Query: A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics

Relevant passages:
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences <=SEP=> We provide two fundamental results on the population (infinite-sample) likelihood function of Gaussian mixture models with M ≥ 3 components. Our first main result shows that the population likelihood function has bad local maxima even in the special case of equally-weighted mixtures of well-separated and spherical Gaussians. We prove that the log-likelihood value of these bad local maxima can be arbitrarily worse than that of any global optimum, thereby resolving an open question of Srebro [21]. Our second main result shows that the EM algorithm (or a first-order variant of it) with random initialization will converge to bad critical points with probability at least 1 − e. We further establish that a first-order variant of EM will not converge to strict saddle points almost surely, indicating that the poor performance of the first-order method can be attributed to the existence of bad local maxima rather than bad saddle points. Overall, our results highlight the necessity of careful initialization when using the EM algorithm in practice, even when applied in highly favorable settings.

Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications <=SEP=> We describe a generalization of the PAC learning model that is based on statistical decision theory. In this model the learner receives randomly drawn examples, each example consisting of an instance x E X and an outcome J E Y, and tries to find a decision rule h: X + A, where h E X, that specifies the appropriate action a E A to take for each instance x in order to minimize the expectation of a loss I( y, a). Here X, Y, and A are arbitrary sets. I is a real-valued function, and examples are generated according to an arbitrary joint distribution on Xx Y. Special cases include the problem of learning a function from X into Y, the problem of learning the conditional probability distribution on Y given X (regression), and the problem of learning a distribution on X (density estimation). We give theorems on the uniform convergence of empirical loss estimates to true expected loss rates for certain decision rule spaces 2, and show how this implies learnability with bounded sample size, disregarding computational complexity. As an application, we give distribution-independent upper bounds on the sample size needed for learning with feedforward neural networks, Our theorems use a generalized notion of VC dimension that applies to classes of real-valued functions, adapted from Vapnik and Pollard’s work, and a notion of capacity and metric dimension for classes of functions that map into a bounded metric space. ‘(‘1 1992 Academic Press, Inc.

Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima <=SEP=> We establish theoretical results concerning all local optima of various regularized M-estimators, where both loss and penalty functions are allowed to be nonconvex. Our results show that as long as the loss function satisfies restricted strong convexity and the penalty function satisfies suitable regularity conditions, any local optimum of the composite objective function lies within statistical precision of the true parameter vector. Our theory covers a broad class of nonconvex objective functions, including corrected versions of the Lasso for errors-in-variables linear models; regression in generalized linear models using nonconvex regularizers such as SCAD and MCP; and graph and inverse covariance matrix estimation. On the optimization side, we show that a simple adaptation of composite gradient descent may be used to compute a global optimum up to the statistical precision εstat in log(1/εstat) iterations, which is the fastest possible rate of any first-order method. We provide a variety of simulations to illustrate the sharpness of our theoretical predictions. Disciplines Statistics and Probability This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/statistics_papers/222 Regularized M -estimators with nonconvexity: Statistical and algorithmic theory for local optima Po-Ling Loh Department of Statistics University of California, Berkeley Berkeley, CA 94720 ploh@berkeley.edu Martin J. Wainwright Departments of Statistics and EECS University of California, Berkeley Berkeley, CA 94720 wainwrig@stat.berkeley.edu

On the Quality of the Initial Basin in Overspecified Neural Networks <=SEP=> Deep learning, in the form of artificial neural networks, has achieved remarkable practical success in recent years, for a variety of difficult machine learning applications. However, a theoretical explanation for this remains a major open problem, since training neural networks involves optimizing a highly non-convex objective function, and is known to be computationally hard in the worst case. In this work, we study the geometric structure of the associated non-convex objective function, in the context of ReLU networks and starting from a random initialization of the network parameters. We identify some conditions under which it becomes more favorable to optimization, in the sense of (i) High probability of initializing at a point from which there is a monotonically decreasing path to a global minimum; and (ii) High probability of initializing at a basin (suitably defined) with a small minimal objective value. A common theme in our results is that such properties are more likely to hold for larger (“overspecified”) networks, which accords with some recent empirical and theoretical observations.

Gradient Descent Converges to Minimizers <=SEP=> We show that gradient descent converges to a local minimizer , almost surely with random initialization. This is proved by applying the Stable Manifold Theorem from dynamical systems theory.

2D electronics - opportunities and limitations <=SEP=> 2D materials (2Ds), in particular the 2Ds beyond graphene, have attracted considerable attention in the transistor community. While rapid progress on 2D transistors has been achieved recently, the prospects of 2D electronics is still under debate. In the present paper we discuss our view of the potential of 2D transistors for digital CMOS and for flexible electronics. We show that 2D MOSFETs show promise for ultimately scaled CMOS due their excellent electrostatic integrity and their ability to suppress source-drain tunneling. Moreover, the 2Ds represent a promising option for radio frequency (RF) flexible electronics since they offer an attractive combination of mechanical (bendability, stretchability), and electrical (reasonable mobility) properties.

Advancing Land-Sea Conservation Planning: Integrating Modelling of Catchments, Land-Use Change, and River Plumes to Prioritise Catchment Management and Protection <=SEP=> Human-induced changes to river loads of nutrients and sediments pose a significant threat to marine ecosystems. Ongoing land-use change can further increase these loads, and amplify the impacts of land-based threats on vulnerable marine ecosystems. Consequently, there is a need to assess these threats and prioritise actions to mitigate their impacts. A key question regarding prioritisation is whether actions in catchments to maintain coastal-marine water quality can be spatially congruent with actions for other management objectives, such as conserving terrestrial biodiversity. In selected catchments draining into the Gulf of California, Mexico, we employed Land Change Modeller to assess the vulnerability of areas with native vegetation to conversion into crops, pasture, and urban areas. We then used SedNet, a catchment modelling tool, to map the sources and estimate pollutant loads delivered to the Gulf by these catchments. Following these analyses, we used modelled river plumes to identify marine areas likely influenced by land-based pollutants. Finally, we prioritised areas for catchment management based on objectives for conservation of terrestrial biodiversity and objectives for water quality that recognised links between pollutant sources and affected marine areas. Our objectives for coastal-marine water quality were to reduce sediment and nutrient discharges from anthropic areas, and minimise future increases in coastal sedimentation and eutrophication. Our objectives for protection of terrestrial biodiversity covered species of vertebrates. We used Marxan, a conservation planning tool, to prioritise interventions and explore spatial differences in priorities for both objectives. Notable differences in the distributions of land values for terrestrial biodiversity and coastal-marine water quality indicated the likely need for trade-offs between catchment management objectives. However, there were priority areas that contributed to both sets of objectives. Our study demonstrates a practical approach to integrating models of catchments, land-use change, and river plumes with conservation planning software to inform prioritisation of catchment management.

Model-Agnostic Interpretability of Machine Learning <=SEP=> Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, interpretability has become a vital concern in machine learning, and work in the area of interpretable models has found renewed interest. In some applications, such models are as accurate as non-interpretable ones, and thus are preferred for their transparency. Even when they are not accurate, they may still be preferred when interpretability is of paramount importance. However, restricting machine learning to interpretable models is often a severe limitation. In this paper we argue for explaining machine learning predictions using model-agnostic approaches. By treating the machine learning models as blackbox functions, these approaches provide crucial flexibility in the choice of models, explanations, and representations, improving debugging, comparison, and interfaces for a variety of users and models. We also outline the main challenges for such methods, and review a recently-introduced model-agnostic explanation approach (LIME) that addresses these challenges.

Machine Learning in Medical Applications <=SEP=> Research in Machine Learning methods to-date remains centered on technological issues and is mostly application driven. This letter summarizes successful applications of machine learning methods that were presented at the Workshop on Machine Learning in Medical Applications. The goals of the workshop were to foster fundamental and applied research in the application of machine learning methods to medical problem solving and to medical research, to provide a forum for reporting significant results, to determine whether Machine Learning methods are able to underpin the research and development on intelligent systems for medical applications, and to identify those areas where increased research is likely to yield advances. A number of recommendations for a research agenda were produced, including both technical and human-centered issues.

Multi-view Clustering via Multi-manifold Regularized Nonnegative Matrix Factorization <=SEP=> Multi-view clustering integrates complementary information from multiple views to gain better clustering performance rather than relying on a single view. NMF based multi-view clustering algorithms have shown their competitiveness among different multi-view clustering algorithms. However, NMF fails to preserve the locally geometrical structure of the data space. In this paper, we propose a multi-manifold regularized nonnegative matrix factorization framework (MMNMF) which can preserve the locally geometrical structure of the manifolds for multi-view clustering. MMNMF regards that the intrinsic manifold of the dataset is embedded in a convex hull of all the views' manifolds, and incorporates such an intrinsic manifold and an intrinsic (consistent) coefficient matrix with a multi-manifold regularizer to preserve the locally geometrical structure of the multi-view data space. We use linear combination to construct the intrinsic manifold, and propose two strategies to find the intrinsic coefficient matrix, which lead to two instances of the framework. Experimental results show that the proposed algorithms outperform existing NMF based algorithms for multi-view clustering.

Extracting 3D Vascular Structures from Microscopy Images using Convolutional Recurrent Networks <=SEP=> Vasculature is known to be of key biological significance, especially in the study of cancer. As such, considerable effort has been focused on the automated measurement and analysis of vasculature in medical and pre-clinical images. In tumors in particular, the vascular networks may be extremely irregular and the appearance of the individual vessels may not conform to classical descriptions of vascular appearance. Typically, vessels are extracted by either a segmentation and thinning pipeline, or by direct tracking. Neither of these methods are well suited to microscopy images of tumor vasculature. In order to address this we propose a method to directly extract a medial representation of the vessels using Convolutional Neural Networks. We then show that these two-dimensional centerlines can be meaningfully extended into 3D in anisotropic and complex microscopy images using the recently popularized Convolutional Long Short-Term Memory units (ConvLSTM). We demonstrate the effectiveness of this hybrid convolutional-recurrent architecture over both 2D and 3D convolutional comparators.

Building fast and compact convolutional neural networks for offline handwritten Chinese character recognition <=SEP=> Like other problems in computer vision, offline handwritten Chinese character recognition (HCCR) has achieved impressive results using convolutional neural network (CNN)-based methods. However, larger and deeper networks are needed to deliver state-of-the-art results in this domain. Such networks intuitively appear to incur high computational cost, and require the storage of a large number of parameters, which render them unfeasible for deployment in portable devices. To solve this problem, we propose a Global Supervised Low-rank Expansion (GSLRE) method and an Adaptive Drop-weight (ADW) technique to solve the problems of speed and storage capacity. We design a nine-layer CNN for HCCR consisting of 3,755 classes, and devise an algorithm that can reduce the network’s computational cost by nine times and compress the network to 1/18 of the original size of the baseline model, with only a 0.21% drop in accuracy. In tests, the proposed algorithm can still surpass the best single-network performance reported thus far in the literature while requiring only 2.3 MB for storage. Furthermore, when integrated with our effective forward implementation, the recognition of an offline character image takes only 9.7 ms on a CPU. Compared with the state-of-the-art CNN model for HCCR, ∗Corresponding author Email addresses: xiaoxuefengchina@gmail.com (Xuefeng Xiao), lianwen.jin@gmail.com (Lianwen Jin) Preprint submitted to Pattern Recognition June 30, 2017

Circuit Fingerprinting Attacks: Passive Deanonymization of Tor Hidden Services <=SEP=> This paper sheds light on crucial weaknesses in the design of hidden services that allow us to break the anonymity of hidden service clients and operators passively. In particular, we show that the circuits, paths established through the Tor network, used to communicate with hidden services exhibit a very different behavior compared to a general circuit. We propose two attacks, under two slightly different threat models, that could identify a hidden service client or operator using these weaknesses. We found that we can identify the users’ involvement with hidden services with more than 98% true positive rate and less than 0.1% false positive rate with the first attack, and 99% true positive rate and 0.07% false positive rate with the second. We then revisit the threat model of previous website fingerprinting attacks, and show that previous results are directly applicable, with greater efficiency, in the realm of hidden services. Indeed, we show that we can correctly determine which of the 50 monitored pages the client is visiting with 88% true positive rate and false positive rate as low as 2.9%, and correctly deanonymize 50 monitored hidden service servers with true positive rate of 88% and false positive rate of 7.8% in an open world setting.

MarrNet: 3D Shape Reconstruction via 2.5D Sketches <=SEP=> 3D object reconstruction from a single image is a highly under-determined problem, requiring strong prior knowledge of plausible 3D shapes. This introduces challenges for learning-based approaches, as 3D object annotations are scarce in real images. Previous work chose to train on synthetic data with ground truth 3D information, but suffered from domain adaptation when tested on real data. In this work, we propose MarrNet, an end-to-end trainable model that sequentially estimates 2.5D sketches and 3D object shape. Our disentangled, two-step formulation has three advantages. First, compared to full 3D shape, 2.5D sketches are much easier to be recovered from a 2D image; models that recover 2.5D sketches are also more likely to transfer from synthetic to real data. Second, for 3D reconstruction from 2.5D sketches, systems can learn purely from synthetic data. This is because we can easily render realistic 2.5D sketches without modeling object appearance variations in real images, including lighting, texture, etc. This further relieves the domain adaptation problem. Third, we derive differentiable projective functions from 3D shape to 2.5D sketches; the framework is therefore end-to-end trainable on real images, requiring no human annotations. Our model achieves state-of-the-art performance on 3D shape reconstruction.

Exploring the influential factors in continuance usage of mobile social Apps: Satisfaction, habit, and customer value perspectives <=SEP=> The emergence of mobile application software (App) has explosively grown in conjunction with the worldwide use of smartphones in recent years. Among numerous categories of mobile Apps, social Apps were one of those with the greatest growth in 2013. Despite abundant research on users’ behavior intention of mobile App usage, few studies have focused on investigating key determinants of users’ continuance intention regarding social Apps. To fill this gap, we integrated customer value perspectives to explore the influential factors in the continuance intention of social App use. Moreover, users’ satisfaction and habit from both the marketing and psychology literature were also incorporated into the research model. A total of 378 valid questionnaires were collected by survey method, and structural equation modeling was employed in the subsequent data analysis. The results indicate that the continuance usage of social Apps is driven by users’ satisfaction, tight connection with others, and hedonic motivation to use the Apps. In addition, full mediation effects of satisfaction and habit were found between perceived usefulness and intention to continue use. These findings extend our understanding of users’ continuance intention in the context of social Apps. Discussion and implications are provided. 2015 Elsevier Ltd. All rights reserved.

Perceivable Light Fields: Matching the Requirements Between the Human Visual System and Autostereoscopic 3-D Displays <=SEP=> Recently, there has been a substantial increase in efforts to develop 3-D visualization technologies that can provide the viewers with a realistic 3-D visual experience. Various terms such as “reality communication” have been used to categorize these efforts. In order to provide the viewers with a complete and realistic visual sensation, the display or visualization system and the displayed content need to match the physiological 3-D information sensing capabilities of the human visual system which can be quite complex. These may include spatial and temporal resolutions, depth perception, dynamic range, spectral contents, nonlinear effects, and vergence accommodation effects. In this paper, first we present an overview of some of the 3-D display research efforts which have been extensively pursued in Asia, Europe, and North America among other areas. Based on the limitations and comfort-based requirements of the human visual system when viewing a nonnatural visual input from 3-D displays, we present an analytical framework that combines main perception and human visual requirements with analytical tools and principles used in related disciplines such as optics, computer graphics, computational imaging, and signal processing. Building on the widely used notion of light fields, we define a notion of perceivable light fields to account for the human visual system physiological requirements, and propagate it back to the display device to determine the display device specifications. This helps us clarify the fundamental and practical requirements of the 3-D display devices for reality viewing communication. In view of the proposed analytical framework, we overview various methods that can be applied to overcome the extensive information needed to be displayed in order to meet the requirements imposed by the human visual system.

Parking lot guidance software based on MQTT Protocol <=SEP=> To reduce the amount of CO2 produced from the personal cars in the world, the parking lot guidance systems are considered to be the solution in shopping malls or department stores in many countries. However, most of the current parking lot systems are located in the parking area of each shopping mall and they cannot show the parking lots information for the driver that are driving on the road. So, The drivers can see the parking lot information if and only if they arrive at the shopping mall area. This is the fact that the CO2 are still produced to the world although we use the parking lot guidance system. From the reason as mentioned, we propose the parking lots guidance software to share a parking lot information to a large number of clients in real-time based on MQTT Protocol. The proposed software can share the real-time parking lot information to the mobile devices of the drivers in any location or when they are driving on the road. The results show that the proposed software can share the parking lot information in real-time for at least 1,000 sessions simultaneously with the high average score of usability, design and the benefits.

Advances in data stream mining <=SEP=> Mining data streams has been a focal point of research interest over the past decade. Hardware and software advances have contributed to the significance of this area of research by introducing faster than ever data generation. This rapidly generated data has been termed as data streams. Credit card transactions, Google searches, phone calls in a city, and many others\are typical data streams. In many important applications, it is inevitable to analyze this streaming data in real time. Traditional data mining techniques have fallen short in addressing the needs of data stream mining. Randomization, approximation, and adaptation have been used extensively in developing new techniques or adopting exiting ones to enable them to operate in a streaming environment. This paper reviews key milestones and state of the art in the data stream mining area. Future insights are also be presented. C © 2011 Wiley Periodicals, Inc.

Visual tracking with online Multiple Instance Learning <=SEP=> In this paper, we address the problem of learning an adaptive appearance model for object tracking. In particular, a class of tracking techniques called “tracking by detection” have been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrades the classifier and can cause further drift. In this paper we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems, and can therefore lead to a more robust tracker with fewer parameter tweaks. We present a novel online MIL algorithm for object tracking that achieves superior results with real-time performance.

Sentence-State LSTM for Text Representation <=SEP=> The baseline BiLSTM model consists of two LSTM components, which process the input in the forward left-to-right and the backward rightto-left directions, respectively. In each direction, the reading of input words is modelled as a recurrent process with a single hidden state. Given an initial value, the state changes its value recurrently, each time consuming an incoming word. Take the forward LSTM component for example. Denoting the initial state as h 0, which is a model parameter, the recurrent state transition step for calculating h 1, . . . , h n+1 is defined as follows (Graves and Schmidhuber, 2005):

Frontal terminations for the inferior fronto-occipital fascicle: anatomical dissection, DTI study and functional considerations on a multi-component bundle <=SEP=> The anatomy and functional role of the inferior fronto-occipital fascicle (IFOF) remain poorly known. We accurately analyze its course and the anatomical distribution of its frontal terminations. We propose a classification of the IFOF in different subcomponents. Ten hemispheres (5 left, 5 right) were dissected with Klingler’s technique. In addition to the IFOF dissection, we performed a 4-T diffusion tensor imaging study on a single healthy subject. We identified two layers of IFOF. The first one is superficial and antero-superiorly directed, terminating in the inferior frontal gyrus. The second is deeper and consists of three portions: posterior, middle and anterior. The posterior component terminates in the middle frontal gyrus (MFG) and dorso-lateral prefrontal cortex. The middle component terminates in the MFG and lateral orbito-frontal cortex. The anterior one is directed to the orbito-frontal cortex and frontal pole. In vivo tractography study confirmed these anatomical findings. We suggest that the distribution of IFOF fibers within the frontal lobe corresponds to a fine functional segmentation. IFOF can be considered as a “multi-function” bundle, with each anatomical subcomponent subserving different brain processing. The superficial layer and the posterior component of the deep layer, which connects the occipital extrastriate, temporo-basal and inferior frontal cortices, might subserve semantic processing. The middle component of the deep layer could play a role in a multimodal sensory–motor integration. Finally, the anterior component of the deep layer might be involved in emotional and behavioral aspects.

Digital Marketing Maturity Models : Overview and Comparison <=SEP=> The variety of available digital tools, strategies and activities might confuse and disorient even an experienced marketer. This applies in particular to B2B companies, which are usually less flexible in uptaking of digital technology than B2C companies. B2B companies are lacking a framework that corresponds to the specifics of the B2B business, and which helps to evaluate a company’s capabilities and to choose an appropriate path. A B2B digital marketing maturity model helps to fill this gap. However, modern marketing offers no widely approved digital marketing maturity model, and thus, some marketing institutions provide their own tools. The purpose of this paper is building an optimized B2B digital marketing maturity model based on a SWOT (strengths, weaknesses, opportunities, and threats) analysis of existing models. The current study provides an analytical review of the existing digital marketing maturity models with open access. The results of the research are twofold. First, the provided SWOT analysis outlines the main advantages and disadvantages of existing models. Secondly, the strengths of existing digital marketing maturity models, helps to identify the main characteristics and the structure of an optimized B2B digital marketing maturity model. The research findings indicate that only one out of three analyzed models could be used as a separate tool. This study is among the first examining the use of maturity models in digital marketing. It helps businesses to choose between the existing digital marketing models, the most effective one. Moreover, it creates a base for future research on digital marketing maturity models. This study contributes to the emerging B2B digital marketing literature by providing a SWOT analysis of the existing digital marketing maturity models and suggesting a structure and main characteristics of an optimized B2B digital marketing maturity model. Keywords—B2B digital marketing strategy, digital marketing, digital marketing maturity model, SWOT analysis.

Watershed Cuts: Minimum Spanning Forests and the Drop of Water Principle <=SEP=> We study the watersheds in edge-weighted graphs. We define the watershed cuts following the intuitive idea of drops of water flowing on a topographic surface. We first establish the consistency of these watersheds: they can be equivalently defined by their "catchment basinsrdquo (through a steepest descent property) or by the "dividing linesrdquo separating these catchment basins (through the drop of water principle). Then, we prove, through an equivalence theorem, their optimality in terms of minimum spanning forests. Afterward, we introduce a linear-time algorithm to compute them. To the best of our knowledge, similar properties are not verified in other frameworks and the proposed algorithm is the most efficient existing algorithm, both in theory and in practice. Finally, the defined concepts are illustrated in image segmentation, leading to the conclusion that the proposed approach improves, on the tested images, the quality of watershed-based segmentations.

Eating when bored: revision of the emotional eating scale with a focus on boredom. <=SEP=> OBJECTIVE
The current study explored whether eating when bored is a distinct construct from other negative emotions by revising the emotional eating scale (EES) to include a separate boredom factor. Additionally, the relative endorsement of eating when bored compared to eating in response to other negative emotions was examined.


METHOD
A convenience sample of 139 undergraduates completed open-ended questions regarding their behaviors when experiencing different levels of emotions. Participants were then given the 25-item EES with 6 additional items designed to measure boredom.


RESULTS
On the open-ended items, participants more often reported eating in response to boredom than the other emotions. Exploratory factor analysis showed that boredom is a separate construct from other negative emotions. Additionally, the most frequently endorsed item on the EES was "eating when bored".


CONCLUSIONS
These results suggest that boredom is an important construct, and that it should be considered a separate dimension of emotional eating.

Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM <=SEP=> The detection of seizure activity in electroencephalogram (EEG) signals is crucial for the classification of epileptic seizures. However, epileptic seizures occur irregularly and unpredictably, automatic seizure detection in EEG recordings is highly required. In this work, we present a new technique for seizure classification of EEG signals using Hilbert–Huang transform (HHT) and support vector machine (SVM). In our method, the HHT based time-frequency representation (TFR) has been considered as time-frequency image (TFI), the segmentation of TFI has been implemented based on the frequency-bands of the rhythms of EEG signals, the histogram of grayscale sub-images has been represented. Statistical features such as ime-frequency image upport vector machine eizure classification mean, variance, skewness and kurtosis of pixel intensity in the histogram have been extracted. The SVM with radial basis function (RBF) kernel has been employed for classification of seizure and nonseizure EEG signals. The classification accuracy and receiver operating characteristics (ROC) curve have been used for evaluating the performance of the classifier. Experimental results show that the best average classification accuracy of this algorithm can reach 99.125% with the theta rhythm of EEG signals.

MagicFuzzer: Scalable deadlock detection for large-scale applications <=SEP=> We present MagicFuzzer, a novel dynamic deadlock detection technique. Unlike existing techniques to locate potential deadlock cycles from an execution, it iteratively prunes lock dependencies that each has no incoming or outgoing edge. Combining with a novel thread-specific strategy, it dramatically shrinks the size of lock dependency set for cycle detection, improving the efficiency and scalability of such a detection significantly. In the real deadlock confirmation phase, it uses a new strategy to actively schedule threads of an execution against the whole set of potential deadlock cycles. We have implemented a prototype and evaluated it on large-scale C/C++ programs. The experimental results confirm that our technique is significantly more effective and efficient than existing techniques.

Positive Definite Kernels in Machine Learning <=SEP=> This survey is an introduction to positive definite kernels and the set of methods they have inspired in the machine learning literature, namely kernel methods. We first discuss some properties of positive definite kernels as well as reproducing kernel Hibert spaces, the natural extension of the set of functions {k(x, ·), x ∈ X} associated with a kernel k defined on a space X . We discuss at length the construction of kernel functions that take advantage of well-known statistical models. We provide an overview of numerous data-analysis methods which take advantage of reproducing kernel Hilbert spaces and discuss the idea of combining several kernels to improve the performance on certain tasks. We also provide a short cookbook of different kernels which are particularly useful for certain datatypes such as images, graphs or speech segments. Remark: This report is a draft. I apologize in advance for the numerous mistakes, typos, and not always well written parts it contains. Comments and suggestions will be highly appreciated.

Single imputation with multilayer perceptron and multiple imputation combining multilayer perceptron and k-nearest neighbours for monotone patterns <=SEP=> The knowledge discovery process is supported by data files information gathered from collected data sets, which often contain errors in the form of missing values. Data imputation is the activity aimed at estimating values for missing data items. This study focuses on the development of automated data imputation models, based on artificial neural networks for monotone patterns of missing values. The present work proposes a single imputation approach relying on a multilayer perceptron whose training is conducted with different learning rules, and a multiple imputation approach based on the combination of multilayer perceptron and k-nearest neighbours. Eighteen real and simulated databases were exposed to a perturbation experiment with random generation of monotone missing data pattern. An empirical test ean/mode model ultilayer perceptron egression model was accomplished on these data sets, including both approaches (single and multiple imputations), and three classical single imputation procedures – mean/mode imputation, regression and hot-deck – were also considered. Therefore, the experiments involved five imputation methods. The results, considering different performance measures, demonstrated that, in comparison with traditional tools, both proposals improve the automation level and data quality offering a satisfactory performance.

Who Uses Bitcoin? An exploration of the Bitcoin community <=SEP=> Many cryptocurrencies have come into existence in recent years, with Bitcoin the most prominent among them. Although its short history has been volatile, the virtual currency maintains a core group of committed users. This paper presents an exploratory analysis of Bitcoin users. As a virtual currency and peer-to-peer payment system, Bitcoin may signal future challenges to state oversight and financial powers through its decentralized structure and offer of instantaneous transactions with relative anonymity. Very little is known about the users of Bitcoin, however. Utilizing publicly available survey data of Bitcoin users, this analysis explores the structure of the Bitcoin community in terms of wealth accumulation, optimism about the future of Bitcoin, and themes that attract users to the cryptocurrency. Results indicate that age, time of initial use, geographic location, mining status, engaging online discourse, and political orientation are all relevant factors that help explain various aspects of Bitcoin wealth, optimism, and attraction.

3-D Reciprocal Collision Avoidance on Physical Quadrotor Helicopters with On-Board Sensing for Relative Positioning <=SEP=> In this paper, we present an implementation of 3D reciprocal collision avoidance on real quadrotor helicopters where each quadrotor senses the relative position and veloc ity of other quadrotors using an on-board camera. We show that using our approach, quadrotors are able to successfully avo id pairwise collisions GPS and motion-capture denied environ ments, without communication between the quadrotors, and even when human operators deliberately attempt to induce collision. To our knowledge, this is the first time that reciprocal collision avoidance has been successfully implemented on r eal robots where each agent independently observes the others using on-board sensors. We theoretically analyze the respo nse of the collision-avoidance algorithm to the violated assumptions by the use of real robots. We quantitatively analyze our experimental results. A particularly striking observation is that at times the quadrotors exhibit “reciprocal dance” behavior, which is also observed when humans move past each other in constrained environments. This seems to be the result of sensing uncertainty, which causes both robots involved to h ave a different belief about the relative positions and velocit ies and, as a result, choose the same side on which to pass.