Query: A proposal of LDMOS using Deep Trench poly field plate

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0.18um BCD technology with best-in-class LDMOS from 6 V to 45 V <=SEP=> We propose a novel nLDMOS structure and a design concept in BCD technology with the best-in-class performance. The drift profile is optimized and the multi-oxide in the drift region is adopted to approach the RESURF limit of on-resistance vs. BVdss characteristic (i.e., 36V DMOS has a Ron_sp of 20mohm-mm2 with a BVdss of 50V; 45V DMOS has a Ron_sp of 28mohm-mm2 with a BVdss of 65V). Moreover, this modification requires merely three extra masks in the Non-Epitaxy LV process to achieve this improvement. Therefore it is not only a high performance but also a low cost solution.

0.35μm, 30V fully isolated and low-Ron nLDMOS for DC-DC applications <=SEP=> In this paper, we present a new approach to integrate in a 0.35 μm BCD technology, low Ron LDMOS power transistors with highly competitive Specific Resistance figure of merit (Rsp, defined as Ron*Area). The LDMOS are fully isolated in order to support applications which may bias the source/drain electrodes below the substrate potential, which is critical for devices used in high-current, high-frequency switching applications. The new devices are suitable for high-efficiency DC-DC converter products with operating voltage of up to 30V, such as mobile PMICs. For maximum performance, two different extended-drain LDMOS structures have been developed to cover the entire operating voltage range: for 16V and below, a planar-gate structure is used and for 20V and above, a non-planar “offset-LOCOS” gate is used for 20V and above.

Ultra-low on-resistance LDMOS implementation in 0.13µm CD and BiCD process technologies for analog power IC's <=SEP=> Toshiba's 5th generation BiCD/CD-0.13 is a new process platform for analog power applications based on 0.13µm CMOS technology. The process platform has six varieties of rated voltage, 5V, 6V, 18V, 25V, 40V, and 60V. 5 to 18V CD-0.13 process use P-type silicon substrate. 25 to 60V BiCD-0.13 process use N-Epi wafer with N+/P+ buried layer on P type silicon substrate. Each LDMOS recode ultra-low on-resistance compared with that of previous papers, and we will realize the highest performance analog power IC's using this technology.

Advanced 0.13um smart power technology from 7V to 70V <=SEP=> This paper presents BCD process integrating 7V to 70V power devices on 0.13um CMOS platform for various power management applications. BJT, Zener diode and Schottky diode are available and non-volatile memory is embedded as well. LDMOS shows best-in-class specific Ron (R<sub>SP</sub>) vs. BV<sub>DSS</sub> characteristics (i.e., 70V NMOS has R<sub>SP</sub> of 69mΩ-mm<sup>2</sup> with BV<sub>DSS</sub> of 89V). Modular process scheme is used for flexibility to various requirements of applications.

BCD8sP: An advanced 0.16 μm technology platform with state of the art power devices <=SEP=> Advanced 0.16 μm BCD technology platform offering dense logic transistors (1.8 V-5 V CMOS) and high performance analog features has been developed. Thanks to dedicated field plate optimization, body and drain engineering, state of the art power devices (8 V to 42 V rated) have been obtained ensuring large Safe Operating Areas with best RONXAREA-BVDSS tradeoff.

HeNet: A Deep Learning Approach on Intel$^\circledR$ Processor Trace for Effective Exploit Detection <=SEP=> This paper presents HeNet, a hierarchical ensemble neural network, applied to classify hardware-generated control flow traces for malware detection. Deep learning-based malware detection has so far focused on analyzing executable files and runtime API calls. Static code analysis approaches face challenges due to obfuscated code and adversarial perturbations. Behavioral data collected during execution is more difficult to obfuscate but recent research has shown successful attacks against API call based malware classifiers. We investigate control flow based characterization of a program execution to build robust deep learning malware classifiers. HeNet consists of a low-level behavior model and a toplevel ensemble model. The low-level model is a per-application behavior model, trained via transfer learning on a time-series of images generated from control flow trace of an execution. We use Intel R © Processor Trace enabled processor for low overhead execution tracing and design a lightweight image conversion and segmentation of the control flow trace. The top-level ensemble model aggregates the behavior classification of all the trace segments and detects an attack. The use of hardware trace adds portability to our system and the use of deep learning eliminates the manual effort of feature engineering. We evaluate HeNet against real-world exploitations of PDF readers. HeNet achieves 100% accuracy and 0% false positive on test set, and higher classification accuracy compared to classical machine learning algorithms.

Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis <=SEP=> Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations. Synthesizing person images conditioned on arbitrary poses is one of the most representative examples where the generation quality largely relies on the capability of identifying and modeling arbitrary transformations on different body parts. Current generative models are often built on local convolutions and overlook the key challenges (e.g. heavy occlusions, different views or dramatic appearance changes) when distinct geometric changes happen for each part, caused by arbitrary pose manipulations. This paper aims to resolve these challenges induced by geometric variability and spatial displacements via a new Soft-Gated Warping Generative Adversarial Network (Warping-GAN), which is composed of two stages: 1) it first synthesizes a target part segmentation map given a target pose, which depicts the region-level spatial layouts for guiding image synthesis with higher-level structure constraints; 2) the Warping-GAN equipped with a soft-gated warping-block learns feature-level mapping to render textures from the original image into the generated segmentation map. Warping-GAN is capable of controlling different transformation degrees given distinct target poses. Moreover, the proposed warping-block is lightweight and flexible enough to be injected into any networks. Human perceptual studies and quantitative evaluations demonstrate the superiority of our WarpingGAN that significantly outperforms all existing methods on two large datasets.

Vehicle Guidance for an Autonomous Vehicle <=SEP=> This paper describes the vehicle guidance system of an autonomous vehicle. This system is part of the control structure of the vehicle and consists of a path generator, a motion planning algorithm and a sensor fusion module. A stereo vision sensor and a DGPS sensor are used as position sensors. The trajectory for the vehicle motion is generated in a rst step by using only information from a digital map. Object-detecting sensors such as the stereo vision sensor, three laserscanner and a radar sensor observe the vehicle environment and report detected objects to the sensor fusion module. This information is used to dynamically update the planned vehicle trajectory to the nal vehicle motion.

A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems <=SEP=> The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing in a wide range of wireless networking applications. In particular, with their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. On the one hand, UAVs can be used as aerial base stations to enhance coverage, capacity, reliability, and energy efficiency of wireless networks. For instance, UAVs can be deployed to complement existing cellular systems by providing additional capacity to hotspot areas as well as to provide network coverage in emergency and public safety situations. On the other hand, UAVs can operate as flying mobile terminals within the cellular networks. Such cellular-connected UAVs can enable a wide range of key applications expanding from real-time video streaming to item delivery. Despite the several benefits and practical applications of using UAVs as aerial wireless devices, one must address many technical challenges. In this paper, a comprehensive tutorial on the potential benefits and applications of UAVs in wireless communications is presented. Moreover, the important challenges and the fundamental tradeoffs in UAV-enabled wireless networks are thoroughly investigated. In particular, the key UAV challenges such as three-dimensional (3D) deployment, performance analysis, air-to-ground channel modeling, and energy efficiency are explored along with representative results. Then, fundamental open problems and potential research directions pertaining to wireless communications and networking with UAVs are introduced. To cope with the open research problems, various analytical frameworks and mathematical tools such as optimization theory, machine learning, stochastic geometry, transport theory, and game theory are described. The use of such tools for addressing unique UAV problems is also presented. In a nutshell, this tutorial provides key guidelines on how to analyze, optimize, and design UAV-based wireless communication systems.

Data Change Exploration Using Time Series Clustering <=SEP=> Analysis of static data is one of the best studied research areas. However, data changes over time. These changes may reveal patterns or groups of similar values, properties, and entities. We study changes in large, publicly available data repositories by modelling them as time series and clustering these series by their similarity. In order to perform change exploration on real-world data we use the publicly available revision data of Wikipedia Infoboxes and weekly snapshots of IMDB. The changes to the data are captured as events, which we call change records. In order to extract temporal behavior we count changes in time periods and propose a general transformation framework that aggregates groups of changes to numerical time series of different resolutions. We use these time series to study different application scenarios of unsupervised clustering. Our explorative results show that changes made to collaboratively edited data sources can help find characteristic behavior, distinguish entities or properties and provide insight into the respective domains.

The survival of direct composite restorations in the management of severe tooth wear including attrition and erosion: A prospective 8-year study. <=SEP=> OBJECTIVES
Survival of directly placed composite to restore worn teeth has been reported in studies with small sample sizes, short observation periods and different materials. This study aimed to estimate survival for a hybrid composite placed by one clinician up to 8-years follow-up.


METHODS
All patients were referred and recruited for a prospective observational cohort study. One composite was used: Spectrum(®) (DentsplyDeTrey). Most restorations were placed on the maxillary anterior teeth using a Dahl approach.


RESULTS
A total of 1010 direct composites were placed in 164 patients. Mean follow-up time was 33.8 months (s.d. 27.7). 71 of 1010 restorations failed during follow-up. The estimated failure rate in the first year was 5.4% (95% CI 3.7-7.0%). Time to failure was significantly greater in older subjects (p=0.005) and when a lack of posterior support was present (p=0.003). Bruxism and an increase in the occlusal vertical dimension were not associated with failure. The proportion of failures was greater in patients with a Class 3 or edge-to-edge incisal relationship than in Class 1 and Class 2 cases but this was not statistically significant. More failures occurred in the lower arch (9.6%) compared to the upper arch (6%) with the largest number of composites having been placed on the maxillary incisors (n=519).


CONCLUSION
The worn dentition presents a restorative challenge but composite is an appropriate restorative material.


CLINICAL SIGNIFICANCE
This study shows that posterior occlusal support is necessary to optimise survival.

On the dimensionality of organizational justice: a construct validation of a measure. <=SEP=> This study explores the dimensionality of organizational justice and provides evidence of construct validity for a new justice measure. Items for this measure were generated by strictly following the seminal works in the justice literature. The measure was then validated in 2 separate studies. Study 1 occurred in a university setting, and Study 2 occurred in a field setting using employees in an automobile parts manufacturing company. Confirmatory factor analyses supported a 4-factor structure to the measure, with distributive, procedural, interpersonal, and informational justice as distinct dimensions. This solution fit the data significantly better than a 2- or 3-factor solution using larger interactional or procedural dimensions. Structural equation modeling also demonstrated predictive validity for the justice dimensions on important outcomes, including leader evaluation, rule compliance, commitment, and helping behavior.

Music Synthesis with Reconstructive Phrase Modeling <=SEP=> This article describes a new synthesis technology called reconstructive phrase modeling (RPM). A goal of RPM is to combine the realistic sound quality of sampling with the performance interaction of functional synthesis. Great importance is placed on capturing the dynamics of note transitions-slurs, legato, bow changes, etc. Expressive results are achieved with conventional keyboard controllers. Mastery of special performance techniques is not needed. RPM is an analysis-synthesis system that is related to two important trends in computer music research. The first is a form of additive synthesis in which sounds are represented as a sum of time-varying harmonics plus noise elements. RPM creates expressive performances by searching a database of idiomatic instrumental phrases and combining modified fragments of these phrases to form a new expressive performance. This approach is related to another research trend called concatenative synthesis

SAR image formation toolbox for MATLAB <=SEP=> While many synthetic aperture radar (SAR) image formation techniques exist, two of the most intuitive methods for implementation by SAR novices are the matched filter and backprojection algorithms. The matched filter and (non-optimized) backprojection algorithms are undeniably computationally complex. However, the backprojection algorithm may be successfully employed for many SAR research endeavors not involving considerably large data sets and not requiring time-critical image formation. Execution of both image reconstruction algorithms in MATLAB is explicitly addressed. In particular, a manipulation of the backprojection imaging equations is supplied to show how common MATLAB functions, ifft and interp1, may be used for straight-forward SAR image formation. In addition, limits for scene size and pixel spacing are derived to aid in the selection of an appropriate imaging grid to avoid aliasing. Example SAR images generated though use of the backprojection algorithm are provided given four publicly available SAR datasets. Finally, MATLAB code for SAR image reconstruction using the matched filter and backprojection algorithms is provided.

Mitigating Sybils in Federated Learning Poisoning <=SEP=> Machine learning (ML) over distributed multiparty data is required for a variety of domains. Existing approaches, such as federated learning, collect the outputs computed by a group of devices at a central aggregator and run iterative algorithms to train a globally shared model. Unfortunately, such approaches are susceptible to a variety of attacks, including model poisoning, which is made substantially worse in the presence of sybils. In this paper we first evaluate the vulnerability of federated learning to sybil-based poisoning attacks. We then describe FoolsGold, a novel defense to this problem that identifies poisoning sybils based on the diversity of client updates in the distributed learning process. Unlike prior work, our system does not bound the expected number of attackers, requires no auxiliary information outside of the learning process, and makes fewer assumptions about clients

An ensemble classifier with random projection for predicting multi-label protein subcellular localization <=SEP=> In protein subcellular localization prediction, a predominant scenario is that the number of available features is much larger than the number of data samples. Among the large number of features, many of them may contain redundant or irrelevant information, causing the prediction systems suffer from overfitting. To address this problem, this paper proposes a dimensionality-reduction method that applies random projection (RP) to construct an ensemble multi-label classifier for predicting protein subcellular localization. Specifically, the frequencies of occurrences of gene-ontology terms are used as feature vectors, which are projected onto lower-dimensional spaces by random projection matrices whose elements conform to a distribution with zero mean and unit variance. The transformed low-dimensional vectors are classified by an ensemble of one-vs-rest multi-label support vector machine (SVM) classifiers, each corresponding to one of the RP matrices. The scores obtained from the ensemble are then fused for making the final decision. Experimental results on two recent datasets suggest that the proposed method can reduce the dimensions by six folds and remarkably improve the classification performance.

Novel Control Method for Multimodule PV Microinverter With Multiple Functions <=SEP=> This paper presents a novel control method for multimodule photovoltaic microinverter (MI). The proposed MI employs a two-stage topology with active-clamped current-fed push–pull converter cascaded with a full-bridge inverter. This system can operate in grid-connected mode to feed power to the grid with a programmable power factor. This system can also operate in line-interactive mode, i.e., share load power without feeding power to the grid. In the event of grid power failure, the MI can operate in a standalone mode to supply uninterruptible power to the load. This paper presents a multiloop control scheme with power programmable capability for achieving the above multiple functions. In addition, the proposed control scheme embedded a multimodule parallel capability that multiple MI modules can be paralleled to enlarge the capacity with autonomous control in all operation modes. Finally, three 250-W MI modules are adopted to demonstrate the effectiveness of the proposed control method in simulations as well as experiments.

A refined PWM scheme for voltage and current source converters <=SEP=> A pulse-width-modulation (PWM) scheme that uses the converter switching frequency to minimize unwanted load current harmonics is described. This results in the reduction of the number of switch communications per cycle. The method is suitable for high-performance variable-speed AC-motor drives that require high-output switching frequencies for near-silent operation. It is also applicable, without change, to voltage or current-source inverters and to two and four-quadrant three-phase PWM rectifiers. Key predicted results have been verified experimentally on a 5 kVA inverter breadboard.<<ETX>>

Path Planning of Unmanned Aerial Vehicles using B-Splines and Particle Swarm Optimization <=SEP=> Military operations are turning to more complex and advanced automation technologies for minimum risk and maximum efficiency.A critical piece to this strategy is unmanned aerial vehicles. Unmanned aerial vehicles require the intelligence to safely maneuver along a path to an intended target and avoiding obstacles such as other aircrafts or enemy threats. This paper presents a unique three-dimensional path planning problem formulation and solution approach using particle swarm optimization. The problem formulation was designed with three objectives: 1) minimize risk owing to enemy threats, 2) minimize fuel consumption incurred by deviating from the original path, and 3) fly over defined reconnaissance targets. The initial design point is defined as the original path of the unmanned aerial vehicles. Using particle swarm optimization, alternate paths are generated using B-spline curves, optimized based on the three defined objectives. The resulting paths can be optimized with a preference toward maximum safety, minimum fuel consumption, or target reconnaissance. This method has been implemented in a virtual environment where the generated alternate paths can be visualized interactively to better facilitate the decision-making process. The problem formulation and solution implementation is described along with the results from several simulated scenarios demonstrating the effectiveness of the method.

Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks <=SEP=> Complex biological systems have been successfully modeled by biochemical and genetic interaction networks, typically gathered from high-throughput (HTP) data. These networks can be used to infer functional relationships between genes or proteins. Using the intuition that the topological role of a gene in a network relates to its biological function, local or diffusionbased “guilt-by-association” and graph-theoretic methods have had success in inferring gene functions. Here we seek to improve function prediction by integrating diffusion-based methods with a novel dimensionality reduction technique to overcome the incomplete and noisy nature of network data. In this paper, we introduce diffusion component analysis (DCA), a framework that plugs in a diffusion model and learns a low-dimensional vector representation of each node to encode the topological properties of a network. As a proof of concept, we demonstrate DCA’s substantial improvement over state-of-the-art diffusion-based approaches in predicting protein function from molecular interaction networks. Moreover, our DCA framework can integrate multiple networks from heterogeneous sources, consisting of genomic information, biochemical experiments and other resources, to even further improve function prediction. Yet another layer of performance gain is achieved by integrating the DCA framework with support vector machines that take our node vector representations as features. Overall, our DCA framework provides a novel representation of nodes in a network that can be used as a plug-in architecture to other machine learning algorithms to decipher topological properties of and obtain novel insights into interactomes. This paper was selected for oral presentation at RECOMB 2015 and an abstract is published in the conference proceedings. ar X iv :1 50 4. 02 71 9v 1 [ qbi o. M N ] 1 0 A pr 2 01 5

Experiments on deep learning for speech denoising <=SEP=> In this paper we present some experiments using a deep learning model for speech denoising. We propose a very lightweight procedure that can predict clean speech spectra when presented with noisy speech inputs, and we show how various parameter choices impact the quality of the denoised signal. Through our experiments we conclude that such a structure can perform better than some comparable single-channel approaches and that it is able to generalize well across various speakers, noise types and signal-to-noise ratios.

Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media <=SEP=> Drug-drug interactions (DDIs) constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.

Fast Min-Sum Algorithms for Decoding of LDPC over GF(q) <=SEP=> In this paper, we present a fast min-sum algorithm for decoding LDPC codes over GF(q). Our algorithm is different from the one presented by David Declercq and Marc Fossorier (2005) only at the way of speeding up the horizontal scan in the min-sum algorithm. The Declercq and Fossorier's algorithm speeds up the computation by reducing the number of configurations, while our algorithm uses the dynamic programming instead. Compared with the configuration reduction algorithm, the dynamic programming one is simpler at the design stage because it has less parameters to tune. Furthermore, it does not have the performance degradation problem caused by the configuration reduction because it searches the whole configuration space efficiently through dynamic programming. Both algorithms have the same level of complexity and use simple operations which are suitable for hardware implementations

Machine Learning Algorithms for Characterization of EMG Signals <=SEP=> —In the last decades, the researchers of the human arm prosthesis are using different types of machine learning algorithms. This review article firstly gives a brief explanation about type of machine learning methods. Secondly, some recent applications of myoelectric control of human arm prosthesis by using machine learning algorithms are compared. This study presents two different comparisons based on feature extraction methods which are time series modeling and wavelet transform of EMG signal. Finally, of characterization of EMG for of human arm prosthesis have been and discussed.

Generating Haptic Textures with a Vibrotactile Actuator <=SEP=> Vibrotactile actuation is mainly used to deliver buzzing sensations. But if vibrotactile actuation is tightly coupled to users' actions, it can be used to create much richer haptic experiences. It is not well understood, however, how this coupling should be done or which vibrotactile parameters create which experiences. To investigate how actuation parameters relate to haptic experiences, we built a physical slider with minimal native friction, a vibrotactile actuator and an integrated position sensor. By vibrating the slider as it is moved, we create an experience of texture between the sliding element and its track. We conducted a magnitude estimation experiment to map how granularity, amplitude and timbre relate to the experiences of roughness, adhesiveness, sharpness and bumpiness. We found that amplitude influences the strength of the perceived texture, while variations in granularity and timbre create distinct experiences. Our study underlines the importance of action in haptic perception and suggests strategies for deploying such tightly coupled feedback in everyday devices.

Characterizing Taxi Flows in New York City <=SEP=> We present an analysis of taxi flows in Manhattan (NYC) using a variety of data mining approaches. The methods presented here can aid in development of representative and accurate models of large-scale traffic flows with applications to many areas, including outlier detection and characterization.

Curriculum Learning Based on Reward Sparseness for Deep Reinforcement Learning of Task Completion Dialogue Management <=SEP=> Learning from sparse and delayed reward is a central issue in reinforcement learning. In this paper, to tackle reward sparseness problem of task oriented dialogue management, we propose a curriculum based approach on the number of slots of user goals. This curriculum makes it possible to learn dialogue management for sets of user goals with large number of slots. We also propose a dialogue policy based on progressive neural networks whose modules with parameters are appended with previous parameters fixed as the curriculum proceeds, and this policy improves performances over the one with single set of parameters.

A system for understanding imaged infographics and its applications <=SEP=> Information graphics, or infographics, are visual representations of information, data or knowledge. Understanding of infographics in documents is a relatively new research problem, which becomes more challenging when infographics appear as raster images. This paper describes technical details and practical applications of the system we built for recognizing and understanding imaged infographics located in document pages. To recognize infographics in raster form, both graphical symbol extraction and text recognition need to be performed. The two kinds of information are then auto-associated to capture and store the semantic information carried by the infographics. Two practical applications of the system are introduced in this paper, including supplement to traditional optical character recognition (OCR) system and providing enriched information for question answering (QA). To test the performance of our system, we conducted experiments using a collection of downloaded and scanned infographic images. Another set of scanned document pages from the University of Washington document image database were used to demonstrate how the system output can be used by other applications. The results obtained confirm the practical value of the system.

Coupling design and performance analysis of rim-driven integrated motor propulsor <=SEP=> Coupling design of rim-driven integrated motor propulsor for vessels and underwater vehicle is presented in this paper. The main characteristic of integrated motor propulsor is that the motor is integrated in the duct of propulsor. So the coupling design of motor and propulsor is the key to the overall design. Considering the influence of the motor and duct size, the propeller was designed, and the CFD Method was used to analyze the hydrodynamic performance of the propulsor. Based on the air-gap magnetic field of permanent magnet motor and the equivalent magnetic circuit, the integrated motor electromagnetic model was proposed, and the finite element method was used to analyze the motor electromagnetic field. Finally, the simulation of the integrated motor starting process with the load of propulsor torque was carried out, and the results meets the design specifications.

Deep keyphrase generation with a convolutional sequence to sequence model <=SEP=> Keyphrases can provide highly condensed and valuable information that allows users to quickly acquire the main ideas. Most previous studies realize the automatic keyphrase extraction through dividing the source text into multiple chunks and then rank and select the most suitable ones. These approaches ignore the deep semantics behind the text and could not predict the keyphrases not appearing in the source text. A sequence to sequence model to generate keyphrases from vocabulary could solve the issues above. However, traditional sequence to sequence model based on recurrent neural network(RNN) suffers from low efficiency problem. We propose an architecture based entirely on convolutional neural networks. Compared to recurrent models, computations can be completely parallelized over all elements so as to better exploit the GPU hardware. Our use of gated linear units alleviates gradient propagation and we equip each decoder layer with a separate attention model. Moreover, we incorporate a copying mechanism to handle out-of-vocabulary phrases. In experiments, we evaluate our model on six datasets, and our proposed model is demonstrated to outperform state-of-the-art baseline models consistently and significantly, both on extracting the keyphrases existing in the source text and generating the absent keyphrases based on the sematic meaning of the text.