title: Trajectory Planning and Optimized Adaptive Control for a Class of Wheeled Inverted Pendulum Vehicle Models

abstract: In this paper, we investigate optimized adaptive control and trajectory generation for a class of wheeled inverted pendulum (WIP) models of vehicle systems. Aiming at shaping the controlled vehicle dynamics to be of minimized motion tracking errors as well as angular accelerations, we employ the linear quadratic regulation optimization technique to obtain an optimal reference model. Adaptive control has then been developed using variable structure method to ensure the reference model to be exactly matched in a finite-time horizon, even in the presence of various internal and external uncertainties. The minimized yaw and tilt angular accelerations help to enhance the vehicle rider's comfort. In addition, due to the underactuated mechanism of WIP, the vehicle forward velocity dynamics cannot be controlled separately from the pendulum tilt angle dynamics. Inspired by the control strategy of human drivers, who usually manipulate the tilt angle to control the forward velocity, we design a neural-network-based adaptive generator of implicit control trajectory (AGICT) of the tilt angle which indirectly “controls” the forward velocity such that it tracks the desired velocity asymptotically. The stability and optimal tracking performance have been rigorously established by theoretic analysis. In addition, simulation studies have been carried out to demonstrate the efficiency of the developed AGICT and optimized adaptive controller.


title: Localized Synaptic Actions of Neurotrophin-4

abstract: Neurotrophins secreted by the postsynaptic target cell may participate in activity-dependent synaptic modification during development and in the mature brain. A fundamental question of how neurotrophins can sculpt synaptic connections is whether neurotrophin-induced synaptic changes are spatially restricted to the site of neurotrophin secretion or whether they can spread to neighboring synapses. Using a model system of nerve-muscle coculture in which neurotrophin-4 (NT-4) is overexpressed in a subpopulation of postsynaptic myocytes, we demonstrated that presynaptic potentiation is restricted to synapses on myocytes overexpressing NT-4 without affecting nearby synapses formed by the same neuron on control myocytes. Likewise, postsynaptic modulation of acetylcholine channels by secreted NT-4 is spatially restricted to <60 micron from the site of NT-4 secretion. Therefore, activity-dependent secretion of neurotrophins can result in highly localized modification of neuronal connections.


title: A robust 3D eye gaze tracking system using noise reduction

abstract: This paper describes a novel real-time 3D gaze estimation system. The system consists of two cameras and two IR light sources. There are three novelties in this method. First, in our system, two IR lights are mounted near the centers of the stereo cameras, respectively. Based on this specific configuration, the 3D position of the corneal center can be simply derived by the 3D reconstruction technique. Then, after extracting the 3D position of the "virtual pupil" correctly, the optical axis of the eye can be obtained directly by connecting the "virtual pupil" with the corneal center. Second, we systematically analyze the noise in our 3D gaze estimation algorithm and propose an effective constraint to reduce this noise. Third, to estimate the user-dependent parameters (i.e. the constraint parameters and the eye parameters), a simple calibration method is proposed by gazing at four positions on the screen. Experimental results show that our system can accurately estimate and track eye gaze under natural head movement.


title: End-to-End Feature Integration for Correlation Filter Tracking With Channel Attention

abstract: Recently, the performance advancement of discriminative correlation filter (DCF) based trackers is predominantly driven by the use of deep convolutional features. As convolutional features from multiple layers capture different target information, existing works integrate hierarchical convolutional features to enhance target representation. However, these works separate feature integration from DCF learning and hardly benefit from end-to-end training. In this letter, we incorporates feature integration and DCF learning in a unified convolutional neural network. This network reformulates feature integration as a differential module that concatenates features from the shallow and deep layers. A channel attention mechanism is introduced to adaptively impose channel-wise weight on the integrated features. Experimental results on OTB100 and UAV123 demonstrate that our method achieves significant performance improvement while running in real-time.


title: Multilayer Antenna-Filter Antenna for Beam-Steering Transmit-Array Applications

abstract: This paper presents a study on reconfigurable transmit-array (TA) antennas based on multilayer tunable elements. The proposed configuration can be referred to as a multilayer antenna-filter antenna (MAFA) and it can be viewed as an extension of the antenna-filter antenna concept. It consists of multiple antenna layers cascaded to form a spatially distributed bandpass filter. Phase agility is achieved by tuning the resonant frequency of each layer. The main objective of this paper is to introduce a new design technique for MAFA TA antennas. As will be shown, the key feature of the proposed approach is that it allows to simultaneously take into account three key design parameters: the phase tuning range, the maximum acceptable losses per unit cell, and the operating bandwidth. Simulations and experimental results for MAFAs based on varactor-loaded slot antennas will be illustrated to prove the validity of the proposed method. In particular, numerical results will demonstrate that MAFA cells can be designed to provide full phase agility over operating bandwidths of up to 10% with maximum insertion losses of 3 dB. Furthermore, a specific example will show how introducing an inter-layer medium with a higher dielectric permittivity it is possible to reduce the array longitudinal size. Finally, experimental results obtained using a waveguide simulator will be presented for a 1 × 3 five-layer MAFA cell.


title: Visualization as Seen through its Research Paper Keywords

abstract: We present the results of a comprehensive multi-pass analysis of visualization paper keywords supplied by authors for their papers published in the IEEE Visualization conference series (now called IEEE VIS) between 1990–2015. From this analysis we derived a set of visualization topics that we discuss in the context of the current taxonomy that is used to categorize papers and assign reviewers in the IEEE VIS reviewing process. We point out missing and overemphasized topics in the current taxonomy and start a discussion on the importance of establishing common visualization terminology. Our analysis of research topics in visualization can, thus, serve as a starting point to (a) help create a common vocabulary to improve communication among different visualization sub-groups, (b) facilitate the process of understanding differences and commonalities of the various research sub-fields in visualization, (c) provide an understanding of emerging new research trends, (d) facilitate the crucial step of finding the right reviewers for research submissions, and (e) it can eventually lead to a comprehensive taxonomy of visualization research. One additional tangible outcome of our work is an online query tool ( http://keyvis.org/ ) that allows visualization researchers to easily browse the 3952 keywords used for IEEE VIS papers since 1990 to find related work or make informed keyword choices.


title: A scalable and low-cost MQTT broker clustering system

abstract: This paper aims at building a scalable MQTT broker by combining low-cost board computers like Raspberry Pi and open source broker software. The MQTT broker can be used to exchange messages between devices and devices in term of publish/subscribe for create Internet of Things network. Nowadays people extremely interest in IoT not only in small room or home but expand to the big company which have a huge amount of IoT devices. Thus, we propose a scalable and low cost MQTT broker clustering which can implement easily but have high performance with a load balancer for a single entry point of MQTT messages and balance the message in clustering. We decide to use Raspberry Pi as it have low cost and can run linux OS with Docker and have enough spec for MQTT broker. This paper also shows the performance of the Raspberry Pi cluster and how to visualize the status of a computer cluster on SAGE2 wall.


title: A taxonomy of generic clinical questions: classification study.

abstract: OBJECTIVE ::: To develop a taxonomy of doctors' questions about patient care that could be used to help answer such questions. ::: ::: ::: DESIGN ::: Use of 295 questions asked by Oregon primary care doctors to modify previously developed taxonomy of 1101 clinical questions asked by Iowa family doctors. ::: ::: ::: SETTING ::: Primary care practices in Iowa and Oregon. ::: ::: ::: PARTICIPANTS ::: Random samples of 103 Iowa family doctors and 49 Oregon primary care doctors. ::: ::: ::: MAIN OUTCOME MEASURES ::: Consensus among seven investigators on a meaningful taxonomy of generic questions; interrater reliability among 11 individuals who used the taxonomy to classify a random sample of 100 questions: 50 from Iowa and 50 from Oregon. ::: ::: ::: RESULTS ::: The revised taxonomy, which comprised 64 generic question types, was used to classify 1396 clinical questions. The three commonest generic types were "What is the drug of choice for condition x?" (150 questions, 11%); "What is the cause of symptom x?" (115 questions, 8%); and "What test is indicated in situation x?" (112 questions, 8%). The mean interrater reliability among 11 coders was moderate (kappa=0.53, agreement 55%). ::: ::: ::: CONCLUSIONS ::: Clinical questions in primary care can be categorised into a limited number of generic types. A moderate degree of interrater reliability was achieved with the taxonomy developed in this study. The taxonomy may enhance our understanding of doctors' information needs and improve our ability to meet those needs.


title: Remote Area Plant Disease Detection Using Image Processing

abstract: India being an agro-based economy, farmers experince alot of problem in detecting andpreventing diseases in fauna. So there is a necessacity in detecting diseases in fauna which proves to be effective and conviennent for researchers. Relying on pure naked-eye observation to detect and classify diseases can be very unprecise and cumbersome. The color and texture features are used to recognize and classify different agriculture/horticulture produce into normal and affected regions. The combinations of features prove to be very effective in disease detection. The experimental results indicate that proposed approach significantly enhances accuracy in automatic detection of normal and affected produce. This paper presents an effective method for detection of diseases in Malus Domestica using methods like K-meanclustering, color and texture analysis. Keywords-CCM method, color & texture segmentation, K-mean clustering, Malus Domestica, nuclei segmentation


title: OWL Rules: A Proposal and Prototype Implementation

abstract: Although the OWL Web Ontology Language adds considerable expressive power to the Semantic Web it does have expressive limitations, particularly with respect to what can be said about properties. We present the Semantic Web Rule Language (SWRL), a Horn clause rules extension to OWL that overcomes many of these limitations. SWRL extends OWL in a syntactically and semantically coherent manner: the basic syntax for SWRL rules is an extension of the abstract syntax for OWL DL and OWL Lite; SWRL rules are given formal meaning via an extension of the OWL DL model-theoretic semantics; SWRL rules are given an XML syntax based on the OWL XML presentation syntax; and a mapping from SWRL rules to RDF graphs is given based on the OWL RDF/XML exchange syntax. We discuss the expressive power of SWRL, showing that the ontology consistency problem is undecidable, provide several examples of SWRL usage, and discuss a prototype implementation of reasoning support for SWRL.


title: Fish-eye image of hemi-cylinder unwrapping plane based on a flexible technique

abstract: Fish-eye camera is a kind of large field of view camera, thus the image from fish-eye camera is severely distorted. So in this paper, we presented unwrapping of a fish-eye image with hemi-cylinder algorithm, which estimates the image distortion by using a flexible technique. The experimental result shows the accuracy of a flexible technique for calibration camera, and the undistorted image obtained from this process can be projected into hemi-cylinder plane. When compared with classical techniques, the proposed procedure is very useful for panoramic vision for many applications in vision field of robot.


title: Game strategies for The Settlers of Catan

abstract: We present an empirical framework for testing game strategies in The Settlers of Catan, a complex win-lose game that lacks any analytic solution. This framework provides the means to change different components of an autonomous agent's strategy, and to test them in suitably controlled ways via performance metrics in game simulations and via comparisons of the agent's behaviours with those exhibited in a corpus of humans playing the game. We provide changes to the game strategy that not only improve the agent's strength, but corpus analysis shows that they also bring the agent closer to a model of human players.


title: Impact of Computed Tomography on Operative Planning for Ankle Fractures Involving the Posterior Malleolus

abstract: BACKGROUND ::: The purpose of this study was to (1) Determine the effect of computed tomography (CT) on identification of fractures involving the posterior malleolus, (2) determine its effect on operative indications, and (3) determine its effect on the overall operative plan. ::: ::: ::: METHODS ::: Patients with ankle fractures involving the posterior malleolus were identified. Only injuries with complete preoperative plain radiographs and a CT scan were included. Spiral tibia fractures and pilon variants were excluded. The plain radiographs were deidentified, randomized, and presented to 3 orthopedic surgeons. They were asked 3 questions: (1) Is this fracture simple or complex? (2) Does the injury require direct visualization and reduction? and (3) How would you position the patient and approach the fracture? The same process was repeated for the CT scans. A total of 376 posterior malleolus injuries were identified and 25 met the inclusion criteria. ::: ::: ::: RESULTS ::: A complex fracture pattern was identified on 44% of plain radiographs and 56% of CT scans. The surgeons chose to operate in 84% of cases based on plain radiographs and 92% of cases based on CT scan. The observers changed their operative approach or positioning 44% of the time after reviewing CT images. The interobserver and intraobserver correlation coefficients were moderate. ::: ::: ::: CONCLUSION ::: The use of CT scan changed operative positioning and approach in 44% of cases. There was no significant change in characterization or operative indications when comparing plain radiographs to CT scan. CT scan may be a valuable tool in the management of ankle fractures involving the posterior malleolus. ::: ::: ::: LEVEL OF EVIDENCE ::: Diagnostic Level III, comparative series.


title: An in vitro evaluation of graphene oxide reduced by Ganoderma spp. in human breast cancer cells (MDA-MB-231)

abstract: BACKGROUND:Recently, graphene and graphene-related materials have attracted much attention due their unique properties, such as their physical, chemical, and biocompatibility properties. This study aimed to determine the cytotoxic effects of graphene oxide (GO) that is reduced biologically using Ganoderma spp. mushroom extracts in MDA-MB-231 human breast cancer cells. METHODS:Herein, we describe a facile and green method for the reduction of GO using extracts of Ganoderma spp. as a reducing agent. GO was reduced without any hazardous chemicals in an aqueous solution, and the reduced GO was characterized using a range of analytical procedures. The Ganoderma extract (GE)-reduced GO (GE-rGO) was characterized by ultraviolet-visible absorption spectroscopy, X-ray diffraction, Fourier-transform infrared spectroscopy, X-ray photoelectron spectroscopy, dynamic light scattering, scanning electron microscopy, Raman spectroscopy, and atomic force microscopy. Furthermore, the toxicity of GE-rGO was evaluated using a sequence of assays such as cell viability, lactate dehydrogenase leakage, and reactive oxygen species generation in human breast cancer cells (MDA-MB-231). RESULTS:The preliminary characterization of reduction of GO was confirmed by the red-shifting of the absorption peak for GE-rGO to 265 nm from 230 nm. The size of GO and GE-rGO was found to be 1,880 and 3,200 nm, respectively. X-ray diffraction results confirmed that reduction processes of GO and the processes of removing intercalated water molecules and the oxide groups. The surface functionalities and chemical natures of GO and GE-rGO were confirmed using Fourier-transform infrared spectroscopy and X-ray photoelectron spectroscopy. The surface morphologies of the synthesized graphene were analyzed using high-resolution scanning electron microscopy. Raman spectroscopy revealed single- and multilayer properties of GE-rGO. Atomic force microscopy images provided evidence for the formation of graphene. Furthermore, the effect of GO and GE-rGO was examined using a series of assays, such as cell viability, membrane integrity, and reactive oxygen species generation, which are key molecules involved in apoptosis. The results obtained from cell viability and lactate dehydrogenase assay suggest that GO and GE-rGO cause dose-dependent toxicity in the cells. Interestingly, it was found that biologically derived GE-rGO is more toxic to cancer cells than GO. CONCLUSION:We describe a simple, green, nontoxic, and cost-effective approach to producing graphene using mushroom extract as a reducing and stabilizing agent. The proposed method could enable synthesis of graphene with potential biological and biomedical applications such as in cancer and angiogenic disorders. To our knowledge, this is the first report using mushroom extract as a reducing agent for the synthesis of graphene. Mushroom extract can be used as a biocatalyst for the production of graphene.


title: Open the Black Box Data-Driven Explanation of Black Box Decision Systems

abstract: Black box systems for automated decision making, often based on machine learning over (big) data, map a user's features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases hidden in the algorithms, due to human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We introduce the local-to-global framework for black box explanation, a novel approach with promising early results, which paves the road for a wide spectrum of future developments along three dimensions: (i) the language for expressing explanations in terms of highly expressive logic-based rules, with a statistical and causal interpretation; (ii) the inference of local explanations aimed at revealing the logic of the decision adopted for a specific instance by querying and auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of the many local explanations into simple global ones, with algorithms that optimize the quality and comprehensibility of explanations.


title: Estimating Market Power in the Internet Backbone - Using the Ip Transit Band-X Database

abstract: Recent studies have found the Internet backbone to be more competitive than was thought before. This paper explores a novel route to monitor market power using prices and quality data from the online trading site Band-X. First the hypothesis that Europe is a connectivity market on its own, is tested and then, by using a Panel data regression, the paper finds that these markets are not fully competitive since company specific reputation effects play a major role and price variations are only marginally affected by qualitative variables. Worryingly, companies with high prices are often able to supply an inferior quality transit.


title: A Generic and Robust System for Automated Patient-Specific Classification of ECG Signals

abstract: This paper presents a generic and patient-specific classification system designed for robust and accurate detection of ECG heartbeat patterns. The proposed feature extraction process utilizes morphological wavelet transform features, which are projected onto a lower dimensional feature space using principal component analysis, and temporal features from the ECG data. For the pattern recognition unit, feedforward and fully connected artificial neural networks, which are optimally designed for each patient by the proposed multidimensional particle swarm optimization technique, are employed. By using relatively small common and patient-specific training data, the proposed classification system can adapt to significant interpatient variations in ECG patterns by training the optimal network structure, and thus, achieves higher accuracy over larger datasets. The classification experiments over a benchmark database demonstrate that the proposed system achieves such average accuracies and sensitivities better than most of the current state-of-the-art algorithms for detection of ventricular ectopic beats (VEBs) and supra-VEBs (SVEBs). Over the entire database, the average accuracy-sensitivity performances of the proposed system for VEB and SVEB detections are 98.3%-84.6% and 97.4%-63.5%, respectively. Finally, due to its parameter-invariant nature, the proposed system is highly generic, and thus, applicable to any ECG dataset.


title: Service Value Networks

abstract: The current industry-driven trend of providing flexible e-services lays the ground for the new research area ``service value networks'' (SVNs). We observe a rising number of industry-oriented publications provided by research departments of large companies such as IBM or SAP as well as the fact that more and more IS conferences offer special tracks on that issue. However, when it comes to formalizing and economically analyzing such SVNs that offer joint complex services to service customers, scientific approaches are in their infancy. We intend to fill this research gap by providing a clear understanding of service value networks by defining their characteristics, their structure, and their components. Mapping these aspects into a formalized model, we intend to establish a reference point for future work in the area of service value networks.


title: Business Intelligence Maturity: Development and Evaluation of a Theoretical Model

abstract: In order to identify and explore the strengths and weaknesses of business intelligence (BI) initiatives, managers in charge need to assess the maturity of their BI efforts. For this, a wide range of maturity models has been developed, but these models often focus on technical details and do not address the potential value proposition of BI. Based on an extensive literature review and an empirical study, we develop and evaluate a theoretical model of impact-oriented BI maturity. Building on established IS theories, the model integrates BI deployment, BI usage, individual impact, and organizational performance. This conceptualization helps to refocus the topic of BI maturity to business needs and can be used as a theoretical foundation for future research.


title: Clustering of Defect Reports Using Graph Partitioning Algorithms

abstract: We present in this paper several solutions to the challenging task of clustering software defect reports. Clustering defect reports can be very useful for prioritizing the testing effort and to better understand the nature of software defects. Despite some challenges with the language used and semi-structured nature of defect reports, our experiments on data collected from the open source project Mozilla show extremely promising results for clustering software defect reports using natural language processing and graph partitioning techniques. We report results with three models for representing the textual information in the defect reports and three clustering algorithms: normalized cut, size regularized cut, and k-means. Our data collection method allowed us to quickly develop a proof-of-concept setup. Experiments showed that normalized cut achieved the best performance in terms of average cluster purity, accuracy, and normalized mutual information.


title: Towards Understanding Action Recognition

abstract: Although action recognition in videos is widely studied, current methods often fail on real-world datasets. Many recent approaches improve accuracy and robustness to cope with challenging video sequences, but it is often unclear what affects the results most. This paper attempts to provide insights based on a systematic performance evaluation using thoroughly-annotated data of human actions. We annotate human Joints for the HMDB dataset (J-HMDB). This annotation can be used to derive ground truth optical flow and segmentation. We evaluate current methods using this dataset and systematically replace the output of various algorithms with ground truth. This enables us to discover what is important - for example, should we work on improving flow algorithms, estimating human bounding boxes, or enabling pose estimation? In summary, we find that high-level pose features greatly outperform low/mid level features, in particular, pose over time is critical, but current pose estimation algorithms are not yet reliable enough to provide this information. We also find that the accuracy of a top-performing action recognition framework can be greatly increased by refining the underlying low/mid level features, this suggests it is important to improve optical flow and human detection algorithms. Our analysis and J-HMDB dataset should facilitate a deeper understanding of action recognition algorithms.


title: The Tactical Load Smoother for Multi-Sector Planning

abstract: The Program for Harmonised ATM Research in Eurocontrol (PHARE) has proposed a number of new concepts to address forecast problems of airspace capacity shortfall. One of these involves a redistribution of workload upward from single sector level, which currently represents a bottleneck in Air Traffic Control, to a wider multi-sector area. A specialist controller known as a multi-sector planner would work this new area. This paper describes a tool, the Tactical Load Smoother, which is designed to support the multisector planning activity by providing functionality to analyse future traffic, and then produce a clear indication of when and where conditions are likely to become excessively difficult for sector-level controllers. Having identified potential problems, the controller may then intervene in some way to reduce or eliminate them in a timely fashion. After a description of the PHARE context and operational scenario, this paper will undertake a technical discussion of the TLS, with descriptions of the algorithms and examples of the resulting displays. Some early results from the PD3 experiments will then be discussed, and finally, the paper will indicate directions for future research.


title: A Powerful Generative Model Using Random Weights for the Deep Image Representation

abstract: To what extent is the success of deep visualization due to the training? Could we do deep visualization using untrained, random weight networks? To address this issue, we explore new and powerful generative models for three popular deep visualization tasks using untrained, random weight convolutional neural networks. First we invert representations in feature spaces and reconstruct images from white noise inputs. The reconstruction quality is statistically higher than that of the same method applied on well trained networks with the same architecture. Next we synthesize textures using scaled correlations of representations in multiple layers and our results are almost indistinguishable with the original natural texture and the synthesized textures based on the trained network. Third, by recasting the content of an image in the style of various artworks, we create artistic images with high perceptual quality, highly competitive to the prior work of Gatys et al. on pretrained networks. To our knowledge this is the first demonstration of image representations using untrained deep neural networks. Our work provides a new and fascinating tool to study the representation of deep network architecture and sheds light on new understandings on deep visualization.


title: Chinese CCGbank: extracting CCG derivations from the Penn Chinese Treebank

abstract: Automated conversion has allowed the development of wide-coverage corpora for a variety of grammar formalisms without the expense of manual annotation. Analysing new languages also tests formalisms, exposing their strengths and weaknesses. ::: ::: We present Chinese CCGbank, a 760,000 word corpus annotated with Combinatory Categorial Grammar (ccg) derivations, induced automatically from the Penn Chinese Treebank (pctb). We design parsimonious ccg analyses for a range of Chinese syntactic constructions, and transform the pctb trees to produce them. Our process yields a corpus of 27,759 derivations, covering 98.1% of the pctb.


title: Supervised dictionary learning for music genre classification

abstract: This paper concerns the development of a music codebook for summarizing local feature descriptors computed over time. Comparing to a holistic representation, this text-like representation better captures the rich and time-varying information of music. We systematically compare a number of existing codebook generation techniques and also propose a new one that incorporates labeled data in the dictionary learning process. Several aspects of the encoding system such as local feature extraction and codeword encoding are also analyzed. Our result demonstrates the superiority of sparsity-enforced dictionary learning over conventional VQ-based or exemplar-based methods. With the new supervised dictionary learning algorithm and the optimal settings inferred from the performance study, we achieve state-of-the-art accuracy of music genre classification using just the log-power spectrogram as the local feature descriptor. The classification accuracies for benchmark datasets GTZAN and IS-MIR2004Genre are 84.7% and 90.8%, respectively.


title: Experimental comfort assessment of an active exoskeleton interface

abstract: Walking assistive devices are designed to improve the mobility of the user. Although promising, active devices have a problem: they become uncomfortable after prolonged usage. Large motor torques provided by active devices effectively assist the movements of the user. However, these torques transfer forces to the user's limbs creating significant discomfort and limiting the duration of usefulness. This work used force mapping in an attempt to identify zones of pressure concentration that may cause discomfort as well as examined the effect that various pads had on these zones. The device tested in this research was the K-SRD TM by B-Temia Inc. Force analysis identified certain zones of high force concentration. Possible causes were identified and potential solutions were suggested. Furthermore, the padding testing showed that the force transfer was similar regardless of the thickness of the pads. However, the stiffer padding materials distributed the forces over a greater area and decreased the pressure at the interface. This seems to indicate that the thickness of padding is less important than the distribution of applied forces. In all, interfaces that distribute forces over large surface areas may be beneficial. Further research efforts are needed in order to develop a physical human-machine interface that will ensure the success of exoskeletons in the future.


title: Towards Efficient Learning of Optimal Spatial Bag-of-Words Representations

abstract: Spatial Pyramid Matching (SPM) assumes that the spatial Bag-of-Words (BoW) representation is independent of data. However, evidence has shown that the assumption usually leads to a suboptimal representation. In this paper, we propose a novel method called Jensen-Shannon (JS) Tiling to learn the BoW representation from data directly at the BoW level. The proposed JS Tiling is especially appropriate for large-scale datasets as it is orders of magnitude faster than existing methods, but with comparable or even better classification precision. Experimental results on four benchmarks including two TRECVID12 datasets validate that JS Tiling outperforms the SPM and the state-of-the-art methods. The runtime comparison demonstrates that selecting BoW representations by JS Tiling is more than 1,000 times faster than running classifiers. Besides, JS Tiling is an important component contributing to CMU Teams' final submission in TRECVID 2012 Multimedia Event Detection.


title: VALORACIÓN DEL PERFIL LIPÍDICO Y DE LA DENSIDAD MINERAL ÓSEA EN PACIENTES TRASPLANTADOS RENALES

abstract: Introduction: alterations in lipid metabolism and bone mineral metabolism disturbances are common disorders among renal transplant patients, contributing to the apparition of oxidative metabolic and cardiovascular diseases that threaten the integrity of the graft. Aims: describe and observe the evolution of alterations in bone mineral density (BMD) and lipid abnormalities in a population of kidney transplant patients. Material and method: the samples consisted of 119 kidney transplant patients of both sexes, measurements were performed pretransplant and posttransplant for five years of biochemical parameters, anthropometric measurements and measurement of bone mineral density at the lumbar spine, femur and radioulnar. Results: during the five years after transplantation a significant increase in biochemical parameters, BMI, dyslipidemia, diabetes and hypertension occurs. At six months there is a high percentage of patients with pathologic BMD increase by 4.1% per year of transplantation. Conclusions: after kidney transplantation, a large increase of hyperlipidemia associated with a characteristic pattern of altered lipid with elevated total cholesterol, low density lipoprotein, high density lipoprotein, and the resulting increase in triglycerides, occurs despite statin therapy, leading to an increase in risk factors for diabetes, hypertension, diseases and cardiovascualres further loss of bone mass which carries a high risk of serious fractures occurs, threatening kidney graft and quality of life of patients.


title: Introduction to Visualization for Computer Security

abstract: Networked computers are ubiquitous, and are subject to attack, misuse, and abuse. Automated systems to combat this threat are one potential solution, but most automated systems require vigilant human oversight. This automated approach undervalues the strong analytic capabilities of humans. While automation affords opportunities for increased scalability, humans provide the ability to handle exceptions and novel patterns. One method to counteracting the ever increasing cyber threat is to provide the human security analysts with better tools to discover patterns, detect anomalies, identify correlations, and communicate their findings. This is what visualization for computer security (VizSec) researchers and developers are doing. VizSec is about putting robust information visualization tools into the hands of humans to take advantage of the power of the human perceptual and cognitive processes in solving computer security problems. This chapter is an introduction to the VizSec research community and the papers in this volume.


A Synthetic Gallophosphate Molecular Sieve with a 20‐Tetrahedral‐Atom Pore Opening.


title: Service transition: A viable option for manufacturing companies with deteriorating financial performance?

abstract: Abstract When manufacturing firms venture into services, they often do so either in reaction to deteriorating performance or to further enhance their financial position. While extant literature argues that firms are most likely to succeed with strategic change processes out of a healthy position, the majority of manufacturers have started their service maneuver in reaction to deteriorating performance. To date, no empirical studies examine whether a healthy financial situation is a necessary requirement for successful service transition. Drawing on configuration theory and employing qualitative comparative analysis, the present study demonstrates that an increasing focus on services can lead to success for both types of firms. However, depending on their financial situation, firms should tap different resource and knowledge sources to implement the service transition: Whereas healthy companies should focus on customers as a knowledge source, companies in an inferior financial situation need strong links with suppliers to turn their services transition into financial success.


title: Gain-of-function mutations in DNMT3A in patients with paraganglioma

abstract: The high percentage of patients carrying germline mutations makes pheochromocytomas/paragangliomas the most heritable of all tumors. However, there are still cases unexplained by mutations in the known genes. We aimed to identify the genetic cause of disease in patients strongly suspected of having hereditary tumors. Whole-exome sequencing was applied to the germlines of a parent–proband trio. Genome-wide methylome analysis, RNA-seq, CRISPR/Cas9 gene editing, and targeted sequencing were also performed. We identified a novel de novo germline mutation in DNMT3A, affecting a highly conserved residue located close to the aromatic cage that binds to trimethylated histone H3. DNMT3A-mutated tumors exhibited significant hypermethylation of homeobox-containing genes, suggesting an activating role of the mutation. CRISPR/Cas9-mediated knock-in in HeLa cells led to global changes in methylation, providing evidence of the DNMT3A-altered function. Targeted sequencing revealed subclonal somatic mutations in six additional paragangliomas. Finally, a second germline DNMT3A mutation, also causing global tumor DNA hypermethylation, was found in a patient with a family history of pheochromocytoma. Our findings suggest that DNMT3A may be a susceptibility gene for paragangliomas and, if confirmed in future studies, would represent the first example of gain-of-function mutations affecting a DNA methyltransferase gene involved in cancer predisposition.


title: Gradient-based low rank method and its application in image inpainting

abstract: Conventional inpainting methods generally apply textures that are most similar to the areas around the missing region or use large image database. Recently, low rank property of data shows that the non-convex optimization decreases measurements. In this paper, we propose a new image prior, which implies the low rank prior knowledge of image gradients. The proposed detail-preserving image inpainting algorithm adopts the low rank regularization to gradient similarity minimization, termed gradient-based low rank approximation (Grad-LR), namely that we employ the low rank constraints in the horizontal and vertical gradients of the image and then reconstruct the desired image using the adaptive iterative singular-value thresholding of both derivatives. In the method, by incorporating the spatially adaptive iterative singular-value thresholding (SAIST) to optimize our gradient scheme, the deterministic annealing iterates the procedure efficiently. As a result, the strength of the algorithm is obvious when filling large missing region. Experimental results consistently demonstrate that the proposed algorithm works well for both structural and texture images and outperforms other techniques, in terms of both objective and subjective performance measures.


title: Wearing Many (Social) Hats: How Different are Your Different Social Network Personae?

abstract: This paper investigates when users create profiles in different social networks, whether they are redundant expressions of the same persona, or they are adapted to each platform. Using the personal webpages of 116,998 users on About.me, we identify and extract matched user profiles on several major social networks including Facebook, Twitter, LinkedIn, and Instagram. We find evidence for distinct site-specific norms, such as differences in the language used in the text of the profile self-description, and the kind of picture used as profile image. By learning a model that robustly identifies the platform given a user's profile image (0.657--0.829 AUC) or self-description (0.608--0.847 AUC), we confirm that users do adapt their behaviour to individual platforms in an identifiable and learnable manner. However, different genders and age groups adapt their behaviour differently from each other, and these differences are, in general, consistent across different platforms. We show that differences in social profile construction correspond to differences in how formal or informal the platform is.


title: A Family of Joint Sparse PCA Algorithms for Anomaly Localization in Network Data Streams

abstract: Determining anomalies in data streams that are collected and transformed from various types of networks has recently attracted significant research interest. Principal component analysis (PCA) has been extensively applied to detecting anomalies in network data streams. However, none of existing PCA-based approaches addresses the problem of identifying the sources that contribute most to the observed anomaly, or anomaly localization. In this paper, we propose novel sparse PCA methods to perform anomaly detection and localization for network data streams. Our key observation is that we can localize anomalies by identifying a sparse low-dimensional space that captures the abnormal events in data streams. To better capture the sources of anomalies, we incorporate the structure information of the network stream data in our anomaly localization framework. Furthermore, we extend our joint sparse PCA framework with multidimensional Karhunen Loeve Expansion that considers both spatial and temporal domains of data streams to stabilize localization performance. We have performed comprehensive experimental studies of the proposed methods and have compared our methods with the state-of-the-art using three real-world data sets from different application domains. Our experimental studies demonstrate the utility of the proposed methods.


title: Stressed speech emotion recognition using feature fusion of teager energy operator and MFCC

abstract: In this paper, a novel feature fusion of Teager Energy Operator (TEO) and Mel Frequency Cepstral Coefficients (MFCC), as Teager-MFCC (T-MFCC) feature extraction technique, is used to recognize the stressed emotions from speech signal. TEO is a non-linear feature of speech, basically designed to identify the stressed emotions. The Gaussian Mixture Model (GMM) is used for classification of these different stressed emotions with reference to neutral speech. It is found that the proposed method achieves better performance compared to the existing feature extraction technique like MFCC.


title: Adult hippocampal neurogenesis is involved in anxiety-related behaviors

abstract: Adult hippocampal neurogenesis is a unique example of structural plasticity, the functional role of which has been a matter of intense debate. New transgenic models have recently shown that neurogenesis participates in hippocampus-mediated learning. Here, we show that transgenic animals, in which adult hippocampal neurogenesis has been specifically impaired, exhibit a striking increase in anxiety-related behaviors. Our results indicate that neurogenesis plays an important role in the regulation of affective states and could be the target of new treatments for anxiety disorders.


Downloading Hidden Web Content


title: OFF-State Degradation of AlGaN/GaN Power HEMTs: Experimental Demonstration of Time-Dependent Drain-Source Breakdown

abstract: This paper reports the experimental demonstration of a novel degradation mechanism of high-power AlGaN/GaN high electron mobility transistors (HEMTs), that is, time-dependent drain-source breakdown. With current-controlled breakdown measurements and constant voltage stress experiments we demonstrate that: 1) when submitted to constant voltage stress, in the OFF-state, the HEMTs can show a significant degradation; 2) the degradation process is time-dependent, and consists of a measurable increase in subthreshold drain-source leakage; this effect is ascribed to the accumulation of positive charge in proximity of the gate, consistently with previous theoretical calculations; and 3) a catastrophic (and permanent) failure is observed for long stress times, possibly due to thermal runaway or to the increase in the electric field in proximity of the localized drain-source leakage paths.


title: A stepped-carrier 77-GHz OFDM MIMO radar system with 4 GHz bandwidth

abstract: This paper presents a 77-GHz orthogonal frequency-division multiplexing (OFDM) multiple-input multiple-output (MIMO) radar system with 4 GHz bandwidth. Due to the limited bandwidth of the analog-to-digital and digital-to-analog converters a stepped-carrier approach is used. OFDM radar measurements with a bandwidth of 200MHz at 20 different carrier frequencies are combined in signal processing to achieve a high range resolution OFDM MIMO radar. Spectrally-interleaved OFDM signals are used to allow for simultaneous transmission and thus MIMO operation. The proposed approach is verified in practical measurements of a static target scenario and a slowly moving target scenario. Therefore a self-developed software defined radar evaluation platform with four transmitters and four receivers is used. The expected high range resolution could be achieved and range/angle/velocity measurements show the suitability for detecting slowly moving targets like pedestrians.


title: Hardening Distributed and Encrypted Keyword Search via Blockchain

abstract: Distributed storage platforms draw much attention due to their high reliability and scalability for handling a massive amount of data. To protect user and data privacy, encryption is considered as a necessary feature for production systems like Storj. But it prohibits the nodes from performing content search. To preserve the functionality, we observe that a protocol of integration with searchable encryption and keyword search via distributed hash table allows the nodes in a network to search over encrypted and distributed data. However, this protocol does not address a practical threat in a fully distributed scenario. Malicious nodes would sabotage search results, and easily infiltrate the system as the network grows. Using primitives such as MAC and verifiable data structure may empower the users to verify the search result, but the robustness of the overall system can hardly be ensured. In this paper, we address this issue by proposing a protocol that is seamlessly incorporated to encrypted search in distributed network to attest and monitor nodes. From the moment a node joins the system, it will be attested and continuously monitored through verifiable search queries. The result of each attestation is determined via a standard quorum-based voting protocol, and then recorded on the blockchain as a consensus view of trusted nodes. Based on the proposed protocols, malicious nodes can be detected and removed by a majority of nodes in a self-determining manner. To demonstrate the security and efficiency, we conduct robustness analysis against several potential attacks, and perform performance and overhead evaluation on the proposed protocol.


title: Hand-gesture sensing Doppler radar with metamaterial-based leaky-wave antennas

abstract: This paper presents a Doppler radar with metamaterial-based leaky-wave antennas for the hand-gesture sensing application. Since the antenna main beam of the developed radar can be arbitrarily pointed to a known azimuth angle, the hand gesture moving in the decomposed orthogonal directions can be successfully detected. In addition, the phase information caused by the hand gesture is accurately retrieved to calculate the distance of the hand movement.


title: Advanced Modulation Schemes for Short-Range Optical Communications

abstract: The performance of advanced modulation schemes for spectrally efficient data transmission is reviewed, targeting short-range intensity-modulated optical channels with direct detection. Hereby, the focus lies on the performance of multilevel pulse-amplitude modulation combined with electronic equalization and, as an alternative modulation scheme, discrete multitone. A comprehensive statistical analysis of clipping noise is presented and exact expressions for the performance of symmetrically clipped discrete multitone are derived. It is shown that the clipping noise is impulsive and obeys a generalized Laplace distribution. The bit-error probability due to clipping is studied in detail, and it is found that the impact of clipping noise is reduced for an increasing number of subchannels. Finally, the optical link margins of multilevel pulse-amplitude modulation in combination with electronic equalization and that of discrete multitone in combination with margin-adaptive bit loading are compared. It is found that even symmetrically clipped discrete multitone suffers from its large crest factor in the peak-power-limited channel and that, in many instances, pulse-amplitude modulation provides higher link margins for the same target bit-error probability.


title: A Faster and More Realistic Flush+Reload Attack on AES

abstract: Cloud's unrivaled cost effectiveness and on the fly operation versatility is attractive to enterprise and personal users. However, the cloud inherits a dangerous behavior from virtualization systems that poses a serious security risk: resource sharing. This work exploits a shared resource optimization technique called memory deduplication to mount a powerful known-ciphertext only cache side-channel attack on a popular OpenSSL implementation of AES. In contrast to the other cross-VM cache attacks, our attack does not require synchronization with the target server and is fully asynchronous, working in a more realistic scenario with much weaker assumption. Also, our attack succeeds in just 15 seconds working across cores in the cross-VM setting. Our results show that there is strong information leakage through cache in virtualized systems and the memory deduplication should be approached with caution.


title: Beyond Automated Essay Scoring

abstract: The ability to communicate in natural language has long been considered a defining characteristic of human intelligence. Furthermore, we hold our ability to express ideas in writing as a pinnacle of this uniquely human language facility—it defies formulaic or algorithmic specification. So it comes as no surprise that attempts to devise computer programs that evaluate writing are often met with resounding skepticism. Nevertheless, automated writing-evaluation systems might provide precisely the platforms we need to elucidate many of the features that characterize good and bad writing, and many of the linguistic, cognitive, and other skills that underlie the human capacity for both reading and writing. Using computers to increase our understanding of the textual features and cognitive skills involved in creating and comprehending written text will have clear benefits. It will help us develop more effective instructional materials for improving reading, writing, and other human communication abilities. It will also help us develop more effective technologies, such as search engines and questionanswering systems, for providing universal access to electronic information. A sketch of the brief history of automated writing-evaluation research and its future directions might lend some credence to this argument.


title: AnN-bit digitally variable ultra-wideband pulse generator for GPR and UWB applications

abstract: This paper presents a low-cost ultra-wideband (UWB) pulse generator that can vary the pulse duration digitally by using a step-recovery diode (SRD), microstrip transmission lines, and PIN diodes. First, a sharp edge is generated by using SRD circuitry. Then a pulse is formed from the sharp edge through the use of transmission lines and the PIN diodes. Based on the number of transmission lines (N), the duration of the pulse can be varied in 2N steps. The UWB pulse generator circuits are implemented on an FR-4 substrate using microstrip-line technology and UWB pulses with durations of 550 to 2400 psec are measured. © 2006 Wiley Periodicals, Inc. Microwave Opt Technol Lett 48: 1334–1339, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21620


title: A Deep Primal-Dual Network for Guided Depth Super-Resolution

abstract: In this paper we present a novel method to increase the spatial resolution of depth images. We combine a deep fully convolutional network with a non-local variational method in a deep primal-dual network. The joint network computes a noise-free, high-resolution estimate from a noisy, low-resolution input depth map. Additionally, a high-resolution intensity image is used to guide the reconstruction in the network. By unrolling the optimization steps of a first-order primal-dual algorithm and formulating it as a network, we can train our joint method end-to-end. This not only enables us to learn the weights of the fully convolutional network, but also to optimize all parameters of the variational method and its optimization procedure. The training of such a deep network requires a large dataset for supervision. Therefore, we generate high-quality depth maps and corresponding color images with a physically based renderer. In an exhaustive evaluation we show that our method outperforms the state-of-the-art on multiple benchmarks.


title: Developing Niching Algorithms in Particle Swarm Optimization

abstract: Niching as an important technique for multimodal optimization has been used widely in the Evolutionary Computation research community. This chapter aims to provide a survey of some recent efforts in developing state-of-the-art PSO niching algorithms. The chapter first discusses some common issues and difficulties faced when using niching methods, then describe several existing PSO niching algorithms and how they combat these problems by taking advantages of the unique characteristics of PSO. This chapter will also describe a recently proposed lbest ring topology based niching PSO. Our experimental results suggest that this lbest niching PSO compares favourably against some existing PSO niching algorithms.


Sentential Paraphrasing as Black-Box Machine Translation


title: A multifunctional RNA recognition motif in poly(A)-specific ribonuclease with cap and poly(A) binding properties.

abstract: Poly(A)-specific ribonuclease (PARN) is an oligomeric, processive and cap-interacting 3' exoribonuclease that efficiently degrades mRNA poly(A) tails. Here we show that the RNA recognition motif (RRM) of PARN harbors both poly(A) and cap binding properties, suggesting that the RRM plays an important role for the two critical and unique properties that are tightly associated with PARN activity, i.e. recognition and dependence on both the cap structure and poly(A) tail during poly(A) hydrolysis. We show that PARN and its RRM have micromolar affinity to the cap structure by using fluorescence spectroscopy and nanomolar affinity for poly(A) by using filter binding assay. We have identified one tryptophan residue within the RRM that is essential for cap binding but not required for poly(A) binding, suggesting that the cap- and poly(A)-binding sites associated with the RRM are both structurally and functionally separate from each other. RRM is one of the most commonly occurring RNA-binding domains identified so far, suggesting that other RRMs may have both cap and RNA binding properties just as the RRM of PARN.


title: Centimeter Positioning with a Smartphone-Quality GNSS Antenna

abstract: This paper demonstrates for the first time that centimeteraccurate positioning is possible based on data sampled from a smartphone-quality Global Navigation Satellite System (GNSS) antenna. Centimeter-accurate smartphone positioning will enable a host of new applications such as globally-registered fiduciary-marker-free augmented reality and location-based contextual advertising, both of which have been hampered by the several-meterlevel errors in traditional GNSS positioning. An empirical analysis of data collected from a smartphone-grade GNSS antenna reveals the antenna to be the primary impediment to fast and reliable resolution of the integer ambiguities which arise when solving for a centimeter-accurate carrierphase differential position. The antenna’s poor multipath suppression and irregular gain pattern result in large timecorrelated phase errors which significantly increase the time to integer ambiguity resolution as compared to even a low-quality stand-alone patch antenna. The time to integer resolution—and to a centimeter-accurate fix—is significantly reduced when more GNSS signals are tracked or when the smartphone experiences gentle wavelength-scale random motion.


title: Distributed network traffic feature extraction for a real-time IDS

abstract: Internet traffic as well as network attacks have been growing rapidly that necessitates efficient network traffic monitoring. Many efforts have been put to address this issue; however, rapid monitoring applications are needed. We propose a distributed architecture based intrusion detection system (IDS) that is capable of detecting the anomalies in the network in real-time. To achieve this, we exploit the Apache Spark framework and Netmap- a line-rate packet capturing tool. In this work, we implement one of the challenging modules of an IDS, i.e., feature extraction, and present the computational results of the same for TCP-based traffic. Related results are presented along with the insight gained for future work.


title: Quantum machine learning over infinite dimensions

abstract: Machine learning is a fascinating and exciting field within computer science. Recently, this excitement has been transferred to the quantum information realm. Currently, all proposals for the quantum version of machine learning utilize the finite-dimensional substrate of discrete variables. Here we generalize quantum machine learning to the more complex, but still remarkably practical, infinite-dimensional systems. We present the critical subroutines of quantum machine learning algorithms for an all-photonic continuous-variable quantum computer that can lead to exponential speedups in situations where classical algorithms scale polynomially. Finally, we also map out an experimental implementation which can be used as a blueprint for future photonic demonstrations.


title: Antiangiogenic Therapy of a Recurrent Giant Cell Tumor of the Mandible With Interferon Alfa-2a

abstract: We report a 5-year-old girl with a large rapidly growing giant cell tumor of the mandible that recurred 2 months after the first surgical excision and 3 months after a second resection. An angiogenic protein, (bFGF), was abnormally elevated in her urine. The patient was treated with interferon alfa-2a for 1 year because this agent inhibits angiogenesis by suppressing bFGF overexpression in infantile hemangiomas and in other human tumors. During this time the bone tumor regressed and disappeared, the urinary bFGF fell to normal levels, and the mandible regenerated. She has remained tumor-free and has been off therapy for 3 years at this writing. This first successful use of interferon alfa-2a to treat a mandibular tumor in a child demonstrates: 1) low grade tumors that overexpress bFGF may respond to interferon alfa-2a, in a manner similar to life-threatening infantile hemangiomas; 2) antiangiogenic therapy, given without interruption for 1 year, was safe and effective in this patient; and 3) treatment may be continued for 1 year without the development of drug resistance.


CUA guideline on adult overactive bladder


title: A smart parking system using WiFi and wireless sensor network

abstract: In the last few years, smart parking has been focused due to the development of indoor position technology. There are several smart parking systems for garage. But there are two major problems in these systems. The first one is the detection of parking space occupation, and the second one is space navigation. To solve these two problems, we propose a smart parking system combining WiFi and wireless sensor network. In this system, geomagnetic sensors are used to detect the occupation of parking spaces, and WiFi is used for navigation. A prototype system has been developed.


title: Flexible affix classification for stemming Indonesian Language

abstract: The stemming is the process to derive the basic word by removing affix of the word. The stemming is tightly related to basic word or lemma and the sub lemmas. The lemma and sub lemma of Indonesian Language have been grown and absorb from foreign languages or Indonesian traditional languages. Our approach provides the easy way of stemming Indonesian language through flexibility affix classification. Therefore, the affix additional can be applied in easy way. We experiment with 1,704 text documents with 255,182 tokens and the stemmed words is 3,648 words. In this experiment, we compare our approach performance to the confix-stripping approach performance. The result shows that our performance can cover the failure in stemming reduplicated words of confix-stripping approach.


title: A new active labeling method for deep learning

abstract: Deep learning has been shown to achieve outstanding performance in a number of challenging real-world applications. However, most of the existing works assume a fixed set of labeled data, which is not necessarily true in real-world applications. Getting labeled data is usually expensive and time consuming. Active labelling in deep learning aims at achieving the best learning result with a limited labeled data set, i.e., choosing the most appropriate unlabeled data to get labeled. This paper presents a new active labeling method, AL-DL, for cost-effective selection of data to be labeled. AL-DL uses one of three metrics for data selection: least confidence, margin sampling, and entropy. The method is applied to deep learning networks based on stacked restricted Boltzmann machines, as well as stacked autoencoders. In experiments on the MNIST benchmark dataset, the method outperforms random labeling consistently by a significant margin.


title: Quantifying social group evolution

abstract: The rich set of interactions between individuals in society results in complex community structure, capturing highly connected circles of friends, families or professional cliques in a social network. Thanks to frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution. Our knowledge of the mechanisms governing the underlying community dynamics is limited, but is essential for a deeper understanding of the development and self-optimization of society as a whole. We have developed an algorithm based on clique percolation that allows us to investigate the time dependence of overlapping communities on a large scale, and thus uncover basic relationships characterizing community evolution. Our focus is on networks capturing the collaboration between scientists and the calls between mobile phone users. We find that large groups persist for longer if they are capable of dynamically altering their membership, suggesting that an ability to change the group composition results in better adaptability. The behaviour of small groups displays the opposite tendency-the condition for stability is that their composition remains unchanged. We also show that knowledge of the time commitment of members to a given community can be used for estimating the community's lifetime. These findings offer insight into the fundamental differences between the dynamics of small groups and large institutions.


title: Interleukin 21 and its receptor are involved in NK cell expansion and regulation of lymphocyte function

abstract: Cytokines are important in the regulation of haematopoiesis and immune responses, and can influence lymphocyte development. Here we have identified a class I cytokine receptor that is selectively expressed in lymphoid tissues and is capable of signal transduction. The full-length receptor was expressed in BaF3 cells, which created a functional assay for ligand detection and cloning. Conditioned media from activated human CD3+ T cells supported proliferation of the assay cell line. We constructed a complementary DNA expression library from activated human CD3+ T cells, and identified a cytokine with a four-helix-bundle structure using functional cloning. This cytokine is most closely related to IL2 and IL15, and has been designated IL21 with the receptor designated IL21R. In vitro assays suggest that IL21 has a role in the proliferation and maturation of natural killer (NK) cell populations from bone marrow, in the proliferation of mature B-cell populations co-stimulated with anti-CD40, and in the proliferation of T cells co-stimulated with anti-CD3.


title: Real-Time Location-Based Rendering of Urban Underground Pipelines

abstract: The concealment and complex spatial relationships of urban underground pipelines present challenges in managing them. Recently, augmented reality (AR) has been a hot topic around the world, because it can enhance our perception of reality by overlaying information about the environment and its objects onto the real world. Using AR, underground pipelines can be displayed accurately, intuitively, and in real time. We analyzed the characteristics of AR and their application in underground pipeline management. We mainly focused on the AR pipeline rendering procedure based on the BeiDou Navigation Satellite System (BDS) and simultaneous localization and mapping (SLAM) technology. First, in aiming to improve the spatial accuracy of pipeline rendering, we used differential corrections received from the Ground-Based Augmentation System to compute the precise coordinates of users in real time, which helped us accurately retrieve and draw pipelines near the users, and by scene recognition the accuracy can be further improved. Second, in terms of pipeline rendering, we used Visual-Inertial Odometry (VIO) to track the rendered objects and made some improvements to visual effects, which can provide steady dynamic tracking of pipelines even in relatively markerless environments and outdoors. Finally, we used the occlusion method based on real-time 3D reconstruction to realistically express the immersion effect of underground pipelines. We compared our methods to the existing methods and concluded that the method proposed in this research improves the spatial accuracy of pipeline rendering and the portability of the equipment. Moreover, the updating of our rendering procedure corresponded with the moving of the user’s location, thus we achieved a dynamic rendering of pipelines in the real environment.


title: Supervising Unsupervised Learning

abstract: We introduce a framework to leverage knowledge acquired from a repository of (heterogeneous) supervised datasets to new unsupervised datasets. Our perspective avoids the subjectivity inherent in unsupervised learning by reducing it to supervised learning, and provides a principled way to evaluate unsupervised algorithms. We demonstrate the versatility of our framework via simple agnostic bounds on unsupervised problems. In the context of clustering, our approach can help choose the number of clusters, the clustering algorithm, and provably circumvents Kleinberg's impossibility result. Experimental results across hundreds of problems demonstrate improved performance on unsupervised data with simple algorithms, despite the fact problems come from different domains. Additionally, a deep learning algorithm learns common features from many small datasets across multiple domains.


title: Identifying Noun Product Features that Imply Opinions

abstract: Identifying domain-dependent opinion words is a key problem in opinion mining and has been studied by several researchers. However, existing work has been focused on adjectives and to some extent verbs. Limited work has been done on nouns and noun phrases. In our work, we used the feature-based opinion mining model, and we found that in some domains nouns and noun phrases that indicate product features may also imply opinions. In many such cases, these nouns are not subjective but objective. Their involved sentences are also objective sentences and imply positive or negative opinions. Identifying such nouns and noun phrases and their polarities is very challenging but critical for effective opinion mining in these domains. To the best of our knowledge, this problem has not been studied in the literature. This paper proposes a method to deal with the problem. Experimental results based on real-life datasets show promising results.


title: Dynamic Topic Modeling for Monitoring Market Competition from Online Text and Image Data

abstract: We propose a dynamic topic model for monitoring temporal evolution of market competition by jointly leveraging tweets and their associated images. For a market of interest (e.g. luxury goods), we aim at automatically detecting the latent topics (e.g. bags, clothes, luxurious) that are competitively shared by multiple brands (e.g. Burberry, Prada, and Chanel), and tracking temporal evolution of the brands' stakes over the shared topics. One of key applications of our work is social media monitoring that can provide companies with temporal summaries of highly overlapped or discriminative topics with their major competitors. We design our model to correctly address three major challenges: multiview representation of text and images, modeling of competitiveness of multiple brands over shared topics, and tracking their temporal evolution. As far as we know, no previous model can satisfy all the three challenges. For evaluation, we analyze about 10 millions of tweets and 8 millions of associated images of the 23 brands in the two categories of luxury and beer. Through experiments, we show that the proposed approach is more successful than other candidate methods for the topic modeling of competition. We also quantitatively demonstrate the generalization power of the proposed method for three prediction tasks.


title: Constrained K-Means Clustering

abstract: We consider practical methods for adding constraints to the K-Means clustering algorithm in order to avoid local solutions with empty clusters or clusters having very few points. We often observe this phenomena when applying K-Means to datasets where the number of dimensions is n 10 and the number of desired clusters is k 20. We propose explicitly adding k constraints to the underlying clustering optimization problem requiring that each cluster have at least a minimum number of points in it. We then investigate the resulting cluster assignment step. Preliminary numerical tests on real datasets indicate the constrained approach is less prone to poor local solutions, producing a better summary of the underlying data. Contrained K-Means Clustering 1


title: Toward understanding respiratory sinus arrhythmia: Relations to cardiac vagal tone, evolution and biobehavioral functions

abstract: Respiratory sinus arrhythmia (RSA, or high-frequency heart-rate variability) is frequently employed as an index of cardiac vagal tone or even believed to be a direct measure of vagal tone. However, there are many significant caveats regarding vagal tone interpretation: 1. Respiratory parameters can confound relations between RSA and cardiac vagal tone. 2. Although intraindividual relations between RSA and cardiac vagal control are often strong, interindividual associations may be modest. 3. RSA measurement is profoundly influenced by concurrent levels of momentary physical activity, which can bias estimation of individual differences in vagal tone. 4. RSA magnitude is affected by beta-adrenergic tone. 5. RSA and cardiac vagal tone can dissociate under certain circumstances. 6. The polyvagal theory contains evolution-based speculations that relate RSA, vagal tone and behavioral phenomena. We present evidence that the polyvagal theory does not accurately depict evolutionof vagal control of heart-ratevariability, and that it ignores the phenomenon of cardiac aliasing and disregards the evolution of a functional role for vagal control of the heart, from cardiorespiratory synchrony in fish to RSA in mammals.


title: Mental Health Consumers' Experience of Stigma

abstract: The extent to which mental health consumers encounter stigma in their daily lives is a matter of substantial importance for their recovery and quality of life. This article summarizes the results of a nationwide survey of 1,301 mental health consumers concerning their experience of stigma and discrimination. Survey results and followup interviews with 100 respondents revealed experience of stigma from a variety of sources, including communities, families, churches, coworkers, and mental health caregivers. The majority of respondents tended to try to conceal their disorders and worried a great deal that others would find out about their psychiatric status and treat them unfavorably. They reported discouragement, hurt, anger, and lowered self-esteem as results of their experiences, and they urged public education as a means for reducing stigma. Some reported that involvement in advocacy and speaking out when stigma and discrimination were encountered helped them to cope with stigma. Limitations to generalization of results include the self-selection, relatively high functioning of participants, and respondent connections to a specific advocacy organization-the National Alliance for the Mentally Ill.


title: Accuracy analysis of surface normal reconstruction in stereo vision

abstract: Estimating surface normals is an important task in computer vision, e.g. in surface reconstruction, registration and object detection. In stereo vision, the error of depth reconstruction increases quadratically with distance. This makes estimation of surface normals an especially demanding task. In this paper, we analyze how error propagates from noisy disparity data to the orientation of the estimated surface normal. Firstly, we derive a transformation for normals between disparity space and world coordinates. Afterwards, the propagation of disparity noise is analyzed by means of a Monte Carlo method. Normal reconstruction at a pixel position requires to consider a certain neighborhood of the pixel. The extent of this neighborhood affects the reconstruction error. Our method allows to determine the optimal neighborhood size required to achieve a pre specified deviation of the angular reconstruction error, defined by a confidence interval. We show that the reconstruction error only depends on the distance of the surface point to the camera, the pixel distance to the principal point in the image plane and the angle at which the viewing ray intersects the surface.


title: Structure-Infused Copy Mechanisms for Abstractive Summarization

abstract: Seq2seq learning has produced promising results on summarization. However, in many cases, system summaries still struggle to keep the meaning of the original intact. They may miss out important words or relations that play critical roles in the syntactic structure of source sentences. In this paper, we present structure-infused copy mechanisms to facilitate copying important words and relations from the source sentence to summary sentence. The approach naturally combines source dependency structure with the copy mechanism of an abstractive sentence summarizer. Experimental results demonstrate the effectiveness of incorporating source-side syntactic information in the system, and our proposed approach compares favorably to state-of-the-art methods.


title: Deep Gaussian Processes with Convolutional Kernels

abstract: Deep Gaussian processes (DGPs) provide a Bayesian non-parametric alternative to standard parametric deep learning models. A DGP is formed by stacking multiple GPs resulting in a well-regularized composition of functions. The Bayesian framework that equips the model with attractive properties, such as implicit capacity control and predictive uncertainty, makes it at the same time challenging to combine with a convolutional structure. This has hindered the application of DGPs in computer vision tasks, an area where deep parametric models (i.e. CNNs) have made breakthroughs. Standard kernels used in DGPs such as radial basis functions (RBFs) are insufficient for handling pixel variability in raw images. In this paper, we build on the recent convolutional GP to develop Convolutional DGP (CDGP) models which effectively capture image level features through the use of convolution kernels, therefore opening up the way for applying DGPs to computer vision tasks. Our model learns local spatial influence and outperforms strong GP based baselines on multi-class image classification. We also consider various constructions of convolution kernel over the image patches, analyze the computational trade-offs and provide an efficient framework for convolutional DGP models. The experimental results on image data such as MNIST, rectangles-image, CIFAR10 and Caltech101 demonstrate the effectiveness of the proposed approaches.


title: A New Wireless Communication Paradigm through Software-Controlled Metasurfaces

abstract: Electromagnetic waves undergo multiple uncontrollable alterations as they propagate within a wireless environment. Free space path loss, signal absorption, as well as reflections, refractions, and diffractions caused by physical objects within the environment highly affect the performance of wireless communications. Currently, such effects are intractable to account for and are treated as probabilistic factors. This article proposes a radically different approach, enabling deterministic, programmable control over the behavior of wireless environments. The key enabler is the so-called HyperSurface tile, a novel class of planar meta-materials that can interact with impinging electromagnetic waves in a controlled manner. The HyperSurface tiles can effectively re-engineer electromagnetic waves, including steering toward any desired direction, full absorption, polarization manipulation, and more. Multiple tiles are employed to coat objects such as walls, furniture, and overall, any objects in indoor and outdoor environments. An external software service calculates and deploys the optimal interaction types per tile to best fit the needs of communicating devices. Evaluation via simulations highlights the potential of the new concept.


title: RGBD-HuDaAct: A color-depth video database for human daily activity recognition

abstract: In this paper, we present a home-monitoring oriented human activity recognition benchmark database, based on the combination of a color video camera and a depth sensor. Our contributions are two-fold: 1) We have created a publicly releasable human activity video database (i.e., named as RGBD-HuDaAct), which contains synchronized color-depth video streams, for the task of human daily activity recognition. This database aims at encouraging more research efforts on human activity recognition based on multi-modality sensor combination (e.g., color plus depth). 2) Two multi-modality fusion schemes, which naturally combine color and depth information, have been developed from two state-of-the-art feature representation methods for action recognition, i.e., spatio-temporal interest points (STIPs) and motion history images (MHIs). These depth-extended feature representation methods are evaluated comprehensively and superior recognition performances over their uni-modality (e.g., color only) counterparts are demonstrated.


title: Complexity of computing optimal Stackelberg strategies in security resource allocation games

abstract: Recently, algorithms for computing game-theoretic solutions have been deployed in real-world security applications, such as the placement of checkpoints and canine units at Los Angeles International Airport. These algorithms assume that the defender (security personnel) can commit to a mixed strategy, a so-called Stackelberg model. As pointed out by Kiek-intveld et al. (2009), in these applications, generally, multiple resources need to be assigned to multiple targets, resulting in an exponential number of pure strategies for the defender. In this paper, we study how to compute optimal Stackelberg strategies in such games, showing that this can be done in polynomial time in some cases, and is NP-hard in others.


title: Effects of oral administration of rotenone on gastrointestinal functions in mice.

abstract: BACKGROUND ::: The systemic rotenone model of Parkinson's disease (PD) accurately replicates many aspects of the pathology of human PD, especially neurodegeneration of the substantia nigra and lesions in the enteric nervous system (ENS). Nevertheless, the precise effects of oral rotenone on the ENS have not been addressed yet. This study was therefore designed to assess the effects of a chronic oral treatment by rotenone on enteric neurochemical phenotype, gastrointestinal (GI) motility, and intestinal epithelial barrier permeability. ::: ::: ::: METHODS ::: Male C57BL6N mice received once daily oral rotenone administration for 28 days. GI functions were analyzed 4 weeks after rotenone treatment. Gastrointestinal motility was assessed by measuring gastric emptying, total transit time, fecal pellet output, and bead latency. Intestinal barrier permeability was evaluated both in vivo and ex vivo. The number of enteric neurons and the enteric neurochemical phenotype were analyzed by immunohistochemistry. Tyrosine hydroxylase (TH) immunostaining of dopaminergic neurons of the substantia nigra was performed in a subset of animals. ::: ::: ::: KEY RESULTS ::: Mice treated orally with rotenone had a decrease in fecal pellet output and in jejunal alpha-synuclein expression as compared with control animals. This was associated with a significant decrease in TH-immunoreactive neurons in the substantia nigra. No change in gastric emptying, total transit time, intestinal epithelial barrier permeability, and enteric neurochemical phenotype was observed. ::: ::: ::: CONCLUSIONS & INFERENCES ::: Chronic oral treatment with rotenone only induced minor changes in the ENS and did not recapitulate the GI abnormalities seen in PD, while it replicates neurodegeneration of the substantia nigra.


title: A Public Domain Dataset for Human Activity Recognition using Smartphones

abstract: Human-centered computing is an emerging research field that aims to understand human behavior and integrate users and their social context with computer systems. One of the most recent, challenging and appealing applications in this framework consists in sensing human body motion using smartphones to gather context information about people actions. In this context, we describe in this work an Activity Recognition database, built from the recordings of 30 subjects doing Activities of Daily Living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors, which is released to public domain on a well-known on-line repository. Results, obtained on the dataset by exploiting a multiclass Support Vector ::: Machine (SVM), are also acknowledged.


title: Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach

abstract: Identification of cancer subtypes plays an important role in revealing useful insights into disease pathogenesis and advancing personalized therapy. The recent development of high-throughput sequencing technologies has enabled the rapid collection of multi-platform genomic data (e.g., gene expression, miRNA expression, and DNA methylation) for the same set of tumor samples. Although numerous integrative clustering approaches have been developed to analyze cancer data, few of them are particularly designed to exploit both deep intrinsic statistical properties of each input modality and complex cross-modality correlations among multi-platform input data. In this paper, we propose a new machine learning model, called multimodal deep belief network (DBN), to cluster cancer patients from multi-platform observation data. In our integrative clustering framework, relationships among inherent features of each single modality are first encoded into multiple layers of hidden variables, and then a joint latent model is employed to fuse common features derived from multiple input modalities. A practical learning algorithm, called contrastive divergence (CD), is applied to infer the parameters of our multimodal DBN model in an unsupervised manner. Tests on two available cancer datasets show that our integrative data analysis approach can effectively extract a unified representation of latent features to capture both intra- and cross-modality correlations, and identify meaningful disease subtypes from multi-platform cancer data. In addition, our approach can identify key genes and miRNAs that may play distinct roles in the pathogenesis of different cancer subtypes. Among those key miRNAs, we found that the expression level of miR-29a is highly correlated with survival time in ovarian cancer patients. These results indicate that our multimodal DBN based data analysis approach may have practical applications in cancer pathogenesis studies and provide useful guidelines for personalized cancer therapy.


title: Predicting equity market price impact with performance weighted ensembles of random forests

abstract: For many players in financial markets, the price impact of their trading activity represents a large proportion of their transaction costs. This paper proposes a novel machine learning method for predicting the price impact of order book events. Specifically, we introduce a prediction system based on performance weighted ensembles of random forests. The system's performance is benchmarked using ensembles of other popular regression algorithms including: liner regression, neural networks and support vector regression using depth-of-book data from the BATS Chi-X exchange. The results show that recency-weighted ensembles of random forests produce over 15% greater prediction accuracy on out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmarks.


title: 'Svenami tu, pastore': Don Giovanni Tenorio antieroe goldoniano

abstract: From the European, well-known ‘myth’ of Don Giovanni spanning the seventeenth century with charm ever new, and with variations to increase their functional capacity stage, Goldoni distances himself in his tragi-comedy Don Giovanni Tenorio, emphasizing an unprecedented feature of the seducer, that of cowardice. Decommissioned the role of the swordsman always ready to duel, the antihero of the Age of Enlightenment, even emptied of its moral value, appears in the eyes of the viewers as a bourgeois any, panicked because hounded by events. The allure for this stage is not less alive, considering that forty years later Mozart will draw his masterpiece perhaps even inspired by Goldoni and his original explicit, far different from that of the baroque scenarios.


title: Synthesis, characterization and electrocatalytic activity of Pt supported on poly (3,4-ethylenedioxythiophene)–V2O5 nanocomposites electrodes for methanol oxidation

abstract: Abstract Platinum nanoparticles have been supported on PEDOT–V 2 O 5 nanocomposite through the reduction of chloroplatinic acid with formic acid. PEDOT–V 2 O 5 hybrid nanocomposites are thus found to be a good support of Pt particles as catalysts for oxidation of methanol. Surface properties and morphology of the Pt deposit on nanocomposite were characterized using XRD, SEM, EDAX and TEM analysis. EDX mapping and TEM images show the fine dispersion of Pt nanoparticle on the nanocomposite support. TEM images show 4-nm Pt nanoparticles dispersed on the nanocomposite surfaces. The electrocatalytic activity of Pt/PEDOT–V 2 O 5 nanocomposite catalysts toward methanol oxidation was investigated and compared with Pt/C. Pt/PEDOT–V 2 O 5 nanocomposite catalysts exhibited enhanced electrocatalytic activity when compared with commercially available Pt/C catalysts and the chronoamperometric response revealed better activity and stability of the nanocomposite electrode when compared with the Pt/C electrode.


title: Cell Counting by Regression Using Convolutional Neural Network

abstract: The ability to accurately quantitate specific populations of cells is important for precision diagnostics in laboratory medicine. For example, the quantization of positive tumor cells can be used clinically to determine the need for chemotherapy in a cancer patient. In this paper, we describe a supervised learning framework with Convolutional Neural Network (CNN) and cast the cell counting task as a regression problem, where the global cell count is taken as the annotation to supervise training, instead of following the classification or detection framework. To further decrease the prediction error of counting, we tune several cutting-edge CNN architectures (e.g. Deep Residual Network) into the regression model. As the final output, not only the cell count is estimated for an image, but also its spatial density map is provided. The proposed method is evaluated with three state-of-the-art approaches on three cell image datasets and obtain superior performance.


Algorithm to Elicit Utilities via the Uncertain Equivalence Method


title: An assessment form for clinical nursing education: a Delphi study.

abstract: AIM ::: This paper reports a study to develop further the existing assessment form and to capture new aspects of assessment for the nursing profession of the future for inclusion in the form. ::: ::: ::: BACKGROUND ::: Since nursing education became part of the higher education system, the assessment of clinical periods of the programme has become more complicated and requirements are more demanding. Changes in the health care sector, such as demographic changes and shorter hospitalization, create demands upon the independent nursing role of the future. Many educational documents, such as an assessment form, must continuously be up-dated and adapted to changes in society. ::: ::: ::: METHOD ::: A Delphi study concerning the content of this assessment form was carried out using two rounds. Through this process, an expert panel gave their opinions about the form and possible changes to it. ::: ::: ::: RESULTS ::: There was general acceptance of the content in the current assessment form. Suggested changes were the addition of two factors concerning collaboration with the family and society, and development of the student's independence. Two new area headings were suggested: one about ability to use the nursing process, and the other about development of a professional stance. ::: ::: ::: CONCLUSIONS ::: The suggested changes in the assessment form match expected changes in the health care sector and the demands of an academic nursing education.


title: Delignified and Densified Cellulose Bulk Materials with Excellent Tensile Properties for Sustainable Engineering.

abstract: Today's materials research aims at excellent mechanical performance in combination with advanced functionality. In this regard, great progress has been made in tailoring the materials by assembly processes in bottom-up approaches. In the field of wood-derived materials, nanocellulose research has gained increasing attention, and materials with advanced properties were developed. However, there are still unresolved issues concerning upscaling for large-scale applications. Alternatively, the sophisticated hierarchical scaffold of wood can be utilized in a top-down approach to upscale functionalization, and one can profit at the same time from its renewable nature, CO2 storing capacity, light weight, and good mechanical performance. Nevertheless, for bulk wood materials, a wider multipurpose industrial use is so far impeded by concerns regarding durability, natural heterogeneity as well as limitations in terms of functionalization, processing, and shaping. Here, we present a novel cellulose bulk material concept based on delignification and densification of wood resulting in a high-performance material. A delignification process using hydrogen peroxide and acetic acid was optimized to delignify the entire bulk wooden blocks and to retain the highly beneficial structural directionality of wood. In a subsequent step, these cellulosic blocks were densified in a process combining compression and lateral shear to gain a very compact cellulosic material with entangled fibers while retaining unidirectional fiber orientation. The cellulose bulk materials obtained by different densification protocols were structurally, chemically, and mechanically characterized revealing superior tensile properties compared to native wood. Furthermore, after delignification, the cellulose bulk material can be easily formed into different shapes, and the delignification facilitates functionalization of the bioscaffold.


title: PD control with on-line gravity compensation for robots with elastic joints: Theory and experiments

abstract: A proportional-derivative (PD) control with on-line gravity compensation is proposed for regulation tasks of robot manipulators with elastic joints. The work extends a previous PD control with constant gravity compensation at the desired configuration. The control law requires measuring only position and velocity on the motor side of the elastic joints, while the on-line gravity compensation torque uses a biased measure of the motor position. It is proved via a Lyapunov argument that the control law globally asymptotically stabilizes the desired robot configuration. A simulation study on a two-joint arm reveals the better performance that can be obtained with the new scheme as compared to the case of constant gravity compensation. Moreover, the proposed controller is experimentally tested on an eight-joint cable-driven robot manipulator, in combination with a point-to-point interpolating trajectory, showing the practical advantages of the on-line compensation.


title: Rediscovering Computational Autopoiesis

abstract: This paper summarizes some initial empirical results from a new computer model (artificial chemistry) which exhibits spontaneous emergence and persistence of autopoietic organization. the model is based on a system originally presented by Varela, Maturana, and Uribe. In carrying out this reimplementation it was found that an additional interaction (chain-based bond inhibition), not documented in the original description by Varela et al., is critical to the realization of the autopoietic phenomena. This required interaction was rediscovered only following careful examination of (unpublished) source code for an early version of the original model. The purpose of the paper is thus two-fold: firstly, to identify and discuss this previously undocumented, but essential, interaction; and secondly, to argue, on the basis of this particular case, for the importance of exploiting the emerging technologies which support publication of completely detailed software models (in addition, of course, to conventional publication of summary experimental results).


title: DNNVM : End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-based CNN Accelerators

abstract: The convolutional neural network (CNN) has become a state-of-the-art method for several artificial intelligence domains in recent years. The increasingly complex CNN models are both computation-bound and I/O-bound. FPGA-based accelerators driven by custom instruction set architecture (ISA) achieve a balance between generality and efficiency, but there is much on them left to be optimized. We propose the full-stack compiler DNNVM, which is an integration of optimizers for graphs, loops and data layouts, and an assembler, a runtime supporter and a validation environment. The DNNVM works in the context of deep learning frameworks and transforms CNN models into the directed acyclic graph: XGraph. Based on XGraph, we transform the optimization challenges for both the data layout and pipeline into graph-level problems. DNNVM enumerates all potentially profitable fusion opportunities by a heuristic subgraph isomorphism algorithm to leverage pipeline and data layout optimizations, and searches for the optimal execution strategies of the whole computing graph. On the Xilinx ZU2 @330 MHz and ZU9 @330 MHz, we achieve equivalently state-of-the-art performance on our benchmarks by naive implementations without optimizations, and the throughput is further improved up to 1.26x by leveraging heterogeneous optimizations in DNNVM. Finally, with ZU9 @330 MHz, we achieve state-of-the-art performance for VGG and ResNet50. We achieve a throughput of 2.82 TOPs/s and an energy efficiency of 123.7 GOPs/s/W for VGG. Additionally, we achieve 1.38 TOPs/s for ResNet50.