Mixing Confidential Transactions: Comprehensive Transaction Privacy for Bitcoin

The public nature of the blockchain has been shown to be a severe threat for the privacy of Bitcoin users. Even worse, since funds can be tracked and tainted, no two coins are equal, and fungibility, a fundamental property required in every currency, is at risk. With these threats in mind, several privacyenhancing technologies have been proposed to make Bitcoin more private. However, they either require a deep redesign of the currency, breaking many currently deployed features, or they address only specific privacy issues and consequently provide only very limited guarantees when deployed separately. The goal of this work is to overcome this trade-off. Building on CoinJoin, we design ValueShuffle, the first coin mixing protocol compatible with Confidential Transactions, a proposal to hide payment values in transactions. ValueShuffle ensures a mixing participant’s anonymity and the confidentiality of her payment values not only against an attacker observing the blockchain but also against the other possibly malicious mixing participants and against network attackers. By combining ValueShuffle with the proposal for Confidential Transactions and additionally Stealth Addresses, our solution provides comprehensive privacy (payer anonymity, payee anonymity, and payment value privacy) without breaking with the design or the features of Bitcoin. We demonstrate that the combination of these three privacyenhancing technologies creates synergies that overcome the two major obstacles which so far have prohibited the deployment of coin mixing in practice, namely that users need to mix funds of the same value, and need to do so before they can actually spend the funds. As a result, our approach unleashes the full potential of coin mixing as a privacy solution for Bitcoin.


Compressed sensing radar

A stylized compressed sensing radar is proposed in which the time- frequency plane is discretized into an N times N grid. Assuming the number of targets K is small (i.e., K Lt N2), then we can transmit a sufficiently "incoherent" pulse and employ the techniques of compressed sensing to reconstruct the target scene. A theoretical upper bound on the sparsity K is presented. Numerical simulations verify that even better performance can be achieved in practice. This novel compressed sensing approach offers great potential for better resolution over classical radar.


ASR-based corrective feedback on pronunciation: does it really work?

We studied a group of immigrants who were following regular, teacher-fronted Dutch classes, and who were assigned to three groups using either a) Dutch CAPT, an ASR-based Computer Assisted Pronunciation Training (CAPT) system that provides feedback on a number of Dutch speech sounds that are problematic for L2 learners b) a CAPT system without feedback c) no CAPT system. Participants were tested before and after the training. The results show that the ASR-based feedback was effective in correcting the errors addressed in the training.


Plant classification system based on leaf features

This paper presents a classification approach based on Random Forests (RF) and Linear Discriminant Analysis (LDA) algorithms for classifying the different types of plants. The proposed approach consists of three phases that are pre-processing, feature extraction, and classification phases. Since most types of plants have unique leaves, so the classification approach presented in this research depends on plants leave. Leaves are different from each other by characteristics such as the shape, color, texture and the margin. The used dataset for this experiments is a database of different plant species with total of only 340 leaf images, was downloaded from UCI- Machine Learning Repository. It was used for both training and testing datasets with 10-fold cross-validation. Experimental results showed that LDA achieved classification accuracy of (92.65%) against the RF that achieved accuracy of (88.82%) with combination of shape, first order texture, Gray Level Co-occurrence Matrix (GLCM), HSV color moments, and vein features.


Interactive Perception: Leveraging Action in Perception and Perception in Action

Recent approaches in robot perception follow the insight that perception is facilitated by interaction with the environment. These approaches are subsumed under the term Interactive Perception (IP). This view of perception provides the following benefits. First, interaction with the environment creates a rich sensory signal that would otherwise not be present. Second, knowledge of the regularity in the combined space of sensory data and action parameters facilitates the prediction and interpretation of the sensory signal. In this survey, we postulate this as a principle for robot perception and collect evidence in its support by analyzing and categorizing existing work in this area. We also provide an overview of the most important applications of IP. We close this survey by discussing remaining open questions. With this survey, we hope to help define the field of Interactive Perception and to provide a valuable resource for future research.


An Unsupervised Approach to Recognizing Discourse Relations

We presentan unsupervisedapproachto recognizingdiscourserelationsof CONTRAST, EXPLANATION-EVIDENCE, CONDITION andELABORATION that hold betweenarbitraryspansof texts. We show that discourserelation classifierstrained on examples that are automaticallyextractedfrom massi ve amountsof text can be usedto distinguishbetweensomeof theserelationswith accuraciesashigh as 93%, even whenthe relationsarenot explicitly markedby cuephrases.


ANCESTRY-CONSTRAINED PHYLOGENETIC ANALYSIS SUPPORTS THE INDO-EUROPEAN STEPPE HYPOTHESIS

Discussion of Indo-European origins and dispersal focuses on two hypotheses. Qualitative evidence from reconstructed vocabulary and correlations with archaeological data suggest that IndoEuropean languages originated in the Pontic-Caspian steppe and spread together with cultural innovations associated with pastoralism, beginning c. 6500–5500 bp. An alternative hypothesis, according to which Indo-European languages spread with the diffusion of farming from Anatolia, beginning c. 9500–8000 bp, is supported by statistical phylogenetic and phylogeographic analyses of lexical traits. The time and place of the Indo-European ancestor language therefore remain disputed. Here we present a phylogenetic analysis in which ancestry constraints permit more accurate inference of rates of change, based on observed changes between ancient or medieval languages and their modern descendants, and we show that the result strongly supports the steppe hypothesis. Positing ancestry constraints also reveals that homoplasy is common in lexical traits, contrary to the assumptions of previous work. We show that lexical traits undergo recurrent evolution due to recurring patterns of semantic and morphological change.*


The business model concept: theoretical underpinnings and empirical illustrations

Received: 13 December 2001 Revised: 27 March 2002 : 26 July 2002 Accepted:15 October 2002 Abstract The business model concept is becoming increasingly popular within IS, management and strategy literature. It is used within many fields of research, including both traditional strategy theory and in the emergent body of literature on e-business. However, the concept is often used independently from theory, meaning model components and their interrelations are relatively obscure. Nonetheless, we believe that the business model concept is useful in explaining the relation between IS and strategy. This paper offers an outline for a conceptual business model, and proposes that it should include customers and competitors, the offering, activities and organisation, resources and factor market interactions. The causal inter-relations and the longitudinal processes by which business models evolve should also be included. The model criticises yet draws on traditional strategy theory and on the literature that addresses business models directly. The business model is illustrated by an ERP implementation in a European multi-national company. European Journal of Information Systems (2003) 12, 49–59. doi:10.1057/ palgrave.ejis.3000446


Design science in information systems research


Balancing customer and network value in business models for mobile services

Designing business models for mobile services is complex. A business model can be seen as a blueprint of four interrelated components: service offering, technical architecture, and organisational and financial arrangements. In this paper the connections among these components are explored by analysing the critical design issues in business models for mobile services, e.g., targeting and branding in the service domain, security and quality of service in the technology domain, network governance in the organisation domain, and revenue sharing in the finance domain. A causal framework is developed linking these critical design issues to expected customer value and expected network value, and hence, to business model viability.


Evaluation in Design-Oriented Research

Design has been recognized for a long time both as art and as science. In the sixties of the previous century design-oriented research began to draw the attention of scientific researchers and methodologists, not only in technical engineering but also in the social sciences. However, a rather limited methodology for design-oriented research has been developed, especially as to the social sciences. In this article we introduce evaluation methodology and research methodology as a systematic input in the process of designing. A designing cycle is formulated with six stages, and for each of these stages operations, guidelines and criteria for evaluation are defined. All this may be used for a considerable improvement of the process and product of designing.


Business Models for Electronic Markets

Introduction Electronic commerce can be defined loosely as “doing business electronically” (European Commission 1997). Electronic commerce includes electronic trading of physical goods and of intangibles such as information. This encompasses all the trading steps such as online marketing, ordering, payment, and support for delivery. Electronic commerce includes the electronic provision of services, such as aftersales support or online legal advice. Finally it also includes electronic support for collaboration between companies, such as collaborative design.


Geometric mechanics of curved crease origami.

Folding a sheet of paper along a curve can lead to structures seen in decorative art and utilitarian packing boxes. Here we present a theory for the simplest such structure: an annular circular strip that is folded along a central circular curve to form a three-dimensional buckled structure driven by geometrical frustration. We quantify this shape in terms of the radius of the circle, the dihedral angle of the fold, and the mechanical properties of the sheet of paper and the fold itself. When the sheet is isometrically deformed everywhere except along the fold itself, stiff folds result in creases with constant curvature and oscillatory torsion. However, relatively softer folds inherit the broken symmetry of the buckled shape with oscillatory curvature and torsion. Our asymptotic analysis of the isometrically deformed state is corroborated by numerical simulations that allow us to generalize our analysis to study structures with multiple curved creases.


Policy Optimization with Demonstrations

Exploration remains a significant challenge to reinforcement learning methods, especially in environments where reward signals are sparse. Recent methods of learning from demonstrations have shown to be promising in overcoming exploration difficulties but typically require considerable highquality demonstrations that are difficult to collect. We propose to effectively leverage available demonstrations to guide exploration through enforcing occupancy measure matching between the learned policy and current demonstrations, and develop a novel Policy Optimization from Demonstration (POfD) method. We show that POfD induces implicit dynamic reward shaping and brings provable benefits for policy improvement. Furthermore, it can be combined with policy gradient methods to produce state-of-the-art results, as demonstrated experimentally on a range of popular benchmark sparse-reward tasks, even when the demonstrations are few and imperfect.


Are You Asking the Right Questions? Teaching Machines to Ask Clarification Questions

Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this thesis work, we explore how can we teach machines to ask clarification questions when faced with uncertainty, a goal of increasing importance in today’s automated society. We do a preliminary study using data from StackExchange, a plentiful online resource where people routinely ask clarifying questions to posts so that they can better offer assistance to the original poster. We build neural network models inspired by the idea of the expected value of perfect information: a good question is one whose expected answer is going to be most useful. To build generalizable systems, we propose two future research directions: a template-based model and a sequence-to-sequence based neural generative model.


Sentiment Adaptive End-to-End Dialog Systems

v This work focuses on incorporating sentiment information into task-oriented dialogue systems. v Current end-to-end approaches only consider user semantic inputs in learning and under-utilize other user information. v But the ultimate evaluator of dialog systems is the end-users and their sentiment is a direct reflection of user satisfaction and should be taken into consideration. v Therefore, we propose to include user sentiment obtained through multimodal information (acoustic, dialogic and textual), in the end-to-end learning framework to make systems more user-adaptive and effective. v We incorporated user sentiment information in both supervised and reinforcement learning settings. v In both settings, adding sentiment information reduced the dialog length and improved the task success rate on a bus information search task. Multimodal Sentiment Detector v We manually annotated 50 dialogs with 517 conversation turns to train this sentiment detector. The annotated set is open to public. v Prediction made by the detector will be used in the supervised learning and reinforcement learning. v Three sets of features: 1) Acoustic features; 2) Dialogic features; 3) Textual features. v Dialogic features include: 1) Interruption; 2) Button usage; 3) Repetitions; 4) Start over. These four categories of dialog features are chosen based on the previous literature and the observed statistics in the dataset.


Social Media for Opioid Addiction Epidemiology: Automatic Detection of Opioid Addicts from Twitter and Case Studies

Opioid (e.g., heroin and morphine) addiction has become one of the largest and deadliest epidemics in the United States. To combat such deadly epidemic, there is an urgent need for novel tools and methodologies to gain new insights into the behavioral processes of opioid abuse and addiction. The role of social media in biomedical knowledge mining has turned into increasingly significant in recent years. In this paper, we propose a novel framework named AutoDOA to automatically detect the opioid addicts from Twitter, which can potentially assist in sharpening our understanding toward the behavioral process of opioid abuse and addiction. In AutoDOA, to model the users and posted tweets as well as their rich relationships, a structured heterogeneous information network (HIN) is first constructed. Then meta-path based approach is used to formulate similarity measures over users and different similarities are aggregated using Laplacian scores. Based on HIN and the combined meta-path, to reduce the cost of acquiring labeled examples for supervised learning, a transductive classification model is built for automatic opioid addict detection. To the best of our knowledge, this is the first work to apply transductive classification in HIN into drug-addiction domain. Comprehensive experiments on real sample collections from Twitter are conducted to validate the effectiveness of our developed system AutoDOA in opioid addict detection by comparisons with other alternate methods. The results and case studies also demonstrate that knowledge from daily-life social media data mining could support a better practice of opioid addiction prevention and treatment.


Integrated Wideband Self-Interference Cancellation in the RF Domain for FDD and Full-Duplex Wireless

A fully integrated technique for wideband cancellation of transmitter (TX) self-interference (SI) in the RF domain is proposed for multiband frequency-division duplexing (FDD) and full-duplex (FD) wireless applications. Integrated wideband SI cancellation (SIC) in the RF domain is accomplished through: 1) a bank of tunable, reconfigurable second-order high-Q RF bandpass filters in the canceller that emulate the antenna interface's isolation (essentially frequency-domain equalization in the RF domain) and 2) a linear N-path Gm-C filter implementation with embedded variable attenuation and phase shifting. A 0.8-1.4 GHz receiver (RX) with the proposed wideband SIC circuits is implemented in a 65 nm CMOS process. In measurement, >20 MHz 20 dB cancellation bandwidth (BW) is achieved across frequency-selective antenna interfaces: 1) a custom-designed LTElike 0.780/0.895 GHz duplexer with TX/RX isolation peak magnitude of 30 dB, peak group delay of 11 ns, and 7 dB magnitude variation across the TX band for FDD and 2) a 1.4 GHz antenna pair for FD wireless with TX/RX isolation peak magnitude of 32 dB, peak group delay of 9 ns, and 3 dB magnitude variation over 1.36-1.38 GHz. For FDD, SIC enhances the effective outof-band (OOB) IIP3 and IIP2 to +25-27 dBm and +90 dBm, respectively (enhancements of 8-10 and 29 dB, respectively). For FD, SIC eliminates RX gain compression for as high as -8 dBm of peak in-band (IB) SI, and enhances effective IB IIP3 and IIP2 by 22 and 58 dB.


The analyse of the various methods for location of Data Matrix codes in images

Data Matrix codes belong to the group of 2-D bar codes, which are widely used in marking the products in storing, production, distribution and sales processes. Their compact matrix structure enables to store big amount of information in a very small area compared to conventional 1-D bar codes. In following paper we compare several methods for detection of Data Matrix codes in images. When locating the position of the code we start from typical bordering of Data Matrix code — which forms “L” so called Finder Pattern and to it the parallel dotting so called Timing Pattern. On the first stage we try to locate Finder Pattern using the edge detection or adaptive thresholding, then continuing with connecting the points into the continuous regions, we are searching for the regions, which could represent the Finder Pattern. On the second stage we verify Timing Pattern where the number of crossing between the background and foreground must be even.


Quorum responses and consensus decision making.

Animal groups are said to make consensus decisions when group members come to agree on the same option. Consensus decisions are taxonomically widespread and potentially offer three key benefits: maintenance of group cohesion, enhancement of decision accuracy compared with lone individuals and improvement in decision speed. In the absence of centralized control, arriving at a consensus depends on local interactions in which each individual's likelihood of choosing an option increases with the number of others already committed to that option. The resulting positive feedback can effectively direct most or all group members to the best available choice. In this paper, we examine the functional form of the individual response to others' behaviour that lies at the heart of this process. We review recent theoretical and empirical work on consensus decisions, and we develop a simple mathematical model to show the central importance to speedy and accurate decisions of quorum responses, in which an animal's probability of exhibiting a behaviour is a sharply nonlinear function of the number of other individuals already performing this behaviour. We argue that systems relying on such quorum rules can achieve cohesive choice of the best option while also permitting adaptive tuning of the trade-off between decision speed and accuracy.


Single-unit pattern generators for quadruped locomotion

Legged robots can potentially venture beyond the limits of wheeled vehicles. While creating controllers for such robots by hand is possible, evolutionary algorithms are an alternative that can reduce the burden of hand-crafting robotic controllers. Although major evolutionary approaches to legged locomotion can generate oscillations through popular techniques such as continuous time recurrent neural networks (CTRNNs) or sinusoidal input, they typically face a challenge in maintaining long-term stability. The aim of this paper is to address this challenge by introducing an effective alternative based on a new type of neuron called a single-unit pattern generator (SUPG). The SUPG, which is indirectly encoded by a compositional pattern producing network (CPPN) evolved by HyperNEAT, produces a flexible temporal activation pattern that can be reset and repeated at any time through an explicit trigger input, thereby allowing it to dynamically recalibrate over time to maintain stability. The SUPG approach, which is compared to CTRNNs and sinusoidal input, is shown to produce natural-looking gaits that exhibit superior stability over time, thereby providing a new alternative for evolving oscillatory locomotion.


A modular control scheme for PMSM speed control with pulsating torque minimization

In this paper, a modular control approach is applied to a permanent-magnet synchronous motor (PMSM) speed control. Based on the functioning of the individual module, the modular approach enables the powerfully intelligent and robust control modules to easily replace any existing module which does not perform well, meanwhile retaining other existing modules which are still effective. Property analysis is first conducted for the existing function modules in a conventional PMSM control system: proportional-integral (PI) speed control module, reference current-generating module, and PI current control module. Next, it is shown that the conventional PMSM controller is not able to reject the torque pulsation which is the main hurdle when PMSM is used as a high-performance servo. By virtue of the internal model, to nullify the torque pulsation it is imperative to incorporate an internal model in the feed-through path. This is achieved by replacing the reference current-generating module with an iterative learning control (ILC) module. The ILC module records the cyclic torque and reference current signals over one entire cycle, and then uses those signals to update the reference current for the next cycle. As a consequence, the torque pulsation can be reduced significantly. In order to estimate the torque ripples which may exceed certain bandwidth of a torque transducer, a novel torque estimation module using a gain-shaped sliding-mode observer is further developed to facilitate the implementation of torque learning control. The proposed control system is evaluated through real-time implementation and experimental results validate the effectiveness.


LIP ACTIVITY DETECTION FOR TALKING FACES CLASSIFICATION IN TV-CONTENT

Our objective is to index people in a TV-Content. In this context, because of multi-face shots and non-speaking face shots, it is difficult to determine which face is speaking. There is no guaranteed synchronization between sequences of a person’s appearance and sequences of his or her speech. In this work, we want to separate talking and non-talking faces by detecting lip motion. We propose a method to detect the lip motion by measuring the degree of disorder of pixel directions around the lip. Results of experiments on a TV-Show database show that a high correct classification rate can be achieved by the proposed method.


Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network

Capturing the compositional process which maps the meaning of words to that of documents is a central challenge for researchers in Natural Language Processing and Information Retrieval. We introduce a model that is able to represent the meaning of documents by embedding them in a low dimensional vector space, while preserving distinctions of word and sentence order crucial for capturing nuanced semantics. Our model is based on an extended Dynamic Convolution Neural Network, which learns convolution filters at both the sentence and document level, hierarchically learning to capture and compose low level lexical features into high level semantic concepts. We demonstrate the effectiveness of this model on a range of document modelling tasks, achieving strong results with no feature engineering and with a more compact model. Inspired by recent advances in visualising deep convolution networks for computer vision, we present a novel visualisation technique for our document networks which not only provides insight into their learning process, but also can be interpreted to produce a compelling automatic summarisation system for texts.


The Computable News project: Research in the Newsroom

We report on a four year academic research project to build a natural language processing platform in support of a large media company. The Computable News platform processes news stories, producing a layer of structured data that can be used to build rich applications. We describe the underlying platform and the research tasks that we explored building it. The platform supports a wide range of prototype applications designed to support different newsroom functions. We hope that this qualitative review provides some insight into the challenges involved in this type of project.


A language modeling framework for resource selection and results merging

Statistical language models have been proposed recently for several information retrieval tasks, including the resource selection task in distributed information retrieval. This paper extends the language modeling approach to integrate resource selection, ad-hoc searching, and merging of results from different text databases into a single probabilistic retrieval model. This new approach is designed primarily for Intranet environments, where it is reasonable to assume that resource providers are relatively homogeneous and can adopt the same kind of search engine. Experiments demonstrate that this new, integrated approach is at least as effective as the prior state-of-the-art in distributed IR.


Frontal fibrosing alopecia: a clinical review of 36 patients.

BACKGROUND
Frontal fibrosing alopecia (FFA) is a primary lymphocytic cicatricial alopecia with a distinctive clinical pattern of progressive frontotemporal hairline recession. Currently, there are no evidence-based studies to guide treatment for patients with FFA; thus, treatment options vary among clinicians.

OBJECTIVES
We report clinical findings and treatment outcomes of 36 patients with FFA, the largest cohort to date. Further, we report the first evidence-based study of the efficacy of hydroxychloroquine in FFA using a quantitative clinical score, the Lichen Planopilaris Activity Index (LPPAI).

METHODS
A retrospective case note review was performed of 36 adult patients with FFA. Data were collected on demographics and clinical findings. Treatment responses to hydroxychloroquine, doxycycline and mycophenolate mofetil were assessed using the LPPAI. Adverse events were monitored.

RESULTS
Most patients in our cohort were female (97%), white (92%) and postmenopausal (83%). Apart from hairline recession, 75% also reported eyebrow loss. Scalp pruritus (67%) and perifollicular erythema (86%) were the most common presenting symptom and sign, respectively. A statistically significant reduction in signs and symptoms in subjects treated with hydroxychloroquine (P < 0·05) was found at both 6- and 12-month follow up.

CONCLUSIONS
In FFA, hairline recession, scalp pruritus, perifollicular erythema and eyebrow loss are common at presentation. Despite the limitations of a retrospective review, our data reveal that hydroxychloroquine is significantly effective in reducing signs and symptoms of FFA after both 6 and 12 months of treatment. However, the lack of a significant reduction in signs and symptoms between 6 and 12 months indicates that the maximal benefits of hydroxychloroquine are evident within the first 6 months of use.


Incorporating Entity Correlation Knowledge into Topic Modeling

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for exploring hidden topics in text corpora. Standard LDA model suffers the problem that the topic assignment of each word is independent and lacks the mechanism to utilize the rich prior background knowledge to learn semantically coherent topics. To address this problem, in this paper, we propose a model called Entity Correlation Latent Dirichlet Allocation (EC-LDA) by incorporating constraints derived from entity correlations as the prior knowledge into LDA topic model. Different from other knowledge-based topic models which extract the knowledge information directly from the train dataset itself or even from the human judgements, for our work, we take advantage of the prior knowledge from the external knowledge base (Freebase 1, in our experiment). Hence, our approach is more suitable to widely kinds of text corpora in different scenarios. We fit our proposed model using Gibbs sampling. Experiment results demonstrate the effectiveness of our model compared with standard LDA.


Learning by Googling

The goal of giving a well-defined meaning to information is currently shared by endeavors such as the Semantic Web as well as by current trends within Knowledge Management. They all depend on the large-scale formalization of knowledge and on the availability of formal metadata about information resources. However, the question how to provide the necessary formal metadata in an effective and efficient way is still not solved to a satisfactory extent. Certainly, the most effective way to provide such metadata as well as formalized knowledge is to let humans encode them directly into the system, but this is neither efficient nor feasible. Furthermore, as current social studies show, individual knowledge is often less powerful than the collective knowledge of a certain community.As a potential way out of the knowledge acquisition bottleneck, we present a novel methodology that acquires collective knowledge from the World Wide Web using the GoogleTM API. In particular, we present PANKOW, a concrete instantiation of this methodology which is evaluated in two experiments: one with the aim of classifying novel instances with regard to an existing ontology and one with the aim of learning sub-/superconcept relations.


The challenge problem for automated detection of 101 semantic concepts in multimedia

We introduce the challenge problem for generic video indexing to gain insight in intermediate steps that affect performance of multimedia analysis methods, while at the same time fostering repeatability of experiments. To arrive at a challenge problem, we provide a general scheme for the systematic examination of automated concept detection methods, by decomposing the generic video indexing problem into 2 unimodal analysis experiments, 2 multimodal analysis experiments, and 1 combined analysis experiment. For each experiment, we evaluate generic video indexing performance on 85 hours of international broadcast news data, from the TRECVID 2005/2006 benchmark, using a lexicon of 101 semantic concepts. By establishing a minimum performance on each experiment, the challenge problem allows for component-based optimization of the generic indexing issue, while simultaneously offering other researchers a reference for comparison during indexing methodology development. To stimulate further investigations in intermediate analysis steps that inuence video indexing performance, the challenge offers to the research community a manually annotated concept lexicon, pre-computed low-level multimedia features, trained classifier models, and five experiments together with baseline performance, which are all available at http://www.mediamill.nl/challenge/.


Robust video denoising using low rank matrix completion

Most existing video denoising algorithms assume a single statistical model of image noise, e.g. additive Gaussian white noise, which often is violated in practice. In this paper, we present a new patch-based video denoising algorithm capable of removing serious mixed noise from the video data. By grouping similar patches in both spatial and temporal domain, we formulate the problem of removing mixed noise as a low-rank matrix completion problem, which leads to a denoising scheme without strong assumptions on the statistical properties of noise. The resulting nuclear norm related minimization problem can be efficiently solved by many recently developed methods. The robustness and effectiveness of our proposed denoising algorithm on removing mixed noise, e.g. heavy Gaussian noise mixed with impulsive noise, is validated in the experiments and our proposed approach compares favorably against some existing video denoising algorithms.


Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information

This paper considers the model problem of reconstructing an object from incomplete frequency samples. Consider a discrete-time signal f/spl isin/C/sup N/ and a randomly chosen set of frequencies /spl Omega/. Is it possible to reconstruct f from the partial knowledge of its Fourier coefficients on the set /spl Omega/? A typical result of this paper is as follows. Suppose that f is a superposition of |T| spikes f(t)=/spl sigma//sub /spl tau//spl isin/T/f(/spl tau/)/spl delta/(t-/spl tau/) obeying |T|/spl les/C/sub M//spl middot/(log N)/sup -1/ /spl middot/ |/spl Omega/| for some constant C/sub M/>0. We do not know the locations of the spikes nor their amplitudes. Then with probability at least 1-O(N/sup -M/), f can be reconstructed exactly as the solution to the /spl lscr//sub 1/ minimization problem. In short, exact recovery may be obtained by solving a convex optimization problem. We give numerical values for C/sub M/ which depend on the desired probability of success. Our result may be interpreted as a novel kind of nonlinear sampling theorem. In effect, it says that any signal made out of |T| spikes may be recovered by convex programming from almost every set of frequencies of size O(|T|/spl middot/logN). Moreover, this is nearly optimal in the sense that any method succeeding with probability 1-O(N/sup -M/) would in general require a number of frequency samples at least proportional to |T|/spl middot/logN. The methodology extends to a variety of other situations and higher dimensions. For example, we show how one can reconstruct a piecewise constant (one- or two-dimensional) object from incomplete frequency samples - provided that the number of jumps (discontinuities) obeys the condition above - by minimizing other convex functionals such as the total variation of f.


A Singular Value Thresholding Algorithm for Matrix Completion

This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem and arises in many important applications as in the task of recovering a large matrix from a small subset of its entries (the famous Netflix problem). Off-the-shelf algorithms such as interior point methods are not directly amenable to large problems of this kind with over a million unknown entries. This paper develops a simple first-order and easy-to-implement algorithm that is extremely efficient at addressing problems in which the optimal solution has low rank. The algorithm is iterative, produces a sequence of matrices {Xk ,Y k}, and at each step mainly performs a soft-thresholding operation on the singular values of the matrix Y k. There are two remarkable features making this attractive for low-rank matrix completion problems. The first is that the soft-thresholding operation is applied to a sparse matrix; the second is that the rank of the iterates {Xk} is empirically nondecreasing. Both these facts allow the algorithm to make use of very minimal storage space and keep the computational cost of each iteration low. On the theoretical side, we provide a convergence analysis showing that the sequence of iterates converges. On the practical side, we provide numerical examples in which 1, 000 × 1, 000 matrices are recovered in less than a minute on a modest desktop computer. We also demonstrate that our approach is amenable to very large scale problems by recovering matrices of rank about 10 with nearly a billion unknowns from just about 0.4% of their sampled entries. Our methods are connected with the recent literature on linearized Bregman iterations for 1 minimization, and we develop a framework in which one can understand these algorithms in terms of well-known Lagrange multiplier algorithms.


Prevalence and Predictors of Maternal Anemia during Pregnancy in Gondar, Northwest Ethiopia: An Institutional Based Cross-Sectional Study

Background. Anaemia is a global public health problem which has an eminence impact on pregnant mother. The aim of this study was to assess the prevalence and predictors of maternal anemia. Method. A cross-sectional study was conducted from March 1 to April 30, 2012, on 302 pregnant women who attended antenatal care at Gondar University Hospital. Interview-based questionnaire, clinical history, and laboratory tests were used to obtain data. Bivariate and multivariate logistic regression was used to identify predictors. Result. The prevalence of anemia was 16.6%. Majority were mild type (64%) and morphologically normocytic normochromic (76%) anemia. Anemia was high at third trimester (18.9%). Low family income (AOR [95% CI] = 3.1 [1.19, 8.33]), large family size (AOR [95% CI] = 4.14 [4.13, 10.52]), hookworm infection (AOR [95% CI] = 2.72 [1.04, 7.25]), and HIV infection (AOR [95% CI] = 5.75 [2.40, 13.69]) were independent predictors of anemia. Conclusion. The prevalence of anemia was high; mild type and normocytic normochromic anemia was dominant. Low income, large family size, hookworm infection, and HIV infection were associated with anemia. Hence, efforts should be made for early diagnosis and management of HIV and hookworm infection with special emphasis on those having low income and large family size.


An IoT based reference architecture for smart water management processes

Water is a vital resource for life, and its management is a key issue nowadays. Information and communications technology systems for water control are currently facing interoperability problems due to the lack of support of standardization in monitory and control equipment. This problem affects various processes in water management, such as water consumption, distribution, system identification and equipment maintenance. OPC UA (Object Linking and Embedding for Process Control Unified Architecture) is a platform independent service-oriented architecture for the control of processes in the logistic and manufacturing sectors. Based on this standard we propose a smart water management model combining Internet of Things technologies with business processes coordination and decision support systems. We provide an architecture for sub-system interaction and a detailed description of the physical scenario in which we will test our implementation, allowing specific vendor equipment to be manageable and interoperable in the specific context of water management processes.


Multimodal Word Distributions

Word embeddings provide point representations of words containing useful semantic information. We introduce multimodal word distributions formed from Gaussian mixtures, for multiple word meanings, entailment, and rich uncertainty information. To learn these distributions, we propose an energy-based max-margin objective. We show that the resulting approach captures uniquely expressive semantic information, and outperforms alternatives, such as word2vec skip-grams, and Gaussian embeddings, on benchmark datasets such as word similarity and entailment.


Learning to dress: synthesizing human dressing motion via deep reinforcement learning

Creating animation of a character putting on clothing is challenging due to the complex interactions between the character and the simulated garment. We take a model-free deep reinforcement learning (deepRL) approach to automatically discovering robust dressing control policies represented by neural networks. While deepRL has demonstrated several successes in learning complex motor skills, the data-demanding nature of the learning algorithms is at odds with the computationally costly cloth simulation required by the dressing task. This paper is the first to demonstrate that, with an appropriately designed input state space and a reward function, it is possible to incorporate cloth simulation in the deepRL framework to learn a robust dressing control policy. We introduce a salient representation of haptic information to guide the dressing process and utilize it in the reward function to provide learning signals during training. In order to learn a prolonged sequence of motion involving a diverse set of manipulation skills, such as grasping the edge of the shirt or pulling on a sleeve, we find it necessary to separate the dressing task into several subtasks and learn a control policy for each subtask. We introduce a policy sequencing algorithm that matches the distribution of output states from one task to the input distribution for the next task in the sequence. We have used this approach to produce character controllers for several dressing tasks: putting on a t-shirt, putting on a jacket, and robot-assisted dressing of a sleeve.


OmniPres project IST-2001-39237 Deliverable 5 Measuring Presence : A Guide to Current Measurement Approaches

This compendium constitutes a comprehensive overview of presence measures described in the literature so far. It contains both subjective and objective approaches to presence measurement. The theoretical basis of measures is described, along with research in which they have been applied, and other relevant literature. Authors: Joy van Baren and Wijnand IJsselsteijn Project funded by the European Community under the "Information Society Technologies" Programme


Clinical applications of the Miniscrew Anchorage System.


Anonymous Walk Embeddings

The task of representing entire graphs has seen a surge of prominent results, mainly due to learning convolutional neural networks (CNNs) on graphstructured data. While CNNs demonstrate stateof-the-art performance in graph classification task, such methods are supervised and therefore steer away from the original problem of network representation in task-agnostic manner. Here, we coherently propose an approach for embedding entire graphs and show that our feature representations with SVM classifier increase classification accuracy of CNN algorithms and traditional graph kernels. For this we describe a recently discovered graph object, anonymous walk, on which we design task-independent algorithms for learning graph representations in explicit and distributed way. Overall, our work represents a new scalable unsupervised learning of state-of-the-art representations of entire graphs.


Evaluation Of Linear Interpolation Smoothing On Naive Bayes Spam Classifier

The inconvenience associated with spams and the cost of having an important mail misclassified as spam have made all efforts at improving spam filtering worthwhile. The Naive Bayes algorithm has been found to be successful in properly classifying mails. However, they are not perfect. Recent researches have introduced the idea of smoothing into the Naive Bayes algorithm and they have been found to produce better classification. This study applies the concept of linear interpolation smoothing to Naive Bayes spam classification. The resulting classifier did well at improving spam classification and also reducing false positives.


CS Unplugged and Middle-School Students' Views, Attitudes, and Intentions Regarding CS

Many students hold incorrect ideas and negative attitudes about computer science (CS). In order to address these difficulties, a series of learning activities called Computer Science Unplugged was developed by Tim Bell and his colleagues. These activities expose young people to central concepts in CS in an entertaining way without requiring a computer. The CS Unplugged activities have become more and more popular among CS educators and several activities are recommended in the ACM K-12 curriculum for elementary schools. CS Unplugged is used worldwide and has been translated into many languages.
 We examined the effect of the CS Unplugged activities on middle-school students’ ideas about CS and their desire to consider and study it in high school. The results indicate that following the activities the ideas of the students on what CS is about were partially improved, but their desire to study CS lessened.
 In order to provide possible explanations to these results, we analyzed the CS Unplugged activities to determine to what extent the objectives of CS Unplugged were addressed in the activities. In addition, we checked whether the activities were designed according to constructivist principles and whether they were explicitly linked to central concepts in CS. We found that only some of the objectives were addressed in the activities, that the activities do not engage with the students’ prior knowledge and that most of the activities are not explicitly linked to central concepts in CS. We offer suggestions for modifying the CS Unplugged activities so that they will be more likely to achieve their objectives.


Assistive Clothing Pattern Recognition for Visually Impaired People

Choosing clothes with complex patterns and colors is a challenging task for visually impaired people. Automatic clothing pattern recognition is also a challenging research problem due to rotation, scaling, illumination, and especially large intraclass pattern variations. We have developed a camera-based prototype system that recognizes clothing patterns in four categories (plaid, striped, patternless, and irregular) and identifies 11 clothing colors. The system integrates a camera, a microphone, a computer, and a Bluetooth earpiece for audio description of clothing patterns and colors. A camera mounted upon a pair of sunglasses is used to capture clothing images. The clothing patterns and colors are described to blind users verbally. This system can be controlled by speech input through microphone. To recognize clothing patterns, we propose a novel Radon Signature descriptor and a schema to extract statistical properties from wavelet subbands to capture global features of clothing patterns. They are combined with local features to recognize complex clothing patterns. To evaluate the effectiveness of the proposed approach, we used the CCNY Clothing Pattern dataset. Our approach achieves 92.55% recognition accuracy which significantly outperforms the state-of-the-art texture analysis methods on clothing pattern recognition. The prototype was also used by ten visually impaired participants. Most thought such a system would support more independence in their daily life but they also made suggestions for improvements.


An O(sqrt(|v|) |E|) Algorithm for Finding Maximum Matching in General Graphs


Automatic prediction of frustration

Predicting when a person might be frustrated can provide an intelligent system with important information about when to initiate interaction. For example, an automated Learning Companion or Intelligent Tutoring System might use this information to intervene, providing support to the learner who is likely to otherwise quit, while leaving engaged learners free to discover things without interruption. This paper presents the first automated method that assesses, using multiple channels of affect-related information, whether a learner is about to click on a button saying ‘‘I’m frustrated.’’ The new method was tested on data gathered from 24 participants using an automated Learning Companion. Their indication of frustration was automatically predicted from the collected data with 79% accuracy ðchance 1⁄4 58%Þ. The new assessment method is based on Gaussian process classification and Bayesian inference. Its performance suggests that non-verbal channels carrying affective cues can help provide important information to a system for formulating a more intelligent response. r 2007 Elsevier Ltd. All rights reserved.


utomatic detection of skin defects in citrus fruits using a multivariate image nalysis approach

One of the main problems in the post-harvest processing of citrus is the detection of visual defects in order to classify the fruit depending on their appearance. Species and cultivars of citrus present a high rate of unpredictability in texture and colour that makes it difficult to develop a general, unsupervised method able of perform this task. In this paper we study the use of a general approach that was originally developed for the detection of defects in random colour textures. It is based on a Multivariate Image Analysis strategy and uses Principal Component Analysis to extract a reference eigenspace from a matrix built by unfolding colour and spatial data from samples of defect-free peel. Test images are also unfolded and projected onto the reference eigenspace and the result is a score matrix which is used to compute defective maps based on the T2 statistic. In addition, a multiresolution scheme is introduced nsupervised Methods in the original method to speed up the process. Unlike the techniques commonly used for the detection of defects in fruits, this is an unsupervised method that only needs a few samples to be trained. It is also a simple approach that is suitable for real-time compliance. Experimental work was performed on 120 samples of oranges and mandarins from four different cultivars: Clemenules, Marisol, Fortune, and Valencia. The success ratio for the detection of individual defects was 91.5%, while the classification ratio of damaged/sound samples was 94.2%. These results show that the studied method can be suitable for on. the task of citrus inspecti


Improved random redundant iterative HDPC decoding

An iterative algorithm for soft-input soft-output (SISO) decoding of classical algebraic cyclic block codes is presented below. Inspired by other approaches for high performance belief propagation (BP) decoding, this algorithm requires up to 10 times less computational complexity than other methods that achieve similar performance. By utilizing multiple BP decoders, and using random permutation taken from the permutation group of the code, this algorithm reaches near maximum likelihood performance. A computational complexity comparison of the proposed algorithm versus other methods is presented as well. This includes complexity versus performance analysis, allowing one to trade between the former and the latter, according to ones needs.


Unsupervised Video Object Segmentation with Motion-Based Bilateral Networks

In this work, we study the unsupervised video object segmentation problem where moving objects are segmented without prior knowledge of these objects. First, we propose a motion-based bilateral network to estimate the background based on the motion pattern of non-object regions. The bilateral network reduces false positive regions by accurately identifying background objects. Then, we integrate the background estimate from the bilateral network with instance embeddings into a graph, which allows multiple frame reasoning with graph edges linking pixels from different frames. We classify graph nodes by defining and minimizing a cost function, and segment the video frames based on the node labels. The proposed method outperforms previous state-of-the-art unsupervised video object segmentation methods against the DAVIS 2016 and the FBMS-59 datasets.


Moving Target Defense - Creating Asymmetric Uncertainty for Cyber Threats

Excellent book is always being the best friend for spending little time in your office, night time, bus, and everywhere. It will be a good way to just look, open, and read the book while in that time. As known, experience and skill don't always come with the much money to acquire them. Reading this book with the PDF moving target defense creating asymmetric uncertainty for cyber threats will let you know more things.


Decade-bandwidth planar balun using CPW-to-slotline transition for UHF applications

This paper presents an improved broadband balun using a coplanar-waveguide(CPW)-to-slotline field transformation. It operates at very wide frequency, and is of compact size since it does not depend on a resonant structure. The measured results show a passband of 200 MHz to 2 GHz, insertion loss less than 0.75 dB and a size of 20 mm × 14 mm. The amplitude imbalance is approximately 0.3 dB and the phase imbalance is less than 6o over the entire operation range.


GHz UWB automotive short range radar – Spectrum allocation and technology trends

Automotive UWB (Ultra-Wideband) short range radar (SSR) is on the market as a key technology for novel comfort and safety systems. SiGe based 79 GHz UWB SRR will be a definite candidate for the long term substitution of the 24 GHz UWB SRR. This paper will give an overview of the finished BMBF joint project KOKON and the recently started successing project RoCC, which concentrate on the development of this technology and sensor demonstrators. In both projects, the responsibilities of Daimler AG deal with application based sensor specification, test and evaluation of realized sensor demonstrators. Recent UWB SRR frequency regulation approaches and activitites will be introduced. Furthermore, some first results of Daimler activities within RoCC will be presented, dealing with the packaging and operation of these sensors within the complex car environment.


JackIn: integrating first-person view with out-of-body vision generation for human-human augmentation

JackIn is a new human-human communication framework for connecting two or more people. With first-person view video streaming from a person (called Body) wearing a transparent head-mounted display and a head-mounted camera, the other person (called Ghost) participates in the shared first-person view. With JackIn, people's activities can be shared and assistance or guidance can be given through other peoples expertise. This can be applied to daily activities such as cooking lessons, shopping navigation, education in craft-work or electrical work, and sharing experiences of sporting and live events. For a better viewing experience with frist-person view, we developed the out-of-body view in which first-person images are integrated to construct a scene around a Body, and a Ghost can virtually control the viewpoint to look around the space surrounding the Body. We also developed a tele-pointing gesture interface. We conducted an experiment to evaluate how effective this framework is and found that Ghosts can understand the spatial situation of the Body.


Adaptive Control of an Electrically Driven Nonholonomic Mobile Robot via Backstepping and Fuzzy Approach

This paper investigates the tracking control of an electrically driven nonholonomic mobile robot with model uncertainties in the robot kinematics, the robot dynamics, and the wheel actuator dynamics. A robust adaptive controller is proposed with the utilization of adaptive control, backstepping and fuzzy logic techniques. The proposed control scheme employs the adaptive control approach to design an auxiliary wheel velocity controller to make the tracking error as small as possible in consideration of uncertainties in the kinematics of the robot, and makes use of the fuzzy logic systems to learn the behaviors of the unknown dynamics of the robot and the wheel actuators. The approximation errors and external disturbances can be efficiently counteracted by employing smooth robust compensators. A major advantage of the proposed method is that previous knowledge of the robot kinematics and the dynamics of the robot and wheel actuators is no longer necessary. This is because the controller learns both the robot kinematics and the robot and wheel actuator dynamics online. Most importantly, all signals in the closed-loop system can be guaranteed to be uniformly ultimately bounded. For the dynamic uncertainties of robot and actuator, the assumption of ldquolinearity in the unknown parametersrdquo and tedious analysis of determining the ldquoregression matricesrdquo in the standard adaptive robust controllers are no longer necessary. The performance of the proposed approach is demonstrated through a simulation example.


Fast and accurate analysis of scanning slotted waveguide arrays

An iterative procedure is presented for a fast and accurate analysis of scanning slotted waveguide arrays including the effects of mutual couplings and their dependence on the scanning angle. The analysis takes also into account the input transitions from the feeding network; each slotted waveguide being excited either at one end or at the centre. The procedure is applied to the simple case of a linear array of four slots, for which a full-wave analysis could be carried out for comparison. The proposed method is shown to provide excellent agreement with the full-wave computation still requiring extremely short computation times.


One Sentence One Model for Neural Machine Translation

Neural machine translation (NMT) becomes a new state-ofthe-art and achieves promising translation results using a simple encoder-decoder neural network. This neural network is trained once on the parallel corpus and the fixed network is used to translate all the test sentences. We argue that the general fixed network cannot best fit the specific test sentences. In this paper, we propose the dynamic NMT which learns a general network as usual, and then fine-tunes the network for each test sentence. The fine-tune work is done on a small set of the bilingual training data that is obtained through similarity search according to the test sentence. Extensive experiments demonstrate that this method can significantly improve the translation performance, especially when highly similar sentences are available.


Natural Image Stitching with the Global Similarity Prior

This paper proposes a method for stitching multiple images together so that the stitched image looks as natural as possible. Our method adopts the local warp model and guides the warping of each image with a grid mesh. An objective function is designed for specifying the desired characteristics of the warps. In addition to good alignment and minimal local distortion, we add a global similarity prior in the objective function. This prior constrains the warp of each image so that it resembles a similarity transformation as a whole. The selection of the similarity transformation is crucial to the naturalness of the results. We propose methods for selecting the proper scale and rotation for each image. The warps of all images are solved together for minimizing the distortion globally. A comprehensive evaluation shows that the proposed method consistently outperforms several state-of-the-art methods, including AutoStitch, APAP, SPHP and ANNAP.


A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

Transfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and semantics would benefit each other by being trained in a single model. We introduce a joint many-task model together with a strategy for successively growing its depth to solve increasingly complex tasks. Higher layers include shortcut connections to lower-level task predictions to reflect linguistic hierarchies. We use a simple regularization term to allow for optimizing all model weights to improve one task’s loss without exhibiting catastrophic interference of the other tasks. Our single end-to-end model obtains state-of-the-art or competitive results on five different tasks from tagging, parsing, relatedness, and entailment tasks.


Word Translation Without Parallel Data

State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed encouraging results, they are not on par with their supervised counterparts and are limited to pairs of languages sharing a common alphabet. In this work, we show that we can build a bilingual dictionary between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way. Without using any character information, our model even outperforms existing supervised methods on cross-lingual tasks for some language pairs. Our experiments demonstrate that our method works very well also for distant language pairs, like English-Russian or EnglishChinese. We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation. Our code, embeddings and dictionaries are publicly available1.


Deep Biaffine Attention for Neural Dependency Parsing

Andor, D., Alberti, C., Weiss, D., Severyn, A., Presta, A., Ganchev, K., Petrov, S., and Collins, M. (2016). Globally normalized transition-based neural networks. In Association for Computational Linguistics. Ballesteros, M., Goldberg, Y., Dyer, C., and Smith, N. A. (2016). Training with exploration improves a greedy stack-LSTM parser. Proceedings of the conference on empirical methods in natural language processing. Chen, D. and Manning, C. D. (2014). A fast and accurate dependency parser using neural networks. In Proceedings of the conference on empirical methods in natural language processing, pages 740–750. Cheng, H., Fang, H., He, X., Gao, J., and Deng, L. (2016). Bi-directional attention with agreement for dependency parsing. arXiv preprint arXiv:1608.02076. Hashimoto, K., Xiong, C., Tsuruoka, Y., and Socher, R. (2016). A joint many-task model: Growing a neural network for multiple nlp tasks. arXiv preprint arXiv:1611.01587. Kiperwasser, E. and Goldberg, Y. (2016). Simple and accurate dependency parsing using bidirectional LSTM feature representations. Transactions of the Association for Computational Linguistics, 4:313–327. Kuncoro, A., Ballesteros, M., Kong, L., Dyer, C., Neubig, G., and Smith, N. A. (2016). What do recurrent neural network grammars learn about syntax? CoRR, abs/1611.05774. McDonald, R. T. and Pereira, F. C. (2006). Online learning of approximate dependency parsing algorithms. In EACL. Nivre, J., Hall, J., and Nilsson, J. (2006). Maltparser: A data-driven parser-generator for dependency parsing. In Proceedings of LREC, volume 6, pages 2216–2219.


Dependency-Based Open Information Extraction

Building shallow semantic representations from text corpora is the first step to perform more complex tasks such as text entailment, enrichment of knowledge bases, or question answering. Open Information Extraction (OIE) is a recent unsupervised strategy to extract billions of basic assertions from massive corpora, which can be considered as being a shallow semantic representation of those corpora. In this paper, we propose a new multilingual OIE system based on robust and fast rule-based dependency parsing. It permits to extract more precise assertions (verb-based triples) from text than state of the art OIE systems, keeping a crucial property of those systems: scaling to Web-size document collections.


Constituency Parsing with a Self-Attentive Encoder

We demonstrate that replacing an LSTM encoder with a self-attentive architecture can lead to improvements to a state-ofthe-art discriminative constituency parser. The use of attention makes explicit the manner in which information is propagated between different locations in the sentence, which we use to both analyze our model and propose potential improvements. For example, we find that separating positional and content information in the encoder can lead to improved parsing accuracy. Additionally, we evaluate different approaches for lexical representation. Our parser achieves new state-ofthe-art results for single models trained on the Penn Treebank: 93.55 F1 without the use of any external data, and 95.13 F1 when using pre-trained word representations. Our parser also outperforms the previous best-published accuracy figures on 8 of the 9 languages in the SPMRL dataset.


A novel wireless power transfer for in-motion EV/PHEV charging

Wireless power transfer (WPT) is a convenient, safe, and autonomous means for electric and plug-in hybrid electric vehicle charging that has seen rapid growth in recent years for stationary applications. WPT does not require bulky contacts, plugs, and wires, is not affected by dirt or weather conditions, and is as efficient as conventional charging systems. When applied in-motion, WPT additionally relives range anxiety, adds further convenience, reduces battery size, and may help increase the battery life through charge sustaining approach. This study summarizes some of the recent activities of Oak Ridge National Laboratory (ORNL) in WPT charging of EV and PHEV's inmotion. Laboratory experimental results that highlight the wireless transfer of power to a moving receiver coil as it passes a pair of transmit coils and investigation of results of insertion loss due to roadway surfacing materials. Some of the experimental lessons learned are also included in this study.


Minoxidil-induced trichostasis spinulosa of terminal hair.

depression after pressure application to the skin and a fold after stretching of the skin are clinical signs of CL. To our knowledge, the exclusive acral involvement of CL has been reported in only 7 patients, and 4 of them also had multiple myeloma amyloidosis. Unlike those 4 patients, our patient manifested elastolytic cutaneous lesions as the only clinical sign of his multiple myeloma. The histopathologic findings were the clue for the diagnosis of underlying monoclonal gammopathy. Destruction of elastic fibers could be immune mediated by deposition of abnormal proteins. Wewould like toemphasize the importanceof recognizing cutaneous laxity on fingertips associated with temporary dimpling of the skin for minutes after pressure is applied, which can be the clue for the diagnosis of a monoclonalgammopathy.Clinicopathologiccorrelationisessential; demonstration of amyloid deposits with monotypic expression of light chains and diminution of dermal elastic fibers are the histopathologic clues of these forms of acrolocalized acquired CL associated with multiple myeloma.


Sequential Deep Learning for Disaster-Related Video Classification

Videos serve to convey complex semantic information and ease the understanding of new knowledge. However, when mixed semantic meanings from different modalities (i.e., image, video, text) are involved, it is more difficult for a computer model to detect and classify the concepts (such as flood, storm, and animals). This paper presents a multimodal deep learning framework to improve video concept classification by leveraging recent advances in transfer learning and sequential deep learning models. Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) models are then used to obtain the sequential semantics for both audio and textual models. The proposed framework is applied to a disaster-related video dataset that includes not only disaster scenes, but also the activities that took place during the disaster event. The experimental results show the effectiveness of the proposed framework.


Treatment of Hyaluronic Acid Filler-Induced Impending Necrosis With Hyaluronidase: Consensus Recommendations.

Injection-induced necrosis is a rare but dreaded consequence of soft tissue augmentation with filler agents. It usually occurs as a result of injection of filler directly into an artery, but can also result from compression or injury. We provide recommendations on the use of hyaluronidase when vascular compromise is suspected. Consensus recommendations were developed by thorough discussion and debate amongst the authors at a roundtable meeting on Wednesday June 18, 2014 in Las Vegas, NV as well as significant ongoing written and verbal communications amongst the authors in the months prior to journal submission. All authors are experienced tertiary care providers. A prompt diagnosis and immediate treatment with high doses of hyaluronidase (at least 200 U) are critically important. It is not felt necessary to do a skin test in cases of impending necrosis. Some experts recommend dilution with saline to increase dispersion or lidocaine to aid vasodilation. Additional hyaluronidase should be injected if improvement is not seen within 60 minutes. A warm compress also aids vasodilation, and massage has been shown to help. Some experts advocate the use of nitroglycerin paste, although this area is controversial. Introducing an oral aspirin regimen should help prevent further clot formation due to vascular compromise. In our experience, patients who are diagnosed promptly and treated within 24 hours will usually have the best outcomes.


What is the amygdala?

'Amygdala' and 'amygdalar complex' are terms that now refer to a highly differentiated region near the temporal pole of the mammalian cerebral hemisphere. Cell groups within it appear to be differentiated parts of the traditional cortex, the claustrum, or the striatum, and these parts belong to four obvious functional systems--accessory olfactory, main olfactory, autonomic and frontotemporal cortical. In rats, the central nucleus is a specialized autonomic-projecting motor region of the striatum, whereas the lateral and anterior basolateral nuclei together are a ventromedial extension of the claustrum for major regions of the temporal and frontal lobes. The rest of the amygdala forms association parts of the olfactory system (accessory and main), with cortical, claustral and striatal parts. Terms such as 'amygdala' and 'lenticular nucleus' combine cell groups arbitrarily rather than according to the structural and functional units to which they now seem to belong. The amygdala is neither a structural nor a functional unit.


Mitigating YARN Container Overhead with Input Splits

We analyze YARN container overhead and present early results of reducing its overhead by dynamically adjusting the input split size. YARN is designed as a generic resource manager that decouples programming models from resource management infrastructures. We demonstrate that YARN’s generic design incurs significant overhead because each con- tainer must perform various initialization steps, including authentication. To reduce container overhead without changing the existing YARN framework significantly, we propose leverag- ing the input split, which is the logical representation of physical HDFS blocks. With input splits, we can combine multiple HDFS blocks and increase the input size of each container, thereby enabling a single map wave and reducing the number of containers and their initialization overhead. Experimental results shows that we can avoid recurring container overhead by selecting the right size for input splits and reducing the number of containers.


Online-specific fear of missing out and Internet-use expectancies contribute to symptoms of Internet-communication disorder

Some of the most frequently used online applications are Facebook, WhatsApp, and Twitter. These applications allow individuals to communicate with other users, to share information or pictures, and to stay in contact with friends all over the world. However, a growing number of users suffer from negative consequences due to their excessive use of these applications, which can be referred to as Internet-communication disorder. The frequent use and easy access of these applications may also trigger the individual's fear of missing out on content when not accessing these applications. Using a sample of 270 participants, a structural equation model was analyzed to investigate the role of psychopathological symptoms and the fear of missing out on expectancies towards Internet-communication applications in the development of symptoms of an Internet-communication disorder. The results suggest that psychopathological symptoms predict higher fear of missing out on the individual's Internet-communication applications and higher expectancies to use these applications as a helpful tool to escape from negative feelings. These specific cognitions mediate the effect of psychopathological symptoms on Internet-communication disorder. Our results are in line with the theoretical model by Brand et al. (2016) as they show how Internet-related cognitive bias mediates the relationship between a person's core characteristics (e.g., psychopathological symptoms) and Internet-communication disorder. However, further studies should investigate the role of the fear of missing out as a specific predisposition, as well as specific cognition in the online context.


Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources

The Web has accumulated a rich source of information, such as text, image, rating, etc, which represent different aspects of user preferences. However, the heterogeneous nature of this information makes it difficult for recommender systems to leverage in a unified framework to boost the performance. Recently, the rapid development of representation learning techniques provides an approach to this problem. By translating the various information sources into a unified representation space, it becomes possible to integrate heterogeneous information for informed recommendation.
 In this work, we propose a Joint Representation Learning (JRL) framework for top-N recommendation. In this framework, each type of information source (review text, product image, numerical rating, etc) is adopted to learn the corresponding user and item representations based on available (deep) representation learning architectures. Representations from different sources are integrated with an extra layer to obtain the joint representations for users and items. In the end, both the per-source and the joint representations are trained as a whole using pair-wise learning to rank for top-N recommendation. We analyze how information propagates among different information sources in a gradient-descent learning paradigm, based on which we further propose an extendable version of the JRL framework (eJRL), which is rigorously extendable to new information sources to avoid model re-training in practice.
 By representing users and items into embeddings offline, and using a simple vector multiplication for ranking score calculation online, our framework also has the advantage of fast online prediction compared with other deep learning approaches to recommendation that learn a complex prediction network for online calculation.


The price of power: power seeking and backlash against female politicians.

Two experimental studies examined the effect of power-seeking intentions on backlash toward women in political office. It was hypothesized that a female politician's career progress may be hindered by the belief that she seeks power, as this desire may violate prescribed communal expectations for women and thereby elicit interpersonal penalties. Results suggested that voting preferences for female candidates were negatively influenced by her power-seeking intentions (actual or perceived) but that preferences for male candidates were unaffected by power-seeking intentions. These differential reactions were partly explained by the perceived lack of communality implied by women's power-seeking intentions, resulting in lower perceived competence and feelings of moral outrage. The presence of moral-emotional reactions suggests that backlash arises from the violation of communal prescriptions rather than normative deviations more generally. These findings illuminate one potential source of gender bias in politics.


Recognition of Car Makes and Models From a Single Traffic-Camera Image

This paper proposes the recognition framework of car makes and models from a single image captured by a traffic camera. Due to various configurations of traffic cameras, a traffic image may be captured in different viewpoints and lighting conditions, and the image quality varies in resolution and color depth. In the framework, cars are first detected using a part-based detector, and license plates and headlamps are detected as cardinal anchor points to rectify projective distortion. Car features are extracted, normalized, and classified using an ensemble of neural-network classifiers. In the experiment, the performance of the proposed method is evaluated on a data set of practical traffic images. The results prove the effectiveness of the proposed method in vehicle detection and model recognition.


The turing test track of the 2012 Mario AI Championship: Entries and evaluation

The Turing Test Track of the Mario AI Championship focused on developing human-like controllers for a clone of the popular game Super Mario Bros. Competitors participated by submitting AI agents that imitate human playing style. This paper presents the rules of the competition, the software used, the voting interface, the scoring procedure, the submitted controllers and the recent results of the competition for the year 2012. We also discuss what can be learnt from this competition in terms of believability in platform games. The discussion is supported by a statistical analysis of behavioural similarities and differences among the agents, and between agents and humans. The paper is co-authored by the organizers of the competition (the first three authors) and the competitors.


A cross-national profile of bullying and victimization among adolescents in 40 countries

(1) To compare the prevalence of bullying and victimization among boys and girls and by age in 40 countries. (2) In 6 countries, to compare rates of direct physical, direct verbal, and indirect bullying by gender, age, and country. Cross-sectional self-report surveys including items on bullying and being bullied were obtained from nationally representative samples of 11, 13 and 15 year old school children in 40 countries, N = 202,056. Six countries (N = 29,127 students) included questions about specific types of bullying (e. g., direct physical, direct verbal, indirect). Exposure to bullying varied across countries, with estimates ranging from 8.6% to 45.2% among boys, and from 4.8% to 35.8% among girls. Adolescents in Baltic countries reported higher rates of bullying and victimization, whereas northern European countries reported the lowest prevalence. Boys reported higher rates of bullying in all countries. Rates of victimization were higher for girls in 29 of 40 countries. Rates of victimization decreased by age in 30 of 40 (boys) and 25 of 39 (girls) countries. There are lessons to be learned from the current research conducted in countries where the prevalence is low that could be adapted for use in countries with higher prevalence.


The Flipped Classroom : An Opportunity To Engage Millennial Students Through Active Learning Strategies

"Flipping" the classroom employs easy-to-use, readily accessihle technology in order to free class time from lecture. This allows for an expanded range of learning activities during class time. Using class time for active learning versus lecture provides opportunities for greater teacher-to-student mentoring, peer-to-peer collaboration and cross-disciplinary engagement. This review of literature addresses the challenges of engaging today's students in lecture-based classrooms and presents an argument for application of the "flipped classroom" model hy educators in the disciplines of family and consumer sciences.


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

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


Machine Learning Paradigms for Speech Recognition: An Overview

Automatic Speech Recognition (ASR) has historically been a driving force behind many machine learning (ML) techniques, including the ubiquitously used hidden Markov model, discriminative learning, structured sequence learning, Bayesian learning, and adaptive learning. Moreover, ML can and occasionally does use ASR as a large-scale, realistic application to rigorously test the effectiveness of a given technique, and to inspire new problems arising from the inherently sequential and dynamic nature of speech. On the other hand, even though ASR is available commercially for some applications, it is largely an unsolved problem - for almost all applications, the performance of ASR is not on par with human performance. New insight from modern ML methodology shows great promise to advance the state-of-the-art in ASR technology. This overview article provides readers with an overview of modern ML techniques as utilized in the current and as relevant to future ASR research and systems. The intent is to foster further cross-pollination between the ML and ASR communities than has occurred in the past. The article is organized according to the major ML paradigms that are either popular already or have potential for making significant contributions to ASR technology. The paradigms presented and elaborated in this overview include: generative and discriminative learning; supervised, unsupervised, semi-supervised, and active learning; adaptive and multi-task learning; and Bayesian learning. These learning paradigms are motivated and discussed in the context of ASR technology and applications. We finally present and analyze recent developments of deep learning and learning with sparse representations, focusing on their direct relevance to advancing ASR technology.


Spell Checking Techniques in NLP: A Survey

Spell checkers in Indian languages are the basic tools that need to be developed. A spell checker is a software tool that identifies and corrects any spelling mistakes in a text. Spell checkers can be combined with other applications or they can be distributed individually. In this paper the authors are discussing both the approaches and their roles in various applications.


2CAuth: A New Two Factor Authentication Scheme Using QR-Code

Password based schemes has been the standard means of authentication over decades. Enhancements use entities like ownership (something one possess), knowledge (something one knows), and inherence (something one is) as first factor and mobile phones as token less second factor, in combinations, to offer different levels of security assurances, trading off usability. In this paper we present ‘2CAuth’ a new two factor authentication scheme that enhances secure usage of application information and preserves usability, without sacrificing user’s privacy. A significant feature of the scheme is that it DOES NOT call for any synchronization between Mobile Network Operator (MNO) and users. The analysis of the scheme clearly brings out its effectiveness in terms of its usability even at times of peak loads on mobile networks. The scheme has the dual advantage of carrying out the verification of transactions which involve the physical presence of the user as well as those to be done in his absence.


Ultraflat Interleaved Triangular Current Mode (TCM) Single-Phase PFC Rectifier

This paper presents the analysis and realization of a topology suitable to realize a power factor correction (PFC) rectifier with a thickness of only a few millimeters. The low height of the converter requires all components to be integrated into the printed circuit board (PCB). Still reasonable dimensions of the converter PCB are feasible (221 mm × 157 mm for a 200 W PFC rectifier), since PCB-integrated inductors and capacitors allow for high energy densities due to their large surface area which facilitates a low thermal resistance to ambient. A multicell totem-pole PFC rectifier employing a soft-switching modulation scheme over the complete mains period is identified as an adequate topology. The mode of operation is entitled triangular current mode (TCM) due to the triangular-shaped inductor currents. The modulation technique requires a reliable description of the switching transition of a half-bridge in order to provide accurate timing parameters. For this purpose, a simplified model of the nonlinear MOSFETs' output capacitances facilitates closed-form analytical expressions for duty cycle and switching frequency. Furthermore, this paper details the control of three interleaved converter cells which yields a reduction of the input current ripple. A 200 W TCM PFC rectifier with a low height of 5 mm has been realized and measurement results are provided in order to validate the theoretical considerations. The presented TCM PFC rectifier achieves an efficiency of 94.6% and a power factor of 99.3% at nominal power.


Detecting False Data Injection Attacks on DC State Estimation

• Y. Liu, M. K. Reiter, and P. Ning. False Data Injection Attacks Against State Estimation in Electric Power Grids. In 16th ACM Conference on Computer and Communications Security (CCS ’09). Defense Against False Data Injection Attacks on DC State Estimation Miao Lu, University of Illinois at Urbana-Champaign ADVISORS: Rakesh Bobba, Kate Morrow I T I S U M M E R U N D E R G R A D U A T E I N T E R N P R O G R A M, 2 0 1 1


EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals

Generative adversarial networks (GANs) are recently highly successful in generative applications involving images and start being applied to time series data. Here we describe EEG-GAN as a framework to generate electroencephalographic (EEG) brain signals. We introduce a modification to the improved training of Wasserstein GANs to stabilize training and investigate a range of architectural choices critical for time series generation (most notably upand down-sampling). For evaluation we consider and compare different metrics such as Inception score, Frechet inception distance and sliced Wasserstein distance, together showing that our EEG-GAN framework generated naturalistic EEG examples. It thus opens up a range of new generative application scenarios in the neuroscientific and neurological context, such as data augmentation in brain-computer interfacing tasks, EEG super-sampling, or restoration of corrupted data segments. The possibility to generate signals of a certain class and/or with specific properties may also open a new avenue for research into the underlying structure of brain signals.


Image inpainting

Inpainting, the technique of modifying an image in an undetectable form, is as ancient as art itself. The goals and applications of inpainting are numerous, from the restoration of damaged paintings and photographs to the removal/replacement of selected objects. In this paper, we introduce a novel algorithm for digital inpainting of still images that attempts to replicate the basic techniques used by professional restorators. After the user selects the regions to be restored, the algorithm automatically fills-in these regions with information surrounding them. The fill-in is done in such a way that isophote lines arriving at the regions' boundaries are completed inside. In contrast with previous approaches, the technique here introduced does not require the user to specify where the novel information comes from. This is automatically done (and in a fast way), thereby allowing to simultaneously fill-in numerous regions containing completely different structures and surrounding backgrounds. In addition, no limitations are imposed on the topology of the region to be inpainted. Applications of this technique include the restoration of old photographs and damaged film; removal of superimposed text like dates, subtitles, or publicity; and the removal of entire objects from the image like microphones or wires in special effects.


An Introduction to Variational Methods for Graphical Models

This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields). We present a number of examples of graphical models, including the QMR-DT database, the sigmoid belief network, the Boltzmann machine, and several variants of hidden Markov models, in which it is infeasible to run exact inference algorithms. We then introduce variational methods, which exploit laws of large numbers to transform the original graphical model into a simplified graphical model in which inference is efficient. Inference in the simpified model provides bounds on probabilities of interest in the original model. We describe a general framework for generating variational transformations based on convex duality. Finally we return to the examples and demonstrate how variational algorithms can be formulated in each case.


Texture Synthesis by Non-parametric Sampling

A non-parametric method for texture synthesis is proposed. The texture synthesis process grows a new image outward from an initial seed, one pixel at a time. A Markov random field model is assumed, and the conditional distribution of a pixel given all its neighbors synthesized so far is estimated by querying the sample image and finding all similar neighborhoods. The degree of randomness is controlled by a single perceptually intuitive parameter. The method aims at preserving as much local structure as possible and produces good results for a wide variety of synthetic and real-world textures.


Nonlinear total variation based noise removal algorithms

A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using the gradient-projection method. This amounts to solving a time dependent partial differential equation on a manifold determined by the constraints. As t → ∞ the solution converges to a steady state which is the denoised image. The numerical algorithm is simple and relatively fast. The results appear to be state-of-the-art for very noisy images. The method is noninvasive, yielding sharp edges in the image. The technique could be interpreted as a first step of moving each level set of the image normal to itself with velocity equal to the curvature of the level set divided by the magnitude of the gradient of the image, and a second step which projects the image back onto the constraint set.


Google and the scholar: the role of Google in scientists' information-seeking behaviour

Purpose – This paper aims to demonstrate the role that the Google general search engine plays in the information-seeking behaviour of scientists, particularly physicists and astronomers. Design/methodology/approach – The paper is based on a mixed-methods study including 56 semi-structured interviews, a questionnaire survey of 114 respondents (47 per cent response rate) and the use of information-event cards to collect critical incident data. The study was conducted at the Department of Physics and Astronomy at University College, London. Findings – The results show that Google is the tool most used for problem-specific information seeking. The results also show the growing reliance of scientists on general search engines, particularly Google, for finding scholarly articles. Initially, finding scholarly articles was a by-product of general searching for information rather than focused searches for papers. However, a growing number of articles read by scientists are identified through the Google general search engine and, as scientists are becoming more aware of the quantity of scholarly papers searchable by Google, they are increasingly relying on Google for finding scholarly literature. Research limitations/implications – As the only fields covered in the study were physics and astronomy, and the research participants were sourced from just one department of one institution, caution should be taken in generalising the findings. Originality/value – The data are based on a mixed-methods in-depth study of scientists’ information-seeking behaviour which sheds some light on a question raised in past studies relating to the reason for the high number of articles identified through Google.


Character-level incremental speech recognition with recurrent neural networks

In real-time speech recognition applications, the latency is an important issue. We have developed a character-level incremental speech recognition (ISR) system that responds quickly even during the speech, where the hypotheses are gradually improved while the speaking proceeds. The algorithm employs a speech-to-character unidirectional recurrent neural network (RNN), which is end-to-end trained with connectionist temporal classification (CTC), and an RNN-based character-level language model (LM). The output values of the CTC-trained RNN are character-level probabilities, which are processed by beam search decoding. The RNN LM augments the decoding by providing long-term dependency information. We propose tree-based online beam search with additional depth-pruning, which enables the system to process infinitely long input speech with low latency. This system not only responds quickly on speech but also can dictate out-of-vocabulary (OOV) words according to pronunciation. The proposed model achieves the word error rate (WER) of 8.90% on the Wall Street Journal (WSJ) Nov'92 20K evaluation set when trained on the WSJ SI-284 training set.


A novel approach to CAD system for the detection of lung nodules in CT images

Detection of pulmonary nodule plays a significant role in the diagnosis of lung cancer in early stage that improves the chances of survival of an individual. In this paper, a computer aided nodule detection method is proposed for the segmentation and detection of challenging nodules like juxtavascular and juxtapleural nodules. Lungs are segmented from computed tomography (CT) images using intensity thresholding; brief analysis of CT image histogram is done to select a suitable threshold value for better segmentation results. Simple morphological closing is used to include juxtapleural nodules in segmented lung regions. K-means clustering is applied for the initial detection and segmentation of potential nodules; shape specific morphological opening is implemented to refine segmentation outcomes. These segmented potential nodules are then divided into six groups on the basis of their thickness and percentage connectivity with lung walls. Grouping not only helped in improving system's efficiency but also reduced computational time, otherwise consumed in calculating and analyzing unnecessary features for all nodules. Different sets of 2D and 3D features are extracted from nodules in each group to eliminate false positives. Small size nodules are differentiated from false positives (FPs) on the basis of their salient features; sensitivity of the system for small nodules is 83.33%. SVM classifier is used for the classification of large nodules, for which the sensitivity of the proposed system is 93.8% applying 10-fold cross-validation. Receiver Operating Characteristic (ROC) curve is used for the analysis of CAD system. Overall sensitivity of the system is 91.65% with 3.19 FPs per case, and accuracy is 96.22%. The system took 3.8 seconds to analyze each image.


Optimal Online Cyberbullying Detection

Cyberbullying has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyber-bullying instances in an effort to mitigate the consequences. While techniques to automatically detect cyberbullying incidents have been developed, the scalability and timeliness of existing cyberbullying detection approaches have largely been ignored. We address this gap by formulating cyberbullying detection as a sequential hypothesis testing problem. Based on this formulation, we propose a novel algorithm designed to reduce the time to raise a cyberbullying alert by drastically reducing the number of feature evaluations necessary for a decision to be made. We demonstrate the effectiveness of our approach using a real-world dataset from Twitter, one of the top five networks with the highest percentage of users reporting cyberbullying instances. We show that our approach is highly scalable while not sacrificing accuracy for scalability.


Oral-motor function and feeding intervention.

This article presents the elements of the Oral Motor Intervention section of the Infant Care Path for Physical Therapy in the Neonatal Intensive Care Unit (NICU). The types of physical therapy interventions presented in this path are evidence based as well as infant driven and family focused. In the context of anticipated maturation of suck-swallow-breathe coordination, the timing and methods for initiation of oral feedings and transition from gavage to full breast or bottle-feedings are presented with supporting evidence.