title: A Market Separations Perspective to Analyze the Role of ICT in Development at the Bottom of the Pyramid

abstract: The “Bottom of the Pyramid” “BOP” defined to include people sustaining on less than US $2 a day is believed to consist of over four billion people. A principal problem facing BOP consumers is lack of access to essential goods and services, due to unavailability (Prahalad 2005) and/or non-affordability. Similarly, BOP producers find it difficult to identify, connect with, and market their products to, potential customers. This research in progress paper examines the research question: How can Information and Communication Technologies (ICT) facilitate market development at the “Bottom of the Pyramid” (BOP)? Combining concepts from the theory of Market Separations and the Automate-Informate-Transform classification of Information Systems (IS) roles, from the marketing and IS literatures respectively, we develop propositions describing how ICT can reduce particular types of market separations between consumers and producers at the BOP, and thus facilitate market development at the BOP. Our study uses qualitative primary data (interviews with 32 respondents in India) and secondary data. Ongoing analysis indicates that consumers and producers in markets at the BOP are indeed separated from one another due to four types of market separations-spatial, temporal, financial and informational. Application of ICT can reduce these four separations to facilitate exchange and consumption for BOP individuals. Expected contributions and implications are discussed.


title: On Optimality of Jury Selection in Crowdsourcing

abstract: Recent advances in crowdsourcing technologies enable computationally challenging tasks (e.g., sentiment analysis and entity resolution) to be performed by Internet workers, driven mainly by monetary incentives. A fundamental question is: how should workers be selected, so that the tasks in hand can be accomplished successfully and economically? In this paper, we study the Jury Selection Problem (JSP): Given a monetary budget, and a set of decision-making tasks (e.g., “Is Bill Gates still the CEO of Microsoft now?”), return the set of workers (called jury), such that their answers yield the highest “Jury Quality” (or JQ). Existing JSP solutions make use of the Majority Voting (MV) strategy, which uses the answer chosen by the largest number of workers. We show that MV does not yield the best solution for JSP. We further prove that among all voting strategies (including deterministic and randomizedstrategies), BayesianVoting(BV)canoptimallysolveJSP. We then examine how to solve JSP based on BV. This is technically challenging, since computing the JQ with BV is NP-hard. We solve this problem by proposing an approximate algorithm that is computationally efficient. Our approximate JQ computation algorithm is also highly accurate, and its error is proved to be bounded within 1%. We extend our solution by considering the task owner’s “belief” (or prior) on the answers of the tasks. Experiments on synthetic and real datasets show that our new approach is consistently better than the best JSP solution known.


title: HoNVis: Visualizing and exploring higher-order networks

abstract: Unlike the conventional first-order network (FoN), the higher-order network (HoN) provides a more accurate description of transitions by creating additional nodes to encode higher-order dependencies. However, there exists no visualization and exploration tool for the HoN. For applications such as the development of strategies to control species invasion through global shipping which is known to exhibit higher-order dependencies, the existing FoN visualization tools are limited. In this paper, we present HoNVis, a novel visual analytics framework for exploring higher-order dependencies of the global ocean shipping network. Our framework leverages coordinated multiple views to reveal the network structure at three levels of detail (i.e., the global, local, and individual port levels). Users can quickly identify ports of interest at the global level and specify a port to investigate its higher-order nodes at the individual port level. Investigating a larger-scale impact is enabled through the exploration of HoN at the local level. Using the global ocean shipping network data, we demonstrate the effectiveness of our approach with a real-world use case conducted by domain experts specializing in species invasion. Finally, we discuss the generalizability of this framework to other real-world applications such as information diffusion in social networks and epidemic spreading through air transportation.


title: A 5-Gbps USB3.0 transmitter and receiver linear equalizer

abstract: Summary ::: ::: A USB3.0 compatible transmitter and the linear equalizer of the corresponding receiver are presented in this paper. The architecture and circuit design techniques used to meet the strict requirements of the overall link design are explored. Output voltage amplitude and de-emphasis levels are programmable, whereas the output impedance is calibrated to 50Ω. A programmable receiver equalizer is also presented with its main purpose being to compensate for the channel losses; this is employed together with a DC offset compensation scheme. The 6.25-GHz equalizer provides a 10 dB overall gain equalization and 5.5-dB peaking at the maximum gain setting. Designed using a mature and well established 65 nm complementary metal oxide semiconductor process, the layout area is 400 µm × 210 µm for the transmitter core, and 140 µm × 70 µm for the equalizer core. The power consumption is 55 and 4 mW, respectively, from a 1.2 V supply at a data rate of 5 Gbps. The target application for such high-speed blocks is to implement the critical part of the physical layer that defines the signaling technology of SuperSpeed USB3 PHY. However, identical iterations of the circuitry discussed can be used for similar high-speed applications like the PCI express (PCIe). Copyright © 2014 John Wiley & Sons, Ltd.


title: Cooperative perception for autonomous vehicle control on the road: Motivation and experimental results

abstract: In this paper, we attempt to develop a reusable framework of cooperative perception for vehicle control on the road that can extend perception range beyond line-of-sight and beyond field-of-view. For this goal, the following problems are addressed: map merging, vehicle identification, sensor multi-modality, impact of communications, and impact on path planning. We provide experimental results using a self-driving vehicle and manned vehicles equipped with the cooperative perception systems that we propose and implement.


title: Design and implementation of digital fractional order PID controller using optimal pole-zero approximation method for magnetic levitation system

abstract: The aim of this paper is to employ fractional order proportional integral derivative ( FO-PID ) controller and integer order PID controller to control the position of the levitated object in a magnetic levitation system ( MLS ), which is inherently nonlinear and unstable system. The proposal is to deploy discrete optimal pole-zero approximation method for realization of digital fractional order controller. An approach of phase shaping by slope cancellation of asymptotic phase plots for zeros and poles within given bandwidth is explored. The controller parameters are tuned using dynamic particle swarm optimization ( dPSO ) technique. Effectiveness of the proposed control scheme is verified by simulation and experimental results. The performance of realized digital FO-PID controller has been compared with that of the integer order PID controllers. It is observed that effort required in fractional order control is smaller as compared with its integer counterpart for obtaining the same system performance.


title: Performance assessment for mountain bike based on WSN and cloud technologies

abstract: The mountain bike is one of the most used equipment's in outdoor sports activities. The present work presents a distributed sensing system expressed by a WSN for cycling assessment to increase performance of athletes involved in this probes. The implemented system performs acquisition, processing and data communication based on ZigBee protocol that also provides the machine-to-machine communication and data storage in a cloud platform. The information stored on the cloud server can be accessed through a mobile application that analyses and correlates all metrics calculated using the training data obtained during training session. The system permits to record the athletes training data and to access it in order to improve their performances during the next competitions based on data analysis. The recorded data of the evolution of each athlete permits to perform appropriate comparisons between different training sessions and different athletes results.


Luna: A Dashboard for Teachers Using Intelligent Tutoring Systems


title: Receptor co-operation in retrovirus entry: recruitment of an auxiliary entry mechanism after retargeted binding

abstract: We have constructed Moloney murine leukemia virus (MoMLV)-derived envelope glycoproteins (AMO) displaying an amino-terminal Ram-1-binding domain in which a variety of different amino acid spacers have been inserted between the displayed domain and the MoMLV surface (SU) subunit. Titres of retroviruses generated with these chimeric envelopes were enhanced on cells expressing both Ram-1 and Rec-1 receptors compared with the titres on cells expressing only one or other receptor type. The absolute viral titres and the degree of titre enhancement due to receptor cooperativity were highly variable between the different chimeric envelopes and were determined primarily by the properties of the interdomain spacer. An extreme example of receptor co-operativity was encountered when testing Ram-1-targeted AMOPRO envelopes with specific proline-rich interdomain spacers. AMOPRO viruses could not enter cells expressing only Rec-1 or only Ram-1 but could efficiently infect cells co-expressing both receptors. The data are consistent with a model for receptor co-operativity in which binding to the targeted (Ram-1) receptor triggers conformational rearrangements of the envelope that lead to complete unmasking of the hidden Rec-1-binding domain, thereby facilitating its interaction with the viral (Rec-1) receptor which leads to optimal fusion triggering.


title: How to read a paper. Statistics for the non-statistician. II: "Significant" relations and their pitfalls.

abstract: It is possible to be seriously misled by taking the statistical competence (and/or the intellectual honesty) of authors for granted. Some common errors committed (deliberately or inadvertently) by the authors of papers are given in the final box.


Gradient Routing in Ad Hoc Networks


title: Forward Light Cuts: A Scalable Approach to Real-Time Global Illumination

abstract: We present Forward Light Cuts, a novel approach to real-time global illumination using forward rendering techniques. We focus on unshadowed diffuse interactions for the first indirect light bounce in the context of large models such as the complex scenes usually encountered in CAD application scenarios. Our approach efficiently generates and uses a multiscale radiance cache by exploiting the geometry-specific stages of the graphics pipeline, namely the tessellator unit and the geometry shader To do so, we assimilate virtual point lights to the scene's triangles and design a stochastic decimation process chained with a partitioning strategy that accounts for both close-by strong light reflections, and distant regions from which numerous virtual point lights collectively contribute strongly to the end pixel. Our probabilistic solution is supported by a mathematical analysis and a number of experiments covering a wide range of application scenarios. As a result, our algorithm requires no precomputation of any kind, is compatible with dynamic view points, lighting condition, geometry and materials, and scales to tens of millions of polygons on current graphics hardware.


title: Review spam detection via time series pattern discovery

abstract: Online reviews play a crucial role in today's electronic commerce. Due to the pervasive spam reviews, customers can be misled to buy low-quality products, while decent stores can be defamed by malicious reviews. We observe that, in reality, a great portion (> 90% in the data we study) of the reviewers write only one review (singleton review). These reviews are so enormous in number that they can almost determine a store's rating and impression. However, existing methods ignore these reviewers. To address this problem, we observe that the normal reviewers' arrival pattern is stable and uncorrelated to their rating pattern temporally. In contrast, spam attacks are usually bursty and either positively or negatively correlated to the rating. Thus, we propose to detect such attacks via unusually correlated temporal patterns. We identify and construct multidimensional time series based on aggregate statistics, in order to depict and mine such correlation. Experimental results show that the proposed method is effective in detecting singleton review attacks. We discover that singleton review is a significant source of spam reviews and largely affects the ratings of online stores.


title: Measuring information systems success: models, dimensions, measures, and interrelationships

abstract: Since DeLone and McLean (D&M) developed their model of IS success, there has been much research on the topic of success as well as extensions and tests of their model. Using the technique of a qualitative literature review, this research reviews 180 papers found in the academic literature for the period 1992–2007 dealing with some aspect of IS success. Using the six dimensions of the D&M model – system quality, information quality, service quality, use, user satisfaction, and net benefits – 90 empirical studies were examined and the results summarized. Measures for the six success constructs are described and 15 pairwise associations between the success constructs are analyzed. This work builds on the prior research related to IS success by summarizing the measures applied to the evaluation of IS success and by examining the relationships that comprise the D&M IS success model in both individual and organizational contexts.


title: Single-phase cascaded H-bridge multilevel inverter with nonactive power compensation for grid-connected photovoltaic generators

abstract: This paper presents a single-phase cascaded H-bridge multilevel inverter for a grid-connected photovoltaic (PV) system with nonactive power compensation. A generalized nonactive power theory is applied to generate the nonactive current reference. Within the inverter's capability, nonactive power required by the local load is provided to improve the grid power quality. To minimize harmonics and achieve zero error tracking, a hybrid controller composed of a proportional controller and a repetitive controller is applied to current control. A single-phase 11-level cascaded multilevel inverter is considered in both simulation and experimental tests. Each H-bridge is connected to a 195 W solar panel. Simulation and experimental results are presented to validate the proposed ideas.


title: Coronally advanced flap and combination therapy for root coverage. Clinical strategies based on scientific evidence and clinical experience

abstract: During the past three decades, several surgical techniques have been proposed to treat single and multiple gingival recessions. Evidence indicates that coronally advanced flap-based approaches result in the best clinical results. Among all the different techniques, the use of a graft under a coronally advanced flap results in the best short- and long-term outcomes in terms of root coverage and gain in keratinized tissue. The use of a coronally advanced flap + connective tissue graft would appear to be the best choice for root coverage. However, harvesting a graft from the palate adds morbidity, surgical chair-time and requires increased surgical skills. A potential alternative could be the use of enamel matrix derivatives under a coronally advanced flap, and this achieves similar clinical outcomes and is less invasive, but adds economic costs to the treatment. Evidence shows that a coronally advanced flap alone in many instances results in complete root coverage and is stable over time. A coronally advanced flap is less invasive for the patient, requires less chair-time and probably less surgical skill. It would therefore be desirable to use a coronally advanced flap approach when indicated. It has been hypothesized that a coronally advanced flap approach alone could be successfully applied when the residual gingiva is thick and wide, although existing evidence does not support this hypothesis in full. Accordingly, the adjunctive use of a graft or enamel matrix derivatives could be restricted to sites at which there is thin and narrow residual gingiva.


title: Photo Wake-Up: 3D Character Animation From a Single Photo

abstract: We present a method and application for animating a human subject from a single photo. E.g., the character can walk out, run, sit, or jump in 3D. The key contributions of this paper are: 1) an application of viewing and animating humans in single photos in 3D, 2) a novel 2D warping method to deform a posable template body model to fit the person's complex silhouette to create an animatable mesh, and 3) a method for handling partial self occlusions. We compare to state-of-the-art related methods and evaluate results with human studies. Further, we present an interactive interface that allows re-posing the person in 3D, and an augmented reality setup where the animated 3D person can emerge from the photo into the real world. We demonstrate the method on photos, posters, and art. The project page is at https://grail.cs.washington.edu/projects/wakeup/.


title: MEAL: Multi-Model Ensemble via Adversarial Learning

abstract: Often the best performing deep neural models are ensembles of multiple base-level networks. Unfortunately, the space required to store these many networks, and the time required to execute them at test-time, prohibits their use in applications where test sets are large (e.g., ImageNet). In this paper, we present a method for compressing large, complex trained ensembles into a single network, where knowledge from a variety of trained deep neural networks (DNNs) is distilled and transferred to a single DNN. In order to distill diverse knowledge from different trained (teacher) models, we propose to use adversarial-based learning strategy where we define a block-wise training loss to guide and optimize the predefined student network to recover the knowledge in teacher models, and to promote the discriminator network to distinguish teacher vs. student features simultaneously. The proposed ensemble method (MEAL) of transferring distilled knowledge with adversarial learning exhibits three important advantages: (1) the student network that learns the distilled knowledge with discriminators is optimized better than the original model; (2) fast inference is realized by a single forward pass, while the performance is even better than traditional ensembles from multi-original models; (3) the student network can learn the distilled knowledge from a teacher model that has arbitrary structures. Extensive experiments on CIFAR-10/100, SVHN and ImageNet datasets demonstrate the effectiveness of our MEAL method. On ImageNet, our ResNet-50 based MEAL achieves top-1/5 21.79%/5.99% val error, which outperforms the original model by 2.06%/1.14%. Code and models are available at: https://github.com/AaronHeee/MEAL


title: Observations and ratings of preschool children's social behavior: Issues of representativeness and validity

abstract: Data were gathered from videotaped recordings of two preschool children engaged in unstructured free play over 12 days each. Observers coded behavior from the videotapes and completed a behavior rating scale for each child after every two observation sessions. Teachers also completed two behavior rating scales per child. Results indicated that at least three 30-min observation sessions were required to reliably represent a child's overall behavior. Moderate correlations were obtained when observations were compared with teachers' and observers' own ratings, indicating the behavior rating scale did an adequate job of reflecting actual observed behavior. The implications of these results for researchers and practitioners are discussed. © 2005 Wiley Periodicals, Inc. Psychol Schs 42: 13–26, 2005.


title: The glycemic and peak incremental indices of honey, sucrose and glucose in patients with type 1 diabetes mellitus: effects on C-peptide level—a pilot study

abstract: Our study was a case–control cross-sectional study that was conducted on 20 children and adolescents suffering from type 1 diabetes mellitus and ten healthy non-diabetic children and adolescents serving as controls. The mean age of patients was 10.95 years. Oral sugar tolerance tests using glucose, sucrose and honey and measurement of fasting and postprandial serum C-peptide levels were done for all subjects in three separate sittings. The glycemic index (GI) and the peak incremental index (PII) were then calculated for each subject. Honey, compared to sucrose, had lower GI and PII in both patients (P < 0.001) and control (P < 0.05) groups. In the patients group, the increase in the level of C-peptide after using honey was not significant when compared with using either glucose or sucrose. However, in the control group, honey produced a significant higher C-peptide level, when compared with either glucose or sucrose. In conclusion, honey, because of its lower GI and PII when compared with sucrose, may be used as a sugar substitute in patients with type 1 diabetes mellitus.


title: Building a shared world: mapping distributional to model-theoretic semantic spaces

abstract: In this paper, we introduce an approach to automatically map a standard distributional semantic space onto a set-theoretic model. We predict that there is a functional relationship between distributional information and vectorial concept representations in which dimensions are predicates and weights are generalised quantifiers. In order to test our prediction, we learn a model of such relationship over a publicly available dataset of feature norms annotated with natural language quantifiers. Our initial experimental results show that, at least for domain-specific data, we can indeed map between formalisms, and generate high-quality vector representations which encapsulate set overlap information. We further investigate the generation of natural language quantifiers from such vectors.


[Moyamoya disease and Down syndrome].


title: Relationship of personality to performance motivation: a meta-analytic review.

abstract: This article provides a meta-analysis of the relationship between the five-factor model of personality and 3 central theories of performance motivation (goal-setting, expectancy, and self-efficacy motivation). The quantitative review includes 150 correlations from 65 studies. Traits were organized according to the five-factor model of personality. Results indicated that Neuroticism (average validity = -.31) and Conscientiousness (average validity = .24) were the strongest and most consistent correlates of performance motivation across the 3 theoretical perspectives. Results further indicated that the validity of 3 of the Big Five trails-Neuroticisin, Extraversion, and Conscientiousness-generalized across studies. As a set, the Big Five traits had an average multiple correlation of .49 with the motivational criteria, suggesting that the Big Five traits are an important source of performance motivation.


title: Non-Cooperation in Dialogue

abstract: This paper presents ongoing research on computational models for non-cooperative dialogue. We start by analysing different levels of cooperation in conversation. Then, inspired by findings from an empirical study, we propose a technique for measuring non-cooperation in political interviews. Finally, we describe a research programme towards obtaining a suitable model and discuss previous accounts for conflictive dialogue, identifying the differences with our work.


title: Management and outcome of 35 cases with foetal/neonatal alloimmune neutropenia.

abstract: AIM ::: The aim of this study was to provide an overview of foetal/neonatal alloimmune neutropenia (FNAIN), together with advice on the clinical management. ::: ::: ::: METHODS ::: Neutrophil serology in the Netherlands is centralised at Sanquin Diagnostic Services. We examined FNAIN cases between January 1, 1991, and July 1, 2013, to determine the number of cases diagnosed, the relationship with human neutrophil antigen (HNA) antibody, the clinical presentation and therapeutic interventions. ::: ::: ::: RESULTS ::: We identified 35 FNAIN cases. The detected HNA antibodies were as follows: anti-HNA-1a (n = 7), anti-HNA-1b (n = 12), anti-HNA-1c (n = 2), anti-HNA-2 (n = 8), anti-HNA-3a (n = 1), anti-HNA-5a (n = 1) and anti-FcγRIIIb (n = 4). No infections were diagnosed in 14 neonates, and the other 21 neonates suffered from omphalitis (n = 6), urinary tract infection (n = 1), candida mucositis (n = 1), fever of unknown origin (n = 6) and sepsis (n = 7, 20%). Parity, gestational age, birthweight, neutrophil counts and antibody specificity were not significantly different for cases with, and without, infections. All the infected children were treated with antibiotics. No children died. ::: ::: ::: CONCLUSION ::: More than half (21) of the 35 cases of FNAIN presented with infections and most implicated were HNA-1a, HNA-1b and HNA-2. Treatment with antibiotics seemed adequate. A neonatal neutropenia workflow model for use in neonatal intensive care units is presented.


title: A measurement study of available bandwidth estimation tools

abstract: Available bandwidth estimation is useful for route selection in overlay networks, QoS verification, and traffic engineering. Recent years have seen a surge in interest in available bandwidth estimation. A few tools have been proposed and evaluated in simulation and over a limited number of Internet paths, but there is still great uncertainty in the performance of these tools over the Internet at large.This paper introduces Spruce, a simple, light-weight tool for measuring available bandwidth, and compares it with two existing tools, IGI and Pathload, over 400 different Internet paths. The comparison focuses on accuracy, failure patterns, probe overhead, and implementation issues. The paper verifies the measured available bandwidth by comparing it to Multi-Router Traffic Grapher (MRTG) data and by measuring how each tool responds to induced changes in available bandwidth.The measurements show that Spruce is more accurate than Pathload and IGI. Pathload tends to overestimate the available bandwidth whereas IGI becomes insensitive when the bottleneck utilization is large.


title: OpenGV: A unified and generalized approach to real-time calibrated geometric vision

abstract: OpenGV is a new C++ library for calibrated realtime 3D geometric vision. It unifies both central and non-central absolute and relative camera pose computation algorithms within a single library. Each problem type comes with minimal and non-minimal closed-form solvers, as well as non-linear iterative optimization and robust sample consensus methods. OpenGV therefore contains an unprecedented level of completeness with regard to calibrated geometric vision algorithms, and it is the first library with a dedicated focus on a unified real-time usage of non-central multi-camera systems, which are increasingly popular in robotics and in the automotive industry. This paper introduces OpenGV's flexible interface and abstraction for multi-camera systems, and outlines the performance of all contained algorithms. It is our hope that the introduction of this open-source platform will motivate people to use it and potentially also include more algorithms, which would further contribute to the general accessibility of geometric vision algorithms, and build a common playground for the fair comparison of different solutions.


title: Machine Learning methods for Quantitative Radiomic Biomarkers

abstract: Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications in clinical care. In this radiomic study, fourteen feature selection methods and twelve classification methods were examined in terms of their performance and stability for predicting overall survival. A total of 440 radiomic features were extracted from pre-treatment computed tomography (CT) images of 464 lung cancer patients. To ensure the unbiased evaluation of different machine-learning methods, publicly available implementations along with reported parameter configurations were used. Furthermore, we used two independent radiomic cohorts for training (n = 310 patients) and validation (n = 154 patients). We identified that Wilcoxon test based feature selection method WLCX (stability = 0.84 ± 0.05, AUC = 0.65 ± 0.02) and a classification method random forest RF (RSD = 3.52%, AUC = 0.66 ± 0.03) had highest prognostic performance with high stability against data perturbation. Our variability analysis indicated that the choice of classification method is the most dominant source of performance variation (34.21% of total variance). Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice.


title: Efficient evolution of neural network topologies

abstract: Neuroevolution, i.e. evolving artificial neural networks with genetic algorithms, has been highly effective in reinforcement learning tasks, particularly those with hidden state information. An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT) that outperforms the best fixed-topology methods on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, making it possible to evolve increasingly complex solutions over time, thereby strengthening the analogy with biological evolution.


title: A generative sketch model for human hair analysis and synthesis

abstract: In this paper, we present a generative sketch model for human hair analysis and synthesis. We treat hair images as 2D piecewise smooth vector (flow) fields and, thus, our representation is view-based in contrast to the physically-based 3D hair models in graphics. The generative model has three levels. The bottom level is the high-frequency band of the hair image. The middle level is a piecewise smooth vector field for the hair orientation, gradient strength, and growth directions. The top level is an attribute sketch graph for representing the discontinuities in the vector field. A sketch graph typically has a number of sketch curves which are divided into 11 types of directed primitives. Each primitive is a small window (say 5 times 7 pixels) where the orientations and growth directions are defined in parametric forms, for example, hair boundaries, occluding lines between hair strands, dividing lines on top of the hair, etc. In addition to the three level representation, we model the shading effects, i.e., the low-frequency band of the hair image, by a linear superposition of some Gaussian image bases and we encode the hair color by a color map. The inference algorithm is divided into two stages: 1) We compute the undirected orientation field and sketch graph from an input image and 2) we compute the hair growth direction for the sketch curves and the orientation field using a Swendsen-Wang cut algorithm. Both steps maximize a joint Bayesian posterior probability. The generative model provides a straightforward way for synthesizing realistic hair images and stylistic drawings (rendering) from a sketch graph and a few Gaussian bases. The latter can be either inferred from a real hair image or input (edited) manually using a simple sketching interface. We test our algorithm on a large data set of hair images with diverse hair styles. Analysis, synthesis, and rendering results are reported in the experiments


title: A Review of the Stereotype Threat Literature and Its Application in a Neurological Population

abstract: Stereotype threat is a situational phenomenon, leading to test performance decrements, in which a member of a stigmatized group feels pressured by the possibility of confirming or being judged by a negative stereotype. This review article highlights the progression of research in the stereotype threat field, and its relevance to neurological populations. Early studies focused on demonstrating this effect in African American, women, and elderly populations. Since this time, research has continued to focus on these populations but has moved to elucidating stereotype threat’s mediating psychological factors, studying the impact of individual differences in response to stereotype threat, and attempting to reduce its overall effect. A proposal for further study in neurological populations, under the framework of stereotype threat, comprises the last portion of the paper. It is argued that this social psychological phenomenon may, at least in part, account for poor neuropsychological test performance for neurologically compromised individuals.


title: A Q-Band Low-Profile Dual Circularly Polarized Array Antenna Incorporating Linearly Polarized Substrate Integrated Waveguide-Fed Patch Subarrays

abstract: An integrated low-profile dual circularly polarized (dual CP) array antenna operating in the Q-band is proposed in this paper, which is realized by sequentially rotating subarrays comprised linearly polarized (LP) substrate integrated waveguide (SIW)-fed patches. The design methodology and working mechanism of the LP SIW-fed patches is presented first, which is implemented with a two-layer printed circuit board structure where the SIW feeding network is located in the bottom substrate and the patches are on the top substrate. Four subarrays are then arranged according to the requirements of a sequential rotation array for producing CP directive beams. A two-layer SIW feeding network for providing the required amplitude and phase distributions for the four subarrays is designed, which includes two input ports for exciting CP radiation with opposite handedness, respectively. Prototypes of the LP subarray and the integrated dual CP array antenna were fabricated and measured. Good agreement between the simulation and measured results validates the designs. The proposed low-cost and lightweight dual CP antenna owns the advantages of wide useful bandwidth (8.9%), high isolation of more than 19 dB, low profile of less than $0.1\lambda _{0}$ , and good orthogonal polarization discrimination higher than 25 dB.


title: Marketing Influences on Perceptions of Reduced Nicotine Content Cigarettes

abstract: INTRODUCTION ::: The Food and Drug Administration announced intent to reduce the nicotine content in cigarettes. There is limited evidence on how reduced nicotine content cigarette (RNC) marketing affects product beliefs and use, and research on this is needed to inform regulations. ::: ::: ::: METHODS ::: In an online experiment, 426 young adult cigarette smokers (aged 18-30 years) were randomized in a 2 (implicit: red package vs. blue package) × 2 (explicit: corrective message vs. no corrective message) design to view an advertisement for previously commercially available RNCs. Outcomes were advertisement content recall, product beliefs, and use intentions. Participants' responses to open-ended assessment of their beliefs about the stimuli were coded to identify prevailing themes. ::: ::: ::: RESULTS ::: Red packaging and corrective messaging were independently associated with greater advertisement content recall (p = .01 and p = .04, respectively). There were no significant main or interaction effects on product beliefs or use intentions. Controlling for condition, advertisement content recall was significantly associated with less favorable product beliefs (p < .001) and favorable product beliefs were associated with intent to use the product (p < .001). Open-ended responses converged on the finding that respondents were interested in RNCs, but expressed skepticism about effectiveness and value. ::: ::: ::: CONCLUSIONS ::: Brief exposure to an RNC advertisement with red packaging and corrective messaging were each independently associated with greater advertisement content recall. The results indicate: (1) interest and confusion among young adult smokers regarding RNCs, (2) beliefs about RNCs are influenced by marketing, and (3) beliefs are associated with intention to use RNCs. ::: ::: ::: IMPLICATIONS ::: Findings from this study demonstrate the importance of advertising effects on beliefs about RNC products and support the need to regulate advertising and labeling alongside product regulation. More detailed study of advertisement features that affect consumers' beliefs about RNCs and how they impact their processing of explicit messaging about product risks will be important to guide regulatory decision-making.


title: Computer-assisted implant placement. A case report: treatment of the mandible.

abstract: The authors present a case of immediate loading of mandibular implants using a 5-step procedure. The first step consists of building a scannographic template, the second step consists of taking a computerized tomographic (CT) scan, and the third step consists of implant planning using SurgiCase software. The final 2 steps consist of implant placement using a drill guide created by stereolithography and placement of the prosthesis. Using a CT scan-based planning system, the surgeon is able to select the optimal locations for implant placement. By incorporating the prosthetic planning using a scannographic template, the treatment is optimized from a prosthetic point of view. Furthermore, the use of a stereolithographic drill guide allows a physical transfer of the implant planning to the patient's mouth. The scannographic template is designed so that it can be transformed into a temporary fixed prosthesis for immediate loading, and the definitive restoration is placed 3 months later.


title: The connection between varying treatment effects and the crisis of unreplicable research : A Bayesian perspective

abstract: We connect the replication crisis in social science to the default model of constant effects coupled with the flawed statistical approach of null hypothesis significance testing and the related problems arising from the default model of constant treatment effects. We argue that Bayesian modeling of interactions could lead to a general improvement in the communication and understanding of research results. Moving to Bayesian methods (or, more generally, multilevel approaches that incorporate external information) offers an opportunity for introspection into how social scientists interact with social phenomena to produce knowledge.


title: Reassembling fractured objects by geometric matching

abstract: We present a system for automatic reassembly of broken 3D solids. Given as input 3D digital models of the broken fragments, we analyze the geometry of the fracture surfaces to find a globally consistent reconstruction of the original object. Our reconstruction pipeline consists of a graph-cuts based segmentation algorithm for identifying potential fracture surfaces, feature-based robust global registration for pairwise matching of fragments, and simultaneous constrained local registration of multiple fragments. We develop several new techniques in the area of geometry processing, including the novel integral invariants for computing multi-scale surface characteristics, registration based on forward search techniques and surface consistency, and a non-penetrating iterated closest point algorithm. We illustrate the performance of our algorithms on a number of real-world examples.


title: Teaching Smart People How to Learn

abstract: Why are your smartest and most successful employees often the worst learners? Likely, they haven't had the opportunities for introspection that failure affords. So when they do fail, instead of critically examining their own behavior, they cast blame outward--on anyone or anything they can. In Teaching Smart People How to Learn, Chris Argyris sheds light on the forces that prevent highly skilled employees for learning from mistakes and offers suggestions for helping talented employees develop more productive responses. Since 1922, Harvard Business Review has been a leading source of breakthrough ideas in management practice-many of which still speak to and influence us today. The HBR Classics series now offers you the opportunity to make these seminal pieces a part of your permanent management library. Each volume contains a groundbreaking idea that has shaped best practices and inspired countless managers around the world-and will change how you think about the business world today.


title: Solving Code-tracing Problems and its Effect on Code-writing Skills Pertaining to Program Semantics

abstract: An earlier study had found that solving code tracing problems helped improve code writing skills of students. But, given the instruments used in the earlier study, the improvement in code writing pertained primarily to language syntax. A follow-up within-subjects controlled study was conducted to investigate whether solving code tracing problems could help improve code writing skills pertaining to the semantics of a program. In the study, students were asked to write code for a control and a test problem both before and after a problem-solving session on code tracing. Increase in the score from pre-quiz to post-quiz was treated as improvement in code writing attributable to code tracing. Repeated measures ANOVA was used to analyze the data collected over four semesters. A statistically significant improvement in code writing skills pertaining to program semantics was observed on the test problems, but not on control concepts in the control problems. The improvement in code-writing skills as they pertain to program semantics accrued to the students who scored 90% or more on code-tracing problems in this study. Finally, the transfer in learning from code-tracing activities to code-writing skills may be near as well as far.


title: Reverse engineering of binary device drivers with RevNIC

abstract: This paper presents a technique that helps automate the reverse engineering of device drivers. It takes a closed-source binary driver, automatically reverse engineers the driver's logic, and synthesizes new device driver code that implements the exact same hardware protocol as the original driver. This code can be targeted at the same or a different OS. No vendor documentation or source code is required. Drivers are often proprietary and available for only one or two operating systems, thus restricting the range of device support on all other OSes. Restricted device support leads to low market viability of new OSes and hampers OS researchers in their efforts to make their ideas available to the 'real world.' Reverse engineering can help automate the porting of drivers, as well as produce replacement drivers with fewer bugs and fewer security vulnerabilities. Our technique is embodied in RevNIC, a tool for reverse engineering network drivers. We use RevNIC to reverse engineer four proprietary Windows drivers and port them to four different OSes, both for PCs and embedded systems. The synthesized network drivers deliver performance nearly identical to that of the original drivers.


title: The pain of a model in the personality of an onlooker: Influence of state-reactivity and personality traits on embodied empathy for pain

abstract: Abstract The study of inter-individual differences at behavioural and neural levels represents a new avenue for neuroscience. The response to socio-emotional stimuli varies greatly across individuals. For example, identification with the feelings of a movie character may be total for some people or virtually absent for others. Inter-individual differences may reflect both the on-line effect (state) of the observed stimuli and more stable personal characteristics (trait). Here we show that somatomotor mirror responses when viewing others' pain are modulated by both state- and trait-differences in empathy. We recorded motor-evoked potentials (MEPs) induced by Transcranial Magnetic Stimulation (TMS) in healthy individuals observing needles penetrating a model's hand. We found a reduction of corticospinal excitability that was specific for the muscle that subjects observed being penetrated. This inhibition correlated with sensory qualities of the pain ascribed to the model. Moreover, it was greater in subjects with high trait-cognitive empathy and lower in subjects with high trait-personal distress and in those with high aversion for the observed movies. Results indicate that somatomotor responses to others' pain are influenced by specific onlookers' personality traits and self-oriented emotional reactions. Our findings suggest that multiple distinct mechanisms shape mirror mapping of others' pain.


title: A Review on Graph Based Segmentation

abstract: Image segmentation plays a crucial role in effective understanding of digital images. Past few decades saw hundreds of research contributions in this field. However, the research on the existence of general purpose segmentation algorithm that suits for variety of applications is still very much active. Among the many approaches in performing image segmentation, graph based approach is gaining popularity primarily due to its ability in reflecting global image properties. This paper critically reviews existing important graph based segmentation methods. The review is done based on the classification of various segmentation algorithms within the framework of graph based approaches. The major four categorizations we have employed for the purpose of review are: graph cut based methods, interactive methods, minimum spanning tree based methods and pyramid based methods. This review not only reveals the pros in each method and category but also explores its limitations. In addition, the review highlights the need for creating a database for benchmarking intensity based algorithms, and the need for further research in graph based segmentation for automated real time applications.


title: Advances in Computational Stereo

abstract: Extraction of three-dimensional structure of a scene from stereo images is a problem that has been studied by the computer vision community for decades. Early work focused on the fundamentals of image correspondence and stereo geometry. Stereo research has matured significantly throughout the years and many advances in computational stereo continue to be made, allowing stereo to be applied to new and more demanding problems. We review recent advances in computational stereo, focusing primarily on three important topics: correspondence methods, methods for occlusion, and real-time implementations. Throughout, we present tables that summarize and draw distinctions among key ideas and approaches. Where available, we provide comparative analyses and we make suggestions for analyses yet to be done.


title: Multi-level deep supervised networks for retinal vessel segmentation

abstract: Purpose ::: Changes in the appearance of retinal blood vessels are an important indicator for various ophthalmologic and cardiovascular diseases, including diabetes, hypertension, arteriosclerosis, and choroidal neovascularization. Vessel segmentation from retinal images is very challenging because of low blood vessel contrast, intricate vessel topology, and the presence of pathologies such as microaneurysms and hemorrhages. To overcome these challenges, we propose a neural network-based method for vessel segmentation.


title: Investigating the Trend of Virtual Reality-Based Stroke Rehabilitation Systems

abstract: The existing techniques used for post stroke rehabilitation have sought to offer stroke survivors with upper extremity motor deficits a system for the practice and re-learning of motor tasks. Some studies suggest that the use of Virtual Reality technologies as an assistive technology in combination with conventional therapies can achieve improved results in post stroke rehabilitation. Despite the wealth of ongoing research attempting to build a virtual reality based system for upper extremity rehabilitation, there still exists a need for a training platform that would provide whole arm rehabilitation. In order to be practical this system should ideally be low cost and involve minimal therapist involvement. This paper describes some of the applications of virtual reality that have undergone clinical trials with patients suffering from upper extremity functional motor deficits. A brief description of VR technologies that are in place, providing benefits to the stroke patients with upper extremity motor and functional deficits, alongside a proof of concept of a whole arm rehabilitation platform is presented.


title: A hybridization of cuckoo search and particle swarm optimization for solving optimization problems

abstract: A new hybrid optimization algorithm, a hybridization of cuckoo search and particle swarm optimization (CSPSO), is proposed in this paper for the optimization of continuous functions and engineering design problems. This algorithm can be regarded as some modifications of the recently developed cuckoo search (CS). These modifications involve the construction of initial population, the dynamic adjustment of the parameter of the cuckoo search, and the incorporation of the particle swarm optimization (PSO). To cover search space with balance dispersion and neat comparability, the initial positions of cuckoo nests are constructed by using the principle of orthogonal Lation squares. To reduce the influence of fixed step size of the CS, the step size is dynamically adjusted according to the evolutionary generations. To increase the diversity of the solutions, PSO is incorporated into CS using a hybrid strategy. The proposed algorithm is tested on 20 standard benchmarking functions and 2 engineering optimization problems. The performance of the CSPSO is compared with that of several meta-heuristic algorithms based on the best solution, worst solution, average solution, standard deviation, and convergence rate. Results show that in most cases, the proposed hybrid optimization algorithm performs better than, or as well as CS, PSO, and some other exiting meta-heuristic algorithms. That means that the proposed hybrid optimization algorithm is competitive to other optimization algorithms.


title: Altered salience network connectivity predicts macronutrient intake after sleep deprivation

abstract: Although insufficient sleep is a well-recognized risk factor for overeating and weight gain, the neural mechanisms underlying increased caloric (particularly fat) intake after sleep deprivation remain unclear. Here we used resting-state functional magnetic resonance imaging and examined brain connectivity changes associated with macronutrient intake after one night of total sleep deprivation (TSD). Compared to the day following baseline sleep, healthy adults consumed a greater percentage of calories from fat and a lower percentage of calories from carbohydrates during the day following TSD. Subjects also exhibited increased brain connectivity in the salience network from the dorsal anterior cingulate cortex (dACC) to bilateral putamen and bilateral anterior insula (aINS) after TSD. Moreover, dACC-putamen and dACC-aINS connectivity correlated with increased fat and decreased carbohydrate intake during the day following TSD, but not during the day following baseline sleep. These findings provide a potential neural mechanism by which sleep loss leads to increased fat intake.


title: Energy Management and Operational Planning of a Microgrid With a PV-Based Active Generator for Smart Grid Applications

abstract: The development of energy management tools for next-generation PhotoVoltaic (PV) installations, including storage units, provides flexibility to distribution system operators. In this paper, the aggregation and implementation of these determinist energy management methods for business customers in a microgrid power system are presented. This paper proposes a determinist energy management system for a microgrid, including advanced PV generators with embedded storage units and a gas microturbine. The system is organized according to different functions and is implemented in two parts: a central energy management of the microgrid and a local power management at the customer side. The power planning is designed according to the prediction for PV power production and the load forecasting. The central and local management systems exchange data and order through a communication network. According to received grid power references, additional functions are also designed to manage locally the power flows between the various sources. Application to the case of a hybrid supercapacitor battery-based PV active generator is presented.


title: Pipelined Decision Tree Classification Accelerator Implementation in FPGA (DT-CAIF)

abstract: Decision tree classification (DTC) is a widely used technique in data mining algorithms known for its high accuracy in forecasting. As technology has progressed and available storage capacity in modern computers increased, the amount of data available to be processed has also increased substantially, resulting in much slower induction and classification times. Many parallel implementations of DTC algorithms have already addressed the issues of reliability and accuracy in the induction process. In the classification process, larger amounts of data require proportionately more execution time, thus hindering the performance of legacy systems. We have devised a pipelined architecture for the implementation of axis parallel binary DTC that dramatically improves the execution time of the algorithm while consuming minimal resources in terms of area. Scalability is achieved when connected to a high-speed communication unit capable of performing data transfers at a rate similar to that of the DTC engine. We propose a hardware accelerated solution composed of parallel processing nodes capable of independently processing data from a streaming source. Each engine processes the data in a pipelined fashion to use resources more efficiently and increase the achievable throughput. The results show that this system is 3.5 times faster than the existing hardware implementation of classification.


title: Autonomous cars: Past, present and future a review of the developments in the last century, the present scenario and the expected future of autonomous vehicle technology

abstract: The field of autonomous automation is of interest to researchers, and much has been accomplished in this area, of which this paper presents a detailed chronology. This paper can help one understand the trends in autonomous vehicle technology for the past, present, and future. We see a drastic change in autonomous vehicle technology since 1920s, when the first radio controlled vehicles were designed. In the subsequent decades, we see fairly autonomous electric cars powered by embedded circuits in the roads. By 1960s, autonomous cars having similar electronic guide systems came into picture. 1980s saw vision guided autonomous vehicles, which was a major milestone in technology and till date we use similar or modified forms of vision and radio guided technologies. Various semi-autonomous features introduced in modern cars such as lane keeping, automatic braking and adaptive cruise control are based on such systems. Extensive network guided systems in conjunction with vision guided features is the future of autonomous vehicles. It is predicted that most companies will launch fully autonomous vehicles by the advent of next decade. The future of autonomous vehicles is an ambitious era of safe and comfortable transportation.


Authentication System Based on Feature Extraction by Edge Based Segmentation Using Sobel Operator


title: Household availability of ultra-processed foods and obesity in nineteen European countries

abstract: OBJECTIVE ::: To assess household availability of NOVA food groups in nineteen European countries and to analyse the association between availability of ultra-processed foods and prevalence of obesity. ::: ::: ::: DESIGN ::: Ecological, cross-sectional study. ::: ::: ::: SETTING ::: Europe. ::: ::: ::: SUBJECTS ::: Estimates of ultra-processed foods calculated from national household budget surveys conducted between 1991 and 2008. Estimates of obesity prevalence obtained from national surveys undertaken near the budget survey time. ::: ::: ::: RESULTS ::: Across the nineteen countries, median average household availability amounted to 33·9 % of total purchased dietary energy for unprocessed or minimally processed foods, 20·3 % for processed culinary ingredients, 19·6 % for processed foods and 26·4 % for ultra-processed foods. The average household availability of ultra-processed foods ranged from 10·2 % in Portugal and 13·4 % in Italy to 46·2 % in Germany and 50·4 % in the UK. A significant positive association was found between national household availability of ultra-processed foods and national prevalence of obesity among adults. After adjustment for national income, prevalence of physical inactivity, prevalence of smoking, measured or self-reported prevalence of obesity, and time lag between estimates on household food availability and obesity, each percentage point increase in the household availability of ultra-processed foods resulted in an increase of 0·25 percentage points in obesity prevalence. ::: ::: ::: CONCLUSIONS ::: The study contributes to a growing literature showing that the consumption of ultra-processed foods is associated with an increased risk of diet-related non-communicable diseases. Its findings reinforce the need for public policies and actions that promote consumption of unprocessed or minimally processed foods and make ultra-processed foods less available and affordable.


title: Review: An overview of vertical handover techniques: Algorithms, protocols and tools

abstract: Wireless technologies, under the ''Anywhere, Anytime'' paradigm, offer users the promise of being always attached to the network. Mobile devices enabled with multiple wireless technologies make possible to maintain seamless connectivity in highly dynamic scenarios such as vehicular networks (VNs), switching from one wireless network to another by using vertical handover techniques (VHO). In this paper we present an overview of VHO techniques, along with the main algorithms, protocols and tools proposed in the literature. In addition we suggest the most appropriate VHO techniques to efficiently communicate in VN environments considering the particular characteristics of this type of networks.


Markerless Human Gait Analysis via Image Sequences


title: Prediction of heat transfer in the wellbore for liquid carbon dioxide flooding in high temperature formations

abstract: Carbon Dioxide wells are used for both injection and production. Injection wells have been used in enhanced oil recovery for many decades. This study opens up opportunities for predicting the downward flow behavior of carbon dioxide in the wellbore. A mathematical model is be developed for pressure-traverse and heat-transfer computations in liquid carbon dioxide injection wells in high temperature formations by solving the governing wellbore equations. The proposed model is based on simultaneous solution of mass and momentum balance in the wellbore and energy balance equation for carbon dioxide in the wellbore and the surrounding formation.


title: Personality, Social Support, and Anxiety among Adolescents Preparing for University Entrance Examinations in Turkey

abstract: This study aimed to investigate the importance of different sources of perceived social support, sociotropic and autonomic personality dispositions, achievement expectation, and importance of academic achievement in predicting anxiety symptoms of male and female students who were preparing for the university entrance exam. Three hundred and forty students with ages varying from 16 to 21 participated in the study. Separate multiple regression analyses were run for males and females in predicting anxiety symptoms. The total Sociotropy score and the total Social Support score predicted anxiety for females, whereas for males, anxiety was predicted by four variables, namely. Achievement Expectancy, Concern over Approval, Social Support from Family, and Social Support from Friends. Implications of these results were discussed.


title: Learning a Mahalanobis Distance-Based Dynamic Time Warping Measure for Multivariate Time Series Classification

abstract: Multivariate time series (MTS) datasets broadly exist in numerous fields, including health care, multimedia, finance, and biometrics. How to classify MTS accurately has become a hot research topic since it is an important element in many computer vision and pattern recognition applications. In this paper, we propose a Mahalanobis distance-based dynamic time warping (DTW) measure for MTS classification. The Mahalanobis distance builds an accurate relationship between each variable and its corresponding category. It is utilized to calculate the local distance between vectors in MTS. Then we use DTW to align those MTS which are out of synchronization or with different lengths. After that, how to learn an accurate Mahalanobis distance function becomes another key problem. This paper establishes a LogDet divergence-based metric learning with triplet constraint model which can learn Mahalanobis matrix with high precision and robustness. Furthermore, the proposed method is applied on nine MTS datasets selected from the University of California, Irvine machine learning repository and Robert T. Olszewski’s homepage, and the results demonstrate the improved performance of the proposed approach.


title: Meta-Learning Probabilistic Inference For Prediction

abstract: This paper introduces a new framework for data efficient and versatile learning. Specifically: 1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods. 2) We introduce \Versa{}, an instance of the framework employing a flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of shots, and outputs a distribution over task-specific parameters in a single forward pass. \Versa{} substitutes optimization at test time with forward passes through inference networks, amortizing the cost of inference and relieving the need for second derivatives during training. 3) We evaluate \Versa{} on benchmark datasets where the method sets new state-of-the-art results, handles arbitrary numbers of shots, and for classification, arbitrary numbers of classes at train and test time. The power of the approach is then demonstrated through a challenging few-shot ShapeNet view reconstruction task.


title: Lion pride optimizer: An optimization algorithm inspired by lion pride behavior

abstract: In this paper, we report a novel optimization algorithm, lion pride optimizer (LPO), which is inspired by lion pride behavior. The framework is mainly based on lion prides’ evolution process and group living theory. In a lion pride, brutal competition of individuals happens among male lions both within and among prides; on the other hand, each member plays an important role in the persistence of a lion pride. Based on this framework, concepts from lion prides behavior, e.g., the strongest males occupy nearly all mating resources, and if a new cohort of males is able to take over a pride, they will seek to kill young cubs sired by their predecessors, are employed metaphorically to design optimum searching strategies for solving continuous optimization problems. From the studies of the algorithm property, it is found that the LPO algorithm is not sensitive to most parameters, which shows the robustness of the algorithm and the parameters are not problemdependent. Central tendency of the algorithm is not found. It is found that the pride update strategy and brutal competition of individuals are two main factors that contribute to the performance of LPO. According to the test results on 23 famous benchmark functions, the LPO algorithm has better performance than the other seven state-of-the-art algorithms on both unimodal and multimodal benchmark functions; in the test of high-dimensional multimodal problems, LPO outperforms the other five algorithms on all benchmark functions.


title: StaRSaC: Stable random sample consensus for parameter estimation

abstract: We address the problem of parameter estimation in presence of both uncertainty and outlier noise. This is a common occurrence in computer vision: feature localization is performed with an inherent uncertainty which can be described as Gaussian, with unknown variance; feature matching in multiple images produces incorrect data points. RANSAC is the preferred method to reject outliers if the variance of the uncertainty noise is known, but fails otherwise, by producing either a tight fit to an incorrect solution, or by computing a solution which includes outliers. We thus propose a new estimator which enforces stability of the solution with respect to the uncertainty bound. We show that the variance of the estimated parameters (VoP) exhibits ranges of stability with respect to this bound. Within this range of stability, we can accurately segment the inliers, and estimate the parameters, the variance of the Gaussian noise. We show how to compute this stable range using RANSAC and a search. We validate our results by extensive tests and comparison with state of the art estimators on both synthetic and real data sets. These include line fitting, homography estimation, and fundamental matrix estimation. The proposed method outperforms all others.


title: Efficient descriptor learning for large scale localization

abstract: Many robotics and Augmented Reality (AR) systems that use sparse keypoint-based visual maps operate in large and highly repetitive environments, where pose tracking and localization are challenging tasks. Additionally, these systems usually face further challenges, such as limited computational power, or insufficient memory for storing large maps of the entire environment. Thus, developing compact map representations and improving retrieval is of considerable interest for enabling large-scale visual place recognition and loop-closure. In this paper, we propose a novel approach to compress descriptors while increasing their discriminability and match-ability, based on recent advances in neural networks. At the same time, we target resource-constrained robotics applications in our design choices. The main contributions of this work are twofold. First, we propose a linear projection from descriptor space to a lower-dimensional Euclidean space, based on a novel supervised learning strategy employing a triplet loss. Second, we show the importance of including contextual appearance information to the visual feature in order to improve matching under strong viewpoint, illumination and scene changes. Through detailed experiments on three challenging datasets, we demonstrate significant gains in performance over state-of-the-art methods.


Position-aware Attention and Supervised Data Improve Slot Filling


title: Relief of chronic neck and shoulder pain by manual acupuncture to tender points--a sham-controlled randomized trial.

abstract: OBJECTIVES ::: To compare the effects of real acupuncture to tender points for neck and shoulder pain and stiffness (Japanese: katakori) with those of sham acupuncture. ::: ::: ::: DESIGN ::: Randomized-controlled trial. ::: ::: ::: METHODS ::: Thirty-four volunteers from an acupuncture school with complaints of chronic pain and stiffness, who had no arm symptoms and gave informed consent, were randomly allocated to acupuncture or sham groups. Acupuncture or sham acupuncture was applied to the tender points once a week for 3 weeks. In the acupuncture group the acupuncture needle was inserted to the muscle, then the sparrow pecking technique was applied five times. Sham acupuncture was done without insertion of the needle. Dull pain and stiffness were evaluated by visual analog scale (VAS) before, and every 2 days after the first needling for 1 month. Pressure pain threshold on the tender points was measured before and after each treatment. ::: ::: ::: RESULTS ::: There was no statistical difference of VAS scores between acupuncture and sham groups 9 days after the last treatment. However, the acupuncture group showed significant reduction of VAS scores immediately after and/or 1 day after the real acupuncture treatments (P<0.01). The effect tended to be prolonged after repeated treatment. Pressure pain thresholds tended to increase after real acupuncture treatment but not after sham acupuncture. ::: ::: ::: CONCLUSIONS ::: Acupuncture applied to tender points appears to have short-term effects on neck and shoulder pain and stiffness, but this study was unable to demonstrate any long-term superiority over sham acupuncture.


How engineers learn: a study of problem-based learning in the engineering classroom and implications for course design


title: A Peek under the Hood of iOS Malware

abstract: Malicious software specially crafted to proliferate in mobile platforms are becoming a serious threat, as reported by numerous software security vendors during last years. Android and iOS are nowadays the leaders of mobile OS market share. While malware targeting Android are largely studied, few attention is paid to iOS malware. In this paper, we fill this gap by studying and characterizing malware targeting iOS devices. To this regard, we study the features of iOS malware and classify samples of 36 iOS malware families discovered between 2009 and 2015. We also show the methodology for iOS malware analysis and provide a detailed analysis of a malware sample. Our findings evidence that most of them are distributed out of official markets, target jailbroken iOS devices, and very few exploit any vulnerability.


Exploring Implicit Feedback for Open Domain Conversation Generation


title: Optimal direct-drive permanent magnet wind generator systems for different rated wind speeds

abstract: The objective of this paper is to investigate possible ranges of rated wind speeds and rated power levels to have the better performance of the annual energy output per cost (AEOPC) for an individual direct-drive wind energy conversion system. Based on a given rated wind speed, the turbine blade sizing and rotor speed can be determined by using the maximal wind energy capture characteristics, the calculation of annual energy output (AEO) of wind turbines are also described. Based on a given wind turbine, a three-phase radial-flux PM synchronous generator (PMSG) with a back-to-back power converter is optimized for the minimal generator system cost. The analytical design models and optimization are developed and demonstrated by a rated power 500 kW PMSG. The optimal results of direct-drive PM wind generator systems with various combinations of different rated wind speeds and different rated power levels of wind turbines are obtained and compared, including the AEO and AEOPC at the high, medium and low wind speed sites, respectively. By comparison with the AEOPC of the optimized PM generators, the appropriate design ranges with the more cost-effective performance are analyzed and discussed for the direct-drive PM wind generator systems.


title: First webpage time algorithm with mining domain name system data

abstract: It is significant for an Internet Service Provider (ISP) or an Internet Content Provider (ICP) to obtain the user experience of the web service that is one of the most widely used Internet services. Mining data from the network systems to measure the web service has become one of the research hot spots recently. In this paper, we propose a novel method to obtain the user experience of the web service, the First Webpage Time (FWT) algorithm. The proposed FWT algorithm is performed by mining the Domain Name System (DNS) data that record the domain name resolution while users browse webpages. And then, it is analyzed in theory that there are a large number of FWTs to be averaged in order to ensure the precision of the final result. Furthermore, we also propose a new Key Quality Indicator (KQI) of measuring the web service, the FWT that is used to evaluate the loading time of textual contents of a webpage. The method of computing the FWT with DNS data is introduced and its correctness is discussed. At last, experiments based on ISP's DNS data are carried out to evaluate the proposed FWT algorithm.


title: Vision-based handwriting recognition for unrestricted text input in mid-air

abstract: We propose a vision-based system that recognizes handwriting in mid-air. The system does not depend on sensors or markers attached to the users and allows unrestricted character and word input from any position. It is the result of combining handwriting recognition based on Hidden Markov Models with multi-camera 3D hand tracking. We evaluated the system for both quantitative and qualitative aspects. The system achieves recognition rates of 86.15% for character and 97.54% for small-vocabulary isolated word recognition. Limitations are due to slow and low-resolution cameras or physical strain. Overall, the proposed handwriting recognition system provides an easy-to-use and accurate text input modality without placing restrictions on the users.


We need new tools to assess Orthorexia Nervosa. A commentary on “Prevalence of Orthorexia Nervosa among College Students Based on Bratman’s Test and Associated Tendencies”


title: PUF-FSM: A Controlled Strong PUF

abstract: Existing strong controlled physical unclonable function (PUF) designs are built to resist modeling attacks and they deal with noisy PUF responses by exploiting error correction logic. These designs are burdened by the costs of the error correction logic and information shown to leak through the associated helper data for assisting error corrections; leaving the design vulnerable to fault attacks or reliability-based attacks. We present a hybrid PUF–finite state machine (PUF-FSM) construction to realize a controlled strong PUF. The PUF-FSM design removes the need for error correction logic and related computation, storage of the helper data and loading it on-chip by only employing error-free responses judiciously determined on demand in the absence of the underlying PUF—an Arbiter PUF—with a large challenge response pair space. The PUF-FSM demonstrates improved security, especially to reliability-based attacks and is able to support a range of applications from authentication to more advanced cryptographic applications built upon shared keys. We experimentally validate the practicability of the PUF-FSM.


title: Avramenko Diode Circuit Topology for Microwave Energy Harvesting in Goubau Line and Wireless Mediums

abstract: Wireless energy harvesting at microwave frequencies is a known challenge. The transmission medium to support delivery of a wireless energy is normally an insulator that conducts mainly a displacement current. However, Nikola Tesla has hinted that an electric energy can be transmitted on the surface of a natural medium in the same manner as it propagates in a single-wire transmission line. Armed with this knowledge, we have successfully employed a single-wire transmission methodology to deliver a wireless power over a distance in excess of 2.5 m. The proposed methodology involves the use of modified Avramenko diode configuration (ADC), which is known to be able to rectify an ac power in a single-wire transmission line into a dc voltage. The rectified voltage of an ADC, though small, can be enormously multiplied to boost the yield of the power of the harvested output. The results of our investigation suggest two facts: 1) the RF ground does not need to have any physical connection with the remote power source and 2) the output voltage is weakly dependent of the input current, proving the feasibility of harvesting energy from a transmission medium of high characteristic impedance. The proposed methodology is applicable to virtually all transmission mediums, including a single-wire transmission line, free space, and the air-ground interface.


title: A big data enabled load-balancing control for smart manufacturing of Industry 4.0

abstract: The concept of "Industry 4.0" that covers the topics of Internet of Things, cyber-physical system, and smart manufacturing, is a result of increasing demand of mass customized manufacturing. In this paper, a smart manufacturing framework of Industry 4.0 is presented. In the proposed framework, the shop-floor entities (machines, conveyers, etc.), the smart products and the cloud can communicate and negotiate interactively through networks. The shop-floor entities can be considered as agents based on the theory of multi-agent system. These agents implement dynamic reconfiguration in a collaborative manner to achieve agility and flexibility. However, without global coordination, problems such as load-unbalance and inefficiency may occur due to different abilities and performances of agents. Therefore, the intelligent evaluation and control algorithms are proposed to reduce the load-unbalance with the assistance of big data feedback. The experimental results indicate that the presented algorithms can easily be deployed in smart manufacturing system and can improve both load-balance and efficiency.


title: Detection and Recognition of Traffic Planar Objects Using Colorized Laser Scan and Perspective Distortion Rectification

abstract: Reliable detection and recognition of planar objects including traffic sign, street sign, and road surface in dynamic cluttered natural scenes are a big challenge for self-driving cars. In this paper, we propose a comprehensive method for planar object detection and recognition. First, the data association of LIDAR and camera is set up to acquire colorized laser scans, which simultaneously contain both color and geometrical information. Second, we combine three color spaces of RGB, HSV, and CIE L*a*b* with laser reflectivity as an aggregation-based feature vector. Third, the 3-D geometrical characteristics of planar objects that contain planarity, size, and aspect ratio are exploited to further reduce false alarm. Fourth, in order to increase robustness to any viewpoint variation, we present a new virtual camera-based rectification method to synthesize fronto-parallel views of refined object descriptors in 3-D space. Finally, experimental results achieved under a variety of challenging conditions show that integration of color space aggregation and laser reflectivity is superior to individuals. Specifically, the proposed perspective distortion rectification method remarkably eliminates false recognition error by 45.5%. Overall, the detection rate of our comprehensive method has up to 95.87% and the recognition rate even reaches 95.07% for traffic signs ranging within 100 m, with about 33.25 ms average running time per frame.


title: Deep Metric Learning with Angular Loss

abstract: The modern image search system requires semantic understanding of image, and a key yet under-addressed problem is to learn a good metric for measuring the similarity between images. While deep metric learning has yielded impressive performance gains by extracting high level abstractions from image data, a proper objective loss function becomes the central issue to boost the performance. In this paper, we propose a novel angular loss, which takes angle relationship into account, for learning better similarity metric. Whereas previous metric learning methods focus on optimizing the similarity (contrastive loss) or relative similarity (triplet loss) of image pairs, our proposed method aims at constraining the angle at the negative point of triplet triangles. Several favorable properties are observed when compared with conventional methods. First, scale invariance is introduced, improving the robustness of objective against feature variance. Second, a third-order geometric constraint is inherently imposed, capturing additional local structure of triplet triangles than contrastive loss or triplet loss. Third, better convergence has been demonstrated by experiments on three publicly available datasets.


Guidelines for Safe Contact Lens Wear


Rethinking Conventional Collaborative Filtering for Recommending Daily Fashion Outfits


title: Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight

abstract: Deep reinforcement learning has shown promising results in learning control policies for complex sequential decision-making tasks. However, these neural network-based policies are known to be vulnerable to adversarial examples. This vulnerability poses a potentially serious threat to safety-critical systems such as autonomous vehicles. In this paper, we propose a defense mechanism to defend reinforcement learning agents from adversarial attacks by leveraging an action-conditioned frame prediction module. Our core idea is that the adversarial examples targeting at a neural network-based policy are not effective for the frame prediction model. By comparing the action distribution produced by a policy from processing the current observed frame to the action distribution produced by the same policy from processing the predicted frame from the action-conditioned frame prediction module, we can detect the presence of adversarial examples. Beyond detecting the presence of adversarial examples, our method allows the agent to continue performing the task using the predicted frame when the agent is under attack. We evaluate the performance of our algorithm using five games in Atari 2600. Our results demonstrate that the proposed defense mechanism achieves favorable performance against baseline algorithms in detecting adversarial examples and in earning rewards when the agents are under attack.


title: Effects of alloying elements (Mn, Co, Al, W, Sn, B, C and S) on biodegradability and in vitro biocompatibility of pure iron.

abstract: Abstract Pure iron was determined to be a valid candidate material for biodegradable metallic stents in recent animal tests; however, a much faster degradation rate in physiological environments was desired. C, Mn, Si, P, S, B, Cr, Ni, Pb, Mo, Al, Ti, Cu, Co, V and W are common alloying elements in industrial steels, with Cr, Ni, Mo, Cu, Ti, V and Si being acknowledged as beneficial in enhancing the corrosion resistance of iron. The purpose of the present work (using Fe– X binary alloy models) is to explore the effect of the remaining alloying elements (Mn, Co, Al, W, B, C and S) and one detrimental impurity element Sn on the biodegradability and biocompatibility of pure iron by scanning electron microscopy, X-ray diffraction, metallographic observation, tensile testing, microhardness testing, electrochemical testing, static (for 6 months) and dynamic (for 1 month with various dissolved oxygen concentrations) immersion testing, cytotoxicity testing, hemolysis and platelet adhesion testing. The results showed that the addition of all alloying elements except for Sn improved the mechanical properties of iron after rolling. Localized corrosion of Fe– X binary alloys was observed in both static and dynamic immersion tests. Except for the Fe–Mn alloy, which showed a significant decrease in corrosion rate, the other Fe– X binary alloy corrosion rates were close to that of pure iron. It was found that compared with pure iron all Fe– X binary alloys decreased the viability of the L929 cell line, none of experimental alloying elements significantly reduced the viability of vascular smooth muscle cells and all the elements except for Mn increased the viability of the ECV304 cell line. The hemolysis percentage of all Fe-X binary alloy models were less than 5%, and no sign of thrombogenicity was observed. In vitro corrosion and the biological behavior of these Fe– X binary alloys are discussed and a corresponding mechanism of corrosion of Fe– X binary alloys in Hank’s solution proposed. As a concluding remark, Co, W, C and S are recommended as alloying elements for biodegradable iron-based biomaterials.


title: A positive feedback passive mixer-first receiver front-end

abstract: This paper presents a technique to reduce the noise figure of a passive mixer-first receiver front-end. By using lower than 50Ω switch resistance in the current-mode passive mixer and introducing a positive feedback from baseband to the RF-input, it can be well matched close to f LO while achieving a noise figure below 3dB, which is otherwise a fundamental limit. A quadrature front-end prototype for a direct conversion receiver has been implemented in 65nm CMOS, occupying an active area of 0.23mm2 with a frequency operation ranging from 0.7 to 3.8GHz. The prototype achieves a minimum noise figure of 2.5dB, an out-of-band 1dB compression point of +3dBm, with IIP3 and IIP2 exceeding +26 and +65dBm, respectively. The current consumption from a 1.2V supply is between 22.8 and 62.8mA, depending on frequency operation.


title: Interference Aware Scheduling of Sensors in IoT Enabled Health-Care Monitoring System

abstract: As the population is increasing worldwide, huge need arises to provide proper health-care services. With the advent of modern technologies the need-gap may be augmented. Sensor is one such technology which can be used to enable Internet of Things based health-care monitoring system. In this paper, implementation of such a system is described. In a real time health monitoring system, there are many sensors connected to a local data processing unit through a shared channel having a fixed bandwidth. These sensors have a wide variety of channel access requirements. The access to the channel should be discrete, so that each and every sensor avails the required bandwidth and delay in the shared channel. In this paper, a scheduling technique is proposed for the IoT based system, which nullify interference among different sensors and consequent distortation of precious health data. Implementation of this technique in our prototype health-care monitoring application is discussed in this paper.


title: A User-Friendly Patent Search Paradigm

abstract: As an important operation for finding existing relevant patents and validating a new patent application, patent search has attracted considerable attention recently. However, many users have limited knowledge about the underlying patents, and they have to use a try-and-see approach to repeatedly issue different queries and check answers, which is a very tedious process. To address this problem, in this paper, we propose a new user-friendly patent search paradigm, which can help users find relevant patents more easily and improve user search experience. We propose three effective techniques, error correction, topic-based query suggestion, and query expansion, to improve the usability of patent search. We also study how to efficiently find relevant answers from a large collection of patents. We first partition patents into small partitions based to their topics and classes. Then, given a query, we find highly relevant partitions and answer the query in each of such highly relevant partitions. Finally, we combine the answers of each partition and generate top-k answers of the patent-search query.


title: Lumped-element equivalent circuit models for multilayer ring resonators filters

abstract: Cascaded block filters are proposed for ultra-wideband (UWB) applications. This class of microwave bandpass filters (BPF) is based on multilayer ring resonators (MRR) structure. The novelty of this configuration, with respect to MRR, is to improve the selectivity of the filters and introduce high order super-compact circuits. For the first time, lumped-element equivalent circuit models are introduced for both MRR structures and cascaded block filters. The mathematical model derived from the lumped circuit shows a close match with the filters frequency responses. Admittance matrix is derived and given for the analysis. Low-temperature co-fired ceramic (LTCC) is a suitable technology to implement these types of filters. Various prototypes are introduced and their frequency responses are demonstrated using electromagnetic simulation software.


title: Decoding of Polar Code by Using Deep Feed-Forward Neural Networks

abstract: With the success of image classification problems, deep learning is expanding its application areas. In this paper, we apply deep learning to decode a polar code. As an initial step for memoryless additive Gaussian noise channel, we consider a deep feed-forward neural network and investigate its decoding performances with respect to numerous configurations: the number of hidden layers, the number of nodes for each layer, and activation functions. Generally, the higher complex network yields a better performance. Comparing the performances of regular list decoding, we provide a guideline for the configuration parameters. Although the training of deep learning may require high computational complexity, it should be noted that the field application of trained networks can be accomplished at a low level complexity. Considering the level of performance and complexity, we believe that deep learning is a competitive decoding tool.


title: Identifying natural images from human brain activity

abstract: Recent functional magnetic resonance imaging (fMRI) studies have shown that, based on patterns of activity evoked by different categories of visual images, it is possible to deduce simple features in the visual scene, or to which category it belongs. Kay et al. take this approach a tantalizing step further. Their newly developed decoding method, based on quantitative receptive field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas, can identify with high accuracy which specific natural image an observer saw, even for an image chosen at random from 1,000 distinct images. This prompts the thought that it may soon be possible to decode subjective perceptual experiences such as visual imagery and dreams, an idea previously restricted to the realm of science fiction. Recent functional magnetic resonance imaging (fMRI) studies have shown that it is possible to deduce simple features in the visual scene or to which category it belongs. A decoding method based on quantitative receptive field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas has now been developed. These models make it possible to identify, out of a large set of completely novel complex images, which specific image was seen by an observer. A challenging goal in neuroscience is to be able to read out, or decode, mental content from brain activity. Recent functional magnetic resonance imaging (fMRI) studies have decoded orientation1,2, position3 and object category4,5 from activity in visual cortex. However, these studies typically used relatively simple stimuli (for example, gratings) or images drawn from fixed categories (for example, faces, houses), and decoding was based on previous measurements of brain activity evoked by those same stimuli or categories. To overcome these limitations, here we develop a decoding method based on quantitative receptive-field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas. These models describe the tuning of individual voxels for space, orientation and spatial frequency, and are estimated directly from responses evoked by natural images. We show that these receptive-field models make it possible to identify, from a large set of completely novel natural images, which specific image was seen by an observer. Identification is not a mere consequence of the retinotopic organization of visual areas; simpler receptive-field models that describe only spatial tuning yield much poorer identification performance. Our results suggest that it may soon be possible to reconstruct a picture of a person’s visual experience from measurements of brain activity alone.


title: Pyramidal displacement mapping: a GPU based artifacts-free ray tracing through an image pyramid

abstract: Displacement mapping enables us to details to polygonal meshes. We present a real-time artifacts-free inverse displacement mapping method using per-pixel ray tracing through an image pyramid on the GPU. In each pixel, we make a ray and trace the ray through a displacement map to find an intersection. To skip empty-regions safely, we use the quad-tree image pyramid of a displacement map in top-down order. For magnification we estimate an intersection between a ray and a bi-linearly interpolated displacement. For minification we perform a mipmap-like prefiltering to improve quality of result images and rendering performance. Results show that our method produces correct images even at steep grazing angles. Rendering speed of test scenes were over hundreds of frames per second and less influence to the resolution of the map. Our method is simple enough to add to existing virtual reality systems easily.


Personalized Spell Checking using Neural Networks


title: Online continuous stereo extrinsic parameter estimation

abstract: Stereo visual odometry and dense scene reconstruction depend critically on accurate calibration of the extrinsic (relative) stereo camera poses. We present an algorithm for continuous, online stereo extrinsic re-calibration operating only on sparse stereo correspondences on a per-frame basis. We obtain the 5 degree of freedom extrinsic pose for each frame, with a fixed baseline, making it possible to model time-dependent variations. The initial extrinsic estimates are found by minimizing epipolar errors, and are refined via a Kalman Filter (KF). Observation covariances are derived from the Cra´mer-Rao lower bound of the solution uncertainty. The algorithm operates at frame rate with unoptimized Matlab code with over 1000 correspondences per frame. We validate its performance using a variety of real stereo datasets and simulations.


title: FootStriker: An EMS-based Foot Strike Assistant for Running

abstract: In running, knee-related injuries are very common. The main cause are high impact forces when striking the ground with the heel first. Mid- or forefoot running is generally known to reduce impact loads and to be a more efficient running style. In this paper, we introduce a wearable running assistant, consisting of an electrical muscle stimulation (EMS) device and an insole with force sensing resistors. It detects heel striking and actuates the calf muscles during the flight phase to control the foot angle before landing. We conducted a user study, in which we compared the classical coaching approach using slow motion video analysis as a terminal feedback to our proposed real-time EMS feedback. The results show that EMS actuation significantly outperforms traditional coaching, i.e., a decreased average heel striking rate, when using the system. As an implication, EMS feedback can generally be beneficial for the motor learning of complex, repetitive movements.


title: Towards a Semantic Driven Framework for Smart Grid Applications: Model-Driven Development Using CIM, IEC 61850 and IEC 61499

abstract: Power and energy systems are on the verge of a profound change where Smart Grid solutions will enhance their efficiency and flexibility. Advanced ICT and control systems are key elements of the Smart Grid to enable efficient integration of a high amount of renewable energy resources which in turn are seen as key elements of the future energy system. The corresponding distribution grids have to become more flexible and adaptable as the current ones in order to cope with the upcoming high share of energy from distributed renewable sources.


title: A two-tier Wireless Sensor Network infrastructure for large-scale real-time groundwater monitoring

abstract: In this paper, we describe the design, implementation and deployment details of a two-tier real-time environmental monitoring network in Nebraska. Our state-wide sensor network infrastructure uses typical Wireless Sensor Network (WSN) structure at tier-two to conduct dynamic sensing tasks with high resolution and flexibility. The satellite communication technology is used at tier-one to provide reliable and low-cost long-haul connectivity between each local WSN and the central base station. By the end of 2009, the entire tier-one infrastructure has been designed and deployed to provide state-wide wireless connectivity for 54 monitoring sites equipped with 1–4 water level transducers. A data and network management framework has also been developed to enable the remote network retasking and network performance analysis from a central base station. The network web portal offers integrated real-time and historical groundwater data to worldwide users. The deployed infrastructure will serve as the prototype network to expedite the commercial adoption of large-scale WSN design for long-term environmental monitoring applications.