query: EvoNN: a customizable evolutionary neural network with heterogenous activation functions

positive: Neuroevolution: from architectures to learning Artificial neural networks (ANNs) are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and the choice of a learning algorithm have to be addressed. Evolutionary search methods can provide an automatic solution to these problems. New insights in both neuroscience and evolutionary biology have led to the development of increasingly powerful neuroevolution techniques over the last decade. This paper gives an overview of the most prominent methods for evolving ANNs with a special focus on recent advances in the synthesis of learning architectures.

positive: Genetic programming - on the programming of computers by means of natural selection Page ii Complex Adaptive Systems John H. Holland, Christopher Langton, and Stewart W. Wilson, advisors Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT Press edition John H. Holland Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life edited by Francisco J. Varela and Paul Bourgine Genetic Programming: On the Programming of Computers by Means of Natural Selection John R. Koza

positive: Survey of Neural Transfer Functions The choice of transfer functions may strongly influence complexity and performance of neural networks. Although sigmoidal transfer functions are the most common there is no a priori reason why models based on such functions should always provide optimal decision borders. A large number of alternative transfer functions has been described in the literature. A taxonomy of activation and output functions is proposed, and advantages of various non-local and local neural transfer functions are discussed. Several less-known types of transfer functions and new combinations of activation/output functions are described. Universal transfer functions, parametrized to change from localized to delocalized type, are of greatest interest. Other types of neural transfer functions discussed here include functions with activations based on nonEuclidean distance measures, bicentral functions, formed from products or linear combinations of pairs of sigmoids, and extensions of such functions making rotations of localized decision borders in highly dimensional spaces practical. Nonlinear input preprocessing techniques are briefly described, offering an alternative way to change the shapes of decision borders.

positive: Back propagation neural network with adaptive differential evolution algorithm for time series forecasting The back propagation neural network (BPNN) can easily fall into the local minimum point in time series forecasting. A hybrid approach that combines the adaptive differential evolution (ADE) algorithm with BPNN, called ADE–BPNN, is designed to improve the forecasting accuracy of BPNN. ADE is first applied to search for the global initial connection weights and thresholds of BPNN. Then, BPNN is employed to thoroughly search for the optimal weights and thresholds. Two comparative real-life series data sets are used to verify the feasibility and effectiveness of the hybrid method. The proposed ADE–BPNN can effectively improve forecasting accuracy relative to basic BPNN, autoregressive integrated moving average model (ARIMA), and other hybrid models. 2014 Elsevier Ltd. All rights reserved.

positive: A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks Research in neuroevolutionthat is, evolving artificial neural networks (ANNs) through evolutionary algorithmsis inspired by the evolution of biological brains, which can contain trillions of connections. Yet while neuroevolution has produced successful results, the scale of natural brains remains far beyond reach. This article presents a method called hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) that aims to narrow this gap. HyperNEAT employs an indirect encoding called connective compositional pattern-producing networks (CPPNs) that can produce connectivity patterns with symmetries and repeating motifs by interpreting spatial patterns generated within a hypercube as connectivity patterns in a lower-dimensional space. This approach can exploit the geometry of the task by mapping its regularities onto the topology of the network, thereby shifting problem difficulty away from dimensionality to the underlying problem structure. Furthermore, connective CPPNs can represent the same connectivity pattern at any resolution, allowing ANNs to scale to new numbers of inputs and outputs without further evolution. HyperNEAT is demonstrated through visual discrimination and food-gathering tasks, including successful visual discrimination networks containing over eight million connections. The main conclusion is that the ability to explore the space of regular connectivity patterns opens up a new class of complex high-dimensional tasks to neuroevolution.

negative: On code reuse from StackOverflow: An exploratory study on Android apps Context: Source code reuse has been widely accepted as a fundamental activity in software development. Recent studies showed that StackOverflow has emerged as one of the most popular resources for code reuse. Therefore, a plethora of work proposed ways to optimally ask questions, search for answers and find relevant code on StackOverflow. However, little work studies the impact of code reuse from Stack-

negative: CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks Generative Adversarial Networks (GAN) have achieved big success in various domains such as image generation, music generation, and natural language generation. In this paper, we propose a novel GAN-based collaborative filtering (CF) framework to provide higher accuracy in recommendation. We first identify a fundamental problem of existing GAN-based methods in CF and highlight it quantitatively via a series of experiments. Next, we suggest a new direction of vector-wise adversarial training to solve the problem and propose our GAN-based CF framework, called CFGAN, based on the direction. We identify a unique challenge that arises when vector-wise adversarial training is employed in CF. We then propose three CF methods realized on top of our CFGAN that are able to address the challenge. Finally, via extensive experiments on real-world datasets, we validate that vector-wise adversarial training employed in CFGAN is really effective to solve the problem of existing GAN-based CF methods. Furthermore, we demonstrate that our proposed CF methods on CFGAN provide recommendation accuracy consistently and universally higher than those of the state-of-the-art recommenders.

negative: A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures Scientific problems that depend on processing largeamounts of data require overcoming challenges in multiple areas:managing large-scale data distribution, co-placement andscheduling of data with compute resources, and storing and transferringlarge volumes of data. We analyze the ecosystems of thetwo prominent paradigms for data-intensive applications, hereafterreferred to as the high-performance computing and theApache-Hadoop paradigm. We propose a basis, common terminologyand functional factors upon which to analyze the two approachesof both paradigms. We discuss the concept of "Big DataOgres" and their facets as means of understanding and characterizingthe most common application workloads found acrossthe two paradigms. We then discuss the salient features of thetwo paradigms, and compare and contrast the two approaches.Specifically, we examine common implementation/approaches ofthese paradigms, shed light upon the reasons for their current"architecture" and discuss some typical workloads that utilizethem. In spite of the significant software distinctions, we believethere is architectural similarity. We discuss the potential integrationof different implementations, across the different levelsand components. Our comparison progresses from a fully qualitativeexamination of the two paradigms, to a semi-quantitativemethodology. We use a simple and broadly used Ogre (K-meansclustering), characterize its performance on a range of representativeplatforms, covering several implementations from bothparadigms. Our experiments provide an insight into the relativestrengths of the two paradigms. We propose that the set of Ogreswill serve as a benchmark to evaluate the two paradigms alongdifferent dimensions.

negative: Towards a Self-Organized Agent-Based Simulation Model for Exploration of Human Synaptic Connections In this paper, the early design of our selforganized agent-based simulation model for exploration of synaptic connections that faithfully generates what is observed in natural situation is given. While we take inspiration from neuroscience, our intent is not to create a veridical model of processes in neurodevelopmental biology, nor to represent a real biological system. Instead, our goal is to design a simulation model that learns acting in the same way of human nervous system by using findings on human subjects using reflex methodologies in order to estimate unknown connections.

negative: BIM: An integrated model for planned and preventive maintenance of architectural heritage Modern digital technologies give us great possibilities to organize knowledge about constructions, regarding multidisciplinary fields like preservation, conservation and valorization of our architectural heritage, in order to suggest restoration projects and related work, or to suppose a planned preventive maintenance. New procedures to archive, analyze and manage architectural information find a natural support in 3D model, thanks to the development of capacities of new modeling software. Moreover, if the model contains or interfaces with a heterogeneous archive of information, as it is for BIM, this model can be considered as the bases of critical studies, projects of restoration, heritage maintenance, integrated management, protection, and valorization, and evaluation of economic aspects, management and planning, that can flow into a planned preventive maintenance [1]. The aspect that limit the use of BIM technology is the set up parametric object library inside programs: the standardized level of these objects deals difficulty with survey and restoration issues, where each single element has its own historical and architectural characterization [2]. From this foreword, the goal of this research is more evident: the possibilities of using BIM modeling to the existing constructions and cultural heritage, as a support for the construction and management of a Plan for planned preventive maintenance.

negative: Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic In this paper we propose a novel energy efficient approach for the recognition of human activities using smartphones as wearable sensing devices, targeting assisted living applications such as remote patient activity monitoring for the disabled and the elderly. The method exploits fixed-point arithmetic to propose a modified multiclass Support Vector Machine (SVM) learning algorithm, allowing to better preserve the smartphone battery lifetime with respect to the conventional floating-point based formulation while maintaining comparable system accuracy levels. Experiments show comparative results between this approach and the traditional SVM in terms of recognition performance and battery consumption, highlighting the advantages of the proposed method.

negative: Design for reliability of power electronics modules 0026-2714/$ see front matter 2009 Published by doi:10.1016/j.microrel.2009.07.055 * Corresponding author. Tel.: +44 (0)20 8331 8660 E-mail address: c.bailey@gre.ac.uk (C. Bailey). Power electronics uses semiconductor technology to convert and control electrical power. Demands for efficient energy management, conversion and conservation, and the increasing take-up of electronics in transport systems has resulted in tremendous growth in the use of power electronics devices such as Insulated Gate Bipolar Transistors (IGBT’s). The packaging of power electronics devices involves a number of challenges for the design engineer in terms of reliability. For example, IGBT modules will contain a number of semiconductor dies within a small footprint bonded to substrates with aluminum wires and wide area solder joints. To a great extent, the reliability of the package will depend on the thermo-mechanical behavior of these materials. This paper details a physics of failure approach to reliability predictions of IGBT modules. It also illustrates the need for a probabilistic approach to reliability predictions that include the effects of design variations. Also discussed are technologies for predicting the remaining life of the package when subjected to qualification stresses or in service stresses using prognostics methods. 2009 Published by Elsevier Ltd.

negative: A Real-time Inversion Attack on the GMR-2 Cipher Used in the Satellite Phones The GMR-2 cipher is a kind of stream cipher currently being used in some Inmarsat satellite phones. It has been proven that such cipher can be cracked using only one frame known keystream but with a moderate executing times. In this paper, we present a new thorough security analysis of the GMR-2 cipher. We first study the inverse properties and the relationship of the cipher’s components to reveal a bad one-way character of the cipher. Then by introducing a new concept called “valid key chain” according to the cipher’s key schedule, we for the first time propose a real-time inversion attack using one frame keystream. This attack contains three phases: (1) table generation (2) dynamic table looks-up, filtration and combination (3) verification. Our analysis shows that, using the proposed attack, the exhaustive search space for the 64-bit encryption key can be reduced to about 2 when one frame (15 bytes) keystream is available. Compared with previous known attacks, this inversion attack is much more efficient. Finally, the proposed attack are carried out on a 3.3GHz platform, and the experimental results demonstrate that the 64-bit encryption-key could be recovered in around 0.02s on average.

negative: Review on virtual synchronous generator (VSG) for enhancing performance of microgrid Increasing penetration level of Distributed Generators (DGs)/ Renewable energy sources (RES) creates low inertia problems, damping effect on grid dynamic performance and stability. Voltage rise by reverse power from PV generations, excessive supply of electricity in the grid due to full generation of the DGs, power fluctuations due to variable nature of Renewable energy sources, and degradation of frequency regulation can be considered as some negative results of mentioned hurdle. For securing such a grid there should add inertia, virtually. The proposed control approach based on an inverter connected in Microgrid, which operated as a virtual synchronous generator (VSG) reacting only in transitory situations. Super capacitor (SC), an energy storage system (ESS) included in MG-forming inverter i.e., a normal inverter structure used for contributing system robustness and reliability. In steady-state load distributed between the MG-supporting inverter are achieved with two restoration controllers for active and reactive powers. The virtual inertia hypothesis can be used for maintaining a large share of DGs in future grids without affect system stability. This proposed system analyzed and simulated using MATLAB/SIMULINK.

negative: Predicting short-term interests using activity-based search context A query considered in isolation offers limited information about a searcher's intent. Query context that considers pre-query activity (e.g., previous queries and page visits), can provide richer information about search intentions. In this paper, we describe a study in which we developed and evaluated user interest models for the current query, its context (from pre-query session activity), and their combination, which we refer to as intent. Using large-scale logs, we evaluate how accurately each model predicts the user's short-term interests under various experimental conditions. In our study we: (i) determine the extent of opportunity for using context to model intent; (ii) compare the utility of different sources of behavioral evidence (queries, search result clicks, and Web page visits) for building predictive interest models, and; (iii) investigate optimally combining the query and its context by learning a model that predicts the context weight for each query. Our findings demonstrate significant opportunity in leveraging contextual information, show that context and source influence predictive accuracy, and show that we can learn a near-optimal combination of the query and context for each query. The findings can inform the design of search systems that leverage contextual information to better understand, model, and serve searchers' information needs.

negative: A 280-KBytes Twin-Bit-Cell Embedded NOR Flash Memory With a Novel Sensing Current Protection Enhanced Technique and High-Voltage Generating Systems A novel sensing current protection enhanced (SCPE) technique and high-voltage (HV) generating systems are adopted to greatly enhance the advantages of the twin-bit cell. The SCPE technique provides the tradeoff of sensing margin loss between “1” and “0” bits sensing case to realize fast read access. The read speed is improved by 7.7%. HV generating systems with parallel-series-transform and capacitance-shared techniques are proposed to satisfy the complicated requirements of output capability and current drivability with lower area penalty. The HV periphery area decreases by 71%. A 1.5-V 280-KBytes embedded NOR flash memory IP has been fabricated in HHGrace (Shanghai Huahong Grace Semiconductor Manufacturing Corporation) 90-nm 4 poly 4 metal CMOS process. The complicated operation voltages and access time of 23.5 ns have been achieved at 1.5 V. The die size of the proposed IP is 0.437 mm<inline-formula> <tex-math notation="LaTeX">$^{2}$ </tex-math></inline-formula> and the area size of charge pump has been reduced to 0.006 mm<inline-formula> <tex-math notation="LaTeX">$^{2}$ </tex-math></inline-formula> on average.

negative: A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing Physiological computing represents a mode of human–computer interaction where the computer monitors, analyzes and responds to the user’s psychophysiological activity in real-time. Within the field, autonomic nervous system responses have been studied extensively since they can be measured quickly and unobtrusively. However, despite a vast body of literature available on the subject, there is still no universally accepted set of rules that would translate physiological data to psychological states. This paper surveys the work performed on data fusion and system adaptation using autonomic nervous system responses in psychophysiology and physiological computing during the last ten years. First, five prerequisites for data fusion are examined: psychological model selection, training set preparation, feature extraction, normalization and dimension reduction. Then, different methods for either classification or estimation of psychological states from the extracted features are presented and compared. Finally, implementations of system adaptation are reviewed: changing the system that the user is interacting with in response to cognitive or affective information inferred from autonomic nervous system responses. The paper is aimed primarily at psychologists and computer scientists who have already recorded autonomic nervous system responses and now need to create algorithms to determine the subject’s psychological state. 2012 British Informatics Society Limited. All rights reserved.

negative: Review: Graph Databases This paper covers NOSQL databases. With the increase in net users and applications it is important to understand graph databases, the future of tomorrow’s data storage and management. Basic architecture of some graph databases has been discussed to know their working and how data is stored in the form of nodes and relationships. KeywordsNOSQL databases, Neo4j, JENA, DEX, InfoGrid, FlockDB.

negative:
Differential effects of atomoxetine on executive functioning and lexical decision in attention-deficit/hyperactivity disorder and reading disorder. OBJECTIVE
The effects of a promising pharmacological treatment for attention-deficit/hyperactivity disorder (ADHD), atomoxetine, were studied on executive functions in both ADHD and reading disorder (RD) because earlier research demonstrated an overlap in executive functioning deficits in both disorders. In addition, the effects of atomoxetine were explored on lexical decision.

METHODS
Sixteen children with ADHD, 20 children with ADHD + RD, 21 children with RD, and 26 normal controls were enrolled in a randomized placebo-controlled crossover study. Children were measured on visuospatial working memory, inhibition, and lexical decision on the day of randomization and following two 28-day medication periods.

RESULTS
Children with ADHD + RD showed improved visuospatial working memory performance and, to a lesser extent, improved inhibition following atomoxetine treatment compared to placebo. No differential effects of atomoxetine were found for lexical decision in comparison to placebo. In addition, no effects of atomoxetine were demonstrated in the ADHD and RD groups.

CONCLUSION
Atomoxetine improved visuospatial working memory and to a lesser degree inhibition in children with ADHD + RD, which suggests differential developmental pathways for co-morbid ADHD + RD as compared to ADHD and RD alone.

CLINICAL TRIAL REGISTRY
B4Z-MC-LYCK, NCT00191906; http://clinicaltrials.gov/ct2/show/NCT00191906.

negative: Gradient Regularization Improves Accuracy of Discriminative Models Regularizing the gradient norm of the output of a neural network with respect to its inputs is a powerful technique, rediscovered several times. This paper presents evidence that gradient regularization can consistently improve classification accuracy on vision tasks, using modern deep neural networks, especially when the amount of training data is small. We introduce our regularizers as members of a broader class of Jacobian-based regularizers. We demonstrate empirically on real and synthetic data that the learning process leads to gradients controlled beyond the training points, and results in solutions that generalize well.

negative: A robust data scaling algorithm to improve classification accuracies in biomedical data Machine learning models have been adapted in biomedical research and practice for knowledge discovery and decision support. While mainstream biomedical informatics research focuses on developing more accurate models, the importance of data preprocessing draws less attention. We propose the Generalized Logistic (GL) algorithm that scales data uniformly to an appropriate interval by learning a generalized logistic function to fit the empirical cumulative distribution function of the data. The GL algorithm is simple yet effective; it is intrinsically robust to outliers, so it is particularly suitable for diagnostic/classification models in clinical/medical applications where the number of samples is usually small; it scales the data in a nonlinear fashion, which leads to potential improvement in accuracy. To evaluate the effectiveness of the proposed algorithm, we conducted experiments on 16 binary classification tasks with different variable types and cover a wide range of applications. The resultant performance in terms of area under the receiver operation characteristic curve (AUROC) and percentage of correct classification showed that models learned using data scaled by the GL algorithm outperform the ones using data scaled by the Min-max and the Z-score algorithm, which are the most commonly used data scaling algorithms. The proposed GL algorithm is simple and effective. It is robust to outliers, so no additional denoising or outlier detection step is needed in data preprocessing. Empirical results also show models learned from data scaled by the GL algorithm have higher accuracy compared to the commonly used data scaling algorithms.

negative: Compliance with Information Security Policies: An Empirical Investigation Information security was the main topic in this paper. An investigation of the compliance to information security policies were discussed. The author mentions that the insignificant relationship between rewards and actual compliance with information security policies does not make sense. Quite possibly this relationship results from not applying rewards for security compliance. Also mentions that based on the survey conducted, careless employee behavior places an organization's assets and reputation in serious jeopardy. The major threat to information security arises from careless employees who fail to comply with organizations' information security policies and procedures.

negative: Fisher Discrimination Dictionary Learning for sparse representation Sparse representation based classification has led to interesting image recognition results, while the dictionary used for sparse coding plays a key role in it. This paper presents a novel dictionary learning (DL) method to improve the pattern classification performance. Based on the Fisher discrimination criterion, a structured dictionary, whose dictionary atoms have correspondence to the class labels, is learned so that the reconstruction error after sparse coding can be used for pattern classification. Meanwhile, the Fisher discrimination criterion is imposed on the coding coefficients so that they have small within-class scatter but big between-class scatter. A new classification scheme associated with the proposed Fisher discrimination DL (FDDL) method is then presented by using both the discriminative information in the reconstruction error and sparse coding coefficients. The proposed FDDL is extensively evaluated on benchmark image databases in comparison with existing sparse representation and DL based classification methods.

negative: More on the fragility of performance: choking under pressure in mathematical problem solving. In 3 experiments, the authors examined mathematical problem solving performance under pressure. In Experiment 1, pressure harmed performance on only unpracticed problems with heavy working memory demands. In Experiment 2, such high-demand problems were practiced until their answers were directly retrieved from memory. This eliminated choking under pressure. Experiment 3 dissociated practice on particular problems from practice on the solution algorithm by imposing a high-pressure test on problems practiced 1, 2, or 50 times each. Infrequently practiced high-demand problems were still performed poorly under pressure, whereas problems practiced 50 times each were not. These findings support distraction theories of choking in math, which contrasts with considerable evidence for explicit monitoring theories of choking in sensorimotor skills. This contrast suggests a skill taxonomy based on real-time control structures.

negative: The Effects of Interaction Goals on Negotiation Tactics and Outcomes: A Dyad-Level Analysis Across Two Cultures This study investigates how negotiators’ interaction goals influence their own and their counterparts’ negotiation tactics and outcomes across two cultures using a simulated employment contract negotiation. Results show that when negotiators placed greater importance on competitive goals, they used more distributive persuasion and fewer priority information exchange tactics, which reduced their counterparts’ profit; negotiators’ competitive goals also caused their counterparts to use fewer priority information exchange tactics, which in turn hurt their own profit. Dyad members’ competitive goals have an indirect, negative impact on joint profit. In addition, Chinese negotiators placed greater importance on competitive goals and used more distributive and fewer integrative tactics than Americans, but the associations between goals and tactics did not differ across cultures. Nevertheless, members of the two cultures took different paths to improve joint profit; as a result, Chinese dyads achieved no less joint profit than American dyads. The study sheds light on culture’s effect on the interactive processes by which goals impact negotiation performance.

negative: Visual Analysis of TED Talk Topic Trends TED Talks are short, powerful talks given by some of the world's brightest minds - scientists, philanthropists, businessmen, artists, and many others. Funded by members and advertising, these talks are free to access by the public on the TED website and TED YouTube channel, and many videos have become viral phenomena. In this research project, we perform a visual analysis of TED Talk videos and playlists to gain a good understanding of the trends and relationships between TED Talk topics.

negative: Solving job-shop scheduling problems by genetic algorithm Job-shop Scheduling Problem (JSP) is one of extremely hard problems because it requires very large combinatorial search space and the precedence constraint between machines. The traditional algorithm used to solve the problem is the branch-andbound method, which takes considerable computing time when the size of problem is large. W e propose a new method for solving JSP using Genetic Algorithm (GA) and demonstrate its efficiency by the standard benchmark of job-shop scheduling problems. Some important points of G A are how t o represent the schedules as an individuals and to design the genetic operators for the representation in order t o produce better results.

negative: Detection of ulcerative colitis severity in colonoscopy video frames Ulcerative colitis (UC) is a chronic inflammatory disease characterized by periods of relapses and remissions affecting more than 500,000 people in the United States. The therapeutic goals of UC are to first induce and then maintain disease remission. However, it is very difficult to evaluate the severity of UC objectively because of non-uniform nature of symptoms associated with UC, and large variations in their patterns. To address this, we objectively measure and classify the severity of UC presented in optical colonoscopy video frames based on the image textures. To extract distinct textures, we are using a hybrid approach in which a new proposed feature based on the accumulation of pixel value differences is combined with an existing feature such as LBP (Local Binary Pattern). The experimental results show the hybrid method can achieve more than 90% overall accuracy.

negative: Finding surprising patterns in a time series database in linear time and space The problem of finding a specified pattern in a time series database (i.e. query by content) has received much attention and is now a relatively mature field. In contrast, the important problem of enumerating all surprising or interesting patterns has received far less attention. This problem requires a meaningful definition of "surprise", and an efficient search technique. All previous attempts at finding surprising patterns in time series use a very limited notion of surprise, and/or do not scale to massive datasets. To overcome these limitations we introduce a novel technique that defines a pattern surprising if the frequency of its occurrence differs substantially from that expected by chance, given some previously seen data.

negative: Everyday Life Information Seeking : Approaching Information Seeking in the Context of “ Way of Life ” The study offers a framework for the study of everyday life information seeking (ELIS) in the context of way of and mastery of life.Way of life is defined as the “order of things,” manifesting itself, for example, in the relationship between work and leisure time, models of consumption, and nature of hobbies. Mastery of life is interpreted as “keeping things in order; ” four ideal types of mastery of life with their implications for ELIS, namely optimistic-cognitive, pessimistic-cognitive, defensiveaffective and pessimistic-affective mastery of life are outlined. The article reviews two major dimensions of ELIS, there are. the seeking of orienting and practical information. The research framework was tested in an empirical study based on interviews with teachers and industrial workers, eleven of both. The main features of seeking orienting and practical information are reviewed, followed by suggestions for refinement of the research framework.

negative: The neural correlates of maternal and romantic love Romantic and maternal love are highly rewarding experiences. Both are linked to the perpetuation of the species and therefore have a closely linked biological function of crucial evolutionary importance. Yet almost nothing is known about their neural correlates in the human. We therefore used fMRI to measure brain activity in mothers while they viewed pictures of their own and of acquainted children, and of their best friend and of acquainted adults as additional controls. The activity specific to maternal attachment was compared to that associated to romantic love described in our earlier study and to the distribution of attachment-mediating neurohormones established by other studies. Both types of attachment activated regions specific to each, as well as overlapping regions in the brain's reward system that coincide with areas rich in oxytocin and vasopressin receptors. Both deactivated a common set of regions associated with negative emotions, social judgment and 'mentalizing', that is, the assessment of other people's intentions and emotions. We conclude that human attachment employs a push-pull mechanism that overcomes social distance by deactivating networks used for critical social assessment and negative emotions, while it bonds individuals through the involvement of the reward circuitry, explaining the power of love to motivate and exhilarate.

negative: Synaptic patterning and the timescales of cortical dynamics Neocortical circuits, as large heterogeneous recurrent networks, can potentially operate and process signals at multiple timescales, but appear to be differentially tuned to operate within certain temporal receptive windows. The modular and hierarchical organization of this selectivity mirrors anatomical and physiological relations throughout the cortex and is likely determined by the regional electrochemical composition. Being consistently patterned and actively regulated, the expression of molecules involved in synaptic transmission constitutes the most significant source of laminar and regional variability. Due to their complex kinetics and adaptability, synapses form a natural primary candidate underlying this regional temporal selectivity. The ability of cortical networks to reflect the temporal structure of the sensory environment can thus be regulated by evolutionary and experience-dependent processes.

negative: A new optimizer using particle swarm theory The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.

negative: Meta-learning with backpropagation This paper introduces gradient descent methods applied to meta-leaming (leaming how to leam) in Neural Networks. Meta-leaning has been of interest in the machine leaming field for decades because of its appealing applications to intelligent agents, non-stationary time series, autonomous robots, and improved leaming algorithms. Many previous neural network-based approaches toward meta-leaming have been based on evolutionary methods. We show how to use gradient descent for meta-leaming in recurrent neural networks. Based on previous work on FixedWeight Leaming Neural Networks, we hypothesize that any recurrent network topology and its corresponding leaming algorithm(s) is a potential meta-leaming system. We tested several recurrent neural network topologies and their corresponding forms of Backpropagation for their ability to meta-leam. One of our systems, based on the Long Short-Term Memory neural network developed a leaming algorithm that could leam any two-dimensional quadratic function (from a set of such functions} after only 30 training examples.

negative: NEVESIM: event-driven neural simulation framework with a Python interface NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events (spikes) between the neurons at a network level from the implementation of the internal dynamics of individual neurons. In this paper we will present the simulation framework of NEVESIM, its concepts and features, as well as some aspects of the object-oriented design approaches and simulation strategies that were utilized to efficiently implement the concepts and functionalities of the framework. We will also give an overview of the Python user interface, its basic commands and constructs, and also discuss the benefits of integrating NEVESIM with Python. One of the valuable capabilities of the simulator is to simulate exactly and efficiently networks of stochastic spiking neurons from the recently developed theoretical framework of neural sampling. This functionality was implemented as an extension on top of the basic NEVESIM framework. Altogether, the intended purpose of the NEVESIM framework is to provide a basis for further extensions that support simulation of various neural network models incorporating different neuron and synapse types that can potentially also use different simulation strategies.

negative: Segmentation of Medical Ultrasound Images Using Convolutional Neural Networks with Noisy Activating Functions The attempts to segment medical ultrasound images have had limited success than the attempts to segment images from other medical imaging modalities. In this project, we attempt to segment medical ultrasound images using convolutional neural networks (CNNs) with a group of noisy activation functions which have recently been demonstrated to improve the performance of neural networks. We report on the segmentation results using a U-Net-like CNN with noisy rectified linear unit (NReLU) functions, noisy hard sigmoid (NHSigmoid) functions, and noisy hard tanh (NHTanh) function on a small data set.

negative: Evolved GANs for generating pareto set approximations In machine learning, generative models are used to create data samples that mimic the characteristics of the training data. Generative adversarial networks (GANs) are neural-network based generator models that have shown their capacity to produce realistic samples in different domains. In this paper we propose a neuro-evolutionary approach for evolving deep GAN architectures together with the loss function and generator-discriminator synchronization parameters. We also propose the problem of Pareto set (PS) approximation as a suitable benchmark to evaluate the quality of neural-network based generators in terms of the accuracy of the solutions they generate. The covering of the Pareto front (PF) by the generated solutions is used as an indicator of the mode-collapsing behavior of GANs. We show that it is possible to evolve GANs that generate good PS approximations. Our method scales to up to 784 variables and that it is capable to create architecture transferable across dimensions and functions.


query: Real-Time Impulse Noise Suppression from Images Using an Efficient Weighted-Average Filtering

positive: A Universal Denoising Framework With a New Impulse Detector and Nonlocal Means Impulse noise detection is a critical issue when removing impulse noise and impulse/Gaussian mixed noise. In this paper, we propose a new detection mechanism for universal noise and a universal noise-filtering framework based on the nonlocal means (NL-means). The operation is carried out in two stages, i.e., detection followed by filtering. For detection, first, we propose the robust outlyingness ratio (ROR) for measuring how impulselike each pixel is, and then all the pixels are divided into four clusters according to the ROR values. Second, different decision rules are used to detect the impulse noise based on the absolute deviation to the median in each cluster. In order to make the detection results more accurate and more robust, the from-coarse-to-fine strategy and the iterative framework are used. In addition, the detection procedure consists of two stages, i.e., the coarse and fine detection stages. For filtering, the NL-means are extended to the impulse noise by introducing a reference image. Then, a universal denoising framework is proposed by combining the new detection mechanism with the NL-means (ROR-NLM). Finally, extensive simulation results show that the proposed noise detector is superior to most existing detectors, and the ROR-NLM produces excellent results and outperforms most existing filters for different noise models. Unlike most of the other impulse noise filters, the proposed ROR-NLM also achieves high peak signal-to-noise ratio and great image quality by efficiently removing impulse/Gaussian mixed noise.

positive: Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median Filter A modified decision based unsymmetrical trimmed median filter algorithm for the restoration of gray scale, and color images that are highly corrupted by salt and pepper noise is proposed in this paper. The proposed algorithm replaces the noisy pixel by trimmed median value when other pixel values, 0's and 255's are present in the selected window and when all the pixel values are 0's and 255's then the noise pixel is replaced by mean value of all the elements present in the selected window. This proposed algorithm shows better results than the Standard Median Filter (MF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), and Progressive Switched Median Filter (PSMF). The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF).

positive: Fast restoration of natural images corrupted by high-density impulse noise In this paper, we suggest a general model for the fixed-valued impulse noise and propose a two-stage method for high density noise suppression while preserving the image details. In the first stage, we apply an iterative impulse detector, exploiting the image entropy, to identify the corrupted pixels and then employ an Adaptive Iterative Mean filter to restore them. The filter is adaptive in terms of the number of iterations, which is different for each noisy pixel, according to the Euclidean distance from the nearest uncorrupted pixel. Experimental results show that the proposed filter is fast and outperforms the best existing techniques in both objective and subjective performance measures.

positive: An Efficient Edge-Preserving Algorithm for Removal of Salt-and-Pepper Noise In this letter, a novel algorithm for removing salt-and-pepper noise from corrupted images is presented. We employ an efficient impulse noise detector to detect the noisy pixels, and an edge-preserving filter to reconstruct the intensity values of noisy pixels. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art impulse denoising techniques. Especially, the proposed method can preserve edges very well while removing impulse noise. Since our algorithm is algorithmically simple, it is very suitable to be applied to many real-time applications.

positive: Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction This letter presents a novel two-stage noise adaptive fuzzy switching median (NAFSM) filter for salt-and-pepper noise detection and removal. Initially, the detection stage will utilize the histogram of the corrupted image to identify noise pixels. These detected ¿noise pixels¿ will then be subjected to the second stage of the filtering action, while ¿noise-free pixels¿ are retained and left unprocessed. Then, the NAFSM filtering mechanism employs fuzzy reasoning to handle uncertainty present in the extracted local information as introduced by noise. Simulation results indicate that the NAFSM is able to outperform some of the salt-and-pepper noise filters existing in literature.

negative: A Communication-Optimal Framework for Contracting Distributed Tensors Tensor contractions are extremely compute intensive generalized matrix multiplication operations encountered in many computational science fields, such as quantum chemistry and nuclear physics. Unlike distributed matrix multiplication, which has been extensively studied, limited work has been done in understanding distributed tensor contractions. In this paper, we characterize distributed tensor contraction algorithms on torus networks. We develop a framework with three fundamental communication operators to generate communication-efficient contraction algorithms for arbitrary tensor contractions. We show that for a given amount of memory per processor, the framework is communication optimal for all tensor contractions. We demonstrate performance and scalability of the framework on up to 262,144 cores on a Blue Gene/Q supercomputer.

negative: \pi Match: Monocular vSLAM and Piecewise Planar Reconstruction Using Fast Plane Correspondences This paper proposes πMatch, a monocular SLAM pipeline that, in contrast to current state-of-the-art feature-based methods, provides a dense Piecewise Planar Reconstruction (PPR) of the scene. It builds on recent advances in planar segmentation from affine correspondences (ACs) for generating motion hypotheses that are fed to a PEaRL framework which merges close motions and decides about multiple motion situations. Among the selected motions, the camera motion is identified and refined, allowing the subsequent refinement of the initial plane estimates. The high accuracy of this two-view approach allows a good scale estimation and a small drift in scale is observed, when compared to prior monocular methods. The final discrete optimization step provides an improved PPR of the scene. Experiments on the KITTI dataset show the accuracy of πMatch and that it robustly handles situations of multiple motions and pure rotation of the camera. A Matlab implementation of the pipeline runs in about 0.7 s per frame.

negative: $K$ -Band CMOS Differential and Quadrature Voltage-Controlled Oscillators for Low Phase-Noise and Low-Power Applications In this paper, modified circuit topologies of a differential voltage-controlled oscillator (VCO) and a quadrature VCO (QVCO) in a standard bulk 90-nm CMOS process are presented for low dc power and low phase-noise applications. By utilizing current-reuse and transformer-feedback techniques, the proposed VCO and QVCO can be operated at reduced dc power consumption while maintaining extraordinary circuit performance in terms of low phase-noise and low amplitude/phase errors. The VCO circuit topology is investigated to obtain the design procedure. The VCO is further applied to the QVCO design with a bottom-series coupling technique. The coupling network between two differential VCOs and device size are properly designed based on our proposed design methodology to achieve low amplitude and phase errors. Moreover, the VCO and the QVCO are fully characterized with amplitude and phase errors via a four-port vector network analyzer. With a dc power of 3 mW, the VCO exhibits a frequency tuning range from 20.3 to 21.3 GHz, a phase noise of - 116.4 dBc/Hz at 1-MHz offset, a figure-of-merit (FOM) of -198 dBc/Hz, a phase error of 3.8° , and an amplitude error of 0.9 dB. With a dc power of 6 mW, the QVCO demonstrates a phase noise of -117.4 dBc/Hz, a FOM of -195.6 dBc/Hz, a phase error of 4° , and an amplitude error of 0.6 dB. The proposed VCO and QVCO can be compared with the previously reported state-of-the-art VCOs and QVCOs in silicon-based technologies.

negative: Abstract syntax graphs for domain specific languages This paper presents a representation for embedded domain specific languages (EDSLs) using abstract syntax graphs (ASGs). The purpose of this representation is to deal with the important problem of defining operations that require observing or preserving sharing and recursion in EDSLs in an expressive, yet easy-to-use way. In contrast to more conventional representations based on abstract syntax trees, ASGs represent sharing and recursion explicitly as binder constructs. We use a functional representation of ASGs based on structured graphs, where binders are encoded with parametric higher-order abstract syntax. We show how adapt to this representation to well-typed ASGs. This is especially useful for EDSLs, which often reuse the type system of the host language. We also show an alternative class-based encoding of(well-typed) ASGs that enables extensible and modular well-typed EDSLs while allowing the manipulation of sharing and recursion.

negative: An Investigation into the Pedagogical Features of Documents Characterizing the content of a technical document in terms of its learning utility can be useful for applications related to education, such as generating reading lists from large collections of documents. We refer to this learning utility as the “pedagogical value” of the document to the learner.While pedagogical value is an important concept that has been studied extensively within the education domain, there has been little work exploring it from a computational, i.e., natural language processing (NLP), perspective. To allow a computational exploration of this concept, we introduce the notion of “pedagogical roles” of documents (e.g., Tutorial and Survey) as an intermediary component for the study of pedagogical value. Given the lack of available corpora for our exploration, we create the first annotated corpus of pedagogical roles and use it to test baseline techniques for automatic prediction of such roles.

negative: Bilevel Model-Based Discriminative Dictionary Learning for Recognition Most supervised dictionary learning methods optimize the combinations of reconstruction error, sparsity prior, and discriminative terms. Thus, the learnt dictionaries may not be optimal for recognition tasks. Also, the sparse codes learning models in the training and the testing phases are inconsistent. Besides, without utilizing the intrinsic data structure, many dictionary learning methods only employ the  $\ell _{0}$  or  $\ell _{1}$  norm to encode each datum independently, limiting the performance of the learnt dictionaries. We present a novel bilevel model-based discriminative dictionary learning method for recognition tasks. The upper level directly minimizes the classification error, while the lower level uses the sparsity term and the Laplacian term to characterize the intrinsic data structure. The lower level is subordinate to the upper level. Therefore, our model achieves an overall optimality for recognition in that the learnt dictionary is directly tailored for recognition. Moreover, the sparse codes learning models in the training and the testing phases can be the same. We further propose a novel method to solve our bilevel optimization problem. It first replaces the lower level with its Karush-Kuhn–Tucker conditions and then applies the alternating direction method of multipliers to solve the equivalent problem. Extensive experiments demonstrate the effectiveness and robustness of our method.

negative: Understanding Replication in Databases and Distributed Systems Replication is an area of interest to both distributed systems and databases. The solutions developed from these two perspectives are conceptually similar but differ in many aspects: model, assumptions, mechanisms, guarantees provided, and implementation. In this paper, we provide an abstract and “neutral” framework to compare replication techniques from both communities in spite of the many subtle differences. The framework has been designed to emphasize the role played by different mechanisms and to facilitate comparisons. With this, it is possible to get a functional comparison of many ideas that is valuable for both didactic and practical purposes. The paper describes the replication techniques used in both communities, compares them, and points out ways in which they can be integrated to arrive to better, more robust replication protocols.

negative: The Ontogenesis of Social Dominance : A Strategy-Based Evolutionary Perspective Social dominance results when members of a social group vary in their ability to acquire resources in the presence of others (i.e., compete). Traditional approaches to social dominance often emphasize coercive behavior, but nonetheless suggest that dominant individuals are socially central (e.g., watched, attractive social partners). These patterns, however, apply to humans only up to a certain age. This apparent discontinuity may give the false impression that social dominance is less relevant to human social organization than it is to animal social organization. This paper reintroduces the ethological concept of social dominance, but reinterprets it from a strategy-based perspective. That is, if social dominance is defined as differential ability to control resources—without reference to how this is done—then children evidently employ different strategies to compete with peers (e.g., coercive and prosocial). Furthermore, the type of strategy children employ and peers’ responses to it depend largely on the ages of the children. By adopting a strategy-based approach to social dominance and explicitly incorporating developmental processes and uniquely human capacities, human social dominance patterns appear to be more similar to primate patterns than commonly believed. Implications for social competence, peer relationships, and the development of the self are discussed.  1999

negative: PaDNet: Pan-Density Crowd Counting Crowd counting in varying density scenes is a challenging problem in artificial intelligence (AI) and pattern recognition. Recently, deep convolutional neural networks (CNNs) are used to tackle this problem. However, the single-column CNN cannot achieve high accuracy and robustness in diverse density scenes. Meanwhile, multi-column CNNs lack effective way to accurately learn the features of different scales for estimating crowd density. To address these issues, we propose a novel pan-density level deep learning model, named as Pan-Density Network (PaDNet). Specifically, the PaDNet learns multi-scale features by three steps. First, several sub-networks are pre-trained on crowd images with different density-levels. Then, a Scale Reinforcement Net (SRN) is utilized to reinforce the scale features. Finally, a Fusion Net fuses all of the scale features to generate the final density map. Experiments on four crowd counting benchmark datasets, the ShanghaiTech, the UCF CC 50, the UCSD, and the UCF-QRNF, indicate that the PaDNet achieves the best performance and has high robustness in pan-density crowd counting compared with other state-of-the-art algorithms.

negative: Offline handwritten signature verification — Literature review The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5–10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.

negative: Improving MQTT by Inclusion of Usage Control Due to the increasing pervasiveness of Internet of Things (IoT) and Internet of Everything (IoE) devices, securing both their communications and operations has become of capital importance. Among the several existing IoT protocols, Message Queue Telemetry Transport (MQTT) is a widely-used general purpose one, usable in both constrained and powerful devices, which coordinates data exchanges through a publish/subscribe approach. In this paper, we propose a methodology to increase the security of the MQTT protocol, by including Usage Control in its operative workflow. The inclusion of usage control enables a fine-grained dynamic control of the rights of subscribers to access data and data-streams over time, by monitoring mutable attributes related to the subscriber, the environment or data itself. We will present the architecture and workflow of MQTT enhanced through Usage Control, also presenting a real implementation on Raspberry Pi 3 for performance

negative: A three-phase ZVS PWM DC/DC converter with asymmetrical duty cycle associated with a three-phase version of the hybridge rectifier This paper proposes the use of a three-phase version of the hybridge rectifier in the three-phase zero-voltage switch (ZVS) DC/DC converter with asymmetrical duty cycle. The use of this new rectifier improves the efficiency of the converter because only three diodes are responsible for the conduction losses in the secondary side. The current in the secondary side of the transformer is half the output current. In addition to this, all the advantages of the three-phase DC/DC converter, i.e., the increased frequency of the output and input currents, the improved distribution of the losses, as well as the soft commutation for a wide load range, are preserved. Therefore, the resulting topology is capable of achieving high efficiency and high power density at high power levels. The theoretical analysis, simulation, and experimental results obtained from a 6-kW prototype, and also a comparison of the efficiency of this converter with the full-bridge rectifier are presented.

negative: Towards a Deeper Understanding of Adversarial Losses Recent work has proposed various adversarial losses for training generative adversarial networks. Yet, it remains unclear what certain types of functions are valid adversarial loss functions, and how these loss functions perform against one another. In this paper, we aim to gain a deeper understanding of adversarial losses by decoupling the effects of their component functions and regularization terms. We first derive some necessary and sufficient conditions of the component functions such that the adversarial loss is a divergence-like measure between the data and the model distributions. In order to systematically compare different adversarial losses, we then propose DANTest—a new, simple framework based on discriminative adversarial networks. With this framework, we evaluate an extensive set of adversarial losses by combining different component functions and regularization approaches. This study leads to some new insights into the adversarial losses. For reproducibility, all source code is available at https: //github.com/salu133445/dan.

negative: Grammatical error correction in non-native English Grammatical error correction (GEC) is the task of automatically correcting grammatical errors in written text. Previous research has mainly focussed on individual error types and current commercial proofreading tools only target limited error types. As sentences produced by learners may contain multiple errors of different types, a practical error correction system should be able to detect and correct all errors. In this thesis, we investigate GEC for learners of English as a Second Language (ESL). Specifically, we treat GEC as a translation task from incorrect into correct English, explore new models for developing end-to-end GEC systems for all error types, study system performance for each error type, and examine model generali-sation to different corpora. First, we apply Statistical Machine Translation (SMT) to GEC and prove that it can form the basis of a competitive all-errors GEC system. We implement an SMT-based GEC system which contributes to our winning system submitted to a shared task in 2014. Next, we propose a ranking model to re-rank correction candidates generated by an SMT-based GEC system. This model introduces new linguistic information and we show that it improves correction quality. Finally, we present the first study using Neural Machine Translation (NMT) for GEC. We demonstrate that NMT can be successfully applied to GEC and help capture new errors missed by an SMT-based GEC system. While we focus on GEC for English, our methods presented in this thesis can be easily applied to any language. Acknowledgements First and foremost, I owe a huge debt of gratitude to my supervisor, Ted Briscoe, who has patiently guided me through my PhD and always been very helpful, understanding and supportive. I cannot thank him enough for providing me with opportunities that helped me grow as a researcher and a critical thinker. I am immensely grateful to my examiners, Paula Buttery and Stephen Pulman, for their thorough reading of my thesis, their valuable comments and an enjoyable viva. My appreciation extends to my fellow members of the Natural Language and Information Processing research group, with whom I have always enjoyed discussing our work and other random things. My gratitude goes to Stephen Clark and Ann Copestake for giving me early feedback on my work as well as Christopher Bryant for generously reading my thesis draft. I would especially like to thank Mariano Felice for being not just a great colleague but also a dear friend. A special mention has …

negative: A Lightweight Component Caching Scheme for Satisfiability Solvers We introduce in this paper a lightweight technique for reducing work repetition caused by non–chronological backtracking commonly practiced by DPLL–based SAT solvers. The presented technique can be viewed as a partial component caching scheme. Empirical evaluation of the technique reveals significant improvements on a broad range of in-

negative: Enhancing DataQuality in DataWarehouse Environments D ecisions by senior management lay the groundwork for lower corporate levels to develop policies and procedures for various corporate activities. However, the potential business contribution of these activities depends on the quality of the decisions and in turn on the quality of the data used to make them. Some inputs are judgmental, others are from transactional systems, and still others are from external sources, but all must have a level of quality appropriate for the decisions they will be part of. Although concern about the quality of one’s data is not new, what is fairly recent is using the same data for multiple purposes, which can be quite different from their original purposes. Users working with a particular data set come to know and internalize its deficiencies and idiosyncrasies. This knowledge is lost when data is made available to other parties, like when data needed for decision making is collected in repositories called data warehouses. Here, we offer a conceptual framework for enhancing data quality in data warehouse environments. We explore the factors that should be considered, such as the current level of data quality, the levels of quality needed by the relevant decision processes, and the potential benefits of projects designed to enhance data quality. Those responsible for data quality have to understand the importance of such factors, as well as the interaction among them. This understanding is mandatory in data warehousing environments characterized by multiple users with differing needs for data quality. For warehouses supporting a limited number of decision processes, awareness of these issues coupled with good judgment should suffice. For more complex situations, however, the number and diversity of trade-offs make reliance on judgment alone problematic. For such situations, we offer a methodology that Nothing is more likely to undermine the performance and business value of a data warehouse than inappropriate, misunderstood, or ignored data quality. Enhancing DataQuality in DataWarehouse Environments

negative: Normative data stratified by age and education for two measures of verbal fluency: FAS and animal naming. Normative data stratified by three levels of age (16-59, 60-79, and 80-95 years) and three levels of education (0-8, 9-12, and 13-21 years) are presented for phonemic verbal fluency (FAS) and categorical verbal fluency (Animal Naming). The normative sample, aged 16 to 95 years, consisted of 1,300 cognitively intact individuals who resided in the community. Years of education ranged from 0 to 21. The total number of words in 1 minute for each of the letters F, A, and S was correlated r =.52 with the number of animal names generated in 1 minute. Regression analyses showed that FAS was more sensitive to the effects of education (18.6% of the variance) than age (11.0% of the variance). The opposite relationship occurred for Animal Naming, where age accounted for 23.4% of the variance and education accounted only for 13.6%. Gender accounted for less than 1% of variance for FAS and Animal Naming. The clinical utility of these norms is discussed.

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

negative:
Heritability for Alzheimer's disease: the study of dementia in Swedish twins. BACKGROUND
Alzheimer's disease has been thought to have familial and sporadic forms, and several genetic defects have been identified that chiefly explain early-onset familial cases. In this study, our purpose was to detect all cases of dementia in an established twin registry and to estimate total extent of genetic contribution to liability to Alzheimer's disease.

METHODS
At the first stage, members of the registry were screened for dementia, using in-person or telephone mental status testing. At the second stage, those who screened positively and their partners were referred for clinical work-ups, including neuropsychological assessment, physician examination, laboratory tests, and neuroimaging. Clinical diagnoses were assigned at a multidisciplinary consensus conference. Probandwise concordance rates were examined by zygosity, and structural modeling was applied to the data to estimate genetic and environmental influences, using both single- and multiple-threshold models.

RESULTS
Sixty-five pairs were identified in which one or both was demented. The probandwise concordance rate for Alzheimer's disease among monozygotic pairs was 67%; the corresponding figure for dizygotic pairs was 22%. Heritability of liability to Alzheimer's disease was estimated to be .74; to any dementia, .43. The other variance is attributable to environmental influences.

CONCLUSIONS
Findings indicate a substantial genetic effect for these predominantly late-onset Alzheimer's disease cases. At the same time, structural modeling results and large intra-pair differences in age of onset suggest that environmental factors are also important in determining whether and when an individual may develop dementia.

negative: SANE: System for Fine Grained Named Entity Typing on Textual Data Assignment of fine-grained types to named entities is gaining popularity as one of the major Information Extraction tasks due to its applications in several areas of Natural Language Processing. Existing systems use huge knowledge bases to improve the accuracy of the fine-grained types. We designed and developed SANE, a system that uses Wikipedia categories to fine grain the type of the named entities recognized in the textual data. The main contribution of this work is building a named entity typing system without the use of knowledge bases. Through our experiments, 1) we establish the usefulness of Wikipedia categories to Named Entity Typing and 2) we show that SANE’s performance is on par with the state-ofthe-art.

negative: Algorithm 799: revolve: an implementation of checkpointing for the reverse or adjoint mode of computational differentiation In its basic form, the reverse mode of computational differentiation yields the gradient of a scalar-valued function at a cost that is a small multiple of the computational work needed to evaluate the function itself. However, the corresponding memory requirement is proportional to the run-time of the evaluation program. Therefore, the practical applicability of the reverse mode in its original formulation is limited despite the availability of ever larger memory systems. This observation leads to the development of checkpointing schedules to reduce the storage requirements. This article presents the function revolve, which generates checkpointing schedules that are provably optimal with regard to a primary and a secondary criterion. This routine is intended to be used as an explicit “controller” for running a time-dependent applications program.

negative: Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-precision accumulators for partial sums in inner-product operations to preserve the quality of convergence. The absence of any framework to analyze the precision requirements of partial sum accumulations results in conservative design choices. This imposes an upper-bound on the reduction of complexity of multiply-accumulate units. We present a statistical approach to analyze the impact of reduced accumulation precision on deep learning training. Observing that a bad choice for accumulation precision results in loss of information that manifests itself as a reduction in variance in an ensemble of partial sums, we derive a set of equations that relate this variance to the length of accumulation and the minimum number of bits needed for accumulation. We apply our analysis to three benchmark networks: CIFAR-10 ResNet 32, ImageNet ResNet 18 and ImageNet AlexNet. In each case, with accumulation precision set in accordance with our proposed equations, the networks successfully converge to the single precision floating-point baseline. We also show that reducing accumulation precision further degrades the quality of the trained network, proving that our equations produce tight bounds. Overall this analysis enables precise tailoring of computation hardware to the application, yielding areaand power-optimal systems.

negative: The nude image identification with adaptive skin chromatic distribution matching scheme This paper presents a new nude image identification system. We propose an adaptive chroma-distribution matching scheme based on face detection to on-line determine the image's skin chromatic distribution such that it can tolerate the color deviation coming from special lighting without increasing false alarm. The object detection based on machine learning approach is used to locate the face region in the test image. According to the color information of face, the matched skin chromatic distribution is selected to detect the skin objects in the test image. The texture feature, namely coarseness, is used to acquire accurate skin segmentation. The low-level but reliable geometrical constraints and the mug shot exclusion procedure are employed to further examine the skin regions. Experimental results that the overall detection rate is 86.3% show our method can achieve satisfactory performance for detecting nude images under special lighting conditions.

negative: Graded Entailment for Compositional Distributional Semantics The categorical compositional distributional model of natural language provides a conceptually motivated procedure to compute the meaning of sentences, given grammatical structure and the meanings of its words. This approach has outperformed other models in mainstream empirical language processing tasks. However, until now it has lacked the crucial feature of lexical entailment – as do other distributional models of meaning. In this paper we solve the problem of entailment for categorical compositional distributional semantics. Taking advantage of the abstract categorical framework allows us to vary our choice of model. This enables the introduction of a notion of entailment, exploiting ideas from the categorical semantics of partial knowledge in quantum computation. The new model of language uses density matrices, on which we introduce a novel robust graded order capturing the entailment strength between concepts. This graded measure emerges from a general framework for approximate entailment, induced by any commutative monoid. Quantum logic embeds in our graded order. Our main theorem shows that entailment strength lifts compositionally to the sentence level, giving a lower bound on sentence entailment. We describe the essential properties of graded entailment such as continuity, and provide a procedure for calculating entailment strength.

negative: Sorry, I only speak natural language: a pattern-based, data-driven and guided approach to mapping natural language queries to SPARQL We present a new interface based on natural language to support users in specifying their queries with respect to RDF datasets. The approach relies on a number of predefined patterns that uniquely determine a type of SPARQL query. The approach is incremental and assisted in that it guides a user step by step in specifying a query by incrementally parsing the input and providing suggestions for completion at every stage. The methodology to specify the patterns is informed by empirical distributions of SPARQL query types as found in standard query logs. So far, we have implemented the approach using two patterns only as proof-of-concept. The coverage of the pattern library will be extended in the future. We will also provide an evaluation of the approach on the well-known QALD dataset.

negative: Importance filtering for image retargeting Content-aware image retargeting has attracted a lot of interests recently. The key and most challenging issue for this task is how to balance the tradeoff between preserving the important contents and minimizing the visual distortions on the consistency of the image structure. In this paper we present a novel filtering-based technique to tackle this issue, called ”importance filtering”. Specifically, we first filter the image saliency guided by the image itself to achieve a structure-consistent importance map. We then use the pixel importance as the key constraint to compute the gradient map of pixel shifts from the original resolution to the target. Finally, we integrate the shift gradient across the image using a weighted filter to construct a smooth shift map and render the target image. The weight is again controlled by the pixel importance. The two filtering processes enforce to maintain the structural consistency and yet preserve the important contents in the target image. Furthermore, the simple nature of filter operations allows highly efficient implementation for real-time applications and easy extension to video retargeting, as the structural constraints from the original image naturally convey the temporal coherence between frames. The effectiveness and efficiency of our importance filtering algorithm are confirmed in extensive experiments.

negative: Motion-aware noise filtering for deblurring of noisy and blurry images Image noise can present a serious problem in motion deblurring. While most state-of-the-art motion deblurring algorithms can deal with small levels of noise, in many cases such as low-light imaging, the noise is large enough in the blurred image that it cannot be handled effectively by these algorithms. In this paper, we propose a technique for jointly denoising and deblurring such images that elevates the performance of existing motion deblurring algorithms. Our method takes advantage of estimated motion blur kernels to improve denoising, by constraining the denoised image to be consistent with the estimated camera motion (i.e., no high frequency noise features that do not match the motion blur). This improved denoising then leads to higher quality blur kernel estimation and deblurring performance. The two operations are iterated in this manner to obtain results superior to suppressing noise effects through regularization in deblurring or by applying denoising as a preprocess. This is demonstrated in experiments both quantitatively and qualitatively using various image examples.

negative: Fingerprint image enhancement using GWT and DMF Fingerprint image enhancement is an essential preprocessing step in fingerprint recognition applications. In this paper we introduce an approach that extracts simultaneously orientation and frequency of local ridge in the fingerprint image by Gabor wavelet filter bank and use them in Gabor Filtering of image. Furthermore, we describes a robust approach to fingerprint image enhancement, which is based on integration of Gabor Filters and Directional Median Filter(DMF). In fact, Gaussian-distributed noises are reduced effectively by Gabor Filters and impulse noises by DMF. the proposed DMF not only can finish its original tasks, it can also join broken fingerprint ridges, fill out the holes of fingerprint images, smooth irregular ridges as well as remove some annoying small artifacts between ridges. Experimental results show our method to be superior to those described in the literature.

negative: A convolutional neural networks denoising approach for salt and pepper noise The salt and pepper noise, especially the one with extremely high percentage of impulses, brings a significant challenge to image denoising. In this paper, we propose a non-local switching filter convolutional neural network denoising algorithm, named NLSF-CNN, for salt and pepper noise. As its name suggested, our NLSF-CNN consists of two steps, i.e., a NLSF processing step and a CNN training step. First, we develop a NLSF pre-processing step for noisy images using non-local information. Then, the pre-processed images are divided into patches and used for CNN training, leading to a CNN denoising model for future noisy images. We conduct a number of experiments to evaluate the effectiveness of NLSF-CNN. Experimental results show that NLSF-CNN outperforms the state-of-the-art denoising algorithms with a few training images.

negative: Adaptive removal of azimuth ambiguities in SAR images We introduce an innovative algorithm that is capable of suppression of strong azimuth ambiguities in single-look complex (SLC) synthetic aperture radar images, to be used both for detected products and for interferometric surveys. The algorithm exploits a band-pass filter to select that portion of the azimuth spectrum that is less influenced by aliasing, the one that corresponds to the nulls in the replicated azimuth antenna pattern. The selectivity of this filter adapts locally depending on the estimated ambiguity intensity: the filter is more selective in the presence of strong ambiguities and becomes all-pass in absence of ambiguities. The design of such filter frames in the Wiener approach with two different normalization options, depending on the use of the SLC image, either for getting multilook averaged detected products or for interferometric applications. Preliminary results achieved by processing ENVISAT Advanced Synthetic Aperture Radar sensor data are provided.

negative: Efficient mesh denoising via robust normal filtering and alternate vertex updating The most challenging problem in mesh denoising is to distinguish features from noise. Based on the robust guided normal estimation and alternate vertex updating strategy, we investigate a new feature-preserving mesh denoising method. To accurately capture local structures around features, we propose a corner-aware neighborhood (CAN) scheme. By combining both overall normal distribution of all faces in a CAN and individual normal influence of the interested face, we give a new consistency measuring method, which greatly improves the reliability of the estimated guided normals. As the noise level lowers, we take as guidance the previous filtered normals, which coincides with the emerging rolling guidance idea. In the vertex updating process, we classify vertices according to filtered normals at each iteration and reposition vertices of distinct types alternately with individual regularization constraints. Experiments on a variety of synthetic and real data indicate that our method adapts to various noise, both Gaussian and impulsive, no matter in the normal direction or in a random direction, with few triangles flipped.

negative: Multi-Sensor Conflict Measurement and Information Fusion In sensing applications where multiple sensors observe the same scene, fusing sensor outputs can provide improved results. However, if some of the sensors are providing lower quality outputs, e.g. when one or more sensors has a poor signal-tonoise ratio (SNR) and therefore provides very noisy data, the fused results can be degraded. In this work, a multi-sensor conflict measure is proposed which estimates multi-sensor conflict by representing each sensor output as interval-valued information and examines the sensor output overlaps on all possible n-tuple sensor combinations. The conflict is based on the sizes of the intervals and how many sensors output values lie in these intervals. In this work, conflict is defined in terms of how little the output from multiple sensors overlap. That is, high degrees of overlap mean low sensor conflict, while low degrees of overlap mean high conflict. This work is a preliminary step towards a robust conflict and sensor fusion framework. In addition, a sensor fusion algorithm is proposed based on a weighted sum of sensor outputs, where the weights for each sensor diminish as the conflict measure increases. The proposed methods can be utilized to (1) assess a measure of multi-sensor conflict, and (2) improve sensor output fusion by lessening weighting for sensors with high conflict. Using this measure, a simulated example is given to explain the mechanics of calculating the conflict measure, and stereo camera 3D outputs are analyzed and fused. In the stereo camera case, the sensor output is corrupted by additive impulse noise, DC offset, and Gaussian noise. Impulse noise is common in sensors due to intermittent interference, a DC offset a sensor bias or registration error, and Gaussian noise represents a sensor output with low SNR. The results show that sensor output fusion based on the conflict measure shows improved accuracy over a simple averaging fusion strategy.