title: Smartphone pedestrian navigation by foot-IMU sensor fusion

abstract: Determining one's own position by means of a smartphone is an important issue for various applications in the fields of personal navigation or location-based services. Places like large airports, shopping malls or extensive underground parking lots require personal navigation but satellite signals and GPS connection cannot be obtained. Thus, alternative or complementary systems are needed. In this paper a system concept to integrate a foot-mounted inertial measurement unit (IMU) with an Android smartphone is presented. We developed a prototype to demonstrate and evaluate the implementation of pedestrian strapdown navigation on a smartphone. In addition to many other approaches we also fuse height measurements from a barometric sensor in order to stabilize height estimation over time. A very low-cost single-chip IMU is used to demonstrate applicability of the outlined system concept for potential commercial applications. In an experimental study we compare the achievable accuracy with a commercially available IMU. The evaluation shows very competitive results on the order of a few percent of traveled distance. Comparing performance, cost and size of the presented IMU the outlined approach carries an enormous potential in the field of indoor pedestrian navigation.


title: Robotics library: An object-oriented approach to robot applications

abstract: We discuss the architecture and software engineering principles of the Robotics Library (RL). Driven by requirements of robot systems, research projects, industrial applications, and education, we identify relevant design requirements and present an approach to manage hardware and real-time, provide a user-friendly, object-oriented interface to powerful kinematics and dynamics calculations, and support various platforms. After over ten years of development that started in 2004 and evaluating many variants of the architecture, we discuss the design choices for the components of the library in its current version.


title: HotFlip: White-Box Adversarial Examples for NLP

abstract: Adversarial examples expose vulnerabilities of machine learning models. We propose an efficient method to generate white-box adversarial examples that trick character-level and word-level neural models. Our method, HotFlip, relies on an atomic flip operation, which swaps one token for another, based on the gradients of the one-hot input vectors. In experiments on text classification and machine translation, we find that only a few manipulations are needed to greatly increase the error rates. We analyze the properties of these examples, and show that employing these adversarial examples in training can improve test-time accuracy on clean examples, as well as defend the models against adversarial examples.


title: Boosted key-frame selection and correlated pyramidal motion-feature representation for human action recognition

abstract: In this paper we propose a novel method for human action recognition based on boosted key-frame selection and correlated pyramidal motion feature representations. Instead of using an unsupervised method to detect interest points, a Pyramidal Motion Feature (PMF), which combines optical flow with a biologically inspired feature, is extracted from each frame of a video sequence. The AdaBoost learning algorithm is then applied to select the most discriminative frames from a large feature pool. In this way, we obtain the top-ranked boosted frames of each video sequence as the key frames which carry the most representative motion information. Furthermore, we utilise the correlogram which focuses not only on probabilistic distributions within one frame but also on the temporal relationships of the action sequence. In the classification phase, a Support-Vector Machine (SVM) is adopted as the final classifier for human action recognition. To demonstrate generalizability, our method has been systematically tested on a variety of datasets and shown to be more effective and accurate for action recognition compared to the previous work. We obtain overall accuracies of: 95.5%, 93.7%, and 36.5% with our proposed method on the KTH, the multiview IXMAS and the challenging HMDB51 datasets, respectively.


title: LC3, a mammalian homologue of yeast Apg8p, is localized in autophagosome membranes after processing

abstract: Little is known about the protein constituents of autophagosome membranes in mammalian cells. Here we demonstrate that the rat microtubule‐associated protein 1 light chain 3 (LC3), a homologue of Apg8p essential for autophagy in yeast, is associated to the autophagosome membranes after processing. Two forms of LC3, called LC3‐I and ‐II, were produced post‐translationally in various cells. LC3‐I is cytosolic, whereas LC3‐II is membrane bound. The autophagic vacuole fraction prepared from starved rat liver was enriched with LC3‐II. Immunoelectron microscopy on LC3 revealed specific labelling of autophagosome membranes in addition to the cytoplasmic labelling. LC3‐II was present both inside and outside of autophagosomes. Mutational analyses suggest that LC3‐I is formed by the removal of the C‐terminal 22 amino acids from newly synthesized LC3, followed by the conversion of a fraction of LC3‐I into LC3‐II. The amount of LC3‐II is correlated with the extent of autophagosome formation. LC3‐II is the first mammalian protein identified that specifically associates with autophagosome membranes.


title: An analysis of the user occupational class through Twitter content

abstract: Social media content can be used as a complementary source to the traditional ::: methods for extracting and studying collective social attributes. This study focuses on the prediction of the occupational class for a public user profile. Our analysis is conducted on a new annotated corpus of Twitter users, their respective job titles, posted textual content and platform-related attributes. We frame our task as classification using latent feature representations such as word clusters and embeddings. The employed linear and, especially, non-linear methods can predict a user’s occupational class with strong accuracy for the coarsest level of a standard occupation taxonomy which includes nine classes. Combined with a qualitative assessment, the derived results confirm the feasibility of our approach in inferring a new user attribute that can be embedded in a multitude of downstream applications.


title: Hybrid recreation by reverse breeding in Arabidopsis thaliana

abstract: Hybrid crop varieties are traditionally produced by selecting and crossing parental lines to evaluate hybrid performance. Reverse breeding allows doing the opposite: selecting uncharacterized heterozygotes and generating parental lines from them. With these, the selected heterozygotes can be recreated as F1 hybrids, greatly increasing the number of hybrids that can be screened in breeding programs. Key to reverse breeding is the suppression of meiotic crossovers in a hybrid plant to ensure the transmission of nonrecombinant chromosomes to haploid gametes. These gametes are subsequently regenerated as doubled-haploid (DH) offspring. Each DH carries combinations of its parental chromosomes, and complementing pairs can be crossed to reconstitute the initial hybrid. Achiasmatic meiosis and haploid generation result in uncommon phenotypes among offspring owing to chromosome number variation. We describe how these features can be dealt with during a reverse-breeding experiment, which can be completed in six generations (∼1 year).


title: A Vision-Based Formation Control Framework

abstract: We describe a framework for cooperative control of a group of nonholonomic mobile robots that allows us to build complex systems from simple controllers and estimators. The resultant modular approach is attractive because of the potential for reusability. Our approach to composition also guarantees stability and convergence in a wide range of tasks. There are two key features in our approach: 1) a paradigm for switching between simple decentralized controllers that allows for changes in formation; 2) the use of information from a single type of sensor, an omnidirectional camera, for all our controllers. We describe estimators that abstract the sensory information at different levels, enabling both decentralized and centralized cooperative control. Our results include numerical simulations and experiments using a testbed consisting of three nonholonomic robots.


title: InvisibleEye: Mobile Eye Tracking Using Multiple Low-Resolution Cameras and Learning-Based Gaze Estimation

abstract: Analysis of everyday human gaze behaviour has significant potential for ubiquitous computing, as evidenced by a large body of work in gaze-based human-computer interaction, attentive user interfaces, and eye-based user modelling. However, current mobile eye trackers are still obtrusive, which not only makes them uncomfortable to wear and socially unacceptable in daily life, but also prevents them from being widely adopted in the social and behavioural sciences. To address these challenges we present InvisibleEye, a novel approach for mobile eye tracking that uses millimetre-size RGB cameras that can be fully embedded into normal glasses frames. To compensate for the cameras’ low image resolution of only a few pixels, our approach uses multiple cameras to capture different views of the eye, as well as learning-based gaze estimation to directly regress from eye images to gaze directions. We prototypically implement our system and characterise its performance on three large-scale, increasingly realistic, and thus challenging datasets: 1) eye images synthesised using a recent computer graphics eye region model, 2) real eye images recorded of 17 participants under controlled lighting, and 3) eye images recorded of four participants over the course of four recording sessions in a mobile setting. We show that InvisibleEye achieves a top person-specific gaze estimation accuracy of 1.79° using four cameras with a resolution of only 5 × 5 pixels. Our evaluations not only demonstrate the feasibility of this novel approach but, more importantly, underline its significant potential for finally realising the vision of invisible mobile eye tracking and pervasive attentive user interfaces.


Deep Analysis of Binary Code to Recover Program Structure


title: Social Network Approach to Anomaly Detection in Network Systems

abstract: The problem of the network security is taken up since eighties (Denning et al., 1987) and is developed up today (Beltilch et al., 2004, Bera, 2010, Dasgupta, 1999, Basile, 2007, Wilson, 1999). A major problem of automatic intrusion detection is that, it is difficult to make a difference between normal and abnormal user behaviour. Intrusion detection system should not only recognise the previously known patterns of attacks, but also react in case of appearance of the new events that violate the network security policy. The distributed nature of the task of the network security monitoring requires applying of the distributed tools for network security maintaining. The most important postulate addressed to the intrusion detection systems is that, such systems should automatically react in case of detecting the security policy breach to prevent the attack execution or to reduce the potential loss in the network systems. Intrusion detection systems should be equipped with the components responsible for the permanent observation of the states of monitored nodes and components that integrate the results of these observations and diagnose the security level of the system (Kolaczek et al., 2005, Nguyen et al., 2006). A comprehensive survey of anomaly detection systems is presented in (Patcha & Park, 2007) and a comparison of different approaches to intrusion detection systems is given in (Bejtlich, 2004). One of the first agent systems for network security monitoring has been proposed in works (Balasubramaniyet et al., 1998, Spafford & Zamboni, 2000). In work (Kolaczek et al., 2005) a framework of an original proposal of the intrusion detection system based on the multi-agent approach was presented. In particular, the architecture of such a system and the task of agents were specified. Proposed ideas were further developed and in work (Nguyen et al., 2006) the problem of anomalies detection on the basis of the nodes traffic analysis was discussed. The proposal of the method for Denial of Service Attack detection was given in (Prusiewicz, 2008a). In this work we propose a novel framework of a multi-agent system for anomaly detection. The originality of our solution consists of applying the social network approach to Man in the Middle Attack (MITM) detection in a network system. Our proposal is based on the social networks discovery and their characteristics measurement to detect anomalies in network traffic. We assume that network communication between nodes constitutes social network of users and their applications, so the appropriate methods of social network formal analysis can be applied. The other important assumption is that values of these social


title: Constrained Convolutional Neural Networks for Weakly Supervised Segmentation

abstract: We present an approach to learn a dense pixel-wise labeling from image-level tags. Each image-level tag imposes constraints on the output labeling of a Convolutional Neural Network (CNN) classifier. We propose Constrained CNN (CCNN), a method which uses a novel loss function to optimize for any set of linear constraints on the output space (i.e. predicted label distribution) of a CNN. Our loss formulation is easy to optimize and can be incorporated directly into standard stochastic gradient descent optimization. The key idea is to phrase the training objective as a biconvex optimization for linear models, which we then relax to nonlinear deep networks. Extensive experiments demonstrate the generality of our new learning framework. The constrained loss yields state-of-the-art results on weakly supervised semantic image segmentation. We further demonstrate that adding slightly more supervision can greatly improve the performance of the learning algorithm.


title: Print and publishing and the future of Augmented Reality

abstract: For Augmented Reality to reach beyond novelty (the simple “wow” factor) and become utilitarian and enjoyable regardless of where the user works or plays, there will need to be: • a nearly infinite amount of content, just as there is today on other digital platforms (e.g., the Web), • an easy system for the owners of content to build or find complementary digital data and have their content be viewed and associated with additional digital information in AR-assisted applications, and • many options for end users to reach the content and experience the AR enhancements.


title: Discrete Signal Processing on Graphs: Sampling Theory

abstract: We propose a sampling theory for signals that are supported on either directed or undirected graphs. The theory follows the same paradigm as classical sampling theory. We show that the perfect recovery is possible for graph signals bandlimited under the graph Fourier transform, and the sampled signal coefficients form a new graph signal, whose corresponding graph structure is constructed from the original graph structure, preserving frequency contents. By imposing a specific structure on the graph, graph signals reduce to finite discrete-time signals and the proposed sampling theory works reduces to classical signal processing. We further establish the connection to frames with maximal robustness to erasures as well as compressed sensing, and show how to choose the optimal sampling operator, how random sampling works on circulant graphs and Erd\H{o}s-R\'enyi graphs, and how to handle full-band graph signals by using graph filter banks. We validate the proposed sampling theory on the simulated datasets of Erd\H{o}s-R\'enyi graphs and small-world graphs, and a real-world dataset of online blogs. We show that for each case, the proposed sampling theory achieves perfect recovery with high probability. Finally, we apply the proposed sampling theory to semi-supervised classification of online blogs and digit images, where we achieve similar or better performance with fewer labeled samples compared to the previous work.


title: Surface charge affects cellular uptake and intracellular trafficking of chitosan-based nanoparticles.

abstract: Chitosan-based nanoparticles (NPs) are widely used in drug delivery, device-based therapy, tissue engineering, and medical imaging. In this aspect, a clear understanding of how physicochemical properties of these NPs affect the cytological response is in high demand. The objective of this study is to evaluate the effect of surface charge on cellular uptake profiles (rate and amount) and intracellular trafficking. We fabricate three kinds of NPs (∼ 215 nm) with different surface charge via SPG membrane emulsification technique and deposition method. They possess uniform size as well as identical other physicochemical properties, minimizing any differences between the NPs except for surface charge. Moreover, we extend our research to eight cell lines, which could help to obtain a representative conclusion. Results show that the cellular uptake rate and amount are both positively correlated with the surface charge in all cell line. Subsequent intracellular trafficking indicates that some of positively charged NPs could escape from lysosome after being internalized and exhibit perinuclear localization, whereas the negatively and neutrally charged NPs prefer to colocalize with lysosome. These results are critical in building the knowledge base required to design chitosan-based NPs to be used efficiently and specifically.


title: 3-D radar imaging using extended 2-D range migration technique

abstract: A three dimensional (3-D) imaging system is implemented by employing 2-D range migration algorithm (RMA) for frequency modulated continuous wave synthetic aperture radar (FMCW-SAR). The backscattered data of a 1-D synthetic aperture at specific altitudes are coherently integrated to form 2-D images. These 2-D images at different altitudes are stitched vertically to form a 3-D image. Numerical simulation for near-field scenario are also presented to validate the proposed algorithm.


COMMON STRUCTURES OF ASSET-BACKED SECURITIES AND THEIR RISKS


title: Context-Aware Pedestrian Motion Prediction In Urban Intersections

abstract: This paper presents a novel context-based approach for pedestrian motion prediction in crowded, urban intersections, with the additional flexibility of prediction in similar, but new, environments. Previously, Chen et. al. combined Markovian-based and clustering-based approaches to learn motion primitives in a grid-based world and subsequently predict pedestrian trajectories by modeling the transition between learned primitives as a Gaussian Process (GP). This work extends that prior approach by incorporating semantic features from the environment (relative distance to curbside and status of pedestrian traffic lights) in the GP formulation for more accurate predictions of pedestrian trajectories over the same timescale. We evaluate the new approach on real-world data collected using one of the vehicles in the MIT Mobility On Demand fleet. The results show 12.5% improvement in prediction accuracy and a 2.65 times reduction in Area Under the Curve (AUC), which is used as a metric to quantify the span of predicted set of trajectories, such that a lower AUC corresponds to a higher level of confidence in the future direction of pedestrian motion.


title: A preliminary investigation into the prevalence and prediction of problematic cell phone use

abstract: Background and aims: Likening mobile phone use dependency to the classification of excessive behaviors may be necessarily equivalent in seriousness to previously established addictions such as problematic computing or excessive gambling. The aim of the study explores into the behavior of excessive use of mobile phones as a pathological behavior. Methods: Two studies investigated criteria for problematic mobile phone usage by examining student (Study 1, N = 301) and nonstudent (Study 2, N = 362) responses to a set of adapted mobile phone addiction inventories. Study 1 investigated cell phone addiction inventories as constructs designed to measure problematic cell phone use. Additionally, Study 2 sought to predict age, depression, extraversion, emotional stability, impulse control, and self-esteem as independent variables that augment respondents’ perceptions of problematic use. Results: The results from Study 1 and Study 2 indicate that 10 to 25% of the participants tested exhibited problematic cell phone usage. Additionally, age, depression, extraversion, and low impulse control are the most suitable predictors for problematic use. Conclusions: The results of the two studies indicate that problematic mobile phone use does occur and ought to be taken seriously by the psychological community. Presently, there is limited data providing conclusive evidence for a comprehensible categorization of cell phone addiction, as well as a unified explanatory model specific to problematic mobile phone use. Studies such as this one may contribute substantial findings, adding scientific significance, and offering a valuable submission for the ongoing progress of creating intervention frameworks relative to “virtual addictions”.


title: Overcoming Shadows in 3-Source Photometric Stereo

abstract: Light occlusions are one of the most significant difficulties of photometric stereo methods. When three or more images are available without occlusion, the local surface orientation is overdetermined so that shape can be computed and the shadowed pixels can be discarded. In this paper, we look at the challenging case when only two images are available without occlusion, leading to a one degree of freedom ambiguity per pixel in the local orientation. We show that, in the presence of noise, integrability alone cannot resolve this ambiguity and reconstruct the geometry in the shadowed regions. As the problem is ill-posed in the presence of noise, we describe two regularization schemes that improve the numerical performance of the algorithm while preserving the data. Finally, the paper describes how this theory applies in the framework of color photometric stereo where one is restricted to only three images and light occlusions are common. Experiments on synthetic and real image sequences are presented.


title: GeoProof: Proofs of Geographic Location for Cloud Computing Environment

abstract: Cloud computing has emerged as a major ICT trend and has been acknowledged as a key theme of industry by prominent ICT organisations. However, one of the major challenges that face the cloud computing concept and its global acceptance is how to secure and protect the data that is the property of the user. The geographic location of cloud data storage centres is an important issue for many organisations and individuals due to the regulations and laws that require data and operations to reside in specific geographic locations. Thus, data owners may need to ensure that their cloud providers do not compromise the SLA contract and move their data into another geographic location. This paper introduces an architecture for a new approach for geographic location assurance, which combines the proof of storage protocol (POS) and the distance-bounding protocol. This allows the client to check where their stored data is located, without relying on the word of the cloud provider. This architecture aims to achieve better security and more flexible geographic assurance within the environment of cloud computing.


title: Real-Time Road Segmentation Using LiDAR Data Processing on an FPGA

abstract: This paper presents the FPGA design of a convolutional neural network (CNN) based road segmentation algorithm for real-time processing of LiDAR data. For autonomous vehicles, it is important to perform road segmentation and obstacle detection such that the drivable region can be identified for path planning. Traditional road segmentation algorithms are mainly based on image data from cameras, which is subjected to the light condition as well as the quality of road markings. LiDAR sensor can obtain the 3D geometry information of the vehicle surroundings with very high accuracy. However, it is a computational challenge to process a large amount of LiDAR data at real-time. In this work, a convolutional neural network model is proposed and trained to perform semantic segmentation using the LiDAR sensor data. Furthermore, an efficient hardware design is implemented on the FPGA that can process each LiDAR scan in 16.9ms, which is much faster than the previous works. Evaluated using KITTI road benchmarks, the proposed solution achieves high accuracy of road segmentation.


title: Basal Cell Carcinoma: Pathogenesis, Epidemiology, Clinical Features, Diagnosis, Histopathology, and Management

abstract: Basal cell carcinoma (BCC) is the most common malignancy. Exposure to sunlight is the most important risk factor. Most, if not all, cases of BCC demonstrate overactive Hedgehog signaling. A variety of treatment modalities exist and are selected based on recurrence risk, importance of tissue preservation, patient preference, and extent of disease. The pathogenesis, epidemiology, clinical features, diagnosis, histopathology, and management of BCC will be discussed in this review.


title: The noble quran Arabic ontology: Domain ontological model and evaluation of human and social relations

abstract: Recently, Arab and Muslim researchers have given a great attention to retrieve information and search about knowledge in the noble Quran. During the past years a lot of websites and applications offered a number of methods to search throughout the noble Quran, some offered a syntactic search, others offered a semantic search and some others offered both. The noble Quran has a special style and metaphorical nature. Furthermore, the Arabic language of the noble Quran has a complicated structure that needs an exceptional extra attention on the issues of searching as well as information retrieval rather than English or any other language. This paper proposes a new ontological modeling that models the human social relations in the noble Quran by employing Web Ontology Language (OWL) as well as Resource Description Framework (RDF). This paper methodology involves a descriptive identification of the human relations related concepts that are described in the Noble Quran with identifying the relations among them. The concept ontological model, in this work, mainly built to support Arabic, ArabEzi (popular chat language) and English languages. As a result, SPARQL queries and DL queries are used in the ontology model to retrieve Quran domains, concepts and Verses in Arabic language. Hence, this work will help in the noble Quran searching and retrieving information.


The Floyd-Warshall Algorithm for Shortest Paths.


title: Interactive visualization of serial periodic data

abstract: Serial periodic data exhibit both serial and periodic properties. For example, time continues forward serially, but weeks, months, and years are periods that recur. While there are extensive visualization techniques for exploring serial data, and a few for exploring periodic data, no existing technique simultaneously displays serial and periodic attributes of a data set. We introduce a spiral visualization technique, which displays data along a spiral to highlight serial attributes along the spiral axis and periodic ones along the radii. We show several applications of the spiral visualization to data exploration tasks, present our implementation, discuss the capacity for data analysis, and present findings of our informal study with users in data-rich scientific domains.


title: Sensitivity analysis of TOPSIS method in water quality assessment: I. Sensitivity to the parameter weights

abstract: Sensitivity analysis is becoming increasingly widespread in many fields of engineering and sciences and has become a necessary step to verify the feasibility and reliability of a model or a method. The sensitivity of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method in water quality assessment mainly includes sensitivity to the parameter weights and sensitivity to the index input data. In the present study, the sensitivity of TOPSIS to the parameter weights was discussed in detail. The present study assumed the original parameter weights to be equal to each other, and then each weight was changed separately to see how the assessment results would be affected. Fourteen schemes were designed to investigate the sensitivity to the variation of each weight. The variation ranges that keep the assessment results unchangeable were also derived theoretically. The results show that the final assessment results will change when the weights increase or decrease by ±20 to ±50 %. The feedback of different samples to the variation of a given weight is different, and the feedback of a given sample to the variation of different weights is also different. The final assessment results can keep relatively stable when a given weight is disturbed as long as the initial variation ratios meet one of the eight derived requirements.


title: The Effects of Mindfulness Meditation Training on Multitasking in a High-Stress Information Environment

abstract: We describe an experiment to determine the effects of meditation training on the multitasking behavior of knowledge workers. Three groups each of 12-15 human resources personnel were tested: (1) those who underwent an 8-week training course on mindfulness-based meditation, (2) those who endured a wait period, were tested, and then underwent the same 8-week training, and (3) those who had 8-weeks of training in body relaxation. We found that only those trained in meditation stayed on tasks longer and made fewer task switches, as well as reporting less negative emotion after task performance, as compared with the other two groups. In addition, both the meditation and the relaxation groups showed improved memory for the tasks they performed.


title: Accurate Word Segmentation and POS Tagging for Japanese Microblogs: Corpus Annotation and Joint Modeling with Lexical Normalization

abstract: Microblogs have recently received widespread interest from NLP researchers. However, current tools for Japanese word segmentation and POS tagging still perform poorly on microblog texts. We developed an annotated corpus and proposed a joint model for overcoming this situation. Our annotated corpus of microblog texts enables not only training of accurate statistical models but also quantitative evaluation of their performance. Our joint model with lexical normalization handles the orthographic diversity of microblog texts. We conducted an experiment to demonstrate that the corpus and model substantially contribute to boosting accuracy.


title: Aerodynamic modeling and practical attitude stabilization of a ducted fan UAV

abstract: In this paper, we address both the aerodynamic modelling of a ducted fan Vertical TakeOff and Landing (VTOL) Unmanned Aerial Vehicle (UAV) and the problem of attitude stabilization when the vehicle is remotely controlled by a human pilot in presence of crosswind. In a first step, we present the main aerodynamic elements, inherent to the presence of the duct, that explain the dynamics of flight of this kind of vehicle. In a second step, we propose an attitude control designed from a linearization of the dynamic model around the hovering flight equilibrium. Experiments led on the HoverEye, a platform designed by Bertin Technologies, show that this attitude control is sufficient to open a large secure flight envelope.


title: Modulated Metasurface Antennas for Space: Synthesis, Analysis and Realizations

abstract: This paper presents design and analysis methods for planar antennas based on modulated metasurfaces (MTSs). These antennas operate on an interaction between a cylindrical surface-wave (SW) excited by an isotropic TM radiator, and an MTS having a spatially modulated equivalent impedance. The MTS is realized by using sub-wavelength patches printed on a grounded slab, thus resulting in a structure with light weight and compact volume. Both features are appealing characteristics for space applications. This paper introduces for the first time an impedance-based amplitude synthesis of the aperture field distribution and shows several new examples of antennas for space applications obtained in recent research projects financed by the European Space Agency.


title: Wave packet methods in cavity QED

abstract: The Jaynes-Cummings model, with and without the rotating wave approximation, is expressed in the conjugate variable representation and solved numerically by wave packet propagation. Both cases are then cast into systems of two coupled harmonic oscillators, reminiscent of coupled bound electronic potential curves of diatomic molecules. Using the knowledge of such models, this approach of the problem gives new insight of the dynamics. The effect of the rotating wave approximation is discussed. The collapse-revival phenomenon is especially analyzed in a non-standard manner. Extensions of the method is briefly mentioned in terms of a three-level atom and the Dicke model.


title: Architectural Control and Value Migration in Layered Ecosystems: The Case of Open-Source Cloud Management Platforms

abstract: Our paper focuses on strategic decision making in layered business ecosystems, highlighting the role of cross-layer interactions in shaping choices about product design and platform governance. Based on evidence from the cloud computing ecosystem, we analyze how concerns about architectural control and expectations regarding future value migration influence the design of product interfaces and the degree of openness to external contributions. We draw on qualitative longitudinal data to trace the development of two open-source platforms for managing cloudbased computing resources. We focus in particular on the emergence of a layered "stack" in which these platforms must compete with both vertically integrated service providers and horizontally focused commercial software vendors. We find that the two platforms adopt distinctly different strategies, which presents an intriguing puzzle since their functionalities are nearly identical. Building on prior theory, we explain these differences in terms of the structure of interdependencies between each platform's lead sponsor and the firms operating in adjacent layers of the ecosystem. While our findings are preliminary due to the ongoing nature of the research, we speculate about their implications for the literatures on modularity and innovation, technology and industry evolution, and strategy in business ecosystems.


title: OpinionFinder: A System For Subjectivity Analysis

abstract: OpinionFinder is a system that performs subjectivity analysis, automatically identifying when opinions, sentiments, speculations, and other private states are present in text. Specifically, OpinionFinder aims to identify subjective sentences and to mark various aspects of the subjectivity in these sentences, including the source (holder) of the subjectivity and words that are included in phrases expressing positive or negative sentiments.


title: Received Signal Strength Database Interpolation by Kriging for a Wi-Fi Indoor Positioning System

abstract: The main approach for a Wi-Fi indoor positioning system is based on the received signal strength (RSS) measurements, and the fingerprinting method is utilized to determine the user position by matching the RSS values with the pre-surveyed RSS database. To build a RSS fingerprint database is essential for an RSS based indoor positioning system, and building such a RSS fingerprint database requires lots of time and effort. As the range of the indoor environment becomes larger, labor is increased. To provide better indoor positioning services and to reduce the labor required for the establishment of the positioning system at the same time, an indoor positioning system with an appropriate spatial interpolation method is needed. In addition, the advantage of the RSS approach is that the signal strength decays as the transmission distance increases, and this signal propagation characteristic is applied to an interpolated database with the Kriging algorithm in this paper. Using the distribution of reference points (RPs) at measured points, the signal propagation model of the Wi-Fi access point (AP) in the building can be built and expressed as a function. The function, as the spatial structure of the environment, can create the RSS database quickly in different indoor environments. Thus, in this paper, a Wi-Fi indoor positioning system based on the Kriging fingerprinting method is developed. As shown in the experiment results, with a 72.2% probability, the error of the extended RSS database with Kriging is less than 3 dBm compared to the surveyed RSS database. Importantly, the positioning error of the developed Wi-Fi indoor positioning system with Kriging is reduced by 17.9% in average than that without Kriging.


title: Social interactions in P2P lending

abstract: Access to capital in the form of credit through money lending requires that the lender to be able to measure the risk of repayment for a given return. In ancient times money lending needed to occur between known parties or required collateral to secure the loan. In the modern era of banking institutions provide loans to individuals who meet a qualification test. Grameen Bank in Bangladesh has demonstrated that small poor communities benefited from the "microcredit" financial innovation, which allowed a priori non-bankable entrepreneurs to engage in self-employment projects. Online P2P (Peer to Peer) lending is considered an evolution of the microcredit concept, and reflects the application of its principles into internet communities. Internet ventures like Prosper.com, Zopa or Lendingclub.com, provide the means for lenders and borrowers to meet, interact and define relationships as part of social groups. This paper measures the influence of social interactions in the risk evaluation of a money request; with special focus on the impact of one-to-one and one-to-many relationships. The results showed that fostering social features increases the chances of getting a loan fully funded, when financial features are not enough to construct a differentiating successful credit request. For this task, a model-based clustering method was applied on actual P2P Lending data provided by Prosper.com.


title: Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility

abstract: Smart grids are capable of two-way communication between individual user devices and the electricity provider, enabling providers to create a control-feedback loop using time-dependent pricing. By charging users more in peak and less in off-peak hours, the provider can induce users to shift their consumption to off-peak periods, thus relieving stress on the power grid and the cost incurred from large peak loads. We formulate the electricity provider's cost minimization problem in setting these prices by considering consumers' device-specific scheduling flexibility and the provider's cost structure of purchasing electricity from an electricity generator. Consumers' willingness to shift their device usage is modeled probabilistically, with parameters that can be estimated from real data. We develop an algorithm for computing day-ahead prices, and another algorithm for estimating and refining user reaction to the prices. Together, these two algorithms allow the provider to dynamically adjust the offered prices based on user behavior. Numerical simulations with data from an Ontario electricity provider show that our pricing algorithm can significantly reduce the cost incurred by the provider.


title: Intelligent distributed surveillance systems: A review

abstract: This survey describes the current state-of-the-art in the development of automated visual surveillance systems so as to provide researchers in the field with a summary of progress achieved to date and to identify areas where further research is needed. The ability to recognise objects and humans, to describe their actions and interactions from information acquired by sensors is essential for automated visual surveillance. The increasing need for intelligent visual surveillance in commercial, law enforcement and military applications makes automated visual surveillance systems one of the main current application domains in computer vision. The emphasis of this review is on discussion of the creation of intelligent distributed automated surveillance systems. The survey concludes with a discussion of possible future directions.


title: Space-Optimized Texture Maps

abstract: A method, system, and computer program product for adjusting a unit image area within an input image according to information importance within the unit area. An image receiver configured to receive the input image. An image warper is coupled to an importance map as is configured to generate a warped image such that regions of higher importance in the input image are expanded in the warped image and regions of lower importance in the input image are compressed in the warped image. The importance map is configured to delineate the regions of higher importance and the regions of lower importance in the input image. Texture coordinates may also be warped in a similar manner. Thus, the image is unwarped automatically by modern graphics adapters having texture mapping capabilities.


title: Algorithmic complexity in coding theory and the minimum distance problem

abstract: We start withan overviewof algorithmiccomplexity problemsin coding theory We then show that the problemof com- puting the minimumdiktanceof a binaryIinwr code is NP-hard,and the corresponding deci~"on problemis W-complete. This constitutes a proof of the conjecture Bedekamp, McEliece,vanTilborg, dating back to 1978. Extensionsand applications of this result to other problemsin codingtheqv are discussed. 1. Introduction. This paper is organized in a manner contrapositive to the talk. In the talk, we give a detailed overview of algorithmic complexity issueain codhg theory. Herein, westart with an overviewof the prominentrole that computational complexity plays in coding theory: we briefly surveythe numerousalgorithmic complexity issuesthat arise in code construction and decoding. Subsequently,we focus on one specific problem. Namely, we consider the problem of computing the minimum distance of a linear code, which is also equivalentto the problem of finding the shortest cycle in a linear matroid over a finite field (90). A long-standing conjecture (12) says that this problem is NP-hard, and we will settle this conjecture in the ai%rmative. We provide a detailed proof. We hope that our proof illustrates some of the elegant techniques used in coding theory today, such as the construction of MDS codes via Vandermonde matri- ces (15, 59, 72) and concatenated coding (23, 31), for instance. It is interesting that algebraic techniques of this kind cart be employed to answer an essentially combinatorial ques- tion. The complexity of computing the minimum distance and the proof of the conjecture of Berlekamp,McEliece, and vanTilborg (12) will be only briefly mentioned in the talk.


title: Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs

abstract: Using unitary (instead of general) matrices in artificial neural networks (ANNs) is a promising way to solve the gradient explosion/vanishing problem, as well as to enable ANNs to learn long-term correlations in the data. This approach appears particularly promising for Recurrent Neural Networks (RNNs). In this work, we present a new architecture for implementing an Efficient Unitary Neural Network (EUNNs); its main advantages can be summarized as follows. Firstly, the representation capacity of the unitary space in an EUNN is fully tunable, ranging from a subspace of SU(N) to the entire unitary space. Secondly, the computational complexity for training an EUNN is merely O(1) per parameter. Finally, we test the performance of EUNNs on the standard copying task, the pixel-permuted MNIST digit recognition benchmark as well as the Speech Prediction Test (TIMIT). We find that our architecture significantly outperforms both other state-of-the-art unitary RNNs and the LSTM architecture, in terms of the final performance and/or the wall-clock training speed. EUNNs are thus promising alternatives to RNNs and LSTMs for a wide variety of applications.


title: Comparison of Machine Learning Approaches for Prediction of Advanced Liver Fibrosis in Chronic Hepatitis C Patients

abstract: Background/Aim: Using machine learning approaches as non-invasive methods have been used recently as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy. This study aims to evaluate different machine learning techniques in prediction of advanced fibrosis by combining the serum bio-markers and clinical information to develop the classification models. Methods: A prospective cohort of 39,567 patients with chronic hepatitis C was divided into two sets—one categorized as mild to moderate fibrosis (F0-F2), and the other categorized as advanced fibrosis (F3-F4) according to METAVIR score. Decision tree, genetic algorithm, particle swarm optimization, and multi-linear regression models for advanced fibrosis risk prediction were developed. Receiver operating characteristic curve analysis was performed to evaluate the performance of the proposed models. Results: Age, platelet count, AST, and albumin were found to be statistically significant to advanced fibrosis. The machine learning algorithms under study were able to predict advanced fibrosis in patients with HCC with AUROC ranging between 0.73 and 0.76 and accuracy between 66.3 and 84.4 percent. Conclusions: Machine-learning approaches could be used as alternative methods in prediction of the risk of advanced liver fibrosis due to chronic hepatitis C.


title: Compact Printed Quadrifilar Helical Antenna With Iso-Flux-Shaped Pattern and High Cross-Polarization Discrimination

abstract: In this letter, a technique for the miniaturization of conventional printed quadrifilar helical antennas using simple sinusoidal profiles is described and supported by experimental results. The challenge is to reduce the size of the antenna while maintaining iso-flux radiation patterns and high cross-polarization discrimination. The miniaturization design approach simulation and measurement results are given in the case of a specific design of L-band telemetry link antenna for meteorological stratospheric balloon payloads, but the achieved results can be extended to any quadrifilar helical antenna with shaped pattern. Size reduction close to 50% is reached while preserving major radiation pattern characteristics and good axial-ratio performance over a wide angular range.


title: A systematic approach for developing a new business model using morphological analysis and integrated fuzzy approach

abstract: Despite the growing importance of developing a novel business model, most previous studies on business model remain conceptual and theoretical. A paucity of empirical studies has hindered the use of the business model concept in practical purposes. Hence, this study proposes a systematic approach to new business model development (NBMD) that helps business practitioners to develop, evaluate and select the best business model to meet the business objectives. The proposed approach comprises two stages: identification of business model alternatives and business model evaluation and selection. During the first stage, a set of business model alternatives are derived by exploring all the possible combinations of a morphological matrix, and in the second stage, we conduct an evaluation and selection of a suitable business model. Morphological analysis (MA) has been employed for the derivation and aggregation of business model alternatives, and decision-making approach that integrates fuzzy extent analytic hierarchy process (FAHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) methods is used as an evaluation and selection tool. Finally, to illustrate the applicability of proposed approach, a case study on the development of a business model for a Telco has been presented.


title: FakeMask: A Novel Privacy Preserving Approach for Smartphones

abstract: Users can enjoy personalized services provided by various context-aware applications that collect users’ contexts through sensor-equipped smartphones. Meanwhile, serious privacy concerns arise due to the lack of privacy preservation mechanisms. Currently, most mechanisms apply passive defense policies in which the released contexts from a privacy preservation system are always real, leading to a great probability with which an adversary infers the hidden sensitive contexts about the users. In this paper, we apply a deception policy for privacy preservation and present a novel technique, FakeMask, in which fake contexts may be released to provably preserve users’ privacy. The output sequence of contexts by FakeMask can be accessed by the untrusted context-aware applications or be used to answer queries from those applications. Since the output contexts may be different from the original contexts, an adversary has greater difficulty in inferring the real contexts. Therefore, FakeMask limits what adversaries can learn from the output sequence of contexts about the user being in sensitive contexts, even if the adversaries are powerful enough to have the knowledge about the system and the temporal correlations among the contexts. The essence of FakeMask is a privacy checking algorithm which decides whether to release a fake context for the current context of the user. We present a novel privacy checking algorithm and an efficient one to accelerate the privacy checking process. Extensive evaluation experiments on real smartphone context traces of users demonstrate the improved performance of FakeMask over other approaches.


title: Micro-Blog: sharing and querying content through mobile phones and social participation

abstract: Recent years have witnessed the impacts of distributed content sharing (Wikipedia, Blogger), social networks (Facebook, MySpace), sensor networks, and pervasive computing. We believe that significant more impact is latent in the convergence of these ideas on the mobile phone platform. Phones can be envisioned as people-centric sensors capable of aggregating participatory as well as sensory inputs from local surroundings. The inputs can be visualized in different dimensions, such as space and time. When plugged into the Internet, the collaborative inputs from phones may enable a high resolution view of the world. This paper presents the architecture and implementation of one such system, called Micro-Blog. New kinds of application-driven challenges are identified and addressed in the context of this system. Implemented on Nokia N95 mobile phones, Micro-Blog was distributed to volunteers for real life use. Promising feedback suggests that Micro-Blog can be a deployable tool for sharing, browsing, and querying global information.


title: Model-optimal optimization by solving bellman equations

abstract: This paper analytically identifies the points chosen by the best black-box optimization methods, measured by assigning values to the history of points examined by stochastic optimization methods applied to randomly chosen but static (single) objective functions. Optimizers that make these "best" choices are called {\it model-optimal}. Model-optimal optimizers may not exist for a given model of static objectives but can always be approximated. If the search domain and the fitness range are both compact, or if other more abstract conditions are satisfied, it can be proven that model-optimal optimizers exist and at least one of them is deterministic. Model-optimality is studied by treating the average performance as a Bellman equation. The overall performance of an optimizer can be assessed based on the sequence of points selected for evaluation. Each choice introduces an error, and the overall performance is in every case just the sum of these errors. It might be possible to extend these results to certain stochastic or dynamic objectives, but they probably do not apply in adaptive or coevolutionary environments. The properties of model-optimality may make it possible to assess the tightness of existing bounds on average performance, and it may be possible to approximate model-optimal optimization decisions directly.


title: A SiGe Terahertz Heterodyne Imaging Transmitter With 3.3 mW Radiated Power and Fully-Integrated Phase-Locked Loop

abstract: A high-power 320 GHz transmitter using 130 nm SiGe BiCMOS technology ( $f_{T}/f_{\max} =$ 220/280 GHz) is reported. This transmitter consists of a 4 × 4 array of radiators based on coupled harmonic oscillators. By incorporating a signal filter structure called return-path gap coupler into a differential self-feeding oscillator, the proposed 320 GHz radiator simultaneously maximizes the fundamental oscillation power, harmonic generation, as well as on-chip radiation. To facilitate the TX-RX synchronization of a future terahertz (THz) heterodyne imaging chipset, a fully-integrated phase-locked loop (PLL) is also implemented in the transmitter. Such on-chip phase-locking capability is the first demonstration for all THz radiators in silicon. In the far-field measurement, the total radiated power and EIRP of the chip is 3.3 mW and 22.5 dBm, respectively. The transmitter consumes 610 mW DC power, which leads to a DC-to-THz radiation efficiency of 0.54%. To the authors' best knowledge, this work presents the highest radiated power and DC-to-THz radiation efficiency in silicon-based THz radiating sources.


title: Carbothermal shock synthesis of high-entropy-alloy nanoparticles

abstract: The controllable incorporation of multiple immiscible elements into a single nanoparticle merits untold scientific and technological potential, yet remains a challenge using conventional synthetic techniques. We present a general route for alloying up to eight dissimilar elements into single-phase solid-solution nanoparticles, referred to as high-entropy-alloy nanoparticles (HEA-NPs), by thermally shocking precursor metal salt mixtures loaded onto carbon supports [temperature ~2000 kelvin (K), 55-millisecond duration, rate of ~10 5 K per second]. We synthesized a wide range of multicomponent nanoparticles with a desired chemistry (composition), size, and phase (solid solution, phase-separated) by controlling the carbothermal shock (CTS) parameters (substrate, temperature, shock duration, and heating/cooling rate). To prove utility, we synthesized quinary HEA-NPs as ammonia oxidation catalysts with ~100% conversion and >99% nitrogen oxide selectivity over prolonged operations.


title: Educational supervision sessions between consultants and trainees

abstract: The weekly educational supervision sessions between consultant psychiatrists and their trai nees have emerged as a central pillar of psychiatric training. The College has set out, in various Statements on Approval of Training Schemes for General Professional Training for the MRCPsych, what it expects of these sessions: they should happen weekly, be devoted entirely to the needs of the trainee, and be applied to the development of the trainee's clinical skills as


title: News Session-Based Recommendations using Deep Neural Networks

abstract: News recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, fast growing number of items, accelerated item's value decay, and users preferences dynamic shift. Some promising results have been recently achieved by the usage of Deep Learning techniques on Recommender Systems, specially for item's feature extraction and for session-based recommendations with Recurrent Neural Networks. In this paper, it is proposed an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News Recommender Systems. This architecture is composed of two modules, the first responsible to learn news articles representations, based on their text and metadata, and the second module aimed to provide session-based recommendations using Recurrent Neural Networks. The recommendation task addressed in this work is next-item prediction for users sessions:"what is the next most likely article a user might read in a session?"Users sessions context is leveraged by the architecture to provide additional information in such extreme cold-start scenario of news recommendation. Users' behavior and item features are both merged in an hybrid recommendation approach. A temporal offline evaluation method is also proposed as a complementary contribution, for a more realistic evaluation of such task, considering dynamic factors that affect global readership interests like popularity, recency, and seasonality. Experiments with an extensive number of session-based recommendation methods were performed and the proposed instantiation of CHAMELEON meta-architecture obtained a significant relative improvement in top-n accuracy and ranking metrics (10% on Hit Rate and 13% on MRR) over the best benchmark methods.


title: Novel high efficiency broadband Ku band power combiner

abstract: High power solid-state power amplifiers require a high efficiency power dividing/combining structure to keep the power loss as low as possible. The heat sinking capability of the divider/combiner also limits its maximum output power with continues wave (CW) configuration. In this paper, we introduce a novel 8-way Ku band power divider/combiner system, it demonstrate advantages of low loss, broadband and good heat sinking capability simultaneously. As its sub-components, low loss probes for waveguide-to-microstrip transition and low loss broadband 1-to-2 power combiners are designed and fabricated. The measured back-to-back insertion loss of the whole 8-way power combiner is lower than 0.5dB in the whole Ku band, and the corresponding combining efficiency is as high as 94.5%. The simulated thermal resistance of the system is as low as 0.21°C/W, indicating the proposed power combiner is able to produce 50W of CW output power with commercial available Monolithic Microwave Integrated Circuits (MMICs).


title: Q-Learning-Based Power Control for LTE Enterprise Femtocell Networks

abstract: Small cells are beneficial for both users and operators to improve communication quality. The femtocell is one type of small cell used for indoor coverage. Enterprise femtocell installation inevitably produces intralayer and interlayer interference management issues. Enterprise femtocell optimization and indoor propagation modeling are built in this work based on the communications environment characteristics. Q-learning-based distributed and hybrid power control strategies are then proposed. The throughput, energy efficiency, and user experience satisfaction with conventional scheduling methods are compared. The simulation results show that distributed Q-learning performs better local optimization while hybrid Q-learning enhances global performance.


title: Do People’s Self-Views Matter? Self-Concept and Self-Esteem in Everyday Life

abstract: Recent scholars have dismissed the utility of self-esteem as well as programs designed to improve it. The authors challenge these contentions on conceptual, methodological, and empirical grounds. They begin by proposing that the scope of recent analyses has been overly narrow and should be broadened to include specific as well as global self-views. Using this conceptualization, the authors place recent critiques in historical context, recalling that similarly skeptical commentaries on global attitudes and traits inspired theorizing and empirical research that subsequently restored faith in the value of both constructs. Specifically, they point to 3 strategies for attaining more optimistic assessments of the predictive validity of self-views: recognizing the utility of incorporating additional variables in predictive schemes, matching the specificity of predictors and criteria, and using theoretically informed standards for evaluating predictor- criterion relationships. The authors conclude that self-views do matter and that it is worthwhile and important to develop and implement theoretically informed programs to improve them.


title: Beyond 5G With UAVs: Foundations of a 3D Wireless Cellular Network

abstract: In this paper, a novel concept of three-dimensional (3D) cellular networks, that integrate drone base stations (drone-BS) and cellular-connected drone users (drone-UEs), is introduced. For this new 3D cellular architecture, a novel framework for network planning for drone-BSs and latency-minimal cell association for drone-UEs is proposed. For network planning, a tractable method for drone-BSs’ deployment based on the notion of truncated octahedron shapes is proposed, which ensures full coverage for a given space with a minimum number of drone-BSs. In addition, to characterize frequency planning in such 3D wireless networks, an analytical expression for the feasible integer frequency reuse factors is derived. Subsequently, an optimal 3D cell association scheme is developed for which the drone-UEs’ latency, considering transmission, computation, and backhaul delays, is minimized. To this end, first, the spatial distribution of the drone-UEs is estimated using a kernel density estimation method, and the parameters of the estimator are obtained using a cross-validation method. Then, according to the spatial distribution of drone-UEs and the locations of drone-BSs, the latency-minimal 3D cell association for drone-UEs is derived by exploiting tools from an optimal transport theory. The simulation results show that the proposed approach reduces the latency of drone-UEs compared with the classical cell association approach that uses a signal-to-interference-plus-noise ratio (SINR) criterion. In particular, the proposed approach yields a reduction of up to 46% in the average latency compared with the SINR-based association. The results also show that the proposed latency-optimal cell association improves the spectral efficiency of a 3D wireless cellular network of drones.


title: Simulation of Absolute Amplitudes of Ultrasound Signals Using Equivalent Circuits

abstract: Equivalent circuits for piezoelectric devices and ultrasonic transmission media can be used to cosimulate electronics and ultrasound parts in simulators originally intended for electronics. To achieve efficient system-level optimization, it is important to simulate correct, absolute amplitude of the ultrasound signal in the system, as this determines the requirements on the electronics regarding dynamic range, circuit noise, and power consumption. This paper presents methods to achieve correct, absolute amplitude of an ultrasound signal in a simulation of a pulse-echo system using equivalent circuits. This is achieved by taking into consideration loss due to diffraction and the effect of the cable that connects the electronics and the piezoelectric transducer. The conductive loss in the transmission line that models the propagation media of the ultrasound pulse is used to model the loss due to diffraction. Results show that the simulated amplitude of the echo follows measured values well in both near and far fields, with an offset of about 10%. The use of a coaxial cable introduces inductance and capacitance that affect the amplitude of a received echo. Amplitude variations of 60% were observed when the cable length was varied between 0.07 m and 2.3 m, with simulations predicting similar variations. The high precision in the achieved results show that electronic design and system optimization can rely on system simulations alone. This will simplify the development of integrated electronics aimed at ultrasound systems.


title: Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis

abstract: In interactive data analysis processes, the dialogue between the human and the computer is the enabling mechanism that can lead to actionable observations about the phenomena being investigated. It is of paramount importance that this dialogue is not interrupted by slow computational mechanisms that do not consider any known temporal human-computer interaction characteristics that prioritize the perceptual and cognitive capabilities of the users. In cases where the analysis involves an integrated computational method, for instance to reduce the dimensionality of the data or to perform clustering, such non-optimal processes are often likely. To remedy this, progressive computations, where results are iteratively improved, are getting increasing interest in visual analytics. In this paper, we present techniques and design considerations to incorporate progressive methods within interactive analysis processes that involve high-dimensional data. We define methodologies to facilitate processes that adhere to the perceptual characteristics of users and describe how online algorithms can be incorporated within these. A set of design recommendations and according methods to support analysts in accomplishing high-dimensional data analysis tasks are then presented. Our arguments and decisions here are informed by observations gathered over a series of analysis sessions with analysts from finance. We document observations and recommendations from this study and present evidence on how our approach contribute to the efficiency and productivity of interactive visual analysis sessions involving high-dimensional data.


title: Intuitionistic fuzzy based DEMATEL method for developing green practices and performances in a green supply chain

abstract: Proposed a intuitionistic fuzzy based DEMATEL method for developing green practices.A case study from automotive industry is used for validation.Sensitivity analysis is performed to check the robustness of the method. Environmental topics have gained much consideration in corporate green operations. Globalization, stakeholder pressures, and stricter environmental regulations have made organizations develop environmental practices. Thus, green supply chain management (GSCM) is now a proactive approach for organizations to enhance their environmental performance and achieve competitive advantages. This study pioneers using the decision-making trial and evaluation laboratory (DEMATEL) method with intuitionistic fuzzy sets to handle the important and causal relationships between GSCM practices and performances. DEMATEL evaluates GSCM practices to find the main practices to improve both environmental and economic performances. This study uses intuitionistic fuzzy set theory to handle the linguistic imprecision and the ambiguity of human being's judgment. A case study from the automotive industry is presented to evaluate the efficiency of the proposed method. The results reveal "internal management support", "green purchasing" and "ISO 14001 certification" are the most significant GSCM practices. The practical results of this study offer useful insights for managers to become more environmentally responsible, while improving their economic and environmental performance goals. Further, a sensitivity analysis of results, managerial implications, conclusions, limitations and future research opportunities are provided.


title: A 1.2-V Dynamic Bias Latch-Type Comparator in 65-nm CMOS With 0.4-mV Input Noise

abstract: A latch-type comparator with a dynamic bias pre-amplifier is implemented in a 65-nm CMOS process. The dynamic bias with a tail capacitor is simple to implement and ensures that the pre-amplifier output nodes are only partially discharged to reduce the energy consumption. The comparator is analyzed and compared to its prior art in terms of energy consumption and input referred noise voltage. First-order equations are presented that show how to optimize the pre-amplifier for low noise and high gain. Both the dynamic bias comparator and the prior art are implemented on the same die and measurements show that the dynamic bias can reduce the average energy consumption by about a factor 2.5 for the same input-equivalent noise at an input common-mode level of half the supply voltage.


title: Ku-band circularly polarized leaky-wave antenna based on lateral sparse substrate integrated waveguide for satellite communication

abstract: This paper presents a user terminal leaky-wave antenna (LWA) for satellite communication system in Ku-band. The motivation is to propose a low-profile broadband high-gain receiving antenna with beam-scanning and circular polarization features. The antenna is based on a lateral sparse substrate integrated waveguide, which is constructed by a microstrip line shorted with two rows of sparse and abundant periodic metallic via-holes on both sides with periodic transverse slots etched on the upper side. To achieve good circularly polarized radiation and compact configuration, non-uniform SIW width configuration is used to realize effective phase shift over the relative broadband. Measured results show that the antenna presents experimentally expected broadband matched impedance with return loss |S 11 | > 10 dB from 15.4 GHz to 19 GHz. The measured radiation pattern exhibits continuous frequency-scanning from 35° at 15.5 GHz to 47° at 18 GHz according to the broadside with maximum gain of 12.3 dBic. The measured axial ratio is within 3 dB from 15.9 GHz to 17.2 GHz. Measured results are in good agreement with predicted ones, and it is demonstrated that the proposed structure is a simple and compact candidate of high performance circularly-polarized antenna for satellite communications. Also the beam scanning capability of proposed antenna could find potential applications in inter-satellite communications.


title: Predicting mild cognitive impairment from spontaneous spoken utterances

abstract: Abstract Introduction Trials in Alzheimer's disease are increasingly focusing on prevention in asymptomatic individuals. We hypothesized that indicators of mild cognitive impairment (MCI) may be present in the content of spoken language in older adults and be useful in distinguishing those with MCI from those who are cognitively intact. To test this hypothesis, we performed linguistic analyses of spoken words in participants with MCI and those with intact cognition participating in a clinical trial. Methods Data came from a randomized controlled behavioral clinical trial to examine the effect of unstructured conversation on cognitive function among older adults with either normal cognition or MCI ( ClinicalTrials.gov : NCT01571427 ). Unstructured conversations (but with standardized preselected topics across subjects) were recorded between interviewers and interviewees during the intervention sessions of the trial from 14 MCI and 27 cognitively intact participants. From the transcription of interviewees recordings, we grouped spoken words using Linguistic Inquiry and Word Count (LIWC), a structured table of words, which categorizes 2500 words into 68 different word subcategories such as positive and negative words, fillers, and physical states. The number of words in each LIWC word subcategory constructed a vector of 68 dimensions representing the linguistic features of each subject. We used support vector machine and random forest classifiers to distinguish MCI from cognitively intact participants. Results MCI participants were distinguished from those with intact cognition using linguistic features obtained by LIWC with 84% classification accuracy which is well above chance 60%. Discussion Linguistic analyses of spoken language may be a powerful tool in distinguishing MCI subjects from those with intact cognition. Further studies to assess whether spoken language derived measures could detect changes in cognitive functions in clinical trials are warrented.


title: Biogeography-based optimisation with chaos

abstract: The biogeography-based optimisation (BBO) algorithm is a novel evolutionary algorithm inspired by biogeography. Similarly, to other evolutionary algorithms, entrapment in local optima and slow convergence speed are two probable problems it encounters in solving challenging real problems. Due to the novelty of this algorithm, however, there is little in the literature regarding alleviating these two problems. Chaotic maps are one of the best methods to improve the performance of evolutionary algorithms in terms of both local optima avoidance and convergence speed. In this study, we utilise ten chaotic maps to enhance the performance of the BBO algorithm. The chaotic maps are employed to define selection, emigration, and mutation probabilities. The proposed chaotic BBO algorithms are benchmarked on ten test functions. The results demonstrate that the chaotic maps (especially Gauss/mouse map) are able to significantly boost the performance of BBO. In addition, the results show that the combination of chaotic selection and emigration operators results in the highest performance.


title: Satellite image-based localization via learned embeddings

abstract: We propose a vision-based method that localizes a ground vehicle using publicly available satellite imagery as the only prior knowledge of the environment. Our approach takes as input a sequence of ground-level images acquired by the vehicle as it navigates, and outputs an estimate of the vehicle's pose relative to a georeferenced satellite image. We overcome the significant viewpoint and appearance variations between the images through a neural multi-view model that learns location-discriminative embeddings in which ground-level images are matched with their corresponding satellite view of the scene. We use this learned function as an observation model in a filtering framework to maintain a distribution over the vehicle's pose. We evaluate our method on different benchmark datasets and demonstrate its ability localize ground-level images in environments novel relative to training, despite the challenges of significant viewpoint and appearance variations.


title: Extended Lateral Rhinotomy Incision for Total Maxillectomy

abstract: An extended lateral rhinotomy incision for total maxillectomy, which avoids postoperative lower eyelid complications, is described. Advantages of the extended lateral rhinotomy incision for total maxillectomy are discussed in comparison with the classic Weber-Fergusson incision. (Otolaryngol Head Neck Surg 2000;123:512-3.)


Classification of radar returns from the ionosphere using neural networks


title: A Survey of Text Mining Techniques and Applications

abstract: Text Mining has become an important research area. Text Mining is the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. In this paper, a Survey of Text Mining techniques and applications have been s presented.


title: Self-adaptive differential evolution algorithm using population size reduction and three strategies

abstract: Many real-world optimization problems are large-scale in nature. In order to solve these problems, an optimization algorithm is required that is able to apply a global search regardless of the problems’ particularities. This paper proposes a self-adaptive differential evolution algorithm, called jDElscop, for solving large-scale optimization problems with continuous variables. The proposed algorithm employs three strategies and a population size reduction mechanism. The performance of the jDElscop algorithm is evaluated on a set of benchmark problems provided for the Special Issue on the Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. Non-parametric statistical procedures were performed for multiple comparisons between the proposed algorithm and three well-known algorithms from literature. The results show that the jDElscop algorithm can deal with large-scale continuous optimization effectively. It also behaves significantly better than other three algorithms used in the comparison, in most cases.


title: Parallalizable deep self-organizing maps for image classification

abstract: Self-organizing Maps (SOMs) are neural network architectures which are used to learn from unlabeled data. Despite being proven to be useful in many areas, SOMs have limited capability of performing high level feature abstraction due to its shallow structure. As a solution, deep self-organizing maps (DSOM), have been proposed. DSOMs enable multiple levels of abstraction in unsupervised learning with a hierarchical deep structure. However, training of DSOMs is computationally expensive, limiting its usability. This paper presents a DSOM architecture that is easily parallelizable and hence more computationally efficient (PD-SOM). The presented architecture has three main advantages: 1) Unsupervised learning based image classification, 2) High-level feature abstraction and 3) Less computationally expensive training while maintaining high accuracy levels. The PD-SOM architecture was implemented on the benchmark MNIST hand written character dataset. To test the robustness and the generalization capability of the presented PD-SOM architecture, testing was done with: 1) small training sets and 2) varying degrees of noise. It was shown that the presented architecture consistently outperformed the previously proposed DSOM while showing ∼18% decrease in training time for the MNIST dataset.


title: Automatic Image Defect Diagnosis.

abstract: Although problems relating to specific image correction have been explored intensively, the problem of simultaneous diagnosis for multiple photographic defects remains relatively untouched. Solutions to this problem attempt to predict the existence, severity, and locations of common defects. This paper proposes a first attempt at a solution to the general defect diagnosis problem based on our novel dataset. We formulate the defect diagnosis problem as a multi-task prediction problem and utilize multi-column deep neural networks (DNN) to approach the problem. We propose DNN models with holistic and multi-patch inputs and combine their predicted scores to integrate multi-scale information. During experiments, we validate the complementarity of both kinds of inputs. We also validate that our combined predictions have a more consistent ranking correlation with our ground truth than the average of individual users' judgments. Furthermore, we apply the fully convolutional version of our trained model to visualize defect severity heat maps, which can effectively identify defective regions of input images. We propose that our work will provide casual photographers with better experiences when using image editing software to improve image quality. Another promising avenue for future application involves the equipping of photo summarization systems with defect cues to focus more on defect-free photos.


title: Predictors of Aspiration Pneumonia: How Important Is Dysphagia?

abstract: Abstract. Aspiration pneumonia is a major cause of morbidity and mortality among the elderly who are hospitalized or in nursing homes. Multiple risk factors for pneumonia have been identified, but no study has effectively compared the relative risk of factors in several different categories, including dysphagia. In this prospective outcomes study, 189 elderly subjects were recruited from the outpatient clinics, inpatient acute care wards, and the nursing home care center at the VA Medical Center in Ann Arbor, Michigan. They were given a variety of assessments to determine oropharyngeal and esophageal swallowing and feeding status, functional status, medical status, and oral/dental status. The subjects were followed for up to 4 years for an outcome of verified aspiration pneumonia. Bivariate analyses identified several factors as significantly associated with pneumonia. Logistic regression analyses then identified the significant predictors of aspiration pneumonia. The best predictors, in one or more groups of subjects, were dependent for feeding, dependent for oral care, number of decayed teeth, tube feeding, more than one medical diagnosis, number of medications, and smoking. The role that each of the significant predictors might play was described in relation to the pathogenesis of aspiration pneumonia. Dysphagia was concluded to be an important risk for aspiration pneumonia, but generally not sufficient to cause pneumonia unless other risk factors are present as well. A dependency upon others for feeding emerged as the dominant risk factor, with an odds ratio of 19.98 in a logistic regression model that excluded tube-fed patients.


title: A new piezoelectric energy harvesting design concept: multimodal energy harvesting skin

abstract: This paper presents an advanced design concept for a piezoelectric energy harvesting (EH), referred to as multimodal EH skin. This EH design facilitates the use of multimodal vibration and enhances power harvesting efficiency. The multimodal EH skin is an extension of our previous work, EH skin, which was an innovative design paradigm for a piezoelectric energy harvester: a vibrating skin structure and an additional thin piezoelectric layer in one device. A computational (finite element) model of the multilayered assembly - the vibrating skin structure and piezoelectric layer - is constructed and the optimal topology and/or shape of the piezoelectric layer is found for maximum power generation from multiple vibration modes. A design rationale for the multimodal EH skin was proposed: designing a piezoelectric material distribution and external resistors. In the material design step, the piezoelectric material is segmented by inflection lines from multiple vibration modes of interests to minimize voltage cancellation. The inflection lines are detected using the voltage phase. In the external resistor design step, the resistor values are found for each segment to maximize power output. The presented design concept, which can be applied to any engineering system with multimodal harmonic-vibrating skins, was applied to two case studies: an aircraft skin and a power transformer panel. The excellent performance of multimodal EH skin was demonstrated, showing larger power generation than EH skin without segmentation or unimodal EH skin.


title: Improved Regularization of Convolutional Neural Networks with Cutout

abstract: Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such representations, they are often susceptible to overfitting and therefore require proper regularization in order to generalize well. In this paper, we show that the simple regularization technique of randomly masking out square regions of input during training, which we call cutout, can be used to improve the robustness and overall performance of convolutional neural networks. Not only is this method extremely easy to implement, but we also demonstrate that it can be used in conjunction with existing forms of data augmentation and other regularizers to further improve model performance. We evaluate this method by applying it to current state-of-the-art architectures on the CIFAR-10, CIFAR-100, and SVHN datasets, yielding new state-of-the-art results of 2.56%, 15.20%, and 1.30% test error respectively. Code is available at this https URL


title: HyDroid: A Hybrid Approach for Generating API Call Traces from Obfuscated Android Applications for Mobile Security

abstract: The growing popularity of Android applications makes them vulnerable to security threats. There exist several studies that focus on the analysis of the behaviour of Android applications to detect the repackaged and malicious ones. These techniques use a variety of features to model the application's behaviour, among which the calls to Android API, made by the application components, are shown to be the most reliable. To generate the APIs that an application calls is not an easy task. This is because most malicious applications are obfuscated and do not come with the source code. This makes the problem of identifying the API methods invoked by an application an interesting research issue. In this paper, we present HyDroid, a hybrid approach that combines static and dynamic analysis to generate API call traces from the execution of an application's services. We focus on services because they contain key characteristics that allure attackers to misuse them. We show that HyDroid can be used to extract API call trace signatures of several malware families.


title: Tracking deformable objects with unscented Kalman filtering and geometric active contours

abstract: Geometric active contours represented as the zero level sets of the graph of a surface have been used very successfully to segment static images. However, tracking involves estimating the global motion of the object and its local deformations as functions of time. Some attempts have been made to use geometric active contours for tracking, but most of these minimize the energy at each frame and do not utilize the temporal coherency of the motion or the deformation. Recently, particle filters for geometric active contours were used for tracking deforming objects. However, the method is computationally very expensive since it requires a large number of particles to approximate the state density. In the present work, we propose to use the unscented Kalman filter together with geometric active contours to track deformable objects in a computationally efficient manner.


title: FastXML: a fast, accurate and stable tree-classifier for extreme multi-label learning

abstract: The objective in extreme multi-label classification is to learn a classifier that can automatically tag a data point with the most relevant subset of labels from a large label set. Extreme multi-label classification is an important research problem since not only does it enable the tackling of applications with many labels but it also allows the reformulation of ranking problems with certain advantages over existing formulations. Our objective, in this paper, is to develop an extreme multi-label classifier that is faster to train and more accurate at prediction than the state-of-the-art Multi-label Random Forest (MLRF) algorithm [2] and the Label Partitioning for Sub-linear Ranking (LPSR) algorithm [35]. MLRF and LPSR learn a hierarchy to deal with the large number of labels but optimize task independent measures, such as the Gini index or clustering error, in order to learn the hierarchy. Our proposed FastXML algorithm achieves significantly higher accuracies by directly optimizing an nDCG based ranking loss function. We also develop an alternating minimization algorithm for efficiently optimizing the proposed formulation. Experiments reveal that FastXML can be trained on problems with more than a million labels on a standard desktop in eight hours using a single core and in an hour using multiple cores.


title: A biterm topic model for short texts

abstract: Uncovering the topics within short texts, such as tweets and instant messages, has become an important task for many content analysis applications. However, directly applying conventional topic models (e.g. LDA and PLSA) on such short texts may not work well. The fundamental reason lies in that conventional topic models implicitly capture the document-level word co-occurrence patterns to reveal topics, and thus suffer from the severe data sparsity in short documents. In this paper, we propose a novel way for modeling topics in short texts, referred as biterm topic model (BTM). Specifically, in BTM we learn the topics by directly modeling the generation of word co-occurrence patterns (i.e. biterms) in the whole corpus. The major advantages of BTM are that 1) BTM explicitly models the word co-occurrence patterns to enhance the topic learning; and 2) BTM uses the aggregated patterns in the whole corpus for learning topics to solve the problem of sparse word co-occurrence patterns at document-level. We carry out extensive experiments on real-world short text collections. The results demonstrate that our approach can discover more prominent and coherent topics, and significantly outperform baseline methods on several evaluation metrics. Furthermore, we find that BTM can outperform LDA even on normal texts, showing the potential generality and wider usage of the new topic model.


title: A comparison of CPUs, GPUs, FPGAs, and massively parallel processor arrays for random number generation

abstract: The future of high-performance computing is likely to rely on the ability to efficiently exploit huge amounts of parallelism. One way of taking advantage of this parallelism is to formulate problems as "embarrassingly parallel" Monte-Carlo simulations, which allow applications to achieve a linear speedup over multiple computational nodes, without requiring a super-linear increase in inter-node communication. However, such applications are reliant on a cheap supply of high quality random numbers, particularly for the three main maximum entropy distributions: uniform, used as a general source of randomness; Gaussian, for discrete-time simulations; and exponential, for discrete-event simulations. In this paper we look at four different types of platform: conventional multi-core CPUs (Intel Core2); GPUs (NVidia GTX 200); FPGAs (Xilinx Virtex-5); and Massively Parallel Processor Arrays (Ambric AM2000). For each platform we determine the most appropriate algorithm for generating each type of number, then calculate the peak generation rate and estimated power efficiency for each device.


Troubling Consequences of Online Political


title: Detection of transmission line-fault through CWT based Bewley lattice diagram

abstract: Any sudden change in power line produces high frequency electromagnetic impulse called travelling wave (TW). Continuous Wavelet Transform (CWT) can detect transmission line abnormalities. CWT has several advantages over Discrete Wavelet Transform (DWT) since CWT is continuous in terms of shifting and it can operate on any scale. Bewley's lattice method, which is a method of detection of fault location from TW, does not consider the noises and interferences. Noises and interferences modify the TW signals. In this paper a novel CWT based Bewley's lattice method of transmission lines-fault detection is presented. The main difference between the conventional CWT method and the method described in this paper is the accumulation of CWT coefficients at all scales. In the proposed method, the coefficients related to transmission line abnormalities are enlarged while the coefficients of noises at different scales counteract one another. The proposed CWT based Bewley's lattice method is more accurate than conventional Bewley's lattice or DWT/CWT based methods.


title: The Impact of Temperature on Outdoor Industrial Sensornet Applications

abstract: Wireless sensor networks are being considered for use in industrial process and control environments. Unlike traditional deployment scenarios for sensor networks, in which energy preservation is the main design principle, industrial environments stress worker safety and uninterrupted production. To fulfill these requirements, sensor networks must be able to provide performance guarantees for radio communication. In this paper, we consider as a case study the deployment of a sensornet in an oil refinery in Portugal, where sensor nodes are deployed outdoors and might experience high temperature fluctuations. We investigate how the variations of ambient temperature influence data delivery performance and link quality in low-power radio communications. We also study the impact that specific implementation requirements, such as the ATEX fire-safety regulations, can have on the design of the overall network. Our experiments show that temperature directly affects the communication between sensor nodes, and that significantly less transmission power is required at low temperatures. We further illustrate that it is possible to save up to 16% energy during nights and cold periods of the year, while still ensuring reliable communication among sensor nodes. In view of these experimental results, we elaborate on how the temperature influences both the design and the deployment of wireless sensor networks in industrial environments.


title: Best Practices for Applying Deep Learning to Novel Applications

abstract: This report is targeted to groups who are subject matter experts in their application but deep learning novices. It contains practical advice for those interested in testing the use of deep neural networks on applications that are novel for deep learning. We suggest making your project more manageable by dividing it into phases. For each phase this report contains numerous recommendations and insights to assist novice practitioners.


title: Hydrogen Gas Sensors Based on Semiconductor Oxide Nanostructures

abstract: Recently, the hydrogen gas sensing properties of semiconductor oxide (SMO) nanostructures have been widely investigated. In this article, we provide a comprehensive review of the research progress in the last five years concerning hydrogen gas sensors based on SMO thin film and one-dimensional (1D) nanostructures. The hydrogen sensing mechanism of SMO nanostructures and some critical issues are discussed. Doping, noble metal-decoration, heterojunctions and size reduction have been investigated and proved to be effective methods for improving the sensing performance of SMO thin films and 1D nanostructures. The effect on the hydrogen response of SMO thin films and 1D nanostructures of grain boundary and crystal orientation, as well as the sensor architecture, including electrode size and nanojunctions have also been studied. Finally, we also discuss some challenges for the future applications of SMO nanostructured hydrogen sensors.


title: Obstacle detection in foliage with ladar and radar

abstract: Autonomous off-road navigation is central to several important applications of unmanned ground vehicles. This requires the ability to detect obstacles in vegetation. We examine the prospects for doing so with scanning ladar and with a linear array of 2.2 GHz micro-impulse radar transceivers. For ladar, we summarize our work to date on algorithms for detecting obstacles in tall grass with single-axis ladar, then present a simple probabilistic model of the distance into tall grass that ladar-based obstacle detection is possible. This model indicates that the ladar “penetration depth” can range from on the order of 10 cm to several meters, depending on the plant type. We also present an experimental investigation of mixed pixel phenomenology for a time-of-flight, SICK ladar and discuss briefly how this bears on the problem. For radar, we show results of applying an existing algorithm for multi-frequency diffraction tomography to a set of 45 scans taken with one sensor translating laterally 4 cm/scan to mimic a linear array of transceivers. This produces a high resolution, 2-D map of scattering surfaces in front of the array and clearly reveals a large tree trunk behind over 2.5 m of thick foliage. Both types of sensor warrant further development and exploitation for this problem.


title: Facebook versus Twitter: Differences in Self-Disclosure and Trait Prediction

abstract: This study compares self-disclosure on Facebook and Twitter through the lens of demographic and psychological traits. Predictive evaluation reveals that language models trained on Facebook posts are more accurate at predicting age, gender, stress, and empathy than those trained on Twitter posts. Qualitative analyses of the underlying linguistic and demographic differences reveal that users are significantly more likely to disclose information about their family, personal concerns, and emotions and provide a more `honest' self-representation on Facebook. On the other hand, the same users significantly preferred to disclose their needs, drives, and ambitions on Twitter. The higher predictive performance of Facebook is also partly due to the greater volume of language on Facebook than Twitter -- Facebook and Twitter are equally good at predicting user traits when the same-sized language samples are used to train language models. We explore the implications of these differences in cross-platform user trait prediction.


title: Central Asian ‘Characteristics’ on China’s New Silk Road: The Role of Landscape and the Politics of Infrastructure

abstract: China’s $1 trillion One Belt, One Road (OBOR) infrastructure project has significant landscape, socio-economic, and political implications in recipient countries. To date, investigation has focused on Chinese motivation and plans rather than OBOR impact in host nations. This paper examines the programme from the perspective of two Central Asian states—Kazakhstan and Kyrgyzstan—that are at the heart of OBOR. We identify geographical factors that constrain infrastructure, recognise geopolitical contestation between Russia and China, address historical and cultural factors, and consider issues of institutional capacity and marginality that may be impediments to China’s initiative. The discussion then focuses on how OBOR may play out in Central Asian landscapes and suggests how to conceive and address the unprecedented transformation in the region’s built environment. Critical issues are that OBOR has not been grounded in the physical geography, practical understanding of OBOR’s impacts is missing, and the state-citizen-China nexus remains unexplored. As pivot nations, OBOR implementation in Kazakhstan and Kyrgyzstan will showcase the Chinese programme’s strengths and highlight its weaknesses.


Neural Joint Model for Transition-based Chinese Syntactic Analysis