title: Convex-lens-on-slide: A simple system for the study of human plasma and blood in narrow spaces

abstract: On clot-promoting surfaces, intact normal blood or plasma deposits fibrinogen and then supplants it with high molecular weight kininogen (HMWK). On glass, plasma layers of less than about 25 μm thick, while still containing enough fibrinogen to coat the surrounding surfaces, lack sufficient HMWK per surface area to remove this fibrinogen deposit. Thus normal intact citrated plasma allowed to enter the space between a glass slide and a convex lens resting belly-down on the slide will leave a disc of fibrinogen where the thickness of plasma layer was below this “critical height” H. The discs of fibrinogen left by plasma that lacks HMWK pathologically or by activation or dilution, are larger—the required H being greater. The present study shows that plasma dilution (final volume divided by original plasma volume) plotted against H yields a straight line. In preliminary series, the slope of this line increases with the atomic weight of five metals whose oxidized surfaces were used as substrates. In whole blood collected in either heparin or ACD, a circle of platelets adheres to oxidized silicon, anodized tantalum, or glass; this circle is similar in size to the one of fibrinogen left by plasma.


A Study of Ontology-based Query Expansion


title: Yes, But Are They Happy? Effects of Trait Self‐Control on Affective Well‐Being and Life Satisfaction

abstract: Does trait self-control (TSC) predict affective well-being and life satisfaction—positively, negatively, or not? We conducted three studies (Study 1: N = 414, 64% female, Mage = 35.0 years; Study 2: N = 208, 66% female, Mage = 25.24 years; Study 3: N = 234, 61% female, Mage = 34.53 years). The key predictor was TSC, with affective well-being and life satisfaction ratings as key outcomes. Potential explanatory constructs including goal conflict, goal balancing, and emotional distress also were investigated. TSC is positively related to affective well-being and life satisfaction, and managing goal conflict is a key as to why. All studies, moreover, showed that the effect of TSC on life satisfaction is at least partially mediated by affect. Study 1's correlational study established the effect. Study 2's experience sampling approach demonstrated that compared to those low in TSC, those high in TSC experience higher levels of momentary affect even as they experience desire, an effect partially mediated through experiencing lower conflict and emotional distress. Study 3 found evidence for the proposed mechanism—that TSC may boost well-being by helping people avoid frequent conflict and balance vice-virtue conflicts by favoring virtues. Self-control positively contributes to happiness through avoiding and dealing with motivational conflict.


title: Automatic query reformulations for text retrieval in software engineering

abstract: There are more than twenty distinct software engineering tasks addressed with text retrieval (TR) techniques, such as, traceability link recovery, feature location, refactoring, reuse, etc. A common issue with all TR applications is that the results of the retrieval depend largely on the quality of the query. When a query performs poorly, it has to be reformulated and this is a difficult task for someone who had trouble writing a good query in the first place. We propose a recommender (called Refoqus) based on machine learning, which is trained with a sample of queries and relevant results. Then, for a given query, it automatically recommends a reformulation strategy that should improve its performance, based on the properties of the query. We evaluated Refoqus empirically against four baseline approaches that are used in natural language document retrieval. The data used for the evaluation corresponds to changes from five open source systems in Java and C++ and it is used in the context of TR-based concept location in source code. Refoqus outperformed the baselines and its recommendations lead to query performance improvement or preservation in 84% of the cases (in average).


title: ConfSys: a kaizen conference management system

abstract: ConfSys is a kaizen(continuous improvement) web-based conference management system. ConfSys3.5 is an upgrade version of ConfSys system, with additional features to facilitate the processes of academic conferences which involve authors, general chair, program chairs program committee and external reviewers and to provide conference services for these and other groups of users and participants. The lessons learned from using the earlier versions of ConfSys have been incorporated in Confsys3.5, which not only improved user interface for ConfSys' useful functions, such as more flexible event configuration, more user-friendly paper submission, paper registration integration, eProceedings, but also introduces new concepts, such as Auto Session Management and proceeding compilation in both pure PDF and PDF with a HTML navigation.


title: Tracking deformable objects with point clouds

abstract: We introduce an algorithm for tracking deformable objects from a sequence of point clouds. The proposed tracking algorithm is based on a probabilistic generative model that incorporates observations of the point cloud and the physical properties of the tracked object and its environment. We propose a modified expectation maximization algorithm to perform maximum a posteriori estimation to update the state estimate at each time step. Our modification makes it practical to perform the inference through calls to a physics simulation engine. This is significant because (i) it allows for the use of highly optimized physics simulation engines for the core computations of our tracking algorithm, and (ii) it makes it possible to naturally, and efficiently, account for physical constraints imposed by collisions, grasping actions, and material properties in the observation updates. Even in the presence of the relatively large occlusions that occur during manipulation tasks, our algorithm is able to robustly track a variety of types of deformable objects, including ones that are one-dimensional, such as ropes; two-dimensional, such as cloth; and three-dimensional, such as sponges. Our implementation can track these objects in real time.


title: Implementation of Super-Twisting Control: Super-Twisting and Higher Order Sliding-Mode Observer-Based Approaches

abstract: In this paper, an output feedback stabilization of perturbed double-integrator systems using super-twisting control (STC) is studied. It is shown that when STC is implemented based on super-twisting observer (STO), then it is not possible to achieve second-order sliding mode (SOSM) using continuous control on the chosen sliding surface. Two methodologies are proposed to circumvent the above-mentioned problem. In the first method, control input is discontinuous, which may not be desirable for practical systems. In the second method, continuous STC is proposed based on higher order sliding mode observer (HOSMO) that achieves SOSM on the chosen sliding surface. For simplicity, we are considering here only the perturbed double integrator, which can be generalized for an arbitrary-order systems. Numerical simulations and experimental validation are also presented to show the effectiveness of the proposed method.


title: Memristor crossbar deep network implementation based on a Convolutional neural network

abstract: This paper presents a simulated memristor crossbar implementation of a deep Convolutional Neural Network (CNN). In the past few years deep neural networks implemented on GPU clusters have become the state of the art in image classification. They provide excellent classification ability at the cost of a more complex data manipulation process. However once these systems are trained, we show that the analog crossbar circuits in this paper can highly parallelize the recognition phase of a CNN algorithm. One of the drawbacks of using memristors to carry out computations is that the data stored will likely have less precision when compared to typical 32-bit floating point memory. However, we show the proposed system is capable of operating with zero loss in classification accuracy if the memristors utilized are able to store at least 16 unique values (essentially acting as 4-bit devices). To the best of our knowledge, this is the first paper that presents a memristor based circuit for implementing CNN recognition. This is also the first paper that provides a circuit for precise memristor based analog convolution.


title: Paper-based nanobiosensors for diagnostics

abstract: In this review we discuss how nanomaterials can be integrated in diagnostic paper-based biosensors for the detection of proteins, nucleic acids and cells. In particular first the different types and properties of paper-based nanobiosensors and nanomaterials are briefly explained. Then several examples of their application in diagnostics of several biomarkers are reported. Finally our opinions regarding future trends in this field are discussed.


title: A design for high speed leading-zero counter

abstract: Leading-zero counter (LZC) is a basic component in floating point operation. This paper aims at speeding up the operation of LZC. Based on new derived Boolean functions and complex logic gates, 8-bit LZC circuit is accomplished. Moreover, 16-bit LZC architecture is implemented in Xilinx field-programmable gate array (FPGA) and 32-bit LZC structure is realized on the platform of Application Specific Integrated Circuit (ASIC) in 65nm technology. Synthesis results are obtained on their special platforms. By comparison, our proposed design is faster than the reported designs.


title: Anomaly Detection in Emergency Call Data The First Step to the Intelligent Emergency Call System Management

abstract: A collaborative Emergency call taking information system in the Czech Republic processes calls on the European 112 emergency number. Amounts of various incident records are stored in its databases. The data can be used for mining spatial and temporal anomalies. When such an anomalous situation is detected so that the system could suffer from local or temporal performance decrease, either a human, or an automatic management module could take measures to reconfigure the system traffic and balance its load. In this paper we describe a method of knowledge discovery and visualization with respect to the emergency call taking information system database characteristics. The method is based on Kohonen Self Organizing Map (SOM) algorithm. Transformations of categorical attributes into numeric values are proposed to prepare training set appropriate for successful SOM generation.


title: Multicasting in delay tolerant networks: a social network perspective

abstract: Node mobility and end-to-end disconnections in Delay Tolerant Networks (DTNs) greatly impair the effectiveness of data dissemination. Although social-based approaches can be used to address the problem, most existing solutions only focus on forwarding data to a single destination. In this paper, we are the first to study multicast in DTNs from the social network perspective. We study multicast in DTNs with single and multiple data items, investigate the essential difference between multicast and unicast in DTNs, and formulate relay selections for multicast as a unified knapsack problem by exploiting node centrality and social community structures. Extensive trace-driven simulations show that our approach has similar delivery ratio and delay to the Epidemic routing, but can significantly reduce the data forwarding cost measured by the number of relays used.


title: System-Level Design Considerations for Digital Pre-Distortion of Wireless Base Station Transmitters

abstract: Over the past 25 years or so there has been much interest in the use of digital pre-distortion (DPD) techniques for the linearization of RF and microwave power amplifiers. In this paper, we describe the important system and hardware requirements for the four main subsystems found in the DPD linearized transmitter: RF/analog, data converters, digital signal processing, and the DPD architecture and algorithms, and illustrate how the overall DPD system architecture is influenced by the design choices that may be made in each of these subsystems. We shall also consider the challenges presented to future applications of DPD systems for wireless communications, such as higher operating frequencies, wider signal bandwidths, greater spectral efficiency signals, resulting in higher peak-to-average power ratios, multiband and multimode operation, lower power consumption requirements, faster adaption, and how these affect the system design choices.


title: Large-scale semantic mapping and reasoning with heterogeneous modalities

abstract: This paper presents a probabilistic framework combining heterogeneous, uncertain, information such as object observations, shape, size, appearance of rooms and human input for semantic mapping. It abstracts multi-modal sensory information and integrates it with conceptual common-sense knowledge in a fully probabilistic fashion. It relies on the concept of spatial properties which make the semantic map more descriptive, and the system more scalable and better adapted for human interaction. A probabilistic graphical model, a chaingraph, is used to represent the conceptual information and perform spatial reasoning. Experimental results from online system tests in a large unstructured office environment highlight the system's ability to infer semantic room categories, predict existence of objects and values of other spatial properties as well as reason about unexplored space.


title: Mining MOOC Clickstreams: Video-Watching Behavior vs. In-Video Quiz Performance

abstract: Student video-watching behavior and quiz performance are studied in two Massive Open Online Courses (MOOCs). In doing so, two frameworks are presented by which video-watching clickstreams can be represented: one based on the sequence of events created, and another on the sequence of positions visited. With the event-based framework, recurring subsequences of student behavior are extracted, which contain fundamental characteristics such as reflecting (i.e., repeatedly playing and pausing) and revising (i.e., plays and skip backs). It is found that some of these behaviors are significantly correlated with changes in the likelihood that a student will be Correct on First Attempt (CFA) or not in answering quiz questions, and in ways that are not necessarily intuitive. Then, with the position-based framework, models of quiz performance are devised based on positions visited in a video. In evaluating these models through CFA prediction, it is found that three of them can substantially improve prediction quality, which underlines the ability to relate this type of behavior to quiz scores. Since this prediction considers videos individually, these benefits also suggest that these models are useful in situations where there is limited training data, e.g., for early detection or in short courses.


title: Learning from Demonstration

abstract: By now it is widely accepted that learning a task from scratch, i.e., without any prior knowledge, is a daunting undertaking. Humans, however, rarely attempt to learn from scratch. They extract initial biases as well as strategies how to approach a learning problem from instructions and/or demonstrations of other humans. For teaming control, this paper investigates how learning from demonstration can be applied in the context of reinforcement learning. We consider priming the Q-function, the value function, the policy, and the model of the task dynamics as possible areas where demonstrations can speed up learning. In general nonlinear learning problems, only model-based reinforcement learning shows significant speed-up after a demonstration, while in the special case of linear quadratic regulator (LQR) problems, all methods profit from the demonstration. In an implementation of pole balancing on a complex anthropomorphic robot arm, we demonstrate that, when facing the complexities of real signal processing, model-based reinforcement learning offers the most robustness for LQR problems. Using the suggested methods, the robot learns pole balancing in just a single trial after a 30 second long demonstration of the human instructor.


title: Segmenting Two-Sided Markets

abstract: Recent years have witnessed the rise of many successful e-commerce marketplace platforms like the Amazon marketplace, AirBnB, Uber/Lyft, and Upwork, where a central platform mediates economic transactions between buyers and sellers. A common feature of many of these two-sided marketplaces is that the platform has full control over search and discovery, but prices are determined by the buyers and sellers. Motivated by this, we study the algorithmic aspects of market segmentation via directed discovery in two-sided markets with endogenous prices. We consider a model where an online platform knows each buyer/seller's characteristics, and associated demand/supply elasticities. Moreover, the platform can use discovery mechanisms (search, recommendation, etc.) to control which buyers/sellers are visible to each other. We develop efficient algorithms for throughput (i.e. volume of trade) and welfare maximization with provable guarantees under a variety of assumptions on the demand and supply functions. We also test the validity of our assumptions on demand curves inferred from NYC taxicab log-data, as well as show the performance of our algorithms on synthetic experiments.


title: A systematic review on regression test selection techniques

abstract: Regression testing is verifying that previously functioning software remains after a change. With the goal of finding a basis for further research in a joint industry-academia research project, we conducted a systematic review of empirical evaluations of regression test selection techniques. We identified 27 papers reporting 36 empirical studies, 21 experiments and 15 case studies. In total 28 techniques for regression test selection are evaluated. We present a qualitative analysis of the findings, an overview of techniques for regression test selection and related empirical evidence. No technique was found clearly superior since the results depend on many varying factors. We identified a need for empirical studies where concepts are evaluated rather than small variations in technical implementations.


title: Discovering popular routes from trajectories

abstract: The booming industry of location-based services has accumulated a huge collection of users' location trajectories of driving, cycling, hiking, etc. In this work, we investigate the problem of discovering the Most Popular Route (MPR) between two locations by observing the traveling behaviors of many previous users. This new query is beneficial to travelers who are asking directions or planning a trip in an unfamiliar city/area, as historical traveling experiences can reveal how people usually choose routes between locations. To achieve this goal, we firstly develop a Coherence Expanding algorithm to retrieve a transfer network from raw trajectories, for indicating all the possible movements between locations. After that, the Absorbing Markov Chain model is applied to derive a reasonable transfer probability for each transfer node in the network, which is subsequently used as the popularity indicator in the search phase. Finally, we propose a Maximum Probability Product algorithm to discover the MPR from a transfer network based on the popularity indicators in a breadth-first manner, and we illustrate the results and performance of the algorithm by extensive experiments.


title: Effects of Intensive Phonomotor Treatment on Reading in Eight Individuals With Aphasia and Phonological Alexia

abstract: Purpose The aim of this study was to investigate effects of a multimodal treatment of phonology, phonomotor treatment, on the reading abilities of persons with aphasia (PWA) with phonological alexia. Method In a retrospective, single-group design, this study presents pre-, post-, and 3-months posttreatment data for 8 PWA with phonological alexia. Participants completed 60 hr of phonomotor treatment over 6 weeks. Wilcoxon signed-ranks tests and group effect sizes comparing pre-, immediately post-, and 3-months posttreatment performance on tests of phonological processing and reading were performed. Results Group data showed phonological processing and oral reading of real words and nonwords improved significantly posttreatment; these gains were maintained 3 months later. No group improvement was found for reading comprehension; however, one individual did show improvement immediately post- and 3-months posttreatment. Conclusions This study provides support that phonomotor treatment is a viable approach to ...


title: Systematic review of the health benefits of physical activity and fitness in school-aged children and youth

abstract: BackgroundThe purpose was to: 1) perform a systematic review of studies examining the relation between physical activity, fitness, and health in school-aged children and youth, and 2) make recommendations based on the findings.MethodsThe systematic review was limited to 7 health indicators: high blood cholesterol, high blood pressure, the metabolic syndrome, obesity, low bone density, depression, and injuries. Literature searches were conducted using predefined keywords in 6 key databases. A total of 11,088 potential papers were identified. The abstracts and full-text articles of potentially relevant papers were screened to determine eligibility. Data was abstracted for 113 outcomes from the 86 eligible papers. The evidence was graded for each health outcome using established criteria based on the quantity and quality of studies and strength of effect. The volume, intensity, and type of physical activity were considered.ResultsPhysical activity was associated with numerous health benefits. The dose-response relations observed in observational studies indicate that the more physical activity, the greater the health benefit. Results from experimental studies indicate that even modest amounts of physical activity can have health benefits in high-risk youngsters (e.g., obese). To achieve substantive health benefits, the physical activity should be of at least a moderate intensity. Vigorous intensity activities may provide even greater benefit. Aerobic-based activities had the greatest health benefit, other than for bone health, in which case high-impact weight bearing activities were required.ConclusionThe following recommendations were made: 1) Children and youth 5-17 years of age should accumulate an average of at least 60 minutes per day and up to several hours of at least moderate intensity physical activity. Some of the health benefits can be achieved through an average of 30 minutes per day. [Level 2, Grade A]. 2) More vigorous intensity activities should be incorporated or added when possible, including activities that strengthen muscle and bone [Level 3, Grade B]. 3) Aerobic activities should make up the majority of the physical activity. Muscle and bone strengthening activities should be incorporated on at least 3 days of the week [Level 2, Grade A].


title: AQUA: an aquatic walking robot

abstract: This paper describes an underwater walking robotic system being developed under the name AQUA, the goals of the AQUA project, the overall hardware and software design, the basic hardware and sensor packages that have been developed, and some initial experiments. The robot is based on the RHex hexapod robot and uses a suite of sensing technologies, primarily based on computer vision and INS, to allow it to navigate and map clear shallow-water environments. The sensor-based navigation and mapping algorithms are based on the use of both artificial floating visual and acoustic landmarks as well as on naturally occurring underwater landmarks and trinocular stereo.


Design and Implementation of Single Phase Inverter


title: The Secretion of miR-200s by a PKCζ/ADAR2 Signaling Axis Promotes Liver Metastasis in Colorectal Cancer

abstract: Summary Most colorectal cancer (CRC)-related deaths are due to liver metastases. PKCζ is a tumor suppressor in CRC with reduced expression in metastasis. Given the importance of microRNAs (miRNAs) in regulating cellular plasticity, we performed an unbiased screening and identified the miR-200 family as the most relevant miRNAs downregulated by PKCζ deficiency. The regulation of the intracellular levels of miR-200 by PKCζ is post-transcriptional and involves their secretion in extracellular vesicles. Here, we identified ADAR2 as a direct substrate of PKCζ in CRC cells. Phosphorylation of ADAR2 regulates its editing activity, which is required to maintain miR-200 steady-state levels, suggesting that the PKCζ/ADAR2 axis regulates miR-200 secretion through RNA editing. Loss of this axis results in epithelial-to-mesenchymal transition (EMT) and increased liver metastases, which can be inhibited in vivo by blocking miR-200 release. Therefore, the PKCζ/ADAR2 axis is a critical regulator of CRC metastases through modulation of miR-200 levels.


title: Combining multispectral aerial imagery and digital surface models to extract urban buildings

abstract: This paper presents an automated classification of buildings in Coleraine, Northern Ireland. The classification was generated using very high spatial resolution data (10 cm) from a Digital Mapping Camera (DMC) for March 2009. The visible to near infrared (VNIR) bands of the DMC enabled a supervised classification to be performed to extract buildings from vegetation. A Digital Surface Model (DSM) was also created from the image to differentiate between buildings and other land classes with similar spectral profiles, such as roads. The supervised classification had the lowest classification accuracy (50%) while the DSM had an accuracy of 81%. The combination of the DSM and the supervised classification achieved an overall classification accuracy of 95%. Two spatial metrics (percentage of the landscape and number of patches) were also used to test the level of agreement between the classification and digitised building data. The results suggest that fine resolution multispectral aerial imagery can automatically detect buildings to a very high level of accuracy. Current space borne sensors, such as IKONOS and QuickBird, lag behind airborne sensors with VNIR bands provided at a much coarser spatial resolution (4m and 2.4m respectively). Techniques must be developed from current airborne sensors that can be applied to new space borne sensors in the future. The ability to generate DSMs from high resolution aerial imagery will afford new insights into the three-dimensional aspects of urban areas which will in turn inform future urban planning.


Eye gaze tracking techniques for interactive applications q


title: Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving

abstract: Autonomous driving has harsh requirements of small model size and energy efficiency, in order to enable the embedded system to achieve real-time on-board object detection. Recent deep convolutional neural network based object detectors have achieved state-of-the-art accuracy. However, such models are trained with numerous parameters and their high computational costs and large storage prohibit the deployment to memory and computation resource limited systems. Low-precision neural networks are popular techniques for reducing the computation requirements and memory footprint. Among them, binary weight neural networks (BWNs) are the extreme case which quantizes the float-point into just 1 bit. BWNs are difficult to train and suffer from accuracy deprecation due to the extreme low-bit representation. To address this problem, we propose a knowledge transfer (KT) method to aid the training of BWN using a full-precision teacher network. We built DarkNet- and MobileNet-based binary weight YOLOv2 detectors and conduct experiments on KITTI benchmark for car, pedestrian and cyclist detection. The experimental results show that the proposed method maintains high detection accuracy while reducing the model size of DarkNet-YOLO from 257 MB to 8.8 MB and MobileNet-YOLO from 193 MB to 7.9 MB.


title: Study on Two-Dimensional Sparse MIMO UWB Arrays for High Resolution Near-Field Imaging

abstract: A novel generic topology for two-dimensional (2-D) sparse multiple-input-multiple-output (MIMO) ultrawideband (UWB) arrays is suggested. Based on the proposed topology, a 2-D MIMO UWB array for high-resolution short-range imaging is developed. The focusing properties of this array are studied both theoretically and experimentally and are shown to be superior to those of arrays with a similar number of antennas and based on known topologies such as Mills Cross, rectangular, and spiral configurations. Decisive impact of a large operational bandwidth and short focusing distance on MIMO array performance is shown. Imaging capabilities of the proposed array are experimentally demonstrated for distributed targets.


title: Music and mirror neurons: from motion to ’e’motion

abstract: The ability to create and enjoy music is a universal human trait and plays an important role in the daily life of most cultures. Music has a unique ability to trigger memories, awaken emotions and to intensify our social experiences. We do not need to be trained in music performance or appreciation to be able to reap its benefits—already as infants, we relate to it spontaneously and effortlessly. There has been a recent surge in neuroimaging investigations of the neural basis of musical experience, but the way in which the abstract shapes and patterns of musical sound can have such profound meaning to us remains elusive. Here we review recent neuroimaging evidence and suggest that music, like language, involves an intimate coupling between the perception and production of hierarchically organized sequential information, the structure of which has the ability to communicate meaning and emotion. We propose that these aspects of musical experience may be mediated by the human mirror neuron system.


title: Shape matching and object recognition using shape contexts

abstract: We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by: (1) solving for correspondences between points on the two shapes; (2) using the correspondences to estimate an aligning transform. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts, enabling us to solve for correspondences as an optimal assignment problem. Given the point correspondences, we estimate the transformation that best aligns the two shapes; regularized thin-plate splines provide a flexible class of transformation maps for this purpose. The dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points, together with a term measuring the magnitude of the aligning transform. We treat recognition in a nearest-neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image. Results are presented for silhouettes, trademarks, handwritten digits, and the COIL data set.


title: Are memory complaints predictive for dementia A review of clinical and population-based studies.

abstract: Objectives. To review studies that have reported on the prevalence of memory complaints and the relationship between memory complaints and impairment or decline (dementia) in elderly individuals ::: ::: Data sources and study selection. All publications in the English language relating to memory complaints, memory impairment, cognitive disorder and dementia in MEDLINE, PSYCHLIT and EMBASE computerized databases, together with a search of relevant citations. ::: ::: Data synthesis. The prevalence of memory complaints, defined as everyday memory problems, shows a large variation of approximately 25 – 50%. A high age, female gender and a low level of education are generally associated with a high prevalence of memory complaints. In community-based samples of elderly subjects an association has been found between memory complaints and memory impairment, after adjustment for depressive symptomatology. Memory complaints predict dementia after a follow-up of at least 2 years, in particular in those with mild cognitive impairment, defined as Mini Mental State Examination (MMSE) > 23. Memory complaints in highly educated elderly subjects may be predictive of dementia even when there is no indication of cognitive impairment on short cognitive screen tests. The shift in methodology which is noticeable in the recently published major studies is discussed as a possible explanation for the established association between memory complaints and decline in memory (or dementia) in elderly subjects. Three methodological factors, in particular, are responsible for the results: community-based sampling, longitudinal design and the treatment of variables such as depression, cognitive impairment and level of education. ::: ::: Conclusion. Memory complaints in elderly people should no longer be considered merely as an innocent age-related phenomenon or a symptom of depression. Instead, these complaints deserve to be taken seriously, at least as a possible early sign of dementia. Copyright © 2000 John Wiley & Sons, Ltd.


title: Believing Is Seeing: The Effects of Racial Labels and Implicit Beliefs on Face Perception

abstract: Two studies tested whether racial category labels and lay beliefs about human traits have a combined effect on people’s perception of, and memory for, racially ambiguous faces. Participants saw a morphed target face accompanied by a racial label (Black or White). Later, they were asked to identify the face from a set of two new morphed faces, one more Black and the other more White than the target. As predicted, entity theorists, who believe traits are immutable, perceived and remembered the target face as consistent with the racial label, whereas incremental theorists, who believe traits are malleable, perceived and remembered the face as inconsistent with the racial label. In Study 2, participants also drew the target face more consistently (entity theorists) or less consistently (incremental theorists) with the racial label. Results of both studies confirm that social variables can affect how physical features are seen and remembered.


title: Behavioral modeling and simulation of jitter and phase noise in fractional-N PLL frequency synthesizer

abstract: A methodology is presented for predicting the phase noise and jitter of a fractional-N PLL based frequency synthesizer. Based on the phase/jitter properties extracted from transistor level through simulation, a voltage-domain behavioral model can give phase noise performance of fractional-N PLL frequency synthesizers in system level accurately, while the simulation efficiency is also improved by merging the VCO block operated at the highest frequency into those operated at lower frequency. Comparing to phase-domain simulation, the improved voltage-domain models do a better job of capturing the details of the behavior of the loop, details such as the signal capturing and escaping traces in fractional-N frequency synthesizer.


title: Targeted Next‐Generation Sequencing Analysis of 1,000 Individuals with Intellectual Disability

abstract: To identify genetic causes of intellectual disability (ID), we screened a cohort of 986 individuals with moderate to severe ID for variants in 565 known or candidate ID-associated genes using targeted next-generation sequencing. Likely pathogenic rare variants were found in ∼11% of the cases (113 variants in 107/986 individuals: ∼8% of the individuals had a likely pathogenic loss-of-function [LoF] variant, whereas ∼3% had a known pathogenic missense variant). Variants in SETD5, ATRX, CUL4B, MECP2, and ARID1B were the most common causes of ID. This study assessed the value of sequencing a cohort of probands to provide a molecular diagnosis of ID, without the availability of DNA from both parents for de novo sequence analysis. This modeling is clinically relevant as 28% of all UK families with dependent children are single parent households. In conclusion, to diagnose patients with ID in the absence of parental DNA, we recommend investigation of all LoF variants in known genes that cause ID and assessment of a limited list of proven pathogenic missense variants in these genes. This will provide 11% additional diagnostic yield beyond the 10%–15% yield from array CGH alone.


title: Sliding Mode Control for Trajectory Tracking of . . .

abstract: Nonholonomic mobile robots have constraints imposed on the motion that are not integrable, i.e., the constraints cannot be written as time derivatives of some function of the generalized coordinates. The position control of nonholonomic mobile robots has been an important class of control problems. In this paper, we propose a robust tracking control of nonholonomic wheeled mobile robots using sliding mode. The posture of a mobile robot is represented by polar coordinates and the dynamic equation of the robot is feedback-linearized by the computed-torque method. A novel sliding mode control law is proposed for asymptotically stabilizing the mobile robot to a desired trajectory. It is shown that the proposed scheme is robust to bounded external disturbances. Experimental results demonstrate the effectiveness of accurate tracking capability and the robust performance of the proposed scheme.


title: A 10 MHz, 48-to-5V synchronous converter with dead time enabled 125 ps resolution zero-voltage switching

abstract: An integrated synchronous buck converter with a high resolution dead time control for input voltages up to 48V and 10MHz switching frequency is presented. The benefit of an enhanced dead time control at light loads to enable zero voltage switching at both the high-side and low-side switch at low output load is studied. This way, compact multi-MHz DCDC converters can be implemented at high efficiency over a wide load current range. The concept also eliminates body diode forward conduction losses and minimizes reverse recovery losses. A dead time resolution of 125 ps is realized by an 8-bit differential delay chain. A further efficiency enhancement by soft switching at the high-side switch at light load is achieved with a voltage boost of the switching node by dead time control in forced continuous conduction mode. The monolithic converter is implemented in an 180nm high-voltage BiCMOS technology. At V IN = 48V, V OUT = 5V, 50mA load, 10MHz switching frequency and 500 nH output inductance, the efficiency is measured to be increased by 14.4% compared to a conventional predictive dead time control. A peak efficiency of 80.9% is achieved at 12V input.


Frisian Reference Grammar


title: Immunoblot assay for serodiagnosis of Helicobacter pylori infections.

abstract: An immunoblot assay for the serological diagnosis of Helicobacter pylori infection was evaluated. Serum samples from patients whose gastric biopsy specimens were known to be positive or negative for H. pylori on culture were used to establish interpretive criteria for the immunoblot assay. A panel of sera from patients with diseases other than H. pylori infection and sera from healthy blood donors were included to validate these criteria. All sera were initially assessed in an enzyme immunoassay (Ge-EIA), based on acid glycine-extracted cell surface proteins of H. pylori NCTC 11637. The same antigen extract was used in the immunoblot assay. In addition, the Ge-EIA and the immunoblot assay were compared with a commercially available EIA (Seradyn, Color Vue Pylori). Bands of 110/120 kDa and/or two of five low-molecular-mass proteins (26, 29, 30, 31, and 33 kDa, in any combination) showed a strong correlation with the H. pylori culture-positive patients (97.5%) compared to the correlation obtained with the EIA results (Ge-EIA, 87.5%; Seradyn EIA, 92.5%), and the antibody responses to these proteins were considered specific reactions. In 37 of 40 serum samples from culture-negative patients and also in sera from patients with other disorders, a moderate antibody reactivity to the medium-size proteins (43 to 66 kDa) was observed, and these were considered not valuable for a specific immunoblot assay. Among sera from culture-positive patients, 39 of 40 serum samples were defined to be immunoblot positive, and from among sera from culture-negative patients, 3 of 40 serum samples were defined to be immunoblot positive. The use of sera from patients with negative cultures for H. pylori as negative controls may decrease the sensitivity due to sampling error and false-negative culture results. Immunoblot assay-positive results were detected among 10% of sera from patients with other diseases, whereas they were detected among 42.5% of sera by the Ge-EIA and 47.5% of sera by the Seradyn-EIA. The higher number of EIA-positive sera in this group reflects a possible cross-reactivity (false-positive EIA result). Of the blood donors, representing asymptomatic but possibly colonized subjects, 24% were immunoblot positive. In conclusion, our data indicate that immunoblotting is more sensitive as well as more specific than EIA. Moreover, it permits detection of antibody responses to specific antigens, e.g., the cytotoxin-associated CagA protein, which may have pathological implications.


title: $1.00 per RT #BostonMarathon #PrayForBoston: Analyzing fake content on Twitter

abstract: Online social media has emerged as one of the prominent channels for dissemination of information during real world events. Malicious content is posted online during events, which can result in damage, chaos and monetary losses in the real world. We analyzed one such media i.e. Twitter, for content generated during the event of Boston Marathon Blasts, that occurred on April, 15th, 2013. A lot of fake content and malicious profiles originated on Twitter network during this event. The aim of this work is to perform in-depth characterization of what factors influenced in malicious content and profiles becoming viral. Our results showed that 29% of the most viral content on Twitter, during the Boston crisis were rumors and fake content; while 51% was generic opinions and comments; and rest was true information.We found that large number of users with high social reputation and verified accounts were responsible for spreading the fake content. Next, we used regression prediction model, to verify that, overall impact of all users who propagate the fake content at a given time, can be used to estimate the growth of that content in future. Many malicious accounts were created on Twitter during the Boston event, that were later suspended by Twitter. We identified over six thousand such user profiles, we observed that the creation of such profiles surged considerably right after the blasts occurred. We identified closed community structure and star formation in the interaction network of these suspended profiles amongst themselves.


title: Compact 28-GHz phased array antenna for 5G access

abstract: Requirements of upcoming 5G millimeter-wave communications, such as high antenna gain and beamforming agility, are addressed by designing, manufacturing and measuring a compact and scalable phased array antenna for 28-GHz access network. The antenna is based on a multi-layer printed circuit board (PCB) with 64 radiating elements and 16 commercial quad-core monolithic microwave integrated circuits (MMICs) providing independent 5-bit phase and amplitude controls for each radiating element. The focus is on measured radiation patterns, which show good characteristics (beamwidth, side lobe level, cross-polar discrimination, beam steering range) and correspondence with theory, even without calibration. The antenna reaches effective isotropic radiated power (EIRP) up to 48 dBm. Measurements with real modulated signal confirm the feasibility of the antenna for multi-Gbps communications.


title: Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies

abstract: Intelligent behaviour in the real-world requires the ability to acquire new knowledge from an ongoing sequence of experiences while preserving and reusing past knowledge. We propose a novel algorithm for unsupervised representation learning from piece-wise stationary visual data: Variational Autoencoder with Shared Embeddings (VASE). Based on the Minimum Description Length principle, VASE automatically detects shifts in the data distribution and allocates spare representational capacity to new knowledge, while simultaneously protecting previously learnt representations from catastrophic forgetting. Our approach encourages the learnt representations to be disentangled, which imparts a number of desirable properties: VASE can deal sensibly with ambiguous inputs, it can enhance its own representations through imagination-based exploration, and most importantly, it exhibits semantically meaningful sharing of latents between different datasets. Compared to baselines with entangled representations, our approach is able to reason beyond surface-level statistics and perform semantically meaningful cross-domain inference.


title: Result Diversification in Automatic Citation Recommendation

abstract: The increase in the number of published papers each year makes manual literature search inefficient and furthermore insufficient. Hence, automatized reference/citation recommendation have been of interest in the last 3-4 decades. Unfortunately, some of the developed approaches, such as keyword-based ones, are prone to ambiguity and synonymy. On the other hand, using the citation information does not suffer from the same problems since they do not consider textual similarity. Today, obtaining the desired information is as hard as looking for a needle in a haystack. And sometimes, we want that small haystack, e.g., a small result set containing only a few recommendations, cover all the important and relevant parts of the literature. That is, the set should be diversified enough. Here, we investigate the problem of result diversification in automatic citation recommendation. We enhance existing techniques, which were designed to recommend a set of citations with satisfactory quality and diversity, with direction-awareness to allow the users to reach either old, well-cited, well-known research papers or recent, less-known ones. We also propose some novel techniques for a better result diversification. Experimental results show that our techniques are very useful in automatic citation recommendation.


title: CLARINET: WAN-aware optimization for analytics queries

abstract: Recent work has made the case for geo-distributed analytics, where data collected and stored at multiple datacenters and edge sites world-wide is analyzed in situ to drive operational and management decisions. A key issue in such systems is ensuring low response times for analytics queries issued against geo-distributed data. A central determinant of response time is the query execution plan (QEP). Current query optimizers do not consider the network when deriving QEPs, which is a key drawback as the geo-distributed sites are connected via WAN links with heterogeneous and modest bandwidths, unlike intra-datacenter networks. We propose CLARINET, a novel WAN-aware query optimizer. Deriving a WAN-aware QEP requires working jointly with the execution layer of analytics frameworks that places tasks to sites and performs scheduling. We design efficient heuristic solutions in CLARINET to make such a joint decision on the QEP. Our experiments with a real prototype deployed across EC2 datacenters, and large-scale simulations using production workloads show that CLARINET improves query response times by ≥ 50% compared to state-of-the-art WAN-aware task placement and scheduling.


title: Effect of optimizing Java deployment artifacts on AWS Lambda

abstract: AWS Lambda (Amazon Web Services) is the most popular serverless architecture provided by Amazon. It currently supports three platforms: JavaScript, Python, and Java Virtual Machine (JVM). The JVM could be the most complicate platform among the three as there are many languages that target the JVM platform besides Java. In addition, the complex hierarchy of dependencies, versioning, and the class loader are major issues that could cause conflict in a project. Deployment in the context of a serverless architecture means deployment as a function that represents a single service rather than as an application that is comprised of many services. AWS Lambda requires a deployment artifact to be self-contained which means all resources and dependencies must be packaged into a single jar file, and this file could be larger than AWS Lambda's allowable limit. Developers usually use build tool plugins to make self-contained artifacts, and those tools are generally unaware of what class and resource files a function needs. As a result, the artifact is not optimized. This paper demonstrates that optimization of an artifact can in general improve its resource usage and runtime performance. This paper also reports the result of an anecdotal experiment regarding the overhead of calling functions remotely in order to support design decisions in the development of AWS Lambda.


title: The Effect of Different Writing Tasks on Linguistic Style: A Case Study of the ROC Story Cloze Task

abstract: A writer's style depends not just on personal traits but also on her intent and mental state. In this paper, we show how variants of the same writing task can lead to measurable differences in writing style. We present a case study based on the story cloze task (Mostafazadeh et al., 2016a), where annotators were assigned similar writing tasks with different constraints: (1) writing an entire story, (2) adding a story ending for a given story context, and (3) adding an incoherent ending to a story. We show that a simple linear classifier informed by stylistic features is able to successfully distinguish among the three cases, without even looking at the story context. In addition, combining our stylistic features with language model predictions reaches state of the art performance on the story cloze challenge. Our results demonstrate that different task framings can dramatically affect the way people write.


title: Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling

abstract: Knowledge distillation is an effective technique that transfers knowledge from a large teacher model to a shallow student. However, just like massive classification, large scale knowledge distillation also imposes heavy computational costs on training models of deep neural networks, as the softmax activations at the last layer involve computing probabilities over numerous classes. In this work, we apply the idea of importance sampling which is often used in Neural Machine Translation on large scale knowledge distillation. We present a method called dynamic importance sampling, where ranked classes are sampled from a dynamic distribution derived from the interaction between the teacher and student in full distillation. We highlight the utility of our proposal prior which helps the student capture the main information in the loss function. Our approach manages to reduce the computational cost at training time while maintaining the competitive performance on CIFAR-100 and Market-1501 person re-identification datasets.


title: Adversary Detection in Neural Networks via Persistent Homology

abstract: We outline a detection method for adversarial inputs to deep neural networks. By viewing neural network computations as graphs upon which information flows from input space to out- put distribution, we compare the differences in graphs induced by different inputs. Specifically, by applying persistent homology to these induced graphs, we observe that the structure of the most persistent subgraphs which generate the first homology group differ between adversarial and unperturbed inputs. Based on this observation, we build a detection algorithm that depends only on the topological information extracted during training. We test our algorithm on MNIST and achieve 98% detection adversary accuracy with F1-score 0.98.


title: It Takes Two to Tango: Deleted Stack Overflow Question Prediction with Text and Meta Features

abstract: Stack Overflow is a popular community-based Qaamp;A website that caters to technical needs of software developers. As of February 2015 -- Stack Overflow has more than 3.9M registered users, 8.8M questions, and 41M comments. Stack Overflow provides explicit and detailed guidelines on how to post questions but, some questions are very poor in quality. Such questions are deleted by the experienced community members and moderators. Deleted questions increase maintenance cost and have an adverse impact on the user experience. Therefore, predicting deleted questions is an important task. In this study, we propose a two stage hybrid approach -- DelPredictor -- which combines text processing and classification techniques to predict deleted questions. In the first stage, DelPredictor converts text in the title, body, and tag fields of questions into numerical textual features via text processing and classification techniques. In the second stage, it extracts meta features that can be categorized into: profile, community, content, and syntactic features. Next, it learns and combines two independent classifiers built on the textual and meta features. We evaluate DelPredictor on 5 years (2008 -- 2013) of deleted questions from Stack Overflow. Our experimental results show that DelPredictor improves the F1-scores over baseline prediction, a prior approach [12] and a text-based approach by 29.50%, 9.34%, and 28.11%, respectively.


title: Approaches to improve the resources management in the simulator cloudsim

abstract: In Cloud Computing, service availability and performance are two significant aspects to be dealt with. These two aspects can deteriorate or even stopping the services of Cloud Computing, if they are not taken into account. Users see that cloud computing delivers elastic computing services to users on the basis of their needs. This paper aims at improving operation of service of the Cloud Computing environment. Cloud service must be available some is the situations and powerful being by a response time reduced at a user's request. To meet this aim, we propose, in this paper, two approaches which aim at returning a better availability of Datacenters without deteriorating the performances for the answers of the users. The first uses the principle of the messages of availability and the second uses the principle of reservation in advance.


title: P.: Trust management through reputation mechanisms

abstract: The members of electronic communities are often unrelated to each other; they may have never met and have no information on each other's reputation. This kind of information is vital in electronic commerce interactions, where the potential counterpart's reputation can be a significant factor in the negotiation strategy. Two complementary reputation mechanisms are investigated which rely on collaborative rating and personalized evaluation of the various ratings assigned to each user. While these reputation mechanisms are developed in the context of electronic commerce, it is believed that they may have applicability in other types of electronic communities such as chatrooms, newsgroups, mailing lists, etc.


title: A three-dimensional requirements elicitation and management decisionmaking scheme for the development of new software components

abstract: Keywords: Software components development, Requirements elicitation, Management decision-making, ReusabilityAbstract: Requirements analysis and general management issues within the development process of new softwarecomponents are addressed in this paper, focusing on factors that result from requirements elicitation andsignificantly affect management decisions and development activities. A new methodology performing acertain form of requirements identification and collection prior to developing new software components isproposed and demonstrated, the essence of which lays on a three-entity model that describes the relationshipbetween different types of component stakeholders: Developers, reusers and end-users. The model issupported by a set of critical factors analysed in the context of three main directions that orient theproduction of a new component, that is, the generality of the services offered, the management approach andthe characteristics of the targeted market. The investigation of the three directions produces critical successfactors that are closely connected and interdependent. Further analysis of the significance of each factoraccording to the priorities set by component developers can provide a detail picture of potentialmanagement implications during the development process and more importantly can support managementdecisions related to if and how development should proceed.


title: Intelligent adaptive cruise control system design and implementation

abstract: Advanced driver assistance systems (ADAS) have a critical role in the development of the active safety systems for vehicles. There are various sub technologies like Adaptive cruise control (ACC), Collision avoidance system, Blind spot detection etc. under ADAS. All these technologies are also accepted as the preliminary technology of autonomous driving. Therefore, during development of these technologies using a system of system (SOS) control approach would help both decreasing the development costs and unifying all these technologies under autonomous driving. In this paper, a SOS based intelligent ACC system design is proposed. The ACC system has high level control, low level control and sensor units.


title: Structual analysis of ball-on-sphere system using bond graph technique

abstract: This paper presents the novel structural analysis of ball-on-sphere system using bond graph technique. The structural analyses carried out are controllability and observability of the system. To achieve these analyses, the system was modelled using bond graph modelling technique. In the modelling procedures, the various subsystems, storage elements, junction structures, transformer elements with appropriate causality assignments and energy exchange that make up the ball-on-sphere system were identified and modelled. The structural controllability and observability properties of the system were carried out on the developed causal bond graph model of the system based on bond graph rules. From the structural analyses, it was established that the developed model was controllable and observable.


title: QDflows: A System Driven by Knowledge Bases for Designing Quality-Aware Data flows

abstract: In the big data era, data integration is becoming increasingly important. It is usually handled by data flows processes that extract, transform, and clean data from several sources, and populate the data integration system (DIS). Designing data flows is facing several challenges. In this article, we deal with data quality issues such as (1) specifying a set of quality rules, (2) enforcing them on the data flow pipeline to detect violations, and (3) producing accurate repairs for the detected violations. We propose QDflows, a system for designing quality-aware data flows that considers the following as input: (1) a high-quality knowledge base (KB) as the global schema of integration, (2) a set of data sources and a set of validated users’ requirements, (3) a set of defined mappings between data sources and the KB, and (4) a set of quality rules specified by users. QDflows uses an ontology to design the DIS schema. It offers the ability to define the DIS ontology as a module of the knowledge base, based on validated users’ requirements. The DIS ontology model is then extended with multiple types of quality rules specified by users. QDflows extracts and transforms data from sources to populate the DIS. It detects violations of quality rules enforced on the data flows, constructs repair patterns, searches for horizontal and vertical matches in the knowledge base, and performs an automatic repair when possible or generates possible repairs. It interactively involves users to validate the repair process before loading the clean data into the DIS. Using real-life and synthetic datasets, the DBpedia and Yago knowledge bases, we experimentally evaluate the generality, effectiveness, and efficiency of QDflows. We also showcase an interactive tool implementing our system.


Breaking down the barriers: the gut microbiome, intestinal permeability and stress-related psychiatric disorders


title: Finger Kinematic Modeling and Real-Time Hand Motion Estimation

abstract: This paper describes methods and experimental studies concerned with quantitative reconstruction of finger movements in real-time, by means of multi-camera system and 24 surface markers. The approach utilizes a kinematic model of the articulated hand which consists in a hierarchical chain of rigid body segments characterized by 22 functional degrees of freedom and the global roto-translation. This work is focused on the experimental evaluation of a kinematical hand model for biomechanical analysis purposes. From a static posture, a completely automatic calibration procedure, based on anthropometric measures and geometric constraints, computes axes, and centers of rotations which are then utilized as the base of an interactive real-time animation of the hand model. The motion tracking, based on automatic marker labeling and predictive filter, is empowered by introducing constraints from functional finger postures. The validation is performed on four normal subjects through different right-handed motor tasks involving voluntary flexion-extension of the thumb, voluntary abduction-adduction of the thumb, grasping, and finger pointing. Performances are tested in terms of repeatability of angular profiles, model-based ability to predict marker trajectories and tracking success during real-time motion estimation. Results show intra-subject repeatability of the model calibration both to different postures and to re-marking in the range of 0.5 and 2 mm, respectively. Kinematic estimation proves satisfactory in terms of prediction capability (index finger: maximum RMSE 2.02 mm; thumb: maximum RMSE 3.25 mm) and motion reproducibility (R (2) coefficients--index finger: 0.96, thumb: 0.94). During fast grasping sequence (60 Hz), the percentage of residual marker occlusions is less than 1% and processing and visualization frequency of 50 Hz confirms the real-time capability of the motion estimation system.


title: Managing Distributed Innovation: Strategic Utilization of Open and User Innovation

abstract: Research from a variety of perspectives has argued that innovation no longer takes place within a single organization, but rather is distributed across multiple stakeholders in a value network. Here we contrast the vertically integrated innovation model to open innovation, user innovation, as well as other distributed processes (cumulative innovation, communities or social production, and co-creation), while we also discuss open source software and crowdsourcing as applications of the perspectives. We consider differences in the nature of distributed innovation, as well as its origins and its effects. From this, we contrast the predictions of the perspectives on the sources, motivation and value appropriation of external innovation, and thereby provide a framework for the strategic management of distributed innovation.


title: HFirst: A Temporal Approach to Object Recognition

abstract: This paper introduces a spiking hierarchical model for object recognition which utilizes the precise timing information inherently present in the output of biologically inspired asynchronous address event representation (AER) vision sensors. The asynchronous nature of these systems frees computation and communication from the rigid predetermined timing enforced by system clocks in conventional systems. Freedom from rigid timing constraints opens the possibility of using true timing to our advantage in computation. We show not only how timing can be used in object recognition, but also how it can in fact simplify computation. Specifically, we rely on a simple temporal-winner-take-all rather than more computationally intensive synchronous operations typically used in biologically inspired neural networks for object recognition. This approach to visual computation represents a major paradigm shift from conventional clocked systems and can find application in other sensory modalities and computational tasks. We showcase effectiveness of the approach by achieving the highest reported accuracy to date (97.5% $\pm$ 3.5%) for a previously published four class card pip recognition task and an accuracy of 84.9% $\pm$ 1.9% for a new more difficult 36 class character recognition task.


title: Composing OS extensions safely and efficiently with Bascule

abstract: Library OS (LibOS) architectures implement the OS personality as a user-mode library, giving each application the flexibility to choose its LibOS. This approach is appealing for many reasons, not least the ability to extend or customise the LibOS. Recent work with Drawbridge [29] showed that an existing commodity OS (Windows 7) could be refactored to produce a LibOS while retaining application compatibility. This paper presents Bascule, an architecture for LibOS extensions based on Drawbridge. Rather than relying on the application developer to customise a LibOS, Bascule allows OS-independent extensions to be attached at runtime. Extensions interpose on a narrow binary interface of primitive OS abstractions, such as files and virtual memory. Thus, they are independent of both guest and host OS, and composable at runtime. Since an extension runs in the same process as an application and its LibOS, it is safe and efficient. Bascule demonstrates extension reuse across diverse guest LibOSes (Windows and Linux) and host OSes (Windows and Barrelfish). Current extensions include file system translation, checkpointing, and architecture adaptation.


title: Becoming syntactic

abstract: Psycholinguistic research has shown that the influence of abstract syntactic knowledge on performance is shaped by particular sentences that have been experienced. To explore this idea, the authors applied a connectionist model of sentence production to the development and use of abstract syntax. The model makes use of (a) error-based learning to acquire and adapt sequencing mechanisms and (b) meaning-form mappings to derive syntactic representations. The model is able to account for most of what is known about structural priming in adult speakers, as well as key findings in preferential looking and elicited production studies of language acquisition. The model suggests how abstract knowledge and concrete experience are balanced in the development and use of syntax.


title: Complete EAP Method: User Efficient and Forward Secure Authentication Protocol for IEEE 802.11 Wireless LANs

abstract: It is necessary to authenticate users who attempt to access resources in Wireless Local Area Networks (WLANs). Extensible Authentication Protocol (EAP) is an authentication framework widely used in WLANs. Authentication mechanisms built on EAP are called EAP methods. The requirements for EAP methods in WLAN authentication have been defined in RFC 4017. To achieve user efficiency and robust security, lightweight computation and forward secrecy, excluded in RFC 4017, are desired in WLAN authentication. However, all EAP methods and authentication protocols designed for WLANs so far do not satisfy all of the above properties. This manuscript will present a complete EAP method that utilizes stored secrets and passwords to verify users so that it can 1) fully meet the requirements of RFC 4017, 2) provide for lightweight computation, and 3) allow for forward secrecy. In addition, we also demonstrate the security of our proposed EAP method with formal proofs.


title: Boosted Deep Neural Networks and Multi-resolution Cochleagram Features for Voice Activity Detection

abstract: Voice activity detection (VAD) is an important frontend of many speech processing systems. In this paper, we describe a new VAD algorithm based on boosted deep neural networks (bDNNs). The proposed algorithm first generates multiple base predictions for a single frame from only one DNN and then aggregates the base predictions for a better prediction of the frame. Moreover, we employ a new acoustic feature, multi-resolution cochleagram (MRCG), that concatenates the cochleagram features at multiple spectrotemporal resolutions and shows superior speech separation results over many acoustic features. Experimental results show that bDNN-based VAD with the MRCG feature outperforms state-of-the-art VADs by a considerable margin.


title: Enforcing dynamic spectrum access with spectrum permits

abstract: Dynamic spectrum access is a maturing technology that allows next generation wireless devices to make highly efficient use of wireless spectrum. Spectrum can be allocated on an on-demand basis for a given geographic location, time duration and frequency range. However, a major obstacle to adoption remains. There are no effective solutions to protect licensed users from spectrum misuse, where users transmit without properly licensing spectrum, and in doing so, interfere and disrupt legitimate flows to whom the spectrum is assigned. Given the flexibility of today's cognitive radios, an application can easily transmit on frequencies outside of its allocated range, either accidentally due to misconfiguration, or intentionally to avoid spectrum licensing costs. In this paper, we propose a system to secure dynamic spectrum transmissions, where authorized users embed secure spectrum permits into data transmissions, thus enabling patrolling trusted devices to detect devices transmitting without authorization. We focus our attention on the development of spectrum permits, and describe Gelato, a spectrum misuse detection system that minimizes both hardware costs and performance overhead on legitimate data transmissions.


title: Image Processing With Neural Networks - a Review

abstract: Abstract We review more than 200 applications of neural networks in image processing and discuss the present and possible future role of neural networks, especially feed-forward neural networks, Kohonen feature maps and Hopfield neural networks. The various applications are categorised into a novel two-dimensional taxonomy for image processing algorithms. One dimension specifies the type of task performed by the algorithm: preprocessing, data reduction/feature extraction, segmentation, object recognition, image understanding and optimisation. The other dimension captures the abstraction level of the input data processed by the algorithm: pixel-level, local feature-level, structure-level, object-level, object-set-level and scene characterisation. Each of the six types of tasks poses specific constraints to a neural-based approach. These specific conditions are discussed in detail. A synthesis is made of unresolved problems related to the application of pattern recognition techniques in image processing and specifically to the application of neural networks. Finally, we present an outlook into the future application of neural networks and relate them to novel developments.


title: Perception and Reasoning for Scene Understanding in Human-Robot Interaction Scenarios

abstract: In this paper, a combination of perception modules and reasoning engines is used for scene understanding in typical Human-Robot Interaction(HRI) scenarios. The major contribution of this work lies in a 3D object detection, recognition and pose estimation module, which can be trained using CAD models and works for noisy data, partial views and in cluttered scenes. This perception module is combined with first-order logic reasoning to provide a semantic description of scenes, which is used for process planning. This abstraction of the scene is an important concept in the design of intelligent robotic systems which can adapt to unstructured and rapidly changing environments since it provides a separation of the process planning problem from its execution and scenario-specific parameters. This work is aimed at HRI applications in industrial settings and has been evaluated in several experiments and demonstration scenarios for autonomous process plan execution, humanrobot interaction and co-operation.


title: Towards Pose Robust Face Recognition

abstract: Most existing pose robust methods are too computational complex to meet practical applications and their performance under unconstrained environments are rarely evaluated. In this paper, we propose a novel method for pose robust face recognition towards practical applications, which is fast, pose robust and can work well under unconstrained environments. Firstly, a 3D deformable model is built and a fast 3D model fitting algorithm is proposed to estimate the pose of face image. Secondly, a group of Gabor filters are transformed according to the pose and shape of face image for feature extraction. Finally, PCA is applied on the pose adaptive Gabor features to remove the redundances and Cosine metric is used to evaluate the similarity. The proposed method has three advantages: (1) The pose correction is applied in the filter space rather than image space, which makes our method less affected by the precision of the 3D model, (2) By combining the holistic pose transformation and local Gabor filtering, the final feature is robust to pose and other negative factors in face recognition, (3) The 3D structure and facial symmetry are successfully used to deal with self-occlusion. Extensive experiments on FERET and PIE show the proposed method outperforms state-of-the-art methods significantly, meanwhile, the method works well on LFW.


title: Strength training effects on physical performance and serum hormones in young soccer players

abstract: To determine the effects of simultaneous explosive strength and soccer training in young men, 8 experimental (S) and 11 control (C) players, aged 17.2 (0.6) years, were tested before and after an 11-week training period with respect to the load-vertical jumping curve [loads of 0-70 kg (counter-movement jump CMJ0-70)], 5- and 15-m sprint performances, submaximal running endurance and basal serum concentrations of testosterone, free testosterone and cortisol. In the S group, the 11-week training resulted in significant increases in the low-force portion of the load-vertical jumping curve (5-14% in CMJ0-30, P<0.01) and in resting serum total testosterone concentrations (7.5%, P<0.05), whereas no changes were observed in sprint running performance, blood lactate during submaximal running, resting serum cortisol and resting serum free testosterone concentrations. In the C group, no changes were observed during the experimental period. In the S group, the changes in CMJ0 correlated ( P<0.05-0.01) with the changes in the 5-m ( r=0.86) and 15-m ( r=0.92) sprints, whereas the changes in CMJ40 correlated negatively with the changes in the testosterone:cortisol ratio ( r=-0.84, -0.92, respectively, P<0.05). These data indicate that young trained soccer players with low initial strength levels can increase explosive strength by adding low-frequency, low-intensity explosive-type strength training. The inverse correlations observed between changes in CMJ40 and changes in the testosterone:cortisol ratio suggest that a transient drop in this ratio below 45% cannot always be interpreted as a sign of overstrain or neuroendocrine dysfunction.


title: Exploring the interdisciplinary evolution of a discipline: the case of Biochemistry and Molecular Biology

abstract: This study explores interdisciplinarity evolution of Biochemistry and Molecular Biology (BMB) over a one-hundred-year period on several fronts, namely: change in interdisciplinarity, identification of core disciplines, disciplinary emergence, and potential discipline detection, in order to assess the evolution of interdisciplinarity over time. Science overlay maps and a StreamGraph were used to visualize interdisciplinary evolution. Our study confirms that interdisciplinarity evolves mainly from neighbouring fields to distant cognitive areas and provides evidence of an increasing tendency of BMB researchers to cite literature from other disciplines. Additionally, from our results, we can see that the top potential interdisciplinary relations belong to distant disciplines of BMB; their share of references is small, but is increasing markedly. On the whole, these results confirm the dynamic nature of interdisciplinary relations, and suggest that current scientific problems are increasingly addressed using knowledge from a wide variety of disciplines.


title: Graph Databases

abstract: Discover how graph databases can help you manage and query highly connected data. With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems. Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution.Model data with the Cypher query language and property graph model Learn best practices and common pitfalls when modeling with graphs Plan and implement a graph database solution in test-driven fashion Explore real-world examples to learn how and why organizations use a graph database Understand common patterns and components of graph database architecture Use analytical techniques and algorithms to mine graph database information


title: Object Bank: An Object-Level Image Representation for High-Level Visual Recognition

abstract: It is a remarkable fact that images are related to objects constituting them. In this paper, we propose to represent images by using objects appearing in them. We introduce the novel concept of object bank (OB), a high-level image representation encoding object appearance and spatial location information in images. OB represents an image based on its response to a large number of pre-trained object detectors, or `object filters', blind to the testing dataset and visual recognition task. Our OB representation demonstrates promising potential in high level image recognition tasks. It significantly outperforms traditional low level image representations in image classification on various benchmark image datasets by using simple, off-the-shelf classification algorithms such as linear SVM and logistic regression. In this paper, we analyze OB in detail, explaining our design choice of OB for achieving its best potential on different types of datasets. We demonstrate that object bank is a high level representation, from which we can easily discover semantic information of unknown images. We provide guidelines for effectively applying OB to high level image recognition tasks where it could be easily compressed for efficient computation in practice and is very robust to various classifiers.


title: Exploiting representations from pre-trained convolutional neural networks for high-resolution remote sensing image retrieval

abstract: With the increasing amount of high-resolution remote sensing images, it becomes more and more urgent to retrieve remote sensing images from large archives efficiently. The existing methods are mainly based on shallow features to retrieve images, while shallow features are easily affected by artificial intervention. Recently, convolutional neural networks (CNNs) are capable of learning feature representations automatically, and CNNs pre-trained on large-scale datasets are generic. This paper exploits representations from pre-trained CNNs for high-resolution remote sensing image retrieval. CNN representations from AlexNet, VGGM, VGG16, and GoogLeNet are first transferred for high-resolution remote sensing images, and then CNN features are extracted via two approaches. One is extracting the outputs of high-level layers directly and the other is aggregating the outputs of mid-level layers by means of average pooling with different pooling regions. Given the generalization and high dimensionality of the CNN features, feature combination and feature compression are also adopted to improve the feature representation. Experimental results demonstrate that aggregated features with pooling region smaller than the feature map size perform excellently, especially for VGG16 and GoogLeNet. Shallow feature makes a great contribution to enhance the retrieval precision when combined with CNN features, and compressed features reduce redundancy effectively. Compared with the state-of-the-art methods, the proposed feature extraction methods are very simple, and the features are able to improve retrieval performance significantly.


title: Techniques for Highly Multiobjective Optimisation: Some Nondominated Points are Better than Others

abstract: The research area of evolutionary multiobjective optimization (EMO) is reaching better understandings of the properties and capabilities of EMO algorithms, and accumulating much evidence of their worth in practical scenarios. An urgent emerging issue is that the favoured EMO algorithms scale poorly when problems have "many" (e.g. five or more) objectives. One of the chief reasons for this is believed to be that, in many-objective EMO search, populations are likely to be largely composed of nondominated solutions. In turn, this means that the commonly-used algorithms cannot distinguish between these for selective purposes. However, there are methods that can be used validly to rank points in a nondominated set, and may therefore usefully underpin selection in EMO search. Here we discuss and compare several such methods. Our main finding is that simple variants of the often-overlooked "Average Ranking" strategy usually outperform other methods tested, covering problems with 5-20 objectives and differing amounts of inter-objective correlation.


title: Cycle time prediction and control based on production line status and manufacturing data mining

abstract: This study aims to develop a methodology for predicting cycle time based on domain knowledge and data mining algorithms given production status including WIP, throughput. The proposed model and derived rules were validated with real data and demonstrated its practical viability for supporting production planning decisions


title: Prodrugs: effective solutions for solubility, permeability and targeting challenges.

abstract: This 2-day inaugural conference on prodrugs was presented by Pharmaceutical Education Associates and covered recent developments in prodrug techniques to solve delivery and targeting issues in drug discovery and development. The speakers were drawn from industry and academia, and the conference was attended mostly by researchers working in the pharmaceutical and biotechnology industries. A number of topics were presented at the conference, from basic prodrug design and functional group considerations to drug metabolism involving cytochrome P450 enzymes, from increasing water solubility, bioavailability, permeability and stability to tumor targeting, from the development of new anti-inflammatory agents to anti-HIV agents, and from the use of transporters and receptor-mediated endocytosis in prodrug delivery to the use of gene therapy for enzyme delivery to cancer cells and tissues. Several case studies were presented including improved pharmaceutical products in the clinic and at various stages of development.


title: Semi-Dense 3D Reconstruction with a Stereo Event Camera

abstract: Event cameras are bio-inspired sensors that offer several advantages, such as low latency, high-speed and high dynamic range, to tackle challenging scenarios in computer vision. This paper presents a solution to the problem of 3D reconstruction from data captured by a stereo event-camera rig moving in a static scene, such as in the context of stereo Simultaneous Localization and Mapping. The proposed method consists of the optimization of an energy function designed to exploit small-baseline spatio-temporal consistency of events triggered across both stereo image planes. To improve the density of the reconstruction and to reduce the uncertainty of the estimation, a probabilistic depth-fusion strategy is also developed. The resulting method has no special requirements on either the motion of the stereo event-camera rig or on prior knowledge about the scene. Experiments demonstrate our method can deal with both texture-rich scenes as well as sparse scenes, outperforming state-of-the-art stereo methods based on event data image representations.


title: Query-focused Multi-Document Summarization: Combining a Topic Model with Graph-based Semi-supervised Learning

abstract: Graph-based learning algorithms have been shown to be an effective approach for query-focused multi-document summarization (MDS). In this paper, we extend the standard graph ranking algorithm by proposing a two-layer (i.e. sentence layer and topic layer) graph-based semi-supervised learning approach based on topic modeling techniques. Experimental results on TAC datasets show that by considering topic information, we can effectively improve the summary performance.


title: Reinforcement learning and human behavior

abstract: The dominant computational approach to model operant learning and its underlying neural activity is model-free reinforcement learning (RL). However, there is accumulating behavioral and neuronal-related evidence that human (and animal) operant learning is far more multifaceted. Theoretical advances in RL, such as hierarchical and model-based RL extend the explanatory power of RL to account for some of these findings. Nevertheless, some other aspects of human behavior remain inexplicable even in the simplest tasks. Here we review developments and remaining challenges in relating RL models to human operant learning. In particular, we emphasize that learning a model of the world is an essential step prior or in parallel to learning the policy in RL and discuss alternative models that directly learn a policy without an explicit world model in terms of state-action pairs.


title: Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances

abstract: The success of the supervised classification of remotely sensed images acquired over large geographical areas or at short time intervals strongly depends on the representativity of the samples used to train the classification algorithm and to define the model. When training samples are collected from an image or a spatial region that is different from the one used for mapping, spectral shifts between the two distributions are likely to make the model fail. Such shifts are generally due to differences in acquisition and atmospheric conditions or to changes in the nature of the object observed. To design classification methods that are robust to data set shifts, recent remote sensing literature has considered solutions based on domain adaptation (DA) approaches. Inspired by machine-learning literature, several DA methods have been proposed to solve specific problems in remote sensing data classification. This article provides a critical review of the recent advances in DA approaches for remote sensing and presents an overview of DA methods divided into four categories: 1) invariant feature selection, 2) representation matching, 3) adaptation of classifiers, and 4) selective sampling. We provide an overview of recent methodologies, examples of applications of the considered techniques to real remote sensing images characterized by very high spatial and spectral resolution as well as possible guidelines for the selection of the method to use in real application scenarios.


title: Perturbation On-Time (POT) Technique in Power Factor Correction (PFC) Controller for Low Total Harmonic Distortion and High Power Factor

abstract: The proposed perturbation on-time technique suppresses total harmonic distortion (THD) and, thus, improves the power factor in the power factor correction (PFC) controller. Besides, the adaptive control of the minimum off time by the proposed inhibit time control can improve efficiency even at low ac input voltage. Therefore, highly integrated PFC converter fabricated in the TSMC 800-V ultrahigh voltage process can achieve low THD of 6%, high PF of 99%, and high efficiency of 95% at the output power of 90 W.


title: Textile Microwave Components in Substrate Integrated Waveguide Technology

abstract: Although substrate integrated waveguide (SIW) technology is well established for the fabrication of microwave circuits on rigid printed circuit boards, and the first implementations of textile SIW antennas have recently appeared in literature, up to now, no complete set of SIW microwave components has been presented. Therefore, this paper describes the design, manufacturing, and testing of a new class of textile microwave components for wearable applications, implemented in SIW technology. After characterizing the adopted textile fabrics material in terms of electrical properties, it is shown that folded textile SIW components, such as interconnections, filters, and antennas form excellent building blocks for wearable microwave circuits, given their low profile, flexibility, and stable characteristics under bending and in proximity of the human body. Hence, they allow the full exploitation of the large area garments offered for the deployment of wearable electronics. Besides SIW interconnections, a folded textile SIW filter operating at 2.45 GHz is designed and tested. The filter combines excellent performance in the band of interest with good out-of-band rejection, even when accounting for the tolerances in the fabrication process. Finally, a folded SIW cavity-backed patch antenna is fabricated and experimentally verified in realistic operating conditions.


title: A consensus-based approach for platooning with inter-vehicular communications

abstract: Automated and coordinated vehicles' driving (platooning) is gaining more and more attention today and it represents a challenging scenario heavily relying on wireless Inter-Vehicular Communication (IVC). In this paper, we propose a novel controller for vehicle platooning based on consensus. Opposed to current approaches where the logical control topology is fixed a priori and the control law designed consequently, we design a system whose control topology can be reconfigured depending on the actual network status. Moreover, the controller does not require the vehicles to be radar equipped and automatically compensates outdated information caused by network delays. We define the control law and analyze it in both analytical and simulative way, showing its robustness in different network scenarios. We consider three different wireless network settings: uncorrelated Bernoullian losses, correlated losses using a Gilbert-Elliott channel, and a realistic traffic scenario with interferences caused by other vehicles. Finally, we compare our strategy with another state of the art controller. The results show the ability of the proposed approach to maintain a stable string of vehicles even in the presence of strong interference, delays, and fading conditions, providing higher comfort and safety for platoon drivers.1


title: Fragment-Based Approaches in Drug Discovery and Chemical Biology

abstract: Fragment-based approaches to finding novel small molecules that bind to proteins are now firmly established in drug discovery and chemical biology. Initially developed primarily in a few centers in the biotech and pharma industry, this methodology has now been adopted widely in both the pharmaceutical industry and academia. After the initial success with kinase targets, the versatility of this approach has now expanded to a broad range of different protein classes. Herein we describe recent fragment-based approaches to a wide range of target types, including Hsp90, β-secretase, and allosteric sites in human immunodeficiency virus protease and fanesyl pyrophosphate synthase. The role of fragment-based approaches in an academic research environment is also examined with an emphasis on neglected diseases such as tuberculosis. The development of a fragment library, the fragment screening process, and the subsequent fragment hit elaboration will be discussed using examples from the literature.


title: Trends in theory building and theory testing: A five-decade study of the Academy of Management Journal.

abstract: We introduce a taxonomy that reflects the theoretical contribution of empirical articles along two dimensions: theory building and theory testing. We used that taxonomy to track trends in the theoretical contributions offered by articles over the past five decades. Results based on data from a sample of 74 issues of the Academy of Management Journal reveal upward trends in theory building and testing over time. In addition, the levels of theory building and testing within articles are significant predictors of citation rates. In particular, articles rated moderate to high on both dimensions enjoyed the highest levels of citations.


title: Open IoT Ecosystem for Sporting Event Management

abstract: By connecting devices, people, vehicles, and infrastructures everywhere in a city, governments and their partners can improve community well-being and other economic and financial aspects (e.g., cost and energy savings). Nonetheless, smart cities are complex ecosystems that comprise many different stakeholders (network operators, managed service providers, logistic centers, and so on), who must work together to provide the best services and unlock the commercial potential of the so-called Internet of Things (IoT). This is one of the major challenges that faces today’s smart city movement, and the emerging “API economy.” Indeed, while new smart connected objects hit the market every day, they mostly feed “vertical silos” (e.g., vertical apps, siloed apps, and so on) that are closed to the rest of the IoT, thus hampering developers to produce new added value across multiple platforms and/or application domains. Within this context, the contribution of this paper is twofold: 1) present the strategic vision and ambition of the EU to overcome this critical vertical silos’ issue and 2) introduce the first building blocks underlying an open IoT ecosystem developed as part of an EU (Horizon 2020) Project and a joint project initiative (IoT-EPI). The practicability of this ecosystem, along with a performance analysis, is carried out considering a proof-of-concept for enhanced sporting event management in the context of the forthcoming FIFA World Cup 2022 in Qatar.


title: Perceptions of Resource Criticality in Times of Resource Scarcity: The Case of University Departments

abstract: In this article the authors discuss research they conducted that examined the relationship between the perceptions of respondents about the scarcity of support staff and faculty at universities and the value they place on research. They note that their research builds on pervious studies that have examined perceptions on the criticality of personnel resources at colleges and universities and that their findings have implications related to the designing of departmental retrenchment strategies for universities. Their findings suggest that research universities place a greater value on support staff during times of resource scarcity, suggesting that if retrenchment is necessary these resources are valued and should be preserved.


title: A One-Layer Recurrent Neural Network With a Discontinuous Hard-Limiting Activation Function for Quadratic Programming

abstract: In this paper, a one-layer recurrent neural network with a discontinuous hard-limiting activation function is proposed for quadratic programming. This neural network is capable of solving a large class of quadratic programming problems. The state variables of the neural network are proven to be globally stable and the output variables are proven to be convergent to optimal solutions as long as the objective function is strictly convex on a set defined by the equality constraints. In addition, a sequential quadratic programming approach based on the proposed recurrent neural network is developed for general nonlinear programming. Simulation results on numerical examples and support vector machine (SVM) learning show the effectiveness and performance of the neural network.


title: Single-photon-sensitive solid-state image sensors for flash lidar

abstract: MIT Lincoln Laboratory has developed lidar systems based on Geiger-mode avalanche photodiodes using both silicon and InGaAs. This technology has enabled terrain mapping and foliage penetration systems with exquisite sensitivity and high area coverage rate.


title: Wideband and Low-Profile H-Plane Ridged SIW Horn Antenna Mounted on a Large Conducting Plane

abstract: This communication presents a wide-band and low-profile H-plane horn antenna based on ridged substrate integrated waveguide (SIW) with a large conducting ground. The horn antenna is implemented in a single substrate with a thickness of 0.13 λ0 at the center frequency. Despite its low profile, the new H-plane horn antenna achieves a very wide bandwidth by employing an arc-shaped copper taper printed on the extended dielectric slab and a three-step ridged SIW transition. The ridged SIW is critical for widening the operation bandwidth and lowering the characteristic impedance so that an excellent impedance matching from the coaxial probe to the narrow SIW can be obtained over a wide frequency range. Measured VSWR of the fabricated horn antenna is below 2.5 from 6.6 GHz to 18 GHz. The antenna also exhibits stable radiation beam over the same frequency range. It is observed that measured results agree well with simulated ones.