title: Performance Evaluation of NoC-Based Multicore Systems: From Traffic Analysis to NoC Latency Modeling

abstract: In this survey, we review several approaches for predicting performance of Network-on-Chip (NoC)-based multicore systems, starting from the traffic models to the complex NoC models for latency evaluation. We first review typical traffic models to represent the application workloads in NoC. Specifically, we review Markovian and non-Markovian (e.g., self-similar or long-range memory-dependent) traffic models and discuss their applications on multicore platform design. Then, we review the analytical techniques to predict NoC performance under given input traffic. We investigate analytical models for average as well as maximum delay evaluation. We also review the developments and design challenges of NoC simulators. One interesting research direction in NoC performance evaluation consists of combining simulation and analytical models in order to exploit their advantages together. Toward this end, we discuss several newly proposed approaches that use hardware-based or learning-based techniques. Finally, we summarize several open problems and our perspective to address these challenges.


title: A closer look at recognition-based graphical passwords on mobile devices

abstract: Graphical password systems based on the recognition of photographs are candidates to alleviate current over-reliance on alphanumeric passwords and PINs. However, despite being based on a simple concept -- and user evaluations consistently reporting impressive memory retention -- only one commercial example exists and overall take-up is low. Barriers to uptake include a perceived vulnerability to observation attacks; issues regarding deployability; and the impact of innocuous design decisions on security not being formalized. Our contribution is to dissect each of these issues in the context of mobile devices -- a particularly suitable application domain due to their increasing significance, and high potential to attract unauthorized access. This produces: 1) A novel yet simple solution to the intersection attack that permits greater variability in login challenges; 2) Detailed analysis of the shoulder surfing threat that considers both simulated and human testing; 3) A first look at image processing techniques to contribute towards automated photograph filtering. We operationalize our observations and gather data in a field context where decentralized mechanisms of varying entropy were installed on the personal devices of participants. Across two working weeks success rates collected from users of a high entropy version were similar to those of a low entropy version at 77%, and login durations decreased significantly across the study.


title: methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles

abstract: DNA methylation is a chemical modification of cytosine bases that is pivotal for gene regulation,cellular specification and cancer development. Here, we describe an R package, methylKit, thatrapidly analyzes genome-wide cytosine epigenetic profiles from high-throughput methylation andhydroxymethylation sequencing experiments. methylKit includes functions for clustering, samplequality visualization, differential methylation analysis and annotation features, thus automatingand simplifying many of the steps for discerning statistically significant bases or regions of DNAmethylation. Finally, we demonstrate methylKit on breast cancer data, in which we find statisticallysignificant regions of differential methylation and stratify tumor subtypes. methylKit is availableat http://code.google.com/p/methylkit.


title: Platelet rich fibrin: a new paradigm in periodontal regeneration

abstract: Among the great challenges facing clinical research is the development of bioactive surgical additives regulating inflammation and increasing healing. Although the use of fibrin adhesives and platelet-rich plasma (PRP) is well documented, they have their own limitations. Hence, reconstructive dental surgeons are looking for an "edge" that jump starts the healing process to maximize predictability as well as the volume of regenerated bone. Overcoming the restrictions related to the reimplantation of blood-derived products, a new family of platelet concentrate, which is neither a fibrin glue nor a classical platelet concentrate, was developed in France. This second generation platelet concentrate called platelet-rich fibrin (PRF), has been widely used to accelerate soft and hard tissue healing. Its advantages over the better known PRP include ease of preparation/application, minimal expense, and lack of biochemical modification (no bovine thrombin or anticoagulant is required). This article serves as an introduction to the PRF "concept" and its potential clinical applications with emphasis on periodontal regeneration.


title: Exponential Moving Average Model in Parallel Speech Recognition Training

abstract: As training data rapid growth, large-scale parallel training with multi-GPUs cluster is widely applied in the neural network model learning currently.We present a new approach that applies exponential moving average method in large-scale parallel training of neural network model. It is a non-interference strategy that the exponential moving average model is not broadcasted to distributed workers to update their local models after model synchronization in the training process, and it is implemented as the final model of the training system. Fully-connected feed-forward neural networks (DNNs) and deep unidirectional Long short-term memory (LSTM) recurrent neural networks (RNNs) are successfully trained with proposed method for large vocabulary continuous speech recognition on Shenma voice search data in Mandarin. The character error rate (CER) of Mandarin speech recognition further degrades than state-of-the-art approaches of parallel training.


title: Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning

abstract: In this paper, we propose an automated computer platform for the purpose of classifying Electroencephalography (EEG) signals associated with left and right hand movements using a hybrid system that uses advanced feature extraction techniques and machine learning algorithms. It is known that EEG represents the brain activity by the electrical voltage fluctuations along the scalp, and Brain-Computer Interface (BCI) is a device that enables the use of the brain's neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements. In our research work, we aspired to find the best feature extraction method that enables the differentiation between left and right executed fist movements through various classification algorithms. The EEG dataset used in this research was created and contributed to PhysioNet by the developers of the BCI2000 instrumentation system. Data was preprocessed using the EEGLAB MATLAB toolbox and artifacts removal was done using AAR. Data was epoched on the basis of Event-Related (De) Synchronization (ERD/ERS) and movement-related cortical potentials (MRCP) features. Mu/beta rhythms were isolated for the ERD/ERS analysis and delta rhythms were isolated for the MRCP analysis. The Independent Component Analysis (ICA) spatial filter was applied on related channels for noise reduction and isolation of both artifactually and neutrally generated EEG sources. The final feature vector included the ERD, ERS, and MRCP features in addition to the mean, power and energy of the activations of the resulting Independent Components (ICs) of the epoched feature datasets. The datasets were inputted into two machine- learning algorithms: Neural Networks (NNs) and Support Vector Machines (SVMs). Intensive experiments were carried out and optimum classification performances of 89.8 and 97.1 were obtained using NN and SVM, respectively. This research shows that this method of feature extraction holds some promise for the classification of various pairs of motor movements, which can be used in a BCI context to mentally control a computer or machine. Keywords—EEG; BCI; ICA; MRCP; ERD/ERS; machine learning; NN; SVM


title: Targeted single-cell electroporation of mammalian neurons in vivo

abstract: In order to link our knowledge of single neurons with theories of network function, it has been a long-standing goal to manipulate the activity and gene expression of identified subsets of mammalian neurons within the intact brain in vivo. This protocol describes a method for delivering plasmid DNA into single identified mammalian neurons in vivo, by combining two-photon imaging with single-cell electroporation. Surgery, mounting of a chronic recording chamber and targeted electroporation of identified neurons can be performed within 1-2 h. Stable transgene expression can reliably be induced with high success rates both in single neurons as well as in small, spatially defined networks of neurons in the cerebral cortex of rodents.


title: A programmable and virtualized network & IT infrastructure for the internet of things: How can NFV & SDN help for facing the upcoming challenges

abstract: The Internet of Things (IoT) revolution has major impacts on the network & Information Technology (IT) infrastructure. As IT in the past decade, network virtualization is simultaneously on its way with for instance Network Functions Virtualization (NFV) and Software-Defined Networking (SDN). NFV and SDN are approaches enhancing the infrastructure agility thus facilitating the design, delivery and operation of network services in a dynamic and scalable manner. IoT will push the infrastructure to its limit with numerous and diverse requirements to fulfill, we therefore believe that the agility brought by the combination of NFV and SDN is essential to face the IoT revolution. In this article, we first highlight some IoT challenges that the network & IT infrastructure will face. The NFV and SDN benefits are presented from a network operator point of view. Following a description of the IoT ecosystem and a recall of some of the IoT stakeholders expectations, a new multi-layered IoT architecture involving SDN and NFV and based upon network & IT resources is put forward. Finally, the article illustrates how the proposed architecture is able to cope with the identified IoT challenges.


title: Information systems in the public sector: The e-Government enactment framework

abstract: Despite the burgeoning number of studies of public sector information systems, very few scholars have focussed on the relationship between e-Government policies and information systems choice and design. Drawing on Fountain's (2001) technology enactment framework, this paper endeavours to conduct an in-depth investigation of the intricacies characterising the choice and design of new technologies in the context of e-Government reforms. By claiming that technologies are carriers of e-Government reform aims, this study investigates the logics embedded in the design of new technology and extant political interests and values inscribed in e-Government policies. The e-Government enactment framework is proposed as a theoretical and analytical approach to understand and study the complexity of these relationships which shape e-Government policies.


title: Context-Aware Analytics in MOM Applications

abstract: Manufacturing Operations Management (MOM) systems are complex in the sense that they integrate data from heterogeneous systems inside the automation pyramid. The need for context-aware analytics arises from the dynamics of these systems that influence data generation and hamper comparability of analytics, especially predictive models (e.g. predictive maintenance), where concept drift affects application of these models in the future. Recently, an increasing amount of research has been directed towards data integration using semantic context models. Manual construction of such context models is an elaborate and error-prone task. Therefore, we pose the challenge to apply combinations of knowledge extraction techniques in the domain of analytics in MOM, which comprises the scope of data integration within Product Life-cycle Management (PLM), Enterprise Resource Planning (ERP), and Manufacturing Execution Systems (MES). We describe motivations, technological challenges and show benefits of context-aware analytics, which leverage from and regard the interconnectedness of semantic context data. Our example scenario shows the need for distribution and effective change tracking of context information.


title: Unbiased MMSE-Based Noise Power Estimation With Low Complexity and Low Tracking Delay

abstract: Recently, it has been proposed to estimate the noise power spectral density by means of minimum mean-square error (MMSE) optimal estimation. We show that the resulting estimator can be interpreted as a voice activity detector (VAD)-based noise power estimator, where the noise power is updated only when speech absence is signaled, compensated with a required bias compensation. We show that the bias compensation is unnecessary when we replace the VAD by a soft speech presence probability (SPP) with fixed priors. Choosing fixed priors also has the benefit of decoupling the noise power estimator from subsequent steps in a speech enhancement framework, such as the estimation of the speech power and the estimation of the clean speech. We show that the proposed speech presence probability (SPP) approach maintains the quick noise tracking performance of the bias compensated minimum mean-square error (MMSE)-based approach while exhibiting less overestimation of the spectral noise power and an even lower computational complexity.


title: Path Planning and Motion Coordination in Multiple Mobile Robot Teams

abstract: Autonomous robot An autonomous robot is a robot that can perform tasks in unstructured environments with minimal human guidance. Planned path A planned path is a pre-determined, obstacle-free, trajectory that a robot can follow to reach its goal position from its starting position. Complete path planner A complete path planner is an algorithm that is guaranteed to find a path, if one exists. Deadlocked path planning A deadlock is a situation in path planning in which a solution cannot be found, even though one exists. Typically, this is caused by robots blocking each other’s paths, and the planner being unable to find a solution in which robots move out of each other’s way.


title: VeriCon: towards verifying controller programs in software-defined networks

abstract: Software-defined networking (SDN) is a new paradigm for operating and managing computer networks. SDN enables logically-centralized control over network devices through a "controller" software that operates independently from the network hardware, and can be viewed as the network operating system. Network operators can run both inhouse and third-party SDN programs (often called applications) on top of the controller, e.g., to specify routing and access control policies. SDN opens up the possibility of applying formal methods to prove the correctness of computer networks. Indeed, recently much effort has been invested in applying finite state model checking to check that SDN programs behave correctly. However, in general, scaling these methods to large networks is challenging and, moreover, they cannot guarantee the absence of errors. We present VeriCon, the first system for verifying that an SDN program is correct on all admissible topologies and for all possible (infinite) sequences of network events. VeriCon either confirms the correctness of the controller program on all admissible network topologies or outputs a concrete counterexample. VeriCon uses first-order logic to specify admissible network topologies and desired network-wide invariants, and then implements classical Floyd-Hoare-Dijkstra deductive verification using Z3. Our preliminary experience indicates that VeriCon is able to rapidly verify correctness, or identify bugs, for a large repertoire of simple core SDN programs. VeriCon is compositional, in the sense that it verifies the correctness of execution of any single network event w.r.t. the specified invariant, and can thus scale to handle large programs. To relieve the burden of specifying inductive invariants from the programmer, VeriCon includes a separate procedure for inferring invariants, which is shown to be effective on simple controller programs. We view VeriCon as a first step en route to practical mechanisms for verifying network-wide invariants of SDN programs.


title: Design procedure for two-stage CMOS transconductance operational amplifiers: a tutorial

abstract: This paper deals with well-defined design criteria for two-stage CMOS transconductance operational amplifiers. A novel and simple design procedure is presented, which allows electrical parameters to be univocally related to the value of each circuit element and biasing value. Unlike previous methods, the proposed one is suited for a pencil-and-paper design and yields accurate performance optimization without introducing unnecessary circuit constraints. Bandwidth optimization strategies are also discussed. SPICE simulations based on the proposed procedures are given which closely agree the expected results.


title: Guidelines for Effective Usage of Text Highlighting Techniques

abstract: Semi-automatic text analysis involves manual inspection of text. Often, different text annotations (like part-of-speech or named entities) are indicated by using distinctive text highlighting techniques. In typesetting there exist well-known formatting conventions, such as bold typeface, italics, or background coloring, that are useful for highlighting certain parts of a given text. Also, many advanced techniques for visualization and highlighting of text exist; yet, standard typesetting is common, and the effects of standard typesetting on the perception of text are not fully understood. As such, we surveyed and tested the effectiveness of common text highlighting techniques, both individually and in combination, to discover how to maximize pop-out effects while minimizing visual interference between techniques. To validate our findings, we conducted a series of crowdsourced experiments to determine: i) a ranking of nine commonly-used text highlighting techniques; ii) the degree of visual interference between pairs of text highlighting techniques; iii) the effectiveness of techniques for visual conjunctive search. Our results show that increasing font size works best as a single highlighting technique, and that there are significant visual interferences between some pairs of highlighting techniques. We discuss the pros and cons of different combinations as a design guideline to choose text highlighting techniques for text viewers.


title: Multisensory integration : methodological approaches and emerging principles in the human brain

abstract: Understanding the conditions under which the brain integrates the different sensory streams and the mechanisms supporting this phenomenon is now a question at the forefront of neuroscience. In this paper, we discuss the opportunities for investigating these multisensory processes using modern imaging techniques, the nature of the information obtainable from each method and their benefits and limitations. Despite considerable variability in terms of paradigm design and analysis, some consistent findings are beginning to emerge. The detection of brain activity in human neuroimaging studies that resembles multisensory integration responses at the cellular level in other species, suggests similar crossmodal binding mechanisms may be operational in the human brain. These mechanisms appear to be distributed across distinct neuronal networks that vary depending on the nature of the shared information between different sensory cues. For example, differing extents of correspondence in time, space or content seem to reliably bias the involvement of different integrative networks which code for these cues. A combination of data obtained from haemodynamic and electromagnetic methods, which offer high spatial or temporal resolution respectively, are providing converging evidence of multisensory interactions at both “early” and “late” stages of processing––suggesting a cascade of synergistic processes operating in parallel at different levels of the cortex.


title: Human resource information systems ( HRIS ) and technology trust

abstract: Scholars in many disciplines have considered the antecedents and consequences of various forms of trust. This paper generates 11 propositions exploring the relationship between Human Resource Information Systems (HRIS) and the trust an individual places in the inanimate technology (technology trust) and models the effect of those relationships on HRIS implementation success. Specifically, organizational, technological, and user factors are considered and modeled to generate a set of testable propositions that can subsequently be investigated in various organizational settings. Eleven propositions are offered suggesting that organizational trust, pooled interdependence, organizational community, organizational culture, technology adoption, technology utility, technology usability, socialization, sensitivity to privacy, and predisposition to trust influence an individual's level of trust in the HRIS technology (technology trust) and ultimately the success of an HRIS implementation process. A summary of the relationships between the key constructs in the model and recommendations for future research are provided.


title: Analysis of coaxial-to-waveguide transitions in end launcher type

abstract: A new structure of different dielectrics with septum for the end launcher coaxial-to-waveguide is proposed in this paper, and an advanced L-loop end launcher is also put forward, manufactured and measured. Both the principle and design methods for the two proposals are detailed discussed. The advantages, the manufacturing and environmental sensitivities of the two structures are also analyzed and the advanced L-loop end launcher is the more superior plan.


title: ReCon: Revealing and Controlling PII Leaks in Mobile Network Traffic

abstract: It is well known that apps running on mobile devices extensively track and leak users' personally identifiable information (PII) [1] and users are generally unaware and unable to stop them [2]; however, these users have little visibility into PII leaked through the network traffic generated by their devices, and have poor control over how, when and where that traffic is sent and handled by third parties. This demo presents the design, implementation, and evaluation of ReCon: a cross-platform (iOS, Android, Windows) system that reveals PII leaks and gives users control over them without requiring any special privileges or custom OSes. ReCon, which has been accepted as a paper to the Mobisys conference, leverages machine learning to reveal potential PII leaks by inspecting network traffic, and provides a visualization tool to empower users with the ability to control these leaks via blocking or substitution of PII. For this demo, we will show how ReCon detects PII in real time, allows users to provide feedback on detected PII, and provides users with controls for blocking or modifying PII leaks. The system is publicly available and we will also help any interested users install it on their mobile devices.


title: UWB antenna for brain stroke and brain tumour detection

abstract: UWB Pentagon antenna is designed to detect brain stroke and brain tumour. It is operating at a band from 3.3568– 12.604 GHz in free space and from 3.818 to 9.16 GHz on the normal head model. The antenna has dimensions of 44×30mm2. It is fabricated on FR4-substrate with relative permittivity 4.4 and thickness 1.5mm. The antenna is simulated on the CST Microwave Studio and measured using the network analyser. There is a good agreement between the measured and simulated results of the return loss of the antenna on human's head and head phantom.


title: Hybrid filter-wrapper feature selection for short-term load forecasting

abstract: Selection of input features plays an important role in developing models for short-term load forecasting (STLF). Previous studies along this line of research have focused pre-dominantly on filter and wrapper methods. Given the potential value of a hybrid selection scheme that includes both filter and wrapper methods in constructing an appropriate pool of features, coupled with the general lack of success in employing filter or wrapper methods individually, in this study we propose a hybrid filter-wrapper approach for STLF feature selection. This proposed approach, which is believed to have taken full advantage of the strengths of both filter and wrapper methods, first uses the Partial Mutual Information based filter method to filter out most of the irrelevant and redundant features, and subsequently applies a wrapper method, implemented via a firefly algorithm, to further reduce the redundant features without degrading the forecasting accuracy. The well-established support vector regression is selected as the modeler to implement the proposed hybrid feature selection scheme. Real-world electricity load datasets from a North-American electric utility and the Global Energy Forecasting Competition 2012 have been used to test the performance of the proposed approach, and the experimental results show its superiority over selected counterparts. A flowchart of the proposed hybrid feature selection method.Display Omitted We propose a filter-wrapper feature selection method for STLF.PMI is first used to filter irrelevant and redundant features.A wrapper method is then used to further reduce the remaining redundant features.The proposed hybrid method can identify less inputs with relatively shorter time.Better forecasting results are obtained based on the selected features.


Deep Learning with Minimal Training Data: TurkuNLP Entry in the BioNLP Shared Task 2016


Managing the development of large software systems: concepts and techniques


title: Design Feasibility of a Vortex Induced Vibration Based Hydro-Kinetic Energy Harvesting System

abstract: The vortex induced vibration (VIV) based power generating system discussed in this paper is a new concept in power generation from fluid flows in oceans, rivers and streams. The possibility of harnessing energy from the vibrations incurred in a bluff body due to the phenomenon known as VIV, in which motion is induced on a body facing an external flow due to the periodic irregularities in the flow caused by boundary layer separation is explored. The VIV system is based on the idea of maximizing rather than spoiling vortex shedding and exploiting rather than suppressing VIV. The vibrating bodies are in turn used to harness energy using an efficient power-take-off system. The study of fluid dynamics, along with maintaining vortex shedding over the synchronization range, plays an important role in the design of such a system. Apart from this, the main challenge in the design of a VIV generator is introducing optimal damping and low mass ratios for high energy conversion and an efficient power take- off mechanism. This work presents a scoping study of such a device. In contrast to previous work on forced vibrations, the present study focuses on free vibrations that arise due to the shedding of vortices from a cylinder. The working and design considerations of the energy converter is outlined starting with a set of basic definitions pertaining to this technology.


title: Data-driven enterprise architecture and the TOGAF ADM phases

abstract: This paper investigates how Data as a disruptive technology could be integrated into TOGAF. Given the recent attention of Big Data and Data Science as disruptors, this paper investigates what the impact on the enterprise could be and how Enterprise Architecture (EA) should accommodate data to enable data-driven EA. There is no model currently available that investigates how Big Data can be incorporated into data-driven EA solutions. This study specifically focuses on how the TOGAF ADM could support a data-driven enterprise. Through document analysis and a systematic literature review, a specific adaption of the TOGAF ADM is proposed that indicates the influence that Data and Big Data has on each phase within the ADM.


title: The past makes the present meaningful: Nostalgia as an existential resource.

abstract: The current research tested the proposition that nostalgia serves an existential function by bolstering a sense of meaning in life. Study 1found that nostalgia was positively associated with a sense of meaning in life. Study 2 experimentally demonstrated that nostalgia increases a sense of meaning in life. In both studies, the link between nostalgia and increased meaning in life was mediated by feelings of social connectedness. Study 3 evidenced that threatened meaning increases nostalgia. Study 4 illustrated that nostalgia, in turn, reduces defensiveness following a meaning threat. Finally, Studies 5 and 6 showed that nostalgia disrupts the link between meaning deficits and compromised psychological well-being. Collectively, these findings indicate that the provision of existential meaning is a pivotal function of nostalgia.


title: PRESENCE OF FIG LEAF MOTTLE-ASSOCIATED VIRUS 1 AND 2 IN LEBANESE FIG ORCHARDS

abstract: An account is given of the presence and incidence of Fig leaf mottle-associated virus 1 (FLMaV-1) and Fig leaf mottle-associated virus 2 (FLMaV-2) in Lebanese fig orchards, as determined by RT-PCR assays. About two-thirds of 102 fig trees tested were infected by at least one virus, with mixed infections in ca. 9% of the samples. FLMaV-1 prevailed (47% infection) especially in cv. Aswad (80% infection) and in Mount Lebanon (95% infection). FLMaV-2 was detected in 29.4% of the trees, with a prevalence in those coming from North Bekaa (57.5% infection). A fragment of the HSP70 gene of four FLMaV-1 and eight FLMaV-2 isolates were sequenced and aligned with the sequences of the homologous genes of the same viruses from Genbank. The comparative analysis showed low levels of variability among both FLMaV-1 (1-3%) and FLMaV-2 (3-9%). isolates.


title: Morphological Inflection Generation Using Character Sequence to Sequence Learning

abstract: Morphological inflection generation is the task of generating the inflected form of a given lemma corresponding to a particular linguistic transformation. We model the problem of inflection generation as a character sequence to sequence learning problem and present a variant of the neural encoder-decoder model for solving it. Our model is language independent and can be trained in both supervised and semi-supervised settings. We evaluate our system on seven datasets of morphologically rich languages and achieve either better or comparable results to existing state-of-the-art models of inflection generation.


title: A 16.8 Gbps/Channel Single-Ended Transceiver in 65 nm CMOS for SiP-Based DRAM Interface on Si-Carrier Channel

abstract: A 16.8 Gbps/channel single-ended transceiver for SiP-based DRAM interface on silicon carrier channel is proposed in this paper. A transmitter, receiver, and channel are all included in a single package as SiP. A current mode 4:1 MUX with 1-tap feed-forward equalizer (FFE) is used as a serializer, and this 4:1 MUX uses 25% duty clock to prevent short circuit current when consecutive 2-phase clocks overlap. Additionally, an open drain output driver with asynchronous type 1-tap FFE is used in the transmitter. Because of its small physical size, a common mode variation of Si-carrier channel from process variation is more serious than that of conventional PCB. This common mode variation degrades bit error rates (BER) at single-ended signaling. To obtain effective single-ended signaling on Si-carrier channel, a source follower-based continuous time linear equalizers and self- ${\rm V}_{\rm REF}$ generator with training algorithm on the receiver are proposed. An implemented Si-carrier channel uses meshed layer as a reference to reduce insertion loss. A BER less than 1e-12 is achieved in 65 nm CMOS and the power efficiency of the transceiver is 5.9 pJ/bit with 120 $\Omega$ terminations at each transceiver side.


title: A learning-based framework for handling dilemmas in urban automated driving

abstract: Over the last decade, automated vehicles have been widely researched and their massive potential has been verified through several milestone demonstrations. However, there are still many challenges ahead. One of the biggest challenges is integrating them into urban environments in which dilemmas occur frequently. Conventional automated driving strategies make automated vehicles foolish in dilemmas such as making lane-change in heavy traffic, handling a yellow traffic light and crossing a double-yellow line to pass an illegally parked car. In this paper, we introduce a novel automated driving strategy that allows automated vehicles to tackle these dilemmas. The key insight behind our automated driving strategy is that expert drivers understand human interactions on the road and comply with mutually-accepted rules, which are learned from countless experiences. In order to teach the driving strategy of expert drivers to automated vehicles, we propose a general learning framework based on maximum entropy inverse reinforcement learning and Gaussian process. Experiments are conducted on a 5.2 km-long campus road at Seoul National University and demonstrate that our framework performs comparably to expert drivers in planning trajectories to handle various dilemmas.


title: Empirical Analysis of Search Advertising Strategies

abstract: Top search ad placement is the coin of today's Internet services realm. An entire industry of search engine marketing companies have emerged to help advertisers optimize their ad campaigns to deliver high returns on investment, peddling a plethora of advertising strategies. Yet, very little is publicly known about the effectiveness of online search advertising, especially when trying to compare the various campaign strategies used by advertisers. This paper presents the first large-scale measurement of the effectiveness-measured in terms of incremental conversion gains'of online search ads. We develop a simple metric called net acquisition benefit (NAB) that admits comparisons between the efficacy of different ad campaign strategies without access to advertisers' private financial information. We study three common campaign strategies used by advertisers on a large search ad network: cannibalization, poaching, and ad extensions. Considering data from a month in the last two years, we employ NAB to identify cases where these campaign strategies are justified. Advertisers and ad agencies can replicate our methodology to apply it to other strategies of interest.


title: Big Data Classification Using Augmented Decision Trees

abstract: We present an algorithm for classification tasks on big data. Experiments conducted as part of this study indicate that the algorithm can be as accurate as ensemble methods such as random forests or gradient boosted trees. Unlike ensemble methods, the models produced by the algorithm can be easily interpreted. The algorithm is based on a divide and conquer strategy and consists of two steps. The first step consists of using a decision tree to segment the large dataset. By construction, decision trees attempt to create homogeneous class distributions in their leaf nodes. However, non-homogeneous leaf nodes are usually produced. The second step of the algorithm consists of using a suitable classifier to determine the class labels for the non-homogeneous leaf nodes. The decision tree segment provides a coarse segment profile while the leaf level classifier can provide information about the attributes that affect the label within a segment.


title: Video Inpainting for Arbitrary Foreground Object Removal

abstract: In this paper, we propose a robust video inpainting method under challenging background conditions such as occlusion, complex visual pattern, overlaid object clutter and depth variation observed in a moving camera. We propose a confidence score based on the normalized difference between observed depth of potential background point and predicted distance in 3D space. Potential points from neighbor frames are collected, refined, and weighted to choose small number of qualified observations to fill in the region of removed object in the current frame. Our method is evaluated with both public dataset and our own video clips and compared to multiple state of the art video inpainting methods showing outperforming performance.


title: A Faster-RCNN Based Chemical Fiber Paper Tube Defect Detection Method

abstract: Chemical fiber paper tubes are the essential spinning equipment on filament high-speed spinning and winding machine of the chemical fiber industry. The precision of its application directly impacts on the formation of the silk, determines the cost of the spinning industry. Due to the accuracy of its application requirements, the paper tubes with defects must be detected and removed. Traditional industrial defect detection methods are usually carried out using the target operator's characteristics, only to obtain surface information, not only the detection efficiency and accuracy is difficult to improve, due to human judgment, it's difficult to give effective algorithm for some targets. And the existing learning algorithms are also difficult to use the deep features, so they can not get good results. Based on the Faster-RCNN method in depth learning, this paper extracts the deep features of the defective target by Convolutional Neural Network (CNN), which effectively solves the internal joint defects that the traditional algorithm can not effectively detect. As to the external joints and damaged flaws that the traditional algorithm can detect, this algorithm has better results, the experimental accuracy rate can be raised up to 98.00%. At the same time, it can be applied to a variety of lighting conditions, reducing the pretreatment steps and improving efficiency. The experimental results show that the method is effective and worthy of further research.


title: DOI: 10.1080/10635150490264699 Bayesian Phylogenetic Analysis of Combined Data

abstract: The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) techniques has facilitated the exploration of parameter-rich evolutionary models. At the same time, stochastic models have become more realistic (and complex) and have been extended to new types of data, such as morphology. Based on this foundation, we developed a Bayesian MCMC approach to the analysis of combined data sets and explored its utility in inferring relationships among gall wasps based on data from morphology and four genes (nuclear and mitochondrial, ribosomal and protein coding). Examined models range in complexity from those recognizing only a morphological and a molecular partition to those having complex substitution models with independent parameters for each gene. Bayesian MCMC analysis deals efficiently with complex models: convergence occurs faster and more predictably for complex models, mixing is adequate for all parameters even under very complex models, and the parameter update cycle is virtually unaffected by model partitioning across sites. Morphology contributed only 5% of the characters in the data set but nevertheless influenced the combined-data tree, supporting the utility of morphological data in multigene analyses. We used Bayesian criteria (Bayes factors) to show that process heterogeneity across data partitions is a significant model component, although not as important as among-site rate variation. More complex evolutionary models are associated with more topological uncertainty and less conflict between morphology and molecules. Bayes factors sometimes favor simpler models over considerably more parameter-rich models, but the best model overall is also the most complex and Bayes factors do not support exclusion of apparently weak parameters from this model. Thus, Bayes factors appear to be useful for selecting among complex models, but it is still unclear whether their use strikes a reasonable balance between model complexity and error in parameter estimates. (Bayes factors; Bayesian analysis; combined data; Cynipidae; gall wasps; MCMC; model heterogeneity; model selection.)


title: Blind dehazing using internal patch recurrence

abstract: Images of outdoor scenes are often degraded by haze, fog and other scattering phenomena. In this paper we show how such images can be dehazed using internal patch recurrence. Small image patches tend to repeat abundantly inside a natural image, both within the same scale, as well as across different scales. This behavior has been used as a strong prior for image denoising, super-resolution, image completion and more. Nevertheless, this strong recurrence property significantly diminishes when the imaging conditions are not ideal, as is the case in images taken under bad weather conditions (haze, fog, underwater scattering, etc.). In this paper we show how we can exploit the deviations from the ideal patch recurrence for "Blind De-hazing" — namely, recovering the unknown haze parameters and reconstructing a haze-free image. We seek the haze parameters that, when used for dehazing the input image, will maximize the patch recurrence in the dehazed output image. More specifically, pairs of co-occurring patches at different depths (hence undergoing different degrees of haze) allow recovery of the airlight color, as well as the relative-transmission of each such pair of patches. This in turn leads to dense recovery of the scene structure, and to full image dehazing.


title: Simple and Effective Multi-Paragraph Reading Comprehension

abstract: We consider the problem of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Our proposed solution trains models to produce well calibrated confidence scores for their results on individual paragraphs. We sample multiple paragraphs from the documents during training, and use a shared-normalization training objective that encourages the model to produce globally correct output. We combine this method with a state-of-the-art pipeline for training models on document QA data. Experiments demonstrate strong performance on several document QA datasets. Overall, we are able to achieve a score of 71.3 F1 on the web portion of TriviaQA, a large improvement from the 56.7 F1 of the previous best system.


title: Learning Surface Text Patterns For A Question Answering System

abstract: In this paper we explore the power of surface text patterns for open-domain question answering systems. In order to obtain an optimal set of patterns, we have developed a method for learning such patterns automatically. A tagged corpus is built from the Internet in a bootstrapping process by providing a few hand-crafted examples of each question type to Altavista. Patterns are then automatically extracted from the returned documents and standardized. We calculate the precision of each pattern, and the average precision for each question type. These patterns are then applied to find answers to new questions. Using the TREC-10 question set, we report results for two cases: answers determined from the TREC-10 corpus and from the web.


title: Sparse convolved Gaussian processes for multi-output regression

abstract: We present a sparse approximation approach for dependent output Gaussian processes (GP). Employing a latent function framework, we apply the convolution process formalism to establish dependencies between output variables, where each latent function is represented as a GP. Based on these latent functions, we establish an approximation scheme using a conditional independence assumption between the output processes, leading to an approximation of the full covariance which is determined by the locations at which the latent functions are evaluated. We show results of the proposed methodology for synthetic data and real world applications on pollution prediction and a sensor network.


title: Rehabilitation of Achilles tendon ruptures: is early functional rehabilitation daily routine?

abstract: INTRODUCTION ::: Ruptures of the Achilles tendon are the most common tendon injuries of the lower extremities. Besides the initial operative or non-operative treatment, rehabilitation of patients plays a crucial role for tendon healing and long-term outcome. As only limited evidence is available for optimized rehabilitation regimen and guidelines for the initial (e.g., first 6 weeks) rehabilitation are limited, this study investigated the current rehabilitation concepts after Achilles tendon rupture. ::: ::: ::: MATERIALS AND METHODS ::: We analyzed 213 written rehabilitation protocols that are provided by orthopedic and trauma surgery institutions throughout Germany in terms of recommendations for weight-bearing, range of motion (ROM), physiotherapy, and choice of orthosis. All protocols for operatively and non-operatively treated Achilles tendon ruptures were included. Descriptive analysis was carried out and statistical analysis applied where appropriate. ::: ::: ::: RESULTS ::: Of 213 institutions, 204 offered rehabilitation protocols for Achilles tendon rupture and, therefore, 243 protocols for operative and non-operative treatment could be analyzed. While the majority of protocols allowed increased weight-bearing over time, significant differences were found for durations of fixed plantar flexion between operative (o) and non-operative (n) treatments [fixed 30° (or 20)° to 15° (or 10)°: 3.6 weeks (±0.1; o) vs 4.7 weeks (±0.3; n) (p ≤ 0.0001) and fixed 15° (or 10)° to 0°: 5.8 weeks (±0.1; o) vs 6.6 weeks (±0.2; n) (p ≤ 0.001)]. The mean time of the recommended start of physiotherapy is at 2.9 weeks (±0.2; o) vs 3.3 weeks (±0.4; n), respectively. ::: ::: ::: CONCLUSION ::: Our study shows that a huge variability in rehabilitation after Achilles tendon rupture exists. This study shows different strategies in rehabilitation of Achilles tendon ruptures using a convertible vacuum brace system. To improve patient care, further clinical as well as biomechanical studies need to be conducted. This study might serve as basis for prospective randomized controlled trials to optimize rehabilitation for Achilles tendon ruptures.


title: Survey of Crime Analysis and Prediction.

abstract: Crime analytics and prediction have long been studied among research communities. In recent years, crime data from different heterogeneous sources have given immense opportunities to the research community to effectively study crime pattern and prediction tasks in actual real data. In this survey paper, we will discuss research that takes into account a variety of crime related variables, and shows where in some cases, information that has been widely accepted as influencing the crime rate, actually does not have an effect.


Encouraging witting participation and performance in digital live art


title: RACE: Large-scale ReAding Comprehension Dataset From Examinations

abstract: We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task. Collected from the English exams for middle and high school Chinese students in the age range between 12 to 18, RACE consists of near 28,000 passages and near 100,000 questions generated by human experts (English instructors), and covers a variety of topics which are carefully designed for evaluating the students' ability in understanding and reasoning. In particular, the proportion of questions that requires reasoning is much larger in RACE than that in other benchmark datasets for reading comprehension, and there is a significant gap between the performance of the state-of-the-art models (43%) and the ceiling human performance (95%). We hope this new dataset can serve as a valuable resource for research and evaluation in machine comprehension. The dataset is freely available at this http URL and the code is available at this https URL.


title: Invertebrate response to snow goose herbivory on moist-soil vegetation

abstract: Foraging activity by snow geese (Chen caerulescens) often creates large areas devoid of vegetation (“eat-outs”) in moist-soil impoundments and coastal wetlands. Open-water habitats that result from eat-outs may be valuable foraging areas for other wetland-dependent birds (i.e., waterfowl and shorebirds). However, few studies have examined the effects of goose-induced habitat changes on invertebrates, an important food source for both waterfowl and shorebirds. We quantified changes in abundance and composition of benthic invertebrates in response to snow goose herbivory in moist-soil impoundments at Prime Hook National Wildlife Refuge, Delaware, USA. We found invertebrate taxon richness and diversity and abundance of Chironomidae, Coleoptera, and Total Invertebrates to be higher in goose-excluded sites than in adjacent eat-outs. These effects were most pronounced during January, February, and early April. We also measured invertebrate abundance in shorebird exclosures in eat-outs but found few detectable effects of shorebird predation on invertebrates. Our study demonstrated that abundant snow geese may negatively influence availability of invertebrates for other nonbreeding waterbirds, suggesting that management actions to reduce local goose populations or deter feeding in impoundments may be warranted.


title: Intrusion of maxillary molar using mini-implants: A clinical report and follow-up at 5 years

abstract: This clinical report describes a straightforward alternative treatment for adult patients with extruded maxillary molars, who are reluctant to use fixed appliances and complex mechanics. The maxillary molar supereruption of a 57-year-old woman was treated using buttons bonded to the buccal and palatal surfaces of the molars, 2 mini-implants, and elastomeric chains. The intrusion was achieved in 5 months. The treatment stability was observed for 5 years after treatment.


title: Unsupervised Learning of Syntactic Structure with Invertible Neural Projections

abstract: Unsupervised learning of syntactic structure is typically performed using generative models with discrete latent variables and multinomial parameters. In most cases, these models have not leveraged continuous word representations. In this work, we propose a novel generative model that jointly learns discrete syntactic structure and continuous word representations in an unsupervised fashion by cascading an invertible neural network with a structured generative prior. We show that the invertibility condition allows for efficient exact inference and marginal likelihood computation in our model so long as the prior is well-behaved. In experiments we instantiate our approach with both Markov and tree-structured priors, evaluating on two tasks: part-of-speech (POS) induction, and unsupervised dependency parsing without gold POS annotation. On the Penn Treebank, our Markov-structured model surpasses state-of-the-art results on POS induction. Similarly, we find that our tree-structured model achieves state-of-the-art performance on unsupervised dependency parsing for the difficult training condition where neither gold POS annotation nor punctuation-based constraints are available.


title: 2.4/5.7-GHz CMOS Dual-Band Low-IF Architecture Using Weaver–Hartley Image-Rejection Techniques

abstract: A 2.4/5.7-GHz dual-band Weaver-Hartley architecture, using 0.18-mum CMOS technology, is demonstrated in this paper. The 2.4-GHz signal is set to be the image signal when the desired signal is at 5.7 GHz, and vice versa. Since the Weaver and Hartley systems are combined into this architecture, the demonstrated architecture rejects not only the first image signal, but also the secondary image signal. The image-rejection ratios of the first image signal and the secondary image signal are better than 40 and 46 dB, respectively. In this paper, a diagrammatic explanation is employed to obtain the image-rejection mechanisms of the Weaver-Hartley architecture.


title: Security Ontologies: Improving Quantitative Risk Analysis

abstract: IT-security has become a much diversified field and small and medium sized enterprises (SMEs), in particular, do not have the financial ability to implement a holistic IT-security approach. We thus propose a security ontology, to provide a solid base for an applicable and holistic IT-security approach for SMEs, enabling low-cost risk management and threat analysis. Based on the taxonomy of computer security and dependability by Landwehr, a heavy-weight ontology can be used to organize and systematically structure knowledge on threats, safeguards, and assets. Using this ontology, each threat scenario can be simulated with a different protection profile as to evaluate the effectiveness and the cost/benefit ratio of individual safeguards


title: Recurrent Neural Network based language modeling with controllable external Memory

abstract: It is crucial for language models to model long-term dependency in word sequences, which can be achieved to some good extent by recurrent neural network (RNN) based language models with long short-term memory (LSTM) units. To accurately model the sophisticated long-term information in human languages, large memory in language models is necessary. However, the size of RNN-based language models cannot be arbitrarily increased because the computational resources required and the model complexity will also be increase accordingly, due to the limitation of the structure. To overcome this problem, inspired from Neural Turing Machine and Memory Network, we equip RNN-based language models with controllable external memory. With a learnable memory controller, the size of the external memory is independent to the number of model parameters, so the proposed language model can have larger memory without increasing the parameters. In the experiments, the proposed model yielded lower perplexities than RNN-based language models with LSTM units on both English and Chinese corpora.


Geometric Concept Acquisition in a Dueling Deep Q-Network.


title: Gender Differences in Emotion Regulation: An fMRI Study of Cognitive Reappraisal.

abstract: Despite strong popular conceptions of gender differences in emotionality and striking gender differences in the prevalence of disorders thought to involve emotion dysregulation, the literature on the neural bases of emotion regulation is nearly silent regarding gender differences (Gross, 2007; Ochsner & Gross, in press). The purpose of the present study was to address this gap in the literature. Using functional magnetic resonance imaging, we asked male and female participants to use a cognitive emotion regulation strategy (reappraisal) to down-regulate their emotional responses to negatively valenced pictures. Behaviorally, men and women evidenced comparable decreases in negative emotion experience. Neurally, however, gender differences emerged. Compared with women, men showed (a) lesser increases in prefrontal regions that are associated with reappraisal, (b) greater decreases in the amygdala, which is associated with emotional responding, and (c) lesser engagement of ventral striatal regions, which are associated with reward processing. We consider two non-competing explanations for these differences. First, men may expend less effort when using cognitive regulation, perhaps due to greater use of automatic emotion regulation. Second, women may use positive emotions in the service of reappraising negative emotions to a greater degree. We then consider the implications of gender differences in emotion regulation for understanding gender differences in emotional processing in general, and gender differences in affective disorders.


title: Discovering texture regularity as a higher-order correspondence problem

abstract: Understanding texture regularity in real images is a challenging computer vision task. We propose a higher-order feature matching algorithm to discover the lattices of near-regular textures in real images. The underlying lattice of a near-regular texture identifies all of the texels as well as the global topology among the texels. A key contribution of this paper is to formulate lattice-finding as a correspondence problem. The algorithm finds a plausible lattice by iteratively proposing texels and assigning neighbors between the texels. Our matching algorithm seeks assignments that maximize both pair-wise visual similarity and higher-order geometric consistency. We approximate the optimal assignment using a recently developed spectral method. We successfully discover the lattices of a diverse set of unsegmented, real-world textures with significant geometric warping and large appearance variation among texels.


title: Classical Statistics and Statistical Learning in Imaging Neuroscience

abstract: Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques.


title: A Compact Multiband Open-Ended Slot Antenna for Mobile Handsets

abstract: A novel compact multiband antenna formed by two printed open-ended slots, with the first one a T-shaped slot and the second one an E-shaped slot, cut at the edge of the ground plane of mobile handsets is presented. This antenna can generate five resonant modes to cover GSM900/DCS1800/ PCS1900/UMTS and 2.4-GHz-based WLAN bands, which can be controlled almost independently by the five respective monopole slots of different lengths. Furthermore, the proposed antenna has a simple planar structure and occupies a small area of only 10 × 42.5 mm2. It is also promising to bend the antenna into a E shape, inverted-T shape and meander line to reduce its volume occupied. Details of the antenna design and experimental results are presented and discussed.


title: PRECYSE: Cyber-attack Detection and Response for Industrial Control Systems

abstract: In this short paper, we present an integrated approach to detecting and mitigating cyber-attacks to modern interconnected industrial control systems. One of the primary goals of this approach is that it is cost-effective, and thus whenever possible it builds on open-source security technologies and open standards, which are complemented with novel security solutions that address the specific challenges of securing critical infrastructures.


title: Flux-Weakening Regime Operation of an Interior Permanent-Magnet Synchronous Motor Drive

abstract: The interior permanent magnet (IPM) synchronous motor is compatible with extended-speed-range constant-power operation by means of flux-weakening control. Flux weakening uses stator current components to counter the fixed-amplitude magnetic airgap flux generated by the rotor magnets, performing a role similar to field weakening in a separately excited dc motor. The nature of current regulator saturation caused by the finite inverter dc source voltage is described, marked by premature torque and power degradation at high speeds in the absence of flux-weakening control. This is followed by presentation of a new flux-weakening control algorithm developed as a modification of an established feedforward IPM torque control algorithm described previously in the literature. Attractive features of this new algorithm include smooth drive transitions into and out of the flux-weakening mode, fast response, as well as automatic adjustment to changes in the dc source voltage. Simulation and empirical test results from a 3-hp laboratory IPM motor drive are used to confirm the constant-power operating envelope achieved using the new flux-weakening control algorithm.


Deep Context: A Neural Language Model for Large-scale Networked Documents


Fast and Accurate Sequence Labeling with Iterated Dilated Convolutions


Implementation of Edge Detection for Images


title: Congestion Avoidance with Incremental Filter Aggregation in Content-Based Routing Networks

abstract: The subscription covering optimization, whereby a general subscription quenches the forwarding of more specific ones, is a common technique to reduce network traffic and routing state in content-based routing networks. Such optimizations, however, leave the system vulnerable to unsubscriptions that trigger the immediate forwarding of all the subscriptions they had previously quenched. These subscription bursts can severely congest the network, and destabilize the system. This paper presents techniques to retain much of the benefits of subscription covering while avoiding bursty subscription traffic. Heuristics are used to estimate the similarity among subscriptions, and a distributed algorithm determines the portions of a subscription propagation tree that should be preserved. Evaluations show that these mechanisms avoid subscription bursts while maintaining relatively compact routing tables.


title: Silver toxicity to ferrous iron and pyrite oxidation and its alleviation by yeast extract in cultures ofThiobacillus ferrooxidans

abstract: Ferrous-ion oxidation byThiobacillusferrooxidans was inhibited by 10−6 M Ag+ while a slight inhibition of growth was apparent with 10−7 M Ag+. The threshold toxic concentration was the seme for four different test strains. While prolonged lag phases resulted from culture exposure to Ag+, Fe2+ oxidation rates after the onset of growth showed little variation under these conditions. Yeast extract (0.02%) partially alleviated the toxicity of silver-ion by reducing the lag periods. Pyrite oxidation byT.ferrooxidans and mixed cultures of acidophiles was tested at 8.3×10−7 to 8.3×10−5 M Ag+. Strong inhibition was apparent at 8.3×10−5 M Ag+ and little to no inhibition was observed at 8.3×10−7 M Ag+.


Predicting the Popularity of Online Content with Knowledge-enhanced Neural Networks


title: DisSent: Sentence Representation Learning from Explicit Discourse Relations

abstract: Sentence vectors represent an appealing approach to meaning: learn an embedding that encompasses the meaning of a sentence in a single vector, that can be used for a variety of semantic tasks. Existing models for learning sentence embeddings either require extensive computational resources to train on large corpora, or are trained on costly, manually curated datasets of sentence relations. We observe that humans naturally annotate the relations between their sentences with discourse markers like "but" and "because". These words are deeply linked to the meanings of the sentences they connect. Using this natural signal, we automatically collect a classification dataset from unannotated text. Training a model to predict these discourse markers yields high quality sentence embeddings. Our model captures complementary information to existing models and achieves comparable generalization performance to state of the art models.


title: Optimal design of mine ventilation system using a ventilation improvement index

abstract: A ventilation improvement index (VII) was developed in an effort to propose an optimal ventilation improvement measure for a designated mine. The index was designed to incorporate both ventilation effectiveness and cost efficiency. To verify the effect of the index, a mine experiencing a ventilation problem was designated and ventilation test and ventilation network analyses were conducted. In the designated mine, 4 areas with ventilations difficulties were detected and an improvement scheme for each area was proposed and evaluation by the ventilation improvement index was carried out. As a result, an optimum ventilation improvement scheme was proposed. The improvement scheme with the highest VII value was incorporated in the design.


title: Category Enhanced Word Embedding

abstract: Distributed word representations have been demonstrated to be effective in capturing semantic and syntactic regularities. Unsupervised representation learning from large unlabeled corpora can learn similar representations for those words that present similar co-occurrence statistics. Besides local occurrence statistics, global topical information is also important knowledge that may help discriminate a word from another. In this paper, we incorporate category information of documents in the learning of word representations and to learn the proposed models in a document-wise manner. Our models outperform several state-of-the-art models in word analogy and word similarity tasks. Moreover, we evaluate the learned word vectors on sentiment analysis and text classification tasks, which shows the superiority of our learned word vectors. We also learn high-quality category embeddings that reflect topical meanings.


title: Implantable SiC based RF antenna biosensor for continuous glucose monitoring

abstract: A continuous glucose sensor employing radio frequency (RF) signals has been developed using the biocompatible material Silicon Carbide (SiC). Unlike biosensors that require direct contact with interstitial fluids to trigger chemical reactions to operate, this biocompatible SiC sensor doesn't require a direct interface. The sensing mechanism is based upon a shift in resonant frequency as a function of change in glucose levels which electrically manifests itself as a change in blood permittivity and conductivity. For invivo applications the antenna sensor needs to operate inside the body environment, and it has been determined that the best operational location of this biocompatible SiC biosensor is within fatty tissue in close proximity to blood vessels. To test the sensor as a function of glucose level, measurements using synthetic body fluid (SBF), which is electrically equivalent to blood plasma, and pig blood were performed. Changes in sensor performance to varying glucose levels were measured and a shift in resonant frequency to lower values observed with increasing glucose level. In-vitro sensor performance demonstrated that the sensor showed a dose dependent response to glucose concentration from 120 mg/dl to 530 mg/dl. A shift of 40 MHz and 26 MHz for blood mimicking liquid and pig blood was observed corresponding to 97 KHz and 67 KHz shift per 1 mg/dl change in blood glucose.


title: Analysis of multistage amplifier-frequency compensation

abstract: Frequency-compensation techniques of single-, two- and three-stage amplifiers based on Miller pole splitting and pole-zero cancellation are re-analyzed. The assumptions made, transfer functions, stability criteria, bandwidths, and important design issues of most of the reported topologies are included. Several proposed methods to improve the published topologies are given. In addition, simulations and experimental results are provided to verify the analysis and to prove the effectiveness of the proposed methods.


title: A Survey of Modeling and Analysis Approaches for Architecting Secure Software Systems

abstract: There has been a growing interest in investigating methodologies to support the development of secure systems in the software engineering research community. Recently, much attention has been focused on the modelling and analysis of security properties for systems at the software architecture design level. The potential benefits of this architecture level work are substantial: security flaws can be detected and removed earlier in the software development life-cycle. This reduces development time, reduces development cost, and improves the quality of the resulting system. As a result of this attention, a wide variety of approaches have been proposed in the literature. At this point, a survey for researchers involved in the problem of systematically modelling and analyzing software architecture design that have security properties would be of value to the community. This paper presents such a survey; it includes a discussion of semi-formal, formal, integrated semi-formal and formal, and aspect-oriented approaches. Comparison criteria are defined including: the kinds of notations used to model the security properties (e.g., Petri nets, temporal logic, etc.), whether the approach supports the manual or automated analysis of security properties, the specific security property modelled (e.g., authentication, role-based access control, etc.), and the kind of example system that has been used to illustrate the approach (information, distributed, etc.).


title: Story and Narrative Structures in Computer Games 1

abstract: Computer games can involve narrative and story elements integrating different forms of interactivity and using different strategies for combining interaction with non-interactive story and narrative elements. While some forms of interactive narrative involve simple selection between fixed narrative sequences, computer games more typically involve the integration of narrative with game play based upon a simulation substrate. These three forms, simulation, game play and narrative, involve pre-authored time structures at different levels of time scale. Simulation involves the lowest levels of time structure, with authored principles specifying how time develops from frame to frame based upon physics, the representation of game objects and their behaviour, and discrete event simulation. Games involve pre-designed game moves, types of actions that may be realized as abstractions over patterns of low level changes at the frame level. Linear and interactive narratives form the highest level of predesigned time structure, framing low-level simulation processes and intermediate level game moves within a high level structure typically based upon classic models of narrative form. Computer games may emphasise one or more of these primary forms as the focus of meaning in the play experience. Story construction within computer games is a function of how these different levels of time structure interact in the play experience, being the result of pre-designed narrative content, story potential and the actual unfolding story created by the actions of the player. There are many strategies for integrating these forms. However, a crucial issue in the design of story content is the relationship between how the resulting game experience relates to user play preferences. In particular, categories of play style can be extended to include preferences for how story content is experienced, based upon audience, performance and immersionist orientations to story. Perceived tensions within computer game form, such as the tension between game play and narrative, are explained, not as fundamental formal issues, but issues of player preferences and how these are satisfied or not by different strategies for story content within a game system.


title: An Experiment in Integrating Sentiment Features for Tech Stock Prediction in Twitter

abstract: Economic analysis indicates a relationship between consumer sentiment and stock price movements. In this study we harness features from Twitter messages to capture public mood related to four Tech companies for predicting the daily up and down price movements of these companies’ NASDAQ stocks. We propose a novel model combining features namely positive and negative sentiment, consumer confidence in the product with respect to ‘bullish’ or ‘bearish’ lexicon and three previous stock market movement days. The features are employed in a Decision Tree classifier using cross-fold validation to yield accuracies of 82.93%,80.49%, 75.61% and 75.00% in predicting the daily up and down changes of Apple (AAPL), Google (GOOG), Microsoft (MSFT) and Amazon (AMZN) stocks respectively in a 41 market day sample.


title: Photogrammetric Analysis of Soft Tissue Facial Profile in Turkish Rhinoplasty Population

abstract: Background ::: The aim of this study was to compare the facial photogrammetric analysis results of the Turkish population who are pleased with their facial appearance with the facial analysis data of patients who underwent rhinoplasty in order to find the most prominent objective facial measurements that lead patients to surgical correction of the nose.


title: Crystal structure of the pregnane X receptor-estradiol complex provides insights into endobiotic recognition.

abstract: The human nuclear pregnane X receptor (PXR) responds to a wide variety of xenobiotic and endobiotic compounds, including pregnanes, progesterones, corticosterones, lithocholic acids, and 17beta-estradiol. In response to these ligands, the receptor controls the expression of genes central to the metabolism and excretion of potentially harmful chemicals from both exogenous and endogenous sources. Although the structural basis of PXR's interaction with small and large xenobiotics has been examined, the detailed nature of its binding to endobiotics, including steroid-like ligands, remains unclear. We report the crystal structure of the human PXR ligand-binding domain (LBD) in complex with 17beta-estradiol, a representative steroid ligand, at 2.65 A resolution. Estradiol is found to occupy only one region of PXR's expansive ligand-binding pocket, leaving a notable 1000 A3 of space unoccupied, and to bridge between the key polar residues Ser-247 and Arg-410 in the PXR LBD. Positioning the steroid scaffold in this way allows it to make several direct contacts to alphaAF of the receptor's AF-2 region. The PXR-estradiol complex was compared with that of other nuclear receptors, including the estrogen receptor, in complexes with analogous ligands. It was found that PXR's placement of the steroid is remarkably distinct relative to other members of the nuclear receptor superfamily. Using the PXR-estradiol complex as a guide, the binding of other steroid- and cholesterol-like molecules was then considered. The results provide detailed insights into the manner in which human PXR responds to a wide range of endobiotic compounds.


title: Cloud-Based OLAP over Big Data: Application Scenarios and Performance Analysis

abstract: Following our previous research results, in this paper we provide two authoritative application scenarios that build on top of OLAP, a middleware for parallel processing of OLAP queries that truly realizes effective and efficiently OLAP over Big Data. We have provided two authoritative case studies, namely parallel OLAP data cube processing and virtual OLAP data cube design, for which we also propose a comprehensive performance evaluation and analysis. Derived analysis clearly confirms the benefits of our proposed framework.


Author ' s personal copy A new hybrid arti fi cial neural networks for rainfall – runoff process modeling


title: Face Detection using Deep Learning: An Improved Faster RCNN Approach

abstract: Abstract In this paper, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detection benchmark evaluation. In particular, we improve the state-of-the-art Faster RCNN framework by combining a number of strategies, including feature concatenation, hard negative mining, multi-scale training, model pre-training, and proper calibration of key parameters. As a consequence, the proposed scheme obtained the state-of-the-art face detection performance and was ranked as one of the best models in terms of ROC curves of the published methods on the FDDB benchmark. 1


title: Embodied language: A review of the role of the motor system in language comprehension

abstract: A growing body of research suggests that comprehending verbal descriptions of actions relies on an internal simulation of the described action. To assess this motor resonance account of language comprehension, we first review recent developments in the literature on perception and action, with a view towards language processing. We then examine studies of language processing from an action simulation perspective. We conclude by discussing several criteria that might be helpful with regard to assessing the role of motor resonance during language comprehension.


title: Health Effects of Coastal Storms and Flooding in Urban Areas: A Review and Vulnerability Assessment

abstract: Coastal storms can take a devastating toll on the public's health. Urban areas like New York City (NYC) may be particularly at risk, given their dense population, reliance on transportation, energy infrastructure that is vulnerable to flood damage, and high-rise residential housing, which may be hard-hit by power and utility outages. Climate change will exacerbate these risks in the coming decades. Sea levels are rising due to global warming, which will intensify storm surge. These projections make preparing for the health impacts of storms even more important. We conducted a broad review of the health impacts of US coastal storms to inform climate adaptation planning efforts, with a focus on outcomes relevant to NYC and urban coastal areas, and incorporated some lessons learned from recent experience with Superstorm Sandy. Based on the literature, indicators of health vulnerability were selected and mapped within NYC neighborhoods. Preparing for the broad range of anticipated effects of coastal storms and floods may help reduce the public health burden from these events.


title: The importance of design for firms׳ competitiveness: A review of the literature

abstract: Scholars dedicated increasing attention towards appreciating how design has changed individuals' perception of new products, firms' understanding and formulation of strategy, or other relevant actors' approach to innovation and technology management. By emphasising the importance of design for the definition of consumers' needs, the restructuring of firms' organisational structures and strategies, and the evolution of firms' value creation processes, this review paper identifies relevant research gaps and questions that would benefit from future scholarly attention. In particular, it is suggested that such effort should address the analysis of: how design consumption can help better comprehend consumers' needs; what are the implications of design thinking on the skill sets of design professionals; the organisational structure of firms, including the reconfiguration of other business functions, and their strategy; and whether and how design thinking can shape firms' value creation processes and contribute to the formalisation of design tasks.


title: Novel Modulation Strategies for Class-D Amplifier

abstract: Recent advances in semiconductor technology have renewed interest in class-D audio amplifiers, especially for portable devices and consumer electronics. In addition to higher efficiency, class-D amplifiers are smaller, lighter, streamlined, cool and quiet with extended battery life compared to the conventional linear amplifiers. A typical class-D amplifier increases the efficiency of amplifiers in consumer and professional applications from the industry norm of approximately 50 percent to 90 percents or better. In this paper, two zero voltage switching (ZVS) modulation strategies are proposed to achieve the goals of high fidelity and high efficiency. Field programmable gate array (FPGA) implementations of the proposed modulation strategies are also provided in this paper. In order to verify the correctness of the proposed methods, a FPGA-based class-D audio amplifier prototype is developed and realized. Experimental results are then presented to validate the proposed modulation strategies. According to the experimental results, the proposed test system is able to deliver more than 15 W into a 4 Omega load with an efficiency of 80% and total harmonic distortion (THD) less than 2 %.


The kinetic order of an interfacial Diels-Alder reaction depends on the environment of the immobilized dienophile


title: Addressing Legal Requirements in Requirements Engineering

abstract: Legal texts, such as regulations and legislation, are playing an increasingly important role in requirements engineering and system development. Monitoring systems for requirements and policy compliance has been recognized in the requirements engineering community as a key area for research. Similarly, regulatory compliance is critical in systems that are governed by regulations and law, especially given that non-compliance can result in both financial and criminal penalties. Working with legal texts can be very challenging, however, because they contain numerous ambiguities, cross-references, domain-specific definitions, and acronyms, and are frequently amended via new regulations and case law. Requirements engineers and compliance auditors must be able to identify relevant regulations, extract requirements and other key concepts, and monitor compliance throughout the software lifecycle. This paper surveys research efforts over the past 50 years in handling legal texts for systems development. These efforts include the use of symbolic logic, logic programming, first-order temporal logic, deontic logic, defeasible logic, goal modeling, and semi-structured representations. This survey can aid requirements engineers and auditors to better specify, monitor, and test software systems for compliance.


title: Bagging Predictors

abstract: Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy.


title: A study on intelligent personalized push notification with user history

abstract: In mobile application, the push notification is an important function to make users consume an item (eg. movie, novel). If a mobile application sends too many notifications to users without considering their preferences, it can cause them to turn off the notification or even deleting the mobile application. Thus, we develop a framework that selects a set of users who are likely to react to the push notification. To the best of our knowledge, we first apply recommendation techniques (i.e., collaborative filtering and content-based models) to the problem of discovering the target of push notification.


title: Research on Innovation: A Review and Agenda for Marketing

abstract: Innovation is one of the most important issues in business research today. It has been studied in many independent research traditions. Our understanding and study of innovation can benefit from an integrative review of these research traditions. In so doing, we identify 16 topics relevant to marketing science, which we classify under five research fields: ::: ::: ::: Consumer response to innovation, including attempts to measure consumer innovativeness, models of new product growth, and recent ideas on network externalities; Organizations and innovation, which are increasingly important as product development becomes more complex and tools more effective but demanding;. ::: Market entry strategies, which includes recent research on technology revolution, extensive marketing science research on strategies for entry, and issues of portfolio management; ::: Prescriptive techniques for product development processes, which have been transformed through global pressures, increasingly accurate customer input, Web-based communication for dispersed and global product design, and new tools for dealing with complexity over time and across product lines; ::: Defending against market entry and capturing the rewards of innovating, which includes extensive marketing science research on strategies of defense, managing through metrics, and rewards to entrants. ::: ::: ::: ::: For each topic, we summarize key concepts and highlight research challenges. For prescriptive research topics, we also review current thinking and applications. For descriptive topics, we review key findings.


title: Monitoring endurance running performance using cardiac parasympathetic function

abstract: The aims of the present study were to (1) assess relationships between running performance and parasympathetic function both at rest and following exercise, and (2) examine changes in heart rate (HR)-derived indices throughout an 8-week period training program in runners. In 14 moderately trained runners (36 ± 7 years), resting vagal-related HR variability (HRV) indices were measured daily, while exercise HR and post-exercise HR recovery (HRR) and HRV indices were measured fortnightly. Maximal aerobic speed (MAS) and 10 km running performance were assessed before and after the training intervention. Correlations (r > 0.60, P 0.5% (responders), resting vagal-related indices showed a progressively increasing trend (time effect P = 0.03) and qualitative indications of possibly and likely higher values during week 7 [+7% (90% CI −3.7;17.0)] and week 9 [+10% (90% CI −1.5;23)] compared with pre-training values, respectively. Post-exercise HRV showed similar changes, despite less pronounced between-group differences. HRR showed a relatively early possible decrease at week 3 [−20% (90% CI −42;10)], with only slight reductions near the end of the program. The results illustrate the potential of resting, exercise and post-exercise HR measurements for both assessing and predicting the impact of aerobic training on endurance running performance.


title: 28nm CPI (Chip/Package Interactions) in Large Size eWLB (Embedded Wafer Level BGA) Fan-Out Wafer Level Packages

abstract: To meet the continued demand for form factor reduction and functional integration of electronic devices, Wafer Level Packaging (WLP) is an attractive packaging solution with many advantages in comparison with standard Ball Grid Array (BGA) packages. The advancement of fan-out WLP has made it a more promising solution as compared with fan-in WLP, because it can offer greater flexibility in enabling more IO's, multi-chips, heterogeneous integration and 3D SiP. In particular, Embedded Wafer Level BGA (eWLB) is a fan-out WLP solution which can enable applications that require higher input/output (I/O) density, smaller form factor, excellent heat dissipation, and thin package profile, and it has the potential to evolve in various configurations with proven integration flexibility, process robustness, manufacturing capacity and production yield. It also provides integration of multiple dies vertically and horizontally in a single package without substrates. For eWLB fan-out WLP, the structural design as well as selection of materials is very important in determining the process yield and long term reliability. Therefore it is necessary to investigate the key design factors affecting the reliability comprehensively. This work is focused on an experimental study on the chip-package interactions in 10x10~15x15mm 28nm eWLB fan-out WLP with multiple redistribution layers (RDLs). Standard JEDEC component and board level tests were carried out to investigate reliability, and both destructive and non-destructive analyses were performed to investigate potential structural defects. Electrical characterization was also studied for both simulation and experimental works. The influence of structural design on the package reliability will be demonstrated. Thermal characterization and thermo-mechanical simulation results will also be discussed.


title: Ontology-based deep learning for human behavior prediction in health social networks

abstract: Human behavior prediction is a key component to studying the spread of wellness and healthy behavior in a social network. In this paper, we introduce an ontology-based Restricted Boltzmann Machine (ORBM) model for human behavior prediction in health social networks. We first propose a bottom-up algorithm to learn the user representation from ontologies. Then the user representation is used to incorporate self-motivation, social influences, and environmental events together in a human behavior prediction model, which extends a well-known deep learning method, Restricted Boltzmann Machines (RBMs), so that the interactions among the behavior determinants are naturally simulated through parameters. To our best knowledge, the ORBM model is the first ontology-based deep learning approach in health informatics for human behavior prediction. Experiments conducted on both real and synthetic data from health social networks have shown the tremendous effectiveness of our approach compared with conventional methods.


title: The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth

abstract: We present statistical analyses of the large-scale structure of 3 types of semantic networks: word associations, WordNet, and Roget’s Thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale-free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities have also been found in certain other complex natural networks, such as the World Wide Web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high-dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small-world statistics and power-law connectivity distributions, and it also suggests one possible mechanistic basis for the effects of learning history variables (age of acquisition, usage frequency) on behavioral performance in semantic processing tasks.


COORDINATED REPLENISHMENTS IN A MULTI-ITEM INVENTORY SYSTEM WITH COMPOUND POISSON


title: Residual-Guide Feature Fusion Network for Single Image Deraining

abstract: Single image rain streaks removal is extremely important since rainy images adversely affect many computer vision systems. Deep learning based methods have found great success in image deraining tasks. In this paper, we propose a novel residual-guide feature fusion network, called ResGuideNet, for single image deraining that progressively predicts highquality reconstruction. Specifically, we propose a cascaded network and adopt residuals generated from shallower blocks to guide deeper blocks. By using this strategy, we can obtain a coarse to fine estimation of negative residual as the blocks go deeper. The outputs of different blocks are merged into the final reconstruction. We adopt recursive convolution to build each block and apply supervision to all intermediate results, which enable our model to achieve promising performance on synthetic and real-world data while using fewer parameters than previous required. ResGuideNet is detachable to meet different rainy conditions. For images with light rain streaks and limited computational resource at test time, we can obtain a decent performance even with several building blocks. Experiments validate that ResGuideNet can benefit other low- and high-level vision tasks.


title: Patient factors influencing dermal filler complications: prevention, assessment, and treatment

abstract: While rare, complications do occur with the esthetic use of dermal fillers. Careful attention to patient factors and technique can do much to avoid these complications, and a well-informed practitioner can mitigate problems when they do occur. Since cosmetic surgery is usually an elective process, requested by the patient, clinical trials are complex to organize and run. For this reason, an international group of practicing physicians in the field of esthetics came together to share knowledge and to try and produce some informed guidance for their colleagues, considering the literature and also pooling their own extensive clinical experience. This manuscript aims to summarize the crucial aspects of patient selection, including absolute contraindications as well as situations that warrant caution, and also covers important considerations for the pre- and posttreatment periods as well as during the procedure itself. Guidance is given on both immediate and long-term management of adverse reactions. The majority of complications are related to accepting patients inappropriate for treatment or issues of sterility, placement, volume, and injection technique. It is clear that esthetic practitioners need an in-depth knowledge of all aspects of treatment with dermal fillers to achieve optimal outcomes for their patients.


title: Evolution on SoC Integration: GSM Baseband-Radio in 0.13 $\mu{\hbox {m}}$ CMOS Extended by Fully Integrated Power Management Unit

abstract: GSM baseband-radios system-on-chip (SoC) fabricated in CMOS technology are well established on the market. The next evolutionary step on the proceeding integration path is the extension of the baseband-radio functionality by further integration of power-management-unit (PMU) functionality. This PMU of a mobile phone is normally realized as a separate chip. The integration of the PMU promises lowest phone cost, eases PCB design, reduces phone development time, and enables overall system optimization. However, several integration challenges have to be tackled. An SoC is presented for which the aforementioned challenges were successfully managed. The achieved performance will be demonstrated with corresponding measurement results.


title: Portfolio optimization using improved artificial bee colony approach

abstract: Nature-inspired optimization methods have been known to have the capability of handling computationally complicated problems, especially when traditional methods have become insufficient to. In this work, we proposed an improved artificial bee colony (IABC) method as the solution approach to trace out an efficiency frontier of the general portfolio performance. Such portfolio optimization problem focuses on balancing the trade-off between risk and return and is also captured in multidimensional nature with cardinality and bounding constraints. The proposed IABC algorithm intends to balance the diversity and quality of solutions, and fulfill the characteristic of the portfolio optimization problem. To do so, we employ a hybrid encoding that mixes integer and real variables in the IABC algorithm, and test its performance on four global stock market indices from the OR-Library. In addition, computational results are compared among four other algorithms. Evidences indicate that IABC performs the best in terms of diversity, convergence, and effectiveness among all four test data sets. The effect of choosing different number of stocks to form a portfolio is also investigated. The results confirm that less number of stocks selected in a portfolio can help to build a better efficiency frontier with lower risk and higher return more quickly.


title: Distributed Ledger Technology for Smart Cities, the Sharing Economy, and Social Compliance

abstract: This paper describes how distributed ledger technologies (DLTs) can be used to enforce social contracts and to orchestrate the behavior of agents trying to access a shared resource. The first part of this paper analyzes the advantages and disadvantages of using DLTs architectures to implement certain control systems in an Internet of Things (IoT) setting and then focuses on a specific type of DLT based on a directed acyclic graph. In this setting, we propose a set of delay differential equations to describe the dynamical behavior of the Tangle, an IoT-inspired directed acyclic graph designed for the cryptocurrency IOTA. The second part proposes an application of DLTs as a mechanism for dynamic deposit pricing , wherein the deposit of digital currency is used to orchestrate access to a network of shared resources. The pricing signal is used as a mechanism to enforce the desired level of compliance according to a predetermined set of rules. After presenting an illustrative example, we analyze the control system and provide sufficient conditions for the stability of the network.