title: Coherent Acoustic Array Processing and Localization on Wireless Sensor Networks

abstract: Advances in microelectronics, array processing, and wireless networking have motivated the analysis and design of low-cost integrated sensing, computing, and communicating nodes capable of performing various demanding collaborative space–time processing tasks. In this paper, we consider the problem of coherent acoustic sensor array processing and localization on distributed wireless sensor networks. We first introduce some basic concepts of beamforming and localization for wide-band acoustic sources. A review of various known localization algorithms based on time-delay followed by least-squares estimations as well as the maximum–likelihood method is given. Issues related to practical implementation of coherent array processing, including the need for fine-grain time synchronization, are discussed. Then we describe the implementation of a Linux-based wireless networked acoustic sensor array testbed, utilizing commercially available iPAQs with built-in microphones, codecs, and microprocessors, plus wireless Ethernet cards, to perform acoustic source localization. Various field-measured results using two localization algorithms show the effectiveness of the proposed testbed. An extensive list of references related to this work is also included.


A Machine Learning Approach for Assessment and Prediction of Teamwork Effectiveness in Software Engineering Education


title: Virtual Reality Exposure Therapy (VRET) effectiveness and improvement.

abstract: The use of virtual reality in the treatment of anxiety disorders is growing. Virtual reality exposure therapy uses virtual reality environments to expose patients to realistic environments in which their fears can decrease. This technique is based on classical exposure therapy. For virtual reality exposure therapy to be effective, three aspects are of importance, and these aspects are related to each other. First of all, emotion eliciting capabilities of the virtual environment have to be strong enough, so that anxiety can be experienced. Next to that, cognitive presence experienced by people in VRET environments is an important factor. At last, the effects of VRET accomplished in virtual reality worlds will have to remain effective when a patient returns to the real physical world. Improvement of VRET may be done by moderating one of the mentioned aspects, but changing one will always result in changing the other two also.


title: Episodic Memory—From Brain to Mind

abstract: Neuronal mechanisms of episodic memory, the conscious recollection of autobiographical events, are largely unknown because electrophysiological studies in humans are conducted only in excep- tional circumstances. Unit recording studies in animals are thus crucial for understanding the neurophysiological substrate that enables people to remember their individual past. Two features of episodic memory— autonoetic consciousness, the self-aware ability to \travel through time", and one-trial learning, the acquisition of information in one occurrence of the event—raise important questions about the validity of animal models and the ability of unit recording studies to capture essen- tial aspects of memory for episodes. We argue that autonoetic experi- ence is a feature of human consciousness rather than an obligatory as- pect of memory for episodes, and that episodic memory is reconstruc- tive and thus its key features can be modeled in animal behavioral tasks that do not involve either autonoetic consciousness or one-trial learning. We propose that the most powerful strategy for investigating neurophys- iological mechanisms of episodic memory entails recording unit activity in brain areas homologous to those required for episodic memory in humans (e.g., hippocampus and prefrontal cortex) as animals perform tasks with explicitly defined episodic-like aspects. Within this frame- work, empirical data suggest that the basic structure of episodic mem- ory is a temporally extended representation that distinguishes the begin- ning from the end of an event. Future research is needed to fully under- stand how neural encodings of context, sequences of items/events, and goals are integrated within mnemonic representations of autobiographi- cal events. V C 2006 Wiley-Liss, Inc.


title: Hair Animation with Collision Detection

abstract: We propose an efficient method for hair animation. The movement of hairs is modeled by simplified physical simulations. In particular the method can treat successfully collisions between hair and a human body or other objects, which provides realistic hair animation. The fast collision detection is achieved using cylindrical representation of the head and human body parts, despite a large number of hairs. The cylindrical representation allows collision detection to be performed by table look-up and interpolation, which assures that the computation time is independent of the complexity of the objects. A reaction constraint algorithm is also applied for the collision reaction to simulate inelastic contact. The efficiency of the method is well illustrated by the animation obtained.


title: Primary and secondary glioblastomas: From concept to clinical diagnosis

abstract: Glioblastomas may develop de novo (primary glioblastomas) or through progression from low-grade or anaplastic astrocytomas, (secondary glioblastomas). These subtypes of glioblastoma constitute distinct disease entities that evolve through different genetic pathways, affect patients at different ages, and are likely to differ in prognosis and response to therapy. Primary glioblastomas develop in older patients and typically show EGFR overexpression, PTEN (MMAC1) mutations, CDKN2A (p16) deletions, and less frequently, MDM2 amplification. Secondary glioblastomas develop in younger patients and often contain TP53 mutations as the earliest detectable alteration. These characteristics are derived largely from patients selected on the basis of clinical history and sequential biopsies. Currently available data are insufficient for a substitution of histologic classification and grading of astrocytic tumors by genetic typing alone. More subtypes of glioblastomas may exist with intermediate clinical and genetic profiles, a factor exemplified by the giant-cell glioblastoma that clinically and genetically occupies a hybrid position between primary (de novo) and secondary glioblastomas. Future research should aim at the identification of criteria for a combined clinical, histologic, and genetic classification of astrocytic tumors.


title: There are planar graphs almost as good as the complete graphs and almost as cheap as minimum spanning trees

abstract: Let S be a set ofn points in the plane. For an arbitrary positive rationalr, we construct a planar straight-line graph onS that approximates the complete Euclidean graph onS within the factor (1 + 1/r)[2π/3 cos(π/6)], and it has length bounded by 2r + 1 times the length of a minimum Euclidean spanning tree onS. Given the Deiaunay triangulation ofS, the graph can be constructed in linear time.


title: A distributed framework for information retrieval, processing and presentation of data

abstract: The Internet is ever growing and the information is highly unstructured. Finding the best priced product when shopping online is a difficult task, but shopping search engines help the user identify the desired product. Starting from this reality, this paper presents a distributed framework for information retrieval, processing and presentation of data, which has three main components: a data server cluster, a distributed crawler with a product extraction feature, and a web server for presenting the processed data. The considered use case consists of extracting information about board and card games from specialized websites and presenting the data in a user-friendly website. Efficiency is determined by means of performance evaluation metrics that include crawl and extraction times, and a discussion in terms of scalability. The high modularization of the framework allows it to be an effective teaching tool in classrooms; each module can be easily swapped with a custom implementation. The proposed framework offers students and researchers an environment for developing and testing a wide range of algorithms and programming solutions, e.g., load balancer, crawler, reverse engineering a web template, product extractor from web pages, data normalization, notification services, web APIs, custom database.


Neural Program Synthesis from Diverse Demonstration Videos


IS'95: Guideline for Undergraduate IS Curriculum


title: Low-Density Parity-Check Codes for ATSC 3.0

abstract: In this paper, we introduce the overview and detailed structure of low-density parity-check (LDPC) codes, which are adopted for the physical layer standard of Advanced Television Systems Committee (ATSC) 3.0. As one of the features to mitigate channel errors efficiently, LDPC codes are used as a main part of forward error correction scheme within the bit interleaved coded modulation chain. This paper presents two different structures of LDPC codes in the ATSC 3.0 standard: 1) irregular repeat accumulate structure and 2) multi-edge type structure. After the detailed encoding methods for the two structures are introduced, the performance results including comparison studies with other broadcasting standards such as DVB-T2 and DVB-S2 are shown.


Cambricon: An Instruction Set Architecture for Neural Networks


Grounded Ontology Methodology – Illustrating the Seed Ontology Creation


title: Beyond formality: informal communication in health practices

abstract: Despite an increasing use of formal healthcare systems such as the Electronic Medical Record (EMR) and communication devices, informal communication continues to play an important role in the highly collaborative, dynamic, and information-rich medical work. Formal systems often fail to support the spontaneous and opportunistic needs of healthcare providers to communicate patient information. Yet it is not clear what constitutes informal communication in healthcare practices, what roles does it play in the patient care process, what types of technologies can be designed to support informal communication, and how to design them. This workshop aims to gather interested researchers to better understand informal communication in healthcare, to discuss their implications for CSCW healthcare research, and to brainstorm technological innovations that can support the informal aspects of health communication.


title: Hidden voice commands

abstract: Voice interfaces are becoming more ubiquitous and are now the primary input method for many devices. We explore in this paper how they can be attacked with hidden voice commands that are unintelligible to human listeners but which are interpreted as commands by devices. ::: ::: We evaluate these attacks under two different threat models. In the black-box model, an attacker uses the speech recognition system as an opaque oracle. We show that the adversary can produce difficult to understand commands that are effective against existing systems in the black-box model. Under the white-box model, the attacker has full knowledge of the internals of the speech recognition system and uses it to create attack commands that we demonstrate through user testing are not understandable by humans. ::: ::: We then evaluate several defenses, including notifying the user when a voice command is accepted; a verbal challenge-response protocol; and a machine learning approach that can detect our attacks with 99.8% accuracy.


title: Fast and general distributed transactions using RDMA and HTM

abstract: Recent transaction processing systems attempt to leverage advanced hardware features like RDMA and HTM to significantly boost performance, which, however, pose several limitations like requiring priori knowledge of read/write sets of transactions and providing no availability support. In this paper, we present DrTM+R, a fast in-memory transaction processing system that retains the performance benefit from advanced hardware features, while supporting general transactional workloads and high availability through replication. DrTM+R addresses the generality issue by designing a hybrid OCC and locking scheme, which leverages the strong atomicity of HTM and the strong consistency of RDMA to preserve strict serializability with high performance. To resolve the race condition between the immediate visibility of records updated by HTM transactions and the unready replication of such records, DrTM+R leverages an optimistic replication scheme that uses seqlock-like versioning to distinguish the visibility of tuples and the readiness of record replication. Evaluation using typical OLTP workloads like TPC-C and SmallBank shows that DrTM+R scales well on a 6-node cluster and achieves over 5.69 and 94 million transactions per second without replication for TPC-C and SmallBank respectively. Enabling 3-way replication on DrTM+R only incurs at most 41% overhead before reaching network bottleneck, and is still an order-of-magnitude faster than a state-of-the-art distributed transaction system (Calvin).


title: Auctions for Social Lending: A Theoretical Analysis

abstract: Prosper, today the second largest social lending marketplace with nearly 1.5 million members and $380 million in funded loans, employed an auction mechanism amongst lenders to finance each borrower's loan until 2010. Given that a basic premise of social lending is cheap loans for borrowers, how does the Prosper auction do in terms of the borrower's payment, when lenders are strategic agents with private true interest rates? We first analyze the Prosper auction as a game of complete information and fully characterize its Nash equilibria, and show that the uniform-price Prosper mechanism, while simple, can lead to much larger payments for the borrower than the VCG mechanism. We next compare the Prosper mechanism against the borrower-optimal auction in an incomplete information setting, and conclude by examining the Prosper mechanism when modeled as a dynamic auction, and provide tight bounds on the price for a general class of bidding strategies.


title: Multimodal Deep Learning for Cervical Dysplasia Diagnosis

abstract: To improve the diagnostic accuracy of cervical dysplasia, it is important to fuse multimodal information collected during a patient’s screening visit. However, current multimodal frameworks suffer from low sensitivity at high specificity levels, due to their limitations in learning correlations among highly heterogeneous modalities. In this paper, we design a deep learning framework for cervical dysplasia diagnosis by leveraging multimodal information. We first employ the convolutional neural network (CNN) to convert the low-level image data into a feature vector fusible with other non-image modalities. We then jointly learn the non-linear correlations among all modalities in a deep neural network. Our multimodal framework is an end-to-end deep network which can learn better complementary features from the image and non-image modalities. It automatically gives the final diagnosis for cervical dysplasia with 87.83 % sensitivity at 90 % specificity on a large dataset, which significantly outperforms methods using any single source of information alone and previous multimodal frameworks.


title: CRISPR/Cas9 in Genome Editing and Beyond.

abstract: The Cas9 protein (CRISPR-associated protein 9), derived from type II CRISPR (clustered regularly interspaced short palindromic repeats) bacterial immune systems, is emerging as a powerful tool for engineering the genome in diverse organisms. As an RNA-guided DNA endonuclease, Cas9 can be easily programmed to target new sites by altering its guide RNA sequence, and its development as a tool has made sequence-specific gene editing several magnitudes easier. The nuclease-deactivated form of Cas9 further provides a versatile RNA-guided DNA-targeting platform for regulating and imaging the genome, as well as for rewriting the epigenetic status, all in a sequence-specific manner. With all of these advances, we have just begun to explore the possible applications of Cas9 in biomedical research and therapeutics. In this review, we describe the current models of Cas9 function and the structural and biochemical studies that support it. We focus on the applications of Cas9 for genome editing, regulation, and imaging, discuss other possible applications and some technical considerations, and highlight the many advantages that CRISPR/Cas9 technology offers.


title: Introduction to the Special Section on Deep Learning for Speech and Language Processing

abstract: Current speech recognition systems, for example, typically use Gaussian mixture models (GMMs), to estimate the observation (or emission) probabilities of hidden Markov models (HMMs), and GMMs are generative models that have only one layer of latent variables. Instead of developing more powerful models, most of the research effort has gone into finding better ways of estimating the GMM parameters so that error rates are decreased or the margin between different classes is increased. The same observation holds for natural language processing (NLP) in which maximum entropy (MaxEnt) models and conditional random fields (CRFs) have been popular for the last decade. Both of these approaches use shallow models whose success largely depends on the use of carefully handcrafted features.


title: A high gain steerable millimeter-wave antenna array for 5G smartphone applications

abstract: In this paper, a phased array antenna is designed at millimeter-wave frequency bands for future 5G based smartphone applications. The proposed antenna is a novel open slot-PIFA antenna made on a low cost FR4 board. The antenna array covers a frequency range of 26–32 GHz with a bandwidth of 6 GHz. The antenna exhibits a very good radiation pattern when integrated with the mobile phone chassis. The 8 — element antenna array exhibits a maximum gain around 13 dBi. The pattern can be steered by varying the phase shift at each antenna element.


title: Improved link level LTE scheduler

abstract: Long Term Evolution (LTE) packet scheduler is a key mechanism in the LTE traffic handling. It is mainly in charge of allocating physical resource blocks for the attached UEs. The main problem in LTE scheduler is to maximize the throughput while maintaining the fairness. The performance of scheduler is very critical, especially when many UEs are located at the cell edge. The new proposed scheduler can give better cell throughput performance and at the same time maintain the fairness. The findings from the performance results show an increased up to 16 percent of cell throughput compared to proportional fair (PF) scheduler. The result also shows that the fairness level is almost the same as the PF scheduler.


title: End-to-end learning of semantic role labeling using recurrent neural networks

abstract: Semantic role labeling (SRL) is one of the basic natural language processing (NLP) problems. To this date, most of the successful SRL systems were built on top of some form of parsing results (Koomen et al., 2005; Palmer et al., 2010; Pradhan et al., 2013), where pre-defined feature templates over the syntactic structure are used. The attempts of building an end-to-end SRL learning system without using parsing were less successful (Collobert et al., 2011). In this work, we propose to use deep bi-directional recurrent network as an end-to-end system for SRL. We take only original text information as input feature, without using any syntactic knowledge. The proposed algorithm for semantic role labeling was mainly evaluated on CoNLL-2005 shared task and achieved F1 score of 81.07. This result outperforms the previous state-of-the-art system from the combination of different parsing trees or models. We also obtained the same conclusion with F1 = 81.27 on CoNLL2012 shared task. As a result of simplicity, our model is also computationally efficient that the parsing speed is 6.7k tokens per second. Our analysis shows that our model is better at handling longer sentences than traditional models. And the latent variables of our model implicitly capture the syntactic structure of a sentence.


title: Deep Generative Model using Unregularized Score for Anomaly Detection with Heterogeneous Complexity

abstract: Accurate and automated detection of anomalous samples in a natural image dataset can be accomplished with a probabilistic model for end-to-end modeling of images. Such images have heterogeneous complexity, however, and a probabilistic model overlooks simply shaped objects with small anomalies. This is because the probabilistic model assigns undesirably lower likelihoods to complexly shaped objects that are nevertheless consistent with set standards. To overcome this difficulty, we propose an unregularized score for deep generative models (DGMs), which are generative models leveraging deep neural networks. We found that the regularization terms of the DGMs considerably influence the anomaly score depending on the complexity of the samples. By removing these terms, we obtain an unregularized score, which we evaluated on a toy dataset and real-world manufacturing datasets. Empirical results demonstrate that the unregularized score is robust to the inherent complexity of samples and can be used to better detect anomalies.


title: A Compact CRLH Circularly Polarized Leaky-Wave Antenna Based on Substrate-Integrated Waveguide

abstract: A compact single-radiator circularly polarized (CP) leaky-wave antenna (LWA) based on composite right/left-handed (CRLH) transmission line is presented. The proposed antenna is based on substrate-integrated waveguide (SIW), and its unit cell consists of a T-shaped interdigital slot, which provides a CRLH property, a proper CP, and also effective compactness for the full-length LWA. The proposed LWA provides beam scanning from −19° to +84° over the frequency range of 7.35–10.15 GHz, while maintaining a good CP. By providing the balanced condition for the CRLH unit cell, a continuous backward-to-forward scanning including broadside is achieved. The maximum gain of the LWA is 8.95 dB which is satisfactory, considering the compactness of the antenna. Moreover, the radiation efficiency has an average of 85.7% over the operating frequency band. The measurement results are in good agreement with the simulation ones and show considerable advantages over the SIW-based CP LWAs proposed in the recent literature.


title: Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation

abstract: In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3. ::: The MobileNetV2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input an MobileNetV2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on Imagenet classification, COCO object detection, VOC image segmentation. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as the number of parameters


title: Regression Conformal Prediction with Nearest Neighbours

abstract: In this paper we apply Conformal Prediction (CP) to the k-Nearest Neighbours Regression (k-NNR) algorithm and propose ways of extending the typical nonconformity measure used for regression so far. Unlike traditional regression methods which produce point predictions, Conformal Predictors output predictive regions that satisfy a given confidence level. The regions produced by any Conformal Predictor are automatically valid, however their tightness and therefore usefulness depends on the nonconformity measure used by each CP. In effect a nonconformity measure evaluates how strange a given example is compared to a set of other examples based on some traditional machine learning algorithm. We define six novel nonconformity measures based on the k-Nearest Neighbours Regression algorithm and develop the corresponding CPs following both the original (transductive) and the inductive CP approaches. A comparison of the predictive regions produced by our measures with those of the typical regression measure suggests that a major improvement in terms of predictive region tightness is achieved by the new measures.


title: Shedding light on the cell biology of extracellular vesicles

abstract: Cells produce a wide variety of extracellular vesicles (subdivided into exosomes and microvesicles), which carry a multitude of cargoes, including proteins, lipids and nucleic acids. These vesicles have emerged as important means of cell–cell communication in physiology and disease, and their use in the clinic is now being explored.


title: Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation

abstract: The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image. Contrary to "instance-level" 6D pose estimation tasks, our problem assumes that no exact object CAD models are available during either training or testing time. To handle different and unseen object instances in a given category, we introduce a Normalized Object Coordinate Space (NOCS)---a shared canonical representation for all possible object instances within a category. Our region-based neural network is then trained to directly infer the correspondence from observed pixels to this shared object representation (NOCS) along with other object information such as class label and instance mask. These predictions can be combined with the depth map to jointly estimate the metric 6D pose and dimensions of multiple objects in a cluttered scene. To train our network, we present a new context-aware technique to generate large amounts of fully annotated mixed reality data. To further improve our model and evaluate its performance on real data, we also provide a fully annotated real-world dataset with large environment and instance variation. Extensive experiments demonstrate that the proposed method is able to robustly estimate the pose and size of unseen object instances in real environments while also achieving state-of-the-art performance on standard 6D pose estimation benchmarks.


title: WinBUGS – A Bayesian modelling framework: Concepts, structure, and extensibility

abstract: WinBUGS is a fully extensible modular framework for constructing and analysing Bayesian full probability models. Models may be specified either textually via the BUGS language or pictorially using a graphical interface called DoodleBUGS. WinBUGS processes the model specification and constructs an object-oriented representation of the model. The software offers a user-interface, based on dialogue boxes and menu commands, through which the model may then be analysed using Markov chain Monte Carlo techniques. In this paper we discuss how and why various modern computing concepts, such as object-orientation and run-time linking, feature in the software's design. We also discuss how the framework may be extended. It is possible to write specific applications that form an apparently seamless interface with WinBUGS for users with specialized requirements. It is also possible to interface with WinBUGS at a lower level by incorporating new object types that may be used by WinBUGS without knowledge of the modules in which they are implemented. Neither of these types of extension require access to, or even recompilation of, the WinBUGS source-code.


title: Face Alignment by Local Deep Descriptor Regression

abstract: We present an algorithm for extracting key-point descriptors using deep convolutional neural networks (CNN). Unlike many existing deep CNNs, our model computes local features around a given point in an image. We also present a face alignment algorithm based on regression using these local descriptors. The proposed method called Local Deep Descriptor Regression (LDDR) is able to localize face landmarks of varying sizes, poses and occlusions with high accuracy. Deep Descriptors presented in this paper are able to uniquely and efficiently describe every pixel in the image and therefore can potentially replace traditional descriptors such as SIFT and HOG. Extensive evaluations on five publicly available unconstrained face alignment datasets show that our deep descriptor network is able to capture strong local features around a given landmark and performs significantly better than many competitive and state-of-the-art face alignment algorithms.


title: Unconstrained face recognition: Establishing baseline human performance via crowdsourcing

abstract: Research focus in face recognition has shifted towards recognition of faces “in the wild” for both still images and videos which are captured in unconstrained imaging environments and without user cooperation. Due to confounding factors of pose, illumination, and expression, as well as occlusion and low resolution, current face recognition systems deployed in forensic and security applications operate in a semi-automatic manner; an operator typically reviews the top results from the face recognition system to manually determine the final match. For this reason, it is important to analyze the accuracies achieved by both the matching algorithms (machines) and humans on unconstrained face recognition tasks. In this paper, we report human accuracy on unconstrained faces in still images and videos via crowd-sourcing on Amazon Mechanical Turk. In particular, we report the first human performance on the YouTube Faces database and show that humans are superior to machines, especially when videos contain contextual cues in addition to the face image. We investigate the accuracy of humans from two different countries (United States and India) and find that humans from the United States are more accurate, possibly due to their familiarity with the faces of the public figures in the YouTube Faces database. A fusion of recognitions made by humans and a commercial-off-the-shelf face matcher improves performance over humans alone.


title: An Online Learning Approach for Dengue Fever Classification

abstract: This paper introduces a novel approach for dengue fever classification based on online learning paradigms. The proposed approach is suitable for practical implementation as it enables learning using only a few training samples. With time, the proposed approach is capable of learning incrementally from the data collected without need for retraining the model or redeployment of the prediction engine. Additionally, we also provide a comprehensive evaluation of machine learning methods for prediction of dengue fever. The input to the proposed pipeline comprises of recorded patient symptoms and diagnostic investigations. Offline classifier models have been employed to obtain baseline scores to establish that the feature set is optimal for classification of dengue. The primary benefit of the online detection model presented in the paper is that it has been established to effectively identify patients with high likelihood of dengue disease, and experiments on scalability in terms of number of training and test samples validate the use of the proposed model.


title: The diagnostic value of digital subtraction arthrography and radionuclide bone scan in revision hip arthroplasty

abstract: A prospective analysis of plain serial radigraphs (PSR), digital subtraction arthrography (DSA), and radionuclide bone scans (RBS) was performed in 56 cemented total hip arthroplasties to evaluate the efficacy and usefulness of each study in the diagnosis of loosening. To avoid selection bias in the evaluation of DSA and RBS, the decision to perform repeat surgery was based exclusively on the clinical history and PSR. Results of each study were compared with intraoperative assessment of the status of components and expressed in terms of sensitivity, specificity, and accuracy. Overall accuracy for the acetabular component by PSR was 66%; by DSA, 93%; by RBS, 46%. Overall accuracy for the femoral component by PSR was 79%; by DSA, 93%; RBS, 50%. Our results indicate that DSA can be recommended as a further analysis in cases with a painful hip prosthesis and no or inconclusive findings on PSR. RBS did not give any useful information and cannot be recommended routinely.


title: Reconfigurable coordination model for dynamic autonomous real-time systems

abstract: In an open environment, in addition to satisfy both temporal and functional requirements, real-time information systems are further complicated by the dynamicity of composing autonomous entities. The paper presents a real-time reconfigurable coordination model (RT-RCC) that decomposes dynamic realtime information systems based on the principle of separation of concerns, in contrast to traditional object-oriented functional decompositions. In particular, dynamic open distributed real-time systems are decomposed into two categories of active entities, namely, functional actors which are responsible for accomplishing tasks, and non-functional coordinators which are responsible for coordination among the functional actors. High level language abstractions and a framework for actors and coordinators are provided to facilitate programming with the RT-RCC model. Our initial performance evaluation of the framework in non real-time environment has shown that the overhead introduced by separating coordinators from functional entities is less than 2%, the benefit gained is the decoupling of non-functional and functional requirements and the improvement of the software modularity and reusability in both functional and non-functional domains.


title: From IT Capabilities to Supply Chain Performance: The Mediating Effects of Supply Chain Agility and Absorptive Capacity

abstract: While information technologies have been taken as the competitive tool in improving supply chain performance, its investment cannot guarantee to meet firms’ performance expectations. Our understanding about the mechanisms by which IT affects supply chain performance remains unclear. Based on the perspective of dynamic capabilities theory, we derive a model to examine the effects of a firm’s IT capabilities, namely IT infrastructure flexibility and IT assimilation on supply chain performance. In particular, we examine the mediating effects of the firm’s higher-order organizational capabilities, namely supply chain agility and absorptive capacity on the relationships between IT capabilities and supply chain performance. Results from a survey show that the firm’s supply chain agility can fully mediate IT capabilities’ influence, while absorptive capacity partially mediate the influences of IT capabilities on supply chain agility. In addition, IT infrastructure flexibility can improve the firm’s IT assimilation. Contributions and implications of this study are discussed.


title: Spatio-Temporally Consistent Correspondence for Dense Dynamic Scene Modeling

abstract: We address the problem of robust two-view correspondence estimation within the context of dynamic scene modeling. To this end, we investigate the use of local spatio-temporal assumptions to both identify and refine dense low-level data associations in the absence of prior dynamic content models. By developing a strictly data-driven approach to correspondence search, based on bottom-up local 3D motion cues of local rigidity and non-local coherence, we are able to robustly address the higher-order problems of video synchronization and dynamic surface modeling. Our findings suggest an important relationship between these two tasks, in that maximizing spatial coherence of surface points serves as a direct metric for the temporal alignment of local image sequences. The obtained results for these two problems on multiple publicly available dynamic reconstruction datasets illustrate both the effectiveness and generality of our proposed approach.


title: Enhancing Chinese Word Segmentation Using Unlabeled Data

abstract: This paper investigates improving supervised word segmentation accuracy with unlabeled data. Both large-scale in-domain data and small-scale document text are considered. We present a unified solution to include features derived from unlabeled data to a discriminative learning model. For the large-scale data, we derive string statistics from Gigaword to assist a character-based segmenter. In addition, we introduce the idea about transductive, document-level segmentation, which is designed to improve the system recall for out-of-vocabulary (OOV) words which appear more than once inside a document. Novel features result in relative error reductions of 13.8% and 15.4% in terms of F-score and the recall of OOV words respectively.


title: Parameter identification of a DC motor via distribution based approach

abstract: In this paper one presents an algorithm for a DC motor parameters identification from sample data using the distribution approach. While most of the latest methods used in identification utilize a discrete-time model, the distribution method is an alternative approach to directly identify a continuous-time model from discrete-time data. The relation between the state variables is represented by functionals using techniques from distribution theory. Based on these relations, an algorithm for off-line parameter identification is developed. The method is applied to identify the parametric model of a real experimental platform and of a simulated model.


title: Partisan Media Exposure and Attitudes Toward the Opposition

abstract: How has the rise of partisan media outlets changed how citizens perceive the other party? In particular, does watching partisan news sources make citizens dislike and distrust the other party? Drawing on social identity theory, I explain how the slanted presentation of the news on partisan outlets leads viewers to perceive the other party more negatively, to trust them less, and to be less supportive of bipartisanship. Using a series of original experiments, I find strong support for my arguments. I conclude by discussing the normative and empirical implications of these findings.[Supplementary material is available for this article. Go to the publisher's online edition of Political Communication for the following free supplemental resource(s): full details of the experiments, including descriptions of the samples, protocol, and text of the stimuli, results of manipulation checks, the replication of experiment 2 described in the text, and additional statistical results.]


Text Mining Infrastructure in R


title: Exploring decision-making processes in Python

abstract: The process by which norms are developed to become policies, the normative decision-making process, is not often explicit to stakeholders of Open Source Software (OSS) projects. Understanding the normative decision-making process is crucial for members if such projects are to evolve and succeed. In this paper, we investigated aspects of the normative decision-making processes of OSS development through the use of Python Enhancement Proposals (PEPs). We compared extracted process models with those that are advertised by the Python community to evaluate the extent to which those processes overlap. In addition, we assess members' involvement and contribution to these processes. Our work used structural and behavioral analysis techniques, and social network analysis metrics. We found that there were differences between the extracted processes and Python's advertised process, with the extracted processes being significantly more complex. These differences also extended to granular models used for managing social and technical aspects of the Python project. Furthermore, some key members were largely responsible for PEPs' success. Our extracted models could go a far way in helping the Python community to quickly understand decision-making processes in Python.


title: On the Use of Zero-Crossing Rate for an Application of Classification of Percussive Sounds

abstract: We address the issue of automatically extracting rhythm descriptors from audio signals, to be eventually used in content-based musical applications such as in the context of MPEG7. Our aim is to approach the comprehension of auditory scenes in raw polyphonic audio signals without preliminary source separation. As a first step towards the automatic extraction of rhythmic structures out of signals taken from the popular music repertoire, we propose an approach for automatically extracting time indexes of occurrences of different percussive timbres in an audio signal. Within this framework, we found that a particular issue lies in the classification of percussive sounds. In this paper, we report on the method currently used to deal with this problem.


title: BIOMETRIC TECHNOLOGIES AND APPLICATIONS

abstract: Biometric technologies focus on the verification and identification of humans using their possessed biological (anatomical, physiological and behavioral) properties. This paper overview current advances in biometric technologies, as well as some aspects of system integration, privacy and security and accompanying problems such as testing and evaluation.


title: Plant leaf recognition using texture and shape features with neural classifiers

abstract: This paper proposes a novel methodology of characterizing and recognizing plant leaves using a combination of texture and shape features. Texture of the leaf is modeled using Gabor filter and gray level co-occurrence matrix (GLCM) while shape of the leaf is captured using a set of curvelet transform coefficients together with invariant moments. Since these features are in general sensitive to the orientation and scaling of the leaf image, a pre-processing stage prior to feature extraction is applied to make corrections for varying translation, rotation and scaling factors. Efficacy of the proposed methods is studied by using two neural classifiers: a neuro-fuzzy controller (NFC) and a feed-forward back-propagation multi-layered perceptron (MLP) to discriminate between 31 classes of leaves. The features have been applied individually as well as in combination to investigate how recognition accuracies can be improved. Experimental results demonstrate that the proposed approach is effective in recognizing leaves with varying texture, shape, size and orientations to an acceptable degree. Methodology for plant leaf recognition using shape and texture features is proposed.Features are made invariant to scaling and orientation of leaf images.Classification is done using two different types of neural classifiers.System is tested using both known and unknown classes of leaf images.System is also designed to handle images with small amounts of deformations.


title: Price of Anarchy of Innovation Diffusion in Social Networks

abstract: There have been great efforts in studying the cascading behavior in social networks such as the innovation diffusion, etc. Game theoretically, in a social network where individuals choose from two strategies: A (the innovation) and B (the status quo) and get payoff from their neighbors for coordination, it has long been known that the Price of Anarchy (PoA) of this game is not 1, since the Nash equilibrium (NE) where all players take B (B Nash) is inferior to the one all players taking A (A Nash). However, no quantitative analysis has been performed to give an accurate upper bound of PoA in this game. In this paper, we adopt a widely used networked coordination game setting [3] to study how bad a Nash equilibrium can be and give a tight upper bound of the PoA of such games. We show that there is an NE that is slightly worse than the B Nash. On the other hand, the PoA is bounded and the worst NE cannot be much worse than the B Nash. In addition, we discuss how the PoA upper bound would change when compatibility between A and B is introduced, and show an intuitive result that the upper bound strictly decreases as the compatibility is increased.


title: A goal-oriented simulation approach for obtaining good private cloud-based system architectures

abstract: Highlights? We show one way of rationally coming up with a suitable design for a private cloud. ? Stakeholder goals are identified early, analyzed and used to drive the proposed process. ? Cloud Computing simulations are used iteratively and in an interleaving manner to evaluate design decisions. ? The proposed approach borrows heavily from existing techniques in Software Engineering. The fast-growing Cloud Computing paradigm makes it possible to use unprecedented amounts of computing resources at lower costs, among other benefits such as fast provisioning and reliability. In designing a good architecture - the numbers, types and layouts of devices - for a cloud-based system, which meets the goals of all stakeholders, such goals need to be factored in from the earliest stages. However, there seems to be a lack of methodologies for incorporating stakeholder goals into the design process for such systems, and for assuring with higher confidence that the designs are likely to be good enough for the stated goals. In this paper, we propose a goal-oriented simulation approach for cloud-based system design whereby stakeholder goals are captured, together with such domain characteristics as workflows, and used in creating a simulation model as a proxy for the cloud-based system architecture. Simulations are then run, in an interleaving manner, against various configurations of the model as a way of rationally exploring, evaluating and selecting among incrementally better architectural alternatives. We illustrate important aspects of this approach for the private cloud deployment model and report on our experiments, using a smartcard-based public transportation system.


title: The potential of automatic methods of classification to identify leaf diseases from multispectral images

abstract: Three methods of automatic classification of leaf diseases are described based on high-resolution multispectral stereo images. Leaf diseases are economically important as they can cause a loss of yield. Early and reliable detection of leaf diseases has important practical relevance, especially in the context of precision agriculture for localized treatment with fungicides. We took stereo images of single sugar beet leaves with two cameras (RGB and multispectral) in a laboratory under well controlled illumination conditions. The leaves were either healthy or infected with the leaf spot pathogen Cercospora beticola or the rust fungus Uromyces betae. To fuse information from the two sensors, we generated 3-D models of the leaves. We discuss the potential of two pixelwise methods of classification: k-nearest neighbour and an adaptive Bayes classification with minimum risk assuming a Gaussian mixture model. The medians of pixelwise classification rates achieved in our experiments are 91% for Cercospora beticola and 86% for Uromyces betae. In addition, we investigated the potential of contextual classification with the so called conditional random field method, which seemed to eliminate the typical errors of pixelwise classification.


title: Usability principles and best practices for the user interface design of complex 3D architectural design and engineering tools

abstract: This study proposes usability principles for the user interfaces (UI) design of complex 3D parametric architectural design and engineering tools. Numerous usability principles have been developed for generic desktop or web applications. The authors tried to apply existing usability principles as guidelines for evaluating complex 3D design and engineering applications. However, the principles were too generic and high-level to be useful as design or evaluation guidelines. The authors, all with more than 10 or 30 years of experience with various CAD systems, selected and reviewed 10 state-of-the-art 3D parametric design and engineering applications and captured what they thought were best practices, as screenshots and videos. The collected best practices were reviewed through a series of discussion sessions. During the discussion sessions, UI design principles underlying the collected best practices were characterized in the line of existing UI principles. Based on the best practices and the derived common UI principles, a new set of refined and detailed UI principles were proposed for improving and evaluating 3D parametric engineering design tools in the future.


title: Fast facial expression recognition based on local binary patterns

abstract: Automatic facial expression analysis is an interesting and challenging problem which impacts important applications in many areas such as human-computer interaction and data-driven animation. Deriving effective facial representative features from face images is a vital step towards successful expression recognition. In this paper, we evaluate facial representation based on statistical local features called Local Binary Patterns (LBP) for facial expression recognition. Simulation results illustrate that LBP features are effective and efficient for facial expression recognition. A real-time implementation of the proposed approach is also demonstrated which can recognize expressions accurately at the rate of 4.8 frames per second.


title: A Review of Relational Machine Learning for Knowledge Graphs: From Multi-Relational Link Prediction to Automated Knowledge Graph Construction

abstract: Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be "trained" on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph). In particular, we discuss two different kinds of statistical relational models, both of which can scale to massive datasets. The first is based on tensor factorization methods and related latent variable models. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. In particular, we discuss Google's Knowledge Vault project.


title: 5G multi-RAT multi-connectivity architecture

abstract: The 5th generation of mobile networks is envisioned to unify different access types under one system in order to enable efficient and performant operations. We propose a radio network architecture for tight integration of multiple radio access technologies supporting traffic steering, link selection, and aggregation of traffic flows from and to different sources. This enables the radio network architecture to support better throughput, and increased reliability with different levels of mobility. Specifically, we propose a common user plane and control plane across different radio technologies utilizing similar principles, such that the joint operation of radio technologies can be optimized.


title: A transparent bending-insensitive pressure sensor

abstract: A composite fibrous material made of carbon nanotubes and graphene responds to small pressure but not to bending deformation.


title: Exploring Topic Coherence over Many Models and Many Topics

abstract: We apply two new automated semantic evaluations to three distinct latent topic models. Both metrics have been shown to align with human evaluations and provide a balance between internal measures of information gain and comparisons to human ratings of coherent topics. We improve upon the measures by introducing new aggregate measures that allows for comparing complete topic models. We further compare the automated measures to other metrics for topic models, comparison to manually crafted semantic tests and document classification. Our experiments reveal that LDA and LSA each have different strengths; LDA best learns descriptive topics while LSA is best at creating a compact semantic representation of documents and words in a corpus.


title: Amino acid substitution matrices from an information theoretic perspective

abstract: Protein sequence alignments have become an important tool for molecular biologists. Local alignments are frequently constructed with the aid of a "substitution score matrix" that specifies a score for aligning each pair of amino acid residues. Over the years, many different substitution matrices have been proposed, based on a wide variety of rationales. Statistical results, however, demonstrate that any such matrix is i.mplicitly a "log-odds" matrix, with a specific target distribution for aligned pairs of amino acid residues. In the light of information theory, it is possible to express the scores of a substitution matrix in bits and to see that different matrices are better adapted to different purposes. The most widely used matrix for protein sequence comparison has been the PAM-250 matrix. It is argued that for database searches the PAM-,I20 matrix generally is more appropriate, while for comparing two specific proteins with.suspecte4 homology the PAM-200 matrix is indicated. Examples discussed include the lipocalins, human a,B-glycoprotein, the cystic fibrosis transmembrane conductance regulator and the globins.


title: Scaling Optoelectronic-VLSI Circuits into the 21st Century: A Technology Roadmap

abstract: Technologies now exist for implementing dense surface-normal optical interconnections for silicon CMOS VLSI using hybrid integration techniques. The critical factors in determining the performance of the resulting photonic chip are the yield on the transceiver device arrays, the sensitivity and power dissipation of the receiver and transmitter circuits, and the total optical power budget available. The use of GaAs-AlGaAs multiple-quantum-well p-i-n diodes for on-chip detection and modulation is one effective means of implementing the optoelectronic transceivers. We discuss a potential roadmap for the scaling of this hybrid optoelectronic VLSI technology as CMOS linewidths shrink and the characteristics of the hybrid optoelectronic transceiver technology improve. An important general conclusion is that, unlike electrical interconnects, such dense optical interconnections directly to an electronic circuit will likely be able to scale in capacity to match the improved performance of future CMOS technology.


title: Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication

abstract: How can we generate realistic graphs? In addition, how can we do so with a mathematically tractable model that makes it feasible to analyze their properties rigorously? Real graphs obey a long list of surprising properties: Heavy tails for the in- and out-degree distribution; heavy tails for the eigenvalues and eigenvectors; small diameters; and the recently discovered "Densification Power Law" (DPL). All published graph generators either fail to match several of the above properties, are very complicated to analyze mathematically, or both. Here we propose a graph generator that is mathematically tractable and matches this collection of properties. The main idea is to use a non-standard matrix operation, the Kronecker product, to generate graphs that we refer to as "Kronecker graphs". ::: ::: We show that Kronecker graphs naturally obey all the above properties; in fact, we can rigorously prove that they do so. We also provide empirical evidence showing that they can mimic very well several real graphs. ::: ::: Work partially supported by the National Science Foundation under Grants No. IIS-0209107, SENSOR-0329549, IIS-0326322, CNS-0433540, CCF-0325453, IIS-0329064, CNS-0403340, CCR-0122581, a David and Lucile Packard Foundation Fellowship, and also by the Pennsylvania Infrastructure Technology Alliance (PITA), a partnership of Carnegie Mellon, Lehigh University and the Commonwealth of Pennsylvania's Department of Community and Economic Development (DCED). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, or other funding parties.


title: Heartbeat Electrocardiogram ( ECG ) Signal Feature Extraction Using Discrete Wavelet Transforms ( DWT ) 1

abstract: ECG is a method used to measure the rate and regularity of heartbeats to detect any irregularity to the heart. An ECG translates the heart electrical activity into wave-line on paper or screen. In this paper, discrete wavelet transform (DWT) will be used to extract the relevant information from the ECG input data in order to perform the classification task. Previous study suggests DWT-based feature extraction technique yields better data. The Wavelet transform is a two-dimensional timescale processing method. DWT is suitable for the nonstationary ECG signals as it has adequate scale values and shifting in time. The data will then be analyzed and classified using neuro-fuzzy which is a hybrid of artificial neural networks and fuzzy logic. Keyword : Electrocardiogram (ECG), DWT, Neuro Fuzzy.


title: NLP for Answer Extraction in Technical Domains

abstract: In this paper we argue that question answering (QA) over technical domains is distinctly different from TREC-based QA or Web-based QA and it cannot benefit from data-intensive approaches. Technical questions arise in situations where concrete problems require specific answers and explanations. Finding a justification of the answer in the context of the document is essential if we have to solve a real-world problem. We show that NLP techniques can be used successfully in technical domains for high-precision access to information stored in documents. We present Extr- Ans, an answer extraction system over technical domains, its architecture, its use of logical forms for answer extractions and how terminology extraction becomes an important part of the system.


title: Is adolescence-onset antisocial behavior developmentally normative?

abstract: Largely because of the influence of Moffitt’s useful distinctionbetween adolescence-limited and life-course persistentantisocial behavior,ithas become increasingly common to view problem behavior that makes its first appearance in adolescence as developmentally normative. This study prospectively examined the lives of individuals in the NICHD Study of Early Child Care and Youth Development whose patterns of antisocial behavior varied with respect to age of onset and stability from kindergarten through age 15. Consistent with past research, early-onset, persistently deviant youth experienced more contextual adversity and evinced higher levels of intraindividual disadvantages than their peers from infancy through midadolescence. However, relative to youth who never showed significantly elevated antisocial behavior through age 15, children who showed antisocial behavior primarily in adolescence also were more disadvantaged from infancy forward, as were youth who only demonstrated significant externalizing problems in childhood. Findings generally replicated across sex and did not vary as a function of whether antisocial behavior groups were defined using T-scores normed within sex or identified using an empirically driven grouping method applied to raw data.


title: SDSM: A secure data service mechanism in mobile cloud computing

abstract: To enhance the security of mobile cloud users, a few proposals have been presented recently. However we argue that most of them are not suitable for mobile cloud where mobile users might join or leave the mobile networks arbitrarily. In this paper, we design a secure mobile user-based data service mechanism (SDSM) to provide confidentiality and fine-grained access control for data stored in the cloud. This mechanism enables the mobile users to enjoy a secure outsourced data services at a minimized security management overhead. The core idea of SDSM is that SDSM outsources not only the data but also the security management to the mobile cloud in a trust way. Our analysis shows that the proposed mechanism has many advantages over the existing traditional methods such as lower overhead and convenient update, which could better cater the requirements in mobile cloud computing scenarios.


title: Predicting Personality from Twitter

abstract: Social media is a place where users present themselves to the world, revealing personal details and insights into their lives. We are beginning to understand how some of this information can be utilized to improve the users' experiences with interfaces and with one another. In this paper, we are interested in the personality of users. Personality has been shown to be relevant to many types of interactions, it has been shown to be useful in predicting job satisfaction, professional and romantic relationship success, and even preference for different interfaces. Until now, to accurately gauge users' personalities, they needed to take a personality test. This made it impractical to use personality analysis in many social media domains. In this paper, we present a method by which a user's personality can be accurately predicted through the publicly available information on their Twitter profile. We will describe the type of data collected, our methods of analysis, and the machine learning techniques that allow us to successfully predict personality. We then discuss the implications this has for social media design, interface design, and broader domains.


title: The Weltmodell: A Data-Driven Commonsense Knowledge Base

abstract: We present the WELTMODELL, a commonsense knowledge base that was automatically generated from aggregated dependency parse fragments gathered from over 3.5 million English language books. We leverage the magnitude and diversity of this dataset to arrive at close to ten million distinct N-ary commonsense facts using techniques from open-domain Information Extraction (IE). Furthermore, we compute a range of measures of association and distributional similarity on this data. We present the results of our efforts using a browsable web demonstrator and publicly release all generated data for use and discussion by the research community. In this paper, we give an overview of our knowledge acquisition method and representation model, and present our web demonstrator.


title: Question Answering from Unstructured Text by Retrieval and Comprehension

abstract: Open domain Question Answering (QA) systems must interact with external knowledge sources, such as web pages, to find relevant information. Information sources like Wikipedia, however, are not well structured and difficult to utilize in comparison with Knowledge Bases (KBs). In this work we present a two-step approach to question answering from unstructured text, consisting of a retrieval step and a comprehension step. For comprehension, we present an RNN based attention model with a novel mixture mechanism for selecting answers from either retrieved articles or a fixed vocabulary. For retrieval we introduce a hand-crafted model and a neural model for ranking relevant articles. We achieve state-of-the-art performance on W IKI M OVIES dataset, reducing the error by 40%. Our experimental results further demonstrate the importance of each of the introduced components.


title: Token-based Chunking of Turn-internal Dialogue Act Sequences

abstract: In this study we compare two sequence learning approaches to chunk dialogue acts within a speaker’s turn. We assign a dialogue act label to each token in the transcribed speech stream of a dialogue participant, additionally classifying if the token is at the beginning of, inside, or outside that specific dialogue act. Experimental findings show that both our approaches ‐ conditional random fields and memory-based tagging ‐ largely improve over local classification methods, obtaining comparable scores on distinct datasets. We discuss the interplay between transcription granularity of turns and dialogue act chunking.


title: PICTIVE—an exploration in participatory design

abstract: This paper describes PICTIVE, an experimental participatory design technique that is intended to enhance user participation in the design process. PICTIVE combines low-tech objects with high(er)-tech video recording. The low-tech objects — i.e., non-computer representations of system functionality — are intended to insure that all participants have equal opportunity to contribute their ideas. The video recording makes record-keeping easy, reduces social distance during the design session, and may give rise to informal video “design documents.” The session proeeedsbyakind ofbrainstorming, with the low-tech objects used to express each participant’s ideas to the others. This paper describes our initial experiences with the PICTIVE technique, informal analyses about why the teehnique works, and several Bellcore projeets and products to which it has been applied.


title: ZeroG: overground gait and balance training system.

abstract: A new overground body-weight support system called ZeroG has been developed that allows patients with severe gait impairments to practice gait and balance activities in a safe, controlled manner. The unloading system is capable of providing up to 300 lb of static support and 150 lb of dynamic (or constant force) support using a custom-series elastic actuator. The unloading system is mounted to a driven trolley, which rides along an overhead rail. We evaluated the performance of ZeroG's unloading system, as well as the trolley tracking system, using benchtop and human-subject testing. Average root-mean-square and peak errors in unloading were 2.2 and 7.2 percent, respectively, over the range of forces tested while trolley tracking errors were less than 3 degrees, indicating the system was able to maintain its position above the subject. We believe training with ZeroG will allow patients to practice activities that are critical to achieving functional independence at home and in the community.


title: ATOMO: Communication-efficient Learning via Atomic Sparsification

abstract: Distributed model training suffers from communication overheads due to frequent gradient updates transmitted between compute nodes. To mitigate these overheads, several studies propose the use of sparsified stochastic gradients. We argue that these are facets of a general sparsification method that can operate on any possible atomic decomposition. Notable examples include element-wise, singular value, and Fourier decompositions. We present ATOMO, a general framework for atomic sparsification of stochastic gradients. Given a gradient, an atomic decomposition, and a sparsity budget, ATOMO gives a random unbiased sparsification of the atoms minimizing variance. We show that methods such as QSGD and TernGrad are special cases of ATOMO and show that sparsifiying gradients in their singular value decomposition (SVD), rather than the coordinate-wise one, can lead to significantly faster distributed training.


title: DESIGN OF OMNIDIRECTIONAL HIGH-GAIN ANTENNA WITH BROADBAND RADIANT LOAD IN C WAVE BAND

abstract: Abstract—Three novel structures of coplanar waveguide (CPW) cross-coupling-fed antenna with different kinds of broadband radiant loads, which are applied in C wave band, are presented. The simulated results by CST MICROWAVE STUDIO R © indicate that these structures of antenna are able to expand bandwidth, improve gain and maintain good omnidirectional radiation characteristics while the sizes of the structures are relatively small. The antenna surface current simulated by CST is extracted, explaining the mechanism of broadband, high-gain and omnidirectional radiation characteristics of the antenna. Three structures of antenna with different kinds of broadband radiant loads are designed, manufactured and measured. The antenna is printed on FR-4 epoxy substrate with 0.5 mm thickness. According to the measured results of these three structures, the operating bandwidths with a reflection coefficient lower than −10 dB are 4.27∼4.90GHz, 4.04∼5.07GHz and 4.05∼4.87GHz. The relative bandwidths are up to 13.7%, 22.6% and 18.4% respectively. The Hplane maximum omnidirectional gains are 6.6 dB (4.8 GHz), 6.8 dB (4.6GHz) and 7.8 dB (4.6GHz), and the maximum magnitudes of unroundness are 3.0 dB, 2.8 dB and 2.8 dB (4.5 GHz) respectively. The measured and simulated results do not differ much from each other. The overall sizes of the antennas are 133.5 mm × 14.4mm (triangular load), 148.2 mm × 18mm (cutting semi-circular load) and 145.65mm × 16.1mm (circular load) respectively, and the gains per electronic length on the polarization direction are 3.1 dB, 3.1 dB and 3.6 dB, which are relatively high. These three structures of antennas are suitable for communication systems working in C wave band.


title: Improving accuracy for cancer classification with a new algorithm for genes selection

abstract: BackgroundEven though the classification of cancer tissue samples based on gene expression data has advanced considerably in recent years, it faces great challenges to improve accuracy. One of the challenges is to establish an effective method that can select a parsimonious set of relevant genes. So far, most methods for gene selection in literature focus on screening individual or pairs of genes without considering the possible interactions among genes. Here we introduce a new computational method named the Binary Matrix Shuffling Filter (BMSF). It not only overcomes the difficulty associated with the search schemes of traditional wrapper methods and overfitting problem in large dimensional search space but also takes potential gene interactions into account during gene selection. This method, coupled with Support Vector Machine (SVM) for implementation, often selects very small number of genes for easy model interpretability.ResultsWe applied our method to 9 two-class gene expression datasets involving human cancers. During the gene selection process, the set of genes to be kept in the model was recursively refined and repeatedly updated according to the effect of a given gene on the contributions of other genes in reference to their usefulness in cancer classification. The small number of informative genes selected from each dataset leads to significantly improved leave-one-out (LOOCV) classification accuracy across all 9 datasets for multiple classifiers. Our method also exhibits broad generalization in the genes selected since multiple commonly used classifiers achieved either equivalent or much higher LOOCV accuracy than those reported in literature.ConclusionsEvaluation of a gene’s contribution to binary cancer classification is better to be considered after adjusting for the joint effect of a large number of other genes. A computationally efficient search scheme was provided to perform effective search in the extensive feature space that includes possible interactions of many genes. Performance of the algorithm applied to 9 datasets suggests that it is possible to improve the accuracy of cancer classification by a big margin when joint effects of many genes are considered.


title: WalkON Suit: A Medalist in the Powered Exoskeleton Race of Cybathlon 2016

abstract: The powered exoskeleton race in Cybathlon 2016 consisted of six challenging tasks that required a pilot with complete paraplegia to walk on a level floor, uphill, downhill, and on stairs; stand up and sit down; step on stones; and even pass through a tilted path. All of these tasks addressed exactly the requirements for a powered exoskeleton designed to assist with activities of daily living (ADL) for paraplegics. Every team brought unique technologies to achieve this goal.


title: The Abyss: Exploring Depression Through a Narrative of the Self

abstract: This article explores particular processes, vulnerabilities, and difficulties of living with clinical depression. To do so, the author draws upon moments from his own narrative of self to highlight how his embodied illness story collides with different selves. In the show- ing, he attempts to evoke the ambiguities, ongoing struggles, and gendered nature of depression in ways that display how the past continues in the present and crafts the future. Finally, the author discusses the therapeutic implications of writing, telling, enacting, and witnessing stories within a political and cultural framework of narrative resources. The digital radio alarm clock reads 5:37 a.m. The day doesn't matter. Days merge into darkness. Darkness lives in my life. Enough of this babbling. Come and join me. Join my pulsating body. Join my fragmented self. Join my life. Join a story. Feel a statistic. Enter into my world. You become me. Two become one. Are we there? Don't be scared. Come on—take the plunge. That's right. Ease yourself in. Are you with me? We look around the spacious bedroom. A Led Zeppelin poster adorns our pine bedroom door. The latest albums from Pearl Jam, Sheryl Crow, and The Smashing Pumpkins sit on top of our expensive music system. How many hours did it take picking cucumbers to pay for the stereo? we think. We recall the vast greenhouses and the intense suffocating heat. The row upon row of tightly packed plants creeps back into our consciousness. The almost palpa- ble rancid stink of rotting, mushy cucumbers makes us shudder. Our embod- ied self recollects the lacerations from the stalks and leaves, the palpitating red raw blisters on our hands, and the constant ache in our back and knees. "Ohmmm the good times," we say smiling. The desk at the end of our room is awash with various psychology articles and textbooks—a thin layer of dust covers them. The striped red curtains are


Context Adaptive Neural Network for Rapid Adaptation of Deep CNN Based Acoustic Models


title: Suppression of Second and Third Harmonics Using $\lambda/$4 Low-Impedance Substrate IntegratedWaveguide Bias Line in Power Amplifier

abstract: In this letter, a low-impedance substrate integrated waveguide (SIW) bias line is proposed to suppress second and third harmonics in power amplifier. Such a bias line consists of a 4 low-impedance microstrip line and a shorted SIW that can operate as a radio frequency block and suppress any harmonic signals from entering the dc source. Since the frequency of the second harmonic components is lower than the inherent cutoff frequency of the SIW, second harmonic components are blocked. At the same time, third harmonic components are shorted by the shorted SIW. Measured results show that second and third harmonic components can be reduced by 25 dB and 13 dB, respectively, compared to the outcomes using conventional 4 high-impedance microstrip bias line. Also, both 1 dB compression point and power added efficiency are improved slightly.


Multi-label Classification of Satellite Images with Deep Learning


title: WYSIWYG (What You See is What You Get) Volume Visualization

abstract: In this paper, we propose a volume visualization system that accepts direct manipulation through a sketch-based What You See Is What You Get (WYSIWYG) approach. Similar to the operations in painting applications for 2D images, in our system, a full set of tools have been developed to enable direct volume rendering manipulation of color, transparency, contrast, brightness, and other optical properties by brushing a few strokes on top of the rendered volume image. To be able to smartly identify the targeted features of the volume, our system matches the sparse sketching input with the clustered features both in image space and volume space. To achieve interactivity, both special algorithms to accelerate the input identification and feature matching have been developed and implemented in our system. Without resorting to tuning transfer function parameters, our proposed system accepts sparse stroke inputs and provides users with intuitive, flexible and effective interaction during volume data exploration and visualization.


Illiteracy and maternal health: educate or die.


title: Compatibility Is Not Transparency: VMM Detection Myths and Realities

abstract: Recent work on applications ranging from realistic honeypots to stealthier rootkits has speculated about building transparent VMMs - VMMs that are indistinguishable from native hardware, even to a dedicated adversary. We survey anomalies between real and virtual hardware and consider methods for detecting such anomalies, as well as possible countermeasures. We conclude that building a transparent VMM is fundamentally infeasible, as well as impractical from a performance and engineering standpoint.


title: Dose history and occurrence of conditional stimuli determine the strength of cocaine-seeking behavior of rhesus monkeys

abstract: Four adult male rhesus monkeys were trained to lever press for cocaine under a daily two-component MIX PR (progressive ratio) schedule. During the first 10 min of experimental sessions, completion of progressive ratios resulted in 1-s presentations of brief visual stimuli (BS; colored lights) associated with cocaine infusions during the second component. Stimulus lights of different colors were associated with doses of 3, 30, and 300 μg/kg cocaine as the available self-administered infusate. A 5-min time out period followed the first component, which in turn was followed by a 60-min component during which completion of progressive ratios resulted in cocaine infusions and the associated visual stimuli. Once reinforcer rates had stabilized under each dosing condition in both components, break point tests were conducted separately for BS as the reinforcer and with cocaine + stimuli as the reinforcer. Break points for lever pressing maintained by BS alone increased as they were paired with increasing doses of cocaine. Break points maintained by actual cocaine delivery, however, demonstrated an inverted U-shaped function to cocaine dose. The results of this study suggest that the strength of cocaine-seeking behavior varies monotonically with the self-administered dose of cocaine and that the level of motivation to obtain cocaine may not be directly revealed by levels of actual cocaine self-administration.


Functional properties of visual tracking neurons in posterior parietal association cortex of the monkey


title: Building interchangeable black-box models of integrated circuits for EMC simulations

abstract: An interchangeable black-box model of an integrated circuit block for time-domain simulations of the direct power injection (DPI) immunity test is presented. An artificial neural network implemented as a Verilog A module is used to build a model of a bandgap reference circuit sub-block. Being a part of a larger schematic of interconnected circuit blocks, the model is able to correctly load the transistor-level block in the previous stage. The simulation time for the transient analysis is significantly improved compared to the transistor-level models, and the time-to-steady-state of the model is negligible. The accuracy of the model is comparable to the state-of-the-art black-box modelling approaches. The model is very practical for obtaining the EMC behaviour of complex integrated circuits at design-time.


title: Model-Driven engineering in a large industrial context — motorola case study

abstract: In an ongoing effort to reduce development costs in spite of increasing system complexity, Motorola has been a long-time adopter of Model-Driven Engineering (MDE) practices. The foundation of this approach is the creation of rigorous models throughout the development process, thereby enabling the introduction of automation. In this paper we present our experiences within Motorola in deploying a top-down approach to MDE for more than 15 years. We describe some of the key competencies that have been developed and the impact of MDE within the organization. Next we present some of the main issues encountered during MDE deployment, together with some possible resolutions.


title: Uncovering vein patterns from color skin images for forensic analysis

abstract: Recent technological advances have allowed for a proliferation of digital evidence images. Using these images as evidence in legal cases (e.g. child sexual abuse, child pornography and masked gunmen) can be very challenging, because the faces of criminals or victims are not visible. Although large skin marks and tattoos have been used, they are ineffective in some legal cases, because the skin exposed in evidence images neither have unique tattoos nor enough skin marks for identification. The blood vessel between the skin and the muscle covering most parts of the human body is a powerful biometric trait, because of its universality, permanence and distinctiveness. Traditionally, it was impossible to use vein patterns for forensic identification, because they were not visible in color images. This paper proposes an algorithm to uncover vein patterns from the skin exposed in color images for personal identification. Based on the principles of optics and skin biophysics, we modeled the inverse process of skin color formation in an image and derived spatial distributions of biophysical parameters from color images, where vein patterns can be observed. Experimental results are very encouraging. The clarity of the vein patterns in resultant images is comparable to or even better than that in near infrared images.


title: Understanding self-reflection: how people reflect on personal data through visual data exploration

abstract: Rapid advancements in consumer technologies enable people to collect a wide range of personal data. With a proper means for people to ask questions and explore their data, longitudinal data feeds from multiple self-tracking tools pose great opportunities to foster deep self-reflection. However, most self-tracking tools lack support for self-reflection beyond providing simple feedback. Our overarching goal is to support self-trackers in reflecting on their data and gaining rich insights through visual data exploration. As a first step toward the goal, we built a web-based application called Visualized Self, and conducted an in-lab think-aloud study (N = 11) to examine how people reflect on their personal data and what types of insights they gain throughout the reflection. We discuss lessons learned from studying with Visualized Self, and suggest directions for designing visual data exploration tools for fostering self-reflection.


title: Using Cross-Entity Inference to Improve Event Extraction

abstract: Event extraction is the task of detecting certain specified types of events that are mentioned in the source language data. The state-of-the-art research on the task is transductive inference (e.g. cross-event inference). In this paper, we propose a new method of event extraction by well using cross-entity inference. In contrast to previous inference methods, we regard entity-type consistency as key feature to predict event mentions. We adopt this inference method to improve the traditional sentence-level event extraction system. Experiments show that we can get 8.6% gain in trigger (event) identification, and more than 11.8% gain for argument (role) classification in ACE event extraction.


title: Sampling-Based Query Re-Optimization

abstract: Despite of decades of work, query optimizers still make mistakes on "difficult" queries because of bad cardinality estimates, often due to the interaction of multiple predicates and correlations in the data. In this paper, we propose a low-cost post-processing step that can take a plan produced by the optimizer, detect when it is likely to have made such a mistake, and take steps to fix it. Specifically, our solution is a sampling-based iterative procedure that requires almost no changes to the original query optimizer or query evaluation mechanism of the system. We show that this indeed imposes low overhead and catches cases where three widely used optimizers (PostgreSQL and two commercial systems) make large errors.


title: Detailed screening for fetal anomalies and cardiac defects at the 11–13-week scan

abstract: OBJECTIVE ::: To assess the diagnostic efficacy of the first-trimester anomaly scan including first-trimester fetal echocardiography as a screening procedure in a 'medium-risk' population. ::: ::: ::: METHODS ::: In a prospective study, we evaluated 3094 consecutive fetuses with a crown-rump length (CRL) of 45-84 mm and gestational age between 11 + 0 and 13 + 6 weeks, using transabdominal and transvaginal ultrasonography. The majority of patients were referred without prior abnormal scan or increased nuchal translucency (NT) thickness, the median maternal age was, however, 35 (range, 15-46) years, and 53.8% of the mothers (1580/2936) were 35 years or older. This was therefore a self-selected population reflecting an increased percentage of older mothers opting for prenatal diagnosis. The follow-up rate was 92.7% (3117/3363). ::: ::: ::: RESULTS ::: The prevalence of major abnormalities in 3094 fetuses was 2.8% (86/3094). The detection rate of major anomalies at the 11 + 0 to 13 + 6-week scan was 83.7% (72/86), 51.9% (14/27) for NT < 2.5 mm and 98.3% (58/59) for NT >or= 2.5 mm. The prevalence of major congenital heart defects (CHD) was 1.2% (38/3094). The detection rate of major CHD at the 11 to 13 + 6-week scan was 84.2% (32/38), 37.5% (3/8) for NT < 2.5 mm and 96.7% (29/30) for NT >or= 2.5 mm. ::: ::: ::: CONCLUSION ::: The overall detection rate of fetal anomalies including fetal cardiac defects following a specialist scan at 11 + 0 to 13 + 6 weeks' gestation is about 84% and is increased when NT >or= 2.5 mm. This extends the possibilities of a first-trimester scan beyond risk assessment for fetal chromosomal defects. In experienced hands with adequate equipment, the majority of severe malformations as well as major CHD may be detected at the end of the first trimester, which offers parents the option of deciding early in pregnancy how to deal with fetuses affected by genetic or structural abnormalities without pressure of time.


title: Unikernels Everywhere: The Case for Elastic CDNs

abstract: Video streaming dominates the Internet's overall traffic mix, with reports stating that it will constitute 90% of all consumer traffic by 2019. Most of this video is delivered by Content Delivery Networks (CDNs), and, while they optimize QoE metrics such as buffering ratio and start-up time, no single CDN provides optimal performance. In this paper we make the case for elastic CDNs, the ability to build virtual CDNs on-the-fly on top of shared, third-party infrastructure at a scale. To bring this idea closer to reality we begin by large-scale simulations to quantify the effects that elastic CDNs would have if deployed, and build and evaluate MiniCache, a specialized, minimalistic virtualized content cache that runs on the Xen hypervisor. MiniCache is able to serve content at rates of up to 32 Gb/s and handle up to 600K reqs/sec on a single CPU core, as well as boot in about 90 milliseconds on x86 and around 370 milliseconds on ARM32.


title: Software Framework for Topic Modelling with Large Corpora

abstract: Large corpora are ubiquitous in today's world and memory ::: quickly becomes the limiting factor in practical applications ::: of the Vector Space Model (VSM). We identify gap in existing ::: VSM implementations, which is their scalability and ease of ::: use. We describe a Natural Language Processing software ::: framework which is based on the idea of document streaming, ::: i.e. processing corpora document after document, in a memory ::: independent fashion. In this framework, we implement several ::: popular algorithms for topical inference, including Latent ::: Semantic Analysis and Latent Dirichlet Allocation, in a way ::: that makes them completely independent of the training corpus ::: size. Particular emphasis is placed on straightforward and ::: intuitive framework design, so that modifications and ::: extensions of the methods and/or their application by ::: interested practitioners are effortless. We demonstrate the ::: usefulness of our approach on a real-world scenario of ::: computing document similarities within an existing digital ::: library DML-CZ.


title: Exemplary design of a DICOM structured report template for CBIR integration into radiological routine

abstract: The large and continuously growing amount of medical image data demands access methods with regards to content ::: rather than simple text-based queries. The potential benefits of content-based image retrieval (CBIR) systems for ::: computer-aided diagnosis (CAD) are evident and have been approved. Still, CBIR is not a well-established part of daily ::: routine of radiologists. We have already presented a concept of CBIR integration for the radiology workflow in ::: accordance with the Integrating the Healthcare Enterprise (IHE) framework. The retrieval result is composed as a Digital ::: Imaging and Communication in Medicine (DICOM) Structured Reporting (SR) document. The use of DICOM SR ::: provides interchange with PACS archive and image viewer. It offers the possibility of further data mining and automatic ::: interpretation of CBIR results. However, existing standard templates do not address the domain of CBIR. We present a ::: design of a SR template customized for CBIR. Our approach is based on the DICOM standard templates and makes use ::: of the mammography and chest CAD SR templates. Reuse of approved SR sub-trees promises a reliable design which is ::: further adopted to the CBIR domain. We analyze the special CBIR requirements and integrate the new concept of similar ::: images into our template. Our approach also includes the new concept of a set of selected images for defining the ::: processed images for CBIR. A commonly accepted pre-defined template for the presentation and exchange of results in a standardized format promotes the widespread application of CBIR in radiological routine.


title: Logsonomy - social information retrieval with logdata

abstract: Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today's search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user's information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.


title: Lattice Based Cryptography for Beginners.

abstract: The purpose of this lecture note is to introduce lattice based cryptography, which is thought to be a cryptosystem of post-quantum age. We have tried to give as many details possible specially for novice on the subject. Something may be trivial to an expert but not to a novice. Many fundamental problems about lattice are thought to be hard even against quantum computer, compared to factorization problem which can be solved easily with quantum computer, via the celebrated Shor factorization quantum algorithm. The first part of our presentation is based on slides of Christ Peikert 2013 Bonn lecture (crypt@b-it2013). We, more or less, give somewhat detailed explanation of Professor Peikert’s lecture slides. We unfortunately could not attend his Bonn class. We are afraid that there are many mistakes in this note; if any, they are due to our misunderstanding of the material. Part II of our lecture note is on ring LWE, based on the paper “A tool-kit for Ring-LWE Cryptography” by Lyubashevsky, Peikert and Regev. Part III is about multilinear maps together with cryptanalysis of GGH map due to Hu and Jia. Our presentation follows professor Steinfeld’s lecture slides on GGHLite, and the paper by Yupu Hu and Huiwen Jia. When you read this lecture note, the corresponding original paper should be accompanied. We thank professor Jung Hee Cheon for introducing the subject and asking Dong Pyo Chi to give a lecture on the subject at the department of mathematics in Seoul National University. We also thank Hyeongkwan Kim for many helps, especially many corrections and improvements of the manuscript during the 2015 Summer session at UNIST. We also thank the students who took the classes at SNU and UNIST. The lecture was given by a novice for novice, so many mistakes are unavoidable. If the reader lets us know any errors, we will very much appreciate it.


Optimization-Based Formation Stabilization : A Centralized Problem with a Distributed Solution


Speech Recognition Systems : A Comparative Review


title: A novel pore extraction method for heterogeneous fingerprint images using Convolutional Neural Networks

abstract: Abstract Most fingerprint recognition systems use Level 1 characteristics (ridge flow, orientation, and frequency) and Level 2 features (minutiae points) to recognize individuals. Level 3 features (sweat pores, incipient ridges and ultra-thin characteristics of the ridges) are less frequently adopted because they can be extracted only from high resolution images, but they have the potential of improving all the steps of the biometric recognition process. In particular, sweat pores can be used for quality assessment, liveness detection, biometric matching in live applications, and matching of partial latent fingerprints in forensic applications. Currently, each type of fingerprint acquisition technique (touch-based, touchless, or latent) requires a different algorithm for pore extraction. In this paper, we propose the first method in the literature able to extract the coordinates of the pores from touch-based, touchless, and latent fingerprint images. Our method uses specifically designed and trained Convolutional Neural Networks (CNN) to estimate and refine the centroid of each pore. Results show that our method is feasible and achieved satisfactory accuracy for all the types of evaluated images, with a better performance with respect to the compared state-of-the-art methods.


title: User privacy and data trustworthiness in mobile crowd sensing

abstract: Smartphones and other trendy mobile wearable devices are rapidly becoming the dominant sensing, computing and communication devices in peoples’ daily lives. Mobile crowd sensing is an emerging technology based on the sensing and networking capabilities of such mobile wearable devices. MCS has shown great potential in improving peoples’ quality of life, including healthcare and transportation, and thus has found a wide range of novel applications. However, user privacy and data trustworthiness are two critical challenges faced by MCS. In this article, we introduce the architecture of MCS and discuss its unique characteristics and advantages over traditional wireless sensor networks, which result in inapplicability of most existing WSN security solutions. Furthermore, we summarize recent advances in these areas and suggest some future research directions.


title: Cancer incidence, mortality, and associated risk factors among Asian Americans of Chinese, Filipino, Vietnamese, Korean, and Japanese ethnicities.

abstract: Many studies demonstrate that cancer incidence and mortality patterns among Asian Americans are heterogeneous, but national statistics on cancer for Asian ethnic groups are not routinely available. This article summarizes data on cancer incidence, mortality, risk factors, and screening for 5 of the largest Asian American ethnic groups in California. California has the largest Asian American population of any state and makes special efforts to collect health information for ethnic minority populations. We restricted our analysis to the 4 most common cancers (prostate, breast, lung, colon/rectum) and for the 3 sites known to be more common in Asian Americans (stomach, liver, cervix). Cancer incidence and mortality were summarized for 5 Asian American ethnic groups in California in order of population size (Chinese, Filipino, Vietnamese, Korean, and Japanese). Chinese Americans had among the lowest incidence and death rate from all cancer combined; however, Chinese women had the highest lung cancer death rate. Filipinos had the highest incidence and death rate from prostate cancer and the highest death rate from female breast cancer. Vietnamese had among the highest incidence and death rates from liver, lung, and cervical cancer. Korean men and women had by far the highest incidence and mortality rates from stomach cancer. Japanese experienced the highest incidence and death rates from colorectal cancer and among the highest death rates from breast and prostate cancer. Variations in cancer risk factors were also observed and were for the most part consistent with variations in cancer incidence and mortality. Differences in cancer burden among Asian American ethnic groups should be considered in the clinical setting and in cancer control planning.