title: Myoelectrically controlled wrist robot for stroke rehabilitation

abstract: BackgroundRobot-assisted rehabilitation is an advanced new technology in stroke rehabilitation to provide intensive training. Post-stroke motor recovery depends on active rehabilitation by voluntary participation of patient’s paretic motor system as early as possible in order to promote reorganization of brain. However, voluntary residual motor efforts to the affected limb have not been involved enough in most robot-assisted rehabilitation for patients after stroke. The objective of this study is to evaluate the feasibility of robot-assisted rehabilitation using myoelectric control on upper limb motor recovery.MethodsIn the present study, an exoskeleton-type rehabilitation robotic system was designed to provide voluntarily controlled assisted torque to the affected wrist. Voluntary intention was involved by using the residual surface electromyography (EMG) from flexor carpi radialis(FCR) and extensor carpi radialis (ECR)on the affected limb to control the mechanical assistance provided by the robotic system during wrist flexion and extension in a 20-session training. The system also applied constant resistant torque to the affected wrist during the training. Sixteen subjects after stroke had been recruited for evaluating the tracking performance and therapeutical effects of myoelectrically controlled robotic system.ResultsWith the myoelectrically-controlled assistive torque, stroke survivors could reach a larger range of motion with a significant decrease in the EMG signal from the agonist muscles. The stroke survivors could be trained in the unreached range with their voluntary residual EMG on the paretic side. After 20-session rehabilitation training, there was a non-significant increase in the range of motion and a significant decrease in the root mean square error (RMSE) between the actual wrist angle and target angle. Significant improvements also could be found in muscle strength and clinical scales.ConclusionsThese results indicate that robot-aided therapy with voluntary participation of patient’s paretic motor system using myoelectric control might have positive effect on upper limb motor recovery.


title: Dynamic route planning framework for minimal air pollution exposure in urban road transportation systems

abstract: Personal human exposure to air pollution in urban areas and route planning to minimize it is a subject that has attracted much research interest in the last decades. Increase in vehicle density and greedy route planning algorithms considering only shortest distance might result in dense traffic junctions in an urban scenario. Increasing exposure of air pollution at these congested traffic junctions to the commuters is an essential factor of consideration for developing next-generation routing algorithms. In this paper, a dynamic route planning algorithm for an urban scenario is proposed to distribute the traffic density in real time to other low dense traffic junctions. The proposed framework tries to minimize the number of congested traffic junctions by uniformly distributing the real time traffic using diversion suggestions and thereby reducing dense air pollution at traffic junctions. Proposed integrated suggestion framework rely on Internet of Things (IoT) architecture for the real time data information and geographical datasets to reduce the amount of air pollution and time of exposure to the commuters passing through that junctions. The cost function based approach is proposed with pollution levels, junction connectivity and road type as the key parameters without neglecting the possible shortest distance metric. This framework provides alternate routes as suggestions with a trade-off of additional distance but less pollution to the commuters thereby minimizing the overall cost function of time, distance and pollution.


title: You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery

abstract: Detection of small objects in large swaths of imagery is one of the primary problems in satellite imagery analytics. While object detection in ground-based imagery has benefited from research into new deep learning approaches, transitioning such technology to overhead imagery is nontrivial. Among the challenges is the sheer number of pixels and geographic extent per image: a single DigitalGlobe satellite image encompasses>64 km2 and over 250 million pixels. Another challenge is that objects of interest are minuscule (often only ~10 pixels in extent), which complicates traditional computer vision techniques. To address these issues, we propose a pipeline (You Only Look Twice, or YOLT) that evaluates satellite images of arbitrary size at a rate of>0.5 km2/s. The proposed approach can rapidly detect objects of vastly different scales with relatively little training data over multiple sensors. We evaluate large test images at native resolution, and yield scores of F1>0.8 for vehicle localization. We further explore resolution and object size requirements by systematically testing the pipeline at decreasing resolution, and conclude that objects only ~5 pixels in size can still be localized with high confidence. Code is available at https://github.com/CosmiQ/yolt.


title: Scattered data interpolation and approximation for computer graphics

abstract: The goal of scattered data interpolation techniques is to construct a (typically smooth) function from a set of unorganized samples. These techniques have a wide range of applications in computer graphics. For instance they can be used to model a surface from a set of sparse samples, to reconstruct a BRDF from a set of measurements, to interpolate motion capture data, or to compute the physical properties of a fluid. This course will survey and compare scattered interpolation algorithms and describe their applications in computer graphics. Although the course is focused on applying these techniques, we will introduce some of the underlying mathematical theory and briefly mention numerical considerations.


title: A Framework for Passengers Demand Prediction and Recommendation

abstract: With the rapid development of mobile internet and wireless network technologies, more and more people use the mobile app to call a taxicab to pick them up. Therefore, understanding the passengers' travel demand becomes crucial to improve the utilization of the taxicabs and reduce their cost. In this paper, based on spatio-temporal clustering, we propose a demand hotspots prediction framework to generate recommendation for taxi drivers. Specially, an adaptive prediction approach is presented to demand hotspots and their hotness, and then combing the driver's location and the hotness, top candidates are recommended and visually presented to drivers. Based on the dataset provided by CAR INC., the experiment shows that our approach gains a significant improvement in hotspots prediction and recommendation, with 15.21% improvement on average f-measure for prediction and 79.6% hit ratio for recommendation.


title: NUMBER PLATE RECOGNITION USING OCR TECHNIQUE

abstract: Automatic Number Plate Recognition (ANPR) is a spec ial form of Optical Character Recognition (OCR). AN PR is an image processing technology which identifies the vehicle from its number plate automatically by digital pict ures. In this paper we have presented an algorithm for vehicle number identific ation based on Optical Character Recognition (OCR). OCR is used to recognize an optically processed printed character number pla te which is based on template matching. This algori thm is tested on different ambient illumination vehicle images. OCR is the las t stage in vehicle number plate recognition . In recognition stage the characters on the number plate are converted into texts. The char acters are then recognized using the template match ing algorithm. Index Terms : Automatic Number Plate Recognition (ANPR), Optical Character Recognition (OCR), Template Matching


title: Short Communication: EEG signals classification using the K-means clustering and a multilayer perceptron neural network model

abstract: We introduced a multilayer perceptron neural network (MLPNN) based classification model as a diagnostic decision support mechanism in the epilepsy treatment. EEG signals were decomposed into frequency sub-bands using discrete wavelet transform (DWT). The wavelet coefficients were clustered using the K-means algorithm for each frequency sub-band. The probability distributions were computed according to distribution of wavelet coefficients to the clusters, and then used as inputs to the MLPNN model. We conducted five different experiments to evaluate the performance of the proposed model in the classifications of different mixtures of healthy segments, epileptic seizure free segments and epileptic seizure segments. We showed that the proposed model resulted in satisfactory classification accuracy rates.


title: Towards deploying decommissioned mobile devices as cheap energy-efficient compute nodes

abstract: The performance of mobile phone processors has been steadily increasing, causing the performance gap between server and mobile processors to narrow with mobile processors sporting superior performance per unit energy. Fueled by the slowing of Moore's Law, the overall performance of single-chip mobile and server processors have likewise plateaued. These trends and the glut of used and partially broken smartphones which become environmental e-waste motivate creating cloud servers out of decommissioned mobile phones. This work proposes creating a compute dense server built out of used and partially broken smartphones (e.g. screen can be broken). This work evaluates the total cost of ownership (TCO) benefit of using servers based on decommissioned mobile devices and analyzes some of the architectural design trade-offs in creating such servers.


title: What is an Opinion About? Exploring Political Standpoints using Opinion Scoring Model

abstract: In this paper, we propose a generative model to automatically discover the hidden associations between topics words and opinion words. By applying those discovered hidden associations, we construct the opinion scoring models to extract statements which best express opinionists' standpoints on certain topics. For experiments, we apply our model to the political area. First, we visualize the similarities and dissimilarities between Republican and Democratic senators with respect to various topics. Second, we compare the performance of the opinion scoring models with 14 kinds of methods to find the best ones. We find that sentences extracted by our opinion scoring models can effectively express opinionists' standpoints.


Computer Vision Face Tracking For Use in a Perceptual User Interface


title: MEG and EEG data analysis with MNE-Python

abstract: Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne.


title: Questioning, promoting and evaluating the use of streaming video to support student learning

abstract: This paper uses case studies to describe how streaming video is currently used to support student learning in post compulsory education in the UK. It describes the current role of streaming video and identifies processes that could extend the application of streaming in education. It attempts to establish a case for more formal evaluation and communication of educa-tional processes involving streaming and identifies elements of a research agenda that could further develop the application of streaming technology in education.


title: A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a Discourse

abstract: This study addresses the problem of identifying the meaning of unknown words or entities in a discourse with respect to the word embedding approaches used in neural language models. We proposed a method for on-the-fly construction and exploitation of word embeddings in both the input and output layers of a neural model by tracking contexts. This extends the dynamic entity representation used in Kobayashi et al. (2016) and incorporates a copy mechanism proposed independently by Gu et al. (2016) and Gulcehre et al. (2016). In addition, we construct a new task and dataset called Anonymized Language Modeling for evaluating the ability to capture word meanings while reading. Experiments conducted using our novel dataset show that the proposed variant of RNN language model outperformed the baseline model. Furthermore, the experiments also demonstrate that dynamic updates of an output layer help a model predict reappearing entities, whereas those of an input layer are effective to predict words following reappearing entities.


title: Occupational safety and health criteria for responsible development of nanotechnology

abstract: Organizations around the world have called for the responsible development of nanotechnology. The goals of this approach are to emphasize the importance of considering and controlling the potential adverse impacts of nanotechnology in order to develop its capabilities and benefits. A primary area of concern is the potential adverse impact on workers, since they are the first people in society who are exposed to the potential hazards of nanotechnology. Occupational safety and health criteria for defining what constitutes responsible development of nanotechnology are needed. This article presents five criterion actions that should be practiced by decision–makers at the business and societal levels—if nanotechnology is to be developed responsibly. These include (1) anticipate, identify, and track potentially hazardous nanomaterials in the workplace; (2) assess workers’ exposures to nanomaterials; (3) assess and communicate hazards and risks to workers; (4) manage occupational safety and health risks; and (5) foster the safe development of nanotechnology and realization of its societal and commercial benefits. All these criteria are necessary for responsible development to occur. Since it is early in the commercialization of nanotechnology, there are still many unknowns and concerns about nanomaterials. Therefore, it is prudent to treat them as potentially hazardous until sufficient toxicology, and exposure data are gathered for nanomaterial-specific hazard and risk assessments. In this emergent period, it is necessary to be clear about the extent of uncertainty and the need for prudent actions.


title: Advances in Very Deep Convolutional Neural Networks for LVCSR

abstract: Very deep CNNs with small 3x3 kernels have recently been shown to achieve very strong performance as acoustic models in hybrid NN-HMM speech recognition systems. In this paper we investigate how to efficiently scale these models to larger datasets. Specifically, we address the design choice of pooling and padding along the time dimension which renders convolutional evaluation of sequences highly inefficient. We propose a new CNN design without timepadding and without timepooling, which is slightly suboptimal for accuracy, but has two significant advantages: it enables sequence training and deployment by allowing efficient convolutional evaluation of full utterances, and, it allows for batch normalization to be straightforwardly adopted to CNNs on sequence data. Through batch normalization, we recover the lost peformance from removing the time-pooling, while keeping the benefit of efficient convolutional evaluation. We demonstrate the performance of our models both on larger scale data than before, and after sequence training. Our very deep CNN model sequence trained on the 2000h switchboard dataset obtains 9.4 word error rate on the Hub5 test-set, matching with a single model the performance of the 2015 IBM system combination, which was the previous best published result.


title: Motor Imagery EEG Classification for Patients with Amyotrophic Lateral Sclerosis Using Fractal Dimension and Fisher’s Criterion-Based Channel Selection

abstract: Motor imagery is based on the volitional modulation of sensorimotor rhythms (SMRs); however, the sensorimotor processes in patients with amyotrophic lateral sclerosis (ALS) are impaired, leading to degenerated motor imagery ability. Thus, motor imagery classification in ALS patients has been considered challenging in the brain–computer interface (BCI) community. In this study, we address this critical issue by introducing the Grassberger–Procaccia and Higuchi’s methods to estimate the fractal dimensions (GPFD and HFD, respectively) of the electroencephalography (EEG) signals from ALS patients. Moreover, a Fisher’s criterion-based channel selection strategy is proposed to automatically determine the best patient-dependent channel configuration from 30 EEG recording sites. An EEG data collection paradigm is designed to collect the EEG signal of resting state and the imagination of three movements, including right hand grasping (RH), left hand grasping (LH), and left foot stepping (LF). Five late-stage ALS patients without receiving any SMR training participated in this study. Experimental results show that the proposed GPFD feature is not only superior to the previously-used SMR features (mu and beta band powers of EEG from sensorimotor cortex) but also better than HFD. The accuracies achieved by the SMR features are not satisfactory (all lower than 80%) in all binary classification tasks, including RH imagery vs. resting, LH imagery vs. resting, and LF imagery vs. resting. For the discrimination between RH imagery and resting, the average accuracies of GPFD in 30-channel (without channel selection) and top-five-channel configurations are 95.25% and 93.50%, respectively. When using only one channel (the best channel among the 30), a high accuracy of 91.00% can still be achieved by the GPFD feature and a linear discriminant analysis (LDA) classifier. The results also demonstrate that the proposed Fisher’s criterion-based channel selection is capable of removing a large amount of redundant and noisy EEG channels. The proposed GPFD feature extraction combined with the channel selection strategy can be used as the basis for further developing high-accuracy and high-usability motor imagery BCI systems from which the patients with ALS can really benefit.


title: Explorations of Honeycomb Topologies for Network-on-Chip

abstract: Rectangular mesh and torus are the mostly used topologies in network-on-chip (NoC) based systems. In this paper, we quantitatively illustrate that the honeycomb topology is an advantageous design alternative in terms of network cost which is one of the most important parameters that reflects both network performance and implementation cost. Comparing with the rectangular mesh and torus, honeycomb mesh and torus topologies lead to 40% decrease of the network cost. Then we explore the NoC related topological properties of both honeycomb mesh and torus topologies. By transforming the honeycomb topologies into rectangular brick shapes, we demonstrate that the honeycomb topologies are feasible to be implemented with rectangular devices. We also propose a 3D honeycomb topology since 3D IC has become an emerging and promising technique. Another contribution of this paper is the proposal of deadlock free routing algorithms. Based on either the concept of turn model or the logical network, deadlock free routing for all the discussed honeycomb topologies can be achieved.


title: Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves

abstract: We propose Sentence Level Recurrent Topic Model (SLRTM), a new topic model that assumes the generation of each word within a sentence to depend on both the topic of the sentence and the whole history of its preceding words in the sentence. Different from conventional topic models that largely ignore the sequential order of words or their topic coherence, SLRTM gives full characterization to them by using a Recurrent Neural Networks (RNN) based framework. Experimental results have shown that SLRTM outperforms several strong baselines on various tasks. Furthermore, SLRTM can automatically generate sentences given a topic (i.e., topics to sentences), which is a key technology for real world applications such as personalized short text conversation.


title: Data structures in the multicore age

abstract: The advent of multicore processors as the standard computing platform will force major changes in software design.


title: Max-margin Class Imbalanced Learning with Gaussian Affinity

abstract: Real-world object classes appear in imbalanced ratios. This poses a significant challenge for classifiers which get biased towards frequent classes. We hypothesize that improving the generalization capability of a classifier should improve learning on imbalanced datasets. Here, we introduce the first hybrid loss function that jointly performs classification and clustering in a single formulation. Our approach is based on an `affinity measure' in Euclidean space that leads to the following benefits: (1) direct enforcement of maximum margin constraints on classification boundaries, (2) a tractable way to ensure uniformly spaced and equidistant cluster centers, (3) flexibility to learn multiple class prototypes to support diversity and discriminability in feature space. Our extensive experiments demonstrate the significant performance improvements on visual classification and verification tasks on multiple imbalanced datasets. The proposed loss can easily be plugged in any deep architecture as a differentiable block and demonstrates robustness against different levels of data imbalance and corrupted labels.


title: SQPR: Stream query planning with reuse

abstract: When users submit new queries to a distributed stream processing system (DSPS), a query planner must allocate physical resources, such as CPU cores, memory and network bandwidth, from a set of hosts to queries. Allocation decisions must provide the correct mix of resources required by queries, while achieving an efficient overall allocation to scale in the number of admitted queries. By exploiting overlap between queries and reusing partial results, a query planner can conserve resources but has to carry out more complex planning decisions. In this paper, we describe SQPR, a query planner that targets DSPSs in data centre environments with heterogeneous resources. SQPR models query admission, allocation and reuse as a single constrained optimisation problem and solves an approximate version to achieve scalability. It prevents individual resources from becoming bottlenecks by re-planning past allocation decisions and supports different allocation objectives. As our experimental evaluation in comparison with a state-of-the-art planner shows SQPR makes efficient resource allocation decisions, even with a high utilisation of resources, with acceptable overheads.


title: Microplastics in aquatic environments: Implications for Canadian ecosystems ☆

abstract: Abstract Microplastics have been increasingly detected and quantified in marine and freshwater environments, and there are growing concerns about potential effects in biota. A literature review was conducted to summarize the current state of knowledge of microplastics in Canadian aquatic environments; specifically, the sources, environmental fate, behaviour, abundance, and toxicological effects in aquatic organisms. While we found that research and publications on these topics have increased dramatically since 2010, relatively few studies have assessed the presence, fate, and effects of microplastics in Canadian water bodies. We suggest that efforts to determine aquatic receptors at greatest risk of detrimental effects due to microplastic exposure, and their associated contaminants, are particularly warranted. There is also a need to address the gaps identified, with a particular focus on the species and conditions found in Canadian aquatic systems. These gaps include characterization of the presence of microplastics in Canadian freshwater ecosystems, identifying key sources of microplastics to these systems, and evaluating the presence of microplastics in Arctic waters and biota.


title: Development of cable climbing robot for maintenance of suspension bridges

abstract: In this paper, we introduce a wheel-based cable climbing robot system developed for maintenance of the suspension bridges. The robot consists of three parts: a wheel based driving mechanism, adhesion mechanism, and safe landing mechanism. The driving mechanism is a combination of pantograph mechanism, and wheels driven by motors. In addition, we propose a special design of safe landing mechanism which can assure the safety of the robot on the cables when the power is lost. Finally, the proposed robotic system is manufactured and validated in the indoor experimental environments.


title: Circular Patch Antenna with Quarter Wave Transformer Feed for Wireless Communication

abstract: In this paper, design of a quarter wave transformer-fed circular patch antenna is presented. The maximum size of proposed antenna is 25.4 mm by 25.4 mm. The substrate material used for this antenna has thickness of substrate 0.762 mm and relative permittivity epsivr 3.2. The design frequency of the antenna is 10 GHz. By selecting optimum parameters of proposed antenna, the measured return loss of proposed antenna at design frequency (10 GHz) is -24 dB. A novel 3-stub broad-band matching network is described, which enhances the bandwidth by a factor of 2. The simulated and measured radiation patterns show that the antenna functions as expected, with a gain of 7.52 dBi at 10 GHz. The proposed antenna should be useful for X band communication systems


title: Chasing the Optimum in Replicated In-Memory Transactional Platforms via Protocol Adaptation

abstract: Replication plays an essential role for in-memory distributed transactional platforms, such as NoSQL data grids, given that it represents the primary mean to ensure data durability. Unfortunately, no single replication technique can ensure optimal performance across a wide range of workloads and system configurations. This paper tackles this problem by presenting MORPHR, a framework that allows to automatically adapt the replication protocol of in-memory transactional platforms according to the current operational conditions. MORPHR presents two key innovative aspects. On one hand, it allows to plug in, in a modular fashion, specialized algorithms to regulate the switching between arbitrary replication protocols. On the other hand, MORPHR relies on state of the art machine learning techniques to autonomously determine the optimal replication in face of varying workloads. We integrated MORPHR in a popular open-source in-memory NoSQL data grid, and evaluated it by means of an extensive experimental study. The results highlight that MORPHR is accurate in identifying the optimal replication strategy in presence of complex, realistic workloads, and does so with minimal overhead.


title: Fast approximation of matrix coherence and statistical leverage

abstract: The statistical leverage scores of a matrix $A$ are the squared row-norms of the matrix containing its (top) left singular vectors and the coherence is the largest leverage score. These quantities are of interest in recently-popular problems such as matrix completion and Nystr\"{o}m-based low-rank matrix approximation as well as in large-scale statistical data analysis applications more generally; moreover, they are of interest since they define the key structural nonuniformity that must be dealt with in developing fast randomized matrix algorithms. Our main result is a randomized algorithm that takes as input an arbitrary $n \times d$ matrix $A$, with $n \gg d$, and that returns as output relative-error approximations to all $n$ of the statistical leverage scores. The proposed algorithm runs (under assumptions on the precise values of $n$ and $d$) in $O(n d \log n)$ time, as opposed to the $O(nd^2)$ time required by the na\"{i}ve algorithm that involves computing an orthogonal basis for the range of $A$. Our analysis may be viewed in terms of computing a relative-error approximation to an underconstrained least-squares approximation problem, or, relatedly, it may be viewed as an application of Johnson-Lindenstrauss type ideas. Several practically-important extensions of our basic result are also described, including the approximation of so-called cross-leverage scores, the extension of these ideas to matrices with $n \approx d$, and the extension to streaming environments.


title: Zone and polygon menus: using relative position to increase the breadth of multi-stroke marking menus

abstract: We present Zone and Polygon menus, two new variants of multi-stroke marking menus that consider both the relative position and orientation of strokes. Our menus are designed to increase menu breadth over the 8 item limit of status quo orientation-based marking menus. An experiment shows that Zone and Polygon menus can successfully increase breadth by a factor of 2 or more over orientation-based marking menus, while maintaining high selection speed and accuracy. We also discuss hybrid techniques that may further increase menu breadth and performance. Our techniques offer UI designers new options for balancing menu breadth and depth against selection speed and accuracy.


title: Electricity Theft Detection in AMI Using Customers’ Consumption Patterns

abstract: As one of the key components of the smart grid, advanced metering infrastructure brings many potential advantages such as load management and demand response. However, computerizing the metering system also introduces numerous new vectors for energy theft. In this paper, we present a novel consumption pattern-based energy theft detector, which leverages the predictability property of customers’ normal and malicious consumption patterns. Using distribution transformer meters, areas with a high probability of energy theft are short listed, and by monitoring abnormalities in consumption patterns, suspicious customers are identified. Application of appropriate classification and clustering techniques, as well as concurrent use of transformer meters and anomaly detectors, make the algorithm robust against nonmalicious changes in usage pattern, and provide a high and adjustable performance with a low-sampling rate. Therefore, the proposed method does not invade customers’ privacy. Extensive experiments on a real dataset of 5000 customers show a high performance for the proposed method.


Image Enhancement Using Fuzzy Technique


title: MGA for feature weight learning in SVM —a novel optimization method in pedestrian detection

abstract: Pedestrian detection is a challenging task in computer vision, which is often treated as classification problem of pattern recognition. However, dealing with the high dimensional features extracted from images, it turns out to be difficult to choose and combine the informative features for classification. In this paper, a novel optimization method—Metropolis based Genetic Algorithm (MGA) is proposed to solve this problem, and a novel pedestrian detector MGA-SVM is presented and implemented. In MGA, the metropolis criterion is adopted into GA for dynamical parents’ selection, which makes the algorithm get a stronger ability to jump out of local minimum as well as achieve convergence. To test the effectiveness of the proposed MGA, we implement it for feature weight learning in SVM pedestrian detector, which is named as MGA-SVM. The experimental results demonstrate the MGA has a better optimization capacity than original GA, which leads to a more accurate pedestrian detection result by using MGA-SVM.


title: Compact Circularly Polarized Patch Antenna With Wide Axial-Ratio Beamwidth

abstract: This letter presents a compact circularly polarized patch antenna with wide axial-ratio (AR) beamwidth. The simulation reveals that by etching two pairs of asymmetric slots on the patch and loading coupled strips, the phase differences between the radiated orthogonal fields become flatter and maintain approximately at 90° over a wide angular range; hence, a wide AR beamwidth is achieved. Experimental results agree well with the simulation results. Particularly, this antenna achieves a wide AR beamwidth of 188° in both the principal and diagonal planes.


title: The robustness of critical period effects in second language acquisition

abstract: This study was designed to test the Fundamental Difference Hypothesis (Bley-Vroman, ::: 1988), which states that, whereas children are known to learn language almost completely ::: through (implicit) domain-specific mechanisms, adults have largely lost the ability to learn a ::: language without reflecting on its structure and have to use alternative mechanisms, drawing ::: especially on their problem-solving capacities, to learn a second language. The hypothesis ::: implies that only adults with a high level of verbal analytical ability will reach near-native ::: competence in their second language, but that this ability will not be a significant predictor of ::: success for childhood second language acquisition. A study with 57 adult Hungarian-speaking ::: immigrants confirmed the hypothesis in the sense that very few adult immigrants scored within ::: the range of child arrivals on a grammaticality judgment test, and that the few who did had high ::: levels of verbal analytical ability; this ability was not a significant predictor for childhood ::: arrivals. This study replicates the findings of Johnson and Newport (1989) and provides an ::: explanation for the apparent exceptions in their study. These findings lead to a ::: reconceptualization of the Critical Period Hypothesis: If the scope of this hypothesis is limited to ::: implicit learning mechanisms, then it appears that there may be no exceptions to the age effects ::: that the hypothesis seeks to explain.


title: Making cross-racial therapy work: A phenomenological study of clients’ experiences of cross-racial therapy.

abstract: A phenomenological and consensual qualitative study of clients' lived experiences of cross-racial therapy was conducted to enhance the understanding of whether, how, and under what conditions race matters in the therapy relationship. The sample consisted of 16 racial and/or ethnic minority clients who received treatment from 16 White, European American therapists across a range of treatment settings. Participants who reported a satisfying experience of cross-racial therapy (n = 8) were examined in relation to gender-matched controls and, in most cases, race/ethnicity-matched controls (n = 8) who reported an overall unsatisfying experience. Therapy satisfaction was assessed during the screening process and was confirmed during the research interview. Therapy narratives were analyzed with consensual qualitative research to identify client, therapist, and relational factors that distinguished satisfied participants from unsatisfied participants. Findings reveal substantial differences at the level of individual characteristics and relational processes, providing evidence of both universal (etic) as well as culture- or context-specific (emic) aspects of healing relationships. Recommendations for facilitating positive alliance formation in cross-racial therapy are provided, based on clients' descriptions of facilitative conditions in the therapy relationship.


title: Face recognition based on fast sparse representation of Gabor dictionary and l_0 norm

abstract: Many classic face recognition algorithms degrade sharply when they are used at identifying an individual under various conditions such as illumination,camouflage.A fast sparse representation face recognition algorithm based on Gabor dictionary and smoothed l0 norm is presented in this paper.Gabor filters,which could effectively extract local directional features of the image at multiple scales,are less sensitive to the variations of illumination and camouflage.Smoothed l0 algorithm requires fewer measurement values by continuously differentiable function approximation l0 norm.The algorithm obtains the local feature by extracting Gabor feature,reduces the dimensions by principal component analysis(PCA) and realizes fast sparse by l0 norm.Under camouflage condition,the algorithm blocks Gabor facial feature and improves the speed of formation of the Gabor dictionary.Experimental results on AR face database show that the proposed algorithm can improve recognition speed and recognition rate and can generalize well to the face recognition,even with a few training image per class.


title: QuadricSLAM: Dual Quadrics From Object Detections as Landmarks in Object-Oriented SLAM

abstract: In this letter, we use two-dimensional (2-D) object detections from multiple views to simultaneously estimate a 3-D quadric surface for each object and localize the camera position. We derive a simultaneous localization and mapping (SLAM) formulation that uses dual quadrics as 3-D landmark representations, exploiting their ability to compactly represent the size, position and orientation of an object, and show how 2-D object detections can directly constrain the quadric parameters via a novel geometric error formulation. We develop a sensor model for object detectors that addresses the challenge of partially visible objects, and demonstrate how to jointly estimate the camera pose and constrained dual quadric parameters in factor graph based SLAM with a general perspective camera.


title: Analysis of Face Detection and Recognition Algorithms Using Viola Jones Algorithm with PCA and LDA

abstract: These days' people are most concerned about their privacy. As we know that many big companies are failing to provide their customers the privacy and security, they have right upon. Many such big security breaches have taken place in recent times even in Banks which are trusted blindly by the people. So to solve the security problem in the world, Face recognition technique has shown high standards in keeping things safe and secured. In the paper, we are utilizing Viola Jones strategy to identify the face, it consists of four stages namely Haar features, Integral Image, Adaboost and Cascading which help in detecting an object out of the whole image. We are contrasting two prominent face recognition methods in particular Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) used to perceive the approved individual's face out of a Database [1]. We are analyzing both the algorithms by running tests on different Databases by varying the test images just to get more clarity on the efficiency and the behaviors of both the algorithms in different lighting conditions, contrasts and brightness of images.


title: A fast semi-inverse approach to detect and remove the haze from a single image

abstract: In this paper we introduce a novel approach to restore a single image degraded by atmospheric phenomena such as fog or haze. The presented algorithm allows for fast identification of hazy regions of an image, without making use of expensive optimization and refinement procedures. By applying a single per pixel operation on the original image, we produce a 'semi-inverse' of the image. Based on the hue disparity between the original image and its semi-inverse, we are then able to identify hazy regions on a per pixel basis. This enables for a simple estimation of the airlight constant and the transmission map. Our approach is based on an extensive study on a large data set of images, and validated based on a metric that measures the contrast but also the structural changes. The algorithm is straightforward and performs faster than existing strategies while yielding comparative and even better results. We also provide a comparative evaluation against other recent single image dehazing methods, demonstrating the efficiency and utility of our approach.


A Local Detection Approach for Named Entity Recognition and Mention Detection


title: Integration of a Microstrip Circulator With Planar Yagi Antennas of Several Directors

abstract: An integration methodology is presented to design antenna systems incorporating a ferrite circulator. Two integrated systems comprised of a microstrip circulator and Yagi planar-type antennas are presented as validation of the proposed methodology. Excellent pass band characteristics are observed in the experimental and numerical data for both systems. Furthermore, one system is magnetically tuned to realize 30 dB isolation over a bandwidth of 850 MHz. Both systems maintain excellent return loss and show improved insertion loss specifications even though the ferrite is biased into partial saturation. Extensive measurement data of the ferrite material and system network parameters are provided to corroborate the claims made herein.


title: Survey of Intrusion Detection Systems towards an End to End Secure Internet of Things

abstract: The Internet of Things (IoT) is one of the largest technological evolutions of computing, by 2022 it is estimated that a trillion IP addresses (objects) will be connected to the Internet. The obscurity and low accessibility of many of these devices in this vast heterogeneous network will make it difficult to holistically monitor information flow. Nonetheless, to safeguard networks, unauthorized intruders must be detected within the constraints of each type of device or subnetwork before any system information can be disseminated. In this paper, a survey of the Intrusion Detection Systems (IDS) using the most recent ideas and methods proposed for the IoT is presented. To understand and illustrate IDS platform differences and the current research trend towards a universal, cross-platform distributed approach, the survey starts with an historical examination of intrusion detection systems. This examination of the foundations of IDS research based on the components that make up the IoT is followed by a look at the current holistic trend and an analysis of these schemes. Finally, guidelines to potential IDS in the IoT are proposed before identifying the open research problems.


title: Applying reinforcement learning for web pages ranking algorithms

abstract: Ranking web pages for presenting the most relevant web pages to user's queries is one of the main issues in any search engine. In this paper, two new ranking algorithms are offered, using Reinforcement Learning (RL) concepts. RL is a powerful technique of modern artificial intelligence that tunes agent's parameters, interactively. In the first step, with formulation of ranking as an RL problem, a new connectivity-based ranking algorithm, called RL_Rank, is proposed. In RL_Rank, agent is considered as a surfer who travels between web pages by clicking randomly on a link in the current page. Each web page is considered as a state and value function of state is used to determine the score of that state (page). Reward is corresponded to number of out links from the current page. Rank scores in RL_Rank are computed in a recursive way. Convergence of these scores is proved. In the next step, we introduce a new hybrid approach using combination of BM25 as a content-based algorithm and RL_Rank. Both proposed algorithms are evaluated by well known benchmark datasets and analyzed according to concerning criteria. Experimental results show using RL concepts leads significant improvements in raking algorithms.


title: Running on the bare metal with GeekOS

abstract: Undergraduate operating systems courses are generally taught using one of two approaches: abstract or concrete. In the abstract approach, students learn the concepts underlying operating systems theory, and perhaps apply them using user-level threads in a host operating system. In the concrete approach, students apply concepts by working on a real operating system kernel. In the purest manifestation of the concrete approach, students implement operating system projects that run on real hardware.GeekOS is an instructional operating system kernel which runs on real hardware. It provides the minimum functionality needed to schedule threads and control essential devices on an x86 PC. On this foundation, we have developed projects in which students build processes, semaphores, a multilevel feedback scheduler, paged virtual memory, a filesystem, and inter-process communication. We use the Bochs emulator for ease of development and debugging. While this approach (tiny kernel run on an emulator) is not new, we believe GeekOS goes further towards the goal of combining realism and simplicity than previous systems have.


title: Classification and evolution of type II CRISPR-Cas systems

abstract: The CRISPR-Cas systems of archaeal and bacterial adaptive immunity are classified into three types that differ by the repertoires of CRISPR-associated (cas) genes, the organization of cas operons a ...


title: Learning to Label Aerial Images from Noisy Data

abstract: When training a system to label images, the amount of labeled training data tends to be a limiting factor. We consider the task of learning to label aerial images from existing maps. These provide abundant labels, but the labels are often incomplete and sometimes poorly registered. We propose two robust loss functions for dealing with these kinds of label noise and use the loss functions to train a deep neural network on two challenging aerial image datasets. The robust loss functions lead to big improvements in performance and our best system substantially outperforms the best published results on the task we consider.


title: On Uncertain Graphs Modeling and Queries

abstract: Large-scale, highly-interconnected networks pervade both our society and the natural world around us. Uncertainty, on the other hand, is inherent in the underlying data due to a variety of reasons, such as noisy measurements, lack of precise information needs, inference and prediction models, or explicit manipulation, e.g., for privacy purposes. Therefore, uncertain, or probabilistic, graphs are increasingly used to represent noisy linked data in many emerging application scenarios, and they have recently become a hot topic in the database research community. While many classical graph algorithms such as reachability and shortest path queries become #P-complete, and hence, more expensive in uncertain graphs; various complex queries are also emerging over uncertain networks, such as pattern matching, information diffusion, and influence maximization queries. In this tutorial, we discuss the sources of uncertain graphs and their applications, uncertainty modeling, as well as the complexities and algorithmic advances on uncertain graphs processing in the context of both classical and emerging graph queries. We emphasize the current challenges and highlight some future research directions.


title: Analysis of the radar return of micro-UAVs in flight

abstract: This paper presents an analysis of the radar signature of micro-UAVs whilst in flight when subjected to realistic environmental flight conditions. These highly dynamic signatures have been found to be significantly different from modelled signatures or those expected from the RCS when measured in a benign environment. The time varying radar returns of different micro-UAV targets, measured in different conditions, are presented and characterized. Also the varying characteristics of the micro-Doppler rotor returns are analyzed. The impact of results on radar systems design is discussed.


title: Keratoacanthoma: an abortive squamous cell carcinoma that does not always fail

abstract: Keratoacanthoma (KA) is an unusual entity, a neoplasm difficult to classify. It was once considered the prototype of a pseudomalignancy, yet it is now viewed in an opposite light as a pseudobenignity. It still challenges the definition of cancer. In addition, the KA may serve as a marker for the important autosomal dominant familial cancer syndrome, the Muir-Torre syndrome.


title: Normalized Gradient with Adaptive Stepsize Method for Deep Neural Network Training

abstract: In this paper, we propose a generic and simple algorithmic framework for first order optimization. The framework essentially contains two consecutive steps in each iteration: 1) computing and normalizing the mini-batch stochastic gradient; 2) selecting adaptive step size to update the decision variable (parameter) towards the negative of the normalized gradient. We show that the proposed approach, when customized to the popular adaptive stepsize methods, such as AdaGrad, can enjoy a sublinear convergence rate, if the objective is convex. We also conduct extensive empirical studies on various non-convex neural network optimization problems, including multi layer perceptron, convolution neural networks and recurrent neural networks. The results indicate the normalized gradient with adaptive step size can help accelerate the training of neural networks. In particular, significant speedup can be observed if the networks are deep or the dependencies are long.


title: A stage-based model of personal informatics systems

abstract: People strive to obtain self-knowledge. A class of systems called personal informatics is appearing that help people collect and reflect on personal information. However, there is no comprehensive list of problems that users experience using these systems, and no guidance for making these systems more effective. To address this, we conducted surveys and interviews with people who collect and reflect on personal information. We derived a stage-based model of personal informatics systems composed of five stages (preparation, collection, integration, reflection, and action) and identified barriers in each of the stages. These stages have four essential properties: barriers cascade to later stages; they are iterative; they are user-driven and/or system-driven; and they are uni-faceted or multi-faceted. From these properties, we recommend that personal informatics systems should 1) be designed in a holistic manner across the stages; 2) allow iteration between stages; 3) apply an appropriate balance of automated technology and user control within each stage to facilitate the user experience; and 4) explore support for associating multiple facets of people's lives to enrich the value of systems.


title: The impact of culture and education on non-verbal neuropsychological measurements: A critical review

abstract: Clinical neuropsychology has frequently considered visuospatial and non-verbal tests to be culturally and educationally fair or at least fairer than verbal tests. This paper reviews the cross-cultural differences in performance on visuoperceptual and visuoconstructional ability tasks and analyzes the impact of education and culture on non-verbal neuropsychological measurements. This paper compares: (1) non-verbal test performance among groups with different educational levels, and the same cultural background (inter-education intra-culture comparison); (2) the test performance among groups with the same educational level and different cultural backgrounds (intra-education inter-culture comparisons). Several studies have demonstrated a strong association between educational level and performance on common non-verbal neuropsychological tests. When neuropsychological test performance in different cultural groups is compared, significant differences are evident. Performance on non-verbal tests such as copying figures, drawing maps or listening to tones can be significantly influence by the individuals culture. Arguments against the use of some current neuropsychological non-verbal instruments, procedures, and norms in the assessment of diverse educational and cultural groups are discussed and possible solutions to this problem are presented. 2003 Elsevier Science (USA). All rights reserved.


title: Deep Learning for Medical Image Processing: Overview, Challenges and Future

abstract: The health care sector is totally different from any other industry. It is a high priority sector and consumers expect the highest level of care and services regardless of cost. The health care sector has not achieved society’s expectations, even though the sector consumes a huge percentage of national budgets. Mostly, the interpretations of medical data are analyzed by medical experts. In terms of a medical expert interpreting images, this is quite limited due to its subjectivity and the complexity of the images; extensive variations exist between experts and fatigue sets in due to their heavy workload. Following the success of deep learning in other real-world applications, it is seen as also providing exciting and accurate solutions for medical imaging, and is seen as a key method for future applications in the health care sector. In this chapter, we discuss state-of-the-art deep learning architecture and its optimization when used for medical image segmentation and classification. The chapter closes with a discussion of the challenges of deep learning methods with regard to medical imaging and open research issue.


title: MODELING AND SIMULATION OF MOS TRANSISTOR MISMATCH

abstract: The paper is an overview of MOS transistor mismatch modeling and simulation over the existent literature. The fluctuations of physical parameters and line width are the main causes of mismatch. There are two types of mismatch. Systematic mismatch can be reduced to great extent with proper layout. Different patterns are available, that are able to reduce from linear to n-th order polynomial systematic mismatch. Stochastic mismatch can only be reduced with better process control and larger transistor areas. There are different approaches for calculating the standard deviation representing stochastic mismatch. Simple formulas (e.g. square root of area rule) are most commonly used. With the reducing of the transistor area some new effects should be considerate and more complex formulas are needed. On the other hand correlation functions and frequency domain analysis with spatial spectra give more accurate results. These two approaches are more general but they do not give physical insight and the final layout should be known. Mismatch can be simulated in several ways. Brute force simulation based on Monte-Carlo analysis is appropriate for any kind of distribution but it is the most time expensive. Simulations based on small signal analysis are faster because less circuit simulations are needed to calculate the sensitivity. Two different approaches to calculate the sensitivity are presented in this paper.


title: Learning how to drive in a real world simulation with deep Q-Networks

abstract: We present a reinforcement learning approach using Deep Q-Networks to steer a vehicle in a 3D physics simulation. Relying solely on camera image input the approach directly learns steering the vehicle in an end-to-end manner. The system is able to learn human driving behavior without the need of any labeled training data. An action-based reward function is proposed, which is motivated by a potential use in real world reinforcement learning scenarios. Compared to a naive distance-based reward function, it improves the overall driving behavior of the vehicle agent. The agent is even able to reach comparable to human driving performance on a previously unseen track in our simulation environment.


Instabranding: Shaping the Personalities of the Top Food Brands on Instagram


title: Splicing MPLS and OpenFlow Tunnels Based on SDN Paradigm

abstract: Software-defined networking has emerged as a promising solution for supporting dynamic network functions and intelligent applications through decoupling control plane from forwarding plane. OpenFlow is the first standardized open management interface of SDN architecture. But it is unrealistic to simply swaping out conventional networks for new infrastructure. How to integrate OpenFlow with existing networks is still a serious challenge. We propose a tunnel splicing mechanism for heterogeneous network with MPLS and OpenFlow routers. Two key mechanisms were suggested: first, abstract the underlying network devices into uniformed nodes in order to shield the details of various equipments, second, strip the manipulation of flow table and lable switch table from controller and fulfill it in an independent module. This new paradigm has been developed on Linux system and tests have been carried out in experiment networks. The emulation results proved its feasibility and efficiency.


title: Sensitivity of PCA for traffic anomaly detection

abstract: Detecting anomalous traffic is a crucial part of managing IP networks. In recent years, network-wide anomaly detection based on Principal Component Analysis (PCA) has emerged as a powerful method for detecting a wide variety of anomalies. We show that tuning PCA to operate effectively in practice is difficult and requires more robust techniques than have been presented thus far. We analyze a week of network-wide traffic measurements from two IP backbones (Abilene and Geant) across three different traffic aggregations (ingress routers, OD flows, and input links), and conduct a detailed inspection of the feature time series for each suspected anomaly. Our study identifies and evaluates four main challenges of using PCA to detect traffic anomalies: (i) the false positive rate is very sensitive to small differences in the number of principal components in the normal subspace, (ii) the effectiveness of PCA is sensitive to the level of aggregation of the traffic measurements, (iii) a large anomaly may in advertently pollute the normal subspace, (iv) correctly identifying which flow triggered the anomaly detector is an inherently challenging problem.


title: Multi-joint soft exosuit for gait assistance

abstract: Exosuits represent a new approach for applying assistive forces to an individual, using soft textiles to interface to the wearer and transmit forces through specified load paths. In this paper we present a body-worn, multi-joint soft exosuit that assists both ankle plantar flexion and hip flexion through a multiarticular load path, and hip extension through a separate load path, at walking speeds up to 1.79m/s (4.0mph). The exosuit applies forces of 300N in the multiarticular load path and 150N in hip extension, which correspond to torques of 21% and 19% of the nominal biological moments at the ankle and hip during unloaded walking. The multi-joint soft exosuit uses a new actuation approach that exploits joint synergies, with one motor actuating the multiarticular load paths on both legs and one motor actuating the hip extension load paths on both legs, in order to reduce the total system weight. Control is accomplished by an algorithm that uses only a gyroscope at the heel and a load cell monitoring the suit tension, and is shown to adapt within a single step to changes in cadence. Additionally, the control algorithm can create slack in the suit during non-level-ground walking motions such as stepping over obstacles so that the system can be transparent to the wearer when required. The resulting system consumes 137W, and has a mass of 6.5kg including batteries.


title: Framing, Agenda Setting, and Priming: The Evolution of Three Media Effects Models

abstract: This special issue of Journal of Communication is devoted to theoretical explanations of news framing, agenda setting, and priming effects. It examines if and how the three models are related and what potential relationships between them tell theorists and researchers about the effects of mass media. As an introduction to this effort, this essay provides a very brief review of the three effects and their roots in media-effects research. Based on this overview, we highlight a few key dimensions along which one can compare, framing, agenda setting, and priming. We conclude with a description of the contexts within which the three models operate, and the broader implications that these conceptual distinctions have for the growth of our discipline. doi:10.1111/j.1460-2466.2006.00326.x In 1997, Republican pollster Frank Luntz sent out a 222-page memo called ‘‘Language of the 21st century’’ to select members of the U.S. Congress. Parts of the memo soon spread among staffers, members of Congress, and also journalists. Luntz’s message was simple: ‘‘It’s not what you say, it’s how you say it’’ (Luntz, in press). Drawing on various techniques for real-time message testing and focus grouping, Frank Luntz had researched Republican campaign messages and distilled terms and phrases that resonated with specific interpretive schemas among audiences and therefore helped shift people’s attitudes. In other words, the effect of the messages was not a function of content differences but of differences in the modes of presentation. The ideas outlined in the memo were hardly new, of course, and drew on decades of existing research in sociology (Goffman, 1974), economics (Kahneman & Tversky, 1979), psychology (Kahneman & Tversky, 1984), cognitive linguistics (Lakoff, 2004), and communication (Entman, 1991; Iyengar, 1991). But Frank Luntz was the first professional pollster to systematically use the concept of framing as a campaign tool. The Democratic Party soon followed and George Lakoff published Don’t Think of an


title: Lenient learners in cooperative multiagent systems

abstract: In concurrent learning algorithms, an agent's perception of the joint search space depends on the actions currently chosen by the other agents. These perceptions change as each agent's action selection is influenced by its learning. We observe that agents that show lenience to their teammates achieve more accurate perceptions of the overall learning task. Additionally, lenience appears more beneficial at early stages of learning, when the agent's teammates are merely exploring their actions, and less helpful as the agents start to converge. We propose two multiagent learning algorithms where agents exhibit a variable degree of lenience, and we demonstrate their advantages in several coordination problems.


title: Measuring personalization of web search

abstract: Web search is an integral part of our daily lives. Recently, there has been a trend of personalization in Web search, where different users receive different results for the same search query. The increasing personalization is leading to concerns about Filter Bubble effects, where certain users are simply unable to access information that the search engines' algorithm decides is irrelevant. Despite these concerns, there has been little quantification of the extent of personalization in Web search today, or the user attributes that cause it. In light of this situation, we make three contributions. First, we develop a methodology for measuring personalization in Web search results. While conceptually simple, there are numerous details that our methodology must handle in order to accurately attribute differences in search results to personalization. Second, we apply our methodology to 200 users on Google Web Search; we find that, on average, 11.7% of results show differences due to personalization, but that this varies widely by search query and by result ranking. Third, we investigate the causes of personalization on Google Web Search. Surprisingly, we only find measurable personalization as a result of searching with a logged in account and the IP address of the searching user. Our results are a first step towards understanding the extent and effects of personalization on Web search engines today.


title: Marginality and Problem-Solving Effectiveness in Broadcast Search

abstract: We examine who the winners are in science problem-solving contests characterized by open broadcast of problem information, self-selection of external solvers to discrete problems from the laboratories of large research and development intensive companies, and blind review of solution submissions. Analyzing a unique data set of 166 science challenges involving over 12,000 scientists revealed that technical and social marginality, being a source of different perspectives and heuristics, plays an important role in explaining individual success in problem solving. The provision of a winning solution was positively related to increasing distance between the solver's field of technical expertise and the focal field of the problem. Female solvers---known to be in the “outer circle” of the scientific establishment---performed significantly better than men in developing successful solutions. Our findings contribute to the emerging literature on open and distributed innovation by demonstrating the value of openness, at least narrowly defined by disclosing problems, in removing barriers to entry to nonobvious individuals. We also contribute to the knowledge-based theory of the firm by showing the effectiveness of a market mechanism to draw out knowledge from diverse external sources to solve internal problems.


title: Airborne network evaluation: challenges and high fidelity emulation solution

abstract: The future airborne network (AN) is envisioned as an IP-based hierarchical network with heterogeneous nodes and waveforms. Due to its highly dynamic nature and bandwidth constraints, new AN technologies are needed to provide reliable network operations with similar levels of mission support as that in terrestrial networks. This paper identifies challenges in designing and evaluating AN technologies and also describes an on-going effort on developing a realistic wireless testing and performance evaluation framework for AN testing and evaluation. A set of use cases are presented to illustrate how the framework can be used to evaluate new architecture and protocols in AN environments.


title: Assessing the role of participants in evolution of topic lifecycles on social networks

abstract: Background ::: Topic lifecycle analysis on social networks aims to analyze and track how topics are born from user-generated content, and how they evolve. Twitter researchers have no agreed-upon definition of topics; topics on Twitter are typically derived in the form of (a) frequently used hashtags, or (b) keywords showing sudden trends of large occurrence in a short span of time ("bursty keywords"), or (c) concepts latent within the tweets that are grouped using variations of semantic clustering techniques. ::: ::: ::: Methods ::: In the current paper, we jointly model the hashtags present and the semantic concepts embedded in the content, which in turn helps us identify hashtag groups that define a "topic"-a concept space-that are used by a large number of tweets. ::: ::: ::: Results ::: We observe that different hashtags belonging to a given cluster are more prominent compared to the others, at different times. We further observe that the participation and influence levels of the different users play important roles in determining which hashtag would be more prominent than the others at given times. We thus observe topics to often morph from one to the other (via morphing of dominant hashtags representing the same semantic concept space), rather than becoming extinct outright, which is a novel insight about topic lifecycles. We further present novel observations about the role of users in determining the lifecycle of discussion topics on Twitter. ::: ::: ::: Conclusions ::: We infer that topic lifecycles are governed by user interests, and not by user influence, which is a key observation made by our work.


title: The Bandgap Reference [A Circuit for All Seasons]

abstract: Since its inception in the late 1960s, the bandgap circuit has served as an essential component in most integrated circuits. This simple, robust idea provides a temperature-independent (TI) voltage and a proportional-toabsolute-temperature (PTAT) current. In this article, we study the principles of bandgap circuit design.


title: FOCA: A Methodology for Ontology Evaluation

abstract: Modeling an ontology is a hard and time-consuming task. Although methodologies are useful for ontologists to create good ontologies, they do not help with the task of evaluating the quality of the ontology to be reused. For these reasons, it is imperative to evaluate the quality of the ontology after constructing it or before reusing it. Few studies usually present only a set of criteria and questions, but no guidelines to evaluate the ontology. The effort to evaluate an ontology is very high as there is a huge dependence on the evaluator's expertise to understand the criteria and questions in depth. Moreover, the evaluation is still very subjective. This study presents a novel methodology for ontology evaluation, taking into account three fundamental principles: i) it is based on the Goal, Question, Metric approach for empirical evaluation; ii) the goals of the methodologies are based on the roles of knowledge representations combined with specific evaluation criteria; iii) each ontology is evaluated according to the type of ontology. The methodology was empirically evaluated using different ontologists and ontologies of the same domain. The main contributions of this study are: i) defining a step-by-step approach to evaluate the quality of an ontology; ii) proposing an evaluation based on the roles of knowledge representations; iii) the explicit difference of the evaluation according to the type of the ontology iii) a questionnaire to evaluate the ontologies; iv) a statistical model that automatically calculates the quality of the ontologies.


title: Development of a FACS-verified set of basic and self-conscious emotion expressions

abstract: In 2 studies, the authors developed and validated of a new set of standardized emotion expressions, which they referred to as the University of California, Davis, Set of Emotion Expressions (UCDSEE). The precise components of each expression were verified using the Facial Action Coding System (FACS). The UCDSEE is the first FACS-verified set to include the three "self-conscious" emotions known to have recognizable expressions (embarrassment, pride, and shame), as well as the 6 previously established "basic" emotions (anger, disgust, fear, happiness, sadness, and surprise), all posed by the same 4 expressers (African and White males and females). This new set has numerous potential applications in future research on emotion and related topics.


title: Practical Differential Privacy via Grouping and Smoothing

abstract: We address one-time publishing of non-overlapping counts with e-differential privacy. These statistics are useful in a wide and important range of applications, including transactional, traffic and medical data analysis. Prior work on the topic publishes such statistics with prohibitively low utility in several practical scenarios. Towards this end, we present GS, a method that pre-processes the counts by elaborately grouping and smoothing them via averaging. This step acts as a form of preliminary perturbation that diminishes sensitivity, and enables GS to achieve e-differential privacy through low Laplace noise injection. The grouping strategy is dictated by a sampling mechanism, which minimizes the smoothing perturbation. We demonstrate the superiority of GS over its competitors, and confirm its practicality, via extensive experiments on real datasets.


title: SemLens: visual analysis of semantic data with scatter plots and semantic lenses

abstract: Querying the Semantic Web and analyzing the query results are often complex tasks that can be greatly facilitated by visual interfaces. A major challenge in the design of these interfaces is to provide intuitive and efficient interaction support without limiting too much the analytical degrees of freedom. This paper introduces SemLens, a visual tool that combines scatter plots and semantic lenses to overcome this challenge and to allow for a simple yet powerful analysis of RDF data. The scatter plots provide a global overview on an object collection and support the visual discovery of correlations and patterns in the data. The semantic lenses add dimensions for local analysis of subsets of the objects. A demo accessing DBpedia data is used for illustration.


title: Wireless charger networking for mobile devices: fundamentals, standards, and applications

abstract: Wireless charging is a technique of transmitting power through an air gap to an electrical device for the purpose of energy replenishment. Recently, wireless charging technology has significantly advanced in terms of efficiency and functionality. This article first presents an overview and fundamentals of wireless charging. We then provide the review of standards, that is, Qi and the Alliance for Wireless Power, and highlight their communication protocols. Next, we propose a novel concept of wireless charger networking that allows chargers to be connected to facilitate information collection and control. We demonstrate the application of the wireless charger network in user-charger assignment, which clearly shows the benefit in terms of reduced costs for users to identify the best chargers to replenish energy for their mobile devices.


title: Contribution of posterior corneal astigmatism to total corneal astigmatism

abstract: Purpose To determine the contribution of posterior corneal astigmatism to total corneal astigmatism and the error in estimating total corneal astigmatism from anterior corneal measurements only using a dual-Scheimpflug analyzer. Setting Cullen Eye Institute, Baylor College of Medicine, Houston, Texas, USA. Design Case series. Methods Total corneal astigmatism was calculated using ray tracing, corneal astigmatism from simulated keratometry, anterior corneal astigmatism, and posterior corneal astigmatism, and the changes with age were analyzed. Vector analysis was used to assess the error produced by estimating total corneal astigmatism from anterior corneal measurements only. Results The study analyzed 715 corneas of 435 consecutive patients. The mean magnitude of posterior corneal astigmatism was −0.30 diopter (D). The steep corneal meridian was aligned vertically (60 to 120 degrees) in 51.9% of eyes for the anterior surface and in 86.6% for the posterior surface. With increasing age, the steep anterior corneal meridian tended to change from vertical to horizontal, while the steep posterior corneal meridian did not change. The magnitudes of anterior and posterior corneal astigmatism were correlated when the steeper anterior meridian was aligned vertically but not when it was aligned horizontally. Anterior corneal measurements underestimated total corneal astigmatism by 0.22 @ 180 and exceeded 0.50 D in 5% of eyes. Conclusions Ignoring posterior corneal astigmatism may yield incorrect estimation of total corneal astigmatism. Selecting toric intraocular lenses based on anterior corneal measurements could lead to overcorrection in eyes that have with-the-rule astigmatism and undercorrection in eyes that have against-the-rule astigmatism. Financial Disclosure The authors received research support from Ziemer Group. In addition, Dr. Koch has a financial interest with Alcon Laboratories, Inc., Abbott Medical Optics, Inc., Calhoun Vision, Inc., NuLens, and Optimedica Corp.


title: Automatic segmentation of MR images of the developing newborn brain q

abstract: This paper describes an automatic tissue segmentation method for newborn brains from magnetic resonance images (MRI). The analysis and study of newborn brain MRI is of great interest due to its potential for studying early growth patterns and morphological changes in neurodevelopmental disorders. Automatic segmentation of newborn MRI is a challenging task mainly due to the low intensity contrast and the growth process of the white matter tissue. Newborn white matter tissue undergoes a rapid myelination process, where the nerves are covered in myelin sheathes. It is necessary to identify the white matter tissue as myelinated or non-myelinated regions. The degree of myelination is a fractional voxel property that represents regional changes of white matter as a function of age. Our method makes use of a registered probabilistic brain atlas. The method first uses robust graph clustering and parameter estimation to find the initial intensity distributions. The distribution estimates are then used together with the spatial priors to perform bias correction. Finally, the method refines the segmentation using training sample pruning and non-parametric kernel density estimation. Our results demonstrate that the method is able to segment the brain tissue and identify myelinated and non-myelinated white matter regions.


title: Sink or Swim: Measures Under International Law for the Conservation of the Patagonian Toothfish in the Southern Ocean

abstract: The Patagonian toothfish is in danger of commercial extinction. As an important element of the Antarctic's fragile ecosystem, its sudden demise could have serious implications for the biodiversity of the Southern Ocean. The high market value of Toothfish has made the species a prime target for illegal, unregulated, and unreported (IUU) fishing in the area under the regulatory control of the Commission established by the Convention for the Conservation of Antarctic Marine Living Resources (CCAMLR). This article reviews the conservation measures adopted by the Commission in an attempt to slow the "race for the Toothfish" and analyzes the likely effectiveness of the CCAMLR Catch Documentation Scheme (CDS), first adopted in 1999, including the consistency of its design and application with the international trade rules of the World Trade Organization (WTO). Finally, the article considers the appropriateness of a possible listing of the toothfish under the Convention on International Trade in Endangered Specie...


Introduction : Creativity in Anthropology


title: Questioning some paradigms of signal processing via concrete examples

abstract: This paper was presented in November 2003 in Mexico City. It gives an overview of recent on-line and non-asymptotic estimation techniques in signal processing, which do not necessitate any precise statistical knowledge of the noises. Several concrete examples with their computer simulations are discussed.


title: Optical frequency discriminator with ultrahigh-amplitude-and-phase linearity for frequency-modulated microwave photonic links

abstract: A new linear optical frequency discriminator filter is proposed and analyzed. It offers high field-response linearity (e.g., >99%) and constant group delay for a wide selectable spectral range. Its structure is simple and compact which is based on mature MZI technology.


title: Evidence-based postoperative pain management after laparoscopic colorectal surgery

abstract: AIM ::: The aim of this systematic review was to evaluate the available literature on the management of pain after laparoscopic colorectal surgery. ::: ::: ::: METHOD ::: Randomized studies, published in English between January 1995 and July 2011, assessing analgesic and anaesthetic interventions in adults undergoing laparoscopic colorectal surgery, and reporting pain scores, were retrieved from the Embase and MEDLINE databases. The efficacy and adverse effects of the analgesic techniques was assessed. The recommendations were based on procedure-specific evidence from a systematic review and supplementary transferable evidence from other relevant procedures. ::: ::: ::: RESULTS ::: Of the 170 randomized studies identified, 12 studies were included. Overall, all approaches including ketorolac, methylprednisolone, intraperitoneal instillation of ropivacaine, intravenous lidocaine infusion, intrathecal morphine and epidural analgesia improved pain relief, reduced opioid requirements and improved bowel function. However, there were significant differences in the study designs and the variables evaluated, precluding quantitative analysis. The L'Abbé plots of the data from the epidural analgesia studies included in this review indicate that the pain scores in the nonepidural groups, although higher than those in the epidural groups, were within an acceptable level (i.e. < 4/10). ::: ::: ::: CONCLUSION ::: Infiltration of surgical incisions with local anaesthetic at the end of surgery, systemic steroids, conventional nonsteroidal anti-inflammatory drugs or cyclooxygenase-2-selective inhibitors in combination with paracetamol with opioid used as rescue are recommended. Intravenous lidocaine infusion is recommended, but not as the first line of therapy. However, neuraxial blocks (i.e. epidural analgesia and spinal morphine) are not necessary based on high risk:benefit ratio.


title: Atomically Thin-Layered Molybdenum Disulfide (MoS2) for Bulk-Heterojunction Solar Cells

abstract: Transition metal dichalcogenides (TMDs) are becoming significant because of their interesting semiconducting and photonic properties. In particular, TMDs such as molybdenum disulfide (MoS2), molybdenum diselenide (MoSe2), tungsten disulfide (WS2), tungsten diselenide (WSe2), titanium disulfide (TiS2), tantalum sulfide (TaS2), and niobium selenide (NbSe2) are increasingly attracting attention for their applications in solar cell devices. In this review, we give a brief introduction to TMDs with a focus on MoS2; and thereafter, emphasize the role of atomically thin MoS2 layers in fabricating solar cell devices, including bulk-heterojunction, organic, and perovskites-based solar cells. Layered MoS2 has been used as the hole-transport layer (HTL), electron-transport layer (ETL), interfacial layer, and protective layer in fabricating heterojunction solar cells. The trilayer graphene/MoS2/n-Si solar cell devices exhibit a power-conversion efficiency of 11.1%. The effects of plasma and chemical doping on the pho...


title: The Effects of Digital Media on Political Knowledge and Participation in Election Campaigns: Evidence From Panel Data

abstract: While the majority of previous research suggests there are positive relationships between digital media use and political participation and knowledge, most studies have relied on cross-sectional surveys and have thus not been able to firmly establish the chain of causality. Also, there is little research investigating use of different forms of digital media and their relative effects on political participation and knowledge. This study examines (a) the effects of digital media use on political participation and knowledge and (b) whether different forms of digital media use affect people differently. Drawing on two representative panel surveys, the study demonstrates that there are only weak effects of digital media use on political learning, but that the use of some digital media forms has appreciable effects on political participation.


title: The DHS wealth index: Approaches for rural and urban areas

abstract: The DHS Wealth Index originally was constructed from existing data on household assets, services, and amenities in order to tabulate health, population, nutrition, education, and other indicators according to economic status. The Wealth Index has proved to be one of the most useful background characteristics available from the Demographic and Health Survey (DHS) data. It is now standard in DHS and UNICEF Multiple Indicator Cluster Survey (MICS) final reports and data sets. However, the Index has been criticized as being too urban in its construction and not able to distinguish the poorest of the poor from other poor households. This paper examines the extent of these problems and suggests and evaluates several possible remedies for them. One remedy that has already been taken by the DHS is to include questions in the standard questionnaires that have been specifically designed to ascertain rural stores of wealth and to distinguish among the poor. For example, questions have been added on rural stores of wealth, such as size of landholdings and number of farm animals by type. To better distinguish among the poor, the surveys ask about possession of furniture items, such as tables, chairs, and beds, because the extremely poor may not have such items. The lack of windows and the lack of windows with glass panes may also indicate an extremely poor household. A second remedy that could be applied is the use of urbanand rural-specific indexes, or quintile classifications of a common index. These approaches imply, respectively, the construction of separate indexes for urban and rural areas, or the calculation of wealth quintiles separately by type of area. A third approach would be to more finely divide the national index into deciles (which may distinguish better among the poor). These approaches are applied to data from the 2003 Bolivia DHS and to the 2007 Zambia DHS (in an appendix), and their advantages and drawbacks are discussed in the text. A fourth approach is to construct totally separate indexes for each area and then scale them so that a given score on each index means the same level of wealth. The paper describes two methods of combining separate rural and urban indexes. A method based on regression coefficients is demonstrated using data from the Bolivia 2003 and Zambia 2007 DHS surveys and proves the feasibility of basing urban and rural indexes on differing sets of indicator variables and then scaling these indicators so that a composite index can be calculated. This composite index allows comparability between urban and rural areas.


title: Bayesian Based Approach Learning for Outcome Prediction of Soccer Matches

abstract: In the current world, sports produce considerable data such as players skills, game results, season matches, leagues management, etc. The big challenge in sports science is to analyze this data to gain a competitive advantage. The analysis can be done using several techniques and statistical methods in order to produce valuable information. The problem of modeling soccer data has become increasingly popular in the last few years, with the prediction of results being the most popular topic. In this paper, we propose a Bayesian Model based on rank position and shared history that predicts the outcome of future soccer matches. The model was tested using a data set containing the results of over 200,000 soccer matches from different soccer leagues around the world.


title: Classification of Five Ambulatory Activities Regarding Stair and Incline Walking Using Smart Shoes

abstract: In this paper, we propose a novel method to classify five ambulatory activities, i.e., level ground, incline descent, incline ascent, stair descent, and stair ascent walking using smart shoes which contain eight plantar-pressure sensors on each shoe. Pressure data are collected using an insole-based monitoring system regarding the walking activities conducted by participants at their self-imposed “normal” speed. We present three new features based on an analysis of step patterns to characterize the ambulatory activities and utilize a k-nearest neighbor algorithm to classify the activities from the created features. In experimental results, we obtain walking activity-recognition error rates of 2.16% at the sixth walking step. Furthermore, a proposed method outperforms two reference methods in terms of F1-score and overall accuracy rate.


title: A Limb-Based Graphical Model for Human Pose Estimation

abstract: Modeling the relationship among human joints is one of the most important components in human pose estimation. Most of previous methods define this relationship as a geometric constraint on the relative locations of two neighboring joints. In this constraint, the local appearance of the region connecting two neighboring joints is ignored. However, discarding this image appearance leads to some severe problems, such as double-counting and localization failure when the human pose is rare in the training dataset. Moreover, this image appearance, called human limb, plays an important role in human pose estimation in human visual system. Due to these reasons, we propose to solve a new task: human limb detection, which aims at detecting and representing this local image appearance. We combine this task with human joint localization as a unified framework. After getting the initial detections, we design a two-steps graphical model to capture the spatial relationship among human joints and limbs in a coarse to fine way. We evaluate the proposed method on two widely used datasets for human pose estimation: 1) frame labeled in cinema and 2) leeds sports pose datasets. The experiments results show the effectiveness of our method.


title: A Compact All-Solid-State Repetitive Pulsed Power Modulator Based on Marx Generator and Pulse Transformer

abstract: In this paper, a compact all-solid-state pulsed power modulator was designed based on Marx generator and pulse transformer. It is consisted of a six-stage Marx generator using power MOSFETs as switches to generate primary voltage pulses and a pulse transformer through a closed magnetic core to boost and convert the polarity of the output high-voltage (HV) pulse. A MOSFET driver circuit based on optical–electrical isolation was designed for controlling the Marx generator. It shows that the output pulse shapes, especially the rising and falling phases, are determined by the design of the pulse transformer such as its leakage inductance, distributed capacitance, magnetization inductance, and step-up ratio. The designed modulator is able to generate HV pulses with amplitude ranging from −35 to 35 kV, repetition frequency up to 10 kHz, minimum pulsewidth 250 ns. Various types of plasma discharges, including air microdischarge for local treatments, plate-to-plate air dielectric-barrier discharge, underwater discharge, are able to be driven with good reproducibility by this compact pulse modulator.


title: A Low-Complexity Full-Duplex Radio Implementation With a Single Antenna

abstract: This paper presents a novel low-complexity full-duplex radio design, which only uses a single patch antenna without any duplexer or circulator for passive suppression of self-interference, and a computationally efficient technique for linear digital cancellation. The proposed full-duplex design is tested for IEEE 802.11g Wireless Standard on the WARP (v3) software-defined radio implementation platform. It is shown that this design provides a total suppression of 88 dB, which is sufficient for low-power or short-range full-duplex communication. The dual polarized slot coupled patch antenna used in our design provides an interport isolation as high as 60 dB in 2.4 GHz band. Additionally, the digital domain cancellation utilizes a frequency domain-based estimation and reconstruction approach, which not only offers up to $61\%$ reduction in the computational complexity but also provides a $5-7$ dB better digital cancellation performance in highly selective channel conditions, as compared to the time-domain-based techniques. The proposed full-duplex implementation can be easily applied in OFDM-based wireless systems, such as IEEE 802.11, which is the considered air interface in this paper.


title: mHealth for mental health: Integrating smartphone technology in behavioral healthcare.

abstract: The rapid growth in the use of smartphones has opened a new world of opportunities for use in behavioral health care. Mobile phone software applications (apps) are available for a variety of useful tasks to include symptom assessment, psychoeducation, resource location, and tracking of treatment progress. The latest two-way communication functionality of smartphones also brings new capabilities for telemental health. There is very little information available, however, regarding the integration of smartphone and other mobile technology into care. In this paper, we provide an overview of smartphone use in behavioral health care and discuss options for integrating mobile technology into clinical practice. We also discuss limitations, practical issues, and recommendations.


title: A Fully Integrated 900-MHz Passive RFID Transponder Front End With Novel Zero-Threshold RF–DC Rectifier

abstract: This paper presents a fully integrated CMOS analog front end for a passive 900-MHz radio-frequency identification (RFID) transponder. The power supply in this front end is generated from the received RF electromagnetic energy by using an RF-dc voltage rectifier. In order to improve the compatibility with standard CMOS technology, Schottky diodes in conventional RF-dc rectifiers are replaced by diode-connected MOS transistors with zero threshold. Meanwhile, theoretical analyses for the proposed rectifier are provided and verified by both simulation and measurement results. The design considerations of the pulsewidth-modulation (PWM) demodulator and the backscatter modulator in the front end are also discussed for low-power applications. The proposed front end is implemented in a 0.35-mum 2P4M CMOS technology. The whole chip occupies a die area of 490 times 780 mum2 and consumes only 2.1 muW in reading mode under a self-generated 1.5-V supply voltage. The measurement results show that the proposed rectifier can properly operate with a -14.7-dBm input RF power at a power conversion efficiency of 13.0%. In the proposed RFID applications, this sensitivity corresponds to 10.88-m communication distance at 4-W equivalent isotropically radiated power from a reader base station.


title: AN AUTOMATED FX TRADING SYSTEM USING ADAPTIVE REINFORCEMENT LEARNING

abstract: This paper introduces adaptive reinforcement learning (ARL) as the basis for a fully automated trading system application. The system is designed to trade foreign exchange (FX) markets and relies on a layered structure consisting of a machine learning algorithm, a risk management overlay and a dynamic utility optimization layer. An existing machine-learning method called recurrent reinforcement learning (RRL) was chosen as the underlying algorithm for ARL. One of the strengths of our approach is that the dynamic optimization layer makes a fixed choice of model tuning parameters unnecessary. It also allows for a risk-return trade-off to be made by the user within the system. The trading system is able to make consistent gains out-of-sample while avoiding large draw-downs.


title: What attributes guide the deployment of visual attention and how do they do it?

abstract: As you drive into the centre of town, cars and trucks approach from several directions, and pedestrians swarm into the intersection. The wind blows a newspaper into the gutter and a pigeon does something unexpected on your windshield. This would be a demanding and stressful situation, but you would probably make it to the other side of town without mishap. Why is this situation taxing, and how do you cope?


title: Wearable Thermoelectric Power Generators Combined With Flexible Supercapacitor for Low-Power Human Diagnosis Devices

abstract: This paper presents a wearable thermoelectric power generator using body heat to supply power to low-power human diagnosis devices (especially sensor nodes), thus a new type of thermoelectric generator (TEG) integrated into clothes that combines the TEG technology with fashion sports equipment: wrist guards, leg guards, headbands, and sports tights is also proposed. As a result of this integration, any difference in the appearance of a wearable thermoelectric power generator is not significant when compared with the original clothing. Through the design and experiment of wearable TEG, here several key factors that affect the human body heat energy: ambient temperatures, skin temperatures, wind, human activity, and the connection mode of the TEG module are analyzed. The experimental results show that the power of a single TEG module can reach about 250 μW at 5 °C, while the power of the multiple TEG modules including only four TEG can be close to 1 mW after the integration of sports tight. Finally, the flexible supercapacitor is utilized as the energy storage element, which shows excellent storage character and good effect of conjunction with clothing.


title: A 6.4-Gb/s Voltage-Mode Near-Ground Receiver With a One-Tap Data and Edge DFE

abstract: A low-power receiver with a one-tap data and edge decision-feedback equalizer (DFE) and a clock recovery circuit is presented. The receiver employs analog adders for the tap-weight summation in both the data and the edge path to simultaneously optimize both the voltage and timing margins. A switched-capacitor input stage allows the receiver to be fully compatible with near-GND input levels without extra level conversion circuits. Furthermore, the critical path of the DFE is simplified to relax the timing margin. Fabricated in the 65-nm CMOS technology, a prototype DFE receiver shows that the data-path DFE extends the voltage and timing margins from 40 $\hbox{mV}_{\rm pp}$ and 0.3 unit interval (UI), respectively, to 70 $\hbox{mV}_{\rm pp}$ and 0.6 UI, respectively. Likewise, the edge-path equalizer reduces the uncertain sampling region (the edge region), which results in 17% reduction of the recovered clock jitter. The DFE core, including adders and samplers, consumes 1.1 mW from a 1.2-V supply while operating at 6.4 Gb/s.