title: Combining Quantitative and Logical Data Cleaning

abstract: Quantitative data cleaning relies on the use of statistical methods to identify and repair data quality problems while logical data cleaning tackles the same problems using various forms of logical reasoning over declarative dependencies. Each of these approaches has its strengths: the logical approach is able to capture subtle data quality problems using sophisticated dependencies, while the quantitative approach excels at ensuring that the repaired data has desired statistical properties. We propose a novel framework within which these two approaches can be used synergistically to combine their respective strengths. ::: ::: We instantiate our framework using (i) metric functional dependencies, a type of dependency that generalizes functional dependencies (FDs) to identify inconsistencies in domains where only large differences in metric data are considered to be a data quality problem, and (ii) repairs that modify the inconsistent data so as to minimize statistical distortion, measured using the Earth Mover's Distance. We show that the problem of computing a statistical distortion minimal repair is NP-hard. Given this complexity, we present an efficient algorithm for finding a minimal repair that has a small statistical distortion using EMD computation over semantically related attributes. To identify semantically related attributes, we present a sound and complete axiomatization and an efficient algorithm for testing implication of metric FDs. While the complexity of inference for some other FD extensions is co-NP complete, we show that the inference problem for metric FDs remains linear, as in traditional FDs. We prove that every instance that can be generated by our repair algorithm is set-minimal (with no unnecessary changes). Our experimental evaluation demonstrates that our techniques obtain a considerably lower statistical distortion than existing repair techniques, while achieving similar levels of efficiency.


title: Numerical Analysis of Turbulent Fluid Flow and Drag Coefficient for Optimizing the AUV Hull Design

abstract: Autonomous Underwater Vehicles (AUVs) are robots able to perform tasks without human intervention (remote operators). Research and development of this class of vehicles has growing, due to the excellent characteristics of the AUVs to operate in different situations. Therefore, this study aims to analyze turbulent single fluid flow over different geometric configurations of an AUV hull, in order to obtain test geometry that generates lower drag force, which reduces the energy consumption of the vehicle, thereby increasing their autonomy during operation. In the numerical analysis was used ANSYS-CFX® 11.0 software, which is a powerful tool for solving problems involving fluid mechanics. Results of the velocity (vectors and streamlines), pressure distribution and drag coefficient are showed and analyzed. Optimum hull geometry was found. Lastly, a relationship between the geometric parameters analyzed and the drag coefficient was obtained.


title: Deeply Learned View-Invariant Features for Cross-View Action Recognition

abstract: Classifying human actions from varied views is challenging due to huge data variations in different views. The key to this problem is to learn discriminative view-invariant features robust to view variations. In this paper, we address this problem by learning view-specific and view-shared features using novel deep models. View-specific features capture unique dynamics of each view while view-shared features encode common patterns across views. A novel sample-affinity matrix is introduced in learning shared features, which accurately balances information transfer within the samples from multiple views and limits the transfer across samples. This allows us to learn more discriminative shared features robust to view variations. In addition, the incoherence between the two types of features is encouraged to reduce information redundancy and exploit discriminative information in them separately. The discriminative power of the learned features is further improved by encouraging features in the same categories to be geometrically closer. Robust view-invariant features are finally learned by stacking several layers of features. Experimental results on three multi-view data sets show that our approaches outperform the state-of-the-art approaches.


title: Annular-ring CMUT arrays for forward-looking IVUS: transducer characterization and imaging

abstract: In this study, a 64-element, 1.15-mm diameter annular-ring capacitive micromachined ultrasonic transducer (CMUT) array was characterized and used for forward-looking intravascular ultrasound (IVUS) imaging tests. The array was manufactured using low-temperature processes suitable for CMOS electronics integration on a single chip. The measured radiation pattern of a 43 /spl times/ 140-/spl mu/m array element depicts a 40/spl deg/ view angle for forward-looking imaging around a 15-MHz center frequency in agreement with theoretical models. Pulse-echo measurements show a -10-dB fractional bandwidth of 104% around 17 MHz for wire targets 2.5 mm away from the array in vegetable oil. For imaging and SNR measurements, RF A-scan data sets from various targets were collected using an interconnect scheme forming a 32-element array configuration. An experimental point spread function was obtained and compared with simulated and theoretical array responses, showing good agreement. Therefore, this study demonstrates that annular-ring CMUT arrays fabricated with CMOS-compatible processes are capable of forward-looking IVUS imaging, and the developed modeling tools can be used to design improved IVUS imaging arrays.


title: Modeling the impact of short- and long-term behavior on search personalization

abstract: User behavior provides many cues to improve the relevance of search results through personalization. One aspect of user behavior that provides especially strong signals for delivering better relevance is an individual's history of queries and clicked documents. Previous studies have explored how short-term behavior or long-term behavior can be predictive of relevance. Ours is the first study to assess how short-term (session) behavior and long-term (historic) behavior interact, and how each may be used in isolation or in combination to optimally contribute to gains in relevance through search personalization. Our key findings include: historic behavior provides substantial benefits at the start of a search session; short-term session behavior contributes the majority of gains in an extended search session; and the combination of session and historic behavior out-performs using either alone. We also characterize how the relative contribution of each model changes throughout the duration of a session. Our findings have implications for the design of search systems that leverage user behavior to personalize the search experience.


Security for 4 G and 5 G Cellular Networks


title: Time-dependent covariates in the proportional subdistribution hazards model for competing risks

abstract: Separate Cox analyses of all cause-specific hazards are the standard technique of choice to study the effect of a covariate in competing risks, but a synopsis of these results in terms of cumulative event probabilities is challenging. This difficulty has led to the development of the proportional subdistribution hazards model. If the covariate is known at baseline, the model allows for a summarizing assessment in terms of the cumulative incidence function. black Mathematically, the model also allows for including random time-dependent covariates, but practical implementation has remained unclear due to a certain risk set peculiarity. We use the intimate relationship of discrete covariates and multistate models to naturally treat time-dependent covariates within the subdistribution hazards framework. The methodology then straightforwardly translates to real-valued time-dependent covariates. As with classical survival analysis, including time-dependent covariates does not result in a model for probability functions anymore. Nevertheless, the proposed methodology provides a useful synthesis of separate cause-specific hazards analyses. We illustrate this with hospital infection data, where time-dependent covariates and competing risks are essential to the subject research question.


title: Identification of High Yielding Investment Programs in Bitcoin via Transactions Pattern Analysis

abstract: Although Bitcoin is one of the most successful decentralized cryptocurrency, recent research has revealed that it can be used as fraudulent activities such as HYIP (High Yield Investment Program). To identify such undesired activities, it is important to obtain Bitcoin addresses related with fraud. So far, the identification of such activities is based upon relating Bitcoin addresses with graph mining procedures. In this paper, we follow a different approach for identifying Bitcoin addresses related with HYIP by analyzing transactions patterns. In particular, based on the individual inspection of HYIP activity in Bitcoin, we propose a number of features that can be extracted from transactions. In particular, a signed integer called pattern is assigned to each transaction and the frequency of each pattern is calculated as key features. By evaluating the classification performance with more than 1,500 labeled Bitcoin addresses, it is shown that about 83% of HYIP addresses are correctly classified while maintaining false positive rate less than 4.4%.


title: Human Upper Body Pose Estimation in Static Images

abstract: Estimating human pose in static images is challenging due to the high dimensional state space, presence of image clutter and am- biguities of image observations. We present an MCMC framework for estimating 3D human upper body pose. A generative model, comprising of the human articulated structure, shape and clothing models, is used to formulate likelihood measures for evaluating solution candidates. We adopt a data-driven proposal mechanism for searching the solution space efficiently. We introduce the use of proposal maps, which is an efficient way of implementing inference proposals derived from multiple types of image cues. Qualitative and quantitative results show that the technique is effective in estimating 3D body pose over a variety of images.


Enhancing Security and Privacy of Tor's Ecosystem by Using Trusted Execution Environments


title: Automatic global vessel segmentation and catheter removal using local geometry information and vector field integration

abstract: Vessel enhancement and segmentation aim at (binary) per-pixel segmentation considering certain local features as probabilistic vessel indicators. We propose a new methodology to combine any local probability map with local directional vessel information. The resulting global vessel segmentation is represented as a set of discrete streamlines populating the vascular structures and providing additional connectivity and geometric shape information. The streamlines are computed by numerical integration of the directional vector field that is obtained from the eigenanalysis of the local Hessian indicating the local vessel direction. The streamline representation allows for sophisticated post-processing techniques using the additional information to refine the segmentation result with respect to the requirements of the particular application such as image registration. We propose different post-processing techniques for hierarchical segmentation, centerline extraction, and catheter removal to be used for X-ray angiograms. We further demonstrate how the global approach is able to significantly improve the segmentation compared to conventional local Hessian-based approaches.


title: Eye gaze tracking with google cardboard using purkinje images

abstract: Mobile phone-based Virtual Reality (VR) is rapidly growing as a platform for stereoscopic 3D and non-3D digital content and applications. The ability to track eye gaze in these devices would be a tremendous opportunity on two fronts: firstly, as an interaction technique, where interaction is currently awkward and limited, and secondly, for studying human visual behavior. We propose a method to add eye gaze tracking to these existing devices using their on-board display and camera hardware, with a minor modification to the headset enclosure. We present a proof-of-concept implementation of the technique and show results demonstrating its feasibility. The software we have developed will be made available as open source to benefit the research community.


title: Blurred boundaries: the therapeutics and politics of medical marijuana.

abstract: For 5 millennia, Cannabis sativa has been used throughout the world medically, recreationally, and spiritually. From the mid-19th century to the 1930s, American physicians prescribed it for a plethora of indications, until the federal government started imposing restrictions on its use, culminating in 1970 with the US Congress classifying it as a Schedule I substance, illegal, and without medical value. Simultaneous with this prohibition, marijuana became the United States' most widely used illicit recreational drug, a substance generally regarded as pleasurable and relaxing without the addictive dangers of opioids or stimulants. Meanwhile, cannabis never lost its cachet in alternative medicine circles, going mainstream in 1995 when California became the first of 16 states to date to legalize its medical use, despite the federal ban. Little about cannabis is straightforward. Its main active ingredient, δ-9-tetrahydrocannabinol, was not isolated until 1964, and not until the 1990s were the far-reaching modulatory activities of the endocannabinoid system in the human body appreciated. This system's elucidation raises the possibility of many promising pharmaceutical applications, even as draconian federal restrictions that hamstring research show no signs of softening. Recreational use continues unabated, despite growing evidence of marijuana's addictive potential, particularly in the young, and its propensity for inducing and exacerbating psychotic illness in the susceptible. Public approval drives medical marijuana legalization efforts without the scientific data normally required to justify a new medication's introduction. This article explores each of these controversies, with the intent of educating physicians to decide for themselves whether marijuana is panacea, scourge, or both. PubMed searches were conducted using the following keywords: medical marijuana, medical cannabis, endocannabinoid system, CB1 receptors, CB2 receptors, THC, cannabidiol, nabilone, dronabinol, nabiximols, rimonabant, marijuana legislation, marijuana abuse, marijuana dependence, and marijuana and schizophrenia. Bibliographies were hand searched for additional references relevant to clarifying the relationships between medical and recreational marijuana use and abuse.


title: Model falsification diagnosis and sensor placement for leak detection in pressurized pipe networks

abstract: Pressurized pipe networks used for fresh-water distribution can take advantage of recent advances in sensing technologies and data-interpretation to evaluate their performance. In this paper, a leak-detection and a sensor placement methodology are proposed based on leak-scenario falsification. The approach includes modeling and measurement uncertainties during the leak detection process. The performance of the methodology proposed is tested on a full-scale water distribution network using simulated data. Findings indicate that when monitoring the flow velocity for 14 pipes over the entire network (295 pipes) leaks are circumscribed within a few potential locations. The case-study shows that a good detectability is expected for leaks of 50L/min or more. A study of measurement configurations shows that smaller leak levels could also be detected if additional pipes are instrumented.


title: A Neostriatal Habit Learning System in Humans

abstract: Amnesic patients and nondemented patients with Parkinson's disease were given a probabilistic classification task in which they learned which of two outcomes would occur on each trial, given the particular combination of cues that appeared. Amnesic patients exhibited normal learning of the task but had severely impaired declarative memory for the training episode. In contrast, patients with Parkinson's disease failed to learn the probabilistic classification task, despite having intact memory for the training episode. This double dissociation shows that the limbic-diencephalic regions damaged in amnesia and the neostriatum damaged in Parkinson's disease support separate and parallel learning systems. In humans, the neostriatum (caudate nucleus and putamen) is essential for the gradual, incremental learning of associations that is characteristic of habit learning. The neostriatum is important not just for motor behavior and motor learning but also for acquiring nonmotor dispositions and tendencies that depend on new associations.


title: Climate change: making certain what the uncertainties are

abstract: At the Copenhagen conference our leaders failed to agree on a course of firm action against climate change. We present three articles on the post-Copenhagen world. In the first of them, Richard Chandler, Jonathan Rougier and Mat Collins describe statistical work that still needs to be done.


title: Generalized Sliced Wasserstein Distances

abstract: The Wasserstein distance and its variations, e.g., the sliced-Wasserstein (SW) distance, have recently drawn attention from the machine learning community. The SW distance, specifically, was shown to have similar properties to the Wasserstein distance, while being much simpler to compute, and is therefore used in various applications including generative modeling and general supervised/unsupervised learning. In this paper, we first clarify the mathematical connection between the SW distance and the Radon transform. We then utilize the generalized Radon transform to define a new family of distances for probability measures, which we call generalized sliced-Wasserstein (GSW) distances. We also show that, similar to the SW distance, the GSW distance can be extended to a maximum GSW (max-GSW) distance. We then provide the conditions under which GSW and max-GSW distances are indeed distances. Finally, we compare the numerical performance of the proposed distances on several generative modeling tasks, including SW flows and SW auto-encoders.


title: A Novel Idea of Classification of E-commerce Products Using Deep Convolutional Neural Network

abstract: The high numbers of non-food products and categories on today E-commerce sites render validation of the data as labor intensive and expensive task. Therefore, there is a recent push to automate validation of correct placement of product in category. The French E-commerce company CDiscount has launched Kaggle competition, sharing huge dataset of over 7 million products, to solve the very problem. The goal is to classify products containing multiple images into one of 5270 categories. This paper proposes, implements and experimentally evaluates deep neural network architecture for classification of non-food E-commerce products. To tackle the complexity of the task on available hardware, hierarchical architecture of neural networks that exploits existing category taxonomy is proposed. The hierarchical architecture achieved the Top-1 accuracy of 0.61061. It has been found, that specific networks in hierarchical architecture can be successfully transferred onto similar datasets, by transferring network that learned on books onto different book dataset. The transferred model performed better than the same model pre-trained on ImageNet dataset.


title: General 3D modelling of novel objects from a single view

abstract: In this paper we present a method for building models for grasping from a single 3D snapshot of a scene composed of objects of daily use in human living environments. We employ fast shape estimation, probabilistic model fitting and verification methods capable of dealing with different kinds of symmetries, and combine these with a triangular mesh of the parts that have no other representation to model previously unseen objects of arbitrary shape. Our approach is enhanced by the information given by the geometric clues about different parts of objects which serve as prior information for the selection of the appropriate reconstruction method. While we designed our system for grasping based on single view 3D data, its generality allows us to also use the combination of multiple views. We present two application scenarios that require complete geometric models: grasp planning and locating objects in camera images.


title: Comparison of Intact Antibody Structures and the Implications for Effector Function

abstract: Publisher Summary This chapter discusses the intact antibody structures and their implications for effector function. Immune systems of vertebrates rely on antibody molecules, structurally dynamic glycoproteins for coupling foreign body recognition with elimination processes. An intact immunoglobulin G (IgG) molecule is composed of four polypeptide chains—two identical light (L) chains and two identical heavy (H) chains—with variable (V) and constant (C) regions that form two Fab segments and one Fc segment for a total of 12 domains. Inherent flexibility of the immunoglobulin yields a spectrum of molecular conformations, making crystallization problematic. Mutational studies pertinent to hinge sequence and effector interactions—taken in the structural context of the intact antibody models—suggest that some ideas regarding antibody flexibility should be reevaluated. Genetic hinge-deleted antibodies such as Dob and Mcg are the most rigid human myeloma proteins. Both Dob and Mcg are of the IgG1 subclass but contain “genetic” hinge deletions. Fragment studies have provided insight into mechanisms of antigen binding by the complementarity-determining regions (CDRs) and of some effector protein interactions with the Fc.


F1-Optimal Thresholding in the Multi-Label Setting.


title: A Practical Guide to Training Restricted Boltzmann Machines

abstract: Restricted Boltzmann machines (RBMs) have been used as generative models of many different types of data. RBMs are usually trained using the contrastive divergence learning procedure. This requires a certain amount of practical experience to decide how to set the values of numerical meta-parameters. Over the last few years, the machine learning group at the University of Toronto has acquired considerable expertise at training RBMs and this guide is an attempt to share this expertise with other machine learning researchers.


title: Regularization Theory and Neural Networks Architectures

abstract: We had previously shown that regularization principles lead to approximation schemes that are equivalent to networks with one layer of hidden units, called regularization networks . In particular, standard smoothness functionals lead to a subclass of regularization networks, the well known radial basis functions approximation schemes. This paper shows that regularization networks encompass a much broader range of approximation schemes, including many of the popular general additive models and some of the neural networks. In particular, we introduce new classes of smoothness functionals that lead to different classes of basis functions. Additive splines as well as some tensor product splines can be obtained from appropriate classes of smoothness functionals. Furthermore, the same generalization that extends radial basis functions (RBF) to hyper basis functions (HBF) also leads from additive models to ridge approximation models, containing as special cases Breiman's hinge functions, some forms of projection pursuit regression, and several types of neural networks. We propose to use the term generalized regularization networks for this broad class of approximation schemes that follow from an extension of regularization. In the probabilistic interpretation of regularization, the different classes of basis functions correspond to different classes of prior probabilities on the approximating function spaces, and therefore to different types of smoothness assumptions. In summary, different multilayer networks with one hidden layer, which we collectively call generalized regularization networks, correspond to different classes of priors and associated smoothness functionals in a classical regularization principle. Three broad classes are (1) radial basis functions that can be generalized to hyper basis functions, (2) some tensor product splines, and (3) additive splines that can be generalized to schemes of the type of ridge approximation, hinge functions, and several perceptron-like neural networks with one hidden layer.


title: Dissecting ghost clicks: ad fraud via misdirected human clicks

abstract: FBI's Operation Ghost Click, the largest cybercriminal takedown in history, recently took down an ad fraud infrastructure that affected 4 million users and made its owners 14 million USD over a period of four years. The attackers hijacked clicks and ad impressions on victim machines infected by a DNS changer malware to earn ad revenue fraudulently. We experimented with the attack infrastructure when it was in operation and present a detailed account of the attackers' modus operandi. We also study the impact of this attack on real-world users and find that 37 subscriber lines were impacted in our data set. Also, 20 ad networks and 257 legitimate Web content publishers lost ad revenue while the attackers earned revenue convincing a dozen other ad networks that their ads were served on websites with real visitors. Our work expands the understanding of modalities of ad fraud and could help guide appropriate defense strategies.


title: Real-Time Heuristic Search ABSTRACT

abstract: Abstract We apply the two-player game assumptions of limited search horizon and commitment to moves in constant time, to single-agent heuristic search problems. We present a variation of minimax lookahead search, and an analog to alpha-beta pruning that significantly improves the efficiency of the algorithm. Paradoxically, the search horizon reachable with this algorithm increases with increasing branching factor. In addition, we present a new algorithm, called Real-Time-A∗, for interleaving planning and execution. We prove that the algorithm makes locally optimal decisions and is guaranteed to find a solution. We also present a learning version of this algorithm that improves its performance over successive problem solving trials by learning more accurate heuristic values, and prove that the learned values converge to their exact values along every optimal path. These algorithms effectively solve significantly larger problems than have previously been solvable using heuristic evaluation functions.


title: Metaphor and Culture

abstract: To see and appreciate the relationship between metaphor and culture in its complexity, we have to deal with a number of basic issues. By metaphor in this paper, I will primarily mean "conceptual metaphor" that can have a number of linguistic manifestations (see Lakoff and Johnson 1980; Kovecses 2002/2010). A conceptual metaphor consists of a set of correspondences, or mappings, between a "source" and a "target" domain. The meaning of particular metaphorical linguistic expressions is based on such correspondences. The issues that form a part of the "metaphor-culture interface" are numerous. In this paper, I will discuss the following six. First, we need to ask if there are at all universal conceptual metaphors that are in a sense culture-independent. Second, if there are (which is the case), how can we account for their universality? Third, if we also find in our metaphors variation (which we do), what are the major cultural dimensions along which the metaphors vary? Fourth, we need to examine whether broad, general cultural dimensions are sufficient to account for all variation in metaphors (they are not), or whether metaphor variation also depends on more fine-grained contextual factors. Fifth, is a more fine-grained theory of metaphor variation helpful in understanding everyday talk, poetic language, or both? Sixth, and finally, what role does metaphor play in the creation and understanding of discourse?


title: Exploring the role of customer relationship management (CRM) systems in customer knowledge creation

abstract: This study explores how customer relationship management (CRM) systems support customer knowledge creation processes [48], including socialization, externalization, combination and internalization. CRM systems are categorized as collaborative, operational and analytical. An analysis of CRM applications in three organizations reveals that analytical systems strongly support the combination process. Collaborative systems provide the greatest support for externalization. Operational systems facilitate socialization with customers, while collaborative systems are used for socialization within an organization. Collaborative and analytical systems both support the internalization process by providing learning opportunities. Three-way interactions among CRM systems, types of customer knowledge, and knowledge creation processes are explored.


title: Semisupervised Autoencoder for Sentiment Analysis

abstract: In this paper, we investigate the usage of autoencoders in modeling textual data. Traditional autoencoders suffer from at least two aspects: scalability with the high dimensionality of vocabulary size and dealing with task-irrelevant words. We address this problem by introducing supervision via the loss function of autoencoders. In particular, we first train a linear classifier on the labeled data, then define a loss for the autoencoder with the weights learned from the linear classifier. To reduce the bias brought by one single classifier, we define a posterior probability distribution on the weights of the classifier, and derive the marginalized loss of the autoencoder with Laplace approximation. We show that our choice of loss function can be rationalized from the perspective of Bregman Divergence, which justifies the soundness of our model. We evaluate the effectiveness of our model on six sentiment analysis datasets, and show that our model significantly outperforms all the competing methods with respect to classification accuracy. We also show that our model is able to take advantage of unlabeled dataset and get improved performance. We further show that our model successfully learns highly discriminative feature maps, which explains its superior performance.


title: Blind Speech Separation and Enhancement With GCC-NMF

abstract: We present a blind source separation algorithm named GCC-NMF that combines unsupervised dictionary learning via non-negative matrix factorization NMF with spatial localization via the generalized cross correlation GCC method. Dictionary learning is performed on the mixture signal, with separation subsequently achieved by grouping dictionary atoms, at each point in time, according to their spatial origins. The resulting source separation algorithm is simple yet flexible, requiring no prior knowledge or information. Separation quality is evaluated for three tasks using stereo recordings from the publicly available SiSEC signal separation evaluation campaign: 3 and 4 concurrent speakers in reverberant environments, speech mixed with real-world background noise, and noisy recordings of a moving speaker. Performance is quantified using perceptually motivated and SNR-based measures with the PEASS and BSS Eval toolkits, respectively. We evaluate the effects of model parameters on separation quality, and compare our approach with other unsupervised and semi-supervised speech separation and enhancement approaches. We show that GCC-NMF is a flexible source separation algorithm, outperforming task-specific approaches in each of the three settings, including both blind as well as several informed approaches that require prior knowledge or information.


title: Data Clustering with Cluster Size Constraints Using a Modified K-Means Algorithm

abstract: Data clustering is a frequently used technique in finance, computer science, and engineering. In most of the applications, cluster sizes are either constrained to particular values or available as prior knowledge. Unfortunately, traditional clustering methods cannot impose constrains on cluster sizes. In this paper, we propose some vital modifications to the standard k-means algorithm such that it can incorporate size constraints for each cluster separately. The modified k-means algorithm can be used to obtain clusters in preferred sizes. A potential application would be obtaining clusters with equal cluster size. Moreover, the modified algorithm makes use of prior knowledge of the given data set for selectively initializing the cluster centroids which helps escaping from local minima. Simulation results on multidimensional data demonstrate that the k-means algorithm with the proposed modifications can fulfill cluster size constraints and lead to more accurate and robust results.


title: Comparison of Least Squares and Errors-in-Variables Regression, with Special Reference to Randomized Analysis of Covariance

abstract: Abstract In an errors-in-variables regression model, the least squares estimate is generally inconsistent for the complete regression parameter but can be consistent for certain linear combinations of this parameter. We explore the conjecture that, when the least squares estimate is consistent for a linear combination of the regression parameter, it will be preferred to an errors-in-variables estimate, at least asymptotically. The conjecture is false, in general, but it is true for some important classes of problems. One such problem is a randomized two-group analysis of covariance, upon which we focus.


title: Adaptive Strassen's matrix multiplication

abstract: Strassen's matrix multiplication (MM) has benefits with respect to any (highly tuned) implementations of MM because Strassen's reduces the total number of operations. Strassen achieved this operation reduction by replacing computationally expensive MMs with matrix additions (MAs). For architectures with simple memory hierarchies, having fewer operations directly translates into an efficient utilization of the CPU and, thus, faster execution. However, for modern architectures with complex memory hierarchies, the operations introduced by the MAs have a limited in-cache data reuse and thus poor memory-hierarchy utilization, thereby overshadowing the (improved) CPU utilization, and making Strassen's algorithm (largely) useless on its own. In this paper, we investigate the interaction between Strassen's effective performance and the memory-hierarchy organization. We show how to exploit Strassen's full potential across different architectures. We present an easy-to-use adaptive algorithm that combines a novel implementation of Strassen's idea with the MM from automatically tuned linear algebra software (ATLAS) or GotoBLAS. An additional advantage of our algorithm is that it applies to any size and shape matrices and works equally well with row or column major layout. Our implementation consists of introducing a final step in the ATLAS/GotoBLAS-installation process that estimates whether or not we can achieve any additional speedup using our Strassen's adaptation algorithm. Then we install our codes, validate our estimates, and determine the specific performance. We show that, by the right combination of Strassen's with ATLAS/GotoBLAS, our approach achieves up to 30%/22% speed-up versus ATLAS/GotoBLAS alone on modern high-performance single processors. We consider and present the complexity and the numerical analysis of our algorithm, and, finally, we show performance for 17 (uniprocessor) systems.


title: Adversarial Reinforcement Learning for Chinese Text Summarization

abstract: This paper proposes a novel Adversarial Reinforcement Learning architecture for Chinese text summarization. Previous abstractive methods commonly use Maximum Likelihood Estimation (MLE) to optimize the generative models, which makes auto-generated summary less incoherent and inaccuracy. To address this problem, we innovatively apply the Adversarial Reinforcement Learning strategy to narrow the gap between the generated summary and the human summary. In our model, we use a generator to generate summaries, a discriminator to distinguish between generated summaries and real ones, and reinforcement learning (RL) strategy to iteratively evolve the generator. Besides, in order to better tackle Chinese text summarization, we use a character-level model rather than a word-level one and append Text-Attention in the generator. Experiments were run on two Chinese corpora, respectively consisting of long documents and short texts. Experimental Results showed that our model significantly outperforms previous deep learning models on rouge score.


title: Virtual Network Function Placement Considering Resource Optimization and SFC Requests in Cloud Datacenter

abstract: Network function virtualization (NFV) brings great conveniences and benefits for the enterprises to outsource their network functions to the cloud datacenter. In this paper, we address the virtual network function (VNF) placement problem in cloud datacenter considering users’ service function chain requests (SFCRs). To optimize the resource utilization, we take two less-considered factors into consideration, which are the time-varying workloads, and the basic resource consumptions (BRCs) when instantiating VNFs in physical machines (PMs). Then the VNF placement problem is formulated as an integer linear programming (ILP) model with the aim of minimizing the number of used PMs. Afterwards, a Two-StAge heurisTic solution (T-SAT) is designed to solve the ILP. T-SAT consists of a correlation-based greedy algorithm for SFCR mapping (first stage) and a further adjustment algorithm for virtual network function requests (VNFRs) in each SFCR (second stage). Finally, we evaluate T-SAT with the artificial data we compose with Gaussian function and trace data derived from Google's datacenters. The simulation results demonstrate that the number of used PMs derived by T-SAT is near to the optimal results and much smaller than the benchmarks. Besides, it improves the network resource utilization significantly.


title: Generating synthetic CTs from magnetic resonance images using generative adversarial networks

abstract: PURPOSE ::: While MR-only treatment planning using synthetic CTs (synCTs) offers potential for streamlining clinical workflow, a need exists for an efficient and automated synCT generation in the brain to facilitate near real-time MR-only planning. This work describes a novel method for generating brain synCTs based on generative adversarial networks (GANs), a deep learning model that trains two competing networks simultaneously, and compares it to a deep convolutional neural network (CNN). ::: ::: ::: METHODS ::: Post-Gadolinium T1-Weighted and CT-SIM images from fifteen brain cancer patients were retrospectively analyzed. The GAN model was developed to generate synCTs using T1-weighted MRI images as the input using a residual network (ResNet) as the generator. The discriminator is a CNN with five convolutional layers that classified the input image as real or synthetic. Fivefold cross-validation was performed to validate our model. GAN performance was compared to CNN based on mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) metrics between the synCT and CT images. ::: ::: ::: RESULTS ::: GAN training took ~11 h with a new case testing time of 5.7 ± 0.6 s. For GAN, MAEs between synCT and CT-SIM were 89.3 ± 10.3 Hounsfield units (HU) and 41.9 ± 8.6 HU across the entire FOV and tissues, respectively. However, MAE in the bone and air was, on average, ~240-255 HU. By comparison, the CNN model had an average full FOV MAE of 102.4 ± 11.1 HU. For GAN, the mean PSNR was 26.6 ± 1.2 and SSIM was 0.83 ± 0.03. GAN synCTs preserved details better than CNN, and regions of abnormal anatomy were well represented on GAN synCTs. ::: ::: ::: CONCLUSIONS ::: We developed and validated a GAN model using a single T1-weighted MR image as the input that generates robust, high quality synCTs in seconds. Our method offers strong potential for supporting near real-time MR-only treatment planning in the brain.


title: Bayesian data analysis

abstract: Bayesian methods have garnered huge interest in cognitive science as an approach to models of cognition and perception. On the other hand, Bayesian methods for data analysis have not yet made much headway in cognitive science against the institutionalized inertia of 20th century null hypothesis significance testing (NHST). Ironically, specific Bayesian models of cognition and perception may not long endure the ravages of empirical verification, but generic Bayesian methods for data analysis will eventually dominate. It is time that Bayesian data analysis became the norm for empirical methods in cognitive science. This article reviews a fatal flaw of NHST and introduces the reader to some benefits of Bayesian data analysis. The article presents illustrative examples of multiple comparisons in Bayesian analysis of variance and Bayesian approaches to statistical power. Copyright © 2010 John Wiley & Sons, Ltd. ::: ::: For further resources related to this article, please visit the WIREs website


title: Are you what you look like? Exploring correlations in personality type and their wearing

abstract: Everyday, people choose their preferred clothing to wear before leaving home for various activities. It has been recognized that each person may consciously or unconsciously express individual personality through their wearing styles. It will be interesting to ask: Are you what you look like? This paper presents a novel scheme developed to infer personality type from their wearing. The proposed research is justifiably rooted in the psychological findings that reveal intrinsic correlations between clothing style and wearer's inner self in self-image, mood, and social aspirations. First, we build a relatively large dataset with more than 300 persons and over 10,000 portraits, each is labeled with personality type. Then, personality-related clothing features are explored through statistical analysis based on psychological theories. To extract the clothing features from the images, a suite of algorithms, including body detection, GrabCut algorithm and saliency detection have been developed. Binary logistic regression is then applied to verify the Significance Level of the extracted features for predicting personality types. Experimental results demonstrate that the proposed scheme is able to predict several types of personality combination or type pairs with relatively high precisions.


Beyond Frequencies: Graph Pattern Mining in Multi-weighted Graphs


title: A perspective on distortions

abstract: A framework for analyzing distortions in non-single viewpoint imaging systems is presented. Such systems possess loci of viewpoints called caustics. In general, perspective (or undistorted) views cannot be computed from images acquired with such systems without knowing scene structure. Views computed without scene structure will exhibit distortions, which we call caustic distortions. We first introduce a taxonomy of distortions based on the geometry of imaging systems. Then, we derive a metric to quantify caustic distortions. We present an algorithm to compute minimally distorted views using simple priors on scene structure. These priors are defined as parameterized primitives such as spheres, planes and cylinders with simple uncertainty models for the parameters. To validate our method, we conducted extensive experiments on rendered and real images. In all cases our method produces nearly undistorted views even though the acquired images were strongly distorted. We also provide an approximation of the above method that warps the entire captured image into a quasi single viewpoint representation that can be used by any "viewer" to compute near-perspective views in real-time.


title: Agent-Based Modeling of the Spread of Influenza-Like Illness in an Emergency Department: A Simulation Study

abstract: The objective of this paper was to develop an agent based modeling framework in order to simulate the spread of influenza virus infection on a layout based on a representative hospital emergency department in Winnipeg, Canada. In doing so, the study complements mathematical modeling techniques for disease spread, as well as modeling applications focused on the spread of antibiotic-resistant nosocomial infections in hospitals. Twenty different emergency department scenarios were simulated, with further simulation of four infection control strategies. The agent based modeling approach represents systems modeling, in which the emergency department was modeled as a collection of agents (patients and healthcare workers) and their individual characteristics, behaviors, and interactions. The framework was coded in C + + using Qt4 libraries running under the Linux operating system. A simple ordinary least squares (OLS) regression was used to analyze the data, in which the percentage of patients that be came infected in one day within the simulation was the dependent variable. The results suggest that within the given instance con text, patient-oriented infection control policies (alternate treatment streams, masking symptomatic patients) tend to have a larger effect than policies that target healthcare workers. The agent-based modeling framework is a flexible tool that can be made to reflect any given environment; it is also a decision support tool for practitioners and policymakers to assess the relative impact of infection control strategies. The framework illuminates scenarios worthy of further investigation, as well as counterintuitive findings.


title: Domain specialization: a post-training domain adaptation for Neural Machine Translation

abstract: Domain adaptation is a key feature in Machine Translation. It generally encompasses terminology, domain and style adaptation, especially for human post-editing workflows in Computer Assisted Translation (CAT). With Neural Machine Translation (NMT), we introduce a new notion of domain adaptation that we call "specialization" and which is showing promising results both in the learning speed and in adaptation accuracy. In this paper, we propose to explore this approach under several perspectives.


title: Cluster-based Web Summarization

abstract: We propose a novel approach to abstractive Web summarization based on the observation that summaries for similar URLs tend to be similar in both content and structure. We leverage existing URL clusters and construct per-cluster word graphs that combine known summaries while abstracting out URL-specific attributes. The resulting topology, conditioned on URL features, allows us to cast the summarization problem as a structured learning task using a lowest cost path search as the decoding step. Early experimental results on a large number of URL clusters show that this approach is able to outperform previously proposed Web summarizers.


title: Multiple-organ segmentation by graph cuts with supervoxel nodes

abstract: Improvement in medical imaging technologies has made it possible for doctors to directly look into patients' bodies in ever finer details. However, since only the cross-sectional image can be directly seen, it is essential to segment the volume into organs so that their shape can be seen as 3D graphics of the organ boundary surfaces. Segmentation is also important for quantitative measurement for diagnosis. Here, we introduce a novel higher-precision method to segment multiple organs using graph cuts within medical images such as CT-scanned images. We utilize super voxels instead of voxels as the units of segmentation, i.e., the nodes in the graphical model, and design the energy function to minimize accordingly. We utilize SLIC super voxel algorithm and verify the performance of our segmentation algorithm by energy minimization comparing to the ground truth.


title: Assessing Consumer Emotional Responses in the Presence and Absence of Critical Quality Attributes: A Case Study with Chicken Eggs

abstract: Effects of attribute presence and absence on the emotional profile and consumer acceptability of products with varying qualities were assessed using eggs as an example. An online survey (n = 320) was used to evaluate emotional responses and acceptability to 5 types of egg quality attributes: intrinsic, aesthetic, extrinsic, expediency, and wholesome/safety, for both present and absent conditions. Attribute absence rather than presence evoked greater consumer discriminating emotions associated with eggs. Mean emotion intensity elicited by the presence of all quality attributes ranged from 1.67 (intrinsic; guilty) to 4.05 (wholesome; good) versus 2.01 (wholesome; satisfied) to 3.29 (wholesome; disgusted) when absent. Key positive emotions elicited by presence of attributes were active, calm, good, interested, happy, safe, and satisfied; while dominant negative emotions elicited by absence of attributes included disgusted and worried. Wholesome quality (constituted by egg freshness, “packing/best-before-date” and absence of visible cracks) exhibited the highest liking (7.65) and emotion intensities, while the emotional responses to both the presence and absence of intrinsic quality (constituted by nutrient-fortified egg, organic egg, and USDA-certified farm egg) were similar, reflecting their dynamic effects on emotions. Emotions and acceptability were more correlated for attribute absence than presence; and good, happy, and satisfied emotions were strongly related to egg acceptability (r ≥ 0.6). Egg product/packaging design can be oriented toward emphasizing wholesome and expedient attributes, since they enhance good, safe, and satisfied emotions, while minimizing disgust, worry, and boredom. The use of emotional responses and hedonic testing regarding attribute presence and absence would allow for improved selection of attributes critical to consumer acceptance of products.


title: Accurate Segmentation of Partially Overlapping Cervical Cells Based on Dynamic Sparse Contour Searching and GVF Snake Model

abstract: Overlapping cells segmentation is one of the challenging topics in medical image processing. In this paper, we propose to approximately represent the cell contour as a set of sparse contour points, which can be further partitioned into two parts: the strong contour points and the weak contour points. We consider the cell contour extraction as a contour points locating problem and propose an effective and robust framework for segmentation of partially overlapping cells in cervical smear images. First, the cell nucleus and the background are extracted by a morphological filtering-based K-means clustering algorithm. Second, a gradient decomposition-based edge enhancement method is developed for enhancing the true edges belonging to the center cell. Then, a dynamic sparse contour searching algorithm is proposed to gradually locate the weak contour points in the cell overlapping regions based on the strong contour points. This algorithm involves the least squares estimation and a dynamic searching principle, and is thus effective to cope with the cell overlapping problem. Using the located contour points, the Gradient Vector Flow Snake model is finally employed to extract the accurate cell contour. Experiments have been performed on two cervical smear image datasets containing both single cells and partially overlapping cells. The high accuracy of the cell contour extraction result validates the effectiveness of the proposed method.


title: A Survey and Comparison of Characteristics of Motor Drives Used in Electric Vehicles

abstract: In recent times, the worldwide price of fuel is showing an upward surge. One of the major factors leading to this can be attributed to the exponential increase in demand. In a country like Canada, where a majority of the people own vehicles, and more being added to the roads, this demand for fuel is surely going to increase in the future and will also be severely damaging to the environment as transportation sector alone is responsible for a larger share of pollutants emitted into the atmosphere. Electric vehicles offer one way to reduce the level of emissions. Electric motor drives are an integral component of an electric vehicle and consist of one or more electric motors. In this paper an effort has been made to compare different characteristics of motor drives used in electric vehicles and also given is a comprehensive list of references papers published in the field of electric vehicles.


title: Resting state networks and consciousness Alterations of multiple resting state network connectivity in physiological, pharmacological, and pathological consciousness states

abstract: In order to better understand the functional contribution of resting state activity to conscious cognition, we aimed to review increases and decreases in fMRI functional connectivity under physiological (sleep), pharmacological (anesthesia) and pathological altered states of consciousness, such as brain death, coma, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state. The reviewed RSNs were the DMN, left and right executive control, salience, sensorimotor, auditory and visual networks. We highlight some methodological issues concerning resting state analyses in severely injured brains mainly in terms of hypothesis-driven seed-based correlation analysis and data-driven independent components analysis approaches. Finally, we attempt to contextualize our discussion within theoretical frameworks of conscious processes. We think that this “lesion” approach allows us to better determine the necessary conditions under which normal conscious cognition takes place. At the clinical level, we acknowledge the technical merits of the resting state paradigm. Indeed, fast and easy acquisitions are preferable to activation paradigms in clinical populations. Finally, we emphasize the need to validate the diagnostic and prognostic value of fMRI resting state measurements in non-communicating brain damaged patients.


title: Recursive Binary Neural Network Learning Model with 2.28b/Weight Storage Requirement

abstract: This paper presents a storage-efficient learning model titled Recursive Binary Neural Networks for embedded and mobile devices having a limited amount of on-chip data storage such as hundreds of kilo-Bytes. The main idea of the proposed model is to recursively recycle data storage of weights (parameters) during training. This enables a device with a given storage constraint to train and instantiate a neural network classifier with a larger number of weights on a chip, achieving better classification accuracy. Such efficient use of on-chip storage reduces off-chip storage accesses, improving energy-efficiency and speed of training. We verified the proposed training model with deep and convolutional neural network classifiers on the MNIST and voice activity detection benchmarks. For the deep neural network, our model achieves data storage requirement of as low as 2 bits/weight, whereas the conventional binary neural network learning models require data storage of 8 to 32 bits/weight. With the same amount of data storage, our model can train a bigger network having more weights, achieving 1% less test error than the conventional binary neural network learning model. To achieve the similar classification error, the conventional binary neural network model requires 4× more data storage for weights than our proposed model. For the convolution neural network classifier, the proposed model achieves 2.4% less test error for the same on-chip storage or 6× storage savings to achieve the similar accuracy.


title: Links between metabolism and cancer

abstract: Metabolism generates oxygen radicals, which contribute to oncogenic mutations. Activated oncogenes and loss of tumor suppressors in turn alter metabolism and induce aerobic glycolysis. Aerobic glycolysis or the Warburg effect links the high rate of glucose fermentation to cancer. Together with glutamine, glucose via glycolysis provides the carbon skeletons, NADPH, and ATP to build new cancer cells, which persist in hypoxia that in turn rewires metabolic pathways for cell growth and survival. Excessive caloric intake is associated with an increased risk for cancers, while caloric restriction is protective, perhaps through clearance of mitochondria or mitophagy, thereby reducing oxidative stress. Hence, the links between metabolism and cancer are multifaceted, spanning from the low incidence of cancer in large mammals with low specific metabolic rates to altered cancer cell metabolism resulting from mutated enzymes or cancer genes.


title: Path planning and intelligent scheduling of multi-AGV systems in workshop

abstract: With the continuous development of intelligent logistics, the application of Automated Guided Vehicles (AGVs) increasingly becomes popular in many industrial fields. However, there are a series of problems in multi-AGV systems, such as resource allocation, conflict and deadlock. It is difficult to plan the shortest path for each AGV without conflict and collision in multi-AGV systems. In this paper, a multi-AGV scheduling system in workshop is established by using the unidirectional directed graph method and the A∗ algorithm for path planning of AGVs. In addition, the system is realized by programming and a simulation experiment of 20 AGVs is set up. Finally, the simulation results show that the system can effectively solve the conflict problem of AGVs, and is stable and high real-time. The system is easily extended to their similar multi-AGV scheduling systems, and has a great application value.


SCHIZOPHRENIA SPECTRUM AND OTHER PSYCHOTIC DISORDERS


title: A DPLL-based per core variable frequency clock generator for an eight-core POWER7™ microprocessor

abstract: A per-core clock generator for the eight-core POWER7™ processor is implemented with a digital PLL. This frequency generator is capable of smooth, controlled frequency slewing, minimizing the impact of di/dt. Frequency can be dynamically adjusted while the clock is running, and without skipping any cycles, thus enabling aggressive power management techniques.


title: Automatic Retrieval and Clustering of Similar Words

abstract: Bootstrapping semantics from text is one of the greatest challenges in natural language learning. We first define a word similarity measure based on the distributional pattern of words. The similarity measure allows us to construct a thesaurus using a parsed corpus. We then present a new evaluation methodology for the automatically constructed thesaurus. The evaluation results show that the thesaurus is significantly closer to WordNet than Roget Thesaurus is.


title: 3D spatial interaction: applications for art, design, and science

abstract: 3D interfaces use motion sensing, physical input, and spatial interaction techniques to effectively control highly dynamic virtual content. Now, with the advent of the Nintendo Wii, Sony Move, and Microsoft Kinect, game developers and researchers must create compelling interface techniques and game-play mechanics that make use of these technologies. At the same time, it is becoming increasingly clear that emerging game technologies are not just going to change the way we play games, they are also going to change the way we make and view art, design new products, analyze scientific datasets, and more. This introduction to 3D spatial interfaces demystifies the workings of modern videogame motion controllers and provides an overview of how it is used to create 3D interfaces for tasks such as 2D and 3D navigation, object selection and manipulation, and gesture-based application control. Topics include the strengths and limitations of various motion-controller sensing technologies in today's peripherals, useful techniques for working with these devices, and current and future applications of these technologies to areas beyond games. The course presents valuable information on how to utilize existing 3D user-interface techniques with emerging technologies, how to develop interface techniques, and how to learn from the successes and failures of spatial interfaces created for a variety of application domains.


title: Impact of carboplatin plus paclitaxel combined with endostar against A375 melanoma cells: An in vitro and in vivo analysis.

abstract: PURPOSE ::: The aim of the present study was to investigate the efficacy of carboplatin plus paclitaxel (CP) combined with endostar against A375 melanoma cells in vitro and in vivo. ::: ::: ::: METHODS ::: Effects on the cell viability and apoptosis induction were estimated with the Cell counting Kit-8 assay and Annexin V-FITC/Propidium Iodide staining. Fifty female BALB/c-nude mice with subcutaneous injection of A375 cells were randomized to be treated with normal saline, dacarbazine alone, dacarbazine plus endostar, carboplatin plus paclitaxel, and CP plus endostar. Tumor volume of mice was monitored after injection and survival time was adopted for survival analysis. ::: ::: ::: RESULTS ::: CP plus endostar significantly decreased the cell survival rate compared with CP (P<0.01). Combination of CP and endostar showed higher cytotoxicity to A375 cells in vitro than endostar plus dacarbazine (P<0.01). The percentage of apoptotic cells in A375 cells treated with CP plus endostar was appreciably higher when compared to CP group (P<0.05). The mean relative tumor size in CP group was definitely larger (p<0.05) than CP plus endostar group. In addition, the mean survival time in CP plus endostar group was notably elevated compared with the CP group (P<0.05). ::: ::: ::: CONCLUSIONS ::: Our data indicated that treatment with CP plus endostar significantly reduced cell growth and induced a high rate of apoptotic cells in the A375 melanoma cell line. CP and endostar exhibited synergistic anti-tumor activities in A375 melanoma cells in vitro. CP plus endostar suppressed the growth of xenograft tumors and prolonged the survival time of mice with xenograft tumors. Combination of CP and endostar may be a promising treatment for melanoma.


title: Tighter Bounds for Graph Steiner Tree Approximation

abstract: The classical Steiner tree problem in weighted graphs seeks a minimum weight connected subgraph containing a given subset of the vertices (terminals). We present a new polynomial-time heuristic that achieves a best-known approximation ratio of $1 + \frac{\ln 3}{2} \approx 1.55$ for general graphs and best-known approximation ratios of $\approx 1.28$ for both quasi-bipartite graphs (i.e., where no two nonterminals are adjacent) and complete graphs with edge weights 1 and 2. Our method is considerably simpler and easier to implement than previous approaches. We also prove the first known nontrivial performance bound ($1.5 \cdot$ OPT) for the iterated 1-Steiner heuristic of Kahng and Robins in quasi-bipartite graphs.


title: Detecting research fronts using different types of weighted citation networks

abstract: In this paper, we investigate the performance of types of weighted citation network for detecting emerging research fronts by a comparative study. Some types of citation network, such as direct citation, co-citation and bibliographic citation were tested in some research domains like complex networks. In this paper, some types of citation networks were constructed for each research domain, and the papers in those domains were divided into clusters to detect the research front. Additionally, we employ some measures for evaluating the research fronts to weighted citation networks. For instance, average publication years and similarities of keywords are effective measures to detect research fronts. By introducing these measures as weights of citation networks to the citation network, we can detect research fronts and promising fields compared with the non-weighted citation networks. We perform a comparative study to investigate the performance of type of weighted citation networks for detecting emerging research field. Especially, we evaluate the performance of each type of weighted citation networks in detecting a research front by using the following measures of papers in the cluster: visibility, measured by normalized cluster size, speed, topological relevance, and density.


title: Visualizing RFM Segmentation ABSTRACT

abstract: Segmentation based on RFM (Recency, Frequency, and Monetary) has been used for over 50 years by direct marketers to target a subset of their customers, save mailing costs, and improve profits. RFM analysis is commonly performed using the Arthur Hughes method, which bins each of the three RFM attributes independently into five equal frequency bins. The resulting 125 cells are depicted in a tabular format or as bar graphs and analyzed by marketers, who determine the best cells (customer segments) to target. We propose an interactive visualization of RFM that helps marketers visualize and quickly identify important customer segments. Additionally, we show an integrated filtering approach that allows marketers to interactively explore the RFM segments in relation to other customer attributes, such as behavioral or demographic, to identify interesting subsegments in the customer base. We depict these RFM visualizations on two large real-world data sets and discuss how customers have used these visualizations in practice to glean interesting insights from their data. Given, the widespread use of RFM as a critical, and many times the only, segmentation tool, we believe that the proposed intuitive and interactive visualization will provide significant business value.


title: Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute Recognition

abstract: The difficulty of image recognition has gradually increased from general category recognition to fine-grained recognition and to the recognition of some subtle attributes such as temperature and geolocation. In this paper, we try to focus on the classification between sunrise and sunset and hope to give a hint about how to tell the difference in subtle attributes. Sunrise vs. sunset is a difficult recognition task, which is challenging even for humans. Towards understanding this new problem, we first collect a new dataset made up of over one hundred webcams from different places. Since existing algorithmic methods have poor accuracy, we propose a new pairwise learning strategy to learn features from selective pairs of images. Experiments show that our approach surpasses baseline methods by a large margin and achieves better results even compared with humans. We also apply our approach to existing subtle attribute recognition problems, such as temperature estimation, and achieve state-of-the-art results.


title: Taxonomy of intrusion risk assessment and response system

abstract: In recent years, we have seen notable changes in the way attackers infiltrate computer systems compromising their functionality. Research in intrusion detection systems aims to reduce the impact of these attacks. In this paper, we present a taxonomy of Intrusion Response Systems (IRS) and Intrusion Risk Assessment (IRA), two important components of an intrusion detection solution. We achieve this by classifying a number of studies published during the last two decades. We discuss the key features of existing IRS and IRA. We show how characterizing security risks and choosing the right countermeasures are an important and challenging part of designing an IRS and an IRA. Poorly designed IRS and IRA may reduce network performance and wrongly disconnect users from a network. We propose techniques on how to address these challenges and highlight the need for a comprehensive defense mechanism approach. We believe that this taxonomy will open up interesting areas for future research in the growing field of intrusion risk assessment and response systems.


title: Entity Linking meets Word Sense Disambiguation: a Unified Approach

abstract: Entity Linking (EL) and Word Sense Disambiguation (WSD) both address the lexical ambiguity of language. But while the two tasks are pretty similar, they differ in a fundamental respect: in EL the textual mention can be linked to a named entity which may or may not contain the exact mention, while in WSD there is a perfect match between the word form (better, its lemma) and a suitable word sense. In this paper we present Babelfy, a unified graph-based approach to EL and WSD based on a loose identification of candidate meanings coupled with a densest subgraph heuristic which selects high-coherence semantic interpretations. Our experiments show state-of- the-art performances on both tasks on 6 different datasets, including a multilingual setting. Babelfy is online at http://babelfy.org


title: 48-V Voltage Regulator Module With PCB Winding Matrix Transformer for Future Data Centers

abstract: Efficient power delivery architecture is gaining more attention in the design of future generations of data centers in order to minimize the ever increasing trend of power consumption. Silicon-based power delivery architectures and designs have already reached a level of maturity in terms of efficiency and density. Recent studies indicate that 48-V voltage regulator modules (VRMs), instead of 12-V VRMs, are deemed a more efficient and cost effective architecture. In this paper a Gallium Nitride (GaN) based design of a two-stage solution is proposed. The first stage is a 48 V/12 V-250 W LLC converter with a matrix transformer design operating at 1.6 MHz with a peak efficiency of 97.3% and a power density of 870 W/in3. The second stage is a 12/1.8 V multiphase buck converter which can be either a conventional silicon-based or a GaN-based design with a higher operating frequency and power density. Furthermore, in this proposed two-stage architecture, a novel light load improvement mechanism is proposed by dynamically changing the bus voltage from 12 to 6 V during light load by which the system can achieve a more than 8% efficiency improvement.


title: Edge-Based Structural Similarity for Image Quality Assessment

abstract: Objective quality assessment has been widely used in image processing for decades and many researchers have been studying the objective quality assessment method based on Human Visual System (HVS). Recently the Structural Similarity (SSIM) is proposed, under the assumption that the HVS is highly adapted for extracting structural information from a scene, and simulation results have proved that it is better than PSNR (or MSE). By deeply studying the SSIM, we find it fails in measuring the badly blurred images. Based on this, we develop an improved method which is called Edge-based Structural Similarity (ESSIM). Experiment results show that ESSIM is more consistent with HVS than SSIM and PSNR especially for the blurred images.


title: Knowledge Base Question Answering Based on Deep Learning Models

abstract: This paper focuses on the task of knowledge-based question answering (KBQA). KBQA aims to match the questions with the structured semantics in knowledge base. In this paper, we propose a two-stage method. Firstly, we propose a topic entity extraction model (TEEM) to extract topic entities in questions, which does not rely on hand-crafted features or linguistic tools. We extract topic entities in questions with the TEEM and then search the knowledge triples which are related to the topic entities from the knowledge base as the candidate knowledge triples. Then, we apply Deep Structured Semantic Models based on convolutional neural network and bidirectional long short-term memory to match questions and predicates in the candidate knowledge triples. To obtain better training dataset, we use an iterative approach to retrieve the knowledge triples from the knowledge base. The evaluation result shows that our system achieves an \(\text {Average} F_1\) measure of 79.57% on test dataset.


title: A Novel Anti-forensics Technique for the Android OS

abstract: In recent years traditional mobile-phones, used only to make calls and send text messages, have evolved into even more versatile and powerful devices (smart phones, tablets, etc.). These devices use a NAND flash memory type to store data, due to it being a memory that has been optimized for the fast updating of data. These flash memory drives usually contain sensitive data that could be a possible danger to the user's privacy. This paper proposes a new anti-forensics technique for mobile devices with the Android OS. The technique makes it possible to modify and erase, securely and selectively, the digital evidence on an Android device without having to use any cryptographic primitives or make any file system changes. While the use of cryptographic primitives or changes to the file system create considerable suspicion in a forensic analysis, the proposed technique uses simple software tools commonly used in *nix-like OSes such as the Android OS.


title: Providing privacy, safety, and security in IoT-based transactive energy systems using distributed ledgers

abstract: Power grids are undergoing major changes due to rapid growth in renewable energy resources and improvements in battery technology. While these changes enhance sustainability and efficiency, they also create significant management challenges as the complexity of power systems increases. To tackle these challenges, decentralized Internet-of-Things (IoT) solutions are emerging, which arrange local communities into transactive microgrids. Within a transactive microgrid, "prosumers" (i.e., consumers with energy generation and storage capabilities) can trade energy with each other, thereby smoothing the load on the main grid using local supply. It is hard, however, to provide security, safety, and privacy in a decentralized and transactive energy system. On the one hand, prosumers' personal information must be protected from their trade partners and the system operator. On the other hand, the system must be protected from careless or malicious trading, which could destabilize the entire grid. This paper describes Privacy-preserving Energy Transactions (PETra), which is a secure and safe solution for transactive microgrids that enables consumers to trade energy without sacrificing their privacy. PETra builds on distributed ledgers, such as blockchains, and provides anonymity for communication, bidding, and trading.


PERCEPTION TOWARDS ONLINE SHOPPING : AN EMPIRICAL STUDY OF INDIAN CONSUMERS


title: Axiomatic Analysis of Smoothing Methods in Language Models for Pseudo-Relevance Feedback

abstract: Pseudo-Relevance Feedback (PRF) is an important general technique for improving retrieval effectiveness without requiring any user effort. Several state-of-the-art PRF models are based on the language modeling approach where a query language model is learned based on feedback documents. In all these models, feedback documents are represented with unigram language models smoothed with a collection language model. While collection language model-based smoothing has proven both effective and necessary in using language models for retrieval, we use axiomatic analysis to show that this smoothing scheme inherently causes the feedback model to favor frequent terms and thus violates the IDF constraint needed to ensure selection of discriminative feedback terms. To address this problem, we propose replacing collection language model-based smoothing in the feedback stage with additive smoothing, which is analytically shown to select more discriminative terms. Empirical evaluation further confirms that additive smoothing indeed significantly outperforms collection-based smoothing methods in multiple language model-based PRF models.


title: Natural Image Denoising with Convolutional Networks

abstract: We present an approach to low-level vision that combines two main ideas: the use of convolutional networks as an image processing architecture and an unsupervised learning procedure that synthesizes training samples from specific noise models. We demonstrate this approach on the challenging problem of natural image denoising. Using a test set with a hundred natural images, we find that convolutional networks provide comparable and in some cases superior performance to state of the art wavelet and Markov random field (MRF) methods. Moreover, we find that a convolutional network offers similar performance in the blind de-noising setting as compared to other techniques in the non-blind setting. We also show how convolutional networks are mathematically related to MRF approaches by presenting a mean field theory for an MRF specially designed for image denoising. Although these approaches are related, convolutional networks avoid computational difficulties in MRF approaches that arise from probabilistic learning and inference. This makes it possible to learn image processing architectures that have a high degree of representational power (we train models with over 15,000 parameters), but whose computational expense is significantly less than that associated with inference in MRF approaches with even hundreds of parameters.


Bricolage: A Structured-Prediction Algorithm for Example-Based Web Design


title: A voice-commandable robotic forklift working alongside humans in minimally-prepared outdoor environments

abstract: One long-standing challenge in robotics is the realization of mobile autonomous robots able to operate safely in existing human workplaces in a way that their presence is accepted by the human occupants. We describe the development of a multi-ton robotic forklift intended to operate alongside human personnel, handling palletized materials within existing, busy, semi-structured outdoor storage facilities.


title: Reduced-sized voltage-mode driver for high-speed I/O utilizing dynamic current-driven bulk biasing

abstract: In this paper, a modified structure for low-swing voltage-mode drivers in high-speed serial links is proposed. A dynamic current-driven bulk-biasing technique is applied to the driver transistors to achieve proper channel impedance termination and to minimize the transistors' sizes. Simulation results of an 8-Gb/s serial link in all process corners of a 0.13-μm CMOS technology confirm that, by using the proposed dynamic current-driven bulk-biasing technique, the total size of the driver transistors is reduced by 23%. Moreover, the power overhead of the proposed technique is only less than 0.02% of the total power consumption of the driver.


title: gSpan: graph-based substructure pattern mining

abstract: We investigate new approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm called gSpan (graph-based substructure pattern mining), which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs, and maps each graph to a unique minimum DFS code as its canonical label. Based on this lexicographic order gSpan adopts the depth-first search strategy to mine frequent connected subgraphs efficiently. Our performance study shows that gSpan substantially outperforms previous algorithms, sometimes by an order of magnitude.


title: FL-GUARD: A Detection and Defense System for DDoS Attack in SDN

abstract: This paper proposed a new detection and prevention system against DDoS (Distributed Denial of Service) attack in SDN (software defined network) architecture, FL-GUARD (Floodlight-based guard system). Based on characteristics of SDN and centralized control, etc., FL-GUARD applies dynamic IP address binding to solve the problem of IP spoofing, and uses 3.3.2 C-SVM algorithm to detect attacks, and finally take advantage of the centralized control of software-defined network to issue flow tables to block attacks at the source port. The experiment results show the effectiveness of our system. The modular design of FL-GUARD lays a good foundation for the future improvement.


title: BSA: A Complete Coverage Algorithm

abstract: The Backtracking Spiral Algorithm (BSA) is a coverage strategy for mobile robots based on the use of spiral filling paths; in order to assure the completeness, unvisited regions are marked and covered by backtracking mechanism. The BSA basic algorithm is designed to work in an environment modeled by a coarse-grain grid. BSA has been extended to cover, not only the free cells, but also the partially occupied ones. In this paper, the concepts and algorithms used to extend BSA are introduced. The ideas used to extend BSA are generic, thus a similar approach can be used to extend most of the grid-based coverage algorithms. Finally, some simulation results that demonstrate that BSA performs a complete coverage are presented.


title: Extended Medium Observation (EMO) — A load-aware metric for routing in wireless mesh backbone networks

abstract: The main advantage of using specific routing algorithms like AODV-ST or OLSR in wireless mesh backbone networks is their optimized handling of throughput from border nodes to each other. However, currently used metrics tend to ignore load situations and thus result in overloaded clusters within the mesh network. Our work presents a novel approach for load-based routing. We developed load observation methods and proved their practical applicability within fixed IEEE 802.11a structures. The Extended Medium Observation (EMO)-metric is based on a sophisticated concept of an advanced channel observation process, in which topological and load sensitive characteristics become elicited, quantified and composed. A direct comparison of EMO-AODV, a modified version of AODV using EMO with conventional metrics show that EMO-AODV achieves a kind of traffic balancing that tends to smooth load peaks.


title: A robotic structured light camera

abstract: Structured Light 3D scanners have released a stampede of successful applications in robotics. This opens up the possibility to identify subtle deficiencies of actual sensors and propose solutions. In this paper we present the design of a novel Structured Light scanner that defies the conventional and simplistic static cameras-projector configuration. We found that by adding some degrees of freedom to the cameras it is possible to improve the capabilities of the system, for example reducing the minimum operating distance and increasing the depth estimation accuracy. This multidisciplinary project involves the fields of mechanics, electronics, control, optics and computer engineering, and aims to design a powerful tool, for academic and industrial applications.


title: LCAO MO CALCULATIONS ON SATURATED HYDROCARBONS AND THEIR SUBSTITUTED DERIVATIVES

abstract: The simple semiempirical molecular orbital method was applied to saturated hydrocarbons in three different approximations. Electronic charge distribution diagrams were obtained. Characteristic differences between saturated and conjugated compounds concerning bond localization and the effect of an electronegative substituent can be interpreted on the basis of these diagrams. The 1s electrons of the hydrogens are introduced for the first time in semiempirical molecular orbital calculations.


title: ExpLOD: A Framework for Explaining Recommendations based on the Linked Open Data Cloud

abstract: In this paper we present ExpLOD, a framework which exploits the information available in the Linked Open Data (LOD) cloud to generate a natural language explanation of the suggestions produced by a recommendation algorithm. The methodology is based on building a graph in which the items liked by a user are connected to the items recommended through the properties available in the LOD cloud. Next, given this graph, we implemented some techniques to rank those properties and we used the most relevant ones to feed a module for generating explanations in natural language. In the experimental evaluation we performed a user study with 308 subjects aiming to investigate to what extent our explanation framework can lead to more transparent, trustful and engaging recommendations. The preliminary results provided us with encouraging findings, since our algorithm performed better than both a non-personalized explanation baseline and a popularity-based one.


title: Performance Limits for Stiffness-Critical Graphitic Foam Structures. Part II: Comparisons of Foams, Foam-Core and Honeycomb-Core Sandwiches in Bending/Shear

abstract: Part I of this study [1] described the concept of graphitic structural foams and indicated their substantial potential for saving mass in stiffness-critical structures. Upper bounds, considered achievable, on ligament moduli were assumed in a well-known, empirically-based relation [2] modeling the foam stiffness versus porosity. The resulting hypothetical graphitic foams were compared to commercially available, high-modulus solids and foams for simple structural applications, and were found to offer the potential for dramatic mass reductions. The present paper compares the potential stiffness-critical performances of neat graphitic foams in plate cylindrical bending to those of honeycombcore sandwich materials, as well as evaluating the foams as potential sandwich core materials. These evaluations require estimates of the foam shear modulus and shear deformation, which were not included in Part I, in order to model the foam deflection to the order standard for sandwich modeling. Three separate foam constitutive models ([2], [3] and elements of [2]-[4]) are considered in the plate bending comparisons, based on upper bound ligament stiffnesses in analogy to [1] ; all three assumptions produce similar results, especially for foam porosities exceeding 90 percent. It is found that the influence of shear deformation [5] grows dramatically at porosities exceeding 97 percent, where higher-order theories should be appealed to for accuracy. As the construction of a sandwich of a given stiffness is not unique for given facing and core materials, only mass-optimum sandwiches were compared ; each sandwich incorporated the same graphite-epoxy material for face sheets. The neat graphitic foams and foam core sandwiches were found to be competitive on mass and thickness bases with the honeycomb-core sandwich materials for porosities exceeding roughly 90 percent. The graphitic foams and foam-core sandwiches offer the potential for increased in-plane stiffnesses together with the bending stiffnesses associated with honeycomb sandwiches. Finally, the graphitic foams and foam-core sandwiches are briefly compared to the solid materials considered in Part I, including now the effects of shear deformation, and the foams are found to offer mass advantages similar to those predicted in Part I. In summary, Parts I and II indicate that graphitic foams offer exciting potential as additional materials for the design engineer's menu, and may be especially advantageous for stiffness-critical applications involving complex loadings/geometries where minimum mass drives the design.


title: Performance variation of morphed face image detection algorithms across different datasets

abstract: In past years, different researchers have shown the vulnerability of face recognition systems to attacks based on morphed face images. More recently, first morph detection subsystems have been proposed to automatically detect this kind of fraud. While some algorithms have been reported to reveal practical detection performance on individual datasets a systematic analysis of proposed detectors with respect to their robustness across different databases has remained elusive. In this work, we evaluate the performance of different morph detection algorithms across disjoint datasets of 2,745 bona fide and 14,337 automatically generated morphed face images. Within a generic evaluation framework a systematic robustness estimation scheme is proposed to identify reliable detection algorithms. Finally, the robustness of algorithms which have been determined as most promising is verified on another disjoint dataset. Hence, this paper represents the first attempt towards a comprehensive cross-database performance evaluation and a systematic evaluation of the robustness of morphed face image detection algorithms.


title: ELM-based speckle reduction for medical ultrasound image

abstract: Reducing the speckle noise in ultrasound images plays an important role in image post-processing, e.g. image segmentation, edge detection and image registration. In this paper, a novel filtering method based on extreme learning machine (ELM) is proposed for ultrasound image denoising. We aim at generating a denoising model learned from the input samples. In the training step, pixel features from the noisy image and the target values from the original image are fed to ELM to learn a speckle reduction model. In the testing step, we obtain the denoising image via the learned model. A pilot study is performed to compare the proposed algorithm with other classical denoising methods using brain tumor ultrasound images. The experimental results show that our proposed method outperforms other methods in reducing speckle noise, and is effective to preserve image details.


title: DOI: 10.1080/10635150802429642 A Rapid Bootstrap Algorithm for the RAxML Web Servers

abstract: Despite recent advances achieved by application of high-performance computing methods and novel algorithmic techniques to maximum likelihood (ML)-based inference programs, the major computational bottleneck still consists in the computation of bootstrap support values. Conducting a probably insufficient number of 100 bootstrap (BS) analyses with current ML programs on large datasets-either with respect to the number of taxa or base pairs-can easily require a month of run time. Therefore, we have developed, implemented, and thoroughly tested rapid bootstrap heuristics in RAxML (Randomized Axelerated Maximum Likelihood) that are more than an order of magnitude faster than current algorithms. These new heuristics can contribute to resolving the computational bottleneck and improve current methodology in phylogenetic analyses. Computational experiments to assess the performance and relative accuracy of these heuristics were conducted on 22 diverse DNA and AA (amino acid), single gene as well as multigene, real-world alignments containing 125 up to 7764 sequences. The standard BS (SBS) and rapid BS (RBS) values drawn on the best-scoring ML tree are highly correlated and show almost identical average support values. The weighted RF (Robinson-Foulds) distance between SBS- and RBS-based consensus trees was smaller than 6% in all cases (average 4%). More importantly, RBS inferences are between 8 and 20 times faster (average 14.73) than SBS analyses with RAxML and between 18 and 495 times faster than BS analyses with competing programs, such as PHYML or GARLI. Moreover, this performance improvement increases with alignment size. Finally, we have set up two freely accessible Web servers for this significantly improved version of RAxML that provide access to the 200-CPU cluster of the Vital-IT unit at the Swiss Institute of Bioinformatics and the 128-CPU cluster of the CIPRES project at the San Diego Supercomputer Center. These Web servers offer the possibility to conduct large-scale phylogenetic inferences to a large part of the community that does not have access to, or the expertise to use, high-performance computing resources.


title: Avoiding Chinese Whispers: Controlling End-to-End Join Quality in Linked Open Data Stores

abstract: Today Linked Open Data is a central trend in information provisioning. Data is collected in distributed data stores, individually curated with high quality, and made available over the Web for a wide variety of Web applications providing their own business logic for data utilization. Thus, the key promise of Linked Open Data is to provide a holistic view for a wide range of data items or entities. But parallel to the problems of database integration or schema matching, linking data over several sources remains a challenge and is currently severely hampering the vision of a working Semantic Web. One possible solution are instance matching systems that automatically create owl:sameAs links between data stores. According to existing benchmarks, the matching quality has even reached a satisfying level. However, our extensive analysis shows that instance matching systems are not yet ready for large-scale data interlinking. This is because query processors joining even via a single incorrectly created link implicitly use also all transitive owl:sameAs links that may in turn be mismatched again. The result is similar to the game Chinese Whispers: watered-down sameAs semantics step-by-step lead to a terrible end-to-end quality of joins. We develop innovative structural mechanisms on top of instance matching systems to significantly improve query processing avoiding Chinese Whispers.


title: Bowden Cable Actuator for Force-Feedback Exoskeletons

abstract: This paper introduces a novel type of actuator that is investigated by ESA for force-reflection to a wearable exoskeleton. The actuator consists of a DC motor that is relocated from the joint by means of Bowden cable transmissions. The actuator shall support the development of truly ergonomic and compact wearable man-machine interfaces. Important Bowden cable transmission characteristics are discussed, which dictate a specific hardware design for such an actuator. A first prototype is shown, which was used to analyze these basic characteristics of the transmissions and to proof the overall actuation concept. A second, improved prototype is introduced, which is currently used to investigate the achievable performance as a master actuator in a master-slave control with force-feedback. Initial experimental results are presented, which show good actuator performance in a 4 channel control scheme with a slave joint. The actuator features low movement resistance in free motion and can reflect high torques during hard contact situations. High contact stability can be achieved. The actuator seems therefore well suited to be implemented into the ESA exoskeleton for space-robotic telemanipulation.


title: In the mood: engaging teenagers in psychotherapy using mobile phones

abstract: Mental illness is a significant and growing problem throughout the world. Many mental health problems have their root in childhood, and early intervention is recommended. Engaging young people in psychotherapeutic activities is challenging, and treatment adherence is often poor. This paper presents a series of studies carried out as part of the development of a mobile and online symptom tracking tool for adolescents with mental health problems. Teenagers use the system to record symptoms on their mobile phones and can view this information in a clinical setting with a therapist. We focus on a clinical pilot of the system with ten users in public mental health clinics. As well as indicating that the mobile diary tool can increase client adherence to therapeutic activities, the study yields insight into the factors influencing the success of the design and informs the design of other systems to be used as adjuncts to therapy.


title: A 82% efficiency 0.5% ripple 16-phase fully integrated capacitive voltage doubler

abstract: A fully integrated voltage doubler is presented. Using a 16-phase switching strategy, the output voltage ripple is reduced to less then 0.5% of the output voltage. To the author's knowledge, the peak efficiency and nominal efficiency of this voltage doubler, respectively 82% and 79%, exceed the efficiency of any known fully integrated converter, inductive as well as capacitive. The converter is fabricated in a 130nm CMOS process.


title: Compact 5.8-GHz Rectenna Using Stepped-Impedance Dipole Antenna

abstract: This letter describes a compact 5.8-GHz rectenna using a stepped-impedance dipole antenna. In comparison to the conventional uniform half-wavelength dipole antenna, the stepped-impedance dipole antenna shows a 23% length reduction. This stepped-impedance dipole antenna is then used to receive the microwave power for a rectenna. The rectenna shows a maximum conversion efficiency of 76% at 5.8 GHz with a load resistance of 250 Omega. Furthermore, the conversion efficiency is better than 67% from 5.725 to 5.875 GHz, which covers the entire 5.8-GHz ISM band.


title: A REVIEW ON DIABETES MILLETUS AND THE HERBAL PLANTS USED FOR ITS TREATMENT

abstract: Over the last century human life style and food habits have drastically changed which lead to various chronic diseases. Diabetes milletus is one such disease which is causing serious problems to human health. Diabetes Mellitus is a metabolic disorder characterized by hyperglycemia due to defect in insulin secretion, insulin action or both. Scientific reports revealed that diabetes cannot be cured completely. In addition to that rapid increase in diabetes milletus is becoming a serious threat to mankind in all parts of the world. Allopathic drugs have not shown any significant effect for the treatment of the disease. Hence focus has been turned towards traditional system of medicine. Medicinal plants play an important role in management of diabetes milletus. During the past few years many bioactive drugs have been isolated from plants. Herbal medicines have shown good clinical practice in the therapy of diabetic milletus. The present review gives detailed information about diabetes milletus and the various medicinal plants used in the treatment of the disease.