title: A review of low-density lipoprotein cholesterol, treatment strategies, and its impact on cardiovascular disease morbidity and mortality

abstract: Abstract Cardiovascular (CV) disease is a leading cause of death worldwide, accounting for approximately 31.4% of deaths globally in 2012. It is estimated that, from 1980 to 2000, reduction in total cholesterol accounted for a 33% decrease in coronary heart disease (CHD) deaths in the United States. In other developed countries, similar decreases in CHD deaths (ranging from 19%–46%) have been attributed to reduction in total cholesterol. Low-density lipoprotein cholesterol (LDL-C) has now largely replaced total cholesterol as a risk marker and the primary treatment target for hyperlipidemia. Reduction in LDL-C levels by statin-based therapies has been demonstrated to result in a reduction in the risk of nonfatal CV events and mortality in a continuous and graded manner over a wide range of baseline risk and LDL-C levels. This article provides a review of (1) the relationship between LDL-C and CV risk from a biologic, epidemiologic, and genetic standpoint; (2) evidence-based strategies for LDL-C lowering; (3) lipid-management guidelines; (4) new strategies to further reduce CV risk through LDL-C lowering; and (5) population-level and health-system initiatives aimed at identifying, treating, and lowering lifetime LDL-C exposure.


title: Mixed-Initiative for Big Data: The Intersection of Human + Visual Analytics + Prediction

abstract: Existing surveys in visual analytics focus on the importance of the topic. However, many do not discuss the increasingly critical area of mixed-initiative systems. In this survey we discuss the importance of research in mixed-initiative systems and how it is different from visual analytics and other research fields. We present the conceptual architecture of a mixed-initiative visual analytics system (MIVAS) and the five key components that make up MIVASs (data wrangling, alternative discovery and comparison, parametric interaction, history tracking and exploration, and system agency and adaptation), which forms our main contribution. We compare and contrast different research that claims to be mixed-initiative against MIVASs and show how there is still a considerable amount of work that needs to be accomplished before any system can truly be mixed-initiative.


title: Energy Management Fuzzy Logic Supervisory for Electric Vehicle Power Supplies System

abstract: This paper introduces an energy management strategy based on fuzzy logic supervisory for road electric vehicle, combining a fuel cell power source and two energy storage devices, i.e., batteries and ultracapacitors. The control strategy is designed to achieve the high-efficiency operation region of the individual power source and to regulate current and voltage at peak and average power demand, without compromising the performance and efficiency of the overall system. A multiple-input power electronic converter makes the interface among generator, energy storage devices, and the voltage dc-link bus. Classical regulators implement the control loops of each input of the converter. The supervisory system coordinates the power flows among the power sources and the load. The paper is mainly focused on the fuzzy logic supervisory for energy management of a specific power electronic converter control algorithm. Nevertheless, the proposed system can be easily adapted to other converters arrangements or to distributed generation applications. Simulation and experimental results on a 3-kW prototype prove that the fuzzy logic is a suitable energy management control strategy.


title: Homomorphic Encryption from Learning with Errors: Conceptually-Simpler, Asymptotically-Faster, Attribute-Based

abstract: We describe a comparatively simple fully homomorphic encryption (FHE) scheme based on the learning with errors (LWE) problem. In previous LWE-based FHE schemes, multiplication is a complicated and expensive step involving “relinearization”. In this work, we propose a new technique for building FHE schemes that we call the approximate eigenvector method. In our scheme, for the most part, homomorphic addition and multiplication are just matrix addition and multiplication. This makes our scheme both asymptotically faster and (we believe) easier to understand.


title: Tag Prediction at Flickr: A View from the Darkroom

abstract: Automated photo tagging has established itself as one of the most compelling applications of deep learning. While deep convolutional neural networks have repeatedly demonstrated top performance on standard datasets for classification, there are a number of often overlooked but important considerations when deploying this technology in a real-world scenario. In this paper, we present our efforts in developing a large-scale photo tagging system for Flickr photo search. We discuss topics including how to 1) select the tags that matter most to our users; 2) develop lightweight, high-performance models for tag prediction; and 3) leverage the power of large amounts of noisy data for training. Our results demonstrate that, for real-world datasets, training exclusively with this noisy data yields performance on par with the standard paradigm of first pre-training on clean data and then fine-tuning. In addition, we observe that the models trained with user-generated data can yield better fine-tuning results when a small amount of clean data is available. As such, we advocate for the approach of harnessing user-generated data in large-scale systems.


title: Pushing the Boundaries of Boundary Detection using Deep Learning

abstract: In this work we show that adapting Deep Convolutional Neural Network training to the task of boundary detection can result in substantial improvements over the current state-of-the-art in boundary detection. ::: Our contributions consist firstly in combining a careful design of the loss for boundary detection training, a multi-resolution architecture and training with external data to improve the detection accuracy of the current state of the art. When measured on the standard Berkeley Segmentation Dataset, we improve theoptimal dataset scale F-measure from 0.780 to 0.808 - while human performance is at 0.803. We further improve performance to 0.813 by combining deep learning with grouping, integrating the Normalized Cuts technique within a deep network. ::: We also examine the potential of our boundary detector in conjunction with the task of semantic segmentation and demonstrate clear improvements over state-of-the-art systems. Our detector is fully integrated in the popular Caffe framework and processes a 320x420 image in less than a second.


title: Big data analysis of Indian premier league using Hadoop and MapReduce

abstract: The exponential growth of the internet has changed the way people used to interact with each other. With the extensive use of social media platforms like Facebook, Instagram, Google+, Twitter etc., people nowadays share their emotions, opinions, and reactions quite actively. Twitter, for example, being one of the prime sources of real-time information, it's being used by millions of netizens all around the globe. Twitterati ardently share their political views, movies' experience as well as react to numerous sporting events on a daily basis. This paper analyzes one such sporting event, the final match of Indian Premier League 2015, using Hadoop and MapReduce programming paradigm. The objective of this paper is to assess the popularity of Indian Premier League 2015 as well as various metrics related to the game. The analysis found that Indian Premier League is not only popular in India but it has gained immense popularity throughout the world, ranging from Ahmedabad (India) to Zurich (Switzerland). Furthermore, the time interval in which cricket fans tweeted the most, most talked about players, and the dominant team — are the various metrics that have also been analyzed from the game's perspective.


title: Mobile customer relationship management: an exploratory analysis of Italian applications

abstract: Purpose – The paper's aims are to estimate the size of the Italian market for mobile customer relationship management (mCRM) services and to try to evaluate some benefits that can be obtained through the use of mCRM applications by companies.Design/methodology/approach – The research involved a census of mCRM applications in order to construct a typology of mCRM applications being used and the analysis of some case studies to assess the impact of such applications on the perspective adopted by the company. This second phase involved interviews with managers of the companies.Findings – In 2005, 1,077 mCRM services were used by 405 companies in Italy. The main benefits found were the improvement of customer satisfaction, an increase in the efficiency of internal processes and an increase in revenue.Research limitations/implications – The present research considers only Italian services based on data and not on voice. Future research will be required to investigate end‐user needs and wants (which are not tre...


title: A 2- $\mu\text{W}$ 45-nV/√Hz Readout Front End With Multiple-Chopping Active-High-Pass Ripple Reduction Loop and Pseudofeedback DC Servo Loop

abstract: This brief presents an ultra-low-power low-noise chopped capacitively coupled instrumentation amplifier (CCIA) that is suitable for neural recording applications. An active high-pass filter is embedded in the ripple reduction loop (RRL) to suppress the residual noise and relax the capacitor size. Multiple chopping is employed to further reduce the residual output ripple due to the RRL offsets. A dc servo loop (DSL) using a 14-nA pseudofeedback amplifier is proposed to achieve a subhertz high-pass corner while using only a 15-pF on-chip capacitor. The complete CCIA is implemented in a standard 0.18- $\mu\text{m}$ CMOS process. It occupies an area of 0.23 mm2 (including the DSL) and consumes 1.7 $\mu\text{A}$ from a 1.25-V supply, achieving a noise efficiency factor of 2.9 that compares favorably with the state of the art.


title: A Fully Distributed Reactive Power Optimization and Control Method for Active Distribution Networks

abstract: This paper presents a fully distributed reactive power optimization algorithm that can obtain the global optimum solution of nonconvex problems for distribution networks (DNs) without requiring a central coordinator. Second-order conic relaxation is used to achieve exact convexification. A fully distributed second-order cone programming solver (D-SOCP) is formulated corresponding to the given division of areas based on the alternating direction method of multipliers (ADMM) algorithm, which is greatly simplified by exploiting the structure of active DNs. The problem is solved for each area with very little interchange of boundary information between neighboring areas. D-SOCP is extended by using a varying penalty parameter to improve convergence. A proof of its convergence is also given. The effectiveness of the method is demonstrated via numerical simulations using the IEEE 69-bus, 123-bus DNs, and a real 1066-bus distribution system.


title: Microglia-mediated recovery from ALS-relevant motor neuron degeneration in a mouse model of TDP-43 proteinopathy

abstract: Though motor neurons selectively degenerate in amyotrophic lateral sclerosis, other cell types are likely involved in this disease. We recently generated rNLS8 mice in which human TDP-43 (hTDP-43) pathology could be reversibly induced in neurons and expected that microglia would contribute to neurodegeneration. However, only subtle microglial changes were detected during disease in the spinal cord, despite progressive motor neuron loss; microglia still reacted to inflammatory triggers in these mice. Notably, after hTDP-43 expression was suppressed, microglia dramatically proliferated and changed their morphology and gene expression profiles. These abundant, reactive microglia selectively cleared neuronal hTDP-43. Finally, when microgliosis was blocked during the early recovery phase using PLX3397, a CSF1R and c-kit inhibitor, rNLS8 mice failed to regain full motor function, revealing an important neuroprotective role for microglia. Therefore, reactive microglia exert neuroprotective functions in this amyotrophic lateral sclerosis model, and definition of the underlying mechanism could point toward novel therapeutic strategies.Using an inducible mouse model of sporadic ALS, Spiller et al. show that spinal microgliosis is not a major feature of TDP-43-triggered disease. Instead, microglia mediate TDP-43 clearance and motor recovery, suggesting a neuroprotective role in ALS.


title: An Electrically Actuated DC-to-11-GHz Liquid-Metal Switch

abstract: A liquid-metal RF shunt switch is presented that combines a dielectric spacer over the RF path with electrolyte-filled capillary troughs to facilitate low-power electrical actuation. The switch demonstrates between 20- and 30-dB isolation in the dc to 5-GHz range, and greater than 10-dB isolation from 5 to 11 GHz. This switch has between 0.2- and 1.2-dB insertion loss from dc to 5 GHz.


title: Structure tensor Log-Euclidean statistical models for texture analysis

abstract: This paper presents local structure tensor (LST) based methods for extracting the anisotropy and orientation information characterizing a textured image sample. A Log-Euclidean (LE) multivariate Gaussian model is proposed for representing the marginal distribution of the LST field of a texture. An extended model is considered as well for describing its spatial dependencies. The potential of these approaches are tested in a rotation invariant content based image retrieval (CBIR) context. Experiments are conducted on real data presenting anisotropic textures: very high resolution (VHR) remote sensing maritime pine forest images and material images representing dense carbons at nanometric scale.


title: High-Performance Stand-Alone Photovoltaic Generation System

abstract: This study develops a high-performance stand-alone photovoltaic (PV) generation system. To make the PV generation system more flexible and expandable, the backstage power circuit is composed of a high step-up converter and a pulsewidth-modulation (PWM) inverter. In the dc-dc power conversion, the high step-up converter is introduced to improve the conversion efficiency in conventional boost converters to allow the parallel operation of low-voltage PV arrays, and to decouple and simplify the control design of the PWM inverter. Moreover, an adaptive total sliding-mode control system is designed for the voltage control of the PWM inverter to maintain a sinusoidal output voltage with lower total harmonic distortion and less variation under various output loads. In addition, an active sun tracking scheme without any light sensors is investigated to make the PV modules face the sun directly for capturing the maximum irradiation and promoting system efficiency. Experimental results are given to verify the validity and reliability of the high step-up converter, the PWM inverter control, and the active sun tracker for the high-performance stand-alone PV generation system.


title: StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments

abstract: Dynamic environments are challenging for visual SLAM as moving objects can impair camera pose tracking and cause corruptions to be integrated into the map. In this paper, we propose a method for robust dense RGB-D SLAM in dynamic environments which detects moving objects and simultaneously reconstructs the background structure. While most methods employ implicit robust penalisers or outlier filtering techniques in order to handle moving objects, our approach is to simultaneously estimate the camera motion as well as a probabilistic static/dynamic segmentation of the current RGB-D image pair. This segmentation is then used for weighted dense RGB-D fusion to estimate a 3D model of only the static parts of the environment. By leveraging the 3D model for frame-to-model alignment, as well as static/dynamic segmentation, camera motion estimation has reduced overall drift - as well as being more robust to the presence of dynamics in the scene. Demonstrations are presented which compare the proposed method to related state-of-the-art approaches using both static and dynamic sequences. The proposed method achieves similar performance in static environments and improved accuracy and robustness in dynamic scenes.


title: Sugar: Secure GPU Acceleration in Web Browsers

abstract: Modern personal computers have embraced increasingly powerful Graphics Processing Units (GPUs). Recently, GPU-based graphics acceleration in web apps (i.e., applications running inside a web browser) has become popular. WebGL is the main effort to provide OpenGL-like graphics for web apps and it is currently used in 53% of the top-100 websites. Unfortunately, WebGL has posed serious security concerns as several attack vectors have been demonstrated through WebGL. Web browsers» solutions to these attacks have been reactive: discovered vulnerabilities have been patched and new runtime security checks have been added. Unfortunately, this approach leaves the system vulnerable to zero-day vulnerability exploits, especially given the large size of the Trusted Computing Base of the graphics plane. We present Sugar, a novel operating system solution that enhances the security of GPU acceleration for web apps by design. The key idea behind Sugar is using a dedicated virtual graphics plane for a web app by leveraging modern GPU virtualization solutions. A virtual graphics plane consists of a dedicated virtual GPU (or vGPU) as well as all the software graphics stack (including the device driver). Sugar enhances the system security since a virtual graphics plane is fully isolated from the rest of the system. Despite GPU virtualization overhead, we show that Sugar achieves high performance. Moreover, unlike current systems, Sugar is able to use two underlying physical GPUs, when available, to co-render the User Interface (UI): one GPU is used to provide virtual graphics planes for web apps and the other to provide the primary graphics plane for the rest of the system. Such a design not only provides strong security guarantees, it also provides enhanced performance isolation.


title: Game Design Patterns

abstract: We present a model to support the design, analysis, and comparison of games through the use of game design patterns, descriptions of reoccurring interaction relevant to game play. The model consists of a structural framework to describe the components of games, and patterns of interaction that describes how components are used by players (or a computer) to affect various aspects of the game play. Focusing on the patterns and identified methods for using them, we describe the development of the model and how we are currently working to enlarge and validate the collection of patterns.


title: Your Neighbors Are My Spies: Location and other Privacy Concerns in Dating Apps

abstract: Trilateration has recently become one of the well-known threat models to the user's location privacy in location-based applications (aka: location-based services or LBS), especially those containing highly sensitive information such as dating applications. The threat model mainly depends on the distance shown from the targeted victim to the adversary to pinpoint the victim's position. As a countermeasure, most of location-based applications have already implemented the "hide distance" function to protect their user's location privacy. The effectiveness of such approach however is still questionable. Therefore, in this paper, we first investigate how popular location-based dating applications are currently protecting their user's privacy by testing the two most popular GLBT-focused applications: Jack'd and Grindr. As one of our findings, we then demonstrate how an adversary can still figure out the location of the targeted victim even when the "hide distance" function is enabled. Our threat model is simply an enhanced version of the trilateration model. Without using sophisticated hacking tools or complex attack techniques, the model is still very effective and efficient at locating the targeted victim, and of course in a so-called "legal" manner since we only utilize the information that can be obtained just as same as any other ordinary user. In addition, we also introduce a potential side channel attack fashion due to the current design of Jack'd. Our study thus raises an urgent alarm to those location-based applications' users in general, and especially to those GLBT-focused dating application's users about their privacy. Finally, the paper concludes by suggesting some possible solutions from the viewpoints of both the provider and the user considering the implementation cost and the trade-off of utility.


title: Low-budget transient imaging using photonic mixer devices

abstract: Transient imaging is an exciting a new imaging modality that can be used to understand light propagation in complex environments, and to capture and analyze scene properties such as the shape of hidden objects or the reflectance properties of surfaces. Unfortunately, research in transient imaging has so far been hindered by the high cost of the required instrumentation, as well as the fragility and difficulty to operate and calibrate devices such as femtosecond lasers and streak cameras. In this paper, we explore the use of photonic mixer devices (PMD), commonly used in inexpensive time-of-flight cameras, as alternative instrumentation for transient imaging. We obtain a sequence of differently modulated images with a PMD sensor, impose a model for local light/object interaction, and use an optimization procedure to infer transient images given the measurements and model. The resulting method produces transient images at a cost several orders of magnitude below existing methods, while simultaneously simplifying and speeding up the capture process.


title: Juxta-articular adiposis dolorosa (Dercum’s disease type IV): report of four cases and treatment by dermolipectomy

abstract: Juxta-articular adiposis dolorosa is a rare subtype of Dercum’s disease. It manifests mainly on the medial parts of the knees. Pain and impaired mobility are common symptoms. We report on four females (aged between 52 and 83 years) who suffered from juxta-articular adiposis dolorosa for more than 10 years. These patients were successfully treated by dermolipectomy resulting in dramatically improved pain and mobility. Adverse effects and complications were minor with a lymph fistula in a single patient which was treated by surgery.


title: Design of Logic Gates and Flip-Flops in High-Performance FinFET Technology

abstract: With the emergence of nonplanar CMOS devices at the 22-nm node and beyond, it is highly likely that multigate device adoption will occur in a high-performance process technology, owing to the increased performance and area benefits. In this paper, for the first time, we evaluate symmetric (Symm-ΦG) and asymmetric (Asymm-ΦG) gate-workfunction FinFETs head to head in a high-performance process, using technology computer-aided design 3-D device simulations. We demonstrate that Asymm-ΦG shorted-gate (a-SG) n/p-FinFETs, which use both workfunctions corresponding to typical high-performance metal-gate n/p-FinFETs, are promising, as they can yield over two orders of magnitude lower leakage without excessive degradation in ON-state current, in comparison to Symm- ΦG shorted-gate (SG) FinFETs, placing them in a better position than back-gate biased independent-gate (IG) FinFETs for leakage reduction. Thereafter, we explore the design space of FinFET logic gates, latches, and flip-flops, for optimal tradeoffs in leakage versus delay and temperature, using mixed-mode 2-D device simulations. Elementary logic gates (such as INV, NAND2, NOR2, XOR2, and XNOR2) using Asymm-ΦG SG-mode FinFETs appear to be located optimally in the leakage-delay spectrum, in comparison to the most versatile configurations possible by mixing corresponding Symm-ΦG SG- and IG-mode FinFETs. Latches and flip-flops, however, require an astute combination of Symm-ΦG and Asymm-ΦG FinFETs to optimize leakage, delay, and setup time simultaneously.


title: Architecture of AppScale-based network teaching platform

abstract: With the emergence of cloud computing, network teaching platforms are built an open, scalable platform for teaching, sharing resources, which can be help to provide the new development direction of network teaching and make it more efficiently. This article states functions and features of the online education platform, and the concept about AppScale and Eucalyptus. In order to propose a reasonable architecture of AppScale-based online education platform, and explore the existing network teaching platform migrating to under the cloud, providing support to set up and research the network teaching platform in the cloud computing environment.


title: Multiple Social Network Learning and Its Application in Volunteerism Tendency Prediction

abstract: We are living in the era of social networks, where people throughout the world are connected and organized by multiple social networks. The views revealed by different social networks may vary according to the different services they offer. They are complimentary to each other and comprehensively characterize a specific user from different perspectives. As compared to the scare knowledge conveyed by a single source, appropriate aggregation of multiple social networks offers us a better opportunity for deep user understanding. The challenges, however, co-exist with opportunities. The first challenge lies in the existence of block-wise missing data, caused by the fact that some users may be very active in certain social networks while inactive in others. The second challenge is how to collaboratively integrate multiple social networks. Towards this end, we first proposed a novel model for data missing completion by seamlessly exploring the knowledge from multiple sources. We then developed a robust multiple social network learning model, and applied it to the application of volunteerism tendency prediction. Extensive experiments on real world dataset verify the effectiveness of our scheme. The proposed scheme is applicable to many other domains, such as demographic inference and interest prediction.


title: Automatic Face Recognition of Newborns, Infants, and Toddlers: A Longitudinal Evaluation

abstract: A number of emerging applications requiring reliable identification of children have called attention to whether biometric traits can be utilized as a solution. While biometric traits based on friction ridge patterns (e.g. fingerprints, footprints) have been evaluated to some extent, to our knowledge, no effort has been made to evaluate the efficacy of automatic face recognition of young children over useful durations of time. Additionally, there are some applications where only the face images of a child are available, such as identification of missing or abducted children and children shown in sexually exploitive media sequestered by law enforcement. In this paper, we introduce the Newborns, Infants, and Toddlers Longitudinal (NITL) face image database, which was collected by the authors during four different sessions over a period of one year (March 2015 to March 2016) at the Saran Ashram Hospital, Dayalbagh, India. The NITL database contains 314 subjects in total in the age range of 0 to 4 years old. The aim of this paper is to provide a comprehensive evaluation of a state-of-the-art commercial-off- the-shelf (COTS) face matcher on the NITL face image database to investigate the feasibility of face recognition of children as they age. Experimental results show that while available face recognition technology is not yet ready to reliably recognize very young children, face recognition enrolled at 3 years of age or older may be feasible.


title: Real-Time Impulse Noise Suppression from Images Using an Efficient Weighted-Average Filtering

abstract: In this letter, we propose a method for real-time high density impulse noise suppression from images. In our method, we first apply an impulse detector to identify the corrupted pixels and then employ an innovative weighted-average filter to restore them. The filter takes the nearest neighboring interpolated image as the initial image and computes the weights according to the relative positions of the corrupted and uncorrupted pixels. Experimental results show that the proposed method outperforms the best existing methods in both PSNR measure and visual quality and is quite suitable for real-time applications.


title: A 630μW zero-crossing-based ΔΣ ADC using switched-resistor current sources in 45nm CMOS

abstract: A delta-sigma analog-to-digital converter employs zero-crossing-based integrators to achieve low power operation at 50MHz sampling rate. Switched resistors, used as current sources, provide good dynamic current matching, eliminating the need for common-mode feedback circuits. The effect of the switched resistors on the linearity of the zero-crossing-based integrator is discussed. Also, a unidirectional 2-phase charging scheme is proposed to improve input signal swing and reduce overshoot voltage. Test chip implemented in a 45nm LP Digital CMOS achieves 54.3dB DR, 52.5dB SNR, and 47.7dB SNDR at 0.833MHz input while dissipating 630μ;W from a 1.1V supply.


Detecting Tomato Flowers in Greenhouses Using Computer Vision


title: DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting

abstract: Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to lack of mining road topology. To address the effect attenuation problem, we propose to take account of the traffic of surrounding locations(wider than adjacent range). We propose an end-to-end framework called DeepTransport, in which Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain spatial-temporal traffic information within a transport network topology. In addition, attention mechanism is introduced to align spatial and temporal information. Moreover, we constructed and released a real-world large traffic condition dataset with 5-minute resolution. Our experiments on this dataset demonstrate our method captures the complex relationship in temporal and spatial domain. It significantly outperforms traditional statistical methods and a state-of-the-art deep learning method.


title: "PTCH"-ing it together: a basal cell nevus syndrome review.

abstract: BACKGROUND ::: Basal cell nevus syndrome (BCNS) has existed at least since Dynastic Egyptian times. In 1960, Gorlin and Goltz first described the classic clinical triad: multiple basal cell carcinomas (BCCs), jaw keratocysts, and bifid ribs. As an autosomal-dominant disorder, it is characterized by tumorigenesis and developmental defects. ::: ::: ::: OBJECTIVE ::: To review the current literature on BCNS, including reports on epidemiology, pathogenesis, clinical presentation, diagnostic criteria, management, treatment, and prognosis. ::: ::: ::: METHODS ::: A literature review of currently available articles related to BCNS. ::: ::: ::: RESULTS ::: Individuals with a mutation in the tumor suppressor gene PTCH1 are predisposed to tumorigenesis and developmental defects. Clinical features include BCCs, often with onset in adolescence, jaw keratocysts, bifid ribs, craniofacial defects, palmar-plantar pits, and ectopic intracranial calcification. Despite high cure rates for individual lesions and various treatment modalities including excision, Mohs micrographic surgery, photodynamic therapy, and topical imiquimod, management of BCCs is challenging. The development of an oral hedgehog pathway inhibitor, vismodegib, has added a new dimension to current treatment algorithms. ::: ::: ::: CONCLUSIONS ::: Adolescents and young adults with BCC should be evaluated for BCNS. Early diagnosis of BCNS is critical for possible prevention of the devastating effects of BCCs and establishment of multidisciplinary care.


Addressing Misconceptions, Threshold Concepts, and Troublesome Knowledge in GIScience Education


title: A first look at traffic on smartphones

abstract: Using data from 43 users across two platforms, we present a detailed look at smartphone traffic. We find that browsing contributes over half of the traffic, while each of email, media, and maps contribute roughly 10%. We also find that the overhead of lower layer protocols is high because of small transfer sizes. For half of the transfers that use transport-level security, header bytes correspond to 40% of the total. We show that while packet loss is the main factor that limits the throughput of smartphone traffic, larger send buffers at Internet servers can improve the throughput of a quarter of the transfers. Finally, by studying the interaction between smartphone traffic and the radio power management policy, we find that the power consumption of the radio can be reduced by 35% with minimal impact on the performance of packet exchanges.


title: Hierarchical Control of Hybrid Energy Storage System in DC Microgrids

abstract: Hybridization of energy storages (ESs) with different characteristics takes advantages of all ESs. Centralized control with high-/low-pass filter (LPF) for system net power decomposition and ESs' power dispatch is usually implemented in hybrid ES system (HESS). In this paper, hierarchical control of HESS, composed of both centralized and distributed control, is proposed to enhance system reliability. The conventional HESS centralized control is refined with implementations of online iteration, secondary voltage regulation, and autonomous state-of-charge (SoC) recovery. ESs' power references are iteratively generated to maximize the utilization of ES ramp rates and power capacities. Secondary voltage regulation and autonomous SoC recovery are applied to minimize bus voltage deviation and limit slack terminal SoC variation, respectively. In case of communication failure, a novel algorithm for HESS distributed control is proposed to retain system operation. Bus voltage is regarded as the global indicator for system power balance, and droop relationships are imposed for ES control. System net power decomposition and ESs' power dispatch are realized with localized LPFs. SoC recovery in distributed control is implemented by tuning the threshold voltage of a slack terminal. A laboratory-scale dc microgrid is developed to verify the proposed hierarchical control of HESS.


title: Rule Mining and Classification in a Situation Assessment Application: A Belief-Theoretic Approach for Handling Data Imperfections

abstract: Management of data imprecision and uncertainty has become increasingly important, especially in situation awareness and assessment applications where reliability of the decision-making process is critical (e.g., in military battlefields). These applications require the following: 1) an effective methodology for modeling data imperfections and 2) procedures for enabling knowledge discovery and quantifying and propagating partial or incomplete knowledge throughout the decision-making process. In this paper, using a Dempster-Shafer belief-theoretic relational database (DS-DB) that can conveniently represent a wider class of data imperfections, an association rule mining (ARM)-based classification algorithm possessing the desirable functionality is proposed. For this purpose, various ARM-related notions are revisited so that they could be applied in the presence of data imperfections. A data structure called belief itemset tree is used to efficiently extract frequent itemsets and generate association rules from the proposed DS-DB. This set of rules is used as the basis on which an unknown data record, whose attributes are represented via belief functions, is classified. These algorithms are validated on a simplified situation assessment scenario where sensor observations may have caused data imperfections in both attribute values and class labels.


title: Activity in fusiform face area modulated as a function of working memory load.

abstract: Previous fMRI results suggest that extrastriate visual areas have a predominant role in perceptual processing while the prefrontal cortex (PFC) has a predominant role in working memory. In contrast, single-unit recording studies in monkeys have demonstrated a relationship between extrastriate visual areas and visual working memory tasks. In this study we tested whether activity in both the PFC and fusiform face area (FFA) changed with increasing demands of an n-back task for gray-scale faces. Since stimulus presentation was identical across conditions, the n-back task allowed us to parametrically vary working memory demands across conditions while holding perceptual and motor demands constant. This study replicated the result of PFC areas of activation that increased directly with load n of the task. The novel finding in all subjects was FFA activation that also increased directly with load n of the task. Since perceptual demands were equivalent across the three task conditions, these findings suggest that activity in both the PFC and the FFA vary with face working memory demands.


title: Location Privacy-Preserving Task Allocation for Mobile Crowdsensing with Differential Geo-Obfuscation

abstract: In traditional mobile crowdsensing applications, organizers need participants' precise locations for optimal task allocation, e.g., minimizing selected workers' travel distance to task locations. However, the exposure of their locations raises privacy concerns. Especially for those who are not eventually selected for any task, their location privacy is sacrificed in vain. Hence, in this paper, we propose a location privacy-preserving task allocation framework with geo-obfuscation to protect users' locations during task assignments. Specifically, we make participants obfuscate their reported locations under the guarantee of differential privacy, which can provide privacy protection regardless of adversaries' prior knowledge and without the involvement of any third-part entity. In order to achieve optimal task allocation with such differential geo-obfuscation, we formulate a mixed-integer non-linear programming problem to minimize the expected travel distance of the selected workers under the constraint of differential privacy. Evaluation results on both simulation and real-world user mobility traces show the effectiveness of our proposed framework. Particularly, our framework outperforms Laplace obfuscation, a state-of-the-art differential geo-obfuscation mechanism, by achieving 45% less average travel distance on the real-world data.


title: Implementing the CANopen protocol for the distributed control of a hybrid electric vehicle

abstract: The paper presents the integration of the hybrid electric vehicle components into a communication CAN network of high speed (1 Mbps) which assure a distributed control of the all resources. By the CANopen protocol implementation two or more elements from different producers can be easy integrated into a CAN network communication. The implementation is realized on an experimental stand of a hybrid electric vehicle.


title: Melanoma in situ of penis: a very rare entity A case report and review of the literature

abstract: Rationale: Melanoma in situ of the penis is very rare and there are no clear guidelines for its surgical treatment. Patient concerns: The authors describe the case of a 69-year-old man who presented with an asymptomatic brown macula on his glans penis and foreskin that appeared about 8 years earlier, enlarged in the last few months. Diagnoses: A diagnostic biopsy showed the characteristics of a melanoma in situ. Interventions: The authors decided to excise the lesion keeping a healthy margin of 1 cm all over around except close to the urethral meatus, where it was impossible, and where only 5 mm of free margin was excised. A full thickness mucosal graft from oral cavity was performed to repair the defect. Outcomes: No recurrence or metastasis occurred during 50 months after the operation. Lessons: Considering that at the sixth clinical follow-up the patient was alive and disease free at 50 months after surgery, the chosen treatment has proved successful.


Data mining applications in public organizations


title: A SWOT Analysis of the Field of Virtual Reality Rehabilitation and Therapy

abstract: The use of virtual-reality technology in the areas of rehabilitation and therapy continues to grow, with encouraging results being reported for applications that address human physical, cognitive, and psychological functioning. This article presents a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis for the field of VR rehabilitation and therapy. The SWOT analysis is a commonly employed framework in the business world for analyzing the factors that influence a company's competitive position in the marketplace with an eye to the future. However, the SWOT framework can also be usefully applied outside of the pure business domain. A quick check on the Internet will turn up SWOT analyses for urban-renewal projects, career planning, website design, youth sports programs, and evaluation of academic research centers, and it becomes obvious that it can be usefully applied to assess and guide any organized human endeavor designed to accomplish a mission. It is hoped that this structured examination of the factors relevant to the current and future status of VR rehabilitation will provide a good overview of the key issues and concerns that are relevant for understanding and advancing this vital application area.


title: Readability Assessment for Text Simplification

abstract: We describe a readability assessment approach to support the process of text simplification for poor literacy readers. Given an input text, the goal is to predict its readability level, which corresponds to the literacy level that is expected from the target reader: rudimentary, basic or advanced. We complement features traditionally used for readability assessment with a number of new features, and experiment with alternative ways to model this problem using machine learning methods, namely classification, regression and ranking. The best resulting model is embedded in an authoring tool for Text Simplification.


title: Lifted Probabilistic Inference by First-Order Knowledge Compilation

abstract: Probabilistic logical languages provide powerful formalisms for knowledge representation and learning. Yet performing inference in these languages is extremely costly, especially if it is done at the propositional level. Lifted inference algorithms, which avoid repeated computation by treating indistinguishable groups of objects as one, help mitigate this cost. Seeking inspiration from logical inference, where lifted inference (e.g., resolution) is commonly performed, we develop a model theoretic approach to probabilistic lifted inference. Our algorithm compiles a first-order probabilistic theory into a first-order deterministic decomposable negation normal form (d-DNNF) circuit. Compilation offers the advantage that inference is polynomial in the size of the circuit. Furthermore, by borrowing techniques from the knowledge compilation literature our algorithm effectively exploits the logical structure (e.g., context-specific independencies) within the first-order model, which allows more computation to be done at the lifted level. An empirical comparison demonstrates the utility of the proposed approach.


title: Very Deep Convolutional Neural Networks for Noise Robust Speech Recognition

abstract: Although great progress has been made in automatic speech recognition, significant performance degradation still exists in noisy environments. Recently, very deep convolutional neural networks (CNNs) have been successfully applied to computer vision and speech recognition tasks. Based on our previous work on very deep CNNs, in this paper this architecture is further developed to improve recognition accuracy for noise robust speech recognition. In the proposed very deep CNN architecture, we study the best configuration for the sizes of filters, pooling, and input feature maps: the sizes of filters and poolings are reduced and dimensions of input features are extended to allow for adding more convolutional layers. Then the appropriate pooling, padding, and input feature map selection strategies are investigated and applied to the very deep CNN to make it more robust for speech recognition. In addition, an in-depth analysis of the architecture reveals key characteristics, such as compact model scale, fast convergence speed, and noise robustness. The proposed new model is evaluated on two tasks: Aurora4 task with multiple additive noise types and channel mismatch, and the AMI meeting transcription task with significant reverberation. Experiments on both tasks show that the proposed very deep CNNs can significantly reduce word error rate (WER) for noise robust speech recognition. The best architecture obtains a 10.0% relative reduction over the traditional CNN on AMI, competitive with the long short-term memory recurrent neural networks (LSTM-RNN) acoustic model. On Aurora4, even without feature enhancement, model adaptation, and sequence training, it achieves a WER of 8.81%, a 17.0% relative improvement over the LSTM-RNN. To our knowledge, this is the best published result on Aurora4.


title: Measuring Peer Pressure, Popularity, and Conformity in Adolescent Boys and Girls: Predicting School Performance, Sexual Attitudes, and Substance Abuse

abstract: Existing measures of peer pressure and conformity may not be suitable for screening large numbers of adolescents efficiently, and few studies have differentiated peer pressure from theoretically related constructs, such as conformity or wanting to be popular. We developed and validated short measures of peer pressure, peer conformity, and popularity in a sample (n = 148) of adolescent boys and girls in grades 11 to 13. Results showed that all measures constructed for the study were internally consistent. Although all measures of peer pressure, conformity, and popularity were intercorrelated, peer pressure and peer conformity were stronger predictors of risk behaviors than measures assessing popularity, general conformity, or dysphoria. Despite a simplified scoring format, peer conformity vignettes were equal to if not better than the peer pressure measures in predicting risk behavior. Findings suggest that peer pressure and peer conformity are potentially greater risk factors than a need to be popular, and that both peer pressure and peer conformity can be measured with short scales suitable for large-scale testing.


title: How components of facial width to height ratio differently contribute to the perception of social traits

abstract: Facial width to height ratio (fWHR) is a morphological cue that correlates with sexual dimorphism and social traits. Currently, it is unclear how vertical and horizontal components of fWHR, distinctly capture faces' social information. Using a new methodology, we orthogonally manipulated the upper facial height and the bizygomatic width to test their selective effect in the formation of impressions. Subjects (n = 90) saw pair of faces and had to select the face expressing better different social traits (trustworthiness, aggressiveness and femininity). We further investigated how sex and fWHR components interact in the formation of these judgements. Across experiments, changes along the vertical component better predicted participants' ratings rather than the horizontal component. Faces with smaller height were perceived as less trustworthy, less feminine and more aggressive. By dissociating fWHR and testing the contribution of its components independently, we obtained a powerful and discriminative measure of how facial morphology guides social judgements.


title: Extending the TAM for a World-Wide-Web context

abstract: Ease of use and usefulness are believed to be fundamental in determining the acceptance and use of various, corporate ITs. These beliefs, however, may not explain the user’s behavior toward newly emerging ITs, such as the World-Wide-Web (WWW). In this study, we introduce playfulness as a new factor that reflects the user’s intrinsic belief in WWW acceptance. Using it as an intrinsic motivation factor, we extend and empirically validate the Technology Acceptance Model (TAM) for the WWW context. # 2001 Elsevier Science B.V. All rights reserved.


title: Understanding Copyright Law in Online Creative Communities

abstract: Copyright law is increasingly relevant to everyday interactions online, from social media status updates to artists showcasing their work. This is especially true in creative spaces where rules about reuse and remix are notoriously gray. Based on a content analysis of public forum postings in eight different online communities featuring different media types (music, video, art, and writing), we found that copyright is a frequent topic of conversation and that much of this discourse stems from problems that copyright causes for creative activities. We identify the major types of problems encountered, including chilling effects that negatively impact technology use. We find that many challenges can be explained by lack of knowledge about legal or policy rules, including breakdowns in user expectations for the sites they use. We argue that lack of clarity is a pervasive usability problem that should be considered more carefully in the design of user-generated content platforms.


title: MC/DC automatic test input data generation

abstract: In regulated domain such as aerospace and in safety critical domains, software quality assurance is subject to strict regulation such as the RTCA DO-178B standard. Among other conditions, the DO-178B mandates for the satisfaction of the modified condition/decision coverage (MC/DC) testing criterion for software where failure condition may have catastrophic consequences. MC/DC is a white box testing criterion aiming at proving that all conditions involved in a predicate can influence the predicate value in the desired way. In this paper, we propose a novel fitness function inspired by chaining test data generation to efficiently generate test input data satisfying the MC/DC criterion. Preliminary results show the superiority of the novel fitness function that is able to avoid plateau leading to a behavior close to random test of traditional white box fitness functions.


title: Cross-lingual lexical language discovery from audio data using multiple translations

abstract: Zero-resource Automatic Speech Recognition (ZR ASR) addresses target languages without given pronunciation dictionary, transcribed speech, and language model. Lexical discovery for ZR ASR aims to extract word-like chunks from speech. Lexical discovery benefits from the availability of written translations in another source language [1, 2, 3]. In this paper, we improve lexical discovery even more by combining multiple source languages. We present a novel method for combining noisy word segmentations resulting in up to 11.2% relative F-score gain. When we extract word pronunciations from the combined segmentations to bootstrap an ASR system, we improve accuracy by 9.1% relative compared to the best system with only one translation, and by 50.1% compared to monolingual lexical discovery.


title: Design of HyQ – a hydraulically and electrically actuated quadruped robot

abstract: A new versatile hydraulically powered quadruped robot (HyQ) has been developed to serve as a platform to study not only highly dynamic motions, such as running and jumping, but also careful navigation over very rough terrain. HyQ stands 1 m tall, weighs roughly 90 kg, and features 12 torque-controlled joints powered by a combination of hydraulic and electric actuators. The hydraulic actuation permits the robot to perform powerful and dynamic motions that are hard to achieve with more traditional electrically actuated robots. This paper describes design and specifications of the robot and presents details on the hardware of the quadruped platform, such as the mechanical design of the four articulated legs and of the torso frame, and the configuration of the hydraulic power system. Results from the first walking experiments are presented, along with test studies using a previously built prototype leg.


title: ACQR: A Novel Framework to Identify and Predict Influential Users in Micro-Blogging.

abstract: As key roles of online social networks, influential users in micro-blogging have the ability to influence the attitudes or behaviour of others. When it comes to marketing, the users’ influence should be associated with a certain topic or field on which people have different levels of preference and expertise. In order to identify and predict influential users in a specific topic more effectively, users’ actual influential capability on a certain topic and potential influence unlimited by topics is combined into a novel comprehensive framework named “ACQR” in this research. ACQR framework depicts the attributes of the influentials from four aspects, including activeness (A), centrality (C), quality of post (Q) and reputation (R). Based on this framework, a data mining method is developed for discovering and forecasting the top influentials. Empirical results reveal that our ACQR framework and the data mining method by TOPSIS and SVMs (with polynomial and RBF kernels) can perform very well in identifying and predicting influential users in a certain topic (such as iPhone 5). Furthermore, the dynamic change processes of users’ influence from longitudinal perspective are analysed and suggestions to the sales managers are provided.


title: Robust sound event classification using deep neural networks

abstract: The automatic recognition of sound events by computers is an important aspect of emerging applications such as automated surveillance, machine hearing and auditory scene understanding. Recent advances in machine learning, as well as in computational models of the human auditory system, have contributed to advances in this increasingly popular research field. Robust sound event classification, the ability to recognise sounds under real-world noisy conditions, is an especially challenging task. Classification methods translated from the speech recognition domain, using features such as mel-frequency cepstral coefficients, have been shown to perform reasonably well for the sound event classification task, although spectrogram-based or auditory image analysis techniques reportedly achieve superior performance in noise. This paper outlines a sound event classification framework that compares auditory image front end features with spectrogram image-based front end features, using support vector machine and deep neural network classifiers. Performance is evaluated on a standard robust classification task in different levels of corrupting noise, and with several system enhancements, and shown to compare very well with current state-of-the-art classification techniques.


title: Effects of lithium on cognitive performance: a meta-analysis.

abstract: Background: Cognitive impairment is under-recognized among patients with bipolar disorder and may represent not only effects of the illness but also adverse effects of its treatments. Among these, lithium is the best-studied mood stabilizer. As its cognitive effects are mixed and not well-known, we assessed reported effects of lithium on cognitive performance. Data sources: MEDLINE, PsycINFO, and EMBASE databases (1950 to December 2008) were queried with the keywords lithium, cognit*, neurocognit * , neuropsych * , psycholog * , attention, concentration, processing speed, memory, executive, and learning. Database searches were supplemented with bibliographic cross-referencing by hand. The literature search was conducted independently by 2 authors (A.P.W and T.S.W) during August and September 2008, and questions about appropriate inclusion or exclusion were resolved between them by consensus. Study selection: Of 586 reports initially identified as being of potential interest, 12, involving 539 subjects, met our inclusion criteria: (1) cognitive performance compared between subjects taking lithium and comparable subjects not taking lithium; comparability was assured by: (2) patients with the same affective disorder diagnoses in euthymic or remitted status or healthy volunteers; (3) groups of similar age and sex; (4) similar intelligence, education, or occupation; (5) similar distribution of other concurrent psychotropic drugs; and (6) cognitive abilities (outcomes) assessed with performance-based measures. Data extraction: Standardized mean-difference effect size (ES), corrected for small-sample bias (Hedges' g), was computed for cognitive tasks in each study. ES estimates were transformed so that positive values indicate poorer performance by lithium-treated subjects. Infrequently, when means and standard deviations were not provided, ES was estimated from reported values of t, F, or z tests. For analysis, similar neurocognitive tests were grouped a priori based on the cognitive domains they aimed to assess. Data synthesis: We identified 12 studies involving 276 lithium-treated and 263 similar or the same subjects, lithium-free. Lithium was taken for a mean duration of 3.9 years by affective disorder patients and 2.5 weeks by healthy volunteers, yielding a mean daily trough serum concentration of 0.80 mEq/L. Overall, lithium treatment was associated with small but significant impairment in immediate verbal learning and memory (ES=0.24; 95% CI, 0.05-0.43) and creativity (ES=0.33; 95% CI, 0.02-0.64), whereas delayed verbal memory, visual memory, attention, executive function, processing speed, and psychomotor performance were not significantly affected. Selectively, among the 326 affective-disorder patients, in addition to these overall impairments, long-term lithium treatment also was associated with even greater impairment in psychomotor performance (ES=0.62; 95% CI, 0.27-0.97), with no evidence of cognitive improvements. Conclusions: Lithium treatment appears to have only few and minor negative effects on cognition.


title: A novel algorithm for incompressible flow using only a coarse grid projection

abstract: Large scale fluid simulation can be difficult using existing techniques due to the high computational cost of using large grids. We present a novel technique for simulating detailed fluids quickly. Our technique coarsens the Eulerian fluid grid during the pressure solve, allowing for a fast implicit update but still maintaining the resolution obtained with a large grid. This allows our simulations to run at a fraction of the cost of existing techniques while still providing the fine scale structure and details obtained with a full projection. Our algorithm scales well to very large grids and large numbers of processors, allowing for high fidelity simulations that would otherwise be intractable.


title: TanDEM-X Pol-InSAR Performance for Forest Height Estimation

abstract: TanDEM-X and TerraSAR-X platforms form together the first spaceborne single-pass polarimetric interferometer in space. This allows, for the first time, the acquisition of spaceborne polarimetric synthetic aperture radar interferometry (Pol-InSAR) data without the disturbing effect of temporal decorrelation. This paper aims to assess the potential of such data for forest applications. For this, single- and dual-pol data acquired over a boreal, a temperate, and a tropical site were investigated to characterize X-band penetration and polarization diversity of the interferometric coherence measurements. Pol-InSAR forest height inversion schemes have been proposed and implemented for the single- and dual-pol cases and cross validated against LIDAR reference measurements for all sites. The single-pol inversion relies on an external ground digital terrain model (DTM) and performed well for all sites with correlation coefficients $r^{2}$ between 0.80 and 0.98. The dual-pol inversion does not require an external DTM but depends on the visibility of the whole forest layer. Accordingly, its performance varied with forest structure and season: The best performance was achieved for the summer acquisition of the boreal test site $(r^{2} = 0.86)$ and for the winter acquisition of the temperate test site $(r^{2} = 0.77)$ . For the tropical test site, only a weak correlation $(r^{2} =\ \sim\!\! 0.50)$ could be established.


title: Optimism Is Universal: Exploring the Presence and Benefits of Optimism in a Representative Sample of the World

abstract: Current theories of optimism suggest that the tendency to maintain positive expectations for the future is an adaptive psychological resource associated with improved well-being and physical health,but the majority of previous optimism research has been conducted in industrialized nations. The present study examined (a) whether optimism is universal, (b) what demographic factors predict optimism,and (c) whether optimism is consistently associated with improved subjective well-being and perceived health worldwide.The present study used representative samples of 142 countries that together represent 95% of the world’s population.The total sample of 150,048 individuals had a mean age of 38.28 (SD = 16.85) and approximately equal sex distribution (51.2% female). The relationships between optimism, subjective well-being, and perceived health were examined using hierarchical linear modeling. Results indicated that most individuals and most countries worldwide are optimistic and that higher levels of optimism are associated with improved subjective well-being and perceived health worldwide.The present study provides compelling evidence that optimism is a universal phenomenon and that the associations between optimism and improved psychological functioning are not limited to industrialized nations. The capacity to maintain an optimistic orientation toward the future has been proposed to be one of the defining features of the human species (Tiger, 1979). The potential benefits of optimism have been a popular research topic in psychology, and there is now extensive research examining whether an optimistic orientation toward the future is adaptive (Carver, Scheier, Miller, & Fulford, 2009; Carver, Scheier, & Segerstrom, 2010). However, the majority of previous optimism research has been conducted in industrialized nations. The present study extends this previous work by examining the origins and benefits of optimism using representative samples from 142 countries that together represent 95% of the world’s population. In addition to examining worldwide variations in individual levels of optimism, we examine whether the associations between optimism and improved psychological functioning are consistent worldwide.


title: Functional Neuroimaging of Anxiety: A Meta-Analysis of Emotional Processing in PTSD, Social Anxiety Disorder, and Specific Phobia

abstract: Objective: The study of human anxiety disorders has benefited greatly from functional neuroimaging approaches. Individual studies, however, vary greatly in their findings. The authors searched for common and disorder-specific functional neurobiological deficits in several anxiety disorders. The authors also compared these deficits to the neural systems engaged during anticipatory anxiety in healthy subjects. Method: Functional magnetic resonance imaging and positron emission tomography studies of posttraumatic stress disorder (PTSD), social anxiety disorder, specific phobia, and fear conditioning in healthy individuals were compared by quantitative meta-analysis. Included studies compared negative emotional processing to baseline, neutral, or positive emotion conditions. Results: Patients with any of the three disorders consistently showed greater activity than matched comparison subjects in the amygdala and insula, structures linked to negative emotional responses. A similar pattern was observed during f...


title: A New Digital Forensics Model of Smart City Automated Vehicles

abstract: In the modern world, cyber societies are full of complications. The Internet has brought so many convenient services to our society but Internet is also a mine field. Mass surveillance from smart phone to PC, from automated car to smart television, any online device seems could be turn to privacy breach toolkit. In order to follow the GDPR (General Data Protection Regulation), protect privacy data, including PII (Personally Identifiable Information), against Cyberstalking and many other cybercrime challenges, a novel Digital Forensics Model served for Smart City Automated Vehicles has been developed working on investigating AAV (Autonomous Automated Vehicle) cases. The proposed development is reported to Big Data 2017. Here, we provide an update for discussion.


title: P2P-NET: Bidirectional Point Displacement Net for Shape Transform

abstract: We introduce P2P-NET, a general-purpose deep neural network that learns geometric transformations between point-based shape representations from two domains (e.g., meso-skeletons and surfaces, partial and complete scans, etc.).


Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization


title: 40-nm Embedded Split-Gate MONOS (SG-MONOS) Flash Macros for Automotive With 160-MHz Random Access for Code and Endurance Over 10 M Cycles for Data at the Junction Temperature of 170 $^{\circ}$C

abstract: First-ever 40-nm embedded split-gate MONOS (SG-MONOS) flash macros for automotive micro-controller unit (MCU) have been successfully developed. A SG-MONOS cell realizes high performance with low power consumption and intrinsically high data reliability thanks to the combination of split-gate and charge-trapping structure. In addition, newly developed circuit techniques greatly enhance the advantages of SG-MONOS cells and enable fast and reliable operations even at the junction temperature (Tj) of 170°C with small peripheral circuit area; 1) a sense amplifier with digital offset cancellation (SA-DOC) provides fast read operation over 160 MHz; 2) adaptable program current control scheme (APCCS) and intelligent erase scheme (IES) significantly decrease the program and erase time, which also results in improving the memory cells' reliability; and, 3) 3-D stacked unit capacitors achieve area-efficient charge pump. Two types of embedded flash (eFlash) macros with these technologies, code macro of 2 MB and data macro of 64 KB, were fabricated in a 40-nm eFlash process. The code macro demonstrates the capability of 160-MHz random read operation at Tj= 170°C, reaching 5.1 GB/s read throughput by simultaneous 256 bits read-out from two code macros. The data macro achieves the program/erase endurance over 10 million cycles at Tj= 170°C without any software-assisted techniques.


title: Resolved momentum control: humanoid motion planning based on the linear and angular momentum

abstract: We introduce a method to generate whole body motion of a humanoid robot such that the resulted total linear/angular momenta become specified values. First, we derive a linear equation, which gives to total momentum of a robot from its physical parameters, the base link speed and the joint speeds. Constraints between the legs and the environment are also considered. The whole body motion is calculated from a given momentum reference by using a pseudo-inverse of the inertia matrix. As examples, we generated the kicking and walking motions and tested on the actual humanoid robot HRP-2. This method, the resolved momentum control, gives us a unified framework to generate various maneuvers of humanoid robots.


title: Androtrace: framework for tracing and analyzing IOs on Android

abstract: In this work, we develop IO trace and analysis framework, Androtrace, which is specifically tailored for Android platform. Unlike earlier works that required prolonged post processing procedures, Androtrace not only traces with low overhead, but also provides efficient solution for storage with--in mobile devices. Captured IO trace is temporarily stored in main memory and storage device, and they are transferred to Androtrace server when the device is connected to WiFi. We use server and client model to support and analyze multiple Android users. Using the framework, we find that write IOs are dominant in mobile workload.


title: Generalization Properties of Learning with Random Features

abstract: We study the generalization properties of regularized learning with random features in the statistical learning theory framework. We show that optimal learning errors can be achieved with a number of features smaller than the number of examples. As a byproduct, we also show that learning with random features can be seen as a form of regularization, rather than only a way to speed up computations.


title: Bilateral recruitment of prefrontal cortex in working memory is associated with task demand but not with age

abstract: Abstract Elderly adults may master challenging cognitive demands by additionally recruiting the cross-hemispheric counterparts of otherwise unilaterally engaged brain regions, a strategy that seems to be at odds with the notion of lateralized functions in cerebral cortex. We wondered whether bilateral activation might be a general coping strategy that is independent of age, task content and brain region. While using functional magnetic resonance imaging (fMRI), we pushed young and old subjects to their working memory (WM) capacity limits in verbal, spatial, and object domains. Then, we compared the fMRI signal reflecting WM maintenance between hemispheric counterparts of various task-relevant cerebral regions that are known to exhibit lateralization. Whereas language-related areas kept their lateralized activation pattern independent of age in difficult tasks, we observed bilaterality in dorsolateral and anterior prefrontal cortex across WM domains and age groups. In summary, the additional recruitment of cross-hemispheric counterparts seems to be an age-independent domain-general strategy to master cognitive challenges. This phenomenon is largely confined to prefrontal cortex, which is arguably less specialized and more flexible than other parts of the brain.


Forearm bone mineral density in healthy children


title: Miniaturized Circularly Polarized Patch Antenna With Low Back Radiation for GPS Satellite Communications

abstract: A new size-reduction technique for circularly polarized (CP) patch antenna with the use of parasitic shorting strips is presented. The operating frequency of the CP patch antenna can be controlled by those parasitic shorting strips. Rectangular, U-shaped, and meandering shorting strips are studied. It is found that the size and the back radiation of the patch antenna can be reduced simultaneously. Good impedance matching and CP radiation characteristics can be achieved with different shapes of the shorting strips. The proposed antenna with meandering shorting strip is measured. Good agreement between analytical and experimental results is obtained.


A neural network approach to predicting urban building energy consumption


title: A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG

abstract: Presents a QT database designed for evaluation of algorithms that detect waveform boundaries in the ECG. The database consists of 105 fifteen-minute excerpts of two-channel ECG Holter recordings, chosen to include a broad variety of QRS and ST-T morphologies. Waveform boundaries for a subset of beats in these recordings have been manually determined by expert annotators using an interactive graphic display to view both signals simultaneously and to insert the annotations. Examples of each morphology were included in this subset of annotated beats; at least 30 beats in each record, 3622 beats in all, were manually annotated in the database. In 11 records, two independent sets of annotations have been included, to allow inter-observer variability studies. The QT Database is available on a CD-ROM in the format previously used for the MIT-BIH Arrhythmia Database and the European ST-T Database, from which some of the recordings in the QT Database have been obtained.


title: Grasp Recognition for Uncalibrated Data Gloves: A Machine Learning Approach

abstract: This paper presents a comparison of various machine learning methods applied to the problem of recognizing grasp types involved in object manipulations performed with a data glove. Conventional wisdom holds that data gloves need calibration in order to obtain accurate results. However, calibration is a time-consuming process, inherently user-specific, and its results are often not perfect. In contrast, the present study aims at evaluating recognition methods that do not require prior calibration of the data glove. Instead, raw sensor readings are used as input features that are directly mapped to different categories of hand shapes. An experiment was carried out in which test persons wearing a data glove had to grasp physical objects of different shapes corresponding to the various grasp types of the Schlesinger taxonomy. The collected data was comprehensively analyzed using numerous classification techniques provided in an open-source machine learning toolbox. Evaluated machine learning methods are composed of (a) 38 classifiers including different types of function learners, decision trees, rule-based learners, Bayes nets, and lazy learners; (b) data preprocessing using principal component analysis (PCA) with varying degrees of dimensionality reduction; and (c) five meta-learning algorithms under various configurations where selection of suitable base classifier combinations was informed by the results of the foregoing classifier evaluation. Classification performance was analyzed in six different settings, representing various application scenarios with differing generalization demands. The results of this work are twofold: (1) We show that a reasonably good to highly reliable recognition of grasp types can be achieveddepending on whether or not the glove user is among those training the classifiereven with uncalibrated data gloves. (2) We identify the best performing classification methods for the recognition of various grasp types. To conclude, cumbersome calibration processes before productive usage of data gloves can be spared in many situations.


title: Benchmark Analysis of Representative Deep Neural Network Architectures

abstract: This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed in the state of the art for image recognition. For each DNN multiple performance indices are observed, such as recognition accuracy, model complexity, computational complexity, memory usage, and inference time. The behavior of such performance indices and some combinations of them are analyzed and discussed. To measure the indices we experiment the use of DNNs on two different computer architectures, a workstation equipped with a NVIDIA Titan X Pascal and an embedded system based on a NVIDIA Jetson TX1 board. This experimentation allows a direct comparison between DNNs running on machines with very different computational capacity. This study is useful for researchers to have a complete view of what solutions have been explored so far and in which research directions are worth exploring in the future; and for practitioners to select the DNN architecture(s) that better fit the resource constraints of practical deployments and applications. To complete this work, all the DNNs, as well as the software used for the analysis, are available online.


title: Life-cycle of the games industry: the specificities of creative industries

abstract: This paper assesses the maturity of the games industry. The indicators of industry maturity posited in the industry life-cycle theory are discussed. The economic characteristics of creative, or cultural, industries are listed. Empirical work is done on the concentration of the international game development sector based on UK sales. The growth of the Finnish games industry is analyzed based on original statistics. The paper concludes that based on this empirical work the software side of the games industry is far from mature. Explanations for this finding are proposed based on the characteristics of the games industry in particular and the creative industries in general.


title: Buffered Bloom Filters on Solid State Storage

abstract: Bloom Filters are widely used in many applications including database management systems. With a certain allowable error rate, this data structure provides an efficient solution for membership queries. The error rate is inversely proportional to the size of the Bloom filter. Currently, Bloom filters are stored in main memory because the low locality of operations makes them impractical on secondary storage. In multi-user database management systems, where there is a high contention for the shared memory heap, the limited memory available for allocating a Bloom filter may cause a high rate of false positives. In this paper we are proposing a technique to reduce the memory requirement for Bloom filters with the help of solid state storage devices (SSD). By using a limited memory space for buffering the read/write requests, we can afford a larger SSD space for the actual Bloom filter bit vector. In our experiments we show that with significantly less memory requirement and fewer hash functions the proposed technique reduces the false positive rate effectively. In addition, the proposed data structure runs faster than the traditional Bloom filters by grouping the inserted records with respect to their locality on the


title: The Distributed Dissimilar Redundancy Architecture of Fly-by-Wire Flight Control System

abstract: This article presents a distributed dissimilar redundancy architecture of fly-by-wire Flight Control System. It is based on many dissimilar flight control computer, redundancy structure which includes the cross arranged hydraulic and power supply, three types of data buses, lots of data correction monitors, a distributed control loop to avoid the correlated faults and achieve the rigid safety airworthiness requirements. A Fault Tree Analysis (FTA) is implemented to verify whether the system safety could meet system design targets.


title: Development and behavioral pattern analysis of a mobile guide system with augmented reality for painting appreciation instruction in an art museum

abstract: A mobile guide system that integrates art appreciation instruction with augmented reality (AR) was designed as an auxiliary tool for painting appreciation, and the learning performance of three groups of visiting participants was explored: AR-guided, audio-guided, and nonguided (i.e., without carrying auxiliary devices). The participants were 135 college students, and a quasi-experimental research design was employed. Several learning performance factors of the museum visitors aided with different guided modes were evaluated, including their learning effectiveness, flow experience, the amount of time spent focusing on the paintings, behavioral patterns, and attitude of using the guide systems. The results showed that compared to the audio- and nonguided participants, the AR guide effectively enhanced visitors' learning effectiveness, promoted their flow experience, and extended the amount of time the visitors spent focusing on the paintings. In addition, the visitors' behavioral patterns were dependent upon the guided mode that they used; the visitors who were the most engaged in the gallery experience were those who were using the AR guide. Most of the visitors using the mobile AR-guide system elicited positive responses and acceptance attitudes.


title: Flexible Transparent Iontronic Film for Interfacial Capacitive Pressure Sensing

abstract: A flexible, transparent iontronic film is introduced as a thin-film capacitive sensing material for emerging wearable and health-monitoring applications. Utilizing the capacitive interface at the ionic-electronic contact, the iontronic film sensor offers a large unit-area capacitance (of 5.4 μF cm(-2) ) and an ultrahigh sensitivity (of 3.1 nF kPa(-1) ), which is a thousand times greater than that of traditional solid-state counterparts.


Septal contributions to olfactory bulb interneuron diversity in the embryonic mouse telencephalon: role of the homeobox gene Gsx2


Daily Fluctuations in Smartphone Use, Psychological Detachment, and Work Engagement: The Role of Workplace Telepressure


title: Effectiveness of a manually controlled infusion scheme of propofol and alfentanil mixture for endotracheal intubation in hypertensive patients: in comparison with thiamylal and nifedipine plus thiamylal.

abstract: BACKGROUND ::: Bolus administration of propofol for induction causes hypotension, especially in elderly hypertensive patient. Carefully titrated infusion of propofol minimizes adverse effects, such as hypotension, and permits a rapid recovery of its central effects. The objective of this study was to investigate the effect of a manually controlled infusion scheme of propofol and alfentanil mixture on hemodynamic stability during induction and endotracheal intubation for hypertensive patient. At the same time, the effectiveness of this scheme was compared with two other induction regimens (thiamylal or nifedipine plus thiamylal). ::: ::: ::: METHODS ::: Sixty hypertensive patients undergoing orthopedic surgery were randomized into 3 groups (n = 20 per each group), None of the patients received premedication. Anesthesia was induced in group 1 (G1) with alfentanil 10 micrograms/kg. 30 s later, manual infusion of a mixture of propofol (10-12 mg/kg/h) and alfentanil (25 micrograms/kg/h) was performed for 2 min, followed by atracurium (5 mg) and propofol (1-1.5 mg/kg) as a bolus induction dose over 20 s, and then Suxamethonium (1.5 mg/kg) at 30-40 s later. Intubation was done while giving a continuous infusion of propofol and alfentanil. After intubation, the infusion rate was adjusted according to the blood pressure (BP) variation. Group 2 patients (G2) were induced with fentanyl (2 micrograms/kg), thiamylal (4-5 mg/kg), atracurium (5 mg) and succinylcholine (1.5 mg/kg). Induction of anesthesia in group 3 patients (G3) was the same as for G2, with additional sublingual nifedipine (1/2 capsule) 10 min prior to induction. Extra bolus dose of propofol (20 mg) or thiamylal (20 mg) was given at every 15 s if the systolic BP was still higher than 160 mmHg after induction by the above 3 regimens. The radial arterial pressure and electrocardiogram were continuously recorded for evaluation of hemodynamic changes. ::: ::: ::: RESULTS ::: Post-intubation peak mean arterial pressure (MAP) in G1 and G3 were below to awake baseline values, while MAP of G2 was significantly higher than over awake baseline level (p < 0.001). The lowest MAP of G3 at post-intubation period before surgical stimulation were significantly lower than those of G1 and G2 (p < 0.001). Peak tachycardiac response to intubation in G2 was significantly higher than G1 (p < 0.05). After intubation, the peak rate pressure product were significantly higher in G2 compared with that in G1 (p < 0.05) and G3 (p < 0.001). ::: ::: ::: CONCLUSIONS ::: The proposed manual infusion scheme of propofol and alfentanil mixture performed during induction and intubation attenuated the subsequent peak pressor response to incubation and reduced the hypotensive effect, in comparison to thiamylal or thiamylal plus nifedipine treatment, during post-intubation period. The same infusion scheme also attenuated the tachycardiac response to intubation.


title: Recognition and defect detection of dot-matrix text via variation-model based learning

abstract: An algorithm for recognition and defect detection of dot-matrix text printed on products is proposed. Extraction and recognition of dot-matrix text contains several difficulties, which are not involved in standard camera-based OCR, that the appearance of dot-matrix characters is corrupted and broken by illumination, complex texture in the background and other standard characters printed on product packages. We propose a dot-matrix text extraction and recognition method which does not require any user interaction. The method employs detected location of corner points and classification score. The result of evaluation experiment using 250 images shows that recall and precision of extraction are 78.60% and 76.03%, respectively. Recognition accuracy of correctly extracted characters is 94.43%. Detecting printing defect of dot-matrix text is also important in the production scene to avoid illegal productions. We also propose a detection method for printing defect of dot-matrix characters. The method constructs a feature vector of which elements are classification scores of each character class and employs support vector machine to classify four types of printing defect. The detection accuracy of the proposed method is 96.68 %.


title: An Interleaved Full Nyquist High-Speed DAC Technique

abstract: A 9 bit 11 GS/s DAC is presented that achieves an SFDR of more than 50 dB across Nyquist and IM3 below 50 dBc across Nyquist. The DAC uses a two-times interleaved architecture to suppress spurs that typically limit DAC performance. Despite requiring two current-steering DACs for the interleaved architecture, the relative low demands on performance of these sub-DACs imply that they can be implemented in an area and power efficient way. Together with a quad-switching architecture to decrease demands on the power supply and bias generation and employing the multiplexer switches in triode, the total core area is only 0.04 mm2 while consuming 110 mW from a single 1.0 V supply.


title: Blind grasping: Stable robotic grasping using tactile feedback and hand kinematics

abstract: We propose a machine learning approach to the perception of a stable robotic grasp based on tactile feedback and hand kinematic data, which we call blind grasping. We first discuss a method for simulating tactile feedback using a soft finger contact model in GraspIt!, which is a robotic grasping simulator [10]. Using this simulation technique, we compute tactile contacts of thousands of grasps with a robotic hand using the Columbia Grasp Database [6]. The tactile contacts along with the hand kinematic data are then input to a Support Vector Machine (SVM) which is trained to estimate the stability of a given grasp based on this tactile feedback and also the robotic hand kinematics. Experimental results indicate that the tactile feedback along with the hand kinematic data carry meaningful information for the prediction of the stability of a blind robotic grasp.


title: Improving automatic query expansion

abstract: Most casual users of IR systems type short queries. Recent research has shown that adding new words to these queries via odhoc feedback improves the retrieval effectiveness of such queries. We investigate ways to improve this query expansion process by refining the set of documents used in feedback. We start by using manually formulated Boolean filters along with proximity constraints. Our approach is similar to the one proposed by Hearst[l2]. Next, we investigate a completely automatic method that makes use of term cooccurrence information to estimate word correlation. Experimental results show that refining the set of documents used in query expansion often prevents the query drift caused by blind expansion and yields substantial improvements in retrieval effectiveness, both in terms of average precision and precision in the top twenty documents. More importantly, the fully automatic approach developed in this study performs competitively with the best manual approach and requires little computational overhead.


title: What is a TOT? Cognate and translation effects on tip-of-the-tongue states in spanish-english and tagalog-english bilinguals

abstract: The authors induced tip-of-the-tongue states (TOTs) for English words in monolinguals and bilinguals using picture stimuli with cognate (e.g., vampire, which is vampiro in Spanish) and noncognate (e.g., funnel, which is embudo in Spanish) names. Bilinguals had more TOTs than did monolinguals unless the target pictures had translatable cognate names, and bilinguals had fewer TOTs for noncognates they were later able to translate. TOT rates for the same targets in monolinguals indicated that these effects could not be attributed to target difficulty. Two popular TOT accounts must be modified to explain cognate and translatability facilitation effects, and cross-language interference cannot explain bilinguals’ increased TOTs rates. Instead the authors propose that, relative to monolinguals, bilinguals are less able to activate representations specific to each language. Tip-of-the-tongue states (TOTs) are word retrieval failures characterized by a feeling of imminent recall and by a greater than chance probability of accurately reporting partial information about the target word (e.g., the first phoneme). Monolinguals are estimated to experience TOTs on about a weekly basis in natural settings and on about 10%–20% of attempts to retrieve lowfrequency targets in laboratory settings (R. Brown & McNeill, 1966; for review papers see A. S. Brown, 1991; Schwartz, 1999). TOT incidence is thought to increase in older age (Burke, MacKay, Worthley, & Wade, 1991; Heine, Ober, & Shenaut, 1999) and in bilinguals compared with monolinguals (Gollan & Silverberg, 2001). In the present study we compared TOT rates in bilinguals and monolinguals, with two goals. First, by observing how bilingualism and TOTs interact we hoped to gain a better understanding of what the TOT retrieval failure entails; and second, we sought to further constrain accounts of the increased TOT rate in bilinguals.


title: Toward Fairness of Cryptocurrency Payments

abstract: Motivated by the great success and adoption of Bitcoin, a number of cryptocurrencies such as Litecoin, Dogecoin, and Ethereum are becoming increasingly popular. Although existing blockchain-based cryptocurrency schemes can ensure reasonable security for transactions, they do not consider any notion of fairness. Fair exchange allows two players to exchange digital “items,” such as digital signatures, over insecure networks fairly, so that either each player gets the other’s item, or neither player does. Given that blockchain participants typically do not trust each other, enabling fairness in existing cryptocurrencies is an essential but insufficiently explored problem. In this article, we explore the solution space for enabling the fair exchange of a cryptocurrency payment for a receipt. We identify the timeliness of an exchange as an important property especially when one of the parties involved in the exchange is resource-constrained. We introduce the notion of strong timeliness for a fair exchange protocol and propose two fair payment-for-receipt protocol instantiations that leverage functionality of the blockchain to achieve strong timeliness. We implement both and compare their security and efficiency.


title: Overcoming Online Information Privacy Concerns : An Information-Processing Theory Approach

abstract: The advent of the Internet has made the transmission of personally identifiable information more common and often unintended by the user. As personal information becomes more accessible, individuals worry that businesses misuse the information that is collected while they are online. Organizations have tried to mitigate this concern in two ways: (1) by offering privacy policies regarding the handling and use of personal information and (2) by offering benefits such as financial gains or convenience. In this paper, we interpret these actions in the context of the information-processing theory of motivation. Information-processing theories, also known as expectancy theories in the context of motivated behavior, are built on the premise that people process information about behavior-outcome relationships. By doing so, they are forming expectations and making decisions about what behavior to choose. Using an experimental setting, we empirically validate predictions that the means to mitigate privacy concerns are associated with positive valences resulting in an increase in motivational score. In a conjoint analysis exercise, 268 participants from the United States and Singapore face trade-off situations, where an organization may only offer incomplete privacy protection or some benefits. While privacy protections (against secondary use, improper access, and error) are associated with positive valences, we also find that financial gains and convenience can significantly increase individuals' motivational score of registering with a Web site. We find that benefits-monetary reward and future convenience-significantly affect individuals' preferences over Web sites with differing privacy policies. We also quantify the value of Web site privacy protection. Among U.S. subjects, protection against errors, improper access, and secondary use of personal information is worth $30.49-$44.62. Finally, our approach also allows us to identify three distinct segments of Internet users-privacy guardians, information sellers, and convenience seekers.


title: Improving virtualization in the presence of software managed translation lookaside buffers

abstract: Virtualization has become an important technology that is used across many platforms, particularly servers, to increase utilization, multi-tenancy and security. Virtualization introduces additional overhead that often relates to memory management, interrupt handling and hypervisor mode switching. Among those, memory management and translation lookaside buffer (TLB) management have been shown to have a significant impact on the performance of systems. Two principal mechanisms for TLB management exist in today's systems, namely software and hardware managed TLBs. In this paper, we analyze and quantify the overhead of a pure software virtualization that is implemented over a software managed TLB. We then describe our design of hardware extensions to support virtualization in systems with software managed TLBs to remove the most dominant overheads. These extensions were implemented in the Power embedded A2 core, which is used in the PowerEN and in the Blue Gene/Q processors. They were used to implement a KVM port. We evaluate each of these hardware extensions to determine their overall contributions to performance and efficiency. Collectively these extensions demonstrate an average improvement of 232% over a pure software implementation.


title: Cross-Evaluation of Entity Linking and Disambiguation Systems for Clinical Text Annotation

abstract: In this paper we study whether state-of-the-art techniques for multi-domain and multilingual entity linking can be ported to the clinical domain. To do so, we compare two known entity linking systems, BabelFly and TagMe, that leverage on Wikipedia and DBpedia, with the standard clinical semantic annotation and disambiguation system, MetaMap, over the SemRep clinical word sense disambiguation gold standard. We show that BabelFly and especially TagMe, while achieving decent precision on clinical annotation, outmatch MetaMap's F1-score.


Sentiment Classification using Distant Supervision


Measuring reshoring trends in the EU and the US


title: Detection and recognition of road markings in panoramic images

abstract: The detection of road lane markings has many practical applications, such as advanced driver assistance systems and road maintenance. In this paper we propose an algorithm to detect and recognize road lane markings from panoramic images. Our algorithm consists of four steps. First, an inverse perspective mapping is applied to the image, and the potential road markings are segmented based on their intensity difference compared to the surrounding pixels. Second, we extract the distance between the center and the boundary at regular angular steps of each considered potential road marking segment into a feature vector. Third, each segment is classified using a Support Vector Machine (SVM). Finally, by modeling the lane markings, previous false positive detected segments can be rejected based on their orientation and position relative to the lane markings. Our experiments show that the system is capable of recognizing \(93\,\%\), \(95\,\%\) and \(91\,\%\) of striped line segments, blocks and arrows respectively, as well as \(94\,\%\) of the lane markings.


title: Whose culture has capital? A critical race theory discussion of community cultural wealth

abstract: This article conceptualizes community cultural wealth as a critical race theory (CRT) challenge to traditional interpretations of cultural capital. CRT shifts the research lens away from a deficit view of Communities of Color as places full of cultural poverty disadvantages, and instead focuses on and learns from the array of cultural knowledge, skills, abilities and contacts possessed by socially marginalized groups that often go unrecognized and unacknowledged. Various forms of capital nurtured through cultural wealth include aspirational, navigational, social, linguistic, familial and resistant capital. These forms of capital draw on the knowledges Students of Color bring with them from their homes and communities into the classroom. This CRT approach to education involves a commitment to develop schools that acknowledge the multiple strengths of Communities of Color in order to serve a larger purpose of struggle toward social and racial justice.


title: Adaptive Submodular Optimization under Matroid Constraints

abstract: Many important problems in discrete optimization require maximization of a monotonic submodular function subject to matroid constraints. For these problems, a simple greedy algorithm is guaranteed to obtain near-optimal solutions. In this article, we extend this classic result to a general class of adaptive optimization problems under partial observability, where each choice can depend on observations resulting from past choices. Specifically, we prove that a natural adaptive greedy algorithm provides a $1/(p+1)$ approximation for the problem of maximizing an adaptive monotone submodular function subject to $p$ matroid constraints, and more generally over arbitrary $p$-independence systems. We illustrate the usefulness of our result on a complex adaptive match-making application.