Query: Incremental visual text analytics of news story development

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TIARA: a visual exploratory text analytic system <=SEP=> In this paper, we present a novel exploratory visual analytic system called TIARA (Text Insight via Automated Responsive Analytics), which combines text analytics and interactive visualization to help users explore and analyze large collections of text. Given a collection of documents, TIARA first uses topic analysis techniques to summarize the documents into a set of topics, each of which is represented by a set of keywords. In addition to extracting topics, TIARA derives time-sensitive keywords to depict the content evolution of each topic over time. To help users understand the topic-based summarization results, TIARA employs several interactive text visualization techniques to explain the summarization results and seamlessly link such results to the original text. We have applied TIARA to several real-world applications, including email summarization and patient record analysis. To measure the effectiveness of TIARA, we have conducted several experiments. Our experimental results and initial user feedback suggest that TIARA is effective in aiding users in their exploratory text analytic tasks.

Dynamic topic models <=SEP=> A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics. Variational approximations based on Kalman filters and nonparametric wavelet regression are developed to carry out approximate posterior inference over the latent topics. In addition to giving quantitative, predictive models of a sequential corpus, dynamic topic models provide a qualitative window into the contents of a large document collection. The models are demonstrated by analyzing the OCR'ed archives of the journal Science from 1880 through 2000.

EventRiver: Visually Exploring Text Collections with Temporal References <=SEP=> Many text collections with temporal references, such as news corpora and weblogs, are generated to report and discuss real life events. Thus, event-related tasks, such as detecting real life events that drive the generation of the text documents, tracking event evolutions, and investigating reports and commentaries about events of interest, are important when exploring such text collections. To incorporate and leverage human efforts in conducting such tasks, we propose a novel visual analytics approach named EventRiver. EventRiver integrates event-based automated text analysis and visualization to reveal the events motivating the text generation and the long term stories they construct. On the visualization, users can interactively conduct tasks such as event browsing, tracking, association, and investigation. A working prototype of EventRiver has been implemented for exploring news corpora. A set of case studies, experiments, and a preliminary user test have been conducted to evaluate its effectiveness and efficiency.

Evolutionary hierarchical dirichlet processes for multiple correlated time-varying corpora <=SEP=> Mining cluster evolution from multiple correlated time-varying text corpora is important in exploratory text analytics. In this paper, we propose an approach called evolutionary hierarchical Dirichlet processes (EvoHDP) to discover interesting cluster evolution patterns from such text data. We formulate the EvoHDP as a series of hierarchical Dirichlet processes~(HDP) by adding time dependencies to the adjacent epochs, and propose a cascaded Gibbs sampling scheme to infer the model. This approach can discover different evolving patterns of clusters, including emergence, disappearance, evolution within a corpus and across different corpora. Experiments over synthetic and real-world multiple correlated time-varying data sets illustrate the effectiveness of EvoHDP on discovering cluster evolution patterns.

The Word Tree, an Interactive Visual Concordance <=SEP=> We introduce the Word Tree, a new visualization and information-retrieval technique aimed at text documents. A Word Tree is a graphical version of the traditional "keyword-in-context" method, and enables rapid querying and exploration of bodies of text. In this paper we describe the design of the technique, along with some of the technical issues that arise in its implementation. In addition, we discuss the results of several months of public deployment of word trees on Many Eyes, which provides a window onto the ways in which users obtain value from the visualization.

Total variation regularization of local-global optical flow <=SEP=> More data fidelity terms in variational optical flow methods improve the estimation's robustness. A robust and anisotropic smoother enhances the specific fill-in process. This work presents a combined local-global (CLG) approach with total variation regularization. The combination of bilateral filtering and anisotropic (image driven) regularization is used to control the propagation phenomena. The resulted method, CLG-TV, is able to compute larger displacements in a reasonable time. The numerical scheme is highly parallelizable and runs in real-time on current generation graphic processing units.

Learning intermediate object affordances: Towards the development of a tool concept <=SEP=> Inspired by the extraordinary ability of young infants to learn how to grasp and manipulate objects, many works in robotics have proposed developmental approaches to allow robots to learn the effects of their own motor actions on objects, i.e., the objects affordances. While holding an object, infants also promote its contact with other objects, resulting in object-object interactions that may afford effects not possible otherwise. Depending on the characteristics of both the held object (intermediate) and the acted object (primary), systematic outcomes may occur, leading to the emergence of a primitive concept of tool. In this paper we describe experiments with a humanoid robot exploring object-object interactions in a playground scenario and learning a probabilistic causal model of the effects of actions as functions of the characteristics of both objects. The model directly links the objects' 2D shape visual cues to the effects of actions. Because no object recognition skills are required, generalization to novel objects is possible by exploiting the correlations between the shape descriptors. We show experiments where an affordance model is learned in a simulated environment, and is then used on the real robotic platform, showing generalization abilities in effect prediction. We argue that, despite the fact that during exploration no concept of tool is given to the system, this very concept may emerge from the knowledge that intermediate objects lead to significant effects when acting on other objects.

Stereovision-Based Object Segmentation for Automotive Applications <=SEP=> Obstacle detection and classification in a complex urban area are highly demanding, but desirable for pedestrian protection, stop & go, and enhanced parking aids. The most difficult task for the system is to segment objects from varied and complicated background. In this paper, a novel position-based object segmentationmethod has been proposed to solve this problem. According to the method proposed, object segmentation is performed in two steps: in depth map (X-Z plane) and in layered images (X-Y planes). The stereovision technique is used to reconstruct image points and generate the depth map. Objects are detected in the depth map. Afterwards, the original edge image is separated into different layers based on the distance of detected objects. Segmentation performed in these layered images can be easier and more reliable. It has been proved that the proposed method offers robust detection of potential obstacles and accurate measurement of their location and size.

Attacks on Copyright Marking Systems <=SEP=> In the last few years, a large number of schemes have been proposed for hiding copyright marks and other information in digital pictures, video, audio and other multimedia objects. We describe some contenders that have appeared in the research literature and in the field; we then present a number of attacks that enable the information hidden by them to be removed or otherwise rendered unusable. 1 Information Hiding Applications The last few years have seen rapidly growing interest in ways to hide information in other information. A number of factors contributed to this. Fears that copyright would be eroded by the ease with which digital media could be copied led people to study ways of embedding hidden copyright marks and serial numbers in audio and video; concern that privacy would be eroded led to work on electronic cash, anonymous remailers, digital elections and techniques for making mobile computer users harder for third parties to trace; and there remain the traditional ‘military’ concerns about hiding one’s own traffic while making it hard for the opponent to do likewise. The first international workshop on information hiding [2] brought these communities together and a number of hiding schemes were presented there; more have been presented elsewhere. We formed the view that useful progress in steganography and copyright marking might come from trying to attack all these first generation schemes. In the related field of cryptology, progress was iterative: cryptographic algorithms were proposed, attacks on them were found, more algorithms were proposed, and so on. Eventually, theory emerged: fast correlation attacks on stream ciphers and differential and linear attacks on block ciphers, now help us understand the strength of cryptographic algorithms in much more detail than before. Similarly, many cryptographic protocols were proposed and almost all the early candidates were broken, leading to concepts of protocol robustness and techniques for formal verification [6]. So in this paper, we first describe the copyright protection context in which most recent schemes have been developed; we then describe a selection of these ? The first author is grateful to Intel Corporation for financial support under the grant ‘Robustness of Information Hiding Systems’ ?? The third author is supported by a European Commission Marie-Curie grant David Aucsmith (Ed.): Information Hiding 1998, LNCS 1525, pp. 218–238, 1998. c © Springer-Verlag Berlin Heidelberg 1998 Attacks on Copyright Marking Systems 219 schemes and present a number of attacks, which break most of them. We finally make some remarks on the meaning of robustness in the context of steganography in general and copyright marking in particular. 1.1 Copyright Protection Issues Digital recording media offer many new possibilities but their uptake has been hindered by widespread fears among intellectual property owners such as Hollywood and the rock music industry that their livelihoods would be threatened if users could make unlimited perfect copies of videos, music and multimedia works. One of the first copy protection mechanisms for digital media was the serial copy management system (SCMS) introduced by Sony and Phillips for digital audio tapes in the eighties [31]. The idea was to allow consumers to make a digital audio tape of a CD they owned in order to use it (say) in their car, but not to make a tape of somebody else’s tape; thus copies would be limited to first generation only. The implementation was to include a Boolean marker in the header of each audio object. Unfortunately this failed because the hardware produced by some manufacturers did not enforce it. More recently the Digital Video Disk, also known as Digital Versatile Disk (DVD) consortium called for proposals for a copyright marking scheme to enforce serial copy management. The idea is that the DVD players sold to consumers will allow unlimited copying of home videos and time-shifted viewing of TV programmes, but cannot easily be abused for commercial piracy [19, 44]. The proposed implementation is that videos will be unmarked, or marked ‘never copy’, or ‘copy once only’; compliant players would not record a video marked ‘never copy’ and when recording one marked ‘copy once only’ would change its mark to ‘never copy’. Commercially sold videos would be marked ‘never copy’, while TV broadcasts and similar material would be marked ‘copy once only’ and home videos would be unmarked. Electronic copyright management schemes have also been proposed by European projects such as Imprimatur and CITED [45, 66, 67], and American projects such as the proposed by the Working Group on Intellectual Property Rights [69].

1 An End-to-End Framework for Evaluating Surface Reconstruction <=SEP=> We present a benchmark for the evaluation and comparison of algorithms which reconstruct a surface from point cloud data. Although a substantial amount of effort has been dedicated to the problem of surface reconstruction, a comprehensive means of evaluating this class of algorithms is noticeably absent. We propose a simple pipeline for measuring surface reconstruction algorithms, consisting of three main phases: surface modeling, sampling, and evaluation.We employ implicit surfaces for modeling shapes which are expressive enough to contain details of varying size, in addition to preserving sharp features. From these implicit surfaces, we produce point clouds by synthetically generating range scans which resemble realistic scan data. We validate our synthetic sampling scheme by comparing against scan data produced via a commercial optical laser scanner, wherein we scan a 3D-printed version of the original implicit surface. Last, we perform evaluation by comparing the output reconstructed surface to a dense uniformly-distributed sampling of the implicit surface. We decompose our benchmark into two distinct sets of experiments. The first set of experiments measures reconstruction against point clouds of complex shapes sampled under a wide variety of conditions. Although these experiments are quite useful for the comparison of surface reconstruction algorithms, they lack a fine-grain analysis. Hence to complement this, the second set of experiments are designed to measure specific properties of surface reconstruction, both from a sampling and surface modeling viewpoint. Together, these experiments depict a detailed examination of the state of surface reconstruction algorithms. An End-to-End Framework for Evaluating Surface Reconstruction

DPIL@FIRE2016: Overview of the Shared task on Detecting Paraphrases in Indian language <=SEP=> This paper explains the overview of the shared task "Detecting Paraphrases in Indian Languages" (DPIL) conducted at FIRE 2016. Given a pair of sentences in the same language, participants are asked to detect the semantic equivalence between the sentences. The shared task is proposed for four Indian languages namely Tamil, Malayalam, Hindi and Punjabi. The dataset created for the shared task has been made available online and it is the first open-source paraphrase detection corpora for Indian languages.

A comparison of trading algorithms based on machine learning classifiers : application on the S & P 500 <=SEP=> In this paper we analyse whether different machine learning classifiers can be useful when it comes to financial data prediction. We first measure the accuracy and the volatility of our algorithm on the stocks included in the S&P 500 stock index. We then back-test it against the same benchmark for two different periods. Overall, our results show that an algorithm that trades just according to the predictions of the classifiers, is not profitable even if the accuracy obtained by the algorithm is higher than the accuracy that should be obtained according to the random walk theory. However, it can boost the results obtained by other strategies, maintaining the same level of volatility. JEL classification: C4, C8, G14.

Visual Perception of Parallel Coordinate Visualizations <=SEP=> Parallel coordinates is a visualization technique that provides an unbiased representation of high-dimensional data. The parallel configuration of axes treats data dimensions uniformly and is well suited for exploratory visualization. However, first-time users of parallel coordinate visualizations can find the representation confusing and difficult to understand.We used eye tracking to study how parallel coordinate visualizations are perceived, and compared the results to the optimal visual scan path required to complete the tasks. The results indicate that even first-time users quickly learn how to use parallel coordinate visualizations, pay attention to the correct task-specific areas in the visualization, and become rapidly proficient with it.

Attentive Explanations: Justifying Decisions and Pointing to the Evidence <=SEP=> Deep models are the defacto standard in visual decision problems due to their impressive performance on a wide array of visual tasks. However, they are frequently seen as opaque and are unable to explain their decisions. In contrast, humans can justify their decisions with natural language and point to the evidence in the visual world which supports their decisions. We propose a method which incorporates a novel explanation attention mechanism; our model is trained using textual rationals, and infers latent attention to visually ground explanations. We collect two novel datasets in domains where it is interesting and challenging to explain decisions. First, we extend the visual question answering task to not only provide an answer but also visual and natural language explanations for the answer. Second, we focus on explaining human activities in a contemporary activity recognition dataset. We extensively evaluate our model, both on the justification and pointing tasks, by comparing it to prior models and ablations using both automatic and human evaluations.

A chaincode based scheme for fingerprint feature extraction <=SEP=> A feature extraction method using the chaincode representation of fingerprint ridge contours is presented for use by Automatic Fingerprint Identification Systems. The representation allows efficient image quality enhancement and detection of fine minutiae feature points. For image enhancement a given fingerprint image is first binarized after a quick averaging to generate its chaincode representation. The direction field is estimated from a set of selected chaincodes. The original gray scale image is then enhanced using connected component analysis and a dynamic filtering scheme that takes advantage of the knowledge gained from the estimated direction flow of the contours. For feature extraction, the enhanced fingerprint image is carefully binarized using a local-global binarization algorithm for generating the chaincode representation. The minutiae are generated using a sophisticated ridge contour following procedure. Visual inspection of the experiment images shows that the method is very effective.

Multi-class AdaBoost ∗ <=SEP=> Boosting has been a very successful technique for solving the two-class classification problem. In going from two-class to multi-class classification, most algorithms have been restricted to reducing the multi-class classification problem to multiple two-class problems. In this paper, we develop a new algorithm that directly extends the AdaBoost algorithm to the multi-class case without reducing it to multiple two-class problems. We show that the proposed multi-class AdaBoost algorithm is equivalent to a forward stagewise additive modeling algorithm that minimizes a novel exponential loss for multi-class classification. Furthermore, we show that the exponential loss is a member of a class of Fisher-consistent loss functions for multi-class classification. As shown in the paper, the new algorithm is extremely easy to implement and is highly competitive in terms of misclassification error rate.

Model-Based Stabilisation of Deep Reinforcement Learning <=SEP=> Though successful in high-dimensional domains, deep reinforcement learning exhibits high sample complexity and suffers from stability issues as reported by researchers and practitioners in the field. These problems hinder the application of such algorithms in real-world and safety-critical scenarios. In this paper, we take steps towards stable and efficient reinforcement learning by following a model-based approach that is known to reduce agent-environment interactions. Namely, our method augments deep Q-networks (DQNs) with model predictions for transitions, rewards, and termination flags. Having the model at hand, we then conduct a rigorous theoretical study of our algorithm and show, for the first time, convergence to a stationary point. En route, we provide a counterexample showing that ’vanilla’ DQNs can diverge confirming practitioners’ and researchers’ experiences. Our proof is novel in its own right and can be extended to other forms of deep reinforcement learning. In particular, we believe exploiting the relation between reinforcement (with deep function approximators) and online learning can serve as a recipe for future proofs in the domain. Finally, we validate our theoretical results in 20 games from the Atari benchmark. Our results show that following the proposed model-based learning approach not only ensures convergence but leads to a reduction in sample complexity and superior performance. Introduction Model-free deep reinforcement learning methods have recently demonstrated impressive performance on a range of complex learning tasks (Hessel et al. 2018; Lillicrap et al. 2017; Jaderberg et al. 2017). Deep Q-Networks (DQNs) (Mnih et al. 2015), in particular, stand out as a versatile and effective tool for a wide range of applications. DQNs offer an end-to-end solution for many reinforcement learning problems by generalizing tabular Q-learning to highdimensional input spaces. Unfortunately, DQNs still suffer from a number of important drawbacks that hinder wide-spread adoption. There is a general lack of theoretical understanding to guide the use of deep Q-learning algorithms. Combining non-linear function approximation with off-policy reinforcement learning is known to lead to possible divergence issues, even in very simple tasks (Boyan and Preliminary work. Moore 1995). Despite improvements introduced by deep Qnetworks (Mnih et al. 2015), Q-learning approaches based on deep neural networks still exhibit occasional instability during the learning process, see for example (van Hasselt, Guez, and Silver 2016). In this paper, we aim to address the instability of current deep RL approaches by augmenting model-free value learning with a predictive model. We propose a hybrid model and value learning objective, which stabilises the learning process and reduces sample complexity when compared to model-free algorithms. We attribute this reduction to the richer feedback signal provided by our method compared to standard deep Q-networks. In particular, our model provides feedback at every time step based on the current prediction error, which in turn eliminates one source of sample inefficiency caused by sparse rewards. These conclusions are also in accordance with previous research conducted on linear function approximation in reinforcement learning (Parr et al. 2008; Sutton et al. 2012; Song et al. 2016). While linear function approximation results provide a motivation for model-based stabilisation, such theories fail to generalise to deep non-linear architectures (Song et al. 2016). To close this gap in the literature and demonstrate stability of our method, we prove convergence in the general deep RL setting with deep value function approximators. Theoretically analysing deep RL algorithms, however, is challenging due to nonlinear functional dependencies and objective non-convexities prohibiting convergence to globally optimal policies. As such, the best one would hope for is a stationary point (e.g., a first-order one) that guarantees vanishing gradients. Even understanding gradient behavior for deep networks in RL can be difficult due to explorationbased policies and replay memory considerations. To alleviate some of these problems, we map deep reinforcement learning to a mathematical framework explicitly designed for understanding the exploration/exploitation trade-off. Namely, we formalise the problem as an online learning game the agent plays against an adversary (i.e., the environment). Such a link allows us to study a more general problem combining notions of regret, reinforcement learning, and optimisation. Given such a link, we prove that for any > 0, regret vanishes as O ( T 1−2 lnT ) with T being the total number of rounds. This, in turn, guarantees convergence to a stationary point. ar X iv :1 80 9. 01 90 6v 1 [ cs .L G ] 6 S ep 2 01 8 Guided by the above theoretical results, we validate that our algorithm leads to faster learning by conducting experiments on 20 Atari games. Due to the high computational and budgeting demands, we chose a benchmark that is closest to our setting. Concretely, we picked DQNs as our template algorithm to improve on both theoretically and empirically. It is worth noting that extending our theoretical results to more complex RL algorithms is an orthogonal future direction. Background: Reinforcement Learning We consider a discrete time, infinite horizon, discounted Markov Decision Process (MDP) 〈 S,A, P , P , γ 〉 . Here S denotes the state space, A the set of possible actions, P (R) : S × A × R → [0, 1] is the reward function, P (S) : S × A × S → [0, 1] is the state transition function, and γ ∈ (0, 1) is a discount factor. An agent, being in state St ∈ S at time step t, executes an action at ∈ A sampled from its policy π(at|St), a conditional probability distribution where π : S × A → [0, 1]. The agent’s action elicits from the environment a reward signal rt ∈ R indicating instantaneous reward, a terminal flag ft ∈ {0, 1} indicating a terminal event that restarts the environment, and a transition to a successor state St+1 ∈ S. We assume that the sets S, A, and R are discrete. The reward rt is sampled from the conditional probability distribution P (rt|St,at). Similarly, ft ∼ P (F (ft|St,at) with P (F ) : S × A × {0, 1} → [0, 1], where a terminal event (ft = 1) restarts the environment according to some initial state distribution S0 ∼ P (S) 0 (S0). The state transition to a successor state is determined by a stochastic state transition function according to St+1 ∼ P (St+1|St,at). The agent’s goal is to maximise future cumulative reward E P (S) 0 ,π,P (R),P (F ),P (S) [ ∞ ∑ t=0 ( t ∏ t′=0 (1− ft′−1) ) γrt ] with respect to the policy π. An important quantity in RL are Q-values Q(St,at), which are defined as the expected future cumulative reward value when executing action at in state St and subsequently following policy π. Qvalues enable us to conveniently phrase the RL problem as maxπ ∑ S p (S) ∑ a π(a|S)Q(S,a), where p(S) is the stationary state distribution obtained when executing π in the environment (starting from S0 ∼ P (S) 0 (S0)). Deep Q-Networks: Value-based reinforcement learning approaches identify optimal Q-values directly using parametric function approximators Qθ(S,a), where θ ∈ Θ represents the parameters (Watkins 1989; Busoniu et al. 2010). Optimal Q-value estimates then correspond to an optimal policy π(a|S) = δa,argmaxa′Qθ(S,a′). Deep Q-networks (Mnih et al. 2015) learn a deep neural network based Qvalue approximation by performing stochastic gradient descent on the following training objective: L(θ) = ES,a,r,f,S′ [( r + (1− f)γmax a′ Qθ−(S ′,a′) −Qθ(S,a) )2] . (1) The expectation ranges over transitions S,a, r, f,S′ sampled from an experience replay memory (S′ denotes the state at the next time step). Use of this replay memory, together with the use of a separate target network Qθ− (with different parameters θ− ∈ Θ) for calculating the bootstrap values maxa′ Qθ−(S ′,a′), helps stabilise the learning process. Background: Online Learning In this paper, we employ a special form of regret minimisation games that we briefly review here. A regret minimisation game is a triple 〈Θ,F , T 〉, where Θ is a non-empty decision set, F is the set of moves of the adversary which contains bounded convex functions from R to R and T is the total number of rounds. The game commences in rounds, where at round j = 1, . . . , T , the agent chooses a prediction θj ∈ Θ and the adversary a loss function Lj ∈ F . At the end of the round, the adversary reveals its choice and the agent suffers a loss Lj(θj). In this paper, we are concerned with the full-information case where the agent can observe the complete loss function Lj at the end of each round. The goal of the game is for the agent to make successive predictions to minimise cumulative regret defined as:

Membrane Bioreactor (MBR) Technology for Wastewater Treatment and Reclamation: Membrane Fouling <=SEP=> The membrane bioreactor (MBR) has emerged as an efficient compact technology for municipal and industrial wastewater treatment. The major drawback impeding wider application of MBRs is membrane fouling, which significantly reduces membrane performance and lifespan, resulting in a significant increase in maintenance and operating costs. Finding sustainable membrane fouling mitigation strategies in MBRs has been one of the main concerns over the last two decades. This paper provides an overview of membrane fouling and studies conducted to identify mitigating strategies for fouling in MBRs. Classes of foulants, including biofoulants, organic foulants and inorganic foulants, as well as factors influencing membrane fouling are outlined. Recent research attempts on fouling control, including addition of coagulants and adsorbents, combination of aerobic granulation with MBRs, introduction of granular materials with air scouring in the MBR tank, and quorum quenching are presented. The addition of coagulants and adsorbents shows a significant membrane fouling reduction, but further research is needed to establish optimum dosages of the various coagulants/adsorbents. Similarly, the integration of aerobic granulation with MBRs, which targets biofoulants and organic foulants, shows outstanding filtration performance and a significant reduction in fouling rate, as well as excellent nutrients removal. However, further research is needed on the enhancement of long-term granule integrity. Quorum quenching also offers a strong potential for fouling control, but pilot-scale testing is required to explore the feasibility of full-scale application.

BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 <=SEP=> We introduce BinaryNet, a method which trains DNNs with binary weights and activations when computing parameters’ gradient. We show that it is possible to train a Multi Layer Perceptron (MLP) on MNIST and ConvNets on CIFAR-10 and SVHN with BinaryNet and achieve nearly state-of-the-art results. At run-time, BinaryNet drastically reduces memory usage and replaces most multiplications by 1-bit exclusive-not-or (XNOR) operations, which might have a big impact on both general-purpose and dedicated Deep Learning hardware. We wrote a binary matrix multiplication GPU kernel with which it is possible to run our MNIST MLP 7 times faster than with an unoptimized GPU kernel, without suffering any loss in classification accuracy. The code for BinaryNet is available.

Lagrangian Texture Advection: Preserving both Spectrum and Velocity Field <=SEP=> Texturing an animated fluid is a useful way to augment the visual complexity of pictures without increasing the simulation time. But texturing flowing fluids is a complex issue, as it creates conflicting requirements: we want to keep the key texture properties (features, spectrum) while advecting the texture with the underlying flow-which distorts it. In this paper, we present a new, Lagrangian, method for advecting textures: the advected texture is computed only locally and follows the velocity field at each pixel. The texture retains its local properties, including its Fourier spectrum, even though it is accurately advected. Due to its Lagrangian nature, our algorithm can perform on very large, potentially infinite scenes in real time. Our experiments show that it is well suited for a wide range of input textures, including, but not limited to, noise textures.

Producing radiologist-quality reports for interpretable artificial intelligence <=SEP=> Current approaches to explaining the decisions of deep learning systems for medical tasks have focused on visualising the elements that have contributed to each decision. We argue that such approaches are not enough to “open the black box” of medical decision making systems because they are missing a key component that has been used as a standard communication tool between doctors for centuries: language. We propose a model-agnostic interpretability method that involves training a simple recurrent neural network model to produce descriptive sentences to clarify the decision of deep learning classifiers. We test our method on the task of detecting hip fractures from frontal pelvic x-rays. This process requires minimal additional labelling despite producing text containing elements that the original deep learning classification model was not specifically trained to detect. The experimental results show that: 1) the sentences produced by our method consistently contain the desired information, 2) the generated sentences are preferred by doctors compared to current tools that create saliency maps, and 3) the combination of visualisations and generated text is better than either alone.

On Divergences and Informations in Statistics and Information Theory <=SEP=> The paper deals with the f-divergences of Csiszar generalizing the discrimination information of Kullback, the total variation distance, the Hellinger divergence, and the Pearson divergence. All basic properties of f-divergences including relations to the decision errors are proved in a new manner replacing the classical Jensen inequality by a new generalized Taylor expansion of convex functions. Some new properties are proved too, e.g., relations to the statistical sufficiency and deficiency. The generalized Taylor expansion also shows very easily that all f-divergences are average statistical informations (differences between prior and posterior Bayes errors) mutually differing only in the weights imposed on various prior distributions. The statistical information introduced by De Groot and the classical information of Shannon are shown to be extremal cases corresponding to alpha=0 and alpha=1 in the class of the so-called Arimoto alpha-informations introduced in this paper for 0<alpha<1 by means of the Arimoto alpha-entropies. Some new examples of f-divergences are introduced as well, namely, the Shannon divergences and the Arimoto alpha-divergences leading for alphauarr1 to the Shannon divergences. Square roots of all these divergences are shown to be metrics satisfying the triangle inequality. The last section introduces statistical tests and estimators based on the minimal f-divergence with the empirical distribution achieved in the families of hypothetic distributions. For the Kullback divergence this leads to the classical likelihood ratio test and estimator

A column store engine for real-time streaming analytics <=SEP=> This paper describes novel aspects of the column store implemented in the MemSQL database engine and describes the design choices made to support real-time streaming workloads. Column stores have traditionally been restricted to data warehouse scenarios where low latency queries are a secondary goal, and where restricting data ingestion to be offline, batched, append-only, or some combination thereof is acceptable. In contrast, the MemSQL column store implementation treats low latency queries and ongoing writes as first class citizens, with a focus on avoiding interference between read, ingest, update, and storage optimization workloads through the use of fragmented snapshot transactions and optimistic storage reordering. This implementation broadens the range of serviceable column store workloads to include those with more stringent demands on query and data latency, such as those backing operational systems used by adtech, financial services, fraud detection and other real-time or data streaming applications.

An Improved Approach for Text-Independent Speaker Recognition <=SEP=> This paper presents new Speaker Identification and Speaker Verification systems based on the use of new feature vectors extracted from the speech signal. The proposed structure combine between the most successful Mel Frequency Cepstral Coefficients and new features which are the Short Time Zero Crossing Rate of the signal. A comparison between speaker recognition systems based on Gaussian mixture models using the well known Mel Frequency Cepstral Coefficients and the novel systems based on the use of a combination between both reduced Mel Frequency Cepstral Coefficients features vectors and Short Time Zero Crossing Rate features is given. This comparison proves that the use of the new reduced feature vectors help to improve the system’s performance and also help to reduce the time and memory complexity of the system which is required for realistic applications that suffer from computational resource limitation. The experiments were performed on speakers from TIMIT database for different training durations. The suggested systems performances are evaluated against the baseline systems. The increase of the proposed systems performances are well observed for identification experiments and the decrease of Equal Error Rates are also remarkable for verification experiments. Experimental results demonstrate the effectiveness of the new approach which avoids the use of more complex algorithms or the combination of different approaches requiring lengthy calculation. Keywords—GMM; speaker verification; speaker recognition; speaker identification

Simple topologies of PWM AC-AC converters <=SEP=> This letter proposes a new family of simple topologies of PWM AC-AC converters with minimal switches. With extension from the basic DC-DC converters, a series of AC-AC converters such as buck, boost, buck-boost, Cuk, and isolated converters are obtained. By PWM duty ratio control, they become a "solid-state transformer" with a continuously variable turns ratio. All the proposed AC-AC converters in this paper employ only two switches. Compared to the existing circuits that use six switches or more, they can reduce cost and improve reliability. The operating principle and control method of the proposed topologies are presented. Analysis and simulation results are given using the Cuk AC-AC converter as an example. The analysis can be easily extended to other converters of the proposed family.

The MOS 36-Item Short-Form Health Survey (SF-36): II. Psychometric and clinical tests of validity in measuring physical and mental health constructs. <=SEP=> Cross-sectional data from the Medical Outcomes Study (MOS) were analyzed to test the validity of the MOS 36-Item Short-Form Health Survey (SF-36) scales as measures of physical and mental health constructs. Results from traditional psychometric and clinical tests of validity were compared. Principal components analysis was used to test for hypothesized physical and mental health dimensions. For purposes of clinical tests of validity, clinical criteria defined mutually exclusive adult patient groups differing in severity of medical and psychiatric conditions. Scales shown in the components analysis to primarily measure physical health (physical functioning and role limitations-physical) best distinguished groups differing in severity of chronic medical condition and had the most pure physical health interpretation. Scales shown to primarily measure mental health (mental health and role limitations-emotional) best distinguished groups differing in the presence and severity of psychiatric disorders and had the most pure mental health interpretation. The social functioning, vitality, and general health perceptions scales measured both physical and mental health components and, thus, had the most complex interpretation. These results are useful in establishing guidelines for the interpretation of each scale and in documenting the size of differences between clinical groups that should be considered very large.

Arrangements for Information Technology Governance: A Theory of Multiple Contingencies <=SEP=> Business world today identifies a need of IT use. It is clear that IT is transforming the business. Since IT offers both opportunities and threats, effective IT governance is required to deliver IT benefits. Enterprises have to understand this issue deeply in wider business perspective in order to successfully implement IT. Effective IT governance can be achieved through enterprise’s ability in identifying, analyzing and dealing with all related factors influencing IT governance arrangements. According to theory of multiple contingencies, an enterprise is subject to impact of some contingency forces by strengthening, weakening, or overriding their mutual effects on the IT governance mode. This paper is a critique about Sambamurthy and Zmud’s research to examine IT governance mode based on this theory in relation to effective IT governance and management. They believe that this approach can be used to explain many different IT governance mode phenomena.

Examining the effects of mass media campaign exposure and interpersonal discussions on youth's drug use: the mediating role of visiting pro-drug websites. <=SEP=> To extend past research on interpersonal communication and campaign effects, we hypothesized that anti-drug mass media campaign message exposure indirectly affects visiting anti- and pro-drug websites through targeted parent-child and friend-to-friend communication against drugs, as well as through having drug-related discussions during organized group activities. Second, we posited that engaging in anti-drug interpersonal communication indirectly affects adolescents' drug use through two intervening variables: visiting anti-drug websites and visiting pro-drug websites. Using self-reported longitudinal data from 2,749 youth, we found that as youth reported higher levels of anti-drug mass media campaign message exposure, they were more likely to talk to friends about the bad consequences of drugs, how to avoid drugs, and anti-drug ads. In turn, however, they were more likely to visit pro-drug websites, and subsequently, to smoke cigarettes.

Experimental results for artificial intelligence-based self-organized 5G networks <=SEP=> Until now, mobile networks are still managed in a manual and semi-automatic manner, which are costly and time-consuming. For the forthcoming Fifth Generation (5G) system, its large-scale, heterogeneous, software-defined and virtualized infrastructure simply become unmanageable if no innovative managing paradigm is applied. Recently, artificial intelligence is proposed to be applied in the 5G system to realize intelligent management and highly self-organized networks. In this paper, proof-of-concept experiments, including the setup of an intelligent 5G testbed, its closed-loop control and enabling algorithms, are presented. The experimental results reveal that applying artificial intelligence to wireless network management is both feasible and effective.

Programming algorithms for multilevel phase-change memory <=SEP=> Phase-change memory (PCM) has emerged as one among the most promising technologies for next-generation nonvolatile solid-state memory. Multilevel storage, namely storage of non-binary information in a memory cell, is a key factor for reducing the total cost-per-bit and thus increasing the competitiveness of PCM technology in the nonvolatile memory market. In this paper, we present a family of advanced programming schemes for multilevel storage in PCM. The proposed schemes are based on iterative write-and-verify algorithms that exploit the unique programming characteristics of PCM in order to achieve significant improvements in resistance-level packing density, robustness to cell variability, programming latency, energy-per-bit and cell storage capacity. Experimental results from PCM test-arrays are presented to validate the proposed programming schemes. In addition, the reliability issues of multilevel PCM in terms of resistance drift and read noise are discussed.