Query: A Fast Learning Algorithm for Deep Belief Nets

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Fields of Experts: a framework for learning image priors <=SEP=> We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The approach extends traditional Markov random field (MRF) models by learning potential functions over extended pixel neighborhoods. Field potentials are modeled using a Products-of-Experts framework that exploits nonlinear functions of many linear filter responses. In contrast to previous MRF approaches all parameters, including the linear filters themselves, are learned from training data. We demonstrate the capabilities of this Field of Experts model with two example applications, image denoising and image inpainting, which are implemented using a simple, approximate inference scheme. While the model is trained on a generic image database and is not tuned toward a specific application, we obtain results that compete with and even outperform specialized techniques.

Projection Pursuit Regression <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis <=SEP=> Neural networks are a powerful technology for classification of visual inputs arising from documents. However, there is a confusing plethora of different neural network methods that are used in the literature and in industry. This paper describes a set of concrete best practices that document analysis researchers can use to get good results with neural networks. The most important practice is getting a training set as large as possible: we expand the training set by adding a new form of distorted data. The next most important practice is that convolutional neural networks are better suited for visual document tasks than fully connected networks. We propose that a simple “do-it-yourself” implementation of convolution with a flexible architecture is suitable for many visual document problems. This simple convolutional neural network does not require complex methods, such as momentum, weight decay, structuredependent learning rates, averaging layers, tangent prop, or even finely-tuning the architecture. The end result is a very simple yet general architecture which can yield state-of-the-art performance for document analysis. We illustrate our claims on the MNIST set of English digit

Gradient-based learning applied to document recognition <=SEP=> 

Nonlinear Component Analysis as a Kernel Eigenvalue Problem <=SEP=> A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear mapfor instance, the space of all possible five-pixel products in 16 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.

CBIR Evaluation using Different Distances and DWT <=SEP=> In this paper, evaluation is made on the result of CBIR system based on haar wavelet transform with different distances for similarity matching to calculate the deviation from query image. So, different distances used are Chessboard distance, Cityblock distance and Euclidean distance. In this paper discrete wavelet transform is used to decompose the image. The image is decomposed till sixth level and last level approximate component is saved as feature vector. Comparison is made between different distances to see the best suited distance for CBIR. The wavelet used is &quot;Haar&quot;. Haar has compact support and it is the simplest orthogonal wavelet. It is very fast also.

Blockchained Post-Quantum Signatures <=SEP=> Inspired by the blockchain architecture and existing Merkle tree based signature schemes, we propose BPQS, an extensible post-quantum (PQ) resistant digital signature scheme best suited to blockchain and distributed ledger technologies (DLTs). One of the unique characteristics of the protocol is that it can take advantage of application-specific chain/graph structures in order to decrease key generation, signing and verification costs as well as signature size. Compared to recent improvements in the field, BPQS outperforms existing hash-based algorithms when a key is reused for reasonable numbers of signatures, while it supports a fallback mechanism to allow for a practically unlimited number of signatures if required. We provide an open source implementation of the scheme and benchmark it.

DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences <=SEP=> Modeling the properties and functions of DNA sequences is an important, but challenging task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the vast majority of which is still poorly understood in terms of function. A powerful predictive model for the function of non-coding DNA can have enormous benefit for both basic science and translational research because over 98% of the human genome is non-coding and 93% of disease-associated variants lie in these regions. To address this need, we propose DanQ, a novel hybrid convolutional and bi-directional long short-term memory recurrent neural network framework for predicting non-coding function de novo from sequence. In the DanQ model, the convolution layer captures regulatory motifs, while the recurrent layer captures long-term dependencies between the motifs in order to learn a regulatory 'grammar' to improve predictions. DanQ improves considerably upon other models across several metrics. For some regulatory markers, DanQ can achieve over a 50% relative improvement in the area under the precision-recall curve metric compared to related models. We have made the source code available at the github repository http://github.com/uci-cbcl/DanQ.

IRIS: A goal-oriented big data analytics framework on Spark for better Business decisions <=SEP=> Big data analytics is the hottest new practice in Business Analytics today. However, recent industrial surveys find that big data analytics may fail to meet business expectations because of lack of business context and lack of expertise to connect the dots, inaccurate scope and batch-oriented Hadoop system. In this paper, we present IRIS - a goal-oriented big data analytics framework using Spark for better business decisions, which consists of a conceptual model which connects a business side and a big data side providing context information around the data, a claim-based evaluation method which enables to focus the most effective solutions, a process on how to use IRIS framework and an assistant tool using Spark which is a real-time big data analytics platform. In this framework, problems against business goals of the current process and solutions for the future process are explicitly hypothesized in the conceptual model and validated on real big data using big queries or big data analytics. As an empirical study, a shipment decision process is used to show how IRIS can support better business decisions in terms of comprehensive understanding both on business and data analytics, high priority and fast decisions.

A socially assistive robot exercise coach for the elderly <=SEP=> We present a socially assistive robot (SAR) system designed to engage elderly users in physical exercise. We discuss the system approach, design methodology, and implementation details, which incorporate insights from psychology research on intrinsic motivation, and we present five clear design principles for SAR-based therapeutic interventions. We then describe the system evaluation, consisting of a multi-session user study with older adults (n = 33), to evaluate the effectiveness of our SAR exercise system and to investigate the role of embodiment by comparing user evaluations of similar physically and virtually embodied coaches. The results validate the system approach and effectiveness at motivating physical exercise in older adults according to a variety of user performance and outcomes measures. The results also show a clear preference by older adults for the physically embodied robot coach over the virtual coach in terms of enjoyableness, helpfulness, and social attraction, among other factors.

Polyphase filters - A model for teaching the art of discovery in DSP <=SEP=> By its very nature DSP is a mathematically heavy topic and to fully understand it students need to understand the mathematical developments underlying DSP topics. However, relying solely on mathematical developments often clouds the true nature of the foundation of a result. It is likely that students who master the mathematics may still not truly grasp the key ideas of a topic. Furthermore, teaching DSP topics by merely “going through the mathematics” deprives students of learning the art of discovery that will make them good researchers. This paper uses the topic of polyphase decimation and interpolation to illustrate how it is possible to maintain rigor yet teach using less mathematical approaches that show students how researchers think when developing new ideas.

Explanation-Based Generalization: A Unifying View <=SEP=> The problem of formulating general concepts from specific training examples has long been a major focus of machine learning research. While most previous research has focused on empirical methods for generalizing from a large number of training examples using no domain-specific knowledge, in the past few years new methods have been developed for applying domain-specific knowledge to formulate valid generalizations from single training examples. The characteristic common to these methods is that their ability to generalize from a single example follows from their ability to explain why the training example is a member of the concept being learned. This paper proposes a general, domain-independent mechanism, called EBG, that unifies previous approaches to explanation-based generalization. The EBG method is illustrated in the context of several example problems, and used to contrast several existing systems for explanation-based generalization. The perspective on explanation-based generalization afforded by this general method is also used to identify open research problems in this area.

Broadband Micro-Coaxial Wilkinson Dividers <=SEP=> This paper presents several micro-coaxial broadband 2 : 1 Wilkinson power dividers operating from 2 to 22 GHz, a 11 : 1 bandwidth. Circuits are fabricated on silicon with PolyStrata technology, and are implemented with 650 mum times 400 mum air-supported micro-coaxial lines. The measured isolation between the output ports is greater than 11 dB and the return loss at each port is more than 13 dB over the entire bandwidth. The footprints of these dividers can be miniaturized due to the high isolation between adjacent coaxial lines and their tight bend radius. For higher power handling, larger lines with a cross section of 1050 mum times 850 mum are also demonstrated. The effect of mismatch at the output ports is investigated in order to find the power loss in the resistors.

Distributed Graph Routing for WirelessHART Networks <=SEP=> Communication reliability in a Wireless Sensor and Actuator Network (WSAN) has a high impact on stability of industrial process monitoring and control. To make reliable and real-time communication in highly unreliable environments, industrial WSANs such as those based on WirelessHART adopt graph routing. In graph routing, each packet is scheduled on multiple time slots using multiple channels, on multiple links along multiple paths on a routing graph between a source and a destination. While high redundancy is crucial to reliable communication, determining and maintaining graph routing is challenging in terms of execution time and energy consumption for resource constrained WSAN. Existing graph routing algorithms use centralized approach, do not scale well in terms of these metrics, and are less suitable under network dynamics. To address these limitations, we propose the first distributed graph routing protocol for WirelessHART networks. Our distributed protocol is based on the Bellman-Ford Algorithm, and generates all routing graphs together using a single algorithm. We prove that our proposed graph routing can include a path between a source and a destination with cost (in terms of hop-count) at most 3 times the optimal cost. We implemented our proposed routing algorithm on TinyOS and evaluated through experiments on TelosB motes and simulations using TOSSIM. The results show that it is scalable and consumes at least 40% less energy and needs at least 65% less time at the cost of 1kB of extra memory compared to the state-of-the-art centralized approach for generating routing graphs.

Traffic Surveillance using Multi-Camera Detection and Multi-Target Tracking <=SEP=> Non-intrusive video-detection for traffic flow observation and surveillance is the primary alternative to conventional inductive loop detectors. Video Image Detection Systems (VIDS) can derive traffic parameters by means of image processing and pattern recognition methods. Existing VIDS emulate the inductive loops. We propose a trajectory based recognition algorithm to expand the common approach and to obtain new types of information (e.g. queue length or erratic movements). Different views of the same area by more than one camera sensor is necessary, because of the typical limitations of single camera systems, resulting from the occlusion effect of other cars, trees and traffic signs. A distributed cooperative multi-camera system enables a significant enlargement of the observation area. The trajectories are derived from multi-target tracking. The fusion of object data from different cameras is done using a tracking method. This approach opens up opportunities to identify and specify traffic objects, their location, speed and other characteristic object information. The system provides new derived and consolidated information about traffic participants. Thus, this approach is also beneficial for a description of individual traffic participants.

Designing Password Policies for Strength and Usability <=SEP=> Password-composition policies are the result of service providers becoming increasingly concerned about the security of online accounts. These policies restrict the space of user-created passwords to preclude easily guessed passwords and thus make passwords more difficult for attackers to guess. However, many users struggle to create and recall their passwords under strict password-composition policies, for example, ones that require passwords to have at least eight characters with multiple character classes and a dictionary check. Recent research showed that a promising alternative was to focus policy requirements on password length instead of on complexity. In this work, we examine 15 password policies, many focusing on length requirements. In doing so, we contribute the first thorough examination of policies requiring longer passwords. We conducted two online studies with over 20,000 participants, and collected both usability and password-strength data. Our findings indicate that password strength and password usability are not necessarily inversely correlated: policies that lead to stronger passwords do not always reduce usability. We identify policies that are both more usable and more secure than commonly used policies that emphasize complexity rather than length requirements. We also provide practical recommendations for service providers who want their users to have strong yet usable passwords.

Mimir: a Market-based Real-time Question and Answer Service <=SEP=> Community-based question and answer (Q&A) systems facilitate information exchange and enable the creation of reusable knowledge repositories. While these systems are growing in usage and are changing how people find and share information, current designs are inefficient, wasting the time and attention of their users. Furthermore, existing systems do not support signaling and screening of joking and non-serious questions. Coupling Q&A services with instant and text messaging for faster questions and answers may exacerbate these issues, causing Q&A services to incur high interruption costs on their users.
 In this paper we present the design and evaluation of a market-based real-time Q&A system. We compared its use to a similar Q&A system without a market. We found that while markets can reduce wasted resources by reducing the number of less important questions and low quality answers, it may also reduce the socially conducive questions and usages that are vital to sustaining a Q&A community.

Tuning of PID controller for an automatic regulator voltage system using chaotic optimization approach <=SEP=> Despite the popularity, the tuning aspect of proportional–integral-derivative (PID) controllers is a challenge for researchers and plant operators. Various controllers tuning methodologies have been proposed in the literature such as auto-tuning, self-tuning, pattern recognition, artificial intelligence, and optimization methods. Chaotic optimization algorithms as an emergent method of global optimization have attracted much attention in engineering applications. Chaotic optimization algorithms, which have the features of easy implementation, short execution time and robust mechanisms of escaping from local optimum, is a promising tool for engineering applications. In this paper, a tuning method for determining the parameters of PID control for an automatic regulator voltage (AVR) system using a chaotic optimization approach based on Lozi map is proposed. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed chaotic optimization introduces chaos mapping using Lozi map chaotic sequences which increases its convergence rate and resulting precision. Simulation results are promising and show the effectiveness of the proposed approach. Numerical simulations based on proposed PID control of an AVR system for nominal system parameters and step reference voltage input demonstrate the good performance of chaotic optimization. 2007 Elsevier Ltd. All rights reserved.

What Constitutes Normal Sleeping Patterns ? Childhood to Adolescence 2 . 1 Developmental Patterns in Sleep Duration : Birth to Adolescence <=SEP=> Healthy, adequate sleep is integral to the process of growth and development during adolescence. At puberty, maturational changes in th e underlying homeostatic and circadian sleep regulatory mechanisms influence the sleep-wak e p tterns of adolescents. These changes interact with psychosocial factors, such as increas ing academic demands, hours spent in paid employment, electronic media use, and social opport unities, and constrict the time available for adolescents to sleep. Survey studies reveal tha t adolescents’ habitual sleep schedules are associated with cumulative sleep loss. As a consequ ence, there is growing concern about the effects of insufficient sleep on adolescents’ wakin g function. This review identifies and examines the characteristics of sleep and sleep los s in adolescents. It highlights the need for more research into the effects of chronic partial s leep deprivation in adolescents, and the process of extending sleep on weekends to recover t he effects of sleep debt. An understanding of chronic sleep deprivation and recovery sleep in adolescents will facilitate the development of evidence-based sleep guidelines and recommendati o s for recovery sleep opportunities when habitual sleep times are insufficient.

Search-based Neural Structured Learning for Sequential Question Answering <=SEP=> Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We collect a dataset of 6,066 question sequences that inquire about semistructured tables from Wikipedia, with 17,553 question-answer pairs in total. To solve this sequential question answering task, we propose a novel dynamic neural semantic parsing framework trained using a weakly supervised reward-guided search. Our model effectively leverages the sequential context to outperform state-of-the-art QA systems that are designed to answer highly complex questions.

Crowdfunding Success Factors: The Characteristics of Successfully Funded Projects on Crowdfunding Platforms <=SEP=> Crowdfunding platforms offer promising opportunities for project founders to publish their project ideas and to collect money in order to be able to realize them. Consequently, the question of what influences the successful funding of projects, i.e., reaching the target amount of money, is very important. Building upon media richness theory and the concept of reciprocity, we extend previous research in the field of crowdfunding success factors. We provide a comprehensive view on factors influencing crowdfunding success by both focusing on project-specific as well as founder-specific aspects. Analyzing a sample of projects of the crowdfunding platform kickstarter.com, we find that the project description, related images and videos as well as the question of whether the founder has previously backed other projects influence funding success. Interestingly, the question of whether the founder has previously created other projects has no significant influence. Our results are of high interest for the stakeholders on crowdfunding platforms.

Predicting Song Popularity <=SEP=> Future Work Dataset and Features Music has been an integral part of our culture all throughout human history. In 2012 alone, the U.S. music industry generated $15 billion. Having a fundamental understanding of what makes a song popular has major implications to businesses that thrive on popular music, namely radio stations, record labels, and digital and physical music market places. Many private companies in these industries have solutions for this problem, but details have been kept private for competitive reasons. For our project, we will predict song popularity based on an song’s audio features and metadata. Methods & Results

Multi-task Deep Reinforcement Learning with PopArt <=SEP=> The reinforcement learning community has made great strides in designing algorithms capable of exceeding human performance on specific tasks. These algorithms are mostly trained one task at the time, each new task requiring to train a brand new agent instance. This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on. In this work, we study the problem of learning to master not one but multiple sequentialdecision tasks at once. A general issue in multi-task learning is that a balance must be found between the needs of multiple tasks competing for the limited resources of a single learning system. Many learning algorithms can get distracted by certain tasks in the set of tasks to solve. Such tasks appear more salient to the learning process, for instance because of the density or magnitude of the in-task rewards. This causes the algorithm to focus on those salient tasks at the expense of generality. We propose to automatically adapt the contribution of each task to the agent’s updates, so that all tasks have a similar impact on the learning dynamics. This resulted in state of the art performance on learning to play all games in a set of 57 diverse Atari games. Excitingly, our method learned a single trained policy with a single set of weights that exceeds median human performance. To our knowledge, this was the first time a single agent surpassed human-level performance on this multi-task domain. The same approach also demonstrated state of the art performance on a set of 30 tasks in the 3D reinforcement learning platform DeepMind Lab.

Many-objective optimization algorithm applied to history matching <=SEP=> Reservoir model calibration, called history matching in the petroleum industry, is an important task to make more accurate predictions for better reservoir management. Providing an ensemble of good matched reservoir models from history matching is essential to reproduce the observed production data from a field and to forecast reservoir performance. The nature of history matching is multi-objective because there are multiple match criteria or misfit from different production data, wells and regions in the field. In many cases, these criteria are conflicting and can be handled by the multi-objective approach. Moreover, multi-objective provides faster misfit convergence and more robust towards stochastic nature of optimization algorithms. However, reservoir history matching may feature far too many objectives that can be efficiently handled by conventional multi-objective algorithms, such as multi-objective particle swarm optimizer (MOPSO) and non-dominated sorting genetic algorithm II (NSGA II). Under an increasing number of objectives, the performance of multi-objective history matching by these algorithms deteriorates (lower match quality and slower misfit convergence). In this work, we introduce a recently proposed algorithm for many-objective optimization problem, known as reference vector-guided evolutionary algorithm (RVEA), to history matching. We apply the algorithm to history matching a synthetic reservoir model and a real field case study with more than three objectives. The paper demonstrates the superiority of the proposed RVEA to the state of the art multi-objective history matching algorithms, namely MOPSO and NSGA II.

An improved LLC resonant inverter for induction heating with asymmetrical control <=SEP=> This paper proposes a modified LLC resonant load configuration of a full-bridge inverter for induction heating applications by using asymmetrical voltage cancellation control. The proposed control method is implemented in a full-bridge inverter topology. With the use of a phase-locked loop control, the operating frequency is automatically adjusted to maintain a small constant lagging phase angle under load parameter variation. The output power is controlled using the asymmetrical voltage cancellation technique. The LLC resonant tank is designed with a matching transformer in between the series inductor and paralleled LC resonant tank for short circuit protection capability of the matching transformer and the induction coil. The validity of the proposed method is verified through computer simulation and hardware experiment at the operating frequency of 108.7 to 110.6 kHz.

An active battery cell balancing topology without using external energy storage elements <=SEP=> Cell balancing circuits are important to extent life-cycle of batteries and to extract maximum power from the batteries. A lot of power electronics topology has been tried for cell balancing in the battery packages. Active cell balancing topologies transfer energy from the cells showing higher performance to the cells showing lower performance to balance voltages across the cells of the battery using energy storage elements like combination of inductor-capacitor or transformer-capacitor or switched capacitor or switched inductor. In this study an active balancing topology without using any energy storage element is proposed. The idea is similar to the switched capacitor topology in which a capacitor or capacitor banks is switched across the cells of battery to balance the voltages. Since a basic battery cell model includes capacitance because of capacitive effect of the cell, this capacitive effect can be utilized in cell balancing. Hence the equalizer capacitors in switched capacitor topology can be eliminated and the cells of battery can be switched with each other. This allows faster energy transfer and hence results in quick equalization. The proposed topology removes the need of extra energy storage elements like capacitors which frequently fails in power electronic circuits, reduces the losses inserted by extra energy storage elements and cost and volume of the circuits and simplifies control algorithm. The proposed balancing circuit can be implemented according to the application requirement. The proposed topology is simulated in MATLAB/Simulink environment and showed better results in terms of balancing speed in comparison to switched capacitor topologies.

Energy-Efficient Resource Allocation in Cellular Networks With Shared Full-Duplex Relaying <=SEP=> Recent advances in self-interference cancelation techniques enable full-duplex relaying (FDR) systems, which transmit and receive simultaneously in the same frequency band with high spectrum efficiency. Unlike most existing works, we study the problem of energy-efficient resource allocation in FDR networks. We consider a shared FDR deployment scenario, where an FDR relay is deployed at the intersection of several adjacent cell sectors. First, a simple but practical transmission strategy is proposed to deal with the involved interference, i.e., multiaccess interference, multiuser interference, and self-interference. Then, the problem of joint power and subcarrier allocation is formulated to maximize the network-level energy efficiency while taking the residual self-interference into account. Since the formulated problem is a mixed combinatorial and nonconvex optimization problem with high computation complexity, we use Dinkelbach and discrete stochastic optimization methods to solve the energy-efficient resource-allocation problem efficiently. Simulation results are presented to show the effectiveness of the proposed scheme.

A novel wideband waveguide-to-microstrip transition with waveguide stepped impedance transformer <=SEP=> A novel wideband waveguide-to-microstrip transition is presented in this paper. A printed fan-shaped probe is used, while a waveguide stepped impedance transformer is utilized to broaden the bandwidth further. With an optimized design, a relative bandwidth of 27.8% for -20dB return loss with a center frequency of 15GHz is achieved. The simulated insertion loss in this bandwidth is less than 0.29dB. A back-to-back transition is fabricated and measured. The measured result is similar to that of simulation.

Graph based E-Government web service composition <=SEP=> Nowadays, e-government has emerged as a government policy to improve the quality and efficiency of public administrations. By exploiting the potential of new information and communication technologies, government agencies are providing a wide spectrum of online services. These services are composed of several web services that comply with well defined processes. One of the big challenges is the need to optimize the composition of the elementary web services. In this paper, we present a solution for optimizing the computation effort in web service composition. Our method is based on Graph Theory. We model the semantic relationship between the involved web services through a directed graph. Then, we compute all shortest paths using for the first time, an extended version of the FloydWarshall algorithm.

Classification of Mixed-Type Defect Patterns in Wafer Bin Maps Using Convolutional Neural Networks <=SEP=> In semiconductor manufacturing, a wafer bin map (WBM) represents the results of wafer testing for dies using a binary pass or fail value. For WBMs, defective dies are often clustered into groups of local systematic defects. Determining their specific patterns is important, because different patterns are related to different root causes of failure. Recently, because wafer sizes have increased and the process technology has become more complicated, the probability of observing mixed-type defect patterns, i.e., two or more defect patterns in a single wafer, has increased. In this paper, we propose the use of convolutional neural networks (CNNs) to classify mixed-type defect patterns in WBMs in the framework of an individual classification model for each defect pattern. Through simulated and real data examples, we show that the CNN is robust to random noise and performs effectively, even if there are many random defects in WBMs.