Query: Iterative deep convolutional encoder-decoder network for medical image segmentation

Relevant passages:
Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique <=SEP=> D EEP learning is a growing trend in general data analysis and has been termed one of the 10 breakthrough technologies of 2013 [1]. Deep learning is an improvement of artificial neural networks, consisting of more layers that permit higher levels of abstraction and improved predictions from data [2]. To date, it is emerging as the leading machine-learning tool in the general imaging and computer vision domains. In particular, convolutional neural networks (CNNs) have proven to be powerful tools for a broad range of computer vision tasks. Deep CNNs automatically learn mid-level and high-level abstractions obtained from raw data (e.g., images). Recent results indicate that the generic descriptors extracted from CNNs are extremely effective in object recognition and localization in natural images. Medical image analysis groups across the world are quickly entering the field and applying CNNs and other deep learning methodologies to a wide variety of applications. Promising results are emerging. In medical imaging, the accurate diagnosis and/or assessment of a disease depends on both image acquisition and image interpretation. Image acquisition has improved substantially over recent years, with devices acquiring data at faster rates and increased resolution. The image interpretation process, however, has only recently begun to benefit from computer technology. Most interpretations of medical images are performed by physicians; however, image interpretation by humans is limited due to its subjectivity, large variations across interpreters, and fatigue. Many diagnostic tasks require an initial search process to detect abnormalities, and to quantify measurements and changes over time. Computerized tools, specifically image analysis and machine learning, are the key enablers to improve diagnosis, by facilitating identification of the findings that require treatment and to support the expert’s workflow. Among these tools, deep learning is rapidly proving to be the state-of-the-art foundation, leading to improved accuracy. It has also opened up new frontiers in data analysis with rates of progress not before experienced.

PH2 - A dermoscopic image database for research and benchmarking <=SEP=> The increasing incidence of melanoma has recently promoted the development of computer-aided diagnosis systems for the classification of dermoscopic images. Unfortunately, the performance of such systems cannot be compared since they are evaluated in different sets of images by their authors and there are no public databases available to perform a fair evaluation of multiple systems. In this paper, a dermoscopic image database, called PH2, is presented. The PH2 database includes the manual segmentation, the clinical diagnosis, and the identification of several dermoscopic structures, performed by expert dermatologists, in a set of 200 dermoscopic images. The PH2 database will be made freely available for research and benchmarking purposes.

Ridge-based vessel segmentation in color images of the retina <=SEP=> A method is presented for automated segmentation of vessels in two-dimensional color images of the retina. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. The system is based on extraction of image ridges, which coincide approximately with vessel centerlines. The ridges are used to compose primitives in the form of line elements. With the line elements an image is partitioned into patches by assigning each image pixel to the closest line element. Every line element constitutes a local coordinate frame for its corresponding patch. For every pixel, feature vectors are computed that make use of properties of the patches and the line elements. The feature vectors are classified using a kNN-classifier and sequential forward feature selection. The algorithm was tested on a database consisting of 40 manually labeled images. The method achieves an area under the receiver operating characteristic curve of 0.952. The method is compared with two recently published rule-based methods of Hoover et al. and Jiang et al. . The results show that our method is significantly better than the two rule-based methods (p<0.01). The accuracy of our method is 0.944 versus 0.947 for a second observer.

Active Contours without Edges for Vector-Valued Images <=SEP=> In this paper, we propose an active contour algorithm for object detection in vectorvalued images (such as RGB or multispectral). The model is an extension of the scalar Chan–Vese algorithm to the vector-valued case [1]. The model minimizes a Mumford–Shah functional over the length of the contour, plus the sum of the fitting error over each component of the vector-valued image. Like the Chan–Vese model, our vector-valued model can detect edges both with or without gradient. We show examples where our model detects vector-valued objects which are undetectable in any scalar representation. For instance, objects with different missing parts in different channels are completely detected (such as occlusion). Also, in color images, objects which are invisible in each channel or in intensity can be detected by our algorithm. Finally, the model is robust with respect to noise, requiring no a priori denoising step. C © 2000 Academic Press

Very Deep Convolutional Networks for Large-Scale Image Recognition <=SEP=> In this work we investigate the effect of the convolutional n etwork depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth, which shows that a significant improvement on the prior-art configurations can be achi eved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first a nd he second places in the localisation and classification tracks respec tively. We also show that our representations generalise well to other datasets, whe re t y achieve the stateof-the-art results. Importantly, we have made our two bestp rforming ConvNet models publicly available to facilitate further research o n the use of deep visual representations in computer vision.

Building Better Detection with Privileged Information <=SEP=> Modern detection systems use sensor outputs available in the deployment environment to probabilistically identify attacks. These systems are trained on past or synthetic feature vectors to create a model of anomalous or normal behavior. Thereafter, run-time collected sensor outputs are compared to the model to identify attacks (or the lack of attack). While this approach to detection has been proven to be effective in many environments, it is limited to training on only features that can be reliably collected at test-time. Hence, they fail to leverage the often vast amount of ancillary information available from past forensic analysis and post-mortem data. In short, detection systems don’t train (and thus don’t learn from) features that are unavailable or too costly to collect at run-time. In this paper, we leverage recent advances in machine learning to integrate privileged information – features available at training time, but not at run-time– into detection algorithms. We apply three different approaches to model training with privileged information; knowledge transfer, model influence, and distillation, and empirically validate their performance in a range of detection domains. Our evaluation shows that privileged information can increase detector precision and recall: we observe an average of 4.8% decrease in detection error for malware traffic detection over a system with no privileged information, 3.53% for fast-flux domain bot detection, 3.33% for malware classification, 11.2% for facial user authentication. We conclude by exploring the limitations and applications of different privileged information techniques in detection systems.

Combining Textual Entailment and Argumentation Theory for Supporting Online Debates Interactions <=SEP=> Blogs and forums are widely adopted by online communities to debate about various issues. However, a user that wants to cut in on a debate may experience some difficulties in extracting the current accepted positions, and can be discouraged from interacting through these applications. In our paper, we combine textual entailment with argumentation theory to automatically extract the arguments from debates and to evaluate their acceptability.

A multimode-beamforming 77-GHz FMCW radar system <=SEP=> In this work a multimode-beamforming 77-GHz frequency-modulated continuous-wave radar system is presented. Four transceiver chips with integrated inphase/quadrature modulators in the transmit path are used in order to simultaneously realize a short-range frequency-division multiple-access (FDMA) multiple-input multiple-output (MIMO) and a long-range transmit phased-array (PA) radar system with the same antennas. It combines the high angular resolution of FDMA MIMO radars and the high-gain and steerable beam of PA transmit antennas. Several measurements were carried out to show the potential benefits of using this concept for a linear antenna array with four antennas and methods of digital beamforming in the receive path.

X509Cloud — Framework for a ubiquitous PKI <=SEP=> The SSL protocol has been widely used for verifying digital identities and to secure Internet traffic since the early days of the web. Although X.509 certificates have been in existence for more than two decades, individual user uptake has been low due to the high cost of issuance and maintenance of such certs. This has led to a situation whereby users are able to verify the identity of an organization or e-commerce retailer via their digital certificate, but organizations have to rely on weak username and password combinations to verify the identity of customers registered with their service. We propose the X509Cloud framework which enables organizations to issue certificates to their users at zero cost, and allows them to securely store and disseminate client certificates using the Bitcoin inspired blockchain protocol. This in turn will enable organizations and individuals to authenticate and to securely communicate with other users on the Internet.

Finding and Optimizing Solvable Priority Schemes for Decoupled Path Planning Techniques for Teams of Mobile Robots <=SEP=> Coordinating the motion of multiple mobile robots is one of the fundamental problems in robotics. The predominant algorithms for coordinating teams of robots are decoupled and prioritized, thereby avoiding combinatorially hard planning problems typically faced by centralized approaches. While these methods are very efficient, they have two major drawbacks. First, they are incomplete, i.e. they sometimes fail to find a solution even if one exists, and second, the resulting solutions are often not optimal. In this paper we present a method for finding and optimizing priority schemes for such prioritized and decoupled planning techniques. Existing approaches apply a single priority scheme which makes them overly prone to failure in cases where valid solutions exist. By searching in the space of priorization schemes, our approach overcomes this limitation. It performs a randomized search with hill-climbing to find solutions and to minimize the overall path length. To focus the search, our algorithm is guided by constraints generated from the task specification. To illustrate the appropriateness of this approach, this paper discusses experimental results obtained with real robots and through systematic robot simulation. The experimental results illustrate the superior performance of our approach, both in terms of efficiency of robot motion and in the ability to find valid plans.

Magic decorator: automatic material suggestion for indoor digital scenes <=SEP=> Assigning textures and materials within 3D scenes is a tedious and labor-intensive task. In this paper, we present Magic Decorator, a system that automatically generates material suggestions for 3D indoor scenes. To achieve this goal, we introduce local material rules, which describe typical material patterns for a small group of objects or parts, and global aesthetic rules, which account for the harmony among the entire set of colors in a specific scene. Both rules are obtained from collections of indoor scene images. We cast the problem of material suggestion as a combinatorial optimization considering both local material and global aesthetic rules. We have tested our system on various complex indoor scenes. A user study indicates that our system can automatically and efficiently produce a series of visually plausible material suggestions which are comparable to those produced by artists.

The Impact of Node Selfishness on Multicasting in Delay Tolerant Networks <=SEP=> Due to the uncertainty of transmission opportunities between mobile nodes, delay tolerant networks (DTNs) exploit the opportunistic forwarding mechanism. This mechanism requires nodes to forward messages in a cooperative and selfish way. However, in the real word, most of the nodes exhibit selfish behaviors, such as individual and social selfishness. In this paper, we are the first to investigate how the selfish behaviors of nodes affect the performance of DTN multicast. We consider two typical multicast relaying schemes, namely, two-hop relaying and epidemic relaying, and study their performance in terms of average message transmission delay and transmission cost. Specifically, we model the message delivery process under selfish behaviors by a 3-D continuous time Markov chain; under this model, we derive closed-form formulas for the message transmission delay and cost. Then, we evaluate the accuracy of the proposed Markov chain model by comparing the theoretical results with the simulation results obtained by simulating the message dissemination under both two-hop and epidemic relaying with different network sizes and mobility models. Our study shows that different selfish behaviors may have different impacts on different performance metrics. In addition, selfish behaviors influence epidemic relaying more than two-hop relaying. Furthermore, our results show that the performance of multicast with selfish nodes depends on the multicast group size.

Implementing & Improvisation of K-means Clustering Algorithm <=SEP=> The clustering techniques are the most important part of the data analysis and k-means is the oldest and popular clustering technique used. The paper discusses the traditional K-means algorithm with advantages and disadvantages of it. It also includes researched on enhanced k-means proposed by various authors and it also includes the techniques to improve traditional K-means for better accuracy and efficiency. There are two area of concern for improving K-means; 1) is to select initial centroids and 2) by assigning data points to nearest cluster by using equations for calculating mean and distance between two data points. The time complexity of the proposed K-means technique will be lesser that then the traditional one with increase in accuracy and efficiency. The main purpose of the article is to proposed techniques to enhance the techniques for deriving initial centroids and the assigning of the data points to its nearest clusters. The clustering technique proposed in this paper is enhancing the accuracy and time complexity but it still needs some further improvements and in future it is also viable to include efficient techniques for selecting value for initial clusters(k). Experimental results show that the improved method can effectively improve the speed of clustering and accuracy, reducing the computational complexity of the k-means. Introduction Clustering is a process of grouping data objects into disjointed clusters so that the data in the same cluster are similar, but data belonging to different cluster differ. A cluster is a collection of data object that are similar to one another are in same cluster and dissimilar to the objects are in other clusters [k-4]. At present the applications of computer technology in increasing rapidly which Unnati R. Raval et al, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.5, May2016, pg. 191-203 © 2016, IJCSMC All Rights Reserved 192 created high volume and high dimensional data sets [10]. These data is stored digitally in electronic media, thus providing potential for the development of automatic data analysis, classification and data retrieval [10]. The clustering is important part of the data analysis which partitioned given dataset in to subset of similar data points in each subset and dissimilar to data from other clusters [1]. The clustering analysis is very useful with increasing in digital data to draw meaningful information or drawing interesting patters from the data sets hence it finds applications in many fields like bioinformatics, pattern recognition, image processing, data mining, marketing and economics etc [4]. There have been many clustering techniques proposed but K-means is one of the oldest and most popular clustering techniques. In this method the number of cluster (k) is predefined prior to analysis and then the selection of the initial centroids will be made randomly and it followed by iterative process of assigning each data point to its nearest centroid. This process will keep repeating until convergence criteria met. However, there are shortcomings of K-means, it is important to proposed techniques that enhance the final result of analysis. This article includes researched on papers [1,2,3,4,5,6] which made some very important improvements towards the accuracy and efficiency of the clustering technique. Basic K-means Algorithm : A centroid-based Clustering technique According to the basic K-mean clustering algorithm, clusters are fully dependent on the selection of the initial clusters centroids. K data elements are selected as initial centers; then distances of all data elements are calculated by Euclidean distance formula. Data elements having less distance to centroids are moved to the appropriate cluster. The process is continued until no more changes occur in clusters[k-1]. This partitioning clustering is most popular and fundamental technique [1]. It is vastly used clustering technique which requires user specified parameters like number of clusters k, cluster initialisation and cluster metric [2]. First it needs to define initial clusters which makes subsets (or groups) of nearest points (from centroid) inside the data set and these subsets (or groups) called clusters [1]. Secondly, it finds means value for each cluster and define new centroid to allocate data points to this new centroid and this iterative process will goes on until centroid [3] does not changes. The simplest algorithm for the traditional K-means [2] is as follows; Input: D = {d1, d2, d3,.......dn} // set of n numbers of data points K // The number of desire Clusters Output: A set of k clusters 1. Select k points as initial centroids. 2. Repeat 3. From K clusters by assigning each data point to its nearest centroid. 4. Recompute the centroid for each cluster until centroid does not change [2]. Unnati R. Raval et al, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.5, May2016, pg. 191-203 © 2016, IJCSMC All Rights Reserved 193 Start

An Analysis on the factors causing telecom churn: First Findings <=SEP=> The exponential growth in number of telecom providers and unceasing competition in the market has caused telecom operators to divert their focus more towards customer retention rather than customer acquisition. Hence it becomes extremely essential to identify the factors causing a customer to switch and take proactive actions in order to retain them. The paper aims to review existing literature to bring up a holistic view of various factors and their relationships that cause a customer to churn in the telecommunication industry. A total of 140 factors related to customer churn were compiled by reviewing several studies on customer churn behavior. The identified factors are then discussed in detail with their impact on customer’s switching behavior. The limitations of the existing studies and directions for future research have also been provided. The paper will aid practitioners and researchers by serving a strong base for their industrial practice and future research.

The role of knowledge in discourse comprehension: a construction-integration model. <=SEP=> In contrast to expectation-based, predictive views of discourse comprehension, a model is developed in which the initial processing is strictly bottom-up. Word meanings are activated, propositions are formed, and inferences and elaborations are produced without regard to the discourse context. However, a network of interrelated items is created in this manner, which can be integrated into a coherent structure through a spreading activation process. Data concerning the time course of word identification in a discourse context are examined. A simulation of arithmetic word-problem understanding provides a plausible account for some well-known phenomena in this area.

Co-evolution of Fitness Maximizers and Fitness Predictors <=SEP=> We introduce an estimation of distribution algorithm (EDA) based on co-evolution of fitness maximizers and fitness predictors for improving the performance of evolutionary search when evaluations are prohibitively expensive. Fitness predictors are lightweight objects which, given an evolving individual, heuristically approximate the true fitness. The predictors are trained by their ability to correctly differentiate between good and bad solutions using reduced computation. We apply co-evolving fitness prediction to symbolic regression and measure its impact. Our results show that the additional computational investment in training the co-evolving fitness predictors can greatly enhance both speed and convergence of individual solutions while overall reducing the number of evaluations. In application to symbolic regression, the advantage of using fitness predictors grows as the complexity of models increases.

An MLP-based representation of neural tensor networks for the RDF data models <=SEP=> In this paper, a new representation of neural tensor networks is presented. Recently, state-of-the-art neural tensor networks have been introduced to complete RDF knowledge bases. However, mathematical model representation of these networks is still a challenging problem, due to tensor parameters. To solve this problem, it is proposed that these networks can be represented as two-layer perceptron network. To complete the network topology, the traditional gradient based learning rule is then developed. It should be mentioned that for tensor networks there have been developed some learning rules which are complex in nature due to the complexity of the objective function used. Indeed, this paper is aimed to show that the tensor network can be viewed and represented by the two-layer feedforward neural network in its traditional form. The simulation results presented in the paper easily verify this claim.

PointCNN: Convolution On $\mathcal{X}$-Transformed Points <=SEP=> We present a simple and general framework for feature learning from point clouds. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local correlation in data represented densely in grids (e.g. images). However, point clouds are irregular and unordered, thus directly convolving kernels against features associated with the points will result in desertion of shape information and variance to point ordering. To address these problems, we propose to learn an X -transformation from the input points to simultaneously promote two causes: the first is the weighting of the input features associated with the points, and the second is the permutation of the points into a latent and potentially canonical order. Element-wise product and sum operations of the typical convolution operator are subsequently applied on the X -transformed features. The proposed method is a generalization of typical CNNs to feature learning from point clouds, thus we call it PointCNN. Experiments show that PointCNN achieves on par or better performance than state-of-the-art methods on multiple challenging benchmark datasets and tasks.

Increased corpus callosum size in musicians <=SEP=> Using in-vivo magnetic resonance morphometry it was investigated whether the midsagittal area of the corpus callosum (CC) would differ between 30 professional musicians and 30 age-, sex- and handedness-matched controls. Our analyses revealed that the anterior half of the CC was significantly larger in musicians. This difference was due to the larger anterior CC in the subgroup of musicians who had begun musical training before the age of 7. Since anatomic studies have provided evidence for a positive correlation between midsagittal callosal size and the number of fibers crossing through the CC, these data indicate a difference in interhemispheric communication and possibly in hemispheric (a)symmetry of sensorimotor areas. Our results are also compatible with plastic changes of components of the CC during a maturation period within the first decade of human life, similar to those observed in animal studies.

Integrated pathology and radiology learning for a musculoskeletal system module: an example of interdisciplinary integrated form <=SEP=> Introduction
Many curricula integrate radiology with anatomy courses but none of these curricula adopt integration of pathology with radiology as interdisciplinary form at the undergraduate level. The aim of the current study was to identify the outcome of interdisciplinary integrated course of pathology and radiology in musculoskeletal system module (MSK).


Methods
A comparative interventional study was conducted on 60 students representing a whole class of the third year of level V. MSK and gastrointestinal module (GIT) were selected as study and control module, respectively, as being adopted for the same level/allocated hours, enriched with many subject areas for both fields, and availability of learning resources for both. A planned interdisciplinary integrated course for MSK pathology and radiology was implemented in the pathology lab. The subject area was selected and taught for both fields in consecutive ways by pathology and radiology experts. After teaching, gross/histopathologic specimens and radiology imaging/reports were distributed over benches and the students investigated the same. Conversely, in GIT control module, both fields were delivered separately, and no interdisciplinary form of integration occurred. Students' scores for both fields were filtered from the objective structured practical exam, quiz, and final exam. Students' marks and satisfaction were subjected to multiple comparisons using independent student's t-test. SPSS version 17 was used.


Results
Significances were obtained between total marks of students for both modules and between radiology courses for both with P=0.0152 and 0.0199, respectively. Number of students who achieved >80% in MSK was 20 and 26 compared to 15 and 17 in GIT for pathology and radiology, respectively. Student satisfaction was high for interdisciplinary integration in MSK with significant difference obtained between MSK and GIT.


Conclusion
The integration of both fields augments student performance for both. This experience must encourage curriculum committee to globalize it over all other modules.

Born Digital: Understanding the First Generation of Digital Natives <=SEP=> Perhaps you have seen them on the train in the morning, adjusting their iPods and texting with their preternaturally agile thumbs. Or perhaps one sits in the cubicle next to yours, cycling through a seemingly endless loop of news feeds, YouTube videos, cell phone calls, and work. Perhaps you have one living in your home, capable of reprogramming the wireless router and leaping insurmountable DVR instructions in a single, tech-savvy bound.

Towards a 99% efficient three-phase buck-type PFC rectifier for 400 V DC distribution systems <=SEP=> In telecom applications, the vision for a total power conversion efficiency from the mains to the output of PoL converters of 95% demands for an optimization of every conversion step, i.e. the PFC rectifier front-end should show an outstanding efficiency in the range of 99%. For recently discussed 400 V DC distribution bus voltages a buck-type PFC rectifier is a logical solution. In this paper, an efficiency-optimized, nearly 99% efficient, 5 kW three-phase buck-type PFC rectifier with 400 V output is presented. Methods for calculating losses of all components are described, and are used to optimize the converter design for efficiency at full load. Special attention is paid to semiconductor losses, which are shown to be dominant, with the parasitic device capacitance losses being a significant component. A prototype of the proposed rectifier is constructed which verifies the accuracy of the models used for loss calculation and optimization.

REVIEW OF FATIGUE DETECTION AND PREDICTION TECHNOLOGIES September 2000 Prepared <=SEP=> This report reviews existing fatigue detection and prediction technologies. Data regarding the different technologies available were collected from a wide variety of worldwide sources. The first half of this report summarises the current state of research and development of the technologies and summarises the status of the technologies with respect to the key issues of sensitivity, reliability, validity and acceptability. The second half evaluates the role of the technologies in transportation, and comments on the place of the technologies vis-a-vis other enforcement and regulatory frameworks, especially in Australia and New Zealand. The report authors conclude that the hardware technologies should never be used as the company fatigue management system. Hardware technologies only have the potential to be a last ditch safety device. Nevertheless, the output of hardware technologies could usefully feed into company fatigue management systems to provide real time risk assessment. However, hardware technology output should never be the only input into a management system. Other inputs should at least come from validated software technologies, mutual assessment of fitness for duty and other risk assessments of work load, schedules and rosters. Purpose: For information: to provide an understanding of the place of fatigue detection and prediction technologies in the management of fatigue in drivers of heavy vehicles.

"Eczema coxsackium" and unusual cutaneous findings in an enterovirus outbreak. <=SEP=> OBJECTIVE
To characterize the atypical cutaneous presentations in the coxsackievirus A6 (CVA6)-associated North American enterovirus outbreak of 2011-2012.


METHODS
We performed a retrospective case series of pediatric patients who presented with atypical cases of hand, foot, and mouth disease (HFMD) from July 2011 to June 2012 at 7 academic pediatric dermatology centers. Patients were included if they tested positive for CVA6 or if they met clinical criteria for atypical HFMD (an enanthem or exanthem characteristic of HFMD with unusual morphology or extent of cutaneous findings). We collected demographic, epidemiologic, and clinical data including history of skin conditions, morphology and extent of exanthem, systemic symptoms, and diagnostic test results.


RESULTS
Eighty patients were included in this study (median age 1.5 years, range 4 months-16 years). Seventeen patients were CVA6-positive, and 63 met clinical inclusion criteria. Ninety-nine percent of patients exhibited a vesiculobullous and erosive eruption; 61% of patients had rash involving >10% body surface area. The exanthem had a perioral, extremity, and truncal distribution in addition to involving classic HFMD areas such as palms, soles, and buttocks. In 55% of patients, the eruption was accentuated in areas of eczematous dermatitis, termed "eczema coxsackium." Other morphologies included Gianotti-Crosti-like (37%), petechial/purpuric (17%) eruptions, and delayed onychomadesis and palm and sole desquamation. There were no patients with serious systemic complications.


CONCLUSIONS
The CVA6-associated enterovirus outbreak was responsible for an exanthem potentially more widespread, severe, and varied than classic HFMD that could be confused with bullous impetigo, eczema herpeticum, vasculitis, and primary immunobullous disease.

Removal Mechanism in Chemical Mechanical Polishing : Theory and Modeling <=SEP=> The abrasion mechanism in solid-solid contact mode of the chemical mechanical polishing (CMP) process is investigated in detail. Based on assumptions of plastic contact over wafer-abrasive and pad-abrasive interfaces, the normal distribution of abrasive size and an assumed periodic roughness of pad surface, a novel model is developed for material removal in CMP. The basic model is = removed, where is the density of wafer, the number of active abrasives, and removed the volume of material removed by a single abrasive. The model proposed integrates process parameters including pressure and velocity and other important input parameters including the wafer hardness, pad hardness, pad roughness, abrasive size, and abrasive geometry into the same formulation to predict the material removal rate ( ). An interface between the chemical effect and mechanical effect has been constructed through a fitting parameter , a “dynamical” hardness value of the wafer surface, in the model. It reflects the influences of chemicals on the mechanical material removal. The fluid effect in the current model is attributed to the number of active abrasives. It is found that the nonlinear down pressure dependence of material removal rate is related to a probability density function of the abrasive size and the elastic deformation of the pad. Compared with experimental results, the model accurately predicts . With further verification of the model, a better understanding of the fundamental mechanism involved in material removal in the CMP process, particularly different roles played by the consumables and their interactions, can be obtained.

Abductive Matching in Question Answering <=SEP=> We study question-answering over semi-structured data. We introduce a new way to apply the technique of semantic parsing by applying machine learning only to provide annotations that the system infers to be missing; all the other parsing logic is in the form of manually authored rules. In effect, the machine learning is used to provide non-syntactic matches, a step that is ill-suited to manual rules. The advantage of this approach is in its debuggability and in its transparency to the end-user. We demonstrate the effectiveness of the approach by achieving state-of-the-art performance of 40.42% on a standard benchmark dataset over tables from Wikipedia.

Academic-Industrial Perspective on the Development and Deployment of a Moderation System for a Newspaper Website <=SEP=> This paper describes an approach and our experiences from the development, deployment and usability testing of a Natural Language Processing (NLP) and Information Retrieval system that supports the moderation of user comments on a large newspaper website. We highlight some of the differences between industry-oriented and academic research settings and their influence on the decisions made in the data collection and annotation processes, selection of document representation and machine learning methods. We report on classification results, where the problems to solve and the data to work with come from a commercial enterprise. In this context typical for NLP research, we discuss relevant industrial aspects. We believe that the challenges faced as well as the solutions proposed for addressing them can provide insights to others working in a similar setting. Data and experiment code related to this paper are available for download at https://ofai.github.io/million-post-corpus.

Divide and conquer: neuroevolution for multiclass classification <=SEP=> Neuroevolution is a powerful and general technique for evolving the structure and weights of artificial neural networks. Though neuroevolutionary approaches such as NeuroEvolution of Augmenting Topologies (NEAT) have been successfully applied to various problems including classification, regression, and reinforcement learning problems, little work has explored application of these techniques to larger-scale multiclass classification problems. In this paper, NEAT is evaluated in several multiclass classification problems, and then extended via two ensemble approaches: One-vs-All and One-vs-One. These approaches decompose multiclass classification problems into a set of binary classification problems, in which each binary problem is solved by an instance of NEAT. These ensemble models exhibit reduced variance and increasingly superior accuracy as the number of classes increases. Additionally, higher accuracy is achieved early in training, even when artificially constrained for the sake of fair comparison with standard NEAT. However, because the approach can be trivially distributed, it can be applied quickly at large scale to solve real problems. In fact, these approaches are incorporated into DarwinTM, an enterprise automatic machine learning solution that also incorporates various other algorithmic enhancements to NEAT. The resulting complete system has proven robust to a wide variety of client datasets.

Homicides with mutilation of the victim's body. <=SEP=> Information on homicide offenders guilty of mutilation is sparse. The current study estimates the rate of mutilation of the victim's body in Finnish homicides and compares sociodemographic characteristics, crime history, life course development, psychopathy, and psychopathology of these and other homicide offenders. Crime reports and forensic examination reports of all offenders subjected to forensic examination and convicted for a homicide in 1995-2004 (n = 676) were retrospectively analyzed for offense and offender variables and scored with the Psychopathy Check List Revised. Thirteen homicides (2.2%) involved mutilation. Educational and mental health problems in childhood, inpatient mental health contacts, self-destructiveness, and schizophrenia were significantly more frequent in offenders guilty of mutilation. Mutilation bore no significant association with psychopathy or substance abuse. The higher than usual prevalence of developmental difficulties and mental disorder of this subsample of offenders needs to be recognized.

Why do they still use paper?: understanding data collection and use in Autism education <=SEP=> Autism education programs for children collect and use large amounts of behavioral data on each student. Staff use paper almost exclusively to collect these data, despite significant problems they face in tracking student data in situ, filling out data sheets and graphs on a daily basis, and using the sheets in collaborative decision making. We conducted fieldwork to understand data collection and use in the domain of autism education to explain why current technology had not met staff needs. We found that data needs are complex and unstandardized, immediate demands of the job interfere with staff ability to collect in situ data, and existing technology for data collection is inadequate. We also identified opportunities for technology to improve sharing and use of data. We found that data sheets are idiosyncratic and not useful without human mediation; improved communication with parents could benefit children's development; and staff are willing, and even eager, to incorporate technology. These factors explain the continued dependence on paper for data collection in this environment, and reveal opportunities for technology to support data collection and improve use of collected data.