers in cQA with appropriate media data. Our scheme consists of three components: answer medium selection, query generation for multimedia search, and multimedia data selection and presentation. This approach automatically determines which type of media information should be added for a textual answer. It then automatically collects data from the web to enrich the answer. By processing a large set of QA pairs and adding them to a pool, our approach can enable a novel multimedia question answering (MMQA) approach as users can find multimedia answers by matching their questions with those in the pool. Different from a lot of MMQA research efforts that attempt to directly answer questions with image and video data, our approach is built based on community-contributed textual answers and thus it is able to deal with more complex questions. We have conducted extensive experiments on a multi-source QA dataset. The results demonstrate the effectiveness of our approach.


title: Three-loop autopilot design and simulation

abstract: In order to realize missile real-time change flight trajectory, three-loop autopilot is setting up. The structure characteristics, autopilot model, and control parameters design method were researched. Firstly, this paper introduced the 11th order three-loop autopilot model. With the principle of systems reduce model order, the 5th order model was deduced. On that basis, open-loop frequency characteristic and closed-loop frequency characteristic were analyzed. The variables of velocity ratio, dynamic pressure ratio and elevator efficiency ratio were leading to correct system nonlinear. And then autopilot gains design method were induced. System flight simulations were done, and result shows that autopilot gains played a good job in the flight trajectory, autopilot satisfied the flight index.


title: Seasonal and diurnal variation in the stomatal conductance and paraheliotropism of tedera (Bituminaria bituminosa var. albomarginata) in the field

abstract: Themechanismsofdroughtresistanceinperenniallegumesarepoorlyunderstood.Weexploredthediurnaland seasonal variation (May, August, February) in stomatal conductance (gs) and paraheliotropism of three tedera accessions (Bituminaria bituminosa (L.) C.H. Stirton var. albomarginata) and lucerne (Medicago sativa L.), both perennial legumes, grown in the field. For the tedera accessions, there was a significant reduction ingsduring the day in May (late autumn) and February(summer),buttherewaslittlereductionforlucerne.Thepeakleafangleinthetederaaccessionsrangedfrom <40 � to 70 � , whereas for lucerne, the leaf angle was nearly parallel to incident light at 85 � . Leaf water-use efficiency, relative leaf water content and leaf retention were higher for the tedera accessions than for lucerne in February. These results highlight thesuperiordroughtresistanceoftederacomparedwithlucerne.Thereductioningsoverthedayintederashowsthecapacity of this species to reduce water loss quickly when conditions for CO2 fixation relative to water loss are highly unfavourable. The high retention of leaves in summer by tedera is a valuable trait for a perennial pasture plant in Mediterranean environments.Leaffolding,combinedwitheffectivestomatalcontrolinsummer,providestederawithasetofphysiological responses that confer high drought resistance.


title: Clinical and Behavioral Characteristics of Adults Receiving Medical Care for HIV Infection — Medical Monitoring Project , United States , 2007

abstract: PROBLEM ::: As of December 31, 2008, an estimated 663,084 persons were living with a diagnosis of human immunodeficiency virus (HIV) infection in the 40 U.S. states that have had confidential name-based HIV infection reporting since at least January 2006. Although HIV surveillance programs in the United States collect information about persons who have received a diagnosis of HIV infection and acquired immunodeficiency syndrome (AIDS), supplemental surveillance projects are needed to collect information about care-seeking behaviors, health-care use, and other behaviors among persons living with HIV. Data on the clinical and behavioral characteristics of persons receiving medical care for HIV infection are critical to reduce HIV-related morbidity and mortality and for program planning to allocate services and resources, guide prevention planning, assess unmet medical and ancillary service needs, and help develop intervention programs and health policies at the local, state, and national levels. ::: ::: ::: REPORTING PERIOD COVERED ::: Data were collected during June 2007-September 2008 for patients who received medical care in 2007 (sampled from January 1-April 30). ::: ::: ::: DESCRIPTION OF THE SYSTEM ::: The Medical Monitoring Project (MMP) is an ongoing, multisite supplemental surveillance project that assesses behaviors, clinical characteristics, and quality of care of HIV-infected persons who are receiving medical care. Participants must be aged ≥ 18 years and have received medical care at sampled facilities that provide HIV medical care within participating MMP project areas. Self-reported behavioral and selected clinical data are collected using an in-person interview. A total of 26 project areas in 19 states and Puerto Rico were funded to collect data during the 2007 MMP data collection cycle. ::: ::: ::: RESULTS ::: The results from the 2007 MMP cycle indicated that among 3,643 participants, a total of 3,040 (84%) had some form of health insurance or coverage during the 12 months before the interview; of these, 45% reported having Medicaid, 37% reported having private health insurance or coverage through a health maintenance organization, and 30% reported having Medicare. A total of 3,091 (85%) of the participants were currently taking antiretroviral medications. Among 3,609 participants who reported ever having a CD4 T-lymphocyte test, 2,996 (83%) reported having three or more CD4 T-lymphocyte tests in the 12 months before the interview. Among 3,567 participants who reported ever having an HIV viral load test, 2,946 (83%) reported having three or more HIV viral load tests in the 12 months before the interview. Among 3,643 participants, 45% needed HIV case management, 33% needed mental health counseling, and 32% needed assistance finding dental services during the 12 months before the interview; 8%, 13%, and 25% of these participants who needed the services, respectively, had not received these services by the time of the interview. Noninjection drugs were used for nonmedical purposes by 1,117 (31%) participants during the 12 months before the interview, and 122 (3%) participants had used injection drugs for nonmedical purposes. Unprotected anal intercourse was reported by 527 (54%) of 970 men who reported having anal sex with a man during the 12 months before the interview. Unprotected anal or vaginal intercourse was reported by 176 (32%) of the 553 men who reported having anal or vaginal intercourse with a woman during the 12 months before the interview. Unprotected anal or vaginal intercourse was reported by 216 (42%) of the 516 women who reported having anal or vaginal intercourse with a man during the 12 months before the interview. ::: ::: ::: INTERPRETATION ::: The findings in this report indicate that in 2007, most persons with HIV infection who were receiving medical care were taking antiretroviral therapy and had some form of health insurance or coverage; however, some persons were not receiving needed critical ancillary services, such as HIV case management or help finding dental services. In addition, some persons living with HIV infection engaged in behaviors, such as unprotected sex, that increase the risk for transmitting HIV to sexual partners, and some used noninjection or injection drugs for nonmedical purposes, which might decrease adherence to antiretroviral therapy and increase health-risk behaviors. ::: ::: ::: PUBLIC HEALTH ACTIONS ::: MMP data can be used to monitor the national HIV/AIDS strategy goal of increasing access to care and optimizing health outcomes among persons living with HIV. Persons infected with HIV who are not receiving needed ancillary services highlight missed opportunities for access to care and other supportive services, information that can be used to advocate for additional resources. Drug use among persons with HIV infection underscores the continued need for substance abuse treatment services for this population. In addition, prevention services and programs are needed to decrease the number of HIV-infected persons engaging in unprotected sex. The data in this report can be included in local, state, and national HIV/AIDS epidemiologic profiles and shared with community stakeholders. Although data from the 2007 MMP cycle might not be representative of all persons receiving medical care for HIV infection in the United States or in the individual project areas, future MMP cycles are expected to yield weighted national estimates representing all HIV-infected persons receiving medical care in the United States.


title: A Survey of Shortest-Path Algorithms

abstract: A shortest-path algorithm finds a path containing the minimal cost between two vertices in a graph. A plethora of shortest-path algorithms is studied in the literature that span across multiple disciplines. This paper presents a survey of shortest-path algorithms based on a taxonomy that is introduced in the paper. One dimension of this taxonomy is the various flavors of the shortest-path problem. There is no one general algorithm that is capable of solving all variants of the shortest-path problem due to the space and time complexities associated with each algorithm. Other important dimensions of the taxonomy include whether the shortest-path algorithm operates over a static or a dynamic graph, whether the shortest-path algorithm produces exact or approximate answers, and whether the objective of the shortest-path algorithm is to achieve time-dependence or is to only be goal directed. This survey studies and classifies shortest-path algorithms according to the proposed taxonomy. The survey also presents the challenges and proposed solutions associated with each category in the taxonomy.


title: Multiphysics Design and Optimization of High-Speed Permanent-Magnet Electrical Machines for Air Blower Applications

abstract: In this paper, a high-speed permanent-magnet (PM) electrical machine is designed for centrifugal air blower application with consideration of the multiphysics constraints, including the mechanical strength, rotor dynamics, mechanical losses, and thermal field. In order to minimize the probabilities of mechanical failure and to realize the compact structure, the length and the outer diameter of rotor from mechanical point of view are designed and optimized. The optimization of sleeve thickness is performed by an accurate finite-element modeling which the interference is equivalent to the contact pressure. The optimization of rotor strength is based on the length–diameter ratio curve by using the finite-element method, which is followed behind the optimization of sleeve thickness. An assessment of machine cooling design from thermal point of view is performed by using the computational fluid dynamics modeling. Finally, in order to verify the accuracy of theoretical analysis, the prototype is fabricated with consideration of all critical values of the design parameters, and measured on the test bench.