title: Competition Between Networks: A Study of the Market for Yellow Pages,”Review of Economic Studies

abstract: This paper estimates the importance of network effects in the market for Yellow Pages. I estimate three simultaneous equations: consumer demand for usage of a directory, advertiser demand for advertising and a publisher's first-order condition (derived from profit-maximizing behaviour). Estimation shows that advertisers value consumer usage and that consumers value advertising, implying a network effect. I find that internalizing network effects would significantly increase surplus. As an application, I consider whether the market benefits from monopoly (which takes advantage of network effects) or oligopoly (which reduces market power). I find that a more competitive market is preferable. Copyright 2004, Wiley-Blackwell.


title: Personal recommendation using deep recurrent neural networks in NetEase

abstract: Each user session in an e-commerce system can be modeled as a sequence of web pages, indicating how the user interacts with the system and makes his/her purchase. A typical recommendation approach, e.g., Collaborative Filtering, generates its results at the beginning of each session, listing the most likely purchased items. However, such approach fails to exploit current viewing history of the user and hence, is unable to provide a real-time customized recommendation service. In this paper, we build a deep recurrent neural network to address the problem. The network tracks how users browse the website using multiple hidden layers. Each hidden layer models how the combinations of webpages are accessed and in what order. To reduce the processing cost, the network only records a finite number of states, while the old states collapse into a single history state. Our model refreshes the recommendation result each time when user opens a new web page. As user's session continues, the recommendation result is gradually refined. Furthermore, we integrate the recurrent neural network with a Feedfoward network which represents the user-item correlations to increase the prediction accuracy. Our approach has been applied to Kaola (http://www.kaola.com), an e-commerce website powered by the NetEase technologies. It shows a significant improvement over previous recommendation service.


title: Planar Antenna Array at D-Band Fed By Rectangular Waveguide for Future Automotive Radar Systems

abstract: Planar antennas are being used in frequencies up to 77 GHz for automotive radar because of their low profile and low-cost manufacturing. Also because of their flexibility when building phased and conformal arrays. In this paper a planar antenna around 140 GHz for the possible next generation of automotive radar is developed. This high frequency can provide narrower beams and better angular resolutions than the current sensor antennas. The structure is designed using a 3D electromagnetic simulator and is fabricated on a commercially available substrate. The fabrication consist of a common photolitography process which yields a very low-cost concept. In addition, this antenna can help to characterize the channel with planar systems, so the capabilities and limits of this technological approach can be tested. Experimental measurements were performed showing a gain of about 11 dBi and a return loss better than -10 dB over a band of 2 GHz.


title: Enhanced vision based vein detection system

abstract: The process of needle infusion is done daily endless times everywhere and even though it is simple procedure the sheer number of erroneous procedures that can be serious and fatal for the patient is attentive. To prevent or at least reduce the number of errors, this paper proposes an easy guiding method for nurse and doctors to be integrated into the procedure. A vision-based imaging technique, that gives a practitioner a new perspective for the needle infusion procedure, is introduced. The idea behind of this process is to use the IR camera to capture video sequences of the arm and then compute the effect of electromagnetic wave imprint on each pixel. Then trace the vein location by locating the NIR illumination that is absorbed by the blood in the vein and highlighting it in comparison to the surrounding tissue. Interestingly, this can lead to other applications for the developed system like locating abdominal bleeding, stroke inducing clots in veins near the skin surface and body part vein map-based individual identification biometric to name a few.


title: Speed planning for solar-powered electric vehicles

abstract: Electric vehicles (EVs) are the trend for future transportation. The major obstacle is range anxiety due to poor availability of charging stations and long charging time. Solar-powered EVs, which mostly rely on solar energy, are free of charging limitations. However, the range anxiety problem is more severe due to the availability of sun light. For example, shadings of buildings or trees may cause a solar-powered EV to stop halfway in a trip. In this paper, we show that by optimally planning the speed on different road segments and thus balancing energy harvesting and consumption, we can enable a solar-powered EV to successfully reach the destination using the shortest travel time. The speed planning problem is essentially a constrained non-linear programming problem, which is generally difficult to solve. We have identified an optimality property that allows us to compute an optimal speed assignment for a partition of the path; then, a dynamic programming method is developed to efficiently compute the optimal speed assignment for the whole trip with significantly low computation overhead compared to the state-of-the-art non-linear programming solver. To evaluate the usability of the proposed method, we have also developed a solar-powered EV prototype. Experiments show that the predictions by the proposed technique match well with the data collected from the physical EV. Issues on practical implementation are also discussed.


title: Magnetite pollution nanoparticles in the human brain

abstract: Abstract Biologically formed nanoparticles of the strongly magnetic mineral, magnetite, were first detected in the human brain over 20 y ago [Kirschvink JL, Kobayashi-Kirschvink A, Woodford BJ (1992) Proc Natl Acad Sci USA 89(16):7683–7687]. Magnetite can have potentially large impacts on the brain due to its unique combination of redox activity, surface charge, and strongly magnetic behavior. We used magnetic analyses and electron microscopy to identify the abundant presence in the brain of magnetite nanoparticles that are consistent with high-temperature formation, suggesting, therefore, an external, not internal, source. Comprising a separate nanoparticle population from the euhedral particles ascribed to endogenous sources, these brain magnetites are often found with other transition metal nanoparticles, and they display rounded crystal morphologies and fused surface textures, reflecting crystallization upon cooling from an initially heated, iron-bearing source material. Such high-temperature magnetite nanospheres are ubiquitous and abundant in airborne particulate matter pollution. They arise as combustion-derived, iron-rich particles, often associated with other transition metal particles, which condense and/or oxidize upon airborne release. Those magnetite pollutant particles which are <∼200 nm in diameter can enter the brain directly via the olfactory bulb. Their presence proves that externally sourced iron-bearing nanoparticles, rather than their soluble compounds, can be transported directly into the brain, where they may pose hazard to human health.


title: CoreTAna: A Trace Analyzer for Reverse Engineering Real-Time Software

abstract: With the availability of the AUTOSAR standard, model-driven methodologies are becoming established in theautomotive domain. However, the process of creating models ofexisting system components is often difficult and time consuming, especially when legacy code has to be re-used or informationabout the exact timing behavior is needed. In order to tackle thisreverse engineering problem, we present CoreTAna, a novel toolthat derives an AUTOSAR compliant model of a real-time systemfrom a dynamic analysis of its trace recordings. This paper givesan overview of CoreTAna's current features and discusses itsbenefits for reverse engineering.


title: Early parental depression and child language development

abstract: Objective:To examine the effects of early maternal and paternal depression on child expressive language at age 24 months and the role that parent-to-child reading may play in this pathway.Participants and methods:The 9-month and 24-month waves from a national prospective study of children and their


title: ORIGINAL PAPER Sexual Regret: Evidence for Evolved Sex Differences

abstract: Regret and anticipated regret enhance decision quality by helping people avoid making and repeating mistakes. Some of people's most intense regrets concern sexual decisions. We hypothesized evolved sex differences in women's and men's experiences of sexual regret. Because of women's higher obligatory costs of reproduction throughout evolutionary his- tory, we hypothesized that sexual actions, particularly those involving casual sex, would be regretted more intensely by women than by men. In contrast, because missed sexual opportunities historically carried higher reproductive fitness costs for men than for women, we hypothesized that poorly chosen sexual inactions would be regretted more by men than by women. Across three studies (Ns = 200, 395, and 24,230), we tested these hypotheses using free responses, written scenarios, detailed checklists, and Internet sampling to achieve participant diversity, including diversity in sexual orientation. Across all data sources, results supported predicted psychological sex differences and these differences were localized in casual sex contexts. These findings are consistent with the notion that the psychology of sexual regret was shaped by recurrent sex dif- ferences in selection pressures operating over deep time.


title: Can you tell who I am? Neuroticism, extraversion, and online self-presentation among young adults

abstract: The present study examined the link between neuroticism, extraversion, as well as presentation of the real, the ideal, and the false self on Facebook. Self-reports were collected from 261 young adults (ages 18-30) about personality, online self-presentation, and Facebook use. Level of extraversion was positively associated with Facebook activity level. A series of regression analyses revealed that young adults high in neuroticism reported presenting their ideal and false self on Facebook to a greater extent whereas those low in extraversion reported engaging in greater online self-exploratory behaviors. Findings suggest that young adults who are experiencing emotional instability may be strategic in their online self-presentation perhaps to seek reassurance, and those who have self-doubt further explore their self online.


title: Competition among memes in a world with limited attention

abstract: The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to, and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they receive. In the emerging dynamics of information diffusion, a few memes go viral while most do not. The predictions of our model are consistent with empirical data from Twitter, a popular microblogging platform. Surprisingly, we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas.


title: 1 Hierarchical Indexing for Out-of-Core Access to Multi-Resolution Data

abstract: Increases in the number and size of volumetric meshes have popularized the use of hierarchical multi-resolution representations for visualization. A key component of these schemes has become the adaptive traversal of hierarchical data-structures to build, in real time, approximate representations of the input geometry for rendering. For very large datasets this process must be performed out-of-core. This paper introduces a new global indexing scheme that accelerates adaptive traversal of geometric data represented as binary trees by improving the locality of hierarchical/spatial data access. Such improvements play a critical role in the enabling of effective out-of-core processing.


3 DCNN for Lung Nodule Detection And False Positive Reduction


title: Mechanics of needle-tissue interaction

abstract: When a needle is inserted into soft tissue, interaction forces are developed at the needle tip and along the needle shaft. The needle tip force is due to cutting of the tissue, and the force along the needle shaft is due to friction between needle and tissue. In this study, the friction force is determined for needles inserted into a gelatine phantom at insertion velocities of 10 mm/s and 20 mm/s. The friction force is found to be dependent on the insertion velocity. The needle tip force is calculated using the friction and insertion force, and is used as input for a mechanics-based model which predicts the amount of needle deflection. In the model, the needle is considered to be a cantilever beam supported by springs which have needle-tissue interaction stiffness (K e ). The value of the interaction stiffness is evaluated by comparing results from experiments and simulation. A mechanical needle insertion device is used to insert needles. Needle deflection during insertion is determined using a needle tip tracking algorithm. Results of this study provide insight into the mechanics of needle-tissue interaction, and can be used in studies for robotically steering needles into soft tissue.


title: Unsupervised speaker adaptation of batch normalized acoustic models for robust ASR

abstract: Batch normalization is a standard technique for training deep neural networks. In batch normalization, the input of each hidden layer is first mean-variance normalized and then linearly transformed before applying non-linear activation functions. We propose a novel unsupervised speaker adaptation technique for batch normalized acoustic models. The key idea is to adjust the linear transformations previously learned by batch normalization for all the hidden layers according to the first-pass decoding results of the speaker-independent model. With the adjusted linear transformations for each test speaker, the test distribution of the input of each hidden layer better matches the training distribution. Experiments on the CHiME-3 dataset demonstrate the effectiveness of the proposed layer-wise adaptation approach. Our overall system obtains 4.24% WER on the real subset of the test data, which represents the best reported result on this dataset to date and a relative 27.3% error reduction over the previous best result.


Supervised Clustering of Questions into Intents for Dialog System Applications


title: SocialQ&A: An Online Social Network Based Question and Answer System

abstract: Question and Answer (Q&A) systems play a vital role in our daily life for information and knowledge sharing. Users post questions and pick questions to answer in the system. Due to the rapidly growing user population and the number of questions, it is unlikely for a user to stumble upon a question by chance that (s)he can answer. Also, altruism does not encourage all users to provide answers, not to mention high quality answers with a short answer wait time. The primary objective of this paper is to improve the performance of Q&A systems by actively forwarding questions to users who are capable and willing to answer the questions. To this end, we have designed and implemented SocialQ&A, an online social network based Q&A system. SocialQ&A leverages the social network properties of common-interest and mutual-trust friend relationship to identify an asker's friends who are most likely to answer the question. We describe the architecture, algorithms and user interface of SocialQ&A, and analyze the Q&A behavior of real users and questions from a small-scale real-world SocialQ&A system. We also conducted comprehensive large-scale simulation to evaluate SocialQ&A in comparison with other methods. Our results suggest that social networks can be leveraged to improve the answer quality and asker's waiting time.


title: Advanced PID attitude control of a quadcopter using asynchronous android flight data

abstract: In this paper, we implement an advanced modelfree PID controller for a DJI F450 quadcopter which is controlled and stabilized by a non-rooted onboard Android smartphone, without the aid of an external IMU. Furthermore, since Android is not a realtime system, the control commands and sensor measurements are subject to significant latencies, and hence the PID controller is modified to account for non-trivial measurement asynchronicities. While baseline PID control is widely used with no further modifications, we show that some features can be added to obtain better performance in the presence of latencies and noise.


Learning What is Essential in Questions


title: Electron-microscopic studies of the respiratory mechanism of spider mite eggs

abstract: The ultrastructure of the respiratory apparatus in the egg of Tetranychus urticae Koch is described. The maturing egg (older than 68 hours, 28°C) has underneath the transparent shell an air-filled duct system which consists of 1 frontal and 2 lateral branches. These ducts are kept open with the aid of a multitude of small pillars (“micropillars”) which expand between the shell and the underlying intermediate lamella. The micropillars arise on the latter in the course of development of the embryo (44 hours, 28°C). Their length varies between 0.1 and 0.45 µ and in consequence so does the airspace between shell and lamella. The intermediate lamella is composed of laminae which frequently show a substructure of arched fibrils. Because of its morphological adaptations, the micropillars and the perforation organs, the intermediate lamella is a most important mediator of embryonic gas exchange. ::: ::: The two perforation organs penetrate the egg shell, causing air to enter the system, which afterwards appears brightly contrasted under incident light. They are situated on 2 curved branches of the frontal part of the air-duct system, and are approximately 6.5 µ wide and 5.5 µ deep. They probably originate within the embryo and rise to the embryo's surface, where they fuse with the intermediate lamella, possibly initiating the differentiation of its superficial zones of micropillars. After perforation, the shell admits air and the perforation organs become part of the air-duct system. Perforation is effected by a slender cone approximately 5 µ long and 1.8 µ wide at its base. It has an internal cavity which is closed toward the embryo but is apparently continuous with one or several fine ducts leading to the tip of the cone. This characteristic may indicate the production of a lytic substance which might play an important part in the process of perforation. Characteristic deformation of the tough and inelastic shell underscores this possibility, as demonstrated by both conventional and scanning electron microscopy. Besides their function of puncturing the shell, the perforation organs serve to conduct the respiratory gases from the orifices in the shell through their chambers into the connecting canal and the airduct system. Several morphological details in the structure of cone and chambers might play important parts in this process.


title: Discrete quadratic curvature energies

abstract: Efficient computation of curvature-based energies is important for practical implementations of geometric modeling and physical simulation applications. Building on a simple geometric observation, we provide a version of a curvature-based energy expressed in terms of the Laplace operator acting on the embedding of the surface. The corresponding energy--being quadratic in positions--gives rise to a constant Hessian in the context of isometric deformations. The resulting isometric bending model is shown to significantly speed up common cloth solvers, and when applied to geometric modeling situations built onWillmore flow to provide runtimes which are close to interactive rates.


title: Guiding Long-Short Term Memory for Image Caption Generation

abstract: In this work we focus on the problem of image caption generation. We propose an extension of the long short term memory (LSTM) model, which we coin gLSTM for short. In particular, we add semantic information extracted from the image as extra input to each unit of the LSTM block, with the aim of guiding the model towards solutions that are more tightly coupled to the image content. Additionally, we explore different length normalization strategies for beam search in order to prevent from favoring short sentences. On various benchmark datasets such as Flickr8K, Flickr30K and MS COCO, we obtain results that are on par with or even outperform the current state-of-the-art.


title: Printed wideband antenna for LTE/GSM/UMTS WWAN wireless USB dongle applications

abstract: This paper proposes a planar printed ultra-wideband antenna for eight-band LTE/GSM/UMTS WWAN wireless USB dongle applications. An inductive shorted strip with a chip capacitor loaded is employed in order to improve the characteristics of small-size terminal antennas which usually have a narrow band over the LTE700/GSM850/900 (698–960 MHz) and DCS1800/PCS1900 /UMTS2100/LTE2300/2500 (1710–2690 MHz) operation. Easily printed on a 0.8-mm thick FR4 dielectric substrate of size 20×70 mm2, the proposed antenna structure occupies a compact size of 20×19 mm2. Then the proposed design can be attached to laptop computer by the USB interface. The proposed antenna is fabricated and tested, and good radiation performances of the fabricated antenna are obtained.


title: Understanding changes in belief and attitude toward information technology usage: a theoretical model and longitudinal test

abstract: User beliefs and attitudes are key perceptions driving information technology usage. These perceptions, however, may change with time as users gain first-hand experience with IT usage, which, in turn, may change their subsequent IT usage behavior. This paper elaborates how users' be liefs and attitudes change during the course of their IT usage, defines emergent constructs driving such change, and proposes a temporal model of belief and attitude change by drawing on expectation-disconfirmation theory and the extant IT usage literature. Student data from two longitudinal studies in end-user computing (computer-based training system usage) and system development (rapid application development software usage) contexts provided empirical support for the hypothesized model, demonstrated its generalizability across technologies and usage contexts, and allowed us to probe context-specific differences. Content analysis of qualitative data validated some of our quantitative results. We report that emergent factors such as disconfirmation and satisfaction are critical to understanding changes in IT users' beliefs and attitudes and recommend that they be included in future process models of IT usage.


title: Nanoparticles in a nanowaveguide: Enhanced-functionality optical systems based on micro- and nanowaveguide structures doped with nanoparticles

abstract: Highly efficient collection of the fluorescent response of silicon nanoparticles loaded in micro- and nanowaveguide fiber structures is experimentally demonstrated. Micro- and nanowaveguide channels of a fiber structure are used to optically excite nanoparticles, capture their fluorescent response, and deliver the fluorescence signal to the detector. Enhancement of the evanescent field in the modes of nanowaveguide channels improves the efficiency of optical excitation of an analyte.


title: Characterization of the mouse organ of Corti cytoarchitecture using a stick representation

abstract: The supporting cells and hair cells (HCs) in the organ of Corti (OoC) are highly organized. The precise 3D micro-structure is hypothesized to play a critical role in cochlear function. Recently, we combined two techniques to obtain the organ of Corti cytoarchitecture. Two-photon imaging allowed us to perform in situ imaging without subjecting the tissue to other potential distortions, while genetically engineered mTmG mice have a fluorophore embedded in the cell membranes. In this contribution we discuss the parameterization step necessary to compare structures obtained with this technique at different locations and in different specimens. First, the z-axis is chosen perpendicular to the basilar membrane. Subsequently, base and apex of cells are indicated by landmarks. As such, the cells are approximated as a stick representation. This representation is used to calculate the 3D lengths and angles of all imaged cells. Since the OoC is not straight but spiral-shaped, the radial (y) and longitudinal (x) directions differ at each location. Therefore, circular arcs are fitted through the 3 rows of outer HCs to define the local radial (y) and longitudinal (x) direction. Novel in this approach is the 3D data of the cell position in the organ of Corti. Cell diameters and tissue areas cannot be quantified with this stick representation and need to be measured separately.


title: Curriculum Learning of Visual Attribute Clusters for Multi-Task Classification

abstract: Visual attributes, from simple objects (e.g., backpacks, hats) to soft-biometrics (e.g., gender, height, clothing) have proven to be a powerful representational approach for many applications such as image description and human identification. In this paper, we introduce a novel method to combine the advantages of both multi-task and curriculum learning in a visual attribute classification framework. Individual tasks are grouped after performing hierarchical clustering based on their correlation. The clusters of tasks are learned in a curriculum learning setup by transferring knowledge between clusters. The learning process within each cluster is performed in a multi-task classification setup. By leveraging the acquired knowledge, we speed-up the process and improve performance. We demonstrate the effectiveness of our method via ablation studies and a detailed analysis of the covariates, on a variety of publicly available datasets of humans standing with their full-body visible. Extensive experimentation has proven that the proposed approach boosts the performance by 4% to 10%.


title: Synchronverters: Inverters That Mimic Synchronous Generators

abstract: In this paper, the idea of operating an inverter to mimic a synchronous generator (SG) is motivated and developed. We call the inverters that are operated in this way synchronverters. Using synchronverters, the well-established theory/algorithms used to control SGs can still be used in power systems where a significant proportion of the generating capacity is inverter-based. We describe the dynamics, implementation, and operation of synchronverters. The real and reactive power delivered by synchronverters connected in parallel and operated as generators can be automatically shared using the well-known frequency- and voltage-drooping mechanisms. Synchronverters can be easily operated also in island mode, and hence, they provide an ideal solution for microgrids or smart grids. Both simulation and experimental results are given to verify the idea.


title: Ending Corruption: The Interplay Among Institutional Logics, Resources, and Institutional Entrepreneurs

abstract: We draw from theories of institutions and collective identities to present a threefold framework of institutional change—involving institutional logics, resources, and social actors—that furthers our understanding of the mitigation of corruption. Those social actors intent on reforming corruption function as institutional entrepreneurs, and their success depends both on articulating an anticorruption institutional logic that incorporates corruption-disabling identities, cognitive schemas, and practices and on having or developing the resources necessary to propagate the new anticorruption institutional logic.


title: Unpaired Photo-to-Caricature Translation on Faces in the Wild

abstract: Recently, image-to-image translation has been made much progress owing to the success of conditional Generative Adversarial Networks (cGANs). However, it's still very challenging for translation tasks with the requirement of high-level visual information conversion, such as photo-to-caricature translation that requires satire, exaggeration, lifelikeness and artistry. We present an approach for learning to translate faces in the wild from the source photo domain to the target caricature domain with different styles, which can also be used for other high-level image-to-image translation tasks. In order to capture global structure with local statistics while translation, we design a dual pathway model of cGAN with one global discriminator and one patch discriminator. Beyond standard convolution (Conv), we propose a new parallel convolution (ParConv) to construct Parallel Convolutional Neural Networks (ParCNNs) for both global and patch discriminators, which can combine the information from previous layer with the current layer. For generator, we provide three more extra losses in association with adversarial loss to constrain consistency for generated output itself and with the target. Also the style can be controlled by the input style info vector. Experiments on photo-to-caricature translation of faces in the wild show considerable performance gain of our proposed method over state-of-the-art translation methods as well as its potential real applications.


title: BOAT—optimistic decision tree construction

abstract: Classification is an important data mining problem. Given a training database of records, each tagged with a class label, the goal of classification is to build a concise model that can be used to predict the class label of future, unlabeled records. A very popular class of classifiers are decision trees. All current algorithms to construct decision trees, including all main-memory algorithms, make one scan over the training database per level of the tree. We introduce a new algorithm (BOAT) for decision tree construction that improves upon earlier algorithms in both performance and functionality. BOAT constructs several levels of the tree in only two scans over the training database, resulting in an average performance gain of 300% over previous work. The key to this performance improvement is a novel optimistic approach to tree construction in which we construct an initial tree using a small subset of the data and refine it to arrive at the final tree. We guarantee that any difference with respect to the “real” tree (i.e., the tree that would be constructed by examining all the data in a traditional way) is detected and corrected. The correction step occasionally requires us to make additional scans over subsets of the data; typically, this situation rarely arises, and can be addressed with little added cost. Beyond offering faster tree construction, BOAT is the first scalable algorithm with the ability to incrementally update the tree with respect to both insertions and deletions over the dataset. This property is valuable in dynamic environments such as data warehouses, in which the training dataset changes over time. The BOAT update operation is much cheaper than completely rebuilding the tree, and the resulting tree is guaranteed to be identical to the tree that would be produced by a complete re-build.


title: Mermaid: Integrating Vertex-Centric with Edge-Centric for Real-World Graph Processing

abstract: There has been increasing interests in processing large-scale real-world graphs, and recently many graph systems have been proposed. Vertex-centric GAS (Gather-Apply-Scatter) and Edge-centric GAS are two graph computation models being widely adopted, and existing graph analytics systems commonly follow only one computation model, which is not the best choice for real-world graph processing. In fact, vertex degrees in real-world graphs often obey skewed power-law distributions: most vertices have relatively few neighbors while a few have many neighbors. We observe that vertex-centric GAS for high-degree vertices and edge-centric GAS for low-degree vertices is a much better choice for real-world graph processing. In this paper, we present Mermaid, a system for processing large-scale real-world graphs on a single machine. Mermaid skillfully integrates vertex-centric GAS with edge-centric GAS through a novel vertex-mapping mechanism, and supports streamlined graph processing. On a total of 6 practical natural graph processing tasks, we demonstrate that, on average, Mermaid achieves 1.83x better performance than the state-of-the-art graph system on a single machine.


title: Photothermolysis of sebaceous glands in human skin ex vivo with a 1,708 nm Raman fiber laser and contact cooling

abstract: BACKGROUND AND OBJECTIVES ::: Wavelengths near ∼1,720 nm are of interest for targeting fat/lipid-rich tissues due to the high absorption coefficient of human fat and low water scattering and absorption. In this study, a 1,708 nm laser was built and shown to selectively target fat/lipid adjacent to porcine heart and dermis and then used to damage dermal sebaceous glands in human skin. STUDY DESIGN AND MATERIALS: An all-fiber 1,708 nm laser with ∼4 W maximum power was designed and built. Selectivity for targeting fat/lipid was studied by exposing porcine heart and skin tissue cross-sections to the 1,708 nm laser. Human skin treatments to damage sebaceous glands were performed both with and without cold window cooling. Histochemical evaluation on the frozen sections was performed using methylthiazolyldiphenyl-tetrazolium bromide (MTT) assay. ::: ::: ::: RESULTS ::: Histochemical analysis of porcine tissue cross-sections showed that 1,708 nm laser can selectively damage pericardial fat(heart) and subcutaneous fat(skin) with little to no damage to the myocardium and the dermis, respectively. In human skin, histochemical evaluation without contact cooling showed damage to both epidermis and dermis. With cooling, epidermis was spared and damage was observed in dermis extending ∼0.4-1.65 mm from the skin surface at an average laser fluence of ∼80 J/cm(2). Selective damage of sebaceous glands was suggested but not definitively demonstrated. ::: ::: ::: CONCLUSIONS ::: We have developed an all-fiber 1,708 nm laser capable of damaging majority of the sebaceous glands in the dermis and thus may have potential applications in the treatment of conditions such as acne vulgaris whose pathophysiology involves disorders of sebaceous glands.


title: Significance analysis of microarrays applied to the ionizing radiation response

abstract: Microarrays can measure the expression of thousands of genes to identify changes in expression between different biological states. Methods are needed to determine the significance of these changes while accounting for the enormous number of genes. We describe a method, Significance Analysis of Microarrays (SAM), that assigns a score to each gene on the basis of change in gene expression relative to the standard deviation of repeated measurements. For genes with scores greater than an adjustable threshold, SAM uses permutations of the repeated measurements to estimate the percentage of genes identified by chance, the false discovery rate (FDR). When the transcriptional response of human cells to ionizing radiation was measured by microarrays, SAM identified 34 genes that changed at least 1.5-fold with an estimated FDR of 12%, compared with FDRs of 60 and 84% by using conventional methods of analysis. Of the 34 genes, 19 were involved in cell cycle regulation and 3 in apoptosis. Surprisingly, four nucleotide excision repair genes were induced, suggesting that this repair pathway for UV-damaged DNA might play a previously unrecognized role in repairing DNA damaged by ionizing radiation.


title: Energy and visual comfort analysis of lighting and daylight control strategies

abstract: Abstract Shading systems have the potential to reduce energy consumption of electric lighting and improve visual comfort. Various automated control systems of shading device and electric lighting have been widely used. However, existing lighting and shading systems typically operate independently, i.e., information is not shared, and thus system performance is not optimal. Therefore, integrated control systems have been proposed to maximize energy efficiency and user comfort. Some problems are still unaddressed. For example, the benefits of sharing control information (e.g., HVAC state and occupancy information) between the lighting and shading control systems have not been quantified; the benefits of integrated controls have not been quantified. To address these issues, improved independent and integrated control strategies were proposed by adding shared HVAC state and occupancy information. To provide a quantitative comparison of these control strategies, a co-simulation platform consisting of BCVTB, EnergyPlus and Matlab was developed to perform an in-depth quantitative study of seven control strategies (manual, independent and integrated control strategies). Simulation results for a reference office building were presented for three climate zones (Baltimore, London, Abu-Dhabi), two types of blinds (interior, exterior) and two window-to-wall ratios (66%, 100%). A dynamic occupancy model was developed from actual office occupancy data and used in the simulations. The lighting, heating and cooling energy consumption, electric demand and visual comfort of different control strategies were evaluated and compared. Overall, in most cases, integrated lighting and daylight control outperforms all other strategies in energy and visual comfort performance.


The Ambiguous Roles of Extracellular Vesicles in HIV Replication and Pathogenesis


title: Memory forensics using virtual machine introspection for Malware analysis

abstract: A security sandbox is a technology that is often used to detect advanced malware. However, current sandboxes are highly dependent on VM hypervisor types and versions. Thus, in this paper, we introduce a new sandbox design, using memory forensics techniques, to provide an agentless sandbox solution that is independent of the VM hypervisor. In particular, we leverage the VM introspection method to monitor malware running memory data outside the VM and analyze its system behaviors, such as process, file, registry, and network activities. We evaluate the feasibility of this method using 20 advanced and 8 script-based malware samples. We furthermore demonstrate how to analyze malware behavior from memory and verify the results with three different sandbox types. The results show that we can analyze suspicious malware activities, which is also helpful for cyber security defense.


title: Signal modeling techniques in speech recognition

abstract: A tutorial on signal processing in state-of-the-art speech recognition systems is presented, reviewing those techniques most commonly used. The four basic operations of signal modeling, i.e. spectral shaping, spectral analysis, parametric transformation, and statistical modeling, are discussed. Three important trends that have developed in the last five years in speech recognition are examined. First, heterogeneous parameter sets that mix absolute spectral information with dynamic, or time-derivative, spectral information, have become common. Second, similarity transform techniques, often used to normalize and decorrelate parameters in some computationally inexpensive way, have become popular. Third, the signal parameter estimation problem has merged with the speech recognition process so that more sophisticated statistical models of the signal's spectrum can be estimated in a closed-loop manner. The signal processing components of these algorithms are reviewed. >


Verification of On-Line Vehicle Collision Avoidance Warning System using DSRC


Integrated multi scale text retrieval visualization


title: Mining structural hole spanners through information diffusion in social networks

abstract: The theory of structural holes suggests that individuals would benefit from filling the "holes" (called as structural hole spanners) between people or groups that are otherwise disconnected. A few empirical studies have verified that structural hole spanners play a key role in the information diffusion. However, there is still lack of a principled methodology to detect structural hole spanners from a given social network. In this work, we precisely define the problem of mining top-k structural hole spanners in large-scale social networks and provide an objective (quality) function to formalize the problem. Two instantiation models have been developed to implement the objective function. For the first model, we present an exact algorithm to solve it and prove its convergence. As for the second model, the optimization is proved to be NP-hard, and we design an efficient algorithm with provable approximation guarantees. We test the proposed models on three different networks: Coauthor, Twitter, and Inventor. Our study provides evidence for the theory of structural holes, e.g., 1% of Twitter users who span structural holes control 25% of the information diffusion on Twitter. We compare the proposed models with several alternative methods and the results show that our models clearly outperform the comparison methods. Our experiments also demonstrate that the detected structural hole spanners can help other social network applications, such as community kernel detection and link prediction. To the best of our knowledge, this is the first attempt to address the problem of mining structural hole spanners in large social networks.


title: Lower-Limb Robotic Rehabilitation: Literature Review and Challenges

abstract: This paper presents a survey of existing robotic systems for lower-limb rehabilitation. It is a general assumption that robotics will play an important role in therapy activities within rehabilitation treatment. In the last decade, the interest in the field has grown exponentially mainly due to the initial success of the early systems and the growing demand caused by increasing numbers of stroke patients and their associate rehabilitation costs. As a result, robot therapy systems have ::: been developed worldwide for training of both the upper and lower extremities. This work reviews all current robotic systems to date for lower-limb rehabilitation, as well as main clinical tests performed with them, with the aim of showing a clear starting point in the field. It also remarks some challenges that current systems still have to meet in order to obtain a broad clinical and market acceptance.


SPIFFY: Inducing Cost-Detectability Tradeoffs for Persistent Link-Flooding Attacks


title: A learning-based coalition formation model for multiagent systems

abstract: In this paper, we present a learning-based coalition formation model that forms sub-optimal coalitions among agents to solve real-time constrained allocation problems in a dynamic, uncertain and noisy environment. This model consists of three stages (coalition planning, coalition instantiation and coalition evaluation) and an integrated learning framework. An agent first derives a coalition formation plan via case-based reasoning (CBR). Guided by this plan, the agent instantiates a coalition through negotiations with other agents. When the process completes, the agent evaluates the outcomes. The integrated learning framework involves multiple levels embedded in the three stages. At a low level on strategic and tactical details, the model allows an agent to learn how to negotiate. At the meta-level, an agent learns how to improve on its planning and the actual execution of the plan. The model uses an approach that synthesizes reinforcement learning (RL) and case-based learning (CBL). We have implemented the model partially and conducted experiments on CPU allocation in a multisensor target-tracking domain.


title: BogusBiter: A transparent protection against phishing attacks

abstract: Many anti-phishing mechanisms currently focus on helping users verify whether a Web site is genuine. However, usability studies have demonstrated that prevention-based approaches alone fail to effectively suppress phishing attacks and protect Internet users from revealing their credentials to phishing sites. In this paper, instead of preventing human users from “biting the bait,” we propose a new approach to protect against phishing attacks with “bogus bites.” We develop BogusBiter, a unique client-side anti-phishing tool, which transparently feeds a relatively large number of bogus credentials into a suspected phishing site. BogusBiter conceals a victim's real credential among bogus credentials, and moreover, it enables a legitimate Web site to identify stolen credentials in a timely manner. Leveraging the power of client-side automatic phishing detection techniques, BogusBiter is complementary to existing preventive anti-phishing approaches. We implemented BogusBiter as an extension to the Firefox 2 Web browser, and evaluated its efficacy through real experiments on both phishing and legitimate Web sites. Our experimental results indicate that it is promising to use BogusBiter to transparently protect against phishing attacks.


title: Leadership Style and Patient Safety

abstract: OBJECTIVE ::: The purpose of this study was to explore the relationship between nurse manager (NM) leadership style and safety climate. ::: ::: ::: BACKGROUND ::: Nursing leaders are needed who will change the environment and increase patient safety. Hospital NMs are positioned to impact day-to-day operations. Therefore, it is essential to inform nurse executives regarding the impact of leadership style on patient safety. ::: ::: ::: METHODS ::: A descriptive correlational study was conducted in 41 nursing departments across 9 hospitals. The hospital unit safety climate survey and multifactorial leadership questionnaire were completed by 466 staff nurses. Bivariate and regression analyses were conducted to determine how well leadership style predicted safety climate. ::: ::: ::: RESULTS ::: Transformational leadership style was demonstrated as a positive contributor to safety climate, whereas laissez-faire leadership style was shown to negatively contribute to unit socialization and a culture of blame. ::: ::: ::: CONCLUSIONS ::: Nursing leaders must concentrate on developing transformational leadership skills while also diminishing negative leadership styles.


title: On mining cross-graph quasi-cliques

abstract: Joint mining of multiple data sets can often discover interesting, novel, and reliable patterns which cannot be obtained solely from any single source. For example, in cross-market customer segmentation, a group of customers who behave similarly in multiple markets should be considered as a more coherent and more reliable cluster than clusters found in a single market. As another example, in bioinformatics, by joint mining of gene expression data and protein interaction data, we can find clusters of genes which show coherent expression patterns and also produce interacting proteins. Such clusters may be potential pathways.In this paper, we investigate a novel data mining problem, mining cross-graph quasi-cliques, which is generalized from several interesting applications such as cross-market customer segmentation and joint mining of gene expression data and protein interaction data. We build a general model for mining cross-graph quasi-cliques, show why the complete set of cross-graph quasi-cliques cannot be found by previous data mining methods, and study the complexity of the problem. While the problem is difficult, we develop an efficient algorithm, Crochet, which exploits several interesting and effective techniques and heuristics to efficaciously mine cross-graph quasi-cliques. A systematic performance study is reported on both synthetic and real data sets. We demonstrate some interesting and meaningful cross-graph quasi-cliques in bioinformatics. The experimental results also show that algorithm Crochet is efficient and scalable.


title: Effects of hydrogen and steam addition on laminar burning velocity of methane–air premixed flame: Experimental and numerical analysis

abstract: Effects of hydrogen enrichment and steam addition on laminar burning velocity of methaneeair premixed flame were studied both experimentally and numerically. Measurements were carried out using the slot burner method at 1 bar for fresh gases temperatures of 27 °C and 57 °C and for variable equivalence ratios going from 0.8 to 1.2. The hydrogen content in the fuel was varied from 0% to 30% in volume and the steam content in the air was varied from 0 to 112 g/kg (0e100% of relative humidity). Numerical calculations were performed using the COSILAB code with the GRI-Mech 3.0 mechanism for one-dimensional premixed flames. The calculations were implemented first at room temperature and pressure and then extended to higher temperatures (up to 917 K) and pressures (up to 50 bar). Measurements of laminar burning velocities of methanee hydrogeneair and methaneeairesteam agree with the GRI-Mech calculations and previous measurements from literature obtained by different methods. Results show that enrich- ment by hydrogen increases of the laminar burning velocity and the adiabatic flame temperature. The addition of steam to a methaneeair mixture noticeably decreases the burning velocity and the adiabatic flame temperature. Modeling shows that isentropic compression of fresh gases leads to the increase of laminar burning velocity.


title: Quantifying Long-Term Scientific Impact

abstract: The lack of predictability of citation-based measures frequently used to gauge impact, from impact factors to short-term citations, raises a fundamental question: Is there long-term predictability in citation patterns? Here, we derive a mechanistic model for the citation dynamics of individual papers, allowing us to collapse the citation histories of papers from different journals and disciplines into a single curve, indicating that all papers tend to follow the same universal temporal pattern. The observed patterns not only help us uncover basic mechanisms that govern scientific impact but also offer reliable measures of influence that may have potential policy implications.


title: Binocular rivalry and perceptual multi-stability

abstract: Abstract When different images are presented to the two eyes, binocular rivalry can occur – that is, perceptual alternations between the two monocular images (bi-stability) or between more than two percepts comprised of complementary portions of the two images (multi-stability). A recent paper reports important findings about the dynamics of multi-stable alternations in binocular rivalry.


title: Keeping One's Distance The Influence of Spatial Distance Cues on Affect and Evaluation

abstract: Current conceptualizations of psychological distance (e.g., construal-level theory) refer to the degree of overlap between the self and some other person, place, or point in time. We propose a complementary view in which perceptual and motor representations of physical distance influence people's thoughts and feelings without reference to the self, extending research and theory on the effects of distance into domains where construal-level theory is silent. Across four experiments, participants were primed with either spatial closeness or spatial distance by plotting an assigned set of points on a Cartesian coordinate plane. Compared with the closeness prime, the distance prime produced greater enjoyment of media depicting embarrassment (Study 1), less emotional distress from violent media (Study 2), lower estimates of the number of calories in unhealthy food (Study 3), and weaker reports of emotional attachments to family members and hometowns (Study 4). These results support a broader conceptualization o...


title: Exploring Various Knowledge In Relation Extraction

abstract: Extracting semantic relationships between entities is challenging. This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based relation extraction using SVM. Our study illustrates that the base phrase chunking information is very effective for relation extraction and contributes to most of the performance improvement from syntactic aspect while additional information from full parsing gives limited further enhancement. This suggests that most of useful information in full parse trees for relation extraction is shallow and can be captured by chunking. We also demonstrate how semantic information such as WordNet and Name List, can be used in feature-based relation extraction to further improve the performance. Evaluation on the ACE corpus shows that effective incorporation of diverse features enables our system outperform previously best-reported systems on the 24 ACE relation subtypes and significantly outperforms tree kernel-based systems by over 20 in F-measure on the 5 ACE relation types.


title: Comparative study of singing voice detection based on deep neural networks and ensemble learning

abstract: This paper investigates various structures of neural network models and various types of stacked ensembles for singing voice detection. The studied models include convolutional neural networks (CNN), long short term memory (LSTM) model, convolutional LSTM model, and capsule net. The input features to the network models are MFCC (mel-frequency cepstrum coefficients), spectrogram from short-time Fourier transformation, or raw PCM samples. The simulation results show that CNN model with spectrogram inputs yields higher detection accuracy, up to 91.8% for Jamendo dataset. Among the studied stacked ensemble methods, performing voting strategy yields comparable performance as the other methods, but with much lower computational cost. By voting with five models, the accuracy reaches 94.2% for Jamendo dataset.


title: Discriminative Unsupervised Feature Learning with Convolutional Neural Networks

abstract: Current methods for training convolutional neural networks depend on large amounts of labeled samples for supervised training. In this paper we present an approach for training a convolutional neural network using only unlabeled data. We train the network to discriminate between a set of surrogate classes. Each surrogate class is formed by applying a variety of transformations to a randomly sampled 'seed' image patch. We find that this simple feature learning algorithm is surprisingly successful when applied to visual object recognition. The feature representation learned by our algorithm achieves classification results matching or outperforming the current state-of-the-art for unsupervised learning on several popular datasets (STL-10, CIFAR-10, Caltech-101).


title: A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models

abstract: Generative adversarial networks (GANs) are a recently proposed class of generative models in which a generator is trained to optimize a cost function that is being simultaneously learned by a discriminator. While the idea of learning cost functions is relatively new to the field of generative modeling, learning costs has long been studied in control and reinforcement learning (RL) domains, typically for imitation learning from demonstrations. In these fields, learning cost function underlying observed behavior is known as inverse reinforcement learning (IRL) or inverse optimal control. While at first the connection between cost learning in RL and cost learning in generative modeling may appear to be a superficial one, we show in this paper that certain IRL methods are in fact mathematically equivalent to GANs. In particular, we demonstrate an equivalence between a sample-based algorithm for maximum entropy IRL and a GAN in which the generator's density can be evaluated and is provided as an additional input to the discriminator. Interestingly, maximum entropy IRL is a special case of an energy-based model. We discuss the interpretation of GANs as an algorithm for training energy-based models, and relate this interpretation to other recent work that seeks to connect GANs and EBMs. By formally highlighting the connection between GANs, IRL, and EBMs, we hope that researchers in all three communities can better identify and apply transferable ideas from one domain to another, particularly for developing more stable and scalable algorithms: a major challenge in all three domains.


title: An energy-stable convex splitting for the phase-field crystal equation

abstract: The phase-field crystal equation is solved using a finite element discretization.A mass-conserving, energy-stable, second-order time discretization is developed.The results are proved rigorously, and verified numerically.The implementation is done in PetIGA, an open source isogeometric analysis framework.Three dimensional results showcase the robustness of the method. The phase-field crystal equation, a parabolic, sixth-order and nonlinear partial differential equation, has generated considerable interest as a possible solution to problems arising in molecular dynamics. Nonetheless, solving this equation is not a trivial task, as energy dissipation and mass conservation need to be verified for the numerical solution to be valid. This work addresses these issues, and proposes a novel algorithm that guarantees mass conservation, unconditional energy stability and second-order accuracy in time. Numerical results validating our proofs are presented, and two and three dimensional simulations involving crystal growth are shown, highlighting the robustness of the method.


title: Lifted Relational Neural Networks

abstract: We propose a method combining relational-logic representations with neural network learning. A general lifted architecture, possibly reflecting some background domain knowledge, is described through relational rules which may be handcrafted or learned. The relational rule-set serves as a template for unfolding possibly deep neural networks whose structures also reflect the structures of given training or testing relational examples. Different networks corresponding to different examples share their weights, which co-evolve during training by stochastic gradient descent algorithm. The framework allows for hierarchical relational modeling constructs and learning of latent relational concepts through shared hidden layers weights corresponding to the rules. Discovery of notable relational concepts and experiments on 78 relational learning benchmarks demonstrate favorable performance of the method.


title: An empirical study of regression test application frequency

abstract: Regression testing is an expensive maintenance process used to revalidate modified software. Regression test selection (RTS) techniques try to lower the cost of regression testing by selecting and running a subset of the existing test cases. Many such techniques have been proposed and initial studies show that they can produce savings. We believe, however, that issues such as the frequency with which testing is done have a strong effect on the behavior of these techniques. Therefore, we conducted an experiment to assess the effects of test application frequency on the costs and benefits of regression test selection techniques. Our results expose essential tradeoffs that should be considered when using these techniques over a series of software releases.


title: Towards mechanisms for detection and prevention of data exfiltration by insiders: keynote talk paper

abstract: Data represent an extremely important asset for any organization. Confidential data such as military secrets or intellectual property must never be disclosed outside the organization. Therefore, one of the most severe threats in the case of cyber-insider attacks is the loss of confidential data due to exfiltration. A malicious insider who has the proper credentials to access the organization databases may, over time, send data outside the organization network through a variety of channels, such as email, crafted HTTP requests that encapsulate data, etc. Existing security tools for detection of cyber-attacks focus on protecting the boundary between the organization and the outside world. Numerous network-level intrusion detection systems (IDS) exist, which monitor the traffic pattern and attempt to infer anomalous behavior. While such tools may be effective in protecting against external attacks, they are less suitable when the data exfiltration is performed by an insider who has the proper credentials and authorization to access resources within the organization. In this paper, we argue that DBMS-layer detection and prevention systems are the best alternative to defend against data exfiltration because: (1) DBMS access is performed through a standard, unique language (SQL) with well-understood semantics; (2) monitoring the potential disclosure of confidential data is more effective if done as close as possible to the data source; and (3) the DBMS layer already has in place a thorough mechanism for enforcing access control based on subject credentials. By analyzing the pattern of interaction between subjects and the DBMS, it is possible to detect anomalous activity that is indicative of early signs of exfiltration. In the paper, we outline a taxonomy of cyber-insider dimensions of activities that are indicative of data exfiltration, and we discuss a high-level architecture and mechanisms for early detection of exfiltration by insiders. We also outline a virtualization-based mechanism that prevents insiders from exfiltrating data, even in the case when they manage to gain control over the network. The protection mechanism relies on explicit authorization of data transfers that cross the organizational boundary.


title: A Novel Word Spotting Method Based on Recurrent Neural Networks

abstract: Keyword spotting refers to the process of retrieving all instances of a given keyword from a document. In the present paper, a novel keyword spotting method for handwritten documents is described. It is derived from a neural network-based system for unconstrained handwriting recognition. As such it performs template-free spotting, i.e., it is not necessary for a keyword to appear in the training set. The keyword spotting is done using a modification of the CTC Token Passing algorithm in conjunction with a recurrent neural network. We demonstrate that the proposed systems outperform not only a classical dynamic time warping-based approach but also a modern keyword spotting system, based on hidden Markov models. Furthermore, we analyze the performance of the underlying neural networks when using them in a recognition task followed by keyword spotting on the produced transcription. We point out the advantages of keyword spotting when compared to classic text line recognition.


title: Enterprise resource planning: business needs and technologies

abstract: Presents briefly ERP as a software solution integrating various functional spheres in an organization. Highlights its role in building a customer process, ERP technologies, available systems, viz. database systems, communication protocols and user interface framework. Details organizational preparedness for embarking and evaluating the investment on ERP. Discusses its application, selection criteria for small and medium organizations and a case study in Indian context. Concludes that ERP is the tool for an integrated information system to stay competitive and customer‐oriented for all organizations.


title: Communication and Trust in Global Virtual Teams

abstract: This paper explores the challenges of creating and maintaining trust in a global virtual team whose members transcend time, space, and culture. The challenges are highlighted by integrating recent literature on work teams, computer-mediated communication groups, cross-cultural communication, and interpersonal and organizational trust. To explore these challenges empirically, we report on a series of descriptive case studies on global virtual teams whose members were separated by location and culture, were challenged by a common collaborative project, and for whom the only economically and practically viable communication medium was asynchronous and synchronous computer-mediated communication. The results suggest that global virtual teams may experience a form of ‘swift’ trust but such trust appears to be very fragile and temporal. The study raises a number of issues to be explored and debated by future research. Pragmatically, the study describes communication behaviors that might facilitate trust in global virtual teams.


Reinforcement Learning and Neural Networks for Tetris


Attaching Semantic Metadata to Cryptocurrency Transactions


Knowledge Management Infrastructure and Product and Process Innovation : The Role of Trust , Technological Support , and Employee Incentives


title: A Study Investigating Typical Concepts and Guidelines for Ontology Building

abstract: In semantic technologies, the shared common understanding of the structure of information among artifacts (people or software agents) can be realized by building an ontology. To do this, it is imperative for an ontology builder to answer several questions: a) what are the main components of an ontology? b) How an ontology look likes and how it works? c) Verify if it is required to consider reusing existing ontologies or not? c) What is the complexity of the ontology to be developed? d) What are the principles of ontology design and development? e) How to evaluate an ontology? This paper answers all the key questions above. The aim of this paper is to present a set of guiding principles to help ontology developers and also inexperienced users to answer such questions.


title: Action recognition based on overcomplete independent components analysis

abstract: Existing works on action recognition rely on two separate stages: (1) designing hand-designed features or learning features from video data; (2) classifying features using a classifier such as SVM or AdaBoost. Motivated by two observations: (1) independent component analysis (ICA) is capable of encoding intrinsic features underlying video data; and (2) videos of different actions can be easily distinguished by their intrinsic features, we propose a simple but effective action recognition framework based on the recently proposed overcomplete ICA model. After a set of overcomplete ICA basis functions are learned from the densely sampled 3D patches from training videos for each action, a test video is classified as the class whose basis functions can reconstruct the sampled 3D patches from the test video with the smallest reconstruction error. The experimental results on five benchmark datasets demonstrate that the proposed approach outperforms several state-of-the-art works.


title: Interfacing carbon nanotubes with living mammalian cells and cytotoxicity issues.

abstract: The unique structures and properties of carbon nanotubes (CNTs) have attracted extensive investigations for many applications, such as those in the field of biomedical materials and devices, biosensors, drug delivery, and tissue engineering. Anticipated large-scale productions for numerous diversified applications of CNTs might adversely affect the environment and human health. For successful applications in the biomedical field, the issue of interfacing between CNTs and mammalian cells in vitro needs to be addressed before in vivo studies can be carried out systematically. We review the important studies pertaining to the internalization of CNTs into the cells and the culturing of cells on the CNT-based scaffold or support materials. The review will focus on the description of a variety of factors affecting CNT cytotoxicity: type of CNTs, impurities, lengths of CNTs, aspect ratios, dispersion, chemical modification, and assaying methods of cytotoxicity.


title: GENETAG: a tagged corpus for gene/protein named entity recognition

abstract: Named entity recognition (NER) is an important first step for text mining the biomedical literature. Evaluating the performance of biomedical NER systems is impossible without a standardized test corpus. The annotation of such a corpus for gene/protein name NER is a difficult process due to the complexity of gene/protein names. We describe the construction and annotation of GENETAG, a corpus of 20K MEDLINE® sentences for gene/protein NER. 15K GENETAG sentences were used for the BioCreAtIvE Task 1A Competition. To ensure heterogeneity of the corpus, MEDLINE sentences were first scored for term similarity to documents with known gene names, and 10K high- and 10K low-scoring sentences were chosen at random. The original 20K sentences were run through a gene/protein name tagger, and the results were modified manually to reflect a wide definition of gene/protein names subject to a specificity constraint, a rule that required the tagged entities to refer to specific entities. Each sentence in GENETAG was annotated with acceptable alternatives to the gene/protein names it contained, allowing for partial matching with semantic constraints. Semantic constraints are rules requiring the tagged entity to contain its true meaning in the sentence context. Application of these constraints results in a more meaningful measure of the performance of an NER system than unrestricted partial matching. The annotation of GENETAG required intricate manual judgments by annotators which hindered tagging consistency. The data were pre-segmented into words, to provide indices supporting comparison of system responses to the "gold standard". However, character-based indices would have been more robust than word-based indices. GENETAG Train, Test and Round1 data and ancillary programs are freely available at ftp://ftp.ncbi.nlm.nih.gov/pub/tanabe/GENETAG.tar.gz ::: . A newer version of GENETAG-05, will be released later this year.


title: Hierarchical Variational Models

abstract: Black box variational inference allows researchers to easily prototype and evaluate an array of models. Recent advances allow such algorithms to scale to high dimensions. However, a central question remains: How to specify an expressive variational distribution that maintains efficient computation? To address this, we develop hierarchical variational models (HVMs). HVMs augment a variational approximation with a prior on its parameters, which allows it to capture complex structure for both discrete and continuous latent variables. The algorithm we develop is black box, can be used for any HVM, and has the same computational efficiency as the original approximation. We study HVMs on a variety of deep discrete latent variable models. HVMs generalize other expressive variational distributions and maintains higher fidelity to the posterior.


title: The Ca2+-extruding ATPase of the human platelet creates and responds to cytoplasmic pH changes, consistent with a 2 Ca2+/nH+ exchange mechanism

abstract: The Ca(2+)-extruding ATPase pump of the human platelet was studied in situ by measuring Ca2+ extrusion from quin2-overloaded platelets (Johansson, J.S., Haynes, D.H. 1988. J. Membrane Biol. 104:147-163). Cytoplasmic pH (pHcyt) was measured by BCECF fluorescence in parallel experiments. The pump was studied by raising the cytoplasmic free Ca2+ to 2.5 microM and monitoring active Ca2+ extrusion into a Ca(2+)-free medium. The pump was shown to perturb pHcyt, to not respond to changes in membrane potential and to respond to imposed changes in pHcyt in a manner consistent with the Ca2+ pump acting as a 2 Ca2+/nH+ exchanger. (i) Raising the external pH (pHext) from 7.40 to 7.60 lowers the Vmax of the pump in basal condition (Vmax,1) from 110 +/- 18 to 73 +/- 12 microM/min (= mumol/liter cell volume/min). (ii) Lowering pHext to 7.13 raised Vmax,1 to 150 +/- 15 microM/min. (iii) In an N-methyl-D-glucamine (NMDG+) medium, the pump operation against high [Ca2+]cyt acidifies the cytoplasm by -0.36 +/- 0.10 pH units, and the pump becomes self-inhibited. (iv) Use of nigericin to drive pHcyt down to 6.23 reduces the Vmax,1 to 18 +/- 11 microM/min. (v) Alkalinization of the cytoplasm by monensin in the presence of Na+ raises the Vmax,1 (basal state with Km,1 = 80 nM) to 136 +/- 24 microM/min, but also activates the pump fourfold (Vmax,2 = 280 +/- 28 microM/min; Km,2 = 502 +/- 36 nM). (vi) Transient elevation of pHcyt by NH4Cl at high [Ca2+]cyt activates the pump eightfold (Vmax,2 > or = 671 +/- 350 microM/min). The large activation by alkaline pHcyt at high [Ca2+]cyt can be explained by Ca(2+)-calmodulin activation of the pump (Valant, P.A., Adjei, P.N., Haynes, D.H. 1992. J. Membrane Biol. 130:63-82) and by increased Ca2+ affinity of calmodulin at high pH.


title: An approach to system modes and dynamics of the evolving Space Station Freedom

abstract: Abstract A relatively general formulation for studying the dynamics of an arbitrary spacecraft with interconnected flexible bodies is developed accounting for transient system inertias, shift in the center of mass, shear deformations, rotary inertias and geometric nonlinearities. The computer implementation has been carried out through symbolic manipulation of the equations of motion. Versatility of the formulation is illustrated through simulation of the dynamics of one of the evolving Space Station configurations.


title: Distilling the neural correlates of consciousness

abstract: Abstract Solving the problem of consciousness remains one of the biggest challenges in modern science. One key step towards understanding consciousness is to empirically narrow down neural processes associated with the subjective experience of a particular content. To unravel these neural correlates of consciousness (NCC) a common scientific strategy is to compare perceptual conditions in which consciousness of a particular content is present with those in which it is absent, and to determine differences in measures of brain activity (the so called “contrastive analysis”). However, this comparison appears not to reveal exclusively the NCC, as the NCC proper can be confounded with prerequisites for and consequences of conscious processing of the particular content. This implies that previous results cannot be unequivocally interpreted as reflecting the neural correlates of conscious experience. Here we review evidence supporting this conjecture and suggest experimental strategies to untangle the NCC from the prerequisites and consequences of conscious experience in order to further develop the otherwise valid and valuable contrastive methodology.


title: Motility at the Origin of Life: Its Characterization and a Model

abstract: Due to recent advances in synthetic biology and artificial life, the origin of life is currently a hot topic of research. We review the literature and argue that the two traditionally competing replicator-first and metabolism-first approaches are merging into one integrated theory of individuation and evolution. We contribute to the maturation of this more inclusive approach by highlighting some problematic assumptions that still lead to an ximpoverished conception of the phenomenon of life. In particular, we argue that the new consensus has so far failed to consider the relevance of intermediate time scales. We propose that an adequate theory of life must account for the fact that all living beings are situated in at least four distinct time scales, which are typically associated with metabolism, motility, development, and evolution. In this view, self-movement, adaptive behavior, and morphological changes could have already been present at the origin of life. In order to illustrate this possibility, we analyze a minimal model of lifelike phenomena, namely, of precarious, individuated, dissipative structures that can be found in simple reaction-diffusion systems. Based on our analysis, we suggest that processes on intermediate time scales could have already been operative in prebiotic systems. They may have facilitated and constrained changes occurring in the faster-and slower-paced time scales of chemical self-individuation and evolution by natural selection, respectively.


title: Boosted Web Named Entity Recognition via Tri-Training

abstract: Named entity extraction is a fundamental task for many natural language processing applications on the web. Existing studies rely on annotated training data, which is quite expensive to obtain large datasets, limiting the effectiveness of recognition. In this research, we propose a semisupervised learning approach for web named entity recognition (NER) model construction via automatic labeling and tri-training. The former utilizes structured resources containing known named entities for automatic labeling, while the latter makes use of unlabeled examples to improve the extraction performance. Since this automatically labeled training data may contain noise, a self-testing procedure is used as a follow-up to remove low-confidence annotation and prepare higher-quality training data. Furthermore, we modify tri-training for sequence labeling and derive a proper initialization for large dataset training to improve entity recognition. Finally, we apply this semisupervised learning framework for person name recognition, business organization name recognition, and location name extraction. In the task of Chinese NER, an F-measure of 0.911, 0.849, and 0.845 can be achieved, for person, business organization, and location NER, respectively. The same framework is also applied for English and Japanese business organization name recognition and obtains models with performance of a 0.832 and 0.803 F-measure.


title: Similarities and differences of e-commerce and e-government: Insights from a pilot study

abstract: So far, empirically grounded studies, which compare the phenomena of e-commerce and e-government, have been in short supply. However, such studies it has been argued would most likely deepen the understanding of the sector-specific similarities and differences leading to potential cross-fertilization between the two sectors as well as to the establishment of performance measures and success criteria. This paper reports on the findings of an empirical research pilot, which is the first in a series of planned exploratory and theory-testing studies on the subject


title: A Tree Search Algorithm for Sequence Labeling

abstract: In this paper we propose a novel reinforcement learning based model for sequence tagging, referred to as MM-Tag. Inspired by the success and methodology of the AlphaGo Zero, MM-Tag formalizes the problem of sequence tagging with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP) model, in which the time steps correspond to the positions of words in a sentence from left to right, and each action corresponds to assign a tag to a word. Two long short-term memory networks (LSTM) are used to summarize the past tag assignments and words in the sentence. Based on the outputs of LSTMs, the policy for guiding the tag assignment and the value for predicting the whole tagging accuracy of the whole sentence are produced. The policy and value are then strengthened with MCTS, which takes the produced raw policy and value as inputs, simulates and evaluates the possible tag assignments at the subsequent positions, and outputs a better search policy for assigning tags. A reinforcement learning algorithm is proposed to train the model parameters. Our work is the first to apply the MCTS enhanced MDP model to the sequence tagging task. We show that MM-Tag can accurately predict the tags thanks to the exploratory decision making mechanism introduced by MCTS. Experimental results show based on a chunking benchmark showed that MM-Tag outperformed the state-of-the-art sequence tagging baselines including CRF and CRF with LSTM.


title: Low-Power Wireless ECG Acquisition and Classification System for Body Sensor Networks

abstract: A low-power biosignal acquisition and classification system for body sensor networks is proposed. The proposed system consists of three main parts: 1) a high-pass sigma delta modulator-based biosignal processor (BSP) for signal acquisition and digitization, 2) a low-power, super-regenerative on–off keying transceiver for short-range wireless transmission, and 3) a digital signal processor (DSP) for electrocardiogram (ECG) classification. The BSP and transmitter circuits, which are the body-end circuits, can be operated for over 80 days using two 605 mAH zinc–air batteries as the power supply; the power consumption is 586.5 μW. As for the radio frequency receiver and DSP, which are the receiving-end circuits that can be integrated in smartphones or personal computers, power consumption is less than 1 mW. With a wavelet transform-based digital signal processing circuit and a diagnosis control by cardiologists, the accuracy of beat detection and ECG classification are close to 99.44% and 97.25%, respectively. All chips are fabricated in TSMC 0.18-μm standard CMOS process.


title: Utilizing Real-World Transportation Data for Accurate Traffic Prediction

abstract: For the first time, real-time high-fidelity spatiotemporal data on transportation networks of major cities have become available. This gold mine of data can be utilized to learn about traffic behavior at different times and locations, potentially resulting in major savings in time and fuel, the two important commodities of 21st century. As a first step towards the utilization of this data, in this paper, we study the real-world data collected from Los Angeles County transportation network in order to incorporate the data's intrinsic behavior into a time-series mining technique to enhance its accuracy for traffic prediction. In particular, we utilized the spatiotemporal behaviors of rush hours and events to perform a more accurate prediction of both short-term and long-term average speed on road-segments, even in the presence of infrequent events (e.g., accidents). Our result shows that taking historical rush-hour behavior we can improve the accuracy of traditional predictors by up to 67% and 78% in short-term and long-term predictions, respectively. Moreover, we can incorporate the impact of an accident to improve the prediction accuracy by up to 91%.


title: Mechanical Sensorless Drives of IPMSM With Online Parameter Identification

abstract: The mechanical sensorless drive system for an interior permanent magnet synchronous motor (IPMSM), for which parameters including the inverter are identified, is proposed in this paper. The rotor position is estimated by a signal injection sensorless scheme at standstill. The resistance, including the on-resistance of the insulated gate bipolar transistor (IGBT), the voltage error caused by the dead time of the inverter, and the d-axis and q-axis inductances are identified. After the motor starts by the signal injection sensorless control, the sensorless scheme changes to a scheme based on the extended electromotive force (EMF) estimation, which uses the identified parameters. The magnet flux linkage is also identified for the sensorless operation. The effectiveness of the proposed method is verified by several experimental results.


title: Qualitative Descriptive Methods in Health Science Research

abstract: OBJECTIVE ::: The purpose of this methodology paper is to describe an approach to qualitative design known as qualitative descriptive that is well suited to junior health sciences researchers because it can be used with a variety of theoretical approaches, sampling techniques, and data collection strategies. ::: ::: ::: BACKGROUND ::: It is often difficult for junior qualitative researchers to pull together the tools and resources they need to embark on a high-quality qualitative research study and to manage the volumes of data they collect during qualitative studies. This paper seeks to pull together much needed resources and provide an overview of methods. ::: ::: ::: METHODS ::: A step-by-step guide to planning a qualitative descriptive study and analyzing the data is provided, utilizing exemplars from the authors' research. ::: ::: ::: RESULTS ::: This paper presents steps to conducting a qualitative descriptive study under the following headings: describing the qualitative descriptive approach, designing a qualitative descriptive study, steps to data analysis, and ensuring rigor of findings. ::: ::: ::: CONCLUSIONS ::: The qualitative descriptive approach results in a summary in everyday, factual language that facilitates understanding of a selected phenomenon across disciplines of health science researchers.


title: An unsupervised self-organizing learning with support vector ranking for imbalanced datasets

abstract: The aim of computational learning algorithm is to establish grounds that work for any types of data, once and for all. However, majority of the classifiers have their base from balanced datasets. This paper discusses the issues related to imbalanced data distribution problem and the common strategy to deal with imbalance datasets. We propose a model capable of handling imbalance datasets well in which other typical classifiers fail to do so. The model adopted a derivation of support vector machines in selecting variables so that the problem of imbalanced data distribution can be relaxed. Then, we used an Emergent Self-Organizing Map (ESOM) to cluster the ranker features so as to provide clusters for unsupervised classification. This work progresses by examining the efficiency of the model in evaluating imbalanced datasets. Experimental results show that the criterion based on weight vector derivative achieves good results and performs consistently well over imbalance datasets. In general, our approach outperforms other classification methods which are unable to handle the imbalanced data distribution in the testing datasets.


title: 10 Steps and Some Tricks to Set up Neural Reinforcement Controllers

abstract: The paper discusses the steps necessary to set up a neural reinforcement controller for successfully solving typical (real world) control tasks. The major intention is to provide a code of practice of crucial steps that show how to transform control task requirements into the specification of a reinforcement learning task. Thereby, we do not necessarily claim that the way we propose is the only one (this would require a lot of empirical work, which is beyond the scope of the paper), but wherever possible we try to provide insights why we do it the one way or the other. Our procedure of setting up a neural reinforcement learning system worked well for a large range of real, realistic or benchmark-style control applications.


title: Analysis and optimization based on reusable knowledge base of process performance models

abstract: In this paper, we propose an architectural design and software framework for fast development of descriptive, diagnostic, predictive, and prescriptive analytics solutions for dynamic production processes. The proposed architecture and framework will support the storage of modular, extensible, and reusable knowledge base (KB) of process performance models. The approach requires developing automated methods that can translate the high-level models in the reusable KB into low-level specialized models required by a variety of underlying analysis tools, including data manipulation, optimization, statistical learning, estimation, and simulation. We also propose an organization and key structure for the reusable KB, composed of atomic and composite process performance models and domain-specific dashboards. Furthermore, we illustrate the use of the proposed architecture and framework by prototyping a decision support system for process engineers. The decision support system allows users to hierarchically compose and optimize dynamic production processes via a graphical user interface.


title: Recommendation with the Right Slice: Speeding Up Collaborative Filtering with Factorization Machines

abstract: We propose an alternative way to efficiently exploit rating data for collaborative filtering with Factorization Machines (FMs). Our approach partitions user-item matrix into ‘slices’ which are mutually exclusive with respect to items. The training phase makes direct use of the slice of interest (target slice), while incorporating information from other slices indirectly. FMs represent user-item interactions as feature vectors, and they offer the advantage of easy incorporation of complementary information. We exploit this advantage to integrate information from other auxiliary slices. We demonstrate, using experiments on two benchmark datasets, that improved performance can be achieved, while the time complexity of training can be reduced significantly.


title: Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications

abstract: The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an active and growing research area and it is now beginning to be applied to many new problems. The rationale behind this fact is that many pattern recognition problems are by nature ranking problems. The main objective of a ranking algorithm is to sort objects according to some criteria, so that, the most relevant items will appear early in the produced result list. Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank. The former aims at studying methods and techniques to sort objects for improving the accuracy of a machine learning model. Enhancing a model performance can be challenging at times. For example, in pattern classification tasks, different data representations can complicate and hide the different explanatory factors of variation behind the data. In particular, hand-crafted features contain many cues that are either redundant or irrelevant, which turn out to reduce the overall accuracy of the classifier. In such a case feature selection is used, that, by producing ranked lists of features, helps to filter out the unwanted information. Moreover, in real-time systems (e.g., visual trackers) ranking approaches are used as optimization procedures which improve the robustness of the system that deals with the high variability of the image streams that change over time. The other way around, learning to rank is necessary in the construction of ranking models for information retrieval, biometric authentication, re-identification, and recommender systems. In this context, the ranking model's purpose is to sort objects according to their degrees of relevance, importance, or preference as defined in the specific application.


title: Dielectric Resonator Integrated high gain SIW antenna array

abstract: In the given paper a Dielectric Resonator Integrated Substrate Integrated Waveguide (SIW) antenna array is proposed for enhanced performance. Two cylindrical structures are mounted on the antenna to form a dielectric resonator, along with periodic coaxial via and resonating slots in the top surface which resulted in an enhanced gain of about 13.6dB. The different antenna parameters are analyzed and thus it can be concluded that the proposed dielectric resonator integrated SIW antenna array can be suitably used for wireless applications.


title: Surgical reduction of hypertrophy of the labia minora

abstract: Abstract Objective To evaluate postoperative satisfaction, including improvements in quality of life, among patients who had undergone labia minora reduction. Methods In a retrospective descriptive study, women who had undergone labiaplasty for hypertrophy of the labia minora between January 2005 and December 2009 were contacted by telephone. The women were asked to answer a satisfaction survey evaluating the esthetic and functional results of the surgery. Results Among 21 patients who had labiaplasty using either a pedicle flap reconstruction (n = 18, 86%) or a nymphectomy technique (n = 3, 14%), 18 (86%) were contacted successfully and took part in the survey. All of the contacted patients were satisfied with the overall outcome of the surgery, with a mean rating of 8.7 out of 10 at a median follow-up of 17 months (interquartile range 6–25 months). From an esthetic point of view, all 18 patients reported an improvement in comfort with respect to any form of original functional discomfort. Concerning sexuality, 95% of the sexually active patients had originally described some form of physical or psychologic discomfort, and all of them reported total disappearance of this symptomatology. Conclusion Labiaplasty provided overall satisfaction in terms of improvements in patient quality of life and sexuality.


title: Network densification: the dominant theme for wireless evolution into 5G

abstract: This article explores network densification as the key mechanism for wireless evolution over the next decade. Network densification includes densification over space (e.g, dense deployment of small cells) and frequency (utilizing larger portions of radio spectrum in diverse bands). Large-scale cost-effective spatial densification is facilitated by self-organizing networks and intercell interference management. Full benefits of network densification can be realized only if it is complemented by backhaul densification, and advanced receivers capable of interference cancellation.


High-quality Voice Conversion Using Spectrogram-Based WaveNet Vocoder


Character-level Language Modeling with Gated Hierarchical Recurrent Neural Networks


title: Knowledge base completion by learning pairwise-interaction differentiated embeddings

abstract: A knowledge base of triples like (subject entity, predicate relation,object entity) is a very important resource for knowledge management. It is very useful for human-like reasoning, query expansion, question answering (Siri) and other related AI tasks. However, such a knowledge base often suffers from incompleteness due to a large volume of increasing knowledge in the real world and a lack of reasoning capability. In this paper, we propose a Pairwise-interaction Differentiated Embeddings model to embed entities and relations in the knowledge base to low dimensional vector representations and then predict the possible truth of additional facts to extend the knowledge base. In addition, we present a probability-based objective function to improve the model optimization. Finally, we evaluate the model by considering the problem of computing how likely the additional triple is true for the task of knowledge base completion. Experiments on WordNet and Freebase show the excellent performance of our model and algorithm.


title: Video object segmentation by Multi-Scale Pyramidal Multi-Dimensional LSTM with generated depth context

abstract: Existing deep neural networks, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), typically treat volumetric video data as several single images and deal with one frame at one time, thus the relevance to frames can hardly be fully exploited. Besides, depth context plays the unique role in motion scenes for primates, but is seldom used in no depth label situations. In this paper, we use a more suitable architecture Multi-Scale Pyramidal Multi-Dimensional Long Short Term Memory (MSPMD-LSTM) to reveal the strong relevance within video frames. Furthermore, depth context is extracted and refined to enhance the performance of the model. Experiments demonstrate that our models yield competitive results on Youtube-Objects dataset and Segtrack v2 dataset.


title: Efficient, generalized indoor WiFi GraphSLAM

abstract: The widespread deployment of wireless networks presents an opportunity for localization and mapping using only signal-strength measurements. The current state of the art is to use Gaussian process latent variable models (GP-LVM). This method works well, but relies on a signature uniqueness assumption which limits its applicability to only signal-rich environments. Moreover, it does not scale computationally to large sets of data, requiring O (N3) operations per iteration. We present a GraphSLAM-like algorithm for signal strength SLAM. Our algorithm shares many of the benefits of Gaussian processes, yet is viable for a broader range of environments since it makes no signature uniqueness assumptions. It is also more tractable to larger map sizes, requiring O (N2) operations per iteration. We compare our algorithm to a laser-SLAM ground truth, showing it produces excellent results in practice.