title: Theory-based predictors of multiple clinician behaviors in the management of diabetes

abstract: Behavioral theory is often tested on one behavior in isolation from other behaviors and theories. We aimed to test the predictive validity of constructs from motivation and action theories of behavior across six diabetes-related clinician behaviors, within the same sample of primary care clinicians. Physicians and nurses (n = 427 from 99 practices in the United Kingdom) completed questionnaires at baseline and 12 months. Primary outcomes: six self-reported clinician behaviors related to advising, prescribing and examining measured at 12 months; secondary outcomes: baseline intention and patient-scenario-based simulated behavior. Across six behaviors, each theory accounted for a medium amount of variance for 12-month behavior (median R adj 2 = 0.15), large and medium amount of variance for two intention measures (median R adj 2 = 0.66; 0.34), and small amount of variance for simulated behavior (median R adj 2 = 0.05). Intention/proximal goals, self-efficacy, and habit predicted all behaviors. Constructs from social cognitive theory (self-efficacy), learning theory (habit) and action and coping planning consistently predicted multiple clinician behaviors and should be targeted by quality improvement interventions.


title: This is how we play it: what a mega-LAN can teach us about games

abstract: Using data gathered through our participant observation and informal interviews at DreamHack Winter 2005 and 2009 we explore a number of themes that not only provide insight into aspects of face-to-face real-time play at LAN parties but also highlight considerations for game studies more generally. In particular, we focus on the heterogeneity of play and experience, the role of spectatorship in computer gaming, the public performance of leisure and gamer identity, and the growing presence of women in game culture. We conclude by suggesting that researchers should begin to consider the much larger trend in which this form of leisure activity is integrating itself into mainstream pop/youth/network culture.


title: Gracker: A Graph-Based Planar Object Tracker

abstract: Matching-based algorithms have been commonly used in planar object tracking. They often model a planar object as a set of keypoints, and then find correspondences between keypoint sets via descriptor matching. In previous work, unary constraints on appearances or locations are usually used to guide the matching. However, these approaches rarely utilize structure information of the object, and are thus suffering from various perturbation factors. In this paper, we proposed a graph-based tracker, named Gracker , which is able to fully explore the structure information of the object to enhance tracking performance. We model a planar object as a graph, instead of a simple collection of keypoints, to represent its structure. Then, we reformulate tracking as a sequential graph matching process, which establishes keypoint correspondence in a geometric graph matching manner. For evaluation, we compare the proposed Gracker with state-of-the-art planar object trackers on three benchmark datasets: two public ones and a newly collected one. Experimental results show that Gracker achieves robust tracking results against various environmental variations, and outperforms other algorithms in general on the datasets.


title: Mapping patient trajectories using longitudinal extraction and deep learning in the MIMIC-III Critical Care Database

abstract: Electronic Health Records (EHRs) contain a wealth of patient data useful to biomedical researchers. At present, both the extraction of data and methods for analyses are frequently designed to work with a single snapshot of a patient's record. Health care providers often perform and record actions in small batches over time. By extracting these care events, a sequence can be formed providing a trajectory for a patient's interactions with the health care system. These care events also offer a basic heuristic for the level of attention a patient receives from health care providers. We show that is possible to learn meaningful embeddings from these care events using two deep learning techniques, unsupervised autoencoders and long short-term memory networks. We compare these methods to traditional machine learning methods which require a point in time snapshot to be extracted from an EHR.


title: 3D Human Body Reconstruction from a Single Image via Volumetric Regression

abstract: This paper proposes the use of an end-to-end Convolutional Neural Network for direct reconstruction of the 3D geometry of humans via volumetric regression. The proposed method does not require the fitting of a shape model and can be trained to work from a variety of input types, whether it be landmarks, images or segmentation masks. Additionally, non-visible parts, either self-occluded or otherwise, are still reconstructed, which is not the case with depth map regression. We present results that show that our method can handle both pose variation and detailed reconstruction given appropriate datasets for training.


title: Advances and Current State of the Security and Privacy in Electronic Health Records: Survey from a Social Perspective

abstract: E-Health systems are experiencing an impulse in these last years, when many medical agencies began to include digital solutions into their platforms. Electronic Health Records (EHRs) are one of the most important improvements, being in its most part a patient-oriented tool. To achieve a completely operational EHR platform, security and privacy problems have to be resolved, due to the importance of the data included within these records. But given all the different methods to address security and privacy, they still remain in most cases as an open issue. This paper studies existing and proposed solutions included in different scenarios, in order to offer an overview of the current state in EHR systems. Bibliographic material has been obtained mainly from MEDLINE and SCOPUS sources, and over 30 publications have been analyzed. Many EHR platforms are being developed, but most of them present weaknesses when they are opened to the public. These architectures gain significance when they cover all the requisites related to security and privacy.


title: Frozen Suit: Designing a Changeable Stiffness Suit and its Application to Haptic Games

abstract: We present the concept of Frozen Suit, a type of clothing that restricts users' movements at joint positions (e.g. elbow, knee) via a changeable stiffness jamming material. The suit can "freeze" users' body parts, for example during a game in order to provide the physical sensation of being frozen by an enemy. In this paper we first present the Frozen Suit concept and its potential applications. We then systematically investigate how to design jamming patches in order to sufficiently restrict an arm or a leg. In particular we used low-fidelity prototypes to explore the restricting power of different material and particles. In order to push this analysis further we conducted a controlled experiment in order to compare the perceived stiffness of different patches sizes attached to the elbow. We performed a paired comparison experience and used a Bradley-Terry-Luce model to analyze the subjective feedback from participants. We found that 20cm long x 7cm large is the most restrictive patch and that an increase in patch area correlates with an increase in perceived stiffness (quadratic). We finish by presenting a use case application with a game that we implemented where enemies can freeze the player.


title: Review: Applications of axiomatic design principles: A literature review

abstract: Although there have been quite a number of theoretical and practical studies where axiomatic design (AD) principles have been used in the last few years, there is a lack of a comprehensive literature survey which evaluates and classifies these papers. This study provides a recognizable overview of literature on AD principles from the past 20 years and introduces a novel classification scheme covering 63 papers. Each article was classified into four main groups, namely the type of the axiom, the application area, the method, and the evaluation type. Findings of our paper indicate that most of the studies in the literature are application-based papers which use typically the independence axiom. While product design is put forward in the application area, the crisp approach is widely used as an evaluation type. A rise in the fuzzy evaluation based research studies using the information axiom for multi-attribute decision making problems has also been noticed.


title: Effects of Buprenorphine Maintenance Dose on μ-Opioid Receptor Availability, Plasma Concentrations, and Antagonist Blockade in Heroin-Dependent Volunteers

abstract: The clinical effectiveness of opioid maintenance for heroin dependence is believed to result from a medication's ability to decrease μ-opioid receptor (μOR) availability thereby replacing agonist effects, alleviating withdrawal symptoms and attenuating heroin effects. We empirically tested this hypothesis in five heroin-dependent volunteers who were successively maintained on 32, 16, 2, and 0 mg daily buprenorphine (BUP) tablet doses. We predicted and confirmed that higher BUP doses would decrease in vivo μOR availability (measured with PET and [11C]carfentanil), increase plasma levels of BUP and its metabolite nor-BUP, and decrease withdrawal symptoms and hydromorphone (HYD) responses. Relative to placebo, BUP significantly decreased mean (±SEM) whole-brain μOR availability 41±8, 80±2, and 84±2% at 2, 16, and 32 mg, respectively. Regions of interest (ROIs) (prefrontal cortex, anterior cingulate, thalamus, amygdala, nucleus accumbens, caudate) showed similar dose-dependent effects. Changes in μOR availability varied across ROIs (prefrontal cortex, 47% vs amygdala, 27%) at BUP 2 mg, but were more homogeneous across ROIs at BUP 32 mg (94–98%; except thalamus, 88%). Relative to placebo (0 ng/ml), peak plasma levels of BUP and nor-BUP were comparable and dose-dependent (0.5–1, 5–6, and 13–14 ng/ml at 2, 16, and 32 mg, respectively). μOR availability decreases were negatively correlated with BUP plasma level and positively correlated with questionnaire-based opioid withdrawal symptoms and attenuation of HYD symptoms. These findings suggest that high-dose BUP maintenance produces near-maximal μOR occupation, μOR availability correlates well with plasma levels, and BUP-related opioid symptoms and antagonist blockade exhibit concentration–effect relationships.


title: Analysis of lignocelluloses and lignins from Arundo donax L. and Miscanthus sinensis Anderss., and hydroliquefaction of Miscanthus

abstract: Abstract In a comparative investigation of the chemical composition of Arundo donax L. and Miscanthus sinensis Anderss. the following experiments were performed: ash determination and ash characterization by energy dispersive X-ray analysis; determination of solubility in cyclohexane/ ethanol, hot water, 1% hydrochloric acid and sodium hydroxide; C, H and N determination; determination of Klason lignin and acid soluble lignin content; sulfuric acid hydrolysis followed by borate complex ion exchange chromatography of the monomeric sugars; isolation of milled wood lignins (MWL) and dioxane lignins (DIL) and their analysis by C, H, and OMe determination; quantitative FTIR spectroscopy of MWL; recording the molecular weight distribution curves using high performance size exclusion chromatography (HPSEC); calculation of average molecular weights, such as Mw and Mn; calculation of the heating values of the lignocellulosics and their components. The quantitative composition of the lignin from the three basic phenylpropane units is presented. M. sinensis was submitted to hydroliquefaction. The conversion process yielded 35% of a net product oil (NPO) with low oxygen content (11%), low viscosity (10−2 NS m−2), low asphaltene content (3·5%) low molecular weight (Mw 200) and with a specific gravity of c. 0·93 g cm−3. The NPO has a heating value of 39·4 MJ kg−1 and contains 55% of the carbon of the starting material and 59% of the combined heating value from the biomass and the hydrogen used for hydroliquefaction. The process yields 28% water which contains 58% of the original oxygen of the biomass. The process gives rise to 9 g solids and 32–35 g gases, whose energy content can easily be recovered.


title: SyMPLES-CVL: A SysML and CVL Based Approach for Product-Line Development of Embedded Systems

abstract: Background: A Software Product Line (SPL) is a group of systems that share a common set of features satisfying the specific needs of a market segment. These systems are systematicaly developed from a common set of core assets. Objective: This paper proposes SyMPLES-CVL, an alternative approach to the management of variability based on SysML models to support the development of SPLs for embedded systems. In addition, we present a comparative study between our previous approach, SyMPLES-SMarty, and SyMPLES-CVL. Method: SyMPLES-CVL was specified based on SyMPLES-SMarty, then an experimental study was carry to compare the approaches. A total of 20 participants with Software Engineering background were divided into two groups, one for each approach. The data were collected with the support of questionnaires which was analysed based on test hypotheses using inferential statistics. Results: SyMPLES-CVL was not considered significantly more effective than SyMPLES-SMarty at an significance level of 0.05 for T-Test test. Limitations: Generalization of results is limited due to the sample size of this study. Conclusion: The results showed that the approaches compared did not have a significant difference in terms of effectiveness, although they use different means (annotative and compositional) for the management of variability in SPLs. Paper Category: Experimental and Technological.


title: Adaptive Beamforming Using Frequency Invariant Uniform Concentric Circular Arrays

abstract: This paper proposes new adaptive beamforming algorithms for a class of uniform concentric circular arrays (UCCAs) having near-frequency invariant characteristics. The basic principle of the UCCA frequency invariant beamformer (FIB) is to transform the received signals to the phase mode representation and remove the frequency dependence of individual phase modes through the use of a digital beamforming or compensation network. As a result, the far field pattern of the array is electronic steerable and is approximately invariant over a wider range of frequencies than the uniform circular arrays (UCAs). The beampattern is governed by a small set of variable beamformer weights. Based on the minimum variance distortionless response (MVDR) and generalized sidelobe canceller (GSC) methods, new recursive adaptive beamforming algorithms for UCCA-FIB are proposed. In addition, robust versions of these adaptive beamforming algorithms for mitigating direction-of-arrival (DOA) and sensor position errors are developed. Simulation results show that the proposed adaptive UCCA-FIBs converge much faster and reach a considerable lower steady-state error than conventional broadband UCCA beamformers without using the compensation network. Since fewer variable multipliers are required in the proposed algorithms, it also leads to lower arithmetic complexity and faster tracking performance than conventional methods.


title: Authorship Verification based on Compression-Models

abstract: Compression models represent an interesting approach for different classification tasks and have been used widely across many research fields. We adapt compression models to the field of authorship verification (AV), a branch of digital text forensics. The task in AV is to verify if a questioned document and a reference document of a known author are written by the same person. We propose an intrinsic AV method, which yields competitive results compared to a number of current state-of-the-art approaches, based on support vector machines or neural networks. However, in contrast to these approaches our method does not make use of machine learning algorithms, natural language processing techniques, feature engineering, hyperparameter optimization or external documents (a common strategy to transform AV from a one-class to a multi-class classification problem). Instead, the only three key components of our method are a compressing algorithm, a dissimilarity measure and a threshold, needed to accept or reject the authorship of the questioned document. Due to its compactness, our method performs very fast and can be reimplemented with minimal effort. In addition, the method can handle complicated AV cases where both, the questioned and the reference document, are not related to each other in terms of topic or genre. We evaluated our approach against publicly available datasets, which were used in three international AV competitions. Furthermore, we constructed our own corpora, where we evaluated our method against state-of-the-art approaches and achieved, in both cases, promising results.


title: Ocular artifacts in EEG and event-related potentials I: Scalp topography

abstract: The ocular artifacts that contaminate the EEG derive from the potential difference between the cornea and the fundus of the eye. This corneofundal or corneoretinal potential can be considered as an equivalent dipole with its positive pole directed toward the cornea. The cornea shows a steady DC potential of approximately +13 mV relative to the forehead. Blink potentials are caused by the eyelids sliding down over the positively charged cornea. The artifacts from eye-movements result from changes in orientation of the corneo-fundal potential. The scalp-distribution of the ocular artifacts can be described in terms of propagation factors — the fraction of the EOG signal at periocular electrodes that is recorded at a particular scalp location. These factors vary with the location of the scalp electrode. Propagation factors for blinks and upward eye-movements are significantly different.


title: Semi-supervised Learning with Encoder-Decoder Recurrent Neural Networks: Experiments with Motion Capture Sequences

abstract: Recent work on sequence to sequence translation using Recurrent Neural Networks (RNNs) based on Long Short Term Memory (LSTM) architectures has shown great potential for learning useful representations of sequential data. A one-to-many encoder-decoder(s) scheme allows for a single encoder to provide representations serving multiple purposes. In our case, we present an LSTM encoder network able to produce representations used by two decoders: one that reconstructs, and one that classifies if the training sequence has an associated label. This allows the network to learn representations that are useful for both discriminative and reconstructive tasks at the same time. This paradigm is well suited for semi-supervised learning with sequences and we test our proposed approach on an action recognition task using motion capture (MOCAP) sequences. We find that semi-supervised feature learning can improve state-of-the-art movement classification accuracy on the HDM05 action dataset. Further, we find that even when using only labeled data and a primarily discriminative objective the addition of a reconstructive decoder can serve as a form of regularization that reduces over-fitting and improves test set accuracy.


Multi-Voxel Pattern Analysis of fMRI Data


title: 30-100-GHz inductors and transformers for millimeter-wave (Bi)CMOS integrated circuits

abstract: Silicon planar and three-dimensional inductors and transformers were designed and characterized on-wafer up to 100 GHz. Self-resonance frequencies (SRFs) beyond 100 GHz were obtained, demonstrating for the first time that spiral structures are suitable for applications such as 60-GHz wireless local area network and 77-GHz automotive RADAR. Minimizing area over substrate is critical to achieving high SRF. A stacked transformer is reported with S/sub 21/ of -2.5 dB at 50 GHz, and which offers improved performance and less area (30 /spl mu/m/spl times/30 /spl mu/m) than planar transformers or microstrip couplers. A compact inductor model is described, along with a methodology for extracting model parameters from simulated or measured y-parameters. Millimeter-wave SiGe BiCMOS mixer and voltage-controlled-oscillator circuits employing spiral inductors are presented with better or comparable performance to previously reported transmission-line-based circuits.


title: Data Curation at Scale: The Data Tamer System

abstract: Data curation is the act of discovering a data source(s) of interest, cleaning and transforming the new data, semantically integrating it with other local data sources, and deduplicating the resulting composite. There has been much research on the various components of curation (especially data integration and deduplication). However, there has been little work on collecting all of the curation components into an integrated end-to-end system. In addition, most of the previous work will not scale to the sizes of problems that we are nding in the eld. For example, one web aggregator requires the curation of 80,000 URLs and a second biotech company has the problem of curating 8000 spreadsheets. At this scale, data curation cannot be a manual (human) eort, but must entail machine learning approaches with a human assist only when necessary. This paper describes Data Tamer, an end-to-end curation system we have built at M.I.T. Brandeis, and Qatar Computing Research Institute (QCRI). It expects as input a sequence of data sources to add to a composite being constructed over time. A new source is subjected to machine learning algorithms to perform attribute identication, grouping of attributes into tables, transformation of incoming data and deduplication. When necessary, a human can be asked for guidance. Also, Data Tamer includes a data visualization component so a human can examine a data source at will and specify manual transformations. We have run Data Tamer on three real world enterprise curation problems, and it has been shown to lower curation cost by about 90%, relative to the currently deployed production software.


title: 3D tilt sensing by using accelerometer-based wireless sensor networks: Real case study: Application in the smart cities

abstract: Real-time tilt sensing in urban areas can provide crucial information regarding the instant evolution of sensitive assets of the city to better safeguard the sites sustainability and security of people. In this paper, we proposed a novel approach based on the accelerometer sensor to recognize tilt angle between the vertical plan (the light poles along the road, the transmission tower, crossing bridges, etc.) and the ground. Indeed, we used the tri-axial accelerometer embedded in a Wireless Sensor Network (WSN) platform manufactured by Libellium, named “Waspmote Starter Kit”. The collected data are sent from one sensor node to another until the gateway and then to the processing center. Our recognition method uses the calculation and mapping of the values sensed by an accelerometer to a corresponding angle. The z-component corresponding to the gravity allows recognizing the real-time tilt of the horizontal plane while the x and y-components indicate in which side the plan is bent. This approach is applied to the smart city to provide an effective solution for real-time electric transmission pole monitoring. A computer program processes the angle results of the mapping and plots the 3D scene on the monitoring screen reproducing the behaviour of electric pole remotely located. The experimental results of our approach are satisfactory because the average error that can be committed by comparing the calculated and measured angle is about 1.0.


title: Logistic Regression by Means of Evolutionary Radial Basis Function Neural Networks

abstract: This paper proposes a hybrid multilogistic methodology, named logistic regression using initial and radial basis function (RBF) covariates. The process for obtaining the coefficients is carried out in three steps. First, an evolutionary programming (EP) algorithm is applied, in order to produce an RBF neural network (RBFNN) with a reduced number of RBF transformations and the simplest structure possible. Then, the initial attribute space (or, as commonly known as in logistic regression literature, the covariate space) is transformed by adding the nonlinear transformations of the input variables given by the RBFs of the best individual in the final generation. Finally, a maximum likelihood optimization method determines the coefficients associated with a multilogistic regression model built in this augmented covariate space. In this final step, two different multilogistic regression algorithms are applied: one considers all initial and RBF covariates (multilogistic initial-RBF regression) and the other one incrementally constructs the model and applies cross validation, resulting in an automatic covariate selection [simplelogistic initial-RBF regression (SLIRBF)]. Both methods include a regularization parameter, which has been also optimized. The methodology proposed is tested using 18 benchmark classification problems from well-known machine learning problems and two real agronomical problems. The results are compared with the corresponding multilogistic regression methods applied to the initial covariate space, to the RBFNNs obtained by the EP algorithm, and to other probabilistic classifiers, including different RBFNN design methods [e.g., relaxed variable kernel density estimation, support vector machines, a sparse classifier (sparse multinomial logistic regression)] and a procedure similar to SLIRBF but using product unit basis functions. The SLIRBF models are found to be competitive when compared with the corresponding multilogistic regression methods and the RBFEP method. A measure of statistical significance is used, which indicates that SLIRBF reaches the state of the art.


title: RainForest—A Framework for Fast Decision Tree Construction of Large Datasets

abstract: Classification of large datasets is an important data mining problem. Many classification algorithms have been proposed in the literature, but studies have shown that so far no algorithm uniformly outperforms all other algorithms in terms of quality. In this paper, we present a unifying framework called Rain Forest for classification tree construction that separates the scalability aspects of algorithms for constructing a tree from the central features that determine the quality of the tree. The generic algorithm is easy to instantiate with specific split selection methods from the literature (including C4.5, CART, CHAID, FACT, ID3 and extensions, SLIQ, SPRINT and QUEST). ::: ::: In addition to its generality, in that it yields scalable versions of a wide range of classification algorithms, our approach also offers performance improvements of over a factor of three over the SPRINT algorithm, the fastest scalable classification algorithm proposed previously. In contrast to SPRINT, however, our generic algorithm requires a certain minimum amount of main memory, proportional to the set of distinct values in a column of the input relation. Given current main memory costs, this requirement is readily met in most if not all workloads.


title: Inverse reinforcement learning in relational domains

abstract: In this work, we introduce the first approach to the Inverse Reinforcement Learning (IRL) problem in relational domains. IRL has been used to recover a more compact representation of the expert policy leading to better generalization performances among different contexts. On the other hand, relational learning allows representing problems with a varying number of objects (potentially infinite), thus provides more generalizable representations of problems and skills. We show how these different formalisms allow one to create a new IRL algorithm for relational domains that can recover with great efficiency rewards from expert data that have strong generalization and transfer properties. We evaluate our algorithm in representative tasks and study the impact of diverse experimental conditions such as : the number of demonstrations, knowledge about the dynamics, transfer among varying dimensions of a problem, and changing dynamics.


title: How to Run a Successful Game-Based AI Competition

abstract: Game-based competitions are commonly used within the Computational Intelligence (CI) and Artificial Intelligence (AI) in games community to benchmark algorithms and to attract new researchers. While many competitions have been organized based on different games, the success of these competitions is highly varied. This short paper is a self-help paper for competition organizers and aspiring competition organizers. After analyzing the fate of a number of recent competitions, some factors likely to contribute to the success or failure of a competition are laid out, and a set of concrete recommendations is offered. There is also a discussion of how to write up game-based AI competitions and what we can ultimately learn from them.


title: A Customizable Matrix Multiplication Framework for the Intel HARPv2 Xeon+FPGA Platform: A Deep Learning Case Study

abstract: General Matrix to Matrix multiplication (GEMM) is the cornerstone for a wide gamut of applications in high performance computing (HPC), scientific computing (SC) and more recently, deep learning. In this work, we present a customizable matrix multiplication framework for the Intel HARPv2 CPU+FPGA platform that includes support for both traditional single precision floating point and reduced precision workloads. Our framework supports arbitrary size GEMMs and consists of two parts: (1) a simple application programming interface (API) for easy configuration and integration into existing software and (2) a highly customizable hardware template. The API provides both compile and runtime options for controlling key aspects of the hardware template including dynamic precision switching; interleaving and block size control; and fused deep learning specific operations. The framework currently supports single precision floating point (FP32), 16, 8, 4 and 2 bit Integer and Fixed Point (INT16, INT8, INT4, INT2) and more exotic data types for deep learning workloads: INT16xTernary, INT8xTernary, BinaryxBinary. We compare our implementation to the latest NVIDIA Pascal GPU and evaluate the performance benefits provided by optimizations built into the hardware template. Using three neural networks (AlexNet, VGGNet and ResNet) we illustrate that reduced precision representations such as binary achieve the best performance, and that the HARPv2 enables fine-grained partitioning of computations over both the Xeon and FPGA. We observe up to 50x improvement in execution time compared to single precision floating point, and that runtime configuration options can improve the efficiency of certain layers in AlexNet up to 4x, achieving an overall 1.3x improvement over the entire network.


title: Design of a Sierpinski patch antenna around 2.4 GHz/5GHz for WiFi (IEEE 802.11n) applications

abstract: In this paper, a low-cost dual-band printed antenna that employs Sierpinski fractal geometry is proposed for possible integration to IEEE 802.11n WLAN standards access points. The designed antenna covers two wireless spectrum bandwidth, they are the ISM 2.4 GHz and the U-NII 5 GHZ. The antenna geometry is confined within a compact size of 75 × 75 × 1.8 mm3. The antenna is mounted on partial ground and fed by microstrip line. It is modelled and optimized using the well-known CST software and the results in terms of reflection coefficient, VSWR, Bandwidth and power Gain are illustrated and compared to previous published literatures.


title: An empirical evaluation of models of text document similarity

abstract: An Empirical Evaluation of Models of Text Document Similarity Michael D. Lee (michael.lee@adelaide.edu.au) Department of Psychology, University of Adelaide South Australia, 5005, AUSTRALIA Brandon Pincombe (brandon.pincombe@dsto.defence.gov.au) Intelligence Surveillance and Reconnaissance Division, Defence Science and Technology Organisation PO Box 1500, Edinburgh SA 5111 AUSTRALIA Matthew Welsh (matthew.welsh@adelaide.edu.au) Australian School of Petroleum Engineering, University of Adelaide South Australia, 5005, AUSTRALIA Abstract Modeling the semantic similarity between text docu- ments presents a significant theoretical challenge for cognitive science, with ready-made applications in in- formation handling and decision support systems deal- ing with text. While a number of candidate models exist, they have generally not been assessed in terms of their ability to emulate human judgments of simi- larity. To address this problem, we conducted an ex- periment that collected repeated similarity measures for each pair of documents in a small corpus of short news documents. An analysis of human performance showed inter-rater correlations of about 0.6. We then considered the ability of existing models—using word- based, n-gram and Latent Semantic Analysis (LSA) approaches—to model these human judgments. The best performed LSA model produced correlations of about 0.6, consistent with human performance, while the best performed word-based and n-gram models achieved correlations closer to 0.5. Many of the re- maining models showed almost no correlation with hu- man performance. Based on our results, we provide some discussion of the key strengths and weaknesses of the models we examined. Introduction Modeling the semantic similarity between text docu- ments is an interesting problem for cognitive science, for both theoretical and practical reasons. Theoret- ically, it involves the study of a basic cognitive pro- cess with richly structured natural stimuli. Practically, search engines, text corpus visualizations, and a vari- ety of other applications for filtering, sorting, retriev- ing, and generally handling text rely fundamentally on similarity measures. For this reason, the ability to as- sess semantic similarity in an accurate, automated, and scalable way is a key determinant of the effectiveness of most information handling and decision support soft- ware that deals with text. A variety of different approaches have been devel- oped for modeling text document similarity. These in- clude simple word-based, keyword-based and n-gram measures (e.g., Salton, 1989; Damashek, 1995), and more complicated approaches such as Latent Seman- tic Analysis (LSA: Deerwester et al., 1990; Landauer and Dumais, 1997). While all of these approaches have achieved some level of practical success, they have gen- erally not been assessed in terms of their ability to model human judgments of text document similarity. The most likely reason for this failure is that no suit- able empirical data exist, and considerable effort is in- volved in collecting pairwise ratings of text document similarity for even a moderate number of documents. This paper reports the collection of data that give ten independent ratings of the similarity of every pair of 50 short text documents, and so represents an attempt to establish a ‘psychological ground truth’ for evaluating models. Using the new data, we report a first eval- uation of the ability of word-based, n-gram and LSA approaches to model human judgments. Experiment Materials The text corpus evaluated by human judges contained 50 documents selected from the Australian Broadcast- ing Corporation’s news mail service, which provides text e-mails of headline stories. The documents varied in length from 51 to 126 words, and covered a number of broad topics. A further 314 documents from the same were collected to act as a larger ‘backgrounding’ corpus for LSA. Both document sets were assessed against a stan- dard corpus of five English texts using four models of language. These were the log-normal, generalized in- verse Gauss-Poisson (with γ = −0.5), Yule-Simon and Zipfian models (Baayen, 2001). Both document sets were within the normal range of English text for word frequency spectrum and vocabulary growth and were therefore regarded as representative of normal English texts. Subjects The subjects were 83 University of Adelaide students (29 males and 54 females), with a mean age of 19.7 years. They were each paid with a ten (Australian) dollar gift voucher for every 100 document pair ratings made.


title: Autonomic neural control of heart rate during dynamic exercise: revisited.

abstract: UNLABELLED ::: The accepted model of autonomic control of heart rate (HR) during dynamic exercise indicates that the initial increase is entirely attributable to the withdrawal of parasympathetic nervous system (PSNS) activity and that subsequent increases in HR are entirely attributable to increases in cardiac sympathetic activity. In the present review, we sought to re-evaluate the model of autonomic neural control of HR in humans during progressive increases in dynamic exercise workload. We analysed data from both new and previously published studies involving baroreflex stimulation and pharmacological blockade of the autonomic nervous system. Results indicate that the PSNS remains functionally active throughout exercise and that increases in HR from rest to maximal exercise result from an increasing workload-related transition from a 4 : 1 vagal-sympathetic balance to a 4 : 1 sympatho-vagal balance. Furthermore, the beat-to-beat autonomic reflex control of HR was found to be dependent on the ability of the PSNS to modulate the HR as it was progressively restrained by increasing workload-related sympathetic nerve activity. ::: ::: ::: IN CONCLUSION ::: (i) increases in exercise workload-related HR are not caused by a total withdrawal of the PSNS followed by an increase in sympathetic tone; (ii) reciprocal antagonism is key to the transition from vagal to sympathetic dominance, and (iii) resetting of the arterial baroreflex causes immediate exercise-onset reflexive increases in HR, which are parasympathetically mediated, followed by slower increases in sympathetic tone as workloads are increased.


title: Applying attribute based access control for privacy preserving health data disclosure

abstract: Patients often willingly share their health data in a controlled manner for the purposes of research. The disclosure of such data in response to requests from researchers must conform to various policies imposed by the patient, the rights of the data requester, and organizational and governmental regulations. This work investigates the applicability of Attribute-Based Access Control (ABAC) model for specifying and enforcing these various types of policies. Specifically, it demonstrates how the XACML framework can be used for specifying and enforcing these policies.


title: The SLAM problem: a survey

abstract: This paper surveys the most recent published techniques in the field of Simultaneous Localization and Mapping (SLAM). In particular it is focused on the existing techniques available to speed up the process, with the purpose to handel large scale scenarios. The main research field we plan to investigate is the filtering algorithms as a way of reducing the amount of data. It seems that almost all the current approaches can not perform consistent maps for large areas, mainly due to the increase of the computational cost and due to the uncertainties that become prohibitive when the scenario becomes larger.


title: On Exploiting Dynamic Execution Patterns for Workload Offloading in Mobile Cloud Applications

abstract: Mobile Cloud Computing (MCC) bridges the gap between limited capabilities of mobile devices and the increasing users' demand of mobile multimedia applications, by offloading the computational workloads from local devices to the remote cloud. Current MCC research focuses on making offloading decisions over different methods of a MCC application, but may inappropriately increase the energy consumption if having transmitted a large amount of program states over expensive wireless channels. Limited research has been done on avoiding such energy waste by exploiting the dynamic patterns of applications' run-time execution for workload offloading. In this paper, we adaptively offload the local computational workload with respect to the run-time application dynamics. Our basic idea is to formulate the dynamic executions of user applications using a semi-Markov model, and to further make offloading decisions based on probabilistic estimations of the offloading operation's energy saving. Such estimation is motivated by experimental investigations over practical smart phone applications, and then builds on analytical modeling of methods' execution times and offloading expenses. Systematic evaluations show that our scheme significantly improves the efficiency of workload offloading compared to existing schemes over various smart phone applications.


title: Testing Database Applications with SQL Semantics

abstract: Testing of database applications is crucial for ensuring high software quality as undetected faults can result in unrecoverable data corruption. The problem of database application testing can be broadly partitioned into the problems of test cases generation, test data preparation and test outcomes verification. Among the three problems, the problem of test cases generation directly affects the effectiveness of testing. Conventionally, database application testing is based upon whether or not the application can perform a set of predefined functions. While it is useful to achieve a basic degree of quality by considering the application to be a black box in the testing process, white box testing is required for more thorough testing. However, the semantics of the Structural Query Language (SQL) statements embedded in database applications are rarely considered in conventional white box testing techniques. In this paper, we propose to complement white box techniques with the inclusion of the SQL semantics. Our approach is to transform the embedded SQL statements to procedures in some general-purpose programming language and thereby generate test cases using conventional white box testing techniques. Additional test cases that are not covered in traditional white box testing are generated to improve the effectiveness of database application testing. The steps of both SQL statements transformation and test cases generation will be explained and illustrated using an example adapted from a course registration system. We successfully identify additional faults involving the internal states of databases.


title: Cardiac arrhythmias related to continuous thermodilution cardiac output measurement in an anaesthetized sheep

abstract: A case of cardiac arrhythmias related to continuous thermodilution cardiac output (CCO) is reported. A sheep anaesthetized for experimental purpose was instrumented with a special Swan-Ganz catheter-type to be used for CCO measurements. One hour after starting the CCO monitoring, isolated ventricular extrasystoles were noticed on the electrocardiogram with an increasing frequency. Subsequently bursts of extrasystoles occurred. Atrioventricular dissociation was also observed. The peaks of temperature of the thermal filament were within the normal range and their presence was noticed when arrhythmias appeared. Mean blood pressure and cardiac output did not change during this episode. When the CCO was switched off, no more arrhythmias were observed. The CCO Swan-Ganz by itself did not generate any arrhythmia. The sheep recovered uneventfully. When arrhythmias occur during anaesthesia where CCO is used, a thermal filament induced origin of the arrhythmia must be considered.


title: Bayesian reputation modeling in e-marketplaces sensitive to subjectivity, deception and change

abstract: We present a model for buying agents in e-marketplaces to interpret evaluations of sellers provided by other buying agents, known as advisors. The interpretation of seller evaluations is complicated by the inherent subjectivity of each advisor, the possibility that advisors may deliberately provide misleading evaluations to deceive competitors and the dynamic nature of seller and advisor behaviours that may naturally change seller evaluations over time. Using a Bayesian approach, we demonstrate how to cope with subjectivity, deception and change in a principled way. More specifically, by modeling seller properties and advisor evaluation functions as dynamic random variables, buyers can progressively learn a probabilistic model that naturally and "correctly" calibrates the interpretation of seller evaluations without having to resort to heuristics to explicitely detect and filter/discount unreliable seller evaluations. Our model, called BLADE, is shown empirically to achieve lower mean error in the estimation of seller properties when compared to other models for reasoning about advisor ratings of sellers in electronic maketplaces.


title: Formalization, Annotation and Analysis of Diverse Drug and Probe Screening Assay Datasets Using the BioAssay Ontology (BAO)

abstract: Huge amounts of high-throughput screening (HTS) data for probe and drug development projects are being generated in the pharmaceutical industry and more recently in the public sector. The resulting experimental datasets are increasingly being disseminated via publically accessible repositories. However, existing repositories lack sufficient metadata to describe the experiments and are often difficult to navigate by non-experts. The lack of standardized descriptions and semantics of biological assays and screening results hinder targeted data retrieval, integration, aggregation, and analyses across different HTS datasets, for example to infer mechanisms of action of small molecule perturbagens. To address these limitations, we created the BioAssay Ontology (BAO). BAO has been developed with a focus on data integration and analysis enabling the classification of assays and screening results by concepts that relate to format, assay design, technology, target, and endpoint. Previously, we reported on the higher-level design of BAO and on the semantic querying capabilities offered by the ontology-indexed triple store of HTS data. Here, we report on our detailed design, annotation pipeline, substantially enlarged annotation knowledgebase, and analysis results. We used BAO to annotate assays from the largest public HTS data repository, PubChem, and demonstrate its utility to categorize and analyze diverse HTS results from numerous experiments. BAO is publically available from the NCBO BioPortal at http://bioportal.bioontology.org/ontologies/1533. BAO provides controlled terminology and uniform scope to report probe and drug discovery screening assays and results. BAO leverages description logic to formalize the domain knowledge and facilitate the semantic integration with diverse other resources. As a consequence, BAO offers the potential to infer new knowledge from a corpus of assay results, for example molecular mechanisms of action of perturbagens.


title: GloMoSim: A scalable network simulation environment

abstract: Large-scale hybrid networks that include wireless, wired, and satellite based communications are becoming common in both military and commercial situations. This paper describes a scalable simulation environment called GloMoSim (for Global Mobile Information System Simulator) that effectively utilizes parallel execution to reduce the simulation time of detailed high-fidelity models of large communication networks. The paper also presents a set of case studies that evaluate the performance of large wireless networks with thousands of nodes and compares the impact of different lower layer protocols on the performance of typical applications.


title: The role of temporal statistics in the transfer of experience in context-dependent reinforcement learning

abstract: Reinforcement learning (RL) is an algorithmic theory for learning by experience optimal action control. Two widely discussed problems within this field are the temporal credit assignment problem and the transfer of experience. The temporal credit assignment problem postulates that deciding whether an action is good or bad may not be done upon right away because of delayed rewards. The problem of transferring experience investigates the question of how experience can be generalized and transferred from a familiar context, where it was acquired, to an unfamiliar context, where it may, nevertheless, prove helpful. We propose a controller for modelling such flexibility in a context-dependent reinforcement learning paradigm. The devised controller combines two alternatives of perfect learner algorithms. In the first alternative, rewards are predicted by individual objects presented in a temporal sequence. In the second alternative, rewards are predicted on the basis of successive pairs of objects. Simulations run on both deterministic and random temporal sequences show that only in case of deterministic sequences, a previously acquired context could be retrieved. This suggests a role of temporal sequence information in the generalization and transfer of experience.


title: Switching losses in a 1.7 kW GaN based full-bridge DC-DC converter with synchronous rectification

abstract: The paper presents the detailed analysis of switching losses in GaN devices for an isolated full-bridge dc-dc converter. Switching losses includes capacitive losses along with turn-on and turn-off losses of the power MOSFET. In hard switched converters, the output capacitance of MOSFET contributes to a significant part of the switching loss. The capacitive loss analysis also helps in determining the optimum number of devices used in parallel for a given power level and switching frequency. The analysis is performed on a 1.7 kW full bridge isolated dc-dc GaN converter at a switching frequency of 50 kHz. The paper also presents the GaN switch efficiency curve for an isolated dc-dc GaN converter with different number of devices in parallel.


title: Enabling Robots to Communicate their Objectives

abstract: Our ultimate goal is to efficiently enable end-users to correctly anticipate a robot's behavior in novel situations. This behavior is often a direct result of the robot's underlying objective function. Our insight is that end-users need to have an accurate mental model of this objective function in order to understand and predict what the robot will do. While people naturally develop such a mental model over time through observing the robot act, this familiarization process may be lengthy. Our approach reduces this time by having the robot model how people infer objectives from observed behavior, and then selecting those behaviors that are maximally informative. The problem of computing a posterior over objectives from observed behavior is known as Inverse Reinforcement Learning (IRL), and has been applied to robots learning human objectives. We consider the problem where the roles of human and robot are swapped. Our main contribution is to recognize that unlike robots, humans will not be \emph{exact} in their IRL inference. We thus introduce two factors to define candidate approximate-inference models for human learning in this setting, and analyze them in a user study in the autonomous driving domain. We show that certain approximate-inference models lead to the robot generating example behaviors that better enable users to anticipate what the robot will do in test situations. Our results also suggest, however, that additional research is needed in modeling how humans extrapolate from examples of robot behavior.


title: Machine Learning for Android Malware Detection Using Permission and API Calls

abstract: The Google Android mobile phone platform is one of the most anticipated smartphone operating systems on the market. The open source Android platform allows developers to take full advantage of the mobile operation system, but also raises significant issues related to malicious applications. On one hand, the popularity of Android absorbs attention of most developers for producing their applications on this platform. The increased numbers of applications, on the other hand, prepares a suitable prone for some users to develop different kinds of malware and insert them in Google Android market or other third party markets as safe applications. In this paper, we propose to combine permission and API (Application Program Interface) calls and use machine learning methods to detect malicious Android Apps. In our design, the permission is extracted from each App's profile information and the APIs are extracted from the packed App file by using packages and classes to represent API calls. By using permissions and API calls as features to characterize each Apps, we can learn a classifier to identify whether an App is potentially malicious or not. An inherent advantage of our method is that it does not need to involve any dynamical tracing of the system calls but only uses simple static analysis to find system functions involved in each App. In addition, because permission settings and APIs are alwaysavailable for each App, our method can be generalized to all mobile applications. Experiments on real-world Apps with more than 1200 malware and 1200 benign samples validate the algorithm performance.


title: LTspice-model of thermoelectric Peltier-Seebeck element

abstract: A simple, symmetric, convertible into electric generator mode LTspice-model of Peltier-Seebeck element is proposed. The model parameters are extracted from the experimental measurement results of the electrical and thermal characteristics of the real elements.


title: Edge envelope based reconstruction of torn document

abstract: The paper proposes a new algorithm for reconstruction of torn document ripped-up by hand using a unique feature, Edge Envelope. The essence of the paper is to match the edges using high curvature points and edge variations as human visual system does while reconstructing the document. Moreover the ambiguity created during the matching of edges due to shearing can be overcome by smoothing the edge envelopes using a filter. Further, a newly defined parameter Normalized Edge Envelope Difference can be used as an effective quantitative measure for matching the edges of torn pieces, which exploits property of human visual system. The effectiveness of the algorithm is demonstrated through preliminary results by taking limited amount of torn pieces.


title: Unsupervised clustering of emotion and voice styles for expressive TTS

abstract: Current text-to-speech synthesis (TTS) systems are often perceived as lacking expressiveness, limiting the ability to fully convey information. This paper describes initial investigations into improving expressiveness for statistical speech synthesis systems. Rather than using hand-crafted definitions of expressive classes, an unsupervised clustering approach is described which is scalable to large quantities of training data. To incorporate this “expression cluster” information into an HMM-TTS system two approaches are described: cluster questions in the decision tree construction; and average expression speech synthesis (AESS) using cluster-based linear transform adaptation. The performance of the approaches was evaluated on audiobook data in which the reader exhibits a wide range of expressiveness. A subjective listening test showed that synthesising with AESS results in speech that better reflects the expressiveness of human speech than a baseline expression-independent system.


title: Automatic Number Plate Recognition System for Vehicle Identification Using Optical Character Recognition

abstract: Automatic Number Plate Recognition (ANPR) is an image processing technology which uses number (license) plate to identify the vehicle. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. The system is implemented on the entrance for security control of a highly restricted area like military zones or area around top government offices e.g. Parliament, Supreme Court etc. The developed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is extracted using the image segmentation in an image. Optical character recognition technique is used for the character recognition. The resulting data is then used to compare with the records on a database so as to come up with the specific information like the vehicle’s owner, place of registration, address, etc. The system is implemented and simulated in Matlab, and it performance is tested on real image. It is observed from the experiment that the developed system successfully detects and recognize the vehicle number plate on real images.


title: Cognitive HF communication system with adaptive complementary codes

abstract: High frequency (HF) band covers frequencies between 3–30 MHz. With wavelengths of 10–100 m, HF band enables communications over a very long distances though a skywave propagation. As HF band has several benefits like versatility and large coverage area, it suffers phenomena like rapidly changing environment and strong interference. The cognitive radio concept which adapts to the changes in the environment offers tools to solve those problems. In this paper, HF band challenges caused by ionospheric break and interference are solved using databases and complementary codes that adapt to the characteristics of different data transmission requirements. The goal is to optimize used frequencies based on the data transmission needs.


A Review of Cleanroom Software Engineering


title: Assessing resilience.

abstract: As more studies reveal the positive relationships between resilience and aging well, mental health practitioners are increasingly interested in recognizing and strengthening resilience among aging Baby Boomers. Although there is much information on characteristics of resilience, there is very little research on assessing for resilience. This article presents a framework for assessing resilience among middle-aged and older adults that includes the Resilience Scale and open-ended questions to provide clients opportunities to reflect on and perhaps rediscover their resilience. This assessment framework may be useful for mental health practitioners using a strengths-based approach with their clients.


title: DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

abstract: We present DeblurGAN, an end-to-end learned method for motion deblurring. The learning is based on a conditional GAN and the content loss. DeblurGAN achieves state-of-the art performance both in the structural similarity measure and visual appearance. The quality of the deblurring model is also evaluated in a novel way on a real-world problem - object detection on (de-)blurred images. The method is 5 times faster than the closest competitor - Deep-Deblur [25]. We also introduce a novel method for generating synthetic motion blurred images from sharp ones, allowing realistic dataset augmentation. The model, code and the dataset are available at https://github.com/KupynOrest/DeblurGAN


title: Orthogonal Security with Cipherbase

abstract: This paper describes the design of the Cipherbase system. Cipherbase is a full-fledged SQL database system that achieves high performance and high data confidentiality by storing and processing strongly encrypted data. The Cipherbase system incorporates customized trusted hardware, extending Microsoft’s SQL Server for efficient execution of queries using both secure hardware and commodity servers. This paper presents the design of the Cipherbase secure hardware and its implementation using FPGAs. Furthermore, this paper shows how we addressed hardware / software co-design in the Cipherbase system.


title: Adaptive Information Gathering via Imitation Learning

abstract: In the adaptive information gathering problem, a policy is required to select an informative sensing location using the history of measurements acquired thus far. While there is an extensive amount of prior work investigating effective practical approximations using variants of Shannon's entropy, the efficacy of such policies heavily depends on the geometric distribution of objects in the world. On the other hand, the principled approach of employing online POMDP solvers is rendered impractical by the need to explicitly sample online from a posterior distribution of world maps. ::: We present a novel data-driven imitation learning framework to efficiently train information gathering policies. The policy imitates a clairvoyant oracle - an oracle that at train time has full knowledge about the world map and can compute maximally informative sensing locations. We analyze the learnt policy by showing that offline imitation of a clairvoyant oracle is implicitly equivalent to online oracle execution in conjunction with posterior sampling. This observation allows us to obtain powerful near-optimality guarantees for information gathering problems possessing an adaptive sub-modularity property. As demonstrated on a spectrum of 2D and 3D exploration problems, the trained policies enjoy the best of both worlds - they adapt to different world map distributions while being computationally inexpensive to evaluate.


title: Toward Domain Independence for Learning-Based Monocular Depth Estimation

abstract: Modern autonomous mobile robots require a strong understanding of their surroundings in order to safely operate in cluttered and dynamic environments. Monocular depth estimation offers a geometry-independent paradigm to detect free, navigable space with minimum space, and power consumption. These represent highly desirable features, especially for microaerial vehicles. In order to guarantee robust operation in real-world scenarios, the estimator is required to generalize well in diverse environments. Most of the existent depth estimators do not consider generalization, and only benchmark their performance on publicly available datasets after specific fine tuning. Generalization can be achieved by training on several heterogeneous datasets, but their collection and labeling is costly. In this letter, we propose a deep neural network for scene depth estimation that is trained on synthetic datasets, which allow inexpensive generation of ground truth data. We show how this approach is able to generalize well across different scenarios. In addition, we show how the addition of long short-term memory layers in the network helps to alleviate, in sequential image streams, some of the intrinsic limitations of monocular vision, such as global scale estimation, with low computational overhead. We demonstrate that the network is able to generalize well with respect to different real-world environments without any fine tuning, achieving comparable performance to state-of-the-art methods on the KITTI dataset.


title: Established – outsider relations in youth football tournaments: an exploration of transnational power figurations between Scandinavian organizers and African teams

abstract: This article examines Scandinavian constructions and readings of potential football migrants from the African continent by providing an investigation of the dynamics and narratives surrounding African football teams’ participation in three youth football tournaments. Drawing on Elias and Scotson’s (1965/1994) theory of established-outsider relations, we explore the transnational power relations and processes of inclusion and exclusion in group relationships. The data were collected between July and August 2012, as part of a wider study examining transnational migration and mobility in Scandinavian women’s football. Ethnographic observations were conducted at three Scandinavian youth football tournaments, and interviews were conducted with tournament organizers and representatives from participating women’s clubs/teams. The data suggest that a particular representation of self, on the part of the tournament organizers, as being providers of ‘development’ and gender equity programmes to African teams, is fu...


title: An observation model for high resolution radar data in the context of an automotive pedestrian safety system

abstract: Within the EU-Project ARTRAC a radar sensor is developed that offers new capabilities for enhanced pedestrian protection. In this paper we describe a sensor model for high resolution measurements as a first step to help improving the object detection and tracking. For the tracking, with the commonly used Kalman filter and its derivations, one makes several assumptions which do not necessarily hold for pedestrian tracking. Therefore we propose to use a non-linear filter e.g. the Particle filter. In order to define an observation model and gain a suitable proposal distribution, we make use of grid structures to represent parts of the measurement and system state space. Also, we set several assumptions to reduce the computational costs of the tracking process.


title: Hard and soft news: A review of concepts, operationalizations and key findings

abstract: Over 30 years, a large body of research on what is often called ‘hard’ and ‘soft news’ has accumulated in communication studies. However, there is no consensus about what hard and soft news exactly is, or how it should be defined or measured. Moreover, the concept has not been clearly differentiated from or systematically related to concepts addressing very similar phenomena – tabloidization and ‘infotainment’. Consequently, the results of various studies are hard to compare and different scientific discourses on related issues remain unconnected. Against this backdrop, this article offers a conceptual analysis of the concept based on studies in English and other languages. We identify key dimensions of the concept and make suggestions for a standardized definition and multi-dimensional measurement of harder and softer news. In doing so, we propose to distinguish thematic, focus and style features as basic dimensions that – in their combination – make up harder and softer types of news.


title: Review: A review of machine learning approaches to Spam filtering

abstract: In this paper, we present a comprehensive review of recent developments in the application of machine learning algorithms to Spam filtering, focusing on both textual- and image-based approaches. Instead of considering Spam filtering as a standard classification problem, we highlight the importance of considering specific characteristics of the problem, especially concept drift, in designing new filters. Two particularly important aspects not widely recognized in the literature are discussed: the difficulties in updating a classifier based on the bag-of-words representation and a major difference between two early naive Bayes models. Overall, we conclude that while important advancements have been made in the last years, several aspects remain to be explored, especially under more realistic evaluation settings.


title: 76–81-GHz CMOS Transmitter With a Phase-Locked-Loop-Based Multichirp Modulator for Automotive Radar

abstract: This paper presents the design of a linear frequency-modulated continuous-wave (FMCW) radar transmitter (TX) for automotive radar applications. The TX has a wide operating range from 76 to 81 GHz by employing a frequency-doubling architecture using an LC voltage-controlled oscillator operating at 38–40.5 GHz and a differential frequency doubler using a single transformer. A chirp generator is integrated into the TX, which provides various frequency chirp profiles with programmable chirp slope and sweep duration. The proposed CMOS FMCW TX can be applied to various modulation algorithms for multiple target detection and the avoidance of ghost targets. The TX is implemented using 65-nm CMOS technology. The chip size is 1.48 $\,\times\,$ 1.85 mm $^{2}$ . The measurement results shows a 76–81-GHz frequency range and 3-dBm output power while dissipating 320 mW. The phase-noise density of the TX is ${-}{\hbox{83.33}}$ dBm/Hz at an offset frequency of 1 MHz when the output frequency is 77 GHz.


Gene Therapy 2017: Progress and Future Directions


title: Avoiding Discrimination through Causal Reasoning

abstract: Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome. While convenient to work with, observational criteria have severe inherent limitations that prevent them from resolving matters of fairness conclusively. ::: Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning. This viewpoint shifts attention from "What is the right fairness criterion?" to "What do we want to assume about the causal data generating process?" Through the lens of causality, we make several contributions. First, we crisply articulate why and when observational criteria fail, thus formalizing what was before a matter of opinion. Second, our approach exposes previously ignored subtleties and why they are fundamental to the problem. Finally, we put forward natural causal non-discrimination criteria and develop algorithms that satisfy them.


title: Controlling shoulder impedance in a rehabilitation arm exoskeleton

abstract: A control methodology is developed for modulating shoulder impedance in an arm exoskeleton during physical therapy. Setting the remote center of compliance at the shoulder will allow the exoskeleton to enact resistance training protocols that strengthen the rotator cuff and other joint musculature supporting the shoulder complex. The rotational kinematics for the shoulder are first derived, and then the torques applied at the shoulder are estimated using force sensors placed at the hand and elbow interfaces. Impedance and admittance control schemes are both developed for realizing isolateral strengthening exercises, and some preliminary experimental results are presented for implementation on an arm exoskeleton currently under development.


title: End-to-End Audiovisual Speech Recognition

abstract: Several end-to-end deep learning approaches have been recently presented which extract either audio or visual features from the input images or audio signals and perform speech recognition. However, research on end-to-end audiovisual models is very limited. In this work, we present an end-to-end audiovisual model based on residual networks and Bidirectional Gated Recurrent Units (BGRUs). To the best of our knowledge, this is the first audiovisual fusion model which simultaneously learns to extract features directly from the image pixels and audio waveforms and performs within-context word recognition on a large publicly available dataset (LRW). The model consists of two streams, one for each modality, which extract features directly from mouth regions and raw waveforms. The temporal dynamics in each stream/modality are modeled by a 2-layer BGRU and the fusion of multiple streams/modalities takes place via another 2-layer BGRU. A slight improvement in the classification rate over an end-to-end audio-only and MFCC-based model is reported in clean audio conditions and low levels of noise. In presence of high levels of noise, the end-to-end audiovisual model significantly outperforms both audio-only models.


title: Bayesian Just-So Stories in Psychology and Neuroscience

abstract: According to Bayesian theories in psychology and neuroscience, minds and brains are (near) optimal in solving a wide range of tasks. We challenge this view and argue that more traditional, non-Bayesian approaches are more promising. We make 3 main arguments. First, we show that the empirical evidence for Bayesian theories in psychology is weak. This weakness relates to the many arbitrary ways that priors, likelihoods, and utility functions can be altered in order to account for the data that are obtained, making the models unfalsifiable. It further relates to the fact that Bayesian theories are rarely better at predicting data compared with alternative (and simpler) non-Bayesian theories. Second, we show that the empirical evidence for Bayesian theories in neuroscience is weaker still. There are impressive mathematical analyses showing how populations of neurons could compute in a Bayesian manner but little or no evidence that they do. Third, we challenge the general scientific approach that characterizes Bayesian theorizing in cognitive science. A common premise is that theories in psychology should largely be constrained by a rational analysis of what the mind ought to do. We question this claim and argue that many of the important constraints come from biological, evolutionary, and processing (algorithmic) considerations that have no adaptive relevance to the problem per se. In our view, these factors have contributed to the development of many Bayesian “just so” stories in psychology and neuroscience; that is, mathematical analyses of cognition that can be used to explain almost any behavior as optimal.


title: Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning

abstract: The development of autonomous unmanned aerial vehicles (UAVs) is of high interest to many governmental and military organizations around the world. An essential aspect of UAV autonomy is the ability for automatic path planning. In this paper, we use the genetic algorithm (GA) and the particle swarm optimization algorithm (PSO) to cope with the complexity of the problem and compute feasible and quasi-optimal trajectories for fixed wing UAVs in a complex 3D environment, while considering the dynamic properties of the vehicle. The characteristics of the optimal path are represented in the form of a multiobjective cost function that we developed. The paths produced are composed of line segments, circular arcs and vertical helices. We reduce the execution time of our solutions by using the “single-program, multiple-data” parallel programming paradigm and we achieve real-time performance on standard commercial off-the-shelf multicore CPUs. After achieving a quasi-linear speedup of 7.3 on 8 cores and an execution time of 10 s for both algorithms, we conclude that by using a parallel implementation on standard multicore CPUs, real-time path planning for UAVs is possible. Moreover, our rigorous comparison of the two algorithms shows, with statistical significance, that the GA produces superior trajectories to the PSO.


title: Visualizing Mobility of Public Transportation System

abstract: Public transportation systems (PTSs) play an important role in modern cities, providing shared/massive transportation services that are essential for the general public. However, due to their increasing complexity, designing effective methods to visualize and explore PTS is highly challenging. Most existing techniques employ network visualization methods and focus on showing the network topology across stops while ignoring various mobility-related factors such as riding time, transfer time, waiting time, and round-the-clock patterns. This work aims to visualize and explore passenger mobility in a PTS with a family of analytical tasks based on inputs from transportation researchers. After exploring different design alternatives, we come up with an integrated solution with three visualization modules: isochrone map view for geographical information, isotime flow map view for effective temporal information comparison and manipulation, and OD-pair journey view for detailed visual analysis of mobility factors along routes between specific origin-destination pairs. The isotime flow map linearizes a flow map into a parallel isoline representation, maximizing the visualization of mobility information along the horizontal time axis while presenting clear and smooth pathways from origin to destinations. Moreover, we devise several interactive visual query methods for users to easily explore the dynamics of PTS mobility over space and time. Lastly, we also construct a PTS mobility model from millions of real passenger trajectories, and evaluate our visualization techniques with assorted case studies with the transportation researchers.


title: Early and Late Discharge after Hospital Birth: Breastfeeding

abstract: Breastfeeding was studied among women discharged early and late after normal delivery in a hospital. Early discharge was defined as leaving the hospital 24–48 h after delivery in combination with domiciliary visits, and late discharge as the regular hospital postpartum care (mean 6 days). 164 women interested in participating in the early discharge study were randomly allocated in late pregnancy to a group offered early discharge (Experimental group = EG) or a group offered the traditional later discharge (Control group = CG). After medical exclusions and non-medical withdrawals, 50 mother-infant couples remained in EG and 54 in CG. Regular breastfeeding at 6 months after birth was reported by 63% of the multiparae in EG and 41% in CG (p=0.06). Thirty-three per cent of the primiparae in each group were still breastfeeding at 6 months. 2% of the infants in EG and 72% in CG received supplementary breastmilk at least once during their first week of life. Infants discharged early were breastfed more often on the 2nd (NS), 3rd (p<0.05) and 4th day (p<0.001) after birth, compared with infants who stayed longer in hospital. There were no statistically significant differences between EG and CG women in their experiences of success in breastfeeding according to daily records from the first 14 days after the birth.


title: Sparse Gaussian graphical models with unknown block structure

abstract: Recent work has shown that one can learn the structure of Gaussian Graphical Models by imposing an L1 penalty on the precision matrix, and then using efficient convex optimization methods to find the penalized maximum likelihood estimate. This is similar to performing MAP estimation with a prior that prefers sparse graphs. In this paper, we use the stochastic block model as a prior. This prefer graphs that are blockwise sparse, but unlike previous work, it does not require that the blocks or groups be specified a priori. The resulting problem is no longer convex, but we devise an efficient variational Bayes algorithm to solve it. We show that our method has better test set likelihood on two different datasets (motion capture and gene expression) compared to independent L1, and can match the performance of group L1 using manually created groups.


title: Intrinsic Scene Decomposition from RGB-D Images

abstract: In this paper, we address the problem of computing an intrinsic decomposition of the colors of a surface into an albedo and a shading term. The surface is reconstructed from a single or multiple RGB-D images of a static scene obtained from different views. We thereby extend and improve existing works in the area of intrinsic image decomposition. In a variational framework, we formulate the problem as a minimization of an energy composed of two terms: a data term and a regularity term. The first term is related to the image formation process and expresses the relation between the albedo, the surface normals, and the incident illumination. We use an affine shading model, a combination of a Lambertian model, and an ambient lighting term. This model is relevant for Lambertian surfaces. When available, multiple views can be used to handle view-dependent non-Lambertian reflections. The second term contains an efficient combination of l2 and l1-regularizers on the illumination vector field and albedo respectively. Unlike most previous approaches, especially Retinex-like techniques, these terms do not depend on the image gradient or texture, thus reducing the mixing shading/reflectance artifacts and leading to better results. The obtained non-linear optimization problem is efficiently solved using a cyclic block coordinate descent algorithm. Our method outperforms a range of state-of-the-art algorithms on a popular benchmark dataset.


title: Agent Based Framework for Scalability in Cloud Computing

abstract: Cloud computing focuses on delivery of reliable, secure, fault- tolerant, sustainable, and scalable infrastructures for hosting internet-based application services. These applications have different composition, configuration, and deployment requirements. Cloud service providers are willing to provide large scaled computing infrastructure at a cheap prices. Quantifying the performance of scheduling and allocation policy on a Cloud infrastructure (hardware, software, services) for different application and service models under varying load, energy performance (power consumption, heat dissipation), and system size is an extremely challenging problem to tackle. This problem can be tackle with the help of mobile agents. Mobile agent being a process that can transport its state from one environment to another, with its data intact, and is capable of performing appropriately in the new environment. This work proposes an agent based framework for providing scalability in cloud computing environments supported with algorithms for searching another cloud when the approachable cloud becomes overloaded and for searching closest datacenters with least response time of virtual machine (VM).


title: Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval

abstract: Most topic models, such as latent Dirichlet allocation, rely on the bag-of-words assumption. However, word order and phrases are often critical to capturing the meaning of text in many text mining tasks. This paper presents topical n-grams, a topic model that discovers topics as well as topical phrases. The probabilistic model generates words in their textual order by, for each word, first sampling a topic, then sampling its status as a unigram or bigram, and then sampling the word from a topic-specific unigram or bigram distribution. Thus our model can model "white house" as a special meaning phrase in the 'politics' topic, but not in the 'real estate' topic. Successive bigrams form longer phrases. We present experiments showing meaningful phrases and more interpretable topics from the NIPS data and improved information retrieval performance on a TREC collection.


title: Everything You Wanted to Know about Smart Health Care: Evaluating the Different Technologies and Components of the Internet of Things for Better Health

abstract: The Internet-of-Things (IoT) has taken over the business spectrum, and its applications vary widely from agriculture and health care to transportation. A hospital environment can be very stressful, especially for senior citizens and children. With the ever-increasing world population, the conventional patient-doctor appointment has lost its effectiveness. Hence, smart health care becomes very important. Smart health care can be implemented at all levels, starting from temperature monitoring for babies to tracking vital signs in the elderly.


title: Bridging the Gap between Software Development

abstract: Traditionally, software development efforts in large corporations have been about as far removed from information security as they were from human resources or any other business function. Software development has also had the tendency to be highly distributed among business units and thus not even practiced in a cohesive, coherent manner. In the worst cases, busy business unit executives trade roving bands of developers like Pokemon cards in a fifth-grade classroom (in an attempt to get ahead). Suffice it to say, none of this is good. The disconnect between security and development has ultimately produced software development efforts that lack any sort of contemporary understanding of technical security risks. Today's complex and highly connected computing environments trigger myriad security concerns, so by blowing off the idea of security entirely, software builders virtually guarantee that their creations have way too many security weaknesses that could - and should - have been avoided. This article presents some recommendations for solving this problem. Our approach is born out of experience in two diverse fields: software security and information security. Central among our recommendations is the notion of using the knowledge inherent in information security organizations to enhance secure software development efforts.


title: Gaze- vs. hand-based pointing in virtual environments

abstract: This paper contributes to the nascent body of literature on pointing performance in Virtual Environments (VEs), comparing gaze- and hand-based pointing. Contrary to previous findings, preliminary results indicate that gaze-based pointing is slower than hand-based pointing for distant objects.


title: Techno economic analysis of smart meter reading implementation in PLN Bali using LoRa technology

abstract: Traditional electricity meters read manually, large workloads, major errors and distributed at different installation locations, need to spend a lot of time and labor costs. In real-time, the accuracy and extension of the application cannot be fulfilled. With technological developments, electric meters now can be used both ways, for real-time remote reading, and can be used as prepaid and postpaid, making it smart meter. Currently the smart meter has become an important part of the smart grid system. Therefore, the national power utility company (PLN) Bali region decided to replace the existing meter with smart meter to support Bali Eco Smart Grid. Long Range (LoRa) wide area network (WAN) is the access technology that used for implementing smart meter because it has benefits including can reach long distance, low power, and lower cost. This study aims to analyze the techno economic smart meter 2 ways implementation at Bali region by using techno-economic method and cost-benefit analysis. In addition to analyze technological and economic aspects, the business model and the regulatory aspect analysis are also included. The results show that LoRa WAN is one of the technology options that can be used today, the business model that can be used is build own or implementation is done by itself, build operate transfer (BOT) or outsourcing to third party, and rental model. According the ministry of communication and information regulation number 35/2015, LoRa WAN that operate at frequencies 923–925 MHz can be implemented to support smart meter implementation by Bali PLN.


title: Real-time Scene Segmentation Using a Light Deep Neural Network Architecture for Autonomous Robot Navigation on Construction Sites

abstract: Camera-equipped unmanned vehicles (UVs) have received a lot of attention in data collection for construction monitoring applications. To develop an autonomous platform, the UV should be able to process multiple modules (e.g., context-awareness, control, localization, and mapping) on an embedded platform. Pixel-wise semantic segmentation provides a UV with the ability to be contextually aware of its surrounding environment. However, in the case of mobile robotic systems with limited computing resources, the large size of the segmentation model and high memory usage requires high computing resources, which a major challenge for mobile UVs (e.g., a small-scale vehicle with limited payload and space). To overcome this challenge, this paper presents a light and efficient deep neural network architecture to run on an embedded platform in real-time. The proposed model segments navigable space on an image sequence (i.e., a video stream), which is essential for an autonomous vehicle that is based on machine vision. The results demonstrate the performance efficiency of the proposed architecture compared to the existing models and suggest possible improvements that could make the model even more efficient, which is necessary for the future development of the autonomous robotics systems.


title: Using High-Level Construal and Perceptions of Changeability to Promote Self-Change Over Self-Protection Motives in Response to Negative Feedback

abstract: Diagnostic negative information presents people with a motivational dilemma. Although negative feedback can provide useful information with which to guide future self-improvement efforts, it also presents short-term affective costs. We propose that construal level, jointly with the perceived changeability of the feedback domain, determines whether people choose to accept or dismiss such information. Whereas low-level construal promotes short-term self-protection motivation (promoting dismissal), high-level construal promotes long-term self-change motivation (promoting acceptance)—to the extent that change is perceived as possible. Four studies support this hypothesis and examine underlying cognitive and motivational mechanisms. The present work may provide an integrative theoretical framework for understanding when people will be open to and accept negative diagnostic information, and has important practical implications for promoting self-change efforts.


title: Neuroscience and “real world” practice: music as a therapeutic resource for children in zones of conflict

abstract: Recent developments in music neuroscience are considered a source for reflection on, and evaluation and development of, musical therapeutic practice in the field, in particular, in relation to traumatized children and postconflict societies. Music neuroscience research is related to practice within a broad biopsychosocial framework. Here, examples are detailed of work from North Uganda, Palestine, and South Thailand.


title: Antibody responses in serum and lung to intranasal immunization with Haemophilus influenzae type b polysaccharide conjugated to cholera toxin B subunit and tetanus toxoid.

abstract: AIM ::: Cholera toxin B subunit (CTB) has previously been used as a mucosal carrier for various vaccine candidate antigens. The objective of this study was to see if coupling a bacterial polysaccharide, Haemophilus influenzae type b capsular polysaccharide (HibCPS), to CTB, either directly or through prior coupling to tetanus toxoid (TT), would improve the immunogenicity of HibCPS after nasal immunization. ::: ::: ::: METHODS ::: HibCPS was conjugated to CTB, TT or via TT to CTB, using glutaraldehyde or 1-ethyl-3(3-dimethylaminopropyl)-carbodiimide (EDAC) and N-succinimidyl 3-(2-pyridyldithio) propionate (SPDP). The conjugates were characterized and used for intranasal (IN) and subcutaneous (SC) immunizations of mice. The anti-Hib, -TT and -CTB antibody titers in serum and lungs after the immunizations were measured with ELISA. ::: ::: ::: RESULTS ::: The HibCTB was poorly immunogenic both given IN and SC compared with HibTT and HibTTCTB, probably because of inefficient coupling. In contrast, the conjugation of CTB to the HibTT conjugate resulted in a preparation which was superior both to the HibTT and the HibCTB conjugates in inducing local IgA and IgG anti-HibCPS antibodies in the lungs. The anti-HibCPS serum IgG titers after IN immunization with the HibTTCTB conjugate were similar to the titers after IN immunization with HibTT, or SC immunization with a commercial HibCRM conjugate vaccine. In contrast to the other conjugates, the HibTTCTB conjugate also gave rise to anti-Hib serum IgA titers. ::: ::: ::: CONCLUSION ::: We conclude that appropriate conjugation to CTB increases the mucosal immunogenicity of HibCPS, and that intranasal immunization with such a conjugate can give rise to both local and systemic anti-HibCPS antibody responses.


title: Efficient Genome-Wide, Privacy-Preserving Similar Patient Query based on Private Edit Distance

abstract: Edit distance has been proven to be an important and frequently-used metric in many human genomic research, with Similar Patient Query (SPQ) being a particularly promising and attractive example. However, due to the widespread privacy concerns on revealing personal genomic data, the scope and scale of many novel use of genome edit distance are substantially limited. While the problem of private genomic edit distance has been studied by the research community for over a decade [6], the state-of-the-art solution [31] is far from even close to be applicable to real genome sequences. In this paper, we propose several private edit distance protocols that feature unprecedentedly high efficiency and precision. Our construction is a combination of a novel genomic edit distance ap- proximation algorithm and new construction of private set difference size protocols. With the private edit distance based secure SPQ primitive, we propose GENSETS, a genome-wide, privacy- preserving similar patient query system. It is able to support search- ing large-scale, distributed genome databases across the nation. We have implemented a prototype of GENSETS. The experimental results show that, with 100 Mbps network connection, it would take GENSETS less than 200 minutes to search through 1 million breast cancer patients (distributed nation-wide in 250 hospitals, each having 4000 patients), based on edit distances between their genomes of lengths about 75 million nucleotides each.


title: The effect of reduced ankle dorsiflexion on lower extremity mechanics during landing: A systematic review

abstract: Abstract Objectives To examine the evidence for effect of restricted ankle dorsiflexion range of motion on lower-extremity landing mechanics. Design Literature review. Methods Systematic search of the literature. Articles critiqued by two reviewers. Results Six studies were identified that investigated the effect of restricted DF ROM on landing mechanics. Overall, results suggest that landing mechanics are altered with restricted DF ROM, but studies disagree as to the particular mechanical variables affected. Conclusions There is evidence that restricted dorsiflexion range of motion may alter lower-extremity landing mechanics in a manner, which predisposes athletes to injury. Interpretation of results was made difficult by the variation in landing tasks investigated and the lack studies investigating sport-specific landing tasks. The focus of studies on specific mechanical variables rather than mechanical patterns and the analysis of pooled data in the presence of different compensation strategies between participants also made interpretation difficult. These areas require further research.


title: Novel Deep Trench Buried-Body-Contact (DBBC) of 4F2 cell for sub 30nm DRAM technology

abstract: Novel Deep Trench Buried-Body-Contac (DBBC) has been successfully developed for 4F2 DRAM cells on sub-30nm technology node. The critical requirements of thermal stability, shallow junction depth, and conformal source-drain doping profile for the contact are achieved by using an ultra thin Ti silicide ohmic layer and PLAD technique, which also show excellent electrical performance and process feasibility for the development of 4F2 DRAM cell on the 30nm node and beyond.


title: Neuromorphic accelerators: a comparison between neuroscience and machine-learning approaches

abstract: A vast array of devices, ranging from industrial robots to self-driven cars or smartphones, require increasingly sophisticated processing of real-world input data (image, voice, radio, ...). Interestingly, hardware neural network accelerators are emerging again as attractive candidate architectures for such tasks. The neural network algorithms considered come from two, largely separate, domains: machine-learning and neuroscience. These neural networks have very different characteristics, so it is unclear which approach should be favored for hardware implementation. Yet, few studies compare them from a hardware perspective. We implement both types of networks down to the layout, and we compare the relative merit of each approach in terms of energy, speed, area cost, accuracy and functionality. Within the limit of our study (current SNN and machine-learning NN algorithms, current best effort at hardware implementation efforts, and workloads used in this study), our analysis helps dispel the notion that hardware neural network accelerators inspired from neuroscience, such as SNN+STDP, are currently a competitive alternative to hardware neural networks accelerators inspired from machine-learning, such as MLP+BP: not only in terms of accuracy, but also in terms of hardware cost for realistic implementations, which is less expected. However, we also outline that SNN+STDP carry potential for reduced hardware cost compared to machine-learning networks at very large scales, if accuracy issues can be controlled (or for applications where they are less important). We also identify the key sources of inaccuracy of SNN+STDP which are less related to the loss of information due to spike coding than to the nature of the STDP learning algorithm. Finally, we outline that for the category of applications which require permanent online learning and moderate accuracy, SNN+STDP hardware accelerators could be a very cost-efficient solution.


title: Comparison of hard-decision and soft-decision channel coded M-ary PPM performance over free space optical links

abstract: Error control coded M-ary pulse position modulation (PPM) can be used over free space optical links to mitigate the atmospheric attenuations and the turbulence induced fading. In this paper, we present performance analysis for coded free space optics (FSO) communication systems operating over various atmospheric conditions. We justify the selection of higher state PPM as an appropriate modulation scheme for the links and provide a comparative analysis between hard decision decoded (Reed-Solomon, RS) and soft decision decoded (Turbo) channel coded PPM over varying link conditions. Simulation results strongly indicate RS-coded PPM as a robust and well-performing coded-modulation scheme for the FSO links.


title: A Broadband Filtering Duplex Patch Antenna With High Isolation

abstract: This letter presents a broadband duplex patch antenna with high isolation and filtering performance. The proposed duplex antenna consists of one radiating patch and two sets of resonator-based filtering channels as a feeding network. The broadband performance is realized by employing multimode resonators to excite the patch antenna. To improve the isolation between two channels, two sets of resonators are employed. The lower band channel resonator can generate a transmission zero in the upper band, and the upper band channel resonator can also produce a transmission zero in the lower band. Thus, high isolation between the two channels can be obtained. For demonstration, the duplex antenna is fabricated and measured. The measured impedance bandwidths of the lower and upper band channels are 10.6% (1.95–2.17 GHz) and 6.9% (2.5–2.68 GHz), respectively. High isolation of better than 45 dB is realized between the two channels.