Optimal control approach for pneumatic artificial muscle with using pressure-force conversion model

In this paper, we propose an optimal control framework for pneumatic actuators. In particular, we consider using Pneumatic Artificial Muscle (PAM) as a part of Pneumatic-Electric (PE) hybrid actuation system. An optimal control framework can be useful for PE hybrid system to properly distribute desired torque outputs to the actuators that have different characteristics. In the optimal control framework, the standard choice to represent control cost is squared force or torque outputs. However, since the control input for PAM is pressure rather than the force or the torque, we should explicitly consider the pressure of PAM as the control cost in an objective function of the optimal control method. We show that we are able to use pressure input as the control cost for PAM by explicitly considering the model which represents a relationship between the pressure input and the force output of PAM. We demonstrate that one-DOF robot with the PE hybrid actuation system can generate pressure-optimized ball throwing movements by using the optimal control method.


Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data

We investigate the problem of generating fast approximate a nswers to queries posed to large sparse binary data sets. We focus in particular on probabilistic mode l-based approaches to this problem and develop a number of techniques that are significantly more accurate t han a baseline independence model. In particular, we introduce two techniques for building probabil ist c models from frequent itemsets: the itemset maximum entropy method, and the itemset inclusion-exclusi on model. In the maximum entropy method we treat itemsets as constraints on the distribution of the q uery variables and use the maximum entropy principle to build a joint probability model for the query at tributes online. In the inclusion-exclusion model itemsets and their frequencies are stored in a data structur e alled an ADtree that supports an efficient implementation of the inclusion-exclusion principle in orde r to answer the query. We empirically compare these two itemset-based models to direct querying of the ori ginal data, querying of samples of the original data, as well as other probabilistic models such as the indep endence model, the Chow-Liu tree model, and the Bernoulli mixture model. These models are able to handle high-dimensionality (hundreds or thousands of attributes), whereas most other work on this topic has foc used on relatively low-dimensional OLAP problems. Experimental results on both simulated and realwor d transaction data sets illustrate various fundamental tradeoffs between approximation error, model complexity, and the online time required to compute a query answer.


MapMarker: Extraction of Postal Addresses and Associated Information for General Web Pages

Address information is essential for people’s daily life. People often need to query addresses of unfamiliar location through Web and then use map services to mark down the location for direction purpose. Although both address information and map services are available online, they are not well combined. Users usually need to copy individual address from a Web site and paste it to another Web site with map services to locate its direction. Such copy and paste operations have to be repeated if multiple addresses are listed on a single page such as public school list or apartment list. Furthermore, associated information with individual address has to be copied and included on each marker for better comprehension. Our research is devoted to automate the above process and make the combination an easier task for users. The main techniques applied here include postal address extraction and associated information extraction. We apply sequence labeling algorithm based on Conditional Random Fields (CRFs) to train models for address extraction. Meanwhile, using the extracted addresses as landmarks, we apply pattern mining to identify the boundaries of address blocks and extract associated information with each individual address. The experimental result shows high F-score at 91% for postal address extraction and 87% accuracy for associated information extraction.


Revising the Structural Framework for Marketing Management

Special thanks to Robert Skipper and Aaron Hyman for their assistance on an earlier version of this manuscript. Also thanks to Shaun McQuitty, Robin Peterson, Chuck Pickett, Kevin Shanahan, and the Journal of Business Research editors and reviewers, for their helpful comments. An earlier version of this manuscript won the Shaw Award for best paper presented at 2001 Society for Marketing Advances conference. An abridged version of this manuscript has been accepted for publication in Journal of Business Research.


End-to-End Learning of Task-Oriented Dialogs

In this thesis proposal, we address the limitations of conventional pipeline design of taskoriented dialog systems and propose end-toend learning solutions. We design neural network based dialog system that is able to robustly track dialog state, interface with knowledge bases, and incorporate structured query results into system responses to successfully complete task-oriented dialog. In learning such neural network based dialog systems, we propose hybrid offline training and online interactive learning methods. We introduce a multi-task learning method in pre-training the dialog agent in a supervised manner using task-oriented dialog corpora. The supervised training agent can further be improved via interacting with users and learning online from user demonstration and feedback with imitation and reinforcement learning. In addressing the sample efficiency issue with online policy learning, we further propose a method by combining the learning-from-user and learningfrom-simulation approaches to improve the online interactive learning efficiency.


Learning model-based strategies in simple environments with hierarchical q-networks

Recent advances in deep learning have allowed artificial agents to rival human-level performance on a wide range of complex tasks; however, the ability of these networks to learn generalizable strategies remains a pressing challenge. This critical limitation is due in part to two factors: the opaque information representation in deep neural networks and the complexity of the task environments in which they are typically deployed. Here we propose a novel Hierarchical Q-Network (HQN), motivated by theories of the hierarchical organization of the human prefrontal cortex, that attempts to identify lower dimensional patterns in the value landscape that can be exploited to construct an internal model of rules in simple environments. We draw on combinatorial games, where there exists a single optimal strategy for winning that generalizes across other features of the game, to probe the strategy generalization of the HQN and other reinforcement learning (RL) agents using variations of Wythoff’s game. Traditional RL approaches failed to reach satisfactory performance on variants of Wythoff’s Game; however, the HQN learned heuristic-like strategies that generalized across changes in board configuration. More importantly, the HQN allowed for transparent inspection of the agent’s internal model of the game following training. Our results show how a biologically inspired hierarchical learner can facilitate learning abstract rules to promote robust and flexible action policies in simplified training environments with clearly delineated optimal strategies. 1 ar X iv :1 80 1. 06 68 9v 1 [ cs .A I] 2 0 Ja n 20 18


Ending the war between Sales & Marketing.

Sales departments tend to believe that marketers are out of touch with what's really going on in the marketplace. Marketing people, in turn, believe the sales force is myopic--too focused on individual customer experiences, insufficiently aware of the larger market, and blind to the future. In short, each group undervalues the other's contributions. Both stumble (and organizational performance suffers) when they are out of sync. Yet few firms seem to make serious overtures toward analyzing and enhancing the relationship between these two critical functions. Curious about the misalignment between Sales and Marketing, the authors interviewed pairs of chief marketing officers and sales vice presidents to capture their perspectives. They looked in depth at the relationship between Sales and Marketing in a variety of companies in different industries. Their goal was to identify best practices that could enhance the joint performance and increase the contributions of these two functions. Among their findings: The marketing function takes different forms in different companies at different product life cycle stages. Marketing's increasing influence in each phase of an organization's growth profoundly affects its relationship with Sales. The strains between Sales and Marketing fall into two main categories: economic (a single budget is typically divided, between Sales and Marketing, and not always evenly) and cultural (the two functions attract very different types of people who achieve success by spending their time in very different ways). In this article, the authors describe the four types of relationships Sales and Marketing typically exhibit. They provide a diagnostic to help readers assess their companies' level of integration, and they offer recommendations for more closely aligning the two functions.


The Enigmatic temporal pole: a review of findings on social and emotional processing.

The function of the anterior-most portion of the temporal lobes, the temporal pole, is not well understood. Anatomists have long considered it part of an extended limbic system based on its location posterior to the orbital frontal cortex and lateral to the amygdala, along with its tight connectivity to limbic and paralimbic regions. Here we review the literature in both non-human primates and humans to assess the temporal pole's putative role in social and emotional processing. Reviewed findings indicate that it has some role in both social and emotional processes, including face recognition and theory of mind, that goes beyond semantic memory. We propose that the temporal pole binds complex, highly processed perceptual inputs to visceral emotional responses. Because perceptual inputs remain segregated into dorsal (auditory), medial (olfactory) and ventral (visual) streams, the integration of emotion with perception is channel specific.


Short-Text Clustering using Statistical Semantics

Short documents are typically represented by very sparse vectors, in the space of terms. In this case, traditional techniques for calculating text similarity results in measures which are very close to zero, since documents even the very similar ones have a very few or mostly no terms in common. In order to alleviate this limitation, the representation of short-text segments should be enriched by incorporating information about correlation between terms. In other words, if two short segments do not have any common words, but terms from the first segment appear frequently with terms from the second segment in other documents, this means that these segments are semantically related, and their similarity measure should be high. Towards achieving this goal, we employ a method for enhancing document clustering using statistical semantics. However, the problem of high computation time arises when calculating correlation between all terms. In this work, we propose the selection of a few terms, and using these terms with the Nystr\"om method to approximate the term-term correlation matrix. The selection of the terms for the Nystr\"om method is performed by randomly sampling terms with probabilities proportional to the lengths of their vectors in the document space. This allows more important terms to have more influence on the approximation of the term-term correlation matrix and accordingly achieves better accuracy.


QUO VADIS , DYNAMIC CAPABILITIES ? ACONTENT-ANALYTICREVIEWOFTHECURRENTSTATEOF KNOWLEDGE AND RECOMMENDATIONS FOR FUTURE RESEARCH

Although the dynamic capabilities perspective has become one of the most frequently used theoretical lenses in management research, critics have repeatedly voiced their frustration with this literature, particularly bemoaning the lack of empirical knowledge and the underspecification of the construct of dynamic capabilities. But research on dynamic capabilities has advanced considerably since its early years, in which most contributions to this literature were purely conceptual. A plethora of empirical studies as well as further theoretical elaborations have shed substantial light on a variety of specific, measurable factors connected to dynamic capabilities. Our article starts out by analyzing these studies to develop a meta-framework that specifies antecedents, dimensions, mechanisms, moderators, and outcomes of dynamic capabilities identified in the literature to date. This framework provides a comprehensive and systematic synthesis of the dynamic capabilities perspective that reflects the richness of the research while at the same time unifying it into a cohesive, overarching model. Such an analysis has not yet been undertaken; no comprehensive framework with this level of detail has previously been presented for dynamic capabilities. Our analysis shows where research has made the most progress and where gaps and unresolved tensions remain. Based on this analysis, we propose a forward-looking research agenda that outlines directions for future research.


Is Tom Cruise Threatened ? Using Netflix Prize Data to Examine the Long Tail of Electronic Commerce

We analyze a large data set from Netflix, the leading online movie rental company, to shed new light on the causes and consequences of the Long Tail effect, which suggests that on the Internet, over time, consumers will increasingly shift away from hit products and toward niche products. We examine the aggregate level demand as well as demand at the individual consumer level and we find that the consumption of both the hit and the niche movies decreased over time when the popularity of the movies is ranked in absolute terms (e.g., the top/bottom 10 titles). However, we also observe that the active product variety has increased dramatically over the study period. To separate out the demand diversification effect from the shift in consumer preferences, we propose to measure the popularity of movies in relative terms by dynamically adjusting for the current product variety (e.g., the top/bottom 1% of titles). Using this alternative definition of popularity, we find that the demand for the hits rises, while the demand for the niches still falls. We conclude that new movie titles appear much faster than consumers discover them. Finally, we find no evidence that niche titles satisfy consumer tastes better than hit titles and that a small number of heavy users are more likely to venture into niches than light users.


Improvasher: A Real-Time Mashup System for Live Musical Input

In this paper we present Improvasher a real-time musical accompaniment system which creates an automatic mashup to accompany live musical input. Improvasher is built around two music processing modules, the first, a performance following technique, makes beat-synchronous predictions of chroma features from a live musical input. The second, a music mashup system, determines the compatibility between beat-synchronous chromagrams from different pieces of music. Through the combination of these two techniques, a real-time predictive mashup can be generated towards a new form of automatic accompaniment for interactive musical performance.


Implementing Intelligent Traffic Control System for Congestion Control, Ambulance Clearance, and Stolen Vehicle Detection

This paper presents an intelligent traffic control system to pass emergency vehicles smoothly. Each individual vehicle is equipped with special radio frequency identification (RFID) tag (placed at a strategic location), which makes it impossible to remove or destroy. We use RFID reader, NSK EDK-125-TTL, and PIC16F877A system-on-chip to read the RFID tags attached to the vehicle. It counts number of vehicles that passes on a particular path during a specified duration. It also determines the network congestion, and hence the green light duration for that path. If the RFID-tag-read belongs to the stolen vehicle, then a message is sent using GSM SIM300 to the police control room. In addition, when an ambulance is approaching the junction, it will communicate to the traffic controller in the junction to turn ON the green light. This module uses ZigBee modules on CC2500 and PIC16F877A system-on-chip for wireless communications between the ambulance and traffic controller. The prototype was tested under different combinations of inputs in our wireless communication laboratory and experimental results were found as expected.


CAD-based pose estimation for random bin-picking of multiple objects using a RGB-D camera

In this paper, we propose a CAD-based 6-DOF pose estimation design for random bin-picking of multiple different objects using a Kinect RGB-D sensor. 3D CAD models of objects are constructed via a virtual camera, which generates a point cloud database for object recognition and pose estimation. A voxel grid filter is suggested to reduce the number of 3D point cloud of objects for reducing computing time of pose estimation. A voting-scheme method was adopted for the 6-DOF pose estimation a swell as object recognition of different type objects in the bin. Furthermore, an outlier filter is designed to filter out bad matching poses and occluded ones, so that the robot arm always picks up the upper object in the bin to increase pick up success rate. A series of experiments on a Kuka 6-axis robot revels that the proposed system works satisfactorily to pick up all random objects in the bin. The average recognition rate of three different type objects is 93.9% and the pickup success rate is 89.7%.


Linear codes over Z4+uZ4: MacWilliams identities, projections, and formally self-dual codes

Linear codes are considered over the ring Z 4 + uZ 4 , a non-chain extension of Z 4. Lee weights, Gray maps for these codes are defined and MacWilliams identities for the complete, symmetrized and Lee weight enumer-ators are proved. Two projections from Z 4 + uZ 4 to the rings Z 4 and F 2 + uF 2 are considered and self-dual codes over Z 4 +uZ 4 are studied in connection with these projections. Finally three constructions are given for formally self-dual codes over Z 4 + uZ 4 and their Z 4-images together with some good examples of formally self-dual Z 4-codes obtained through these constructions.


Millimeter-Wave Compact EBG Structure for Mutual Coupling Reduction Applications

A new millimeter-wave (MMW), electromagnetic band-gap (EBG) structure is presented. The proposed EBG structure without the use of metallic vias or vertical components is formed by etching two slots and adding two connecting bridges to a conventional uniplanar EBG unit-cell. The transmission characteristics of the proposed EBG structure are measured. Results show that the proposed EBG structure has a wide bandgap around the 60 GHz band. The size of the proposed EBG unit-cell is 78% less than a conventional uniplanar EBG, and 72% less than a uniplanar-compact EBG (UC-EBG) operating at the same frequency band. Moreover, and despite the fabrication limitations at the 60 GHz band, the proposed EBG unit-cell provides at least 12% more size reduction than any other planar EBG structures at microwave frequencies. Its enhanced performance and applicability to reduce mutual coupling in antenna arrays are then investigated. Results show a drastic decrease in the mutual coupling level. This EBG structure can find its applications in MMW wireless communication systems.


Alternative activation of macrophages

The classical pathway of interferon-γ-dependent activation of macrophages by T helper 1 (TH1)-type responses is a well-established feature of cellular immunity to infection with intracellular pathogens, such as Mycobacterium tuberculosis and HIV. The concept of an alternative pathway of macrophage activation by the TH2-type cytokines interleukin-4 (IL-4) and IL-13 has gained credence in the past decade, to account for a distinctive macrophage phenotype that is consistent with a different role in humoral immunity and repair. In this review, I assess the evidence in favour of alternative macrophage activation in the light of macrophage heterogeneity, and define its limits and relevance to a range of immune and inflammatory conditions.


EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks

Decision support systems have been utilised since 1960, providing physicians with fast and accurate means towards more accurate diagnoses and increased tolerance when handling missing or incomplete data. This paper describes the application of neural network models for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: initially, a feature extraction scheme using the wavelet transform (WT) has been applied and then a learning-based algorithm classifier performed the classification. The performance of the neural model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals


ProM: The Process Mining Toolkit

Nowadays, all kinds of information systems store detailed information in logs. Process mining has emerged as a way to analyze these systems based on these detailed logs. Unlike classical data mining, the focus of process mining is on processes. First, process mining allows us to extract a process model from an event log. Second, it allows us to detect discrepancies between a modeled process (as it was envisioned to be) and an event log (as it actually is). Third, it can enrich an existing model with knowledge derived from an event log. This paper presents our tool ProM, which is the world-leading tool in the area of process mining.


Artificial neural networks: fundamentals, computing, design, and application.

Artificial neural networks (ANNs) are relatively new computational tools that have found extensive utilization in solving many complex real-world problems. The attractiveness of ANNs comes from their remarkable information processing characteristics pertinent mainly to nonlinearity, high parallelism, fault and noise tolerance, and learning and generalization capabilities. This paper aims to familiarize the reader with ANN-based computing (neurocomputing) and to serve as a useful companion practical guide and toolkit for the ANNs modeler along the course of ANN project development. The history of the evolution of neurocomputing and its relation to the field of neurobiology is briefly discussed. ANNs are compared to both expert systems and statistical regression and their advantages and limitations are outlined. A bird's eye review of the various types of ANNs and the related learning rules is presented, with special emphasis on backpropagation (BP) ANNs theory and design. A generalized methodology for developing successful ANNs projects from conceptualization, to design, to implementation, is described. The most common problems that BPANNs developers face during training are summarized in conjunction with possible causes and remedies. Finally, as a practical application, BPANNs were used to model the microbial growth curves of S. flexneri. The developed model was reasonably accurate in simulating both training and test time-dependent growth curves as affected by temperature and pH.


What Size Net Gives Valid Generalization?

We address the question of when a network can be expected to generalize from m random training examples chosen from some arbitrary probability distribution, assuming that future test examples are drawn from the same distribution. Among our results are the following bounds on appropriate sample vs. network size. Assume 0 < ∊ 1/8. We show that if m O(W/∊ log N/∊) random examples can be loaded on a feedforward network of linear threshold functions with N nodes and W weights, so that at least a fraction 1 ∊/2 of the examples are correctly classified, then one has confidence approaching certainty that the network will correctly classify a fraction 1 ∊ of future test examples drawn from the same distribution. Conversely, for fully-connected feedforward nets with one hidden layer, any learning algorithm using fewer than (W/∊) random training examples will, for some distributions of examples consistent with an appropriate weight choice, fail at least some fixed fraction of the time to find a weight choice that will correctly classify more than a 1 ∊ fraction of the future test examples.


Learning in Artificial Neural Networks: A Statistical Perspective

The premise of this article is that learning procedures used to train artificial neural networks are inherently statistical techniques. It follows that statistical theory can provide considerable insight into the properties, advantages, and disadvantages of different network learning methods. We review concepts and analytical results from the literatures of mathematical statistics, econometrics, systems identification, and optimization theory relevant to the analysis of learning in artificial neural networks. Because of the considerable variety of available learning procedures and necessary limitations of space, we cannot provide a comprehensive treatment. Our focus is primarily on learning procedures for feedforward networks. However, many of the concepts and issues arising in this framework are also quite broadly relevant to other network learning paradigms. In addition to providing useful insights, the material reviewed here suggests some potentially useful new training methods for artificial neural networks.


Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights

A two-layer neural network can be used to approximate any nonlinear function. T h e behavior of the hidden nodes tha t allows the network to do this is described. Networks w i th one input are analyzed first, and the analysis is then extended to networks w i t h mult iple inputs. T h e result of th is analysis is used to formulate a method for ini t ial izat ion o f the weights o f neural networks to reduce t ra in ing t ime. Training examples are given and the learning curve for these examples are shown to illustrate the decrease in necessary training t ime. Introduction Two-layer feed forward neural networks have been proven capable of approximating any arbitrary functions [l], given that they have sufficient numbers of nodes in their hidden layers. We offer a description of how this works, along with a method of speeding up the training process by choosing the networks’ initial weights. The relationship between the inputs and the output of a two-layer neural network may be described by Equation (1) H l y = wi . sigmoid(LqX + W b i ) (1) i=O where y is the network’s output, X is the input vector, H is the number of hidden nodes, Wi is the weight vector of the i th node of the hidden layer, Wbi is the bias weight of the ith hidden node, w i is the weight of the output layer which connects the i th hidden unit to the output. The behavior of hidden nodes in two-layer networks with one input To illustrate the behavior of the hidden nodes, a two-layer network with one input is trained to approximate a function of one variable d(z) . That is, the network is trained to produce d(z) given z as input using the back-propagation algorithm [2]. The output of the network is given as


Large Margin Metric Learning for Multi-Label Prediction

Canonical correlation analysis (CCA) and maximum margin output coding (MMOC) methods have shown promising results for multi-label prediction, where each instance is associated with multiple labels. However, these methods require an expensive decoding procedure to recover the multiple labels of each testing instance. The testing complexity becomes unacceptable when there are many labels. To avoid decoding completely, we present a novel large margin metric learning paradigm for multi-label prediction. In particular, the proposed method learns a distance metric to discover label dependency such that instances with very different multiple labels will be moved far away. To handle many labels, we present an accelerated proximal gradient procedure to speed up the learning process. Comprehensive experiments demonstrate that our proposed method is significantly faster than CCA and MMOC in terms of both training and testing complexities. Moreover, our method achieves superior prediction performance compared with state-of-the-art methods.


Optimal Design of Energy-Efficient Multi-User MIMO Systems: Is Massive MIMO the Answer?

Assume that a multi-user multiple-input multiple-output (MIMO) system is designed from scratch to uniformly cover a given area with maximal energy efficiency (EE). What are the optimal number of antennas, active users, and transmit power? The aim of this paper is to answer this fundamental question. We consider jointly the uplink and downlink with different processing schemes at the base station and propose a new realistic power consumption model that reveals how the above parameters affect the EE. Closed-form expressions for the EE-optimal value of each parameter, when the other two are fixed, are provided for zero-forcing (ZF) processing in single-cell scenarios. These expressions prove how the parameters interact. For example, in sharp contrast to common belief, the transmit power is found to increase (not to decrease) with the number of antennas. This implies that energy-efficient systems can operate in high signal-to-noise ratio regimes in which interference-suppressing signal processing is mandatory. Numerical and analytical results show that the maximal EE is achieved by a massive MIMO setup wherein hundreds of antennas are deployed to serve a relatively large number of users using ZF processing. The numerical results show the same behavior under imperfect channel state information and in symmetric multi-cell scenarios.


A systematic analysis of textual variability modeling languages

Industrial variability models tend to grow in size and complexity due to ever-increasing functionality and complexity of software systems. Some authors report on variability models specifying several thousands of variabilities. However, traditional variability modeling approaches do not seem to scale adequately to cope with size and complexity of such models. Recently, textual variability modeling languages have been advocated as one scalable solution.
 In this paper, we provide a systematic analysis of the capabilities of current textual variability modeling languages, in particular regarding variability management in the large. Towards this aim, we define a classification schema consisting of five dimensions, classify ten different textual variability modeling languages using the classification schema and provide an analysis. In summary, some textual variability modeling languages go beyond textual representations of traditional variability modeling approaches and provide sophisticated modeling concepts and constraint languages. Three textual variability modeling approaches already support mechanisms for large-scale variability modeling such as model composition, modularization, or evolution support.


Cracking the Code of Biodiversity Responses to Past Climate Change.

How individual species and entire ecosystems will respond to future climate change are among the most pressing questions facing ecologists. Past biodiversity dynamics recorded in the paleoecological archives show a broad array of responses, yet significant knowledge gaps remain. In particular, the relative roles of evolutionary adaptation, phenotypic plasticity, and dispersal in promoting survival during times of climate change have yet to be clarified. Investigating the paleo-archives offers great opportunities to understand biodiversity responses to future climate change. In this review we discuss the mechanisms by which biodiversity responds to environmental change, and identify gaps of knowledge on the role of range shifts and tolerance. We also outline approaches at the intersection of paleoecology, genomics, experiments, and predictive models that will elucidate the processes by which species have survived past climatic changes and enhance predictions of future changes in biological diversity.


Application of Artificial Intelligence to Real-Time Fault Detection in Permanent-Magnet Synchronous Machines

This paper discusses faults in rotating electrical machines in general and describes a fault detection technique using artificial neural network (ANN) which is an expert system to detect short-circuit fault currents in the stator windings of a permanent-magnet synchronous machine (PMSM). The experimental setup consists of PMSM coupled mechanically to a dc motor configured to run in torque mode. Particle swarm optimization is used to adjust the weights of the ANN. All simulations are carried out in MATLAB/SIMULINK environment. The technique is shown to be effective and can be applied to real-time fault detection.


Language Models with Pre-Trained (GloVe) Word Embeddings

In this work we present a step-by-step implementation of training a Language Model (LM) , using Recurrent Neural Network (RNN) and pre-trained GloVe word embeddings, introduced by Pennigton et al. in [1]. The implementation is following the general idea of training RNNs for LM tasks presented in [2] , but is rather using Gated Recurrent Unit (GRU) [3] for a memory cell, and not the more commonly used LSTM [4]. The implementation presented is based on using keras1 [5].


Comparison study of non-orthogonal multiple access schemes for 5G

With the development of mobile Internet and Internet of things (IoT), the 5th generation (5G) wireless communications will foresee explosive increase in mobile traffic. To address challenges in 5G such as higher spectral efficiency, massive connectivity, and lower latency, some non-orthogonal multiple access (NOMA) schemes have been recently actively investigated, including power-domain NOMA, multiple access with low-density spreading (LDS), sparse code multiple access (SCMA), multiuser shared access (MUSA), pattern division multiple access (PDMA), etc. Different from conventional orthogonal multiple access (OMA) schemes, NOMA can realize overloading by introducing some controllable interferences at the cost of slightly increased receiver complexity, which can achieve significant gains in spectral efficiency and accommodate much more users. In this paper, we will discuss basic principles and key features of three typical NOMA schemes, i.e., SCMA, MUSA, and PDMA. What's more, their performance in terms of uplink bit error rate (BER) will be compared. Simulation results show that in typical Rayleigh fading channels, SCMA has the best performance, while the BER performance of MUSA and PDMA are very close to each other. In addition, we also analyze the performance of PDMA using the same factor graph as SCMA, which indicates that the performance gain of SCMA over PDMA comes from both the difference of factor graph and the codebook optimization.


RPL-based multipath Routing Protocols for Internet of Things on Wireless Sensor Networks

In the last few years, Wireless Sensor Network (WSN) emerges and appears as an essential platform for prominent concept of Internet of Things (IoT). Their application ranges from so-called “smart cities”, “smart homes” over environmental monitoring. The connectivity in IoT mainly relies on RPL (IPv6 Routing Protocol for Low Power and Lossy Network) - a routing algorithm that constructs and maintains DODAGs (Destination Oriented Directed Acyclic Graph) to transmit data from sensors to root over a single path. However, due to the resource constraints of sensor nodes and the unreliability of wireless links, single-path routing approaches cannot be considered effective techniques to meet the performance demands of various applications. In order to overcome these problems, many individuals and group research focuses on multi-path solutions for RPL routing protocol. In this paper, we propose three multipath schemes based on RPL (Energy Load Balancing-ELB, Fast Local Repair-FLR and theirs combination-ELB-FLR) and integrate them in a modified IPv6 communication stack for IoT. These schemes are implemented in OMNET++ simulator and the experiment outcomes show that our approaches have achieved better energy efficiency, better end-to-end delay, packet delivery rate and network load balance compared to traditional solution of RPL.


Cognitive architectures: Research issues and challenges

In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representation, organization, performance, and learning, and some criteria for evaluating such architectures at the systems level. In closing, we discuss some open issues that should drive future research in this important area. 2008 Published by Elsevier B.V.


GRS: The green, reliability, and security of emerging machine to machine communications

Machine-to-machine communications is characterized by involving a large number of intelligent machines sharing information and making collaborative decisions without direct human intervention. Due to its potential to support a large number of ubiquitous characteristics and achieving better cost efficiency, M2M communications has quickly become a market-changing force for a wide variety of real-time monitoring applications, such as remote e-healthcare, smart homes, environmental monitoring, and industrial automation. However, the flourishing of M2M communications still hinges on fully understanding and managing the existing challenges: energy efficiency (green), reliability, and security (GRS). Without guaranteed GRS, M2M communications cannot be widely accepted as a promising communication paradigm. In this article, we explore the emerging M2M communications in terms of the potential GRS issues, and aim to promote an energy-efficient, reliable, and secure M2M communications environment. Specifically, we first formalize M2M communications architecture to incorporate three domains - the M2M, network, and application domains - and accordingly define GRS requirements in a systematic manner. We then introduce a number of GRS enabling techniques by exploring activity scheduling, redundancy utilization, and cooperative security mechanisms. These techniques hold promise in propelling the development and deployment of M2M communications applications.


Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients.

OBJECTIVE
To ascertain the current burden of adverse drug reactions (ADRs) through a prospective analysis of all admissions to hospital.

DESIGN
Prospective observational study.

SETTING
Two large general hospitals in Merseyside, England.

PARTICIPANTS
18 820 patients aged > 16 years admitted over six months and assessed for cause of admission.

MAIN OUTCOME MEASURES
Prevalence of admissions due to an ADR, length of stay, avoidability, and outcome.

RESULTS
There were 1225 admissions related to an ADR, giving a prevalence of 6.5%, with the ADR directly leading to the admission in 80% of cases. The median bed stay was eight days, accounting for 4% of the hospital bed capacity. The projected annual cost of such admissions to the NHS is 466m pounds sterling (706m Euros, 847m dollars). The overall fatality was 0.15%. Most reactions were either definitely or possibly avoidable. Drugs most commonly implicated in causing these admissions included low dose aspirin, diuretics, warfarin, and non-steroidal anti-inflammatory drugs other than aspirin, the most common reaction being gastrointestinal bleeding.

CONCLUSION
The burden of ADRs on the NHS is high, accounting for considerable morbidity, mortality, and extra costs. Although many of the implicated drugs have proved benefit, measures need to be put into place to reduce the burden of ADRs and thereby further improve the benefit:harm ratio of the drugs.


Health-related rumour detection on Twitter

In the last years social networks have emerged as a critical mean for information spreading. In spite of all the positive consequences this phenomenon brings, unverified and instrumentally relevant information statements in circulation, named as rumours, are becoming a potential threat to the society. Recently, there have been several studies on topic-independent rumour detection on Twitter. In this paper we present a novel rumour detection system which focuses on a specific topic, that is health-related rumours on Twitter. To this aim, we constructed a new subset of features including influence potential and network characteristics features. We tested our approach on a real dataset observing promising results, as it is able to correctly detect about 89% of rumours, with acceptable levels of precision.


Full bridge phase-shifted soft switching high-frequency inverter with boost PFC function for induction heating system

This paper is mainly concerned with a high frequency soft-switching PWM inverter suitable for consumer induction heating system. Proposed system is composed of soft switching chopper based voltage boost PFC input stage and phase shifted PWM controlled full bridge ZCZVS high frequency inverter stage. Its fundamentals of operating performances are illustrated and evaluated on the experimental results. Its effectiveness is substantially proved on the basis of the experimental results from a practical point of view.


Automatic Modulation Classification of Overlapped Sources Using Multi-Gene Genetic Programming With Structural Risk Minimization Principle

As the spectrum environment becomes increasingly crowded and complicated, primary users may be interfered by secondary users and other illegal users. Automatic modulation classification (AMC) of a single source cannot recognize the overlapped sources. Consequently, the AMC of overlapped sources attracts much attention. In this paper, we propose a genetic programming-based modulation classification method for overlapped sources (GPOS). The proposed GPOS consists of two stages, the training stage, and the classification stage. In the training stage, multi-gene genetic programming (MGP)-based feature engineering transforms sample estimates of cumulants into highly discriminative MGP-features iteratively, until optimal MGP-features (OMGP-features) are obtained, where the structural risk minimization principle (SRMP) is employed to evaluate the classification performance of MGP-features and train the classifier. Moreover, a self-adaptive genetic operation is designed to accelerate the feature engineering process. In the classification stage, the classification decision is made by the trained classifier using the OMGP-features. Through simulation results, we demonstrate that the proposed scheme outperforms other existing methods in terms of classification performance and robustness in case of varying power ratios and fading channel.


Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection

Recently, microblogging sites such as Twitter have garnered a great deal of attention as an advanced form of location-aware social network services, whereby individuals can easily and instantly share their most recent updates from any place. In this study, we aim to develop a geo-social event detection system by monitoring crowd behaviors indirectly via Twitter. In particular, we attempt to find out the occurrence of local events such as local festivals; a considerable number of Twitter users probably write many posts about these events. To detect such unusual geo-social events, we depend on geographical regularities deduced from the usual behavior patterns of crowds with geo-tagged microblogs. By comparing these regularities with the estimated ones, we decide whether there are any unusual events happening in the monitored geographical area. Finally, we describe the experimental results to evaluate the proposed unusuality detection method on the basis of geographical regularities obtained from a large number of geo-tagged tweets around Japan via Twitter.


Classic Hallucinogens and Mystical Experiences: Phenomenology and Neural Correlates.

This chapter begins with a brief review of descriptions and definitions of mystical-type experiences and the historical connection between classic hallucinogens and mystical experiences. The chapter then explores the empirical literature on experiences with classic hallucinogens in which claims about mystical or religious experiences have been made. A psychometrically validated questionnaire is described for the reliable measurement of mystical-type experiences occasioned by classic hallucinogens. Controlled laboratory studies show that under double-blind conditions that provide significant controls for expectancy bias, psilocybin can occasion complete mystical experiences in the majority of people studied. These effects are dose-dependent, specific to psilocybin compared to placebo or a psychoactive control substance, and have enduring impact on the moods, attitudes, and behaviors of participants as assessed by self-report of participants and ratings by community observers. Other studies suggest that enduring personal meaning in healthy volunteers and therapeutic outcomes in patients, including reduction and cessation of substance abuse behaviors and reduction of anxiety and depression in patients with a life-threatening cancer diagnosis, are related to the occurrence of mystical experiences during drug sessions. The final sections of the chapter draw parallels in human neuroscience research between the neural bases of experiences with classic hallucinogens and the neural bases of meditative practices for which claims of mystical-type experience are sometimes made. From these parallels, a functional neural model of mystical experience is proposed, based on changes in the default mode network of the brain that have been observed after the administration of classic hallucinogens and during meditation practices for which mystical-type claims have been made.


Sparsely sampled Fourier ptychography.

Fourier ptychography (FP) is an imaging technique that applies angular diversity functions for high-resolution complex image recovery. The FP recovery routine switches between two working domains: the spectral and spatial domains. In this paper, we investigate the spectral-spatial data redundancy requirement of the FP recovery process. We report a sparsely sampled FP scheme by exploring the sampling interplay between these two domains. We demonstrate the use of the reported scheme for bypassing the high-dynamic-range combination step in the original FP recovery routine. As such, it is able to shorten the acquisition time of the FP platform by ~50%. As a special case of the sparsely sample FP, we also discuss a sub-sampled scheme and demonstrate its application in solving the pixel aliasing problem plagued in the original FP algorithm. We validate the reported schemes with both simulations and experiments. This paper provides insights for the development of the FP approach.


Threats to Feminist Identity and Reactions to Gender Discrimination

The aim of this research was to examine conditions that modify feminists' support for women as targets of gender discrimination. In an experimental study we tested a hypothesis that threatened feminist identity will lead to greater differentiation between feminists and conservative women as victims of discrimination and, in turn, a decrease in support for non-feminist victims. The study was conducted among 96 young Polish female professionals and graduate students from Gender Studies programs in Warsaw who self-identified as feminists (Mage  = 22.23). Participants were presented with a case of workplace gender discrimination. Threat to feminist identity and worldview of the discrimination victim (feminist vs. conservative) were varied between research conditions. Results indicate that identity threat caused feminists to show conditional reactions to discrimination. Under identity threat, feminists perceived the situation as less discriminatory when the target held conservative views on gender relations than when the target was presented as feminist. This effect was not observed under conditions of no threat. Moreover, feminists showed an increase in compassion for the victim when she was portrayed as a feminist compared to when she was portrayed as conservative. Implications for the feminist movement are discussed.


Dynamic Filters in Graph Convolutional Networks

Convolutional neural networks (CNNs) have massively impacted visual recognition in 2D images, and are now ubiquitous in state-of-the-art approaches. While CNNs naturally extend to other domains, such as audio and video, where data is also organized in rectangular grids, they do not easily generalize to other types of data such as 3D shape meshes, social network graphs or molecular graphs. To handle such data, we propose a novel graph-convolutional network architecture that builds on a generic formulation that relaxes the 1-to-1 correspondence between filter weights and data elements around the center of the convolution. The main novelty of our architecture is that the shape of the filter is a function of the features in the previous network layer, which is learned as an integral part of the neural network. Experimental evaluations on digit recognition, semi-supervised document classification, and 3D shape correspondence yield state-of-the-art results, significantly improving over previous work for shape correspondence.


BINSEC/SE: A Dynamic Symbolic Execution Toolkit for Binary-Level Analysis

When it comes to software analysis, several approaches exist from heuristic techniques to formal methods, which are helpful at solving different kinds ofproblems. Unfortunately very few initiative seek to aggregate this techniques in the same platform. BINSEC intend to fulfill this lack of binary analysis platform by allowing to perform modular analysis. This work focusses on BINSEC/SE, the new dynamic symbolic execution engine (DSE) implemented in BINSEC. We will highlight the novelties of the engine, especially in terms of interactions between concrete and symbolic execution or optimization of formula generation. Finally, two reverse engineering applications are shown in order to emphasize the tool effectiveness.


Artificial intelligent firewall

Firewalls are now an integral part of network security. An intelligent firewall that prevents unauthorized access to a system has been developed. Artificial intelligence applications are uniquely suited for the ever-changing, ever-evolving world of network security. Typical firewalls are only as good as the information provided by the Network Administrator. A new type of attack creates vulnerabilities, which a static firewall does not have the ability to avoid without human direction. An AI-managed firewall service, however, can protect a computer network from known and future threats. We report in this paper on research in progress concerning the integration of different security techniques. A main purpose of the project is to integrate a smart detection engine into a firewall. The smart detection engine will aim at not only detecting anomalous network traffic as in classical IDSs, but also detecting unusual structures data packets that suggest the presence of virus data. We will report in this paper on the concept of an intelligent firewall that contains a smart detection engine for potentially malicious data packets.


Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking

Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. We present a benchmark for Multiple Object Tracking launched in the late 2014, with the goal of creating a framework for the standardized evaluation of multiple object tracking methods. This paper collects the two releases of the benchmark made so far, and provides an in-depth analysis of almost 50 state-of-the-art trackers that were tested on over 11000 frames. We show the current trends and weaknesses of multiple people tracking methods, and provide pointers of what researchers should be focusing on to push the field forward.


Personality Analysis Through Handwriting


Search Based Software Engineering

The use of evolutionary algorithms for solving multi-objective optimization problems has become increasingly popular, mainly within the last 15 years. From among the several research trends that have originated in recent years, one of the most promising is the use of hybrid approaches that allow to improve the performance of multi-objective evolutionary algorithms (MOEAs). In this talk, some of the most representative research on the use of hybrid approaches in evolutionary multi-objective optimization will be discussed. The topics discussed will include multi-objective memetic algorithms, hybridization of MOEAs with gradient-based methods and with direct search methods, as well as multi-objective hyperheuristics. Some applications of these approaches as well as some potential paths for future research in this area will also be briefly discussed. Towards Evolutionary Multitasking: A New Paradigm


Real-time 3D eyelids tracking from semantic edges

State-of-the-art real-time face tracking systems still lack the ability to realistically portray subtle details of various aspects of the face, particularly the region surrounding the eyes. To improve this situation, we propose a technique to reconstruct the 3D shape and motion of eyelids in real time. By combining these results with the full facial expression and gaze direction, our system generates complete face tracking sequences with more detailed eye regions than existing solutions in real-time. To achieve this goal, we propose a generative eyelid model which decomposes eyelid variation into two low-dimensional linear spaces which efficiently represent the shape and motion of eyelids. Then, we modify a holistically-nested DNN model to jointly perform semantic eyelid edge detection and identification on images. Next, we correspond vertices of the eyelid model to 2D image edges, and employ polynomial curve fitting and a search scheme to handle incorrect and partial edge detections. Finally, we use the correspondences in a 3D-to-2D edge fitting scheme to reconstruct eyelid shape and pose. By integrating our fast fitting method into a face tracking system, the estimated eyelid results are seamlessly fused with the face and eyeball results in real time. Experiments show that our technique applies to different human races, eyelid shapes, and eyelid motions, and is robust to changes in head pose, expression and gaze direction.


Recurrent Network Models for Human Dynamics

We propose the Encoder-Recurrent-Decoder (ERD) model for recognition and prediction of human body pose in videos and motion capture. The ERD model is a recurrent neural network that incorporates nonlinear encoder and decoder networks before and after recurrent layers. We test instantiations of ERD architectures in the tasks of motion capture (mocap) generation, body pose labeling and body pose forecasting in videos. Our model handles mocap training data across multiple subjects and activity domains, and synthesizes novel motions while avoiding drifting for long periods of time. For human pose labeling, ERD outperforms a per frame body part detector by resolving left-right body part confusions. For video pose forecasting, ERD predicts body joint displacements across a temporal horizon of 400ms and outperforms a first order motion model based on optical flow. ERDs extend previous Long Short Term Memory (LSTM) models in the literature to jointly learn representations and their dynamics. Our experiments show such representation learning is crucial for both labeling and prediction in space-time. We find this is a distinguishing feature between the spatio-temporal visual domain in comparison to 1D text, speech or handwriting, where straightforward hard coded representations have shown excellent results when directly combined with recurrent units [31].


DeepPose: Human Pose Estimation via Deep Neural Networks

We propose a method for human pose estimation based on Deep Neural Networks (DNNs). The pose estimation is formulated as a DNN-based regression problem towards body joints. We present a cascade of such DNN regres- sors which results in high precision pose estimates. The approach has the advantage of reasoning about pose in a holistic fashion and has a simple but yet powerful formula- tion which capitalizes on recent advances in Deep Learn- ing. We present a detailed empirical analysis with state-of- art or better performance on four academic benchmarks of diverse real-world images.


Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments

We introduce a new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and algorithms. Besides increasing the size of the datasets in the current state-of-the-art by several orders of magnitude, we also aim to complement such datasets with a diverse set of motions and poses encountered as part of typical human activities (taking photos, talking on the phone, posing, greeting, eating, etc.), with additional synchronized image, human motion capture, and time of flight (depth) data, and with accurate 3D body scans of all the subject actors involved. We also provide controlled mixed reality evaluation scenarios where 3D human models are animated using motion capture and inserted using correct 3D geometry, in complex real environments, viewed with moving cameras, and under occlusion. Finally, we provide a set of large-scale statistical models and detailed evaluation baselines for the dataset illustrating its diversity and the scope for improvement by future work in the research community. Our experiments show that our best large-scale model can leverage our full training set to obtain a 20% improvement in performance compared to a training set of the scale of the largest existing public dataset for this problem. Yet the potential for improvement by leveraging higher capacity, more complex models with our large dataset, is substantially vaster and should stimulate future research. The dataset together with code for the associated large-scale learning models, features, visualization tools, as well as the evaluation server, is available online at http://vision.imar.ro/human3.6m.


Sparse deep belief net model for visual area V2

Motivated in part by the hierarchical organization of the cortex, a number of algorithms have recently been proposed that try to learn hierarchical, or “deep,” structure from unlabeled data. While several authors have formally or informally compared their algorithms to computations performed in visual area V1 (and the cochlea), little attempt has been made thus far to evaluate these algorithms in terms of their fidelity for mimicking computations at deeper levels in the cortical hierarchy. This paper presents an unsupervised learning model that faithfully mimics certain properties of visual area V2. Specifically, we develop a sparse variant of the deep belief networks of Hinton et al. (2006). We learn two layers of nodes in the network, and demonstrate that the first layer, similar to prior work on sparse coding and ICA, results in localized, oriented, edge filters, similar to the Gabor functions known to model V1 cell receptive fields. Further, the second layer in our model encodes correlations of the first layer responses in the data. Specifically, it picks up both colinear (“contour”) features as well as corners and junctions. More interestingly, in a quantitative comparison, the encoding of these more complex “corner” features matches well with the results from the Ito & Komatsu’s study of biological V2 responses. This suggests that our sparse variant of deep belief networks holds promise for modeling more higher-order features.


Modeling Human Motion Using Binary Latent Variables

We propose a non-linear generative model for human motion data that uses an undirected model with binary latent variables and real-valued “visible” variables that represent joint angles. The latent and visible variables at each time step receive directed connections from the visible variables at the last few time-steps. Such an architecture makes on-line inference efficient and allows us to use a simple approximate learning procedure. After training, the model finds a single set of parameters that simultaneously capture several different kinds of motion. We demonstrate the power of our approach by synthesizing various motion sequences and by performing on-line filling in of data lost during motion capture. Website: http://www.cs.toronto.edu/ ∼gwtaylor/publications/nips2006mhmublv/


Efficient Large Message Broadcast using NCCL and CUDA-Aware MPI for Deep Learning

Emerging paradigms like High Performance Data Analytics (HPDA) and Deep Learning (DL) pose at least two new design challenges for existing MPI runtimes. First, these paradigms require an efficient support for communicating unusually large messages across processes. And second, the communication buffers used by HPDA applications and DL frameworks generally reside on a GPU's memory. In this context, we observe that conventional MPI runtimes have been optimized over decades to achieve lowest possible communication latency for relatively smaller message sizes (up-to 1 Megabyte) and that too for CPU memory buffers. With the advent of CUDA-Aware MPI runtimes, a lot of research has been conducted to improve performance of GPU buffer based communication. However, little exists in current state of the art that deals with very large message communication of GPU buffers. In this paper, we investigate these new challenges by analyzing the performance bottlenecks in existing CUDA-Aware MPI runtimes like MVAPICH2-GDR, and propose hierarchical collective designs to improve communication latency of the MPI_Bcast primitive by exploiting a new communication library called NCCL. To the best of our knowledge, this is the first work that addresses these new requirements where GPU buffers are used for communication with message sizes surpassing hundreds of megabytes. We highlight the design challenges for our work along with the details of design and implementation. In addition, we provide a comprehensive performance evaluation using a Micro-benchmark and a CUDA-Aware adaptation of Microsoft CNTK DL framework. We report up to 47% improvement in training time for CNTK using the proposed hierarchical MPI_Bcast design.


Bifurcation Analysis of a Mathematical Model for Malaria Transmission

We present an ordinary differential equation mathematical model for the spread of malaria in human and mosquito populations. Susceptible humans can be infected when they are bitten by an infectious mosquito. They then progress through the exposed, infectious, and recovered classes, before reentering the susceptible class. Susceptible mosquitoes can become infected when they bite infectious or recovered humans, and once infected they move through the exposed and infectious classes. Both species follow a logistic population model, with humans having immigration and disease-induced death. We define a reproductive number, R0, for the number of secondary cases that one infected individual will cause through the duration of the infectious period. We find that the disease-free equilibrium is locally asymptotically stable when R0 < 1 and unstable when R0 > 1. We prove the existence of at least one endemic equilibrium point for all R0 > 1. In the absence of disease-induced death, we prove that the transcritical bifurcation at R0 = 1 is supercritical (forward). Numerical simulations show that for larger values of the disease-induced death rate, a subcritical (backward) bifurcation is possible at R0 = 1.


The Distributed Dissimilar Redundancy Architecture of Fly-by-Wire Flight Control System

This article presents a distributed dissimilar redundancy architecture of fly-by-wire Flight Control System. It is based on many dissimilar flight control computer, redundancy structure which includes the cross arranged hydraulic and power supply, three types of data buses, lots of data correction monitors, a distributed control loop to avoid the correlated faults and achieve the rigid safety airworthiness requirements. A Fault Tree Analysis (FTA) is implemented to verify whether the system safety could meet system design targets.


Handwritten Digit Recognition: A Neural Network Demo

A handwritten digit recognition system was used in a demonstration project to visualize artificial neural networks, in particular Kohonen’s self-organizing feature map. The purpose of this project was to introduce neural networks through a relatively easy-to-understand application to the general public. This paper describes several techniques used for preprocessing the handwritten digits, as well as a number of ways in which neural networks were used for the recognition task. Whereas the main goal was a purely educational one, a moderate recognition rate of 98% was reached on a test set.


Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring

Marine environments are delicate ecosystems which directly influence local climates, flora, fauna, and human activities. Their monitorization plays a key role in their preservation, which is most commonly done through the use of environmental sensing buoy networks. These devices transmit data by means of satellite communications or close-range base stations, which present several limitations and elevated infrastructure costs. Unmanned Aerial Vehicles (UAV) are another alternative for remote environmental monitoring which provide new types of data and ease of use. These aircraft are mainly used in video capture related applications, in its various light spectrums, and do not provide the same data as sensing buoys, nor can they be used for such extended periods of time. The aim of this research is to provide a flexible, easy to deploy and cost-effective Wireless Sensor Network (WSN) for monitoring marine environments. This proposal uses a UAV as a mobile data collector, low-power long-range communications and sensing buoys as part of a single WSN. A complete description of the design, development, and implementation of the various parts of this system is presented, as well as its validation in a real-world scenario.


A Modularization Method for Battery Equalizers Using Multiwinding Transformers

This paper proposes a modularized global architecture using multi-winding transformers for battery cell balancing. The global balancing for a series-connected battery string is achieved based on forward conversion in each battery module and based on flyback conversion among modules. The demagnetization of the multiwinding transformers is also simultaneously achieved by the flyback conversion among modules without the need of additional demagnetizing circuits. Moreover, all MOSFET switches are driven by two complementary pulse width modulation signals without the requirement of cell voltage sensors, and energy can be automatically and simultaneously delivered from any high voltage cells to any low voltage cells. Compared with existing equalizers requiring additional balancing circuits for battery modules, the proposed modularized equalizer shares one circuit for the balancing among cells and modules. The balancing performance of the proposed equalizer is perfectly verified through experimental results, and the maximum balancing efficiency is up to 91.3%. In summary, the proposed modularized equalizer has the advantages of easier modularization, simpler control, higher efficiency, smaller size, and lower cost, ensuring the battery system higher reliability and easier implementation.


Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?

We present a direct extension of probabilistic diffusion tractography to the case of multiple fibre orientations. Using automatic relevance determination, we are able to perform online selection of the number of fibre orientations supported by the data at each voxel, simplifying the problem of tracking in a multi-orientation field. We then apply the identical probabilistic algorithm to tractography in the multi- and single-fibre cases in a number of example systems which have previously been tracked successfully or unsuccessfully with single-fibre tractography. We show that multi-fibre tractography offers significant advantages in sensitivity when tracking non-dominant fibre populations, but does not dramatically change tractography results for the dominant pathways.


CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision

Knowledge of 3D properties of objects is a necessity in order to build effective computer vision systems. However, lack of large scale 3D datasets can be a major constraint for datadriven approaches in learning such properties. We consider the task of single image 3D point cloud reconstruction, and aim to utilize multiple foreground masks as our supervisory data to alleviate the need for large scale 3D datasets. A novel differentiable projection module, called ‘CAPNet’, is introduced to obtain such 2D masks from a predicted 3D point cloud. The key idea is to model the projections as a continuous approximation of the points in the point cloud. To overcome the challenges of sparse projection maps, we propose a loss formulation termed ‘affinity loss’ to generate outlierfree reconstructions. We significantly outperform the existing projection based approaches on a large-scale synthetic dataset. We show the utility and generalizability of such a 2D supervised approach through experiments on a real-world dataset, where lack of 3D data can be a serious concern. To further enhance the reconstructions, we also propose a test stage optimization procedure to obtain reconstructions that display high correspondence with the observed input image.


A Learning Scheme for Microgrid Islanding and Reconnection

This paper introduces a robust learning scheme that can dynamically predict the stability of the reconnection of subnetworks to a main grid. As the future electrical power systems tend towards smarter and greener technology, the deploymen t of self sufficient networks, or microgrids, becomes more likel y. Microgrids may operate on their own or synchronized with the main grid, thus control methods need to take into account islandi ng and reconnecting said networks. The ability to optimally and safely reconnect a portion of the grid is not well understood and, as of now, limited to raw synchronization between interconnection points. A support vector machine (SVM) leveraging real-time data from phasor measurement units (PMUs) is proposed to predict in real time whether the reconnection of a sub-network to the main grid would lead to stability or instability. A dynam ics simulator fed with pre-acquired system parameters is used t o create training data for the SVM in various operating states. The classifier was tested on a variety of cases and operating poin ts to ensure diversity. Accuracies of approximately 90% were obs erved throughout most conditions when making dynamic predictions of a given network. Keywords—Synchrophasor, machine learning, microgrid, islanding, reconnection I. I NTRODUCTION As we make strides towards a smarter power system, it is important to explore new techniques and innovations to fully capture the potential of such a dynamic entity. Many large blackout events, such as the blackout of 2003, could have been prevented with smarter controls and better monito ring [1]. Phasor measurement units, or PMUs, are one such breakthrough that will allow progress to be made in both monitoring and implementing control to the system [2]. PMUs allow for direct measurement of bus voltages and angles at high sample rates which makes dynamic state estimation more feasible [3], [4]. With the use of PMUs, it is possible to improve upon current state estimation [5] and potentially o pen up new ways to control the grid. The addition of control techniques and dynamic monitoring will be important as we begin to integrate newer solutions, such as microgrids, int o the power network. With these advanced monitoring devices, microgrids become more feasible due to the potential for rea ltime monitoring schemes. The integration of microgrids bri ng many benefits such as the ability to operate while islanded as well as interconnected with the main grid; they provide a smooth integration for renewable energy sources that matc h C. Lassetter, E. Cotilla-Sanchez, and J. Kim are with the Sch ool of Electrical Engineering & Computer Science, Oregon State University, C orvallis, OR, 97331 USA e-mail:{lassettc,ecs,kimjinsu }@oregonstate.edu local demand. Unfortunately the implementation of microgr ids is still challenging due to lacking experience with the beha vior of control schemes during off-nominal operation. Currently, microgrids are being phased in slowly due in part to the difficulty of operating subnetworks independent ly as well as determining when they can be reconnected to the main grid. Works in the literature have focused on the potential of reconnecting microgrids to the main grid, in particular aiming at synchronizing the buses at points of interconnect with respects to their voltages, frequencies , and angles [6]. Effort has been directed at creating control sch emes to minimize power flow at the point of common coupling (PCC) using direct machine control, load shedding, as well a s energy storage, to aid in smooth reconnection [7], [8]. Upon reconnection of an islanded sub-network to the main grid, instability can cause damage on both ends. It is important to track instabilities on both the microgrid and main grid upon reconnection to accurately depict the outcome of reconnect ion. Most works focus on the synchronization of two networks before reconnection [9], [10]. In some cases we may need to look at larger microgrids or subnetworks in which multipl e PCCs exist. In such scenarios, it becomes much more difficult to implement a control scheme that satisfies goodreconnection tolerances in regards to minimizing bus frequency, angle, a nd voltage differences at each PCC. In addition to the possibil ity of multiple PCCs, it is possible that direct manipulation of the system becomes limited, compromised, or unsupported with respect to synchronization. In order to address these short comings, we implement an algorithm that dynamically tracks and makes predictions based on the system states, providing real-time stability information of potential reconnectio ns. An algorithm that tracks potential reconnection and island ing times needs to be robust to prevent incorrect operation during these scenarios. PMUs make use of GPS synchronization [11] which can create an attack platform for adversarie s by changing or shifting the time synchronization. Incorrec t or compromised usage could lead to incorrect predictions that would degrade system stability due to hidden failures that remain dormant until triggered by contingencies [12]. We propose an algorithm that can make accurate predictions in face of potentially compromised measurements. Due to the complexity of the power grid, it is difficult to come up with a verbatim standard depicting the potential sta bility after reconnection of a subnetwork. With advances in the artificial intelligence community, we can make use of machin e learning algorithms in order to explore vast combinations o f sensor inputs, states, and control actions. This can be done in a


Notification and awareness: synchronizing task-oriented collaborative activity

People working collaboratively must establish and maintain awareness of one another’s intentions, actions and results. Notification systems typically support awareness of the presence, tasks and actions of collaborators, but they do not adequately support awareness of persistent and complex activities. We analysed awareness breakdowns in use of our Virtual School system—stemming from problems related to the collaborative situation, group, task and tool support—to motivate the concept of activity awareness. Activity awareness builds on prior conceptions of social and action awareness, but emphasizes the importance of activity context factors like planning and coordination. This work suggests design strategies for notification systems to better support collaborative activity. r 2003 Elsevier Science Ltd. All rights reserved.


Design Challenges/Solutions for Environments Supporting the Analysis of Social Media Data in Crisis Informatics Research

Crisis informatics investigates how society's pervasive access to technology is transforming how it responds to mass emergency events. To study this transformation, researchers require access to large sets of data that because of their volume and heterogeneous nature are difficult to collect and analyze. To address this concern, we have designed and implemented an environment - EPIC Analyze - that supports researchers with the collection and analysis of social media data. Our research has identified the types of components - such as NoSQL, MapReduce, caching, and search - needed to ensure that these services are reliable, scalable, extensible, and efficient. We describe the design challenges encountered - such as data modeling, time vs. Space tradeoffs, and the need for a useful and usable system - when building EPIC Analyze and discuss its scalability, performance, and functionality.


Anomaly Detection and Attribution Using Bayesian Networks

We present a novel approach to anomaly detection in Bayesian networks, enabling both the detection and explanation of anomalous cases in a dataset. By exploiting the structure of a Bayesian network, our algorithm is able to efficiently search for local maxima of data conflict between closely related variables. Benchmark tests using data simulated from complex Bayesian networks show that our approach provides a significant improvement over techniques that search for anomalies using the entire network, rather than its subsets. We conclude with demonstrations of the unique explanatory power of our approach in determining the observation(s) responsible for an anomaly. APPROVED FOR PUBLIC RELEASE


Automatic Detection of Diabetic Retinopathy using Deep Convolutional Neural Network

The purpose of this project is to design an automated and efficient solution that could detect the symptoms of DR from a retinal image within seconds and simplify the process of reviewing and examination of images. Diabetic Retinopathy (DR) is a complication of diabetes that is caused by changes in the blood vessel of the retina and it is one of the leading causes of blindness in the developed world. Currently, detecting DR symptoms is a manual and time-consuming process. Recently, fullyconnected and convolutional neural networks have been trained to achieve state-of-the-art performance on a wide variety of tasks such as speech recognition, image classification, natural language processing, and bioinformatics. In our approach, we trained a deep Convolutional Neural Network model on a large dataset consisting around 35,000 images and used dropout layer techniques to achieve higher accuracy.


STDP-based spiking deep convolutional neural networks for object recognition

Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively shallow architectures, and only one layer was trainable. Another line of research has demonstrated - using rate-based neural networks trained with back-propagation - that having many layers increases the recognition robustness, an approach known as deep learning. We thus designed a deep SNN, comprising several convolutional (trainable with STDP) and pooling layers. We used a temporal coding scheme where the most strongly activated neurons fire first, and less activated neurons fire later or not at all. The network was exposed to natural images. Thanks to STDP, neurons progressively learned features corresponding to prototypical patterns that were both salient and frequent. Only a few tens of examples per category were required and no label was needed. After learning, the complexity of the extracted features increased along the hierarchy, from edge detectors in the first layer to object prototypes in the last layer. Coding was very sparse, with only a few thousands spikes per image, and in some cases the object category could be reasonably well inferred from the activity of a single higher-order neuron. More generally, the activity of a few hundreds of such neurons contained robust category information, as demonstrated using a classifier on Caltech 101, ETH-80, and MNIST databases. We also demonstrate the superiority of STDP over other unsupervised techniques such as random crops (HMAX) or auto-encoders. Taken together, our results suggest that the combination of STDP with latency coding may be a key to understanding the way that the primate visual system learns, its remarkable processing speed and its low energy consumption. These mechanisms are also interesting for artificial vision systems, particularly for hardware solutions.


Weld : A Common Runtime for High Performance Data Analytics

Modern analytics applications combine multiple functions from different libraries and frameworks to build increasingly complex workflows. Even though each function may achieve high performance in isolation, the performance of the combined workflow is often an order of magnitude below hardware limits due to extensive data movement across the functions. To address this problem, we propose Weld, a runtime for data-intensive applications that optimizes across disjoint libraries and functions. Weld uses a common intermediate representation to capture the structure of diverse dataparallel workloads, including SQL, machine learning and graph analytics. It then performs key data movement optimizations and generates efficient parallel code for the whole workflow. Weld can be integrated incrementally into existing frameworks like TensorFlow, Apache Spark, NumPy and Pandas without changing their user-facing APIs. We show that Weld can speed up these frameworks, as well as applications that combine them, by up to 30×.


Passivity-Based Controller Design of Grid-Connected VSCs for Prevention of Electrical Resonance Instability

The time delay in the current control loop of a grid-connected voltage-source converter (VSC) may cause destabilization of electrical resonances in the grid or in the VSC's input filter. Instability is prevented if the input admittance of the VSC can be made passive. This paper presents an analytical controller design method for obtaining passivity. The method is equally applicable to single- and three-phase systems, i.e., in the latter case, for both stationary- and synchronous-frame control. Simulations and experiments verify the theoretical results.


Automatic Recognition of Deceptive Facial Expressions of Emotion

Humans modify facial expressions in order to mislead observers regarding their true emotional states. Being able to recognize the authenticity of emotional displays is notoriously difficult for human observers. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of genuine and deceptive facial expressions of emotions for automatic recognition1 . We show that overall the problem of recognizing deceptive facial expressions can be successfully addressed by learning spatio-temporal representations of the data. For this purpose, we propose a method that aggregates features along fiducial trajectories in a deeply learnt feature space. Interesting additional results show that on average it is easier to distinguish among genuine expressions than deceptive ones and that certain emotion pairs are more difficult to distinguish than others.


Coalition Structure Generation: Dynamic Programming Meets Anytime Optimization

Coalition structure generation involves partitioning a set of agents into exhaustive and disjoint coalitions so as to maximize the social welfare. What makes this such a challenging problem is that the number of possible solutions grows exponentially as the number of agents increases. To date, two main approaches have been developed to solve this problem, each with its own strengths and weaknesses. The state of the art in the first approach is the Improved Dynamic Programming (IDP) algorithm, due to Rahwan and Jennings, that is guaranteed to find an optimal solution in O(3), but which cannot generate a solution until it has completed its entire execution. The state of the art in the second approach is an anytime algorithm called IP, due to Rahwan et al., that provides worst-case guarantees on the quality of the best solution found so far, but which is O(n). In this paper, we develop a novel algorithm that combines both IDP and IP, resulting in a hybrid performance that exploits the strength of both algorithms and, at the same, avoids their main weaknesses. Our approach is also significantly faster (e.g. given 25 agents, it takes only 28% of the time required by IP, and 0.3% of the time required by IDP).


Privacy-Preserving User-Auditable Pseudonym Systems

Personal information is often gathered and processed in a decentralized fashion. Examples include health records and governmental data bases. To protect the privacy of individuals, no unique user identifier should be used across the different databases. At the same time, the utility of the distributed information needs to be preserved which requires that it be nevertheless possible to link different records if they relate to the same user. Recently, Camenisch and Lehmann (CCS 15) have proposed a pseudonym scheme that addresses this problem by domain-specific pseudonyms. Although being unlinkable, these pseudonyms can be converted by a central authority (the converter). To protect the users' privacy, conversions are done blindly without the converter learning the pseudonyms or the identity of the user. Unfortunately, their scheme sacrifices a crucial privacy feature: transparency. Users are no longer able to inquire with the converter and audit the flow of their personal data. Indeed, such auditability appears to be diametral to the goal of blind pseudonym conversion. In this paper we address these seemingly conflicting requirements and provide a system where user-centric audits logs are created by the oblivious converter while maintaining all privacy properties. We prove our protocol to be UC-secure and give an efficient instantiation using novel building blocks.


Boosting slow oscillations during sleep potentiates memory

There is compelling evidence that sleep contributes to the long-term consolidation of new memories. This function of sleep has been linked to slow (<1 Hz) potential oscillations, which predominantly arise from the prefrontal neocortex and characterize slow wave sleep. However, oscillations in brain potentials are commonly considered to be mere epiphenomena that reflect synchronized activity arising from neuronal networks, which links the membrane and synaptic processes of these neurons in time. Whether brain potentials and their extracellular equivalent have any physiological meaning per se is unclear, but can easily be investigated by inducing the extracellular oscillating potential fields of interest. Here we show that inducing slow oscillation-like potential fields by transcranial application of oscillating potentials (0.75 Hz) during early nocturnal non-rapid-eye-movement sleep, that is, a period of emerging slow wave sleep, enhances the retention of hippocampus-dependent declarative memories in healthy humans. The slowly oscillating potential stimulation induced an immediate increase in slow wave sleep, endogenous cortical slow oscillations and slow spindle activity in the frontal cortex. Brain stimulation with oscillations at 5 Hz—another frequency band that normally predominates during rapid-eye-movement sleep—decreased slow oscillations and left declarative memory unchanged. Our findings indicate that endogenous slow potential oscillations have a causal role in the sleep-associated consolidation of memory, and that this role is enhanced by field effects in cortical extracellular space.


Gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) is defined as glucose intolerance of various degrees that is first detected during pregnancy. GDM is detected through the screening of pregnant women for clinical risk factors and, among at-risk women, testing for abnormal glucose tolerance that is usually, but not invariably, mild and asymptomatic. GDM appears to result from the same broad spectrum of physiological and genetic abnormalities that characterize diabetes outside of pregnancy. Indeed, women with GDM are at high risk for having or developing diabetes when they are not pregnant. Thus, GDM provides a unique opportunity to study the early pathogenesis of diabetes and to develop interventions to prevent the disease.


From the Shortest Vector Problem to the Dihedral Hidden Subgroup Problem

In Quantum Computation and Lattice Problems [11] Oded Regev presented the first known connection between lattices and quantum computation, in the form of a quantum reduction from the poly(n)-unique shortest vector problem to the dihedral hidden subgroup problem by sampling cosets. This article contains a summary of Regev’s result.


Ontologies in E-Learning: Review of the Literature

We have witnessed a great interest in ontologies as an emerging technology within the Semantic Web. Its foundation that was laid in the last decade paved the way for the development of ontologies and systems that use ontologies in various domains, including the E-Learning domain. In this article, we survey key contributions related to the development and use of ontologies in the domain of E-Learning systems. We provide a framework for classification of the literature that is useful for the community of practice in categorizing work in this field and for determining possible research lines. We also discuss future trends in this area.


Vision-Based Gesture Recognition: A Review

The use of gesture as a natural interface serves as a motivating force for research in modeling, analyzing and recognition of gestures. In particular, human computer intelligent interaction needs vision-based gesture recognition, which involves many interdisciplinary studies. A survey on recent vision-based gesture recognition approaches is given in this paper. We shall review methods of static hand posture and temporal gesture recognition. Several application systems of gesture recognition are also described in this paper. We conclude with some thoughts about future research directions.


Methods for Designing Multiple Classifier Systems

In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. In this paper, the problem of design of multiple classifier system is discussed. Six design methods based on the so-called “overproduce and choose” paradigm are described and compared by experiments. Although these design methods exhibited some interesting features, they do not guarantee to design the optimal multiple classifier system for the classification task at hand. Accordingly, the main conclusion of this paper is that the problem of the optimal MCS design still remains open.


Bagging predictors

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.


A Real-Time Continuous Gesture Recognition System for Sign Language

In this paper, a large vocabulary sign language interpreter is presented with real-time continuous gesture recognition of sign language using a DataGlove . The most critical problem, end-point detection in a stream of gesture input is first solved and then statistical analysis is done according to 4 parameters in a gesture : posture, position, orientation, and motion. We have implemented a prototype system with a lexicon of 250 vocabularies in Taiwanese Sign Language (TWL). This system uses hidden Markov models (HMMs) for 51 fundamental postures, 6 orientations, and 8 motion primitives. In a signerdependent way, a sentence of gestures based on these vocabularies can be continuously recognized in real-time and the average recognition rate is 80.4%.


Methods of combining multiple classifiers and their applications to handwriting recognition

Method of combining the classification powers of several classifiers is regarded as a general problem in various application areas of pattern recognition, and a systematic investigation has been made. Possible solutions to the problem can be divided into three categories according to the levels of information available from the various classifiers. Four approaches are proposed based on different methodologies for solving this problem. One is suitable for combining individual classifiers such as Bayesian, k-NN and various distance classifiers. The other three could be used for combining any kind of individual classifiers. On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers could be improved significantly. For example, on the U.S. zipcode database, the result of 98.9% recognition with 0.90% substitution and 0.2% rejection can be obtained, as well as a high reliability with 95% recognition, 0% substitution and 5% rejection. These results compared favorably to other research p u p s in Europe, Asia, and North America.


A single-stage three-phase high power factor rectifier with high-frequency isolation and regulated DC-bus based on the DCM SEPIC converter

In this paper, the main concepts related to the development of a single-stage three-phase high power factor rectifier, with high-frequency isolation and regulated DC bus are described. The structure operation is presented, being based on the DC-DC SEPIC converter operating in the discontinuous conduction mode. This operational mode provides to the rectifier a high power factor feature, with sinusoidal input current, without the use of any current sensors and current loop control. A design example and simulation results are presented in order to validate the theoretical analysis.


SYSTEM DYNAMICS : SYSTEMIC FEEDBACK MODELING FOR POLICY ANALYSIS


Practical Matrix Completion and Corruption Recovery Using Proximal Alternating Robust Subspace Minimization

Low-rank matrix completion is a problem of immense practical importance. Recent works on the subject often use nuclear norm as a convex surrogate of the rank function. Despite its solid theoretical foundation, the convex version of the problem often fails to work satisfactorily in real-life applications. Real data often suffer from very few observations, with support not meeting the randomness requirements, ubiquitous presence of noise and potentially gross corruptions, sometimes with these simultaneously occurring. This paper proposes a Proximal Alternating Robust Subspace Minimization method to tackle the three problems. The proximal alternating scheme explicitly exploits the rank constraint on the completed matrix and uses the $$\ell _0$$ ℓ 0 pseudo-norm directly in the corruption recovery step. We show that the proposed method for the non-convex and non-smooth model converges to a stationary point. Although it is not guaranteed to find the global optimal solution, in practice we find that our algorithm can typically arrive at a good local minimizer when it is supplied with a reasonably good starting point based on convex optimization. Extensive experiments with challenging synthetic and real data demonstrate that our algorithm succeeds in a much larger range of practical problems where convex optimization fails, and it also outperforms various state-of-the-art algorithms.