title: A Compact Tunable Dielectric Filter with Constant Absolute Bandwidth

abstract: This paper presents a compact tunable filter, consisting of two coaxial dielectric resonators and a pair of parallel-coupled microstrip lines. Coaxial dielectric resonators are directly coupled to each other through the aperture on the side surface of the resonators. The aperture provides magnetic coupling, the parallel-coupled microstrip lines produce electrical coupling, electrical coupling and magnetic coupling has canceling effect for the total coupling coefficient. Constant absolute bandwidth can be achieved by adjusting the gap of coupled lines and the size of the aperture simultaneously. A cross-coupling between the load and the source is created to introduce two transmission zeros in both the lower and the upper stopbands. Coaxial dielectric resonators are made of dielectric ceramic with dielectric constant of 88, parallel-coupled microstrip lines are made on Rogers RT/duroid 5880 substrate, the fabricated filter is measured, the results show that tuning range of center frequency is 0. $67\mathrm{GHz} \sim 0.980\mathrm{GHz}$ at $0\sim10\mathrm{V}$ , 3dB bandwidth is $42\sim 47\mathrm{MRz}$ the volume of filter containing bias networks is $24\times 22\times 7\mathrm{mm}^{3}(0.052\lambda_{0}\times 0.048\lambda_{0}\times 0.015\lambda_{0})$ . The measured results meet with the requirement of constant absolute bandwidth within tuning range, and verified the proposed design approach.


title: Torque Ripple Minimization in Six-Step Modulated PMSM Drives via Fast DC Bus Dynamics

abstract: Six-step modulated drive systems have been shown to have higher power density and lower switching loss than pulse-width-modulation (PWM) drive systems. It has also been shown that by manipulating the dc bus voltage, six-step mode can drive permanent magnet synchronous machines (PMSMs) over a wide speed range, and maintain the machine operating point on the maximum torque per ampere (MTPA) curve below the base speed to reduce the machine copper loss. However, high torque ripple exists while in this type of six-step mode. This paper develops the control design methodologies to leverage fast dc bus manipulation, provided by 2-step finite-settling-step (FSS) dc-to-dc converter control, to minimize torque ripple in six-step PMSM drives. Two torque control algorithms: existing voltage angle-based torque control (VATC) and deadbeat-direct torque and flux control (DB-DTFC) are used to evaluate torque ripple reduction capability and energy savings potential of six-step PMSM drive systems.


title: Applied Federated Learning: Improving Google Keyboard Query Suggestions

abstract: Federated learning is a distributed form of machine learning where both the training data and model training are decentralized. In this paper, we use federated learning in a commercial, global-scale setting to train, evaluate and deploy a model to improve virtual keyboard search suggestion quality without direct access to the underlying user data. We describe our observations in federated training, compare metrics to live deployments, and present resulting quality increases. In whole, we demonstrate how federated learning can be applied end-to-end to both improve user experiences and enhance user privacy.


title: Progressive graph matching: Making a move of graphs via probabilistic voting

abstract: Graph matching is widely used in a variety of scientific fields, including computer vision, due to its powerful performance, robustness, and generality. Its computational complexity, however, limits the permissible size of input graphs in practice. Therefore, in real-world applications, the initial construction of graphs to match becomes a critical factor for the matching performance, and often leads to unsatisfactory results. In this paper, to resolve the issue, we propose a novel progressive framework which combines probabilistic progression of graphs with matching of graphs. The algorithm efficiently re-estimates in a Bayesian manner the most plausible target graphs based on the current matching result, and guarantees to boost the matching objective at the subsequent graph matching. Experimental evaluation demonstrates that our approach effectively handles the limits of conventional graph matching and achieves significant improvement in challenging image matching problems.


title: A Frequency-based Stochastic Blockmodel

abstract: Abstract: We propose a frequency-based inﬁnite relational model (FIRM), which takesinto account the frequency of relation whereas stochastic blockmodels ignore frequency.We also derive a variational inference method for the FIRM to apply to a large dataset.Experimental results show that the FIRM gives better clustering results than a stochasticblockmodel on a dataset which has the frequency of relation.key words : stochastic blockmodels, Dirichlet process, variational inference 1 Introduction Recently in machine learning, relationallearning hasreceived a great deal of attention, for example, to ﬁndsocial roles in social network. While the stochasticblockmodel has been popular for such relational learn-ing in sociology, it is now widely used for various appli-cations including clustering proteins, discovering con-cepts, etc [9, 13].Inﬁnite relational models (IRMs) [9, 13] are stochas-tic blockmodels exploiting the Dirichlet process [6, 2]sothatwedonotneedtodeterminethenumberofclus-ters a priori. Stochastic blockmodels of mixed mem-bership (SBMM) are also stochastic blockmodels thatmodel multiple observation of tables [1].In this paper, we propose a frequency-based inﬁ-nite relational model (FIRM). The FIRM takes intoaccount the frequency of relation, which is statisti-cally informative but ignored by stochastic blockmod-els. Our model generalizes the IRM and can also ob-servemultipletablesastheSBMM.ToapplytheFIRMto a large dataset, we also derived a variational in-ference algorithm for the FIRM. Experimental resultsshow the FIRM gives better clustering results than theIRM on a dataset which has the frequency of relation.


title: Hierarchical Bayesian approaches for detecting inconsistency in network meta-analysis.

abstract: Network meta-analysis (NMA), also known as multiple treatment comparisons, is commonly used to incorporate direct and indirect evidence comparing treatments. With recent advances in methods and software, Bayesian approaches to NMA have become quite popular and allow models of previously unanticipated complexity. However, when direct and indirect evidence differ in an NMA, the model is said to suffer from inconsistency. Current inconsistency detection in NMA is usually based on contrast-based (CB) models; however, this approach has certain limitations. In this work, we propose an arm-based random effects model, where we detect discrepancy of direct and indirect evidence for comparing two treatments using the fixed effects in the model while flagging extreme trials using the random effects. We define discrepancy factors to characterize evidence of inconsistency for particular treatment comparisons, which is novel in NMA research. Our approaches permit users to address issues previously tackled via CB models. We compare sources of inconsistency identified by our approach and existing loop-based CB methods using real and simulated datasets and demonstrate that our methods can offer powerful inconsistency detection. Copyright © 2016 John Wiley & Sons, Ltd.


title: Emotional expressivity in men and women: Stereotypes and self-perceptions

abstract: Three studies were conducted to assess prevalent stereotypes regarding men's and women's emotional expressivity as well as self-perceptions of their emotional behaviour. Emotion profiles were employed to assess both modal emotional reactions and secondary emotional reactions to hypothetical events and personal experiences. In Study 1 we asked how men and women in general would react to a series of hypothetical emotional events. In Study 2 we asked how participants themselves expected to react to these same situations and in Study 3 we asked participants to report a personal emotional event in narrative form. Two gender differences emerged across all three studies. Specifically, women were expected to be more likely to react with sadness to negative emotion-eliciting events in general. They also expected themselves to be more likely to react with sadness as well as to cry and to withdraw more when experiencing negative emotional events. Finally, women report more sadness when describing personal events. In...


title: Web usage mining to improve the design of an e-commerce website: OrOliveSur.com

abstract: Web usage mining is the process of extracting useful information from users history databases associated to an e-commerce website. The extraction is usually performed by data mining techniques applied on server log data or data obtained from specific tools such as Google Analytics. This paper presents the methodology used in an e-commerce website of extra virgin olive oil sale called www.OrOliveSur.com. We will describe the set of phases carried out including data collection, data preprocessing, extraction and analysis of knowledge. The knowledge is extracted using unsupervised and supervised data mining algorithms through descriptive tasks such as clustering, association and subgroup discovery; applying classical and recent approaches. The results obtained will be discussed especially for the interests of the designer team of the website, providing some guidelines for improving its usability and user satisfaction.


ProcessProfiler 3 D : A visualisation framework for log-based process performance comparison


title: Callisto: A Configurable Annotation Workbench

abstract: In order to support a range of textual annotation tasks, we have developed a new annotation tool called Callisto. To promote taskspecific specialization of the interface and associated constraint checking, Callisto provides a facility for the independent development, compilation and installation of task module plug-ins (in the form of Java Archive jar files). The common Callisto backend provides a set of annotation services to which all separate GUI components can subscribe, enabling a common framework through which annotation updates are propagated to all components. A number of annotation task models have already been defined, and those that are of very general applicability have been made easily re-configurable for small changes in task definition. Callisto is implemented in Java to make use of Java s considerable support for Unicode-encoded multilingual data. Callisto is freely available for downloading


title: The Pain Experience of Patients Hospitalized With Inflammatory Bowel Disease: A Phenomenological Study

abstract: Pain is personal, subjective, and best treated when the patient's experience is fully understood. Hospitalization contributes to the physical and psychological complications of acute and chronic pain experienced by patients with inflammatory bowel disease (IBD). The purpose of this qualitative phenomenological study was to develop an understanding of the unique experience of pain in hospitalized patients with an admitting diagnosis of IBD and related care or surgery. Following institutional review board approval, purposeful sampling was used to recruit 16 patients (11 female, 5 male, mean age 41.8 years) from two 36-bed colorectal units of a large academic medical center in the Midwest. Individual, audio-recorded interviews were conducted by a researcher at each participant's bedside. Recordings and transcripts were systematically reviewed by the research team using Van Manen's approach to qualitative analysis. Subsequently, 5 major themes were identified among the data: feeling discredited and misunderstood, desire to dispel the stigma, frustration with constant pain, need for caregiver knowledge and understanding, and nurse as connector between patient and physician. Hospitalized patients with IBD have common issues with pain care. Nurses caring for them can provide better pain management when they understand these issues/themes. Further research into the themes discovered here is recommended.


title: Mobile Robot Control on a Reference Path

abstract: In this paper a control design of a nonholonomic mobile robot with a differential drive is presented. On the basis of robot kinematics equations a robot control is designed where the robot is controlled to follow the arbitrary path reference with a predefined velocity profile. The designed control algorithm proved stable and robust to the errors in robot initial positions, to input and output noises and to other disturbances. The obtained control law is demonstrated on a simple trajectory example, however, for a more general applicability a time-optimal motion planning algorithm considering acceleration constraints is presented as well


title: Real Time Acquisition of EMG Signal and Head Movement Recognition

abstract: paper presents the work on a technology for Real time EMG acquisition and recognition of head movement. EMG signal detection and analysis is useful in various clinical and biomedical applications such as generation of control signal for prosthetic tool, but better solutions for detecting noise free EMG signals need to be developed. In this paper a simple and cost effective method for estimating human head movements from neck EMG signals is proposed. In this work, surface EMG signal is acquired from four main neck muscles (right and left sternocleidomastoid muscles and right and left trapezius muscles) using simple computer interface and processed by MATLAB based filter algorithm for online clean display. Keywordsography, Real time acquisition, Muscle contractions


title: Chronic Physical Illness: A Psychophysiological Approach for Chronic Physical Illness

abstract: Growing evidence demonstrates that psychological risk variables can contribute to physical disease. In an effort to thoroughly investigate potential etiological origins and optimal interventions, this broad review is divided into five sections: the stress response, chronic diseases, mind-body theoretical models, psychophysiological interventions, and integrated health care solutions. The stress response and its correlation to chronic disorders such as cardiovascular, gastrointestinal, autoimmune, metabolic syndrome, and chronic pain are comprehensively explored. Current mind-body theoretical models, including peripheral nerve pathway, neurophysiological, and integrative theories, are reviewed to elucidate the biological mechanisms behind psychophysiological interventions. Specific interventions included are psychotherapy, mindfulness meditation, yoga, and psychopharmacology. Finally, the author advocates for an integrated care approach as a means by which to blur the sharp distinction between physical and psychological health. Integrated care approaches can utilize psychiatric nurse practitioners for behavioral assessment, intervention, research, advocacy, consultation, and education to optimize health outcomes.


Web-based Smart Home Automation: PLC-controlled Implementation


Oscillators - an approach for a better understanding (tutorial presented at ECCTD'03)


title: HEIGHT ESTIMATION FROM A SINGLE CAMERA VIEW

abstract: This paper presents a novel technique for the estimation of the height of an object using a single camera view. In the proposed method, the only information required is the knowledge about the pose of the camera with respect to the world (i.e., height and pitch angle of the camera with respect to the ground) and a vanishing point. In the developed theory, the focal length may also be known, but in the proposed experiments it has not been employed: an approximation for small pitch angles has been taken into account and the consequent committed error has been then analysed. The presented method gives accurate results for any object placed in unstructured environments, regardless of the relative distance from the camera. The method has been tested in a series of outdoor and indoor environments, and the experimental results are presented in this paper.


title: Outperforming LRU with an adaptive replacement cache algorithm

abstract: The self-tuning, low-overhead, scan-resistant adaptive replacement cache algorithm outperforms the least-recently-used algorithm by dynamically responding to changing access patterns and continually balancing between workload recency and frequency features. Caching, a fundamental metaphor in modern computing, finds wide application in storage systems, databases, Web servers, middleware, processors, file systems, disk drives, redundant array of independent disks controllers, operating systems, and other applications such as data compression and list updating. In a two-level memory hierarchy, a cache performs faster than auxiliary storage, but it is more expensive. Cost concerns thus usually limit cache size to a fraction of the auxiliary memory's size.


title: An All-Textile Louis Vuitton Logo Antenna

abstract: An all-textile Louis Vuitton logo antenna is proposed. For proper antenna design, the electrical properties of various leathers are measured. The crossed LV-shaped logo is composed of two longer thin arms for the lower frequency band and two shorter thick arms for the higher frequency band. The proposed logo antenna is fabricated from a conductive textile and a leather substrate, and it has an appropriate logo size for practical bag utilization. The proposed antenna operates in the industrial, scientific, and medical 2.45 GHz band and in the 4.5 GHz band. The corresponding fractional $-\hbox{10}\hbox{-dB}$ reflection coefficient bandwidths and maximum gains are 17.1% and $-\hbox{0.29}~\hbox{dBi}$ , respectively, for the lower frequency band and 13% and 3.05 dBi, respectively, for the higher frequency band.


title: User-Centered Robot Head Design: a Sensing Computing Interaction Platform for Robotics Research (SCIPRR)

abstract: We developed and evaluated a novel humanoid head, SCIPRR (Sensing, Computing, Interacting Platform for Robotics Research). SCIPRR is a head shell that was iteratively created with additive manufactur- ing. SCIPRR contains internal sca olding that allows sensors, small form computers, and a back-projection system to display an ani- mated face on a front-facing screen. SCIPRR was developed using User Centered Design principles and evaluated using three di erent methods. First, we created multiple, small-scale prototypes through additive manufacturing and performed polling and re nement of the overall head shape. Second, we performed usability evaluations of expert HRI mechanics as they swapped sensors and computers within the the SCIPRR head. Finally, we ran and analyzed an ex- periment to evaluate how much novices would like a robot with our head design to perform di erent social and traditional robot tasks. We made both major and minor changes a er each evalu- ation and iteration. Overall, expert users liked the SCIPRR head and novices wanted a robot with the SCIPRR head to perform more tasks (including social tasks) than a more traditional robot.


title: Comparative evaluation of text- and citation-based plagiarism detection approaches using guttenplag

abstract: Various approaches for plagiarism detection exist. All are based on more or less sophisticated text analysis methods such as string matching, fingerprinting or style comparison. In this paper a new approach called Citation-based Plagiarism Detection is evaluated using a doctoral thesis, in which a volunteer crowd-sourcing project called GuttenPlag identified substantial amounts of plagiarism through careful manual inspection. This new approach is able to identify similar and plagiarized documents based on the citations used in the text. It is shown that citation-based plagiarism detection performs significantly better than text-based procedures in identifying strong paraphrasing, translation and some idea plagiarism. Detection rates can be improved by combining citation-based with text-based plagiarism detection.


title: Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans

abstract: Robust and fast detection of anatomical structures is a prerequisite for both diagnostic and interventional medical image analysis. Current solutions for anatomy detection are typically based on machine learning techniques that exploit large annotated image databases in order to learn the appearance of the captured anatomy. These solutions are subject to several limitations, including the use of suboptimal feature engineering techniques and most importantly the use of computationally suboptimal search-schemes for anatomy detection. To address these issues, we propose a method that follows a new paradigm by reformulating the detection problem as a behavior learning task for an artificial agent. We couple the modeling of the anatomy appearance and the object search in a unified behavioral framework, using the capabilities of deep reinforcement learning and multi-scale image analysis. In other words, an artificial agent is trained not only to distinguish the target anatomical object from the rest of the body but also how to find the object by learning and following an optimal navigation path to the target object in the imaged volumetric space. We evaluated our approach on 1487 3D-CT volumes from 532 patients, totaling over 500,000 image slices and show that it significantly outperforms state-of-the-art solutions on detecting several anatomical structures with no failed cases from a clinical acceptance perspective, while also achieving a 20-30 percent higher detection accuracy. Most importantly, we improve the detection-speed of the reference methods by 2-3 orders of magnitude, achieving unmatched real-time performance on large 3D-CT scans.


title: Authoring Augmented Reality Learning Experiences as Learning Objects

abstract: Engineers and educators alike have prototyped a variety of augmented reality learning experiences (ARLEs). However, adapting ARLEs in educational practice would require an interdisciplinary approach that considers learning theory, pedagogy and instructional design. To address this requirement, we model ARLEs as learning objects by outlining the necessary components, and we propose a participatory design to demonstrate the authoring process of an augmented reality learning object (ARLO). ARLOs can be made useful in many scenarios if teachers are empowered to edit its context elements, content and instructional activity. Lastly, we point to the research questions entailed in modeling ARLEs as ARLOs.


title: Robust Guided Image Filtering

abstract: The process of using one image to guide the filtering process of another one is called Guided Image Filtering (GIF). The main challenge of GIF is the structure inconsistency between the guidance image and the target image. Besides, noise in the target image is also a challenging issue especially when it is heavy. In this paper, we propose a general framework for Robust Guided Image Filtering (RGIF), which contains a data term and a smoothness term, to solve the two issues mentioned above. The data term makes our model simultaneously denoise the target image and perform GIF which is robust against the heavy noise. The smoothness term is able to make use of the property of both the guidance image and the target image which is robust against the structure inconsistency. While the resulting model is highly non-convex, it can be solved through the proposed Iteratively Re-weighted Least Squares (IRLS) in an efficient manner. For challenging applications such as guided depth map upsampling, we further develop a data-driven parameter optimization scheme to properly determine the parameter in our model. This optimization scheme can help to preserve small structures and sharp depth edges even for a large upsampling factor (8x for example). Moreover, the specially designed structure of the data term and the smoothness term makes our model perform well in edge-preserving smoothing for single-image tasks (i.e., the guidance image is the target image itself). This paper is an extension of our previous work [1], [2].


title: What Does a Chief Digital Officer Do? Managerial Tasks and Roles of a New C-Level Position in the Context of Digital Transformation

abstract: Integrating "digital" into the DNA of their business models is an essential part of business success for companies across industries today. The digital transformation has become a critical management issue and requires new ways of managerial thinking. In this context, we address one of the fastest-growing phenomena at top management level: the establishment of Chief Digital Officers (CDOs). We adopt a phenomenon-based research methodology and describe the managerial tasks and roles of CDOs by conducting case studies across different industries. As a result, we present a first conceptualization of the CDO position, suggesting that CDOs primarily focus on the strategic and communicational aspects of the digital transformation and closely collaborate with their CIOs if both positions exist in a company.


title: Inferring gender of a Twitter user using celebrities it follows

abstract: This paper addresses the task of user gender classification in social media, with an application to Twitter. The approach automatically predicts gender by leveraging observable information such as the tweet behavior, linguistic content of the user's Twitter feed and the celebrities followed by the user. This paper first evaluates linguistic content based features using LIWC dictionary and popular neighborhood features using Wikipedia and Freebase. Then augments both features which yielded a significant increase in the accuracy for gender prediction. Results show that rich linguistic features combined with popular neighborhood prove valuables and promising for additional user classification needs.


title: A High Input Impedance Low Noise Integrated Front-End Amplifier for Neural Monitoring

abstract: Within neural monitoring systems, the front-end amplifier forms the critical element for signal detection and pre-processing, which determines not only the fidelity of the biosignal, but also impacts power consumption and detector size. In this paper, a novel combined feedback loop-controlled approach is proposed to compensate for input leakage currents generated by low noise amplifiers when in integrated circuit form alongside signal leakage into the input bias network. This loop topology ensures the Front-End Amplifier (FEA) maintains a high input impedance across all manufacturing and operational variations. Measured results from a prototype manufactured on the AMS 0.35 $\mu{\text m}$ CMOS technology is provided. This FEA consumes 3.1 $\mu{\text W}$ in 0.042 ${\text{mm}}^{2}$ , achieves input impedance of 42 ${\text G}\Omega$ , and 18.2 ${\text{nv}}/\sqrt{\text{Hz}}$ input-referred noise.


Distributed adaptive simulated annealing for synthesis design space exploration


title: Data preprocessing algorithm for Web Structure Mining

abstract: World Wide Web is an extremely large collection of information, i.e. beyond our imagination. It provides enough information according to user's need. Web is rising dreadfully as approximately 70 million pages are added daily. Knowledge Discovery on web data is referred as Web Mining. Web Structure Mining based on the analysis of patterns from hyperlink structure in the web. Like as Data Mining, Web Mining has four stages i.e. Data Collection, Preprocessing, Knowledge Discovery and Knowledge Analysis. This paper based on the first two stages Data collection and Preprocessing. Data collection is to collect the data required for analysis. Data preprocessing is considered as an important stage of Web Structure mining because of data available on web is unstructured, heterogeneous and noisy.


title: Interpretive Research Aiming at Theory Building: Adopting and Adapting the Case Study Design

abstract: Although the advantages of case study design are widely recognised, its original positivist underlying assumptions may mislead interpretive researchers aiming at theory building. The paper discusses the limitations of the case study design for theory building and explains how grounded theory systemic process adds to the case study design. The author reflects upon his experience in conducting research on the articulation of both traditional social networks and new virtual networks in six rural communities in Peru, using both case study design and grounded theory in a combined fashion in order to discover an emergent theory. Key Words: Case Study, Interpretive Approach, Theory Building, and Grounded Theory


title: Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning

abstract: This paper proposes a novel tracker which is controlled by sequentially pursuing actions learned by deep reinforcement learning. In contrast to the existing trackers using deep networks, the proposed tracker is designed to achieve a light computation as well as satisfactory tracking accuracy in both location and scale. The deep network to control actions is pre-trained using various training sequences and fine-tuned during tracking for online adaptation to target and background changes. The pre-training is done by utilizing deep reinforcement learning as well as supervised learning. The use of reinforcement learning enables even partially labeled data to be successfully utilized for semi-supervised learning. Through evaluation of the OTB dataset, the proposed tracker is validated to achieve a competitive performance that is three times faster than state-of-the-art, deep network–based trackers. The fast version of the proposed method, which operates in real-time on GPU, outperforms the state-of-the-art real-time trackers.


title: SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

abstract: Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels. Despite its simplicity, SLIC adheres to boundaries as well as or better than previous methods. At the same time, it is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.


title: Deformed Lattice Detection in Real-World Images Using Mean-Shift Belief Propagation

abstract: We propose a novel and robust computational framework for automatic detection of deformed 2D wallpaper patterns in real-world images. The theory of 2D crystallographic groups provides a sound and natural correspondence between the underlying lattice of a deformed wallpaper pattern and a degree-4 graphical model. We start the discovery process with unsupervised clustering of interest points and voting for consistent lattice unit proposals. The proposed lattice basis vectors and pattern element contribute to the pairwise compatibility and joint compatibility (observation model) functions in a Markov random field (MRF). Thus, we formulate the 2D lattice detection as a spatial, multitarget tracking problem, solved within an MRF framework using a novel and efficient mean-shift belief propagation (MSBP) method. Iterative detection and growth of the deformed lattice are interleaved with regularized thin-plate spline (TPS) warping, which rectifies the current deformed lattice into a regular one to ensure stability of the MRF model in the next round of lattice recovery. We provide quantitative comparisons of our proposed method with existing algorithms on a diverse set of 261 real-world photos to demonstrate significant advances in accuracy and speed over the state of the art in automatic discovery of regularity in real images.


title: An Ultralow-Power Low-Noise CMOS Biopotential Amplifier for Neural Recording

abstract: This brief presents a design strategy for a neural recording amplifier array with ultralow-power low-noise operation that is suitable for large-scale integration. The topology combines a highly efficient but supply-sensitive single-ended first stage with a shared reference channel and a differential second stage to effect feedforward supply noise cancellation, combining the low power of single-ended amplifiers with improved supply rejection. For a two-channel amplifier, the measurements show a midband gain of 58.7 dB and a passband from 490 mHz to 10.5 kHz. The amplifier consumes 2.85 $\mu\mbox{A}$ per channel from a 1-V supply and exhibits an input-referred noise of 3.04 $\mu\mbox{V}_\mathrm{rms}$ from 0.1 Hz to 100 kHz, corresponding to a noise efficiency factor of 1.93. The power supply rejection ratio is better than 50 dB in the passband. The amplifier is fabricated in a 90-nm CMOS process and occupies 0.137 $\mbox{mm}^{2}$ of chip area.


title: Angiosperm wood structure: Global patterns in vessel anatomy and their relation to wood density and potential conductivity

abstract: Woody stems comprise a large biological carbon fraction and determine water transport between roots and leaves; their structure and function can influence both carbon and hydrological cycles. While angiosperm wood anatomy and density determine hydraulic conductivity and mechanical strength, little is known about interrelations across many species. We compiled a global data set comprising two anatomical traits for 3005 woody angiosperms: mean vessel lumen area (Ā) and number per unit area (N). From these, we calculated vessel lumen fraction (F = ĀN) and size to number ratio (S = Ā/N), a new vessel composition index. We examined the extent to which F and S influenced potential sapwood specific stem conductivity (K(S)) and wood density (D; dry mass/fresh volume). F and S varied essentially independently across angiosperms. Variation in K(S) was driven primarily by S, and variation in D was virtually unrelated to F and S. Tissue density outside vessel lumens (D(N)) must predominantly influence D. High S should confer faster K(S) but incur greater freeze-thaw embolism risk. F should also affect K(S), and both F and D(N) should influence mechanical strength, capacitance, and construction costs. Improved theory and quantification are needed to better understand ecological costs and benefits of these three distinct dimensions.


title: Frequency-Domain Transient Imaging

abstract: A transient image is the optical impulse response of a scene, which also visualizes the propagation of light during an ultra-short time interval. In contrast to the previous transient imaging which samples in the time domain using an ultra-fast imaging system, this paper proposes transient imaging in the frequency domain using a multi-frequency time-of-flight (ToF) camera. Our analysis reveals the Fourier relationship between transient images and the measurements of a multi-frequency ToF camera, and identifies the causes of the systematic error—non-sinusoidal and frequency-varying waveforms and limited frequency range of the modulation signal. Based on the analysis we propose a novel framework of frequency-domain transient imaging. By removing the systematic error and exploiting the harmonic components inside the measurements, we achieves high quality reconstruction results. Moreover, our technique significantly reduces the computational cost of ToF camera based transient image reconstruction, especially reduces the memory usage, such that it is feasible for the reconstruction of transient images at extremely small time steps. The effectiveness of frequency-domain transient imaging is tested on synthetic data, real data from the web, and real data acquired by our prototype camera.


title: Open Data Portal Quality Comparison using AHP

abstract: During recent years, more and more Open Data becomes available and used as part of the Open Data movement. However, there are reported issues with the quality of the metadata in data portals and the data itself. This is a serious risk that could disrupt the Open Data project, as well as e-government initiatives since the data quality needs to be managed to guarantee the reliability of e-government to the public. First quality assessment frameworks emerge to evaluate the quality for a given dataset or portal along various dimensions (e.g., information completeness). Nonetheless, a common problem with such frameworks is to provide meaningful ranking mechanisms that are able to integrate several quality dimensions and user preferences (e.g., a portal provider is likely to have different quality preferences than a portal consumer). To address this multi-criteria decision making problem, our research work applies AHP (Analytic Hierarchy Process), which compares 146 active Open Data portals across 44 countries, powered by the CKAN software.


title: A survey of computational methods for protein complex prediction from protein interaction networks

abstract: Complexes of physically interacting proteins are one of the fundamental functional units responsible for driving key biological mechanisms within the cell. Their identification is therefore necessary not only to understand complex formation but also the higher level organization of the cell. With the advent of high-throughput techniques in molecular biology, significant amount of physical interaction data has been cataloged from organisms such as yeast, which has in turn fueled computational approaches to systematically mine complexes from the network of physical interactions among proteins (PPI network). In this survey, we review, classify and evaluate some of the key computational methods developed till date for the identification of protein complexes from PPI networks. We present two insightful taxonomies that reflect how these methods have evolved over the years towards improving automated complex prediction. We also discuss some open challenges facing accurate reconstruction of complexes, the crucial ones being presence of high proportion of errors and noise in current high-throughput datasets and some key aspects overlooked by current complex detection methods. We hope this review will not only help to condense the history of computational complex detection for easy reference, but also provide valuable insights to drive further research in this area.


title: A novel big-data processing framwork for healthcare applications: Big-data-healthcare-in-a-box

abstract: Herein we present a novel big-data framework for healthcare applications. Healthcare data is well suited for bigdata processing and analytics because of the variety, veracity and volume of these types of data. In recent times, many areas within healthcare have been identified that can directly benefit from such treatment. However, setting up these types of architecture is not trivial. We present a novel approach of building a big-data framework that can be adapted to various healthcare applications with relative use, making this a one-stop “Big-Data-Healthcare-in-a-Box”.


title: Irreflexive and Hierarchical Relations as Translations

abstract: We consider the problem of embedding entities and relations of knowledge bases in low-dimensional vector spaces. Unlike most existing approaches, which are primarily efficient for modeling equivalence relations, our approach is designed to explicitly model irreflexive relations, such as hierarchies, by interpreting them as translations operating on the low-dimensional embeddings of the entities. Preliminary experiments show that, despite its simplicity and a smaller number of parameters than previous approaches, our approach achieves state-of-the-art performance according to standard evaluation protocols on data from WordNet and Freebase.


title: A GPU implementation of a real-time MIMO detector

abstract: Multiple-input multiple-output (MIMO) is an existing technique that can significantly increase throughput of the system by employing multiple antennas at the transmitter and the receiver. Realizing maximum benefit from this technique requires computationally intensive detectors which poses significant challenges to receiver design. Furthermore, a flexible detector or multiple detectors are needed to handle different configurations. Graphical Processor Unit (GPU), a highly parallel commodity programmable co-processor, can deliver extremely high computation throughput and is well suited for signal processing applications. However, careful architecture aware design is needed to leverage performance offered by GPU. We show we can achieve good performance while maintaining flexibility by employing an optimized trellis-based MIMO detector on GPU.


title: cGAN-Based Manga Colorization Using a Single Training Image

abstract: The Japanese comic format known as Manga is popular all over the world. It is traditionally produced in black and white, and colorization is time consuming and costly. Automatic colorization methods generally rely on greyscale values, which are not present in manga. Furthermore, due to copyright protection, colorized manga available for training is scarce. We propose a manga colorization method based on conditional Generative Adversarial Networks (cGAN). Unlike previous cGAN approaches that use many hundreds or thousands of training images, our method requires only a single colorized reference image for training, avoiding the need of a large dataset. Colorizing manga using cGANs can produce blurry results with artifacts, and the resolution is limited. We therefore also propose a method of segmentation and color-correction to mitigate these issues. The final results are sharp, clear, and in high resolution, and stay true to the character's original color scheme.


The Integrated Design Process on Paper and In Practice : A Case Study


title: DNA Sequence Variants in PPARGC1A, a Gene Encoding a Coactivator of the ω-3 LCPUFA Sensing PPAR-RXR Transcription Complex, Are Associated with NV AMD and AMD-Associated Loci in Genes of Complement and VEGF Signaling Pathways

abstract: BACKGROUND ::: Increased intake of ω-3 long-chain polyunsaturated fatty acids (LCPUFAs) and use of peroxisome proliferator activator receptor (PPAR)-activating drugs are associated with attenuation of pathologic retinal angiogenesis. ω-3 LCPUFAs are endogenous agonists of PPARs. We postulated that DNA sequence variation in PPAR gamma (PPARG) co-activator 1 alpha (PPARGC1A), a gene encoding a co-activator of the LCPUFA-sensing PPARG-retinoid X receptor (RXR) transcription complex, may influence neovascularization (NV) in age-related macular degeneration (AMD). ::: ::: ::: METHODS ::: We applied exact testing methods to examine distributions of DNA sequence variants in PPARGC1A for association with NV AMD and interaction of AMD-associated loci in genes of complement, lipid metabolism, and VEGF signaling systems. Our sample contained 1858 people from 3 elderly cohorts of western European ancestry. We concurrently investigated retinal gene expression profiles in 17-day-old neonatal mice on a 2% LCPUFA feeding paradigm to identify LCPUFA-regulated genes both associated with pathologic retinal angiogenesis and known to interact with PPARs or PPARGC1A. ::: ::: ::: RESULTS ::: A DNA coding variant (rs3736265) and a 3'UTR-resident regulatory variant (rs3774923) in PPARGC1A were independently associated with NV AMD (exact P = 0.003, both SNPs). SNP-SNP interactions existed for NV AMD (P<0.005) with rs3736265 and a AMD-associated variant in complement factor B (CFB, rs512559). PPARGC1A influences activation of the AMD-associated complement component 3 (C3) promoter fragment and CFB influences activation and proteolysis of C3. We observed interaction (P ≤ 0.003) of rs3736265 with a variant in vascular endothelial growth factor A (VEGFA, rs3025033), a key molecule in retinal angiogenesis. Another PPARGC1A coding variant (rs8192678) showed statistical interaction with a SNP in the VEGFA receptor fms-related tyrosine kinase 1 (FLT1, rs10507386; P ≤ 0.003). C3 expression was down-regulated 2-fold in retinas of ω-3 LCPUFA-fed mice - these animals also showed 70% reduction in retinal NV (P ≤ 0.001). ::: ::: ::: CONCLUSION ::: Ligands and co-activators of the ω-3 LCPUFA sensing PPAR-RXR axis may influence retinal angiogenesis in NV AMD via the complement and VEGF signaling systems. We have linked the co-activator of a lipid-sensing transcription factor (PPARG co-activator 1 alpha, PPARGC1A) to age-related macular degeneration (AMD) and AMD-associated genes.


title: Multi-level partition of unity implicits

abstract: We present a new shape representation, the multi-level partition of unity implicit surface, that allows us to construct surface models from very large sets of points. There are three key ingredients to our approach: 1) piecewise quadratic functions that capture the local shape of the surface, 2) weighting functions (the partitions of unity) that blend together these local shape functions, and 3) an octree subdivision method that adapts to variations in the complexity of the local shape.Our approach gives us considerable flexibility in the choice of local shape functions, and in particular we can accurately represent sharp features such as edges and corners by selecting appropriate shape functions. An error-controlled subdivision leads to an adaptive approximation whose time and memory consumption depends on the required accuracy. Due to the separation of local approximation and local blending, the representation is not global and can be created and evaluated rapidly. Because our surfaces are described using implicit functions, operations such as shape blending, offsets, deformations and CSG are simple to perform.


title: Abstract The Q-matrix Method: Mining Student Response Data for Knowledge

abstract: Although many talented researchers have created excellent tools for computer-assisted instruction and intelligent tutoring systems, creating high-quality, effective, scalable but individualized tools for learning at a low cost is still an open research challenge. Many learning tools create complex models of student behavior that require extensive time on the part of subject experts, as well as cognitive science researchers, to create effective help and feedback strategies. In this research, we propose a different approach, called the q-matrix method, where data from student behavior is “mined” to create concept models of the material being taught. These models are then used to both understand student behavior and direct learning paths for future students. We describe the q-matrix method and present preliminary results that imply that the method can effectively predict which concepts need further review.


title: Noise power spectral density estimation based on optimal smoothing and minimum statistics

abstract: We describe a method to estimate the power spectral density of nonstationary noise when a noisy speech signal is given. The method can be combined with any speech enhancement algorithm which requires a noise power spectral density estimate. In contrast to other methods, our approach does not use a voice activity detector. Instead it tracks spectral minima in each frequency band without any distinction between speech activity and speech pause. By minimizing a conditional mean square estimation error criterion in each time step we derive the optimal smoothing parameter for recursive smoothing of the power spectral density of the noisy speech signal. Based on the optimally smoothed power spectral density estimate and the analysis of the statistics of spectral minima an unbiased noise estimator is developed. The estimator is well suited for real time implementations. Furthermore, to improve the performance in nonstationary noise we introduce a method to speed up the tracking of the spectral minima. Finally, we evaluate the proposed method in the context of speech enhancement and low bit rate speech coding with various noise types.


title: Predictive Model for Lime Dosage in Water Treatment

abstract: A predictive model for determining the quantity of lime required for water treatment was developed by considering the inter-relationship between water quality parameter such as pH and the quantity of lime required for water treatment. The model constants were obtained using least square regression method and solving the resultant equation using MATLAB program. The resultant model equation was found to be Q= - 1.5402+ 1735.539((10)^(-∆pH) ). Comparison of the results from the simulation of the model and experimental data showed a good prediction with a correlation coefficient of 89%.


title: Solid Waste Management Architecture Using Wireless Sensor Network Technology

abstract: In many application fields such as home, industry, environment and health, different Wireless Sensor Network (WSN) applications have been developed to solve management problems with smart implementations. This approach can be applied in the filed of solid waste management. In this paper a new architecture is proposed with the aim to improve the on-site handling and transfer optimization in the waste management process. The system architecture is based on sensor nodes and makes use of Data Transfer Nodes (DTN) in order to provide to a remote server the retrieved data measurements from the garbage bins filling. A remote monitoring solution has been implemented, providing user the possibility to interact with the system by using a web browser. Several activities with the aim to provide a Decision Support System (DSS) able to find solutions for resources organization problems linked to solid waste management have been started.


title: DATA ANALYSIS IN PUBLIC SOCIAL NETWORKS

abstract: Public social networks affect significant number of people with different professional and personal background. Presented paper deals with data analysis and in addition with safety of information managed by online social networks. We will show methods for data analysis in social networks based on its scale-free characteristics. Experimental results will be discussed for the biggest social network in Slovakia which is popular for more than 10 years. 1 , big data 2 , scale-free networks 3 , data security 4 , graph theory 5


title: Photophysical properties and intracellular imaging of water-soluble porphyrin dimers for two-photon excited photodynamic therapy

abstract: We have investigated the photophysical properties and intracellular behaviour of a series of hydrophilic conjugated porphyrin dimers. All the dimers exhibit intense linear absorption at 650–800 nm and high singlet oxygen quantum yields (0.5–0.9 in methanol), as required for an efficient sensitiser for photodynamic therapy (PDT). They also exhibit fluorescence at 700–800 nm, with fluorescence quantum yields of up to 0.13 in methanol, and show extremely large two-photon absorption maxima of 8,000–17,000 GM in the near-IR. The dimers aggregate in aqueous solution, but aggregation is reduced by binding to bovine serum albumin (BSA), as manifested by an increase in fluorescence intensity and a sharpening in the emission bands. This process can be regarded as a model for the interaction with proteins under physiological conditions. Confocal fluorescence microscopy of live cells was used to monitor the rate of cellular uptake, intracellular localisation and photostability. Porphyrin dimers with positively charged substituents partition into cells more efficiently than the negatively charged dimers. The photostability of these dimers, in living cells, is significantly better than that of the clinical photosensitiser verteporfin. Analysis of the photophysical parameters and intracellular imaging data indicates that these dimers are promising candidates for one-photon and two-photon excited PDT.


title: Ka Band High Power Multi-Port Amplifier (MPA) Configured with TWTA for Winds Satellite

abstract: It is important to use satellite RF power efficiently in restricted satellite resource, especially in case of Ka-band communication systems. For the purpose of efficient use of RF power in satellite systems Ka-band high power multi-port amplifier (MPA) is adopted in WINDS (wideband internetworking engineering test and demonstration satellite) system, this will be launched by the H-IIA launch vehicle in 2008. This paper describes outline of the MPA for WINDS and its test results.


title: Mitigation of reflection symmetry assumption and negative power problems for the model based decomposition

abstract: The assumption of reflection symmetry is one of the major deficiencies for model-based decompositions. The introduction of helix scattering components can mitigate some of the impact of this assumption while simultaneously generating some new negative power problems. The helix mechanism has been developed in the Krogager coherent target decomposition where there is no negative power problem. Why do these problems happen in model-based decompositions? In this paper, we review the Krogager decomposition based on the coherency matrix form of the unified CTD model and come to the conclusion that the imaginary part of $T_{23}$ is an overestimate of the helix contribution, which is the main cause of the problem. Furthermore, we review the techniques of symmetric scatterer transformation in the Cameron and Huynen decompositions and expand them to the multilook coherency matrix. Two new models of the nonlinear programming problem are proposed to get the transformed coherency matrix: 1) by subtracting the asymmetric component; and 2) by the procedure of con-diagonalization. A new parameter is established to measure the degree of reflection asymmetry of the coherency matrices. Experimental results in the L-band Airborne SAR (AIRSAR) San Francisco data show that the two new models can reduce the correlations between copolarized and cross-polarized channels significantly. In this case, the assumption of reflection symmetry is reasonable for the transformed coherency matrices, and negative power problems related to helix scattering no longer exist in the model-based decompositions; hence, the helix scattering component is not needed. Lastly, the Freeman and Durden decomposition has been applied to the transformed coherency matrices. Experimental results showed that the decomposition results can reflect the predominant characteristics of the ground objects.


title: Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction

abstract: Novel 4,5-diaryl-2-halo imidazoles, for example, 2-bromo-4,5-bis-(4-chorophenyl)imidazole, and their acid addition salts are useful for treating inflammation in mammals. Some of the compounds are also useful as analgesics.


title: A distributed frequent itemset mining algorithm using Spark for Big Data analytics

abstract: Frequent itemset mining is an essential step in the process of association rule mining. Conventional approaches for mining frequent itemsets in big data era encounter significant challenges when computing power and memory space are limited. This paper proposes an efficient distributed frequent itemset mining algorithm (DFIMA) which can significantly reduce the amount of candidate itemsets by applying a matrix-based pruning approach. The proposed algorithm has been implemented using Spark to further improve the efficiency of iterative computation. Numeric experiment results using standard benchmark datasets by comparing the proposed algorithm with the existing algorithm, parallel FP-growth, show that DFIMA has better efficiency and scalability. In addition, a case study has been carried out to validate the feasibility of DFIMA.


An investigation of spoofing speech detection under additive noise and reverberant conditions


title: Aqua-Sim: An NS-2 based simulator for underwater sensor networks

abstract: In this paper, we present a network simulator, Aqua-Sim, for underwater sensor networks. Aqua-Sim is based on NS-2, one of the most widely used network simulators, and it follows object-oriented design style and all network entities are implemented as classes. Aqua-Sim effectively simulates the attenuation of underwater acoustic channels and the collision behaviors in long delay acoustic networks. Moreover, Aqua-Sim supports three-dimensional network deployment and provides a rich set of basic and advanced protocols. Through several case studies, we show that Aqua-Sim can "reproduce" the real world with high fidelity and flexibility.


title: Face liveness detection based on texture and frequency analyses

abstract: This paper proposes a single image-based face liveness detection method for discriminating 2-D paper masks from the live faces. Still images taken from live faces and 2-D paper masks were found to bear the differences in terms of shape and detailedness. In order to effectively employ such differences, we exploit frequency and texture information by using power spectrum and Local Binary Pattern (LBP), respectively. In the experiments, three liveness detectors utilizing the power spectrum, LBP, and fusion of the two were trained and tested with two databases which consist of images taken from live and four types of 2-D paper masks. One database was acquired from a web camera while the other was from the camera on the automated teller machine. Experimental results show that the proposed methods can efficiently classify 2-D paper masks and live faces.


title: Android based smart home system with control via Bluetooth and internet connectivity

abstract: Automation plays an important role in today's human life and people's life is gradually changing with smart living due to modern technology development and Android Smartphone. This paper presents a low-cost Smart Living System, which uses Android based User Interface for control of home appliances. Connection to the smart living system can be made from the designed app via Bluetooth or internet connection. It also integrates home security and alert system.


title: Coordination of muscle activity to assure stability of the lumbar spine.

abstract: The intention of this paper is to introduce some of the issues surrounding the role of muscles to ensure spine stability for discussion -- it is not intended to provide an exhaustive review and integration of the relevant literature. The collection of works synthesized here point to the notion that stability results from highly coordinated muscle activation patterns involving many muscles, and that the recruitment patterns must continually change, depending on the task. This has implications on both the prevention of instability and clinical interventions with patients susceptible to sustaining unstable events.


title: Microglia emerge as central players in brain disease

abstract: There has been an explosion of new findings recently giving us insights into the involvement of microglia in central nervous system (CNS) disorders. A host of new molecular tools and mouse models of disease are increasingly implicating this enigmatic type of nervous system cell as a key player in conditions ranging from neurodevelopmental disorders such as autism to neurodegenerative disorders such as Alzheimer's disease and chronic pain. Contemporaneously, diverse roles are emerging for microglia in the healthy brain, from sculpting developing neuronal circuits to guiding learning-associated plasticity. Understanding the physiological functions of these cells is crucial to determining their roles in disease. Here we focus on recent developments in our rapidly expanding understanding of the function, as well as the dysfunction, of microglia in disorders of the CNS.


title: Performance Optimization of Virtual Keyboards

abstract: Text entry has been a bottleneck of nontraditional computing devices. One of the promising methods is the virtual keyboard for touch screens. Correcting previous estimates on virtual keyboard efficiency in the literature, we estimated the potential performance of the existing Qwerty, FITALY, and OPTI designs of virtual keyboards to be in the neighborhood of 28, 36, and 38 words per minute (wpm), respectively. This article presents 2 quantitative design techniques to search for virtual keyboard layouts. The first technique simulated the dynamics of a keyboard with digraph springs between keys, which produced a Hooke keyboard with 41.6 wpm movement efficiency. The second technique used a Metropolis random walk algorithm guided by a "Fitts-digraph energy" objective function that quantifies the movement efficiency of a virtual keyboard. This method produced various Metropolis keyboards with different shapes and structures with approximately 42.5 wpm movement efficiency, which was 50% higher than Qwerty and 10% higher than OPTI. With a small reduction (41.16 wpm) of movement efficiency, we introduced 2 more design objectives that produced the ATOMIK layout. One was alphabetical tuning that placed the keys with a tendency from A to Z so a novice user could more easily locate the keys. The other was word connectivity enhancement so the most frequent words were easier to find, remember, and type.


title: Evolutionary analyses of base-pairing interactions in DNA and RNA secondary structures

abstract: Pairs of nucleotides within functional nucleic acid secondary structures often display evidence of coevolution that is consistent with the maintenance of base-pairing. Here we introduce a sequence evolution model, MESSI, that infers coevolution associated with base-paired sites in DNA or RNA sequence alignments. MESSI can estimate coevolution whilst accounting for an unknown secondary structure. MESSI can also use GPU parallelism to increase computational speed. We used MESSI to infer coevolution associated with GC, AU (AT in DNA), GU (GT in DNA) pairs in non-coding RNA alignments, and in single-stranded RNA and DNA virus alignments. Estimates of GU pair coevolution were found to be higher at base-paired sites in single-stranded RNA viruses and non-coding RNAs than estimates of GT pair coevolution in single-stranded DNA viruses, suggesting that GT pairs do not stabilise DNA secondary structures to the same extent that GU pairs do in RNA. Additionally, MESSI estimates the degrees of coevolution at individual base-paired sites in an alignment. These estimates were computed for a SHAPE-MaP-determined HIV-1 NL4-3 RNA secondary structure and two corresponding alignments. We found that estimates of coevolution were more strongly correlated with experimentally-determined SHAPE-MaP pairing scores than three non-evolutionary measures of base-pairing covariation. To assist researchers in prioritising substructures with potential functionality, MESSI automatically ranks substructures by degrees of coevolution at base-paired sites within them. Such a ranking was created for an HIV-1 subtype B alignment, revealing an excess of top-ranking substructures that have been previously identified as having structure-related functional importance, amongst several uncharacterised top-ranking substructures.


title: A staircase hexagonal shaped microstrip patch antenna for multiband applications

abstract: As seeing the more demand of multiband antennas for wireless communication, this Staircase-Hexagonal shaped antenna has designed. The merit of antenna eight resonance frequencies are 3.13 GHz, 8.89 GHz, 10.69 GHz, 16.79 GHz, 20.37 GHz, 26.06 GHz, 30.13 GHz, 36.26 GHz with widely used gain 5.17 dBi, 9.12 dBi, 15.83 dBi, 6.05 dBi, 5 dBi, 5.84 dBi, 5.34 dBi, 3.79 dBi respectively. These resonance frequencies are achieved less than −10dB return loss. These output simulated by Ansoft HFSS (High Frequency Structure simulator) software. This antenna is printed on Rogers substrate whose full size has been taken 30mm × 30mm and 1.6mm height. The relative permittivity and loss tangent of Roger substrate are 3.0 and 0.0013 respectively. In this paper, a novel U-Shaped and Hexagonal has been abolished on rectangular radiator patch, also staircase introduced on upper and lower edges. Simulated return loss (S11), gain, directivity, and VSWR are discussed.


title: Mechanisms of Vascular Hyporesponsiveness in Septic Shock

abstract: Purpose: To define some of the most common characteristics of vascular hyporesponsiveness to catecholamines during septic shock and outline current therapeutic approaches and future perspectives. Methods: Source data were obtained from a PubMed search of the medical literature with the following MeSH terms: Muscle, smooth, vascular/physiopathology; hypotension/etiology; shock/physiopathology; vasodilation/physiology; shock/therapy; vasoconstrictor agents. Results: NO and peroxynitrite are mainly responsible for vasoplegia and vascular hyporeactivity while COX 2 enzyme is responsible for the increase in PGI2, which also contributes to hyporeactivity. Moreover, K+ATP and BKCa channels are over-activated during septic shock and participate in hypotension. Finally, other mechanisms are involved in vascular hyporesponsiveness such as critical illness-related corticosteroid insufficiency, vasopressin depletion, dysfunction and desensitization of adrenoreceptors as well as inactivation of catecholamines by oxidation. Conclusion: In animal models, several therapeutic approaches, targeted on one particular compound have proven their efficacy in preventing or reversing vascular hyporesponsiveness to catecholamines. Unfortunately, none have been successfully tested in clinical trials. Nevertheless, very high doses of catecholamines (>5 μg/kg/min), hydrocortisone, terlipressin or vasopressin could represent an alternative for the treatment of refractory septic shock.


title: Electron-beam computed tomography for coronary calcium: a useful test to screen for coronary heart disease?

abstract: ELECTRON-BEAM COMPUTED TOMOGRAPHY (EBCT) USES x-rays generated by electron-beam irradiation of a tungsten target to identify and quantify coronary artery calcium (CAC). Initial studies focused on the use of EBCT for risk stratification or diagnosis among individuals with clinical symptoms of coronary heart disease (CHD). However, more recent articles, as well as marketing by for-profit companies, have focused on using EBCT for screening; that is, for detection of CHD in asymptomatic individuals. When considering the potential value of EBCT for screening, it is critical to keep in mind generally accepted principles for what constitutes an effective screening test: Disease.—The disease should be common and of public health consequence, have an extended preclinical stage, and result in preventable morbidity or mortality. Diagnosis.—The screening test must accurately classify asymptomatic individuals in the population of interest as likely to have or not have the disease. Therapy.—Detection of subclinical disease must result in treatment that reduces morbidity or mortality in comparison with treatment that would have been received in the absence of the screening result. Practicality.—The test must be acceptable, safe, costeffective, and available for use in the population of interest. Ethics.—The screening program must be ethically justifiable after accounting for intended and unintended consequences of both positive and negative test results.


title: Multimethodological approach to study and characterize Forum Novum (Vescovio, central Italy)

abstract: The paper presents the results of an on-going study of the Roman town and early medieval bishopric of Forum Novum at Vescovio in the middle Tiber valley to the north of Rome. The work forms part of the British School at Rome's Tiber Valley Project, which studies the changing landscapes of the middle Tiber valley as the hinterland of Rome through two millennia, from 1000 BC to AD 1000. At Forum Novum a range of remote sensing techniques are being combined with excavation to examine the extent and organization of a Roman town and its development through time. ::: ::: ::: ::: From 1997 to 2001 a series of topographic and high-resolution ground-based remote sensing surveys took place at Forum Novum, which provide a basic map of the ancient centre. The integrated remote sensing surveys were carried out using the magnetic, geoelectric and ground-penetrating radar (GPR) methods. The analysis of the gradiometric, resistivity and GPR time-slice maps indicate that many structural foundations and walls of the buildings are still well preserved below the surface. ::: ::: ::: ::: The results, which are presented and discussed in this paper, demonstrate the potential of remote sensing techniques for our understanding of the extent and organization of urban centres. In the case of Forum Novum, where much of the ancient centre lies under modern structures, GPR survey, in particular, proved fundamental. Further, the results of the georadar surveys permitted archaeologists to select key areas of the town for more detailed investigation through excavation. Copyright © 2004 John Wiley & Sons, Ltd.


title: Application of Machine Learning Approaches in Intrusion Detection System: A Survey

abstract: Network security is one of the major concerns of the modern era. With the rapid development and massive usage of internet over the past decade, the vulnerabilities of network security have become an important issue. Intrusion detection system is used to identify unauthorized access and unusual attacks over the secured networks. Over the past years, many studies have been conducted on the intrusion detection system. However, in order to understand the current status of implementation of machine learning techniques for solving the intrusion detection problems this survey paper enlisted the 49 related studies in the time frame between 2009 and 2014 focusing on the architecture of the single, hybrid and ensemble classifier design. This survey paper also includes a statistical comparison of classifier algorithms, datasets being used and some other experimental setups as well as consideration of feature selection step.


title: Geometry and Meaning

abstract: From Pythagoras's harmonic sequence to Einstein's theory of relativity, geometric models of position, proximity, ratio, and the underlying properties of physical space have provided us with powerful ideas and accurate scientific tools. Currently, similar geometric models are being applied to another type of space the conceptual space of information and meaning, where the contributions of Pythagoras and Einstein are a part of the landscape itself. The rich geometry of conceptual space can be glimpsed, for instance, in internet documents: while the documents themselves define a structure of visual layouts and point-to-point links, search engines create an additional structure by matching keywords to nearby documents in a spatial arrangement of content. What the "Geometry of Meaning" provides is a much-needed exploration of computational techniques to represent meaning and of the conceptual spaces on which these representations are founded."


title: Classification and Regression by randomForest

abstract: Recently there has been a lot of interest in “ensemble learning” — methods that generate many classifiers and aggregate their results. Two well-known methods are boosting (see, e.g., Shapire et al., 1998) and bagging Breiman (1996) of classification trees. In boosting, successive trees give extra weight to points incorrectly predicted by earlier predictors. In the end, a weighted vote is taken for prediction. In bagging, successive trees do not depend on earlier trees — each is independently constructed using a bootstrap sample of the data set. In the end, a simple majority vote is taken for prediction. Breiman (2001) proposed random forests, which add an additional layer of randomness to bagging. In addition to constructing each tree using a different bootstrap sample of the data, random forests change how the classification or regression trees are constructed. In standard trees, each node is split using the best split among all variables. In a random forest, each node is split using the best among a subset of predictors randomly chosen at that node. This somewhat counterintuitive strategy turns out to perform very well compared to many other classifiers, including discriminant analysis, support vector machines and neural networks, and is robust against overfitting (Breiman, 2001). In addition, it is very user-friendly in the sense that it has only two parameters (the number of variables in the random subset at each node and the number of trees in the forest), and is usually not very sensitive to their values. The randomForest package provides an R interface to the Fortran programs by Breiman and Cutler (available at http://www.stat.berkeley.edu/ users/breiman/). This article provides a brief introduction to the usage and features of the R functions.


title: Active Learning based on Random Forest and Its Application to Terrain Classification

abstract: In this paper, a novel active learning technique was proposed for solving multiclass classification problem with random forest classifier. By combining uncertainty, density, and diversity criteria, the most informative samples are selected for manually labeling. The uncertainty criterion is implemented by analyzing the difference between the most votes and second most votes from classifier’s output. Samples in dense regions are thought to be more informative than samples in sparse regions. The average distance of a sample to its k-nearest unlabeled neighbors is computed to describe the sample’s density. The distance between a sample and its nearest labeled sample is used to measure the diversity of the sample. The larger the distance is, the less redundancy the sample is. To assess the effectiveness of the proposed method, it was compared with other techniques like traditional active learning based on random forest and SVM. The results of the experiment on terrain classification have demonstrated the effectiveness of the proposed approach.


in Calculating and Using Confidence Intervals for Model Validation


title: Orion: a power-performance simulator for interconnection networks

abstract: With the prevalence of server blades and systems-on-a-chip (SoCs), interconnection networks are becoming an important part of the microprocessor landscape. However, there is limited tool support available for their design. While performance simulators have been built that enable performance estimation while varying network parameters, these cover only one metric of interest in modern designs. System power consumption is increasingly becoming equally, if not more important than performance. It is now critical to get detailed power-performance tradeoff information early in the microarchitectural design cycle. This is especially so as interconnection networks consume a significant fraction of total system power. It is exactly this gap that the work presented in this paper aims to fill.We present Orion, a power-performance interconnection network simulator that is capable of providing detailed power characteristics, in addition to performance characteristics, to enable rapid power-performance trade-offs at the architectural-level. This capability is provided within a general framework that builds a simulator starting from a microarchitectural specification of the interconnection network. A key component of this construction is the architectural-level parameterized power models that we have derived as part of this effort. Using component power models and a synthesized efficient power (and performance) simulator, a microarchitect can rapidly explore the design space. As case studies, we demonstrate the use of Orion in determining optimal system parameters, in examining the effect of diverse traffic conditions, as well as evaluating new network microarchitectures. In each of the above, the ability to simultaneously monitor power and performance is key in determining suitable microarchitectures.


title: 3-HMG-Coa Reductase Inhibitor Modulates Parasitological Response in Malaria Patients Treated with Amodiaquine

abstract: The occurrence of parasitological resistance to amodiaquine has been reported globally. The 3-HMG CoA reductase inhibitors, otherwise known as statins have been shown to inhibit the growth of malaria parasite.This study was aimed at evaluating the effects of simvastatin in modulating parasitological response to amodiaquine in the chemotherapy of malaria. Subjects with frank malaria (n=60) diagnosed by thick blood film and and confirmed using immunological tests were nominated for the study. Informed written content was obtained and subjects randomized into amodiaquine plus simvastatin (test) and amodiaquine alone (control) groups. The ethical clearance certificate was obtained from the University of Nigeria Teaching Hospital Research Ethics Committee (NHREC/05/01/2008B). The assessment of parasitological response was done in line with WHO criteria and patients followed up on days D0, D3, D7, D14 and D28 posttreatment. The GraphPad Prism 4.0 was employed in the analysis of data which was presented as tables and graphs. Revealed a statistically significant difference in parasitological response (p<0.05) between test and control groups. The mean value of low level resistance, RI was given as 1.3±0.14%, mid-level resistance, RII as 2.7±0.15%, high level parasitological resistance, RIII as 2.4±0.17% and the late parasitological failure, LPF as 3.3±0.26% in the test group. This contrasts with the value of RI given as 8.7±0.42%, RII as 12.8±0.49%, RIII as 4.6±0.17% and the LPF given as 6.7±0.21% in the control group. The outcome of this study suggests that the 3-HMG-CoA reductase inhibitor, simvastatin, is implicated in modulating parasitological response to amodiaquine in the chemotherapy of malaria.


title: Expert finding for community-based question answering via ranking metric network learning

abstract: Expert finding for question answering is a challenging problem in Community-based Question Answering (CQA) site, arising in many applications such as question routing and the identification of best answers. In order to provide high-quality experts, many existing approaches learn the user model mainly from their past question-answering activities in CQA sites, which suffer from the sparsity problem of CQA data. In this paper, we consider the problem of expert finding from the viewpoint of learning ranking metric embedding. We propose a novel ranking metric network learning framework for expert finding by exploiting both users' relative quality rank to given questions and their social relations. We then develop a random-walk based learning method with recurrent neural networks for ranking metric network embedding. The extensive experiments on a large-scale dataset from a real world CQA site show that our method achieves better performance than other state-of-the-art solutions to the problem.


title: TimeML: Robust Specification of Event and Temporal Expressions in Text

abstract: In this paper we provide a description of TimeML, a rich specification language for event and temporal expressions in natural language text, developed in the context of the AQUAINT program on Question Answering Systems. Unlik em ost previous work on event annotation, TimeML capture st hree distinct phenomena in temporal markup: (1) it systematically anchors event predicates to a broad range of temporally denotating expressions; (2) it orders event expressions in text relative to one another, both intrasententially and in discourse; and (3) it allows for a delayed (underspecified) interpretation of partially determined temporal expressions. We demonstrate the expressiveness of TimeML for a broad range of syntactic and semantic contexts, including aspectual predication, modal subordination, and an initial treatment of lexical and constructional causation in text.


title: Toward Long-Distance Subdiffraction Imaging Using Coherent Camera Arrays

abstract: In this work, we propose using camera arrays coupled with coherent illumination as an effective method of improving spatial resolution in long distance images by a factor of ten and beyond. Recent advances in ptychography have demonstrated that one can image beyond the diffraction limit of the objective lens in a microscope. We demonstrate a similar imaging system to image beyond the diffraction limit in long range imaging. We emulate a camera array with a single camera attached to an X-Y translation stage. We show that an appropriate phase retrieval based reconstruction algorithm can be used to effectively recover the lost high resolution details from the multiple low resolution acquired images. We analyze the effects of noise, required degree of image overlap, and the effect of increasing synthetic aperture size on the reconstructed image quality. We show that coherent camera arrays have the potential to greatly improve imaging performance. Our simulations show resolution gains of 10x and more are achievable. Furthermore, experimental results from our proof-of-concept systems show resolution gains of 4x-7x for real scenes. Finally, we introduce and analyze in simulation a new strategy to capture macroscopic Fourier Ptychography images in a single snapshot, albeit using a camera array.


title: Scene image classification method based on Alex-Net model

abstract: Deep convolutional neural network (DCNN) is a powerful method of learning image features with more discriminative and has been studied deeply and applied widely in the field of computer vision and pattern recognition. In order to further explore the superior performance of DCNN and improve the accuracy of the scene image classification, this paper presents a novel algorithm of scene classification, which fully learning the deep characteristics of the images based on the classical Alex-Net model and support vector machine. In the first place, we use the Alex-Net model learning scene image features and extract the last layer with 4096 neurons of the Alex-Net model as the image features in this method; Then, we use the Lib-SVM training model for scene image classification and compare with classification method based on the regression model; Finally, we carried out the experiments on two common datasets in this paper. The experimental results have shown that DCNN can extract the image features effectively. Meanwhile, the trained scene model also has stronger generalization performance and achieves the state-of-the-art classification accuracy.


title: Secure and Trustable Electronic Medical Records Sharing using Blockchain

abstract: Electronic medical records (EMRs) are critical, highly sensitive private information in healthcare, and need to be frequently shared among peers. Blockchain provides a shared, immutable and transparent history of all the transactions to build applications with trust, accountability and transparency. This provides a unique opportunity to develop a secure and trustable EMR data management and sharing system using blockchain. In this paper, we present our perspectives on blockchain based healthcare data management, in particular, for EMR data sharing between healthcare providers and for research studies. We propose a framework on managing and sharing EMR data for cancer patient care. In collaboration with Stony Brook University Hospital, we implemented our framework in a prototype that ensures privacy, security, availability, and fine-grained access control over EMR data. The proposed work can significantly reduce the turnaround time for EMR sharing, improve decision making for medical care, and reduce the overall cost


Methodology for Twitter Sentiment Analysis


title: Synapses and Memory Storage

abstract: The synapse is the functional unit of the brain. During the last several decades we have acquired a great deal of information on its structure, molecular components, and physiological function. It is clear that synapses are morphologically and molecularly diverse and that this diversity is recruited to different functions. One of the most intriguing findings is that the size of the synaptic response in not invariant, but can be altered by a variety of homo- and heterosynaptic factors such as past patterns of use or modulatory neurotransmitters. Perhaps the most difficult challenge in neuroscience is to design experiments that reveal how these basic building blocks of the brain are put together and how they are regulated to mediate the information flow through neural circuits that is necessary to produce complex behaviors and store memories. In this review we will focus on studies that attempt to uncover the role of synaptic plasticity in the regulation of whole-animal behavior by learning and memory.


title: Global goodness‐of‐fit tests in logistic regression with sparse data

abstract: The logistic regression model has become the standard analysing tool for binary responses in medical statistics. Methods for assessing goodness-of-fit, however, are less developed where this problem is especially pronounced in performing global goodness-of-fit tests with sparse data, that is, if the data contain only a small numbers of observations for each pattern of covariate values. In this situation it has been known for a long time that the standard goodness-of-fit tests (residual deviance and Pearson chi-square) behave unsatisfactorily if p-values are calculated from the χ2-distribution. As a remedy in this situation the Hosmer–Lemeshow test is frequently recommended; it relies on a new grouping of the observations to avoid sparseness, where this grouping depends on the estimated probabilities from the model. It has been shown, however, that the Hosmer–Lemeshow test also has some deficiencies, for example, it depends heavily on the calculating algorithm and thus different implementations might lead to different conclusions regarding the fit of the model. We present some alternative tests from the statistical literature which should also perform well with sparse data. Results from a simulation study are given which show that there exist some goodness-of-fit tests (for example, the Farrington test) that have good properties regarding size and power and that even outperform the Hosmer–Lemeshow test. We illustrate the various tests with an example from dermatology on occupational hand eczema in hairdressers. Copyright © 2002 John Wiley & Sons, Ltd.


title: An enhancement transient response of capless LDO with improved dynamic biasing control for SoC applications

abstract: This paper presents an enhancement transient capless low dropout voltage regulator (LDO). To eliminate the external capacitor, the miller effect is implemented through the use of a current amplifier. The proposed regulator LDO provides a load current of 50 mA with a dropout voltage of 200 mV, consuming 14µA quiescent current at light loads, and the regulated output voltage is 1.6 V with an input voltage range from 1.2 to 1.8 V. The proposed system is designed in 0.18 µm CMOS technology. A folded cascode amplifier with high transconductance and high power efficiency is proposed to improve the transient response of the LDO. In addition, multiloop feedback strategy employs a direct dynamic biasing technique to provide a high speed path during the load transient responses. The simulation results presented in this paper will be compared with other results of SoC LDOs demonstrate the advantage of the proposed topology.


title: Fixed-Frequency PWM-Controlled Bidirectional Current-Fed Soft-Switching Series-Resonant Converter for Energy Storage Applications

abstract: A fixed-frequency pulse width modulation (PWM) controlled bidirectional current-fed series-resonant converter is proposed in this paper. The proposed bidirectional resonant converter is derived based on integration of a bidirectional boost/buck converter and a dual-active-bridge series-resonant converter. The most attractive feature of the proposed bidirectional resonant converter is that its voltage gain is only determined by the duty cycle of primary-side switches and has nothing to do with the transferred power. Therefore, the direction and amplitude of the transferred power can be easily regulated continuously and smoothly. In addition, with the help of an auxiliary inductor, zero voltage switching can be achieved for all the active switches on both primary and secondary sides within the entire operation range. Furthermore, the converter is suitable for wide voltage applications because its voltage gain can be regulated in a wide range. The operational principles and characteristics of the proposed bidirectional resonant converter are analyzed in detail. The feasibility and effectiveness of the proposed converter are fully verified by the experimental results from a 1.6 kW prototype.


title: Raman characterization of defects and dopants in graphene

abstract: In this article we review Raman studies of defects and dopants in graphene as well as the importance of both for device applications. First a brief overview of Raman spectroscopy of graphene is presented. In the following section we discuss the Raman characterization of three defect types: point defects, edges, and grain boundaries. The next section reviews the dependence of the Raman spectrum on dopants and highlights several common doping techniques. In the final section, several device applications are discussed which exploit doping and defects in graphene. Generally defects degrade the figures of merit for devices, such as carrier mobility and conductivity, whereas doping provides a means to tune the carrier concentration in graphene thereby enabling the engineering of novel material systems. Accurately measuring both the defect density and doping is critical and Raman spectroscopy provides a powerful tool to accomplish this task.


title: Airbrush simulation for artwork and computer modeling

abstract: A computerized airbrush system with a full three dimensional airbrush interface is presented. The position and orientation of an electronic airbrush tool is tracked in space, and, when the trigger is pulled, paint is sprayed onto two and three dimensional objects displayed on a computer monitor. The experimentally derived paint spray model used for the airbrush takes into account factors such as air to paint ratio, viscosity, and distance of the airbrush from the work. Paint mixing between colors applied to the surface is modeled using Kubelka-Munk theory. Computerized stencils, including semi-permeable stencils, can be manually positioned by the artist or projected onto the object's surface. Two and three dimensional examples of traditional airbrush artwork, produced using the system, are presented. The system can also be used as a modeling tool to decorate three dimensional objects.


title: Critical success factors in enterprise wide information management systems projects

abstract: In the past several years many organizations have initiated enterprise-wide information management systems projects, using such packages as SAP, Peoplesoft, and Oracle. These projects often represent the single largest investment in an information systems project in the history of these companies, and in many cases the largest single investment in any corporate-wide project. These enterprise-wide information management systems projects bring about a host of new questions. Some of these questions and issues are: . How are these projects justified? . Do firms need to change their fundamental business processes, organizational structures, and business strategies to fit the package? . What are the technical challenges associated with implementing a large-scale package such as SAP? . What is the best way to implement a large-scale package: To work through the organization unit-byunit? Or to use a cross-functional approach? . What are the critical success factors in the implementation of a large-scale package? . What are the implications of these projects for the skill and knowledge requirements of the IT workforce? This paper will provide case studies of seven organizations implementing enterprise-wide information management systems projects and will provide insight into each of these questions based upon their experiences.


title: Consumer video understanding: a benchmark database and an evaluation of human and machine performance

abstract: Recognizing visual content in unconstrained videos has become a very important problem for many applications. Existing corpora for video analysis lack scale and/or content diversity, and thus limited the needed progress in this critical area. In this paper, we describe and release a new database called CCV, containing 9,317 web videos over 20 semantic categories, including events like "baseball" and "parade", scenes like "beach", and objects like "cat". The database was collected with extra care to ensure relevance to consumer interest and originality of video content without post-editing. Such videos typically have very little textual annotation and thus can benefit from the development of automatic content analysis techniques. We used Amazon MTurk platform to perform manual annotation, and studied the behaviors and performance of human annotators on MTurk. We also compared the abilities in understanding consumer video content by humans and machines. For the latter, we implemented automatic classifiers using state-of-the-art multi-modal approach that achieved top performance in recent TRECVID multimedia event detection task. Results confirmed classifiers fusing audio and video features significantly outperform single-modality solutions. We also found that humans are much better at understanding categories of nonrigid objects such as "cat", while current automatic techniques are relatively close to humans in recognizing categories that have distinctive background scenes or audio patterns.


title: CycleGAN Face-off

abstract: Face-off is an interesting case of style transfer where the facial expressions and attributes of one person could be fully transformed to another face. We are interested in the unsupervised training process which only requires two sequences of unaligned video frames from each person and learns what shared attributes to extract automatically. In this project, we explored various improvements for adversarial training (i.e. CycleGAN[Zhu et al., 2017]) to capture details in facial expressions and head poses and thus generate transformation videos of higher consistency and stability.


title: Constructions: a new theoretical approach to language

abstract: Abstract A new theoretical approach to language has emerged in the past 10–15 years that allows linguistic observations about form–meaning pairings, known as ‘constructions', to be stated directly. Constructionist approaches aim to account for the full range of facts about language, without assuming that a particular subset of the data is part of a privileged ‘core'. Researchers in this field argue that unusual constructions shed light on more general issues, and can illuminate what is required for a complete account of language.


title: Smart homes automation using Z-wave protocol

abstract: The rapid development of information technology and computer networks make them part of almost everything in our daily life and it became impossible to abandon their use. One of the main and important applications of technology in homes is home automation including controlling and automation of electronic and electrical machines remotely. Wireless Home Automation Networks (WHANs) are used in homes to connect the different devices together and to the Internet. In order to control home devices remotely, there are many popular protocols such as INSTEON, ZigBee, and Home Plug. In this paper, we focus on the relatively new protocol called Z-Wave protocol and we discuss it development and applications in smart homes. This wireless protocol has many advantages over the popular and widely used ZigBee protocol as it provides better reliability, low radio rebirth, easy usage, and easy Interoperability.


title: Different Contexts Lead to Different Word Embeddings

abstract: Recent work for learning word representations has applied successfully to many NLP applications, such as sentiment analysis and question answering. However, most of these models assume a single vector per word type without considering polysemy and homonymy. In this paper, we present an extension to the CBOW model which not only improves the quality of embeddings but also makes embeddings suitable for polysemy. It differs from most of the related work in that it learns one semantic center embedding and one context bias instead of training multiple embeddings per word type. Different context leads to different bias which is defined as the weighted average embeddings of local context. Experimental results on similarity task and analogy task show that the word representations learned by the proposed method outperform the competitive baselines.


title: Designing materials to direct stem-cell fate

abstract: Proper tissue function and regeneration rely on robust spatial and temporal control of biophysical and biochemical microenvironmental cues through mechanisms that remain poorly understood. Biomaterials are rapidly being developed to display and deliver stem-cell-regulatory signals in a precise and near-physiological fashion, and serve as powerful artificial microenvironments in which to study and instruct stem-cell fate both in culture and in vivo. Further synergism of cell biological and biomaterials technologies promises to have a profound impact on stem-cell biology and provide insights that will advance stem-cell-based clinical approaches to tissue regeneration.


title: Online Product Quantization

abstract: Approximate nearest neighbor (ANN) search has achieved great success in many tasks. However, existing popular methods for ANN search, such as hashing and quantization methods, are designed for static databases only. They cannot handle well the database with data distribution evolving dynamically, due to the high computational effort for retraining the model based on the new database. In this paper, we address the problem by developing an online product quantization (online PQ) model and incrementally updating the quantization codebook that accommodates to the incoming streaming data. Moreover, to further alleviate the issue of large scale computation for the online PQ update, we design two budget constraints for the model to update partial PQ codebook instead of all. We derive a loss bound which guarantees the performance of our online PQ model. Furthermore, we develop an online PQ model over a sliding window with both data insertion and deletion supported, to reflect the real-time behaviour of the data. The experiments demonstrate that our online PQ model is both time-efficient and effective for ANN search in dynamic large scale databases compared with baseline methods and the idea of partial PQ codebook update further reduces the update cost.