title: RPAN: An End-to-End Recurrent Pose-Attention Network for Action Recognition in Videos

abstract: Recent studies demonstrate the effectiveness of Recurrent Neural Networks (RNNs) for action recognition in videos. However, previous works mainly utilize video-level category as supervision to train RNNs, which may prohibit RNNs to learn complex motion structures along time. In this paper, we propose a recurrent pose-attention network (RPAN) to address this challenge, where we introduce a novel pose-attention mechanism to adaptively learn pose-related features at every time-step action prediction of RNNs. More specifically, we make three main contributions in this paper. Firstly, unlike previous works on pose-related action recognition, our RPAN is an end-toend recurrent network which can exploit important spatialtemporal evolutions of human pose to assist action recognition in a unified framework. Secondly, instead of learning individual human-joint features separately, our poseattention mechanism learns robust human-part features by sharing attention parameters partially on the semanticallyrelated human joints. These human-part features are then fed into the human-part pooling layer to construct a highlydiscriminative pose-related representation for temporal action modeling. Thirdly, one important byproduct of our RPAN is pose estimation in videos, which can be used for coarse pose annotation in action videos. We evaluate the proposed RPAN quantitatively and qualitatively on two popular benchmarks, i.e., Sub-JHMDB and PennAction. Experimental results show that RPAN outperforms the recent state-of-the-art methods on these challenging datasets.


title: Message equivocality, media selection and manager performance: implications for information systems

abstract: A field study of middle- and upper-level managers was undertaken to explain managers' selection of communication media. The findings indicate that media vary in their capacity to convey information cues. Managers prefer rich media for ambiguous communications and less rich media for unequivocal communications. The data suggest that high performing managers are more sensitive to the relationship between message ambiguity and media richness than low performing managers. Implications for managers' use of information systems and electronic media are discussed.


title: Twitter-enhanced Android malware detection

abstract: In data-driven Android malware detection, large numbers of both malicious and benign apps are used to train machine learning classifiers to detect malware. Existing approaches have nearly exclusively focused on app contents to extract features for classification. We seek to understand if auxiliary data, specifically Twitter data, can be used to improve the performance of existing approaches for Android malware detection. Throughout the course of our research, we collected over 50 million tweets potentially related to Android apps. We propose to link tweets with apps using approaches inspired from the standard vector space model, and subsequently study the usefulness of the linked tweets in malware detection. We find that Twitter data accurately linked to apps through HTTP links can be used to improve the machine learning classifier performance across a variety of common malware detection classifiers. However, classification experiments with Twitter data automatically linked to apps reveal the need for future work on more robust linking approaches.


title: End-to-End Recovery of Human Shape and Pose

abstract: We describe Human Mesh Recovery (HMR), an end-to-end framework for reconstructing a full 3D mesh of a human body from a single RGB image. In contrast to most current methods that compute 2D or 3D joint locations, we produce a richer and more useful mesh representation that is parameterized by shape and 3D joint angles. The main objective is to minimize the reprojection loss of keypoints, which allow our model to be trained using in-the-wild images that only have ground truth 2D annotations. However, reprojection loss alone is highly under constrained. In this work we address this problem by introducing an adversary trained to tell whether a human body parameter is real or not using a large database of 3D human meshes. We show that HMR can be trained with and without using any coupled 2D-to-3D supervision. We do not rely on intermediate 2D keypoint detection and infer 3D pose and shape parameters directly from image pixels. Our model runs in real-time given a bounding box containing the person. We demonstrate our approach on various images in-the-wild and out-perform previous optimizationbased methods that output 3D meshes and show competitive results on tasks such as 3D joint location estimation and part segmentation.


title: Continual Match Based Training in Pommerman: Technical Report

abstract: Continual learning is the ability of agents to improve their capacities throughout multiple tasks continually. While recent works in the literature of continual learning mostly focused on developing either particular loss functions or specialized structures of neural network explaining the episodic memory or neural plasticity, we study continual learning from the perspective of the training mechanism. Specifically, we propose a COnitnual Match BAsed Training (COMBAT) framework for training a population of advantage-actor-critic (A2C) agents in Pommerman, a partially observable multi-agent environment with no communication. Following the COMBAT framework, we trained an agent, namely, Navocado, that won the title of the top 1 learning agent in the NeurIPS 2018 Pommerman Competition. Two critical features of our agent are worth mentioning. Firstly, our agent did not learn from any demonstrations. Secondly, our agent is highly reproducible. As a technical report, we articulate the design of state space, action space, reward, and most importantly, the COMBAT framework for our Pommerman agent. We show in the experiments that Pommerman is a perfect environment for studying continual learning, and the agent can improve its performance by continually learning new skills without forgetting the old ones. Finally, the result in the Pommerman Competition verifies the robustness of our agent when competing with various opponents.


Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks


title: Narcolepsy-like sleep disturbance in orexin knockout mice are normalized by the 5-HT1A receptor agonist 8-OH-DPAT

abstract: Rationale ::: Orexin knockout (KO) mice exhibit a phenotype that is similar to human narcolepsy, and monoamine-related compounds, such as psychostimulants and 5-HT uptake inhibitors, have been used for the treatment of narcoleptic disorders. However, little information is available regarding the pathophysiological features of orexin KO mice, particularly with respect to their narcoleptic-like disorder and how it is affected by monoamine-related compounds.


Nonlinear Model Predictive Control of an Aerial Manipulator using a Recurrent Neural Network Model


title: A Unified Control for the DC–AC Interlinking Converters in Hybrid AC/DC Microgrids

abstract: A novel unified control of the dc–ac interlinking converters (ICs) for autonomous operation of hybrid ac/dc microgrids (MGs) has been proposed in this paper. When the slack terminals in the ac and dc MGs are available, the ICs will operate in autonomous control of interlinking power between the ac and dc subgrids, with the total load demand proportionally shared among the existing ac and dc slack terminals. With a flexible control variable added in power control loop, design of the interlinking power control, and droop features of ac and dc MGs can be decoupled. Moreover, this control variable can be tuned flexibly according to different power control objectives, such as proportional power sharing in terms of capacity (which is considered in this paper), interlinking power dispatch, and other optimal power dispatch algorithms, ensuring a well-designed flexibility and compatibility. Furthermore, if the dc MG or the ac MG loses dc voltage control or ac voltage and frequency control capability due to failures of operation of its slack terminals, the ICs can automatically and seamlessly transfer to dc MG support or ac MG support control modes without operation mode detection, communication, control scheme switching, and control saturation. In order to enhance the stability of the proposed unified control in different modes with different control plants, a phase compensation transfer function has been added in the power control loop. After thorough theoretical analysis and discussions, detailed simulation verifications based on PSCAD/EMTDC and experimental results based on a hardware experimental MG platform have been presented.


title: Wisdom Computing: Toward a Framework for Wisdom Research in Information Systems

abstract: Wisdom sits atop the knowledge pyramid in the Information Systems (IS) literature. Yet there has been little research on wisdom in the IS field despite the need to transform knowledge to wisdom. Perhaps there is no other subject central to human existence whose exploration holds so much promise as wisdom to help address myriad crises affecting the world. Wisdom is intricately connected to many topics in IS research such as knowledge management, decision support, modeling and symbolic representations, problem solving, systems thinking, and artificial intelligence among others. In this paper, we outline a framework for wisdom research for IS and explore research themes for the critically relevant phenomenon that may be called wisdom support using information technologies or “wisdom computing” for short. Wisdom computing is concerned with the design, management, use, and implications of information technologies for discovering, creating, sharing, and supporting wisdom, and may be conceived as part of an emerging area of wisdom management. We propose research questions that arise from the six major focal areas of the framework and their interactions. We believe that the IS discipline with its interdisciplinary focus on the relationships between information technologies and diverse individual, organizational, and societal issues including knowledge management and decision support systems is well positioned for wisdom computing research.


title: Creativity and bipolar disorder: Touched by fire or burning with questions? ☆

abstract: Substantial literature has linked bipolar disorder with creative accomplishment. Much of the thinking in this area has been inspired by biographical accounts of poets, musicians, and other highly accomplished groups, which frequently document signs of bipolar disorder in these samples. A smaller literature has examined quantitative measures of creativity among people with bipolar disorder or at risk for the disorder. In this paper, we provide a critical review of such evidence. We then consider putative mechanisms related to the link of bipolar disorder with creativity, by drawing on literature outside of bipolar disorder on personality, motivational, and affective predictors of creativity. Because so little research has directly evaluated whether these factors could help explain the elevations of creativity in bipolar disorder, we conclude with an agenda for future research on the theoretically and clinically compelling topic of creativity in bipolar disorder.


title: Non-Invasive Control Solution inside Higher-Level OPC UA based Wrapper for Optimizing Groups of Wastewater Systems

abstract: Interoperability is a key attribute of systems nowadays in the industry. It enables local devices towards integration into higher-level supervisory/management structures and assures communication among same level systems. In the wastewater industry, local automation and SCADA structures are functioning independently in most situations and sometimes implemented using proprietary protocols causing higher-level integration and scalability/extensibility problems. With interdependences at the process level but without correlation in a higher-level supervisory algorithm, some real scenarios are causing undesired behaviors (e.g. wastewater overflows, untreated wastewater directed through bypasses into outlet). The current paper proposes a higher-level control solution (HLCS) that is modularly implemented inside a OPC UA based wrapper. The solution is designed for wastewater pumping stations and wastewater treatment plant to optimize their behavior as a group. Besides achieving interoperability of structures, the noninvasive character of the solution eliminates specific and frequent problems of interference in local functioning systems.


title: nGraph-HE: A Graph Compiler for Deep Learning on Homomorphically Encrypted Data

abstract: Homomorphic encryption (HE)---the ability to perform computation on encrypted data---is an attractive remedy to increasing concerns about data privacy in deep learning (DL). However, building DL models that operate on ciphertext is currently labor-intensive and requires simultaneous expertise in DL, cryptography, and software engineering. DL frameworks and recent advances in graph compilers have greatly accelerated the training and deployment of DL models to various computing platforms. We introduce nGraph-HE, an extension of nGraph, Intel's DL graph compiler, which enables deployment of trained models with popular frameworks such as TensorFlow while simply treating HE as another hardware target. Our graph-compiler approach enables HE-aware optimizations- implemented at compile-time, such as constant folding and HE-SIMD packing, and at run-time, such as special value plaintext bypass. Furthermore, nGraph-HE integrates with DL frameworks such as TensorFlow, enabling data scientists to benchmark DL models with minimal overhead.


title: Daylight Design of Office Buildings: Optimisation of External Solar Shadings by Using Combined Simulation Methods

abstract: Abstract: Integrating daylight and energy performance with optimization into the design process has always been a challenge for designers. Most of the building environmental performance simulation tools require a considerable amount of time and iterations for achieving accurate results. Moreover the combination of daylight and energy performances has always been an issue, as different software packages are needed to perform detailed calculations. A simplified method to overcome both issues using recent advances in software integration is explored here. As a case study; the optimization of external shadings in a typical office space in Australia is presented. Results are compared against common solutions adopted as industry standard practices. Visual comfort and energy efficiency are analysed in an integrated approach. The DIVA (Design, Iterate, Validate and Adapt) plug-in for Rhinoceros/Grasshopper software is used as the main tool, given its ability to effectively calculate daylight metrics (using the Radiance/Daysim engine) and energy consumption (using the EnergyPlus engine). The optimization process is carried out parametrically controlling the shadings’ geometries. Genetic Algorithms (GA) embedded in the evolutionary solver Galapagos are adopted in order to achieve close to optimum results by controlling iteration parameters. The optimized result, in comparison with conventional design techniques, reveals significant enhancement of comfort levels and energy efficiency. Benefits and drawbacks of the proposed strategy are then discussed.


title: Development of a new humanoid robot WABIAN-2

abstract: A new humanoid robot-WABIAN-2- that can be used as a human motion simulator is proposed in this paper. Its trunk is designed in order to permit rotation, and forward, backward, and sideway movement. Further, its arms are designed to support its complete weight when pushing a walk-assist machine. Moreover, it can lean on a walk-assist machine by forearm control using trunk motion. Basic walking experiments with WABIAN-2 are conducted with and without a walk-assist machine, thereby confirming its effectiveness


European Society of Hypertension – European Society of Cardiology guidelines for the management of arterial hypertension


title: Evolutionary population dynamics and grey wolf optimizer

abstract: Evolutionary population dynamics (EPD) deal with the removal of poor individuals in nature. It has been proven that this operator is able to improve the median fitness of the whole population, a very effective and cheap method for improving the performance of meta-heuristics. This paper proposes the use of EPD in the grey wolf optimizer (GWO). In fact, EPD removes the poor search agents of GWO and repositions them around alpha, beta, or delta wolves to enhance exploitation. The GWO is also required to randomly reinitialize its worst search agents around the search space by EPD to promote exploration. The proposed GWO---EPD algorithm is benchmarked on six unimodal and seven multi-modal test functions. The results are compared to the original GWO algorithm for verification. It is demonstrated that the proposed operator is able to significantly improve the performance of the GWO algorithm in terms of exploration, local optima avoidance, exploitation, local search, and convergence rate.


title: Maternal and Paternal Filicides: A Retrospective Review of Filicides in Finland

abstract: The purpose of this retrospective study was to illustrate the differences in maternal and paternal filicides in Finland during a 25-year period. In the sample of 200 filicides [neonaticides (n = 56), filicide-suicides (n = 75), other filicides (n = 69)], the incidence was 5.09 deaths per 100,000 live births: 59 percent of filicides were committed by mothers, 39 percent by fathers, and 2 percent by stepfathers. The mean age of the maternal victims (1.6 y) was significantly lower than that of the paternal victims (5.6 y), but no correlation between the sex of the victim and the sex of the perpetrator was found, and the number of female and male victims was equal. The sample of other filicides (n = 65) was studied more closely by forensic psychiatric examination and review of collateral files. Filicidal mothers showed mental distress and often had psychosocial stressors of marital discord and lack of support. They often killed for altruistic reasons and in association with suicide. Maternal perpetrators also dominated in filicide cases in which death was caused by a single episode or recurrent episodes of battering. Psychosis and psychotic depression were diagnosed in 51 percent of the maternal perpetrators, and 76 percent of the mothers were deemed not responsible for their actions by reason of insanity. Paternal perpetrators, on the other hand, were jealous of their mates, had a personality disorder (67%), abused alcohol (45%), or were violent toward their mates. In 18 percent of the cases, they were not held responsible for their actions by reason of insanity. During childhood, most of the perpetrators had endured emotional abuse from their parents or guardians, some of whom also engaged in alcohol abuse and domestic violence. The purpose of this study was to examine the differences between maternal and paternal filicides in a sample of 200 cases in Finland. This report also provides a psychosocial profile of the perpetrator and victim in 65 filicides and a discussion of the influence of diagnoses on decisions regarding criminal responsibility.


title: Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions

abstract: This issue of JAMIA focuses on natural language processing (NLP) techniques for clinical-text information extraction. Several articles are offshoots of the yearly ‘Informatics for Integrating Biology and the Bedside’ (i2b2) (http://www.i2b2.org) NLP shared-task challenge, introduced by Uzuner et al ( see page 552 )1 and co-sponsored by the Veteran's Administration for the last 2 years. This shared task follows long-running challenge evaluations in other fields, such as the Message Understanding Conference (MUC) for information extraction,2 TREC3 for text information retrieval, and CASP4 for protein structure prediction. Shared tasks in the clinical domain are recent and include annual i2b2 Challenges that began in 2006, a challenge for multi-label classification of radiology reports sponsored by Cincinnati Children's Hospital in 2007,5 a 2011 Cincinnati Children's Hospital challenge on suicide notes,6 and the 2011 TREC information retrieval shared task involving retrieval of clinical cases from narrative records.7 ::: ::: Although NLP research in the clinical domain has been active since the 1960s, progress in the development of NLP applications for clinical text has been slow and lags behind progress in the general NLP domain. There are several barriers to NLP development in the clinical domain, and shared tasks like the i2b2/VA Challenge address some of these barriers. Nevertheless, many barriers remain and unless the community takes a more active role in developing novel approaches for addressing the barriers, advancement and innovation will continue to be slow. ::: ::: Historically, there have been substantial barriers to NLP development in the clinical domain. These barriers are not unique to the clinical domain: they also occur in the fields of software engineering and general NLP. ::: ::: ### Lack of access to shared data ::: ::: Because of concerns regarding patient privacy and worry about revealing unfavorable institutional practices, hospitals and clinics have been extremely reluctant to allow access to clinical data for researchers from outside … ::: ::: Correspondence to Dr Wendy W Chapman, Department of Biomedical Informatics, University of California San Diego, 9500 Gilman Dr, Bldg 2 #0728, La Jolla, California, USA; wwchapman{at}ucsd.edu


title: Varactor-Loaded Pattern Reconfigurable Array for Wide-Angle Scanning With Low Gain Fluctuation

abstract: An improved phased array using pattern reconfigurable antenna elements in this communication is proposed to realize wide-angle scanning performance with low gain fluctuation. The pattern reconfigurable element is a microstrip Yagi antenna with its parasitic strips loaded with varactors. The reconfigurability of radiation pattern from the element is enabled by tuning the capacitive reactance of the varactors. Five elements were arranged in an equally spaced linear array, and the effects of several key parameter variations on the radiation characteristics of the array are provided. The proposed array is fabricated and experimentally verified. It is seen from the measured results that the main beam of the array can scan from $- 70^\circ $ to 70° in the $H$ -plane with a gain fluctuation less than 2 dB. Meanwhile, the coverage of 3-dB beamwidth of the array is from about $- 87^\circ $ to 87°. The agreement between simulated and measured results validates our design exhibiting a good performance of wide-angle scanning.


title: AR registration by merging multiple planar markers at arbitrary positions and poses via projective space

abstract: This paper presents a vision-based registration method for Augmented Reality by merging multiple planar markers, which are placed at arbitrary positions and poses. In our approach, we use planar markers like AR-Toolkit markers. We need no information about geometrical relationship of the markers while most of marker-based approaches require the measurement of the markers' 3D positions and poses. Thus we can implement an AR system very easily without time-consuming calibration of markers and a camera.In the proposed registration method, we assign a 3D co-ordinate system for each marker independently, and compute a projection matrix between each marker's 3D coordinate system and the input image coordinate system. For merging all markers' 3D coordinate, we construct a projective 3D space defined by projective geometry between two reference images which are selected while the on-line process automatically. This merging process enables to use the multiple markers at arbitrary positions and poses, and also to reduce errors in registration. For demonstrating the effectiveness of the proposed method, we implement a real-time on-line AR system. Virtual objects are superimposed onto input image sequences that are captured with a handy camera. Since the multiple markers are placed in the wide area, the virtual objects can move around the real world widely. Moreover, the registration can be carried out stably, because the markers face to various directions and some of them can be always recognized from most view points.


title: Nonlocal Sparse and Low-Rank Regularization for Optical Flow Estimation

abstract: Designing an appropriate regularizer is of great importance for accurate optical flow estimation. Recent works exploiting the nonlocal similarity and the sparsity of the motion field have led to promising flow estimation results. In this paper, we propose to unify these two powerful priors. To this end, we propose an effective flow regularization technique based on joint low-rank and sparse matrix recovery. By grouping similar flow patches into clusters, we effectively regularize the motion field by decomposing each set of similar flow patches into a low-rank component and a sparse component. For better enforcing the low-rank property, instead of using the convex nuclear norm, we use the log det (·) function as the surrogate of rank, which can also be efficiently minimized by iterative singular value thresholding. Experimental results on the Middlebury benchmark show that the performance of the proposed nonlocal sparse and low-rank regularization method is higher than (or comparable to) those of previous approaches that harness these same priors, and is competitive to current state-of-the-art methods.


title: An Evolutionary Game Theory Model of Spontaneous Brain Functioning

abstract: Our brain is a complex system of interconnected regions spontaneously organized into distinct networks. The integration of information between and within these networks is a continuous process that can be observed even when the brain is at rest, i.e. not engaged in any particular task. Moreover, such spontaneous dynamics show predictive value over individual cognitive profile and constitute a potential marker in neurological and psychiatric conditions, making its understanding of fundamental importance in modern neuroscience. Here we present a theoretical and mathematical model based on an extension of evolutionary game theory on networks (EGN), able to capture brain's interregional dynamics by balancing emulative and non-emulative attitudes among brain regions. This results in the net behavior of nodes composing resting-state networks identified using functional magnetic resonance imaging (fMRI), determining their moment-to-moment level of activation and inhibition as expressed by positive and negative shifts in BOLD fMRI signal. By spontaneously generating low-frequency oscillatory behaviors, the EGN model is able to mimic functional connectivity dynamics, approximate fMRI time series on the basis of initial subset of available data, as well as simulate the impact of network lesions and provide evidence of compensation mechanisms across networks. Results suggest evolutionary game theory on networks as a new potential framework for the understanding of human brain network dynamics.


title: Selective intervention and internal hybrids: Interpreting and learning from the rise and decline of the Oticon spaghetti organization

abstract: Infusing hierarchies with elements of market control has become a much-used way of simultaneously increasing entrepreneurialism and motivation in firms. However, this paper argues that such "internal hybrids," particularly in their radical forms, are inherently hard to successfully design and implement because of a fundamental incentive problem of establishing credible managerial commitments to not intervene in delegated decision making. This theme is developed and illustrated, using the case of the world-leading hearing aids producer, Oticon. In the beginning of the 1990s, Oticon became famous for its radical internal hybrid, the "spaghetti organization." Recent work has interpreted the spaghetti organization as a radical attempt to foster dynamic capabilities by organizational means, neglecting, however, that about a decade later the spaghetti organization has given way to a more traditional matrix organization. In contrast, an organizational economics interpretation of Oticon organizational changes is developed. This lens suggests that a strong liability of the spaghetti organization was the above incentive problem: Frequent managerial meddling with delegated rights led to a severe loss of motivation, and arguably caused the change to a more structured organization. Refutable implications are developed, and the discussion is broadened to more general issues of economic organization.


title: Visual Search and Attention A Signal Detection Theory Approach

abstract: Abstract A framework based on signal detection theory shows how visual attention influences tasks that require searching for a target among distractors. Data from physiology and psychophysics show that performance in a search task is largely determined by the discriminability of the target from the distractors. Attention acts by enhancing the response to the attended stimulus and by restricting the range and number of units responding to the distractors. Both processes improve performance by increasing the discriminability of the attended signal.


title: Fructooligosaccharide (FOS) and Galactooligosaccharide (GOS) Increase Bifidobacterium but Reduce Butyrate Producing Bacteria with Adverse Glycemic Metabolism in healthy young population

abstract: The gut microbiota has been implicated in glucose intolerance and its progression towards type-2 diabetes mellitus (T2DM). Relevant randomized clinical trial with prebiotic intervention was inadequate. We sought to evaluate the impact of fructooligosaccharides (FOS) and galactooligosaccharides (GOS) on glycemia during oral glucose tolerance test (OGTT) and intestinal microbiota. A randomized double-blind cross-over study was performed with 35 adults treated with FOS and GOS for 14 days (16 g/day). Faeces sampling, OGTT and anthropometric parameters were performed. Short-term intake of high-dose prebiotics had adverse effect on glucose metabolism, as in FOS intervention demonstrated by OGTT (P < 0.001), and in GOS intervention demonstrated by fasting glucose (P < 0.05). A significant increase in the relative abundance of Bifidobacterium was observed both in FOS and GOS group, while the butyrate-producing bacteria like Phascolarctobacterium in FOS group and Ruminococcus in GOS group were decreased. A random forest model using the initial microbiota was developed to predict OGTT levels after prebiotic intervention with relative success (R = 0.726). Our study alerted even though FOS and GOS increased Bifidobacterium, they might have adverse effect on glucose metabolism by reducing butyrate-producing microbes. Individualized prebiotics intervention based on gut microbiome needs to be evaluated in future.


title: Distributed Data Clustering in Peer -to-Peer Networks: A Technical Review

abstract: Clustering as one of the main branches of data mining, has gained an important place in the different applied fields. On the other hand, Peer-to-Peer (P2P) networks with features such as simplicity, low cost communication, and high availability resources, have gained a worldwide popularity in the present days. In P2P network, high volumes of data are distributed between dispersed data sources. In such distributed systems, sending data to a central site is not possible, due to processing, storage and handling costs. This issue makes the need for specific methods for distributed data clustering in P2P networks. This paper makes a technical review of distributed data clustering approaches in P2P network, to understand the research trends in this area. Keywords—Peer to Peer Networks, Distributed Data Mining, Distributed Data Clustering


title: PAPER MOS-Bounded Diodes for On-Chip ESD Protection in Deep

abstract: SUMMARY New diode structures without the field-oxide boundary across the p/n junction for ESD protection are proposed. A NMOS (PMOS) is especially inserted into the diode structure to form the NMOS-bounded (PMOS-bounded) diode, which is used to block the field oxide isolation across the p/n junction in the diode structure. The proposed N(P)MOSbounded diodes can provide more efficient ESD protection to the internal circuits, as compared to the other diode structures. The N(P)MOS-bounded diodes can be used in the I/O ESD protection circuits, power-rail ESD clamp circuits, and the ESD conduction cells between the separated power lines. From the experimental results, the human-body-model ESD level of ESD protection circuit with the proposed N(P)MOS-bounded diodes is greater than 8 kV in a 0.35-µm CMOS process.


title: A Review of Methodological Approaches for the Design and Optimization of Wind Farms

abstract: This article presents a review of the state of the art of the Wind Farm Design and Optimization (WFDO) problem. The WFDO problem refers to a set of advanced planning actions needed to extremize the performance of wind farms, which may be composed of a few individual Wind Turbines (WTs) up to thousands of WTs. The WFDO problem has been investigated in different scenarios, with substantial differences in main objectives, modelling assumptions, constraints, and numerical solution methods. The aim of this paper is: (1) to present an exhaustive survey of the literature covering the full span of the subject, an analysis of the state-of-the-art models describing the performance of wind farms as well as its extensions, and the numerical approaches used to solve the problem; (2) to provide an overview of the available knowledge and recent progress in the application of such strategies to real onshore and offshore wind farms; and (3) to propose a comprehensive agenda for future research.


title: Microbiome Selection Could Spur Next-Generation Plant Breeding Strategies

abstract: "No plant is an island too…" Plants, though sessile, have developed a unique strategy to counter biotic and abiotic stresses by symbiotically co-evolving with microorganisms and tapping into their genome for this purpose. Soil is the bank of microbial diversity from which a plant selectively sources its microbiome to suit its needs. Besides soil, seeds, which carry the genetic blueprint of plants during trans-generational propagation, are home to diverse microbiota that acts as the principal source of microbial inoculum in crop cultivation. Overall, a plant is ensconced both on the outside and inside with a diverse assemblage of microbiota. Together, the plant genome and the genes of the microbiota that the plant harbors in different plant tissues, i.e., the 'plant microbiome,' form the holobiome which is now considered as unit of selection: 'the holobiont.' The 'plant microbiome' not only helps plants to remain fit but also offers critical genetic variability, hitherto, not employed in the breeding strategy by plant breeders, who traditionally have exploited the genetic variability of the host for developing high yielding or disease tolerant or drought resistant varieties. This fresh knowledge of the microbiome, particularly of the rhizosphere, offering genetic variability to plants, opens up new horizons for breeding that could usher in cultivation of next-generation crops depending less on inorganic inputs, resistant to insect pest and diseases and resilient to climatic perturbations. We surmise, from ever increasing evidences, that plants and their microbial symbionts need to be co-propagated as life-long partners in future strategies for plant breeding. In this perspective, we propose bottom-up approach to co-propagate the co-evolved, the plant along with the target microbiome, through - (i) reciprocal soil transplantation method, or (ii) artificial ecosystem selection method of synthetic microbiome inocula, or (iii) by exploration of microRNA transfer method - for realizing this next-generation plant breeding approach. Our aim, thus, is to bring closer the information accrued through the advanced nucleotide sequencing and bioinformatics in conjunction with conventional culture-dependent isolation method for practical application in plant breeding and overall agriculture.


title: Approximating the minimum degree spanning tree to within one from the optimal degree

abstract: We consider the problem of constructing a spanning tree for a graph G = (V,E) with n vertices whose maximal degree is the smallest among all spanning trees of G. This problem is easily shown to be NP-hard. We describe an iterative polynomial time approximation algorithm for this problem. This algorithm computes a spanning tree whose maximal degree is at most O(Δ + log n ), where Δ is the degree of some optimal tree. The result is generalized to the case where only some vertices need to be connected (Steiner case) and to the case of directed graphs. It is then shown that our algorithm can be refined to produce a spanning tree of degree at most Δ + 1. Unless P = NP, this is the best bound achievable in polynomial time.


A method for watermarking java programs via opaque predicates


title: Animating Chinese ink painting through generating reproducible brush strokes

abstract: By generating reproducible brush strokes of Chinese ink painting, we propose an image-based tool for lively displaying real painting process of Chinese ink painting. Contrasted with previous work where the brush trajectory is explicitly specified as manual input, we automatically extract this trajectory from the outlines of the shapes to paint and model the brush footprints; first complex shape of an input image is decomposed into brush strokes. Second, we estimate the brush trajectory with shape outline and build a brush footprint model to measure the quality of the trajectory constrained by the variation along a stroke of the painting process parameters, such as location, size, and orientation of the footprints. Finally, with our real-time rendering model, the estimated trajectories are dynamically rendered into brush strokes by mapping footprint textures sampled from the input images. It is particularly useful for analysis and appreciation of Chinese ink painting, as well as for art practice and education.


title: Delay-constrained energy-efficient cluster-based multi-hop routing in wireless sensor networks

abstract: Energy efficiency is the main objective in the design of a wireless sensor network (WSN). In many applications, sensing data must be transmitted from sources to a sink in a timely manner. This paper describes an investigation of the trade-off between two objectives in WSN design: minimizing energy consumption and minimizing end-to-end delay. We first propose a new distributed clustering approach to determining the best clusterhead for each cluster by considering both energy consumption and end-to-end delay requirements. Next, we propose a new energy-cost function and a new end-to-end delay function for use in an inter-cluster routing algorithm. We present a multi-hop routing algorithm for use in disseminating sensing data from clusterheads to a sink at the minimum energy cost subject to an end-to-end delay constraint. The results of a simulation are consistent with our theoretical analysis results and show that our proposed performs much better than similar protocols in terms of energy consumption and end-to-end delay.


É Kurasoleña Nobo: Expressions of the Curaçaoan Woman in the Paintings of Jean Girigori, Minerva Lauffer, and Viviana Cornet


title: Clustering for improving educational process mining

abstract: In this paper, we propose to use clustering to improve educational process mining. We want to improve both the performance and comprehensibility of the models obtained. We have used data from 84 undergraduate students who followed an online course using Moodle 2.0. We propose to group students firstly starting from data about Moodle's usage summary and/or the students' final marks in the course. Then, we propose to use data from Moodle's logs about each cluster/group of students separately in order to be able to obtain more specific and accurate models of students' behaviour. The results show that the fitness of the specific models is greater than the general model obtained using all the data, and the comprehensibility of the models can be also improved in some cases.


title: The first years in an L2-speaking environment: A comparison of Japanese children and adults learning American English

abstract: This study examined Japanese speakers’ learning of American English during their first years of immersion in the United States (U.S.). Native Japanesespeaking (NJ) children (n=16) and adults (n=16) were tested on two occasions, averaging 0.5 (T1) and 1.6 years (T2) after arrival in the U.S. Age-matched groups of native English-speaking children (n=16) and adults (n=16) also participated. The NJ adults’ scores for segmental perception and production were higher than the NJ children’s at T1. The NJ children’s foreign accent scores and pronunciation of English fricatives improved significantly between T1 and T2, whereas the NJ adults’ scores did not. These findings suggest that adults may have an advantage over children initially but that children may improve in production more quickly than adults when immersed in an L2-speaking environment.


title: Simulation of the Hydraulic Circuit of a Wheel Loader by Using the Bond Graph Technique

abstract: Hydraulic systems are widely used in Engineering because they are highly versatile and can be combined with other domains of physics (mechanics, electronics, etc.). For this reason, it is of vital importance to studyand develop them with a view to obtaining mechanical hydraulic systems that are increasingly efficient. The beginning of this paper briefly analyses the method usually used to perform simulation in the hydraulic domain. Subsequently, after performing an analysis of different Bond-Graph technique-based works, different characteristic elements will be individually analysed. Finally, the different elements analysed will be interconnected and a simulation of a hydraulic circuit. Will be performed in order to obtain a series of results lhat will enable the different rationales set out in this paper to be validated. Finally, a series of conclusions will be reached based on the work presented.


title: Does happiness adapt? A longitudinal study of disability with implications for economists and judges. University of Warwick: working paper

abstract: Economics ignores the possibility of hedonic adaptation (the idea that people bounce back from utility shocks). This paper argues that economists are wrong to do so. It provides longitudinal evidence that individuals who become disabled go on to exhibit recovery in mental wellbeing. Adaptation to severe disability, however, is shown to be incomplete. The paper suggests ways to calculate the level of compensatory damages for the pain and suffering from disablement. Courts all over the world currently use ad hoc methods.


title: Dutch version of the Strengths and Difficulties Questionnaire (SDQ)

abstract: A Dutch translation of the Strengths and Difficulties Questionnaire (SDQ) was made. In the first wave of data collection, self-report data of 11- to 16-year-olds (N = 970) were collected on tht SDQ and other measures of psychopathology. In the second wave of data collection, extended versions of the SDQ were completed by 11- to 16-year-olds (N = 268), by parents of 8- to 16-year-olds (N = 300) and by teachers of 8- to 12-year-olds (N = 208); in addition, the Child Behaviour Checklist (CBCL) was completed by the parents and the Youth Self Report (YSR) by the 11- to 16-year-olds. The results reveal that the internal consistency of the teacher SDQ is good; and the parent and self-report SDQ are generally acceptable and comparable with the internal consistencies of CBCL/YSR. The mean inter-informant product-moment correlations of the SDQ scales were satisfactory (parent-teacher 0.38; teacher-self-report 0.27; parent-self-report 0.35) and comparable with the mean inter-informant correlations of the CBCL and YSR (0.34). The inter-informant rank correlations of the impact questions were also satisfactory (mean parent-teacher 0.48; mean parent-self-report 0.24). Concurrent validity with the other measures of psychopathology used in the present study was good.


title: Patient Activation and Health Literacy as Predictors of Health Information Use in a General Sample of Dutch Health Care Consumers

abstract: In demand-led health care systems, consumers are expected to play an informed, active role in health care decisions by making use of health information. The ability to seek and use this information depends on specific knowledge, skills, and self-confidence. In this study, the authors validated a translated instrument to measure patient activation (Dutch PAM-13) in a general sample of Dutch health care consumers. Furthermore, the authors examined the relative contribution of patient activation and functional health literacy to the seeking and use of health information in The Netherlands. The mean patient activation score in the Dutch sample was higher for younger health care consumers and for those with a higher education, higher income, and better self-reported general and mental health status. More activated consumers were more likely to seek and use health information. Patient activation proved to be a stronger predictor for seeking and using health information than functional health literacy.


title: Cortical Mechanisms Involved in the Processing of Verbs: An fMRI Study

abstract: In this study, we investigated two aspects of verb processing: first, whether verbs are processed differently from nouns; and second, how verbal morphology is processed. For this purpose, we used functional magnetic resonance imaging to compare three types of lexical processing in Japanese: the processing of nouns, unmarked active verbs, and inflected passive verbs. Twenty-eight healthy subjects were shown a lexical item and asked to judge whether the presented item was a legal word. Although all three conditions activated the bilateral inferior frontal, occipital, the left middle, and inferior temporal cortices, we found differences in the degree of activation for each condition. Verbs elicited greater activation in the left middle temporal gyrus than nouns, and inflected verbs showed greater activation in the left inferior frontal gyrus than unmarked verbs. This study demonstrates that although verbs are basically processed in the same cortical network as nouns, nouns and verbs elicit different degrees of activation due to the cognitive demands involved in lexical semantic processing. Furthermore, this study also shows that the left inferior frontal cortex is related to the processing of verbal inflectional morphology.


title: The ACS Fornax Cluster Survey. X. Color Gradients of Globular Cluster Systems in Early-Type Galaxies

abstract: We use the largest homogeneous sample of globular clusters (GCs), drawn from the ACSVCS and ACSFCS, to investigate the color gradients of GC systems in 76 early-type galaxies. We find that most GC systems possess an obvious negative gradient in g-z color (bluer outwards). For GC systems displaying color bimodality, both metal-rich and metal-poor GC subpopulations present shallower but significant color gradients on average, and the mean gradients of these two subpopulations are of roughly equal strength. The FOV of ACS mainly restricts us to measuring the inner gradients of GC systems. These gradients, however, can introduce an aperture bias when measuring the mean colors of GC subpopulations from relatively narrow central pointings. Inferred corrections to previous work imply a reduced significance for the relation between the mean color of metal-poor GCs and their host galaxy luminosity. The GC color gradients also show a dependence with host galaxy mass where the gradiens are weakest at the ends of the mass spectrum--in massive galaxies and dwarf galaxies--and strongest in galaxies of intermediate mass, around a stellar mass of M_stellar~10^10M_sun. We also measure color gradients for field stars in the host galaxies. We find that GC color gradients are systematically steeper than field star color gradients, but the shape of the gradient-mass relation is the same for both. If gradients are caused by rapid dissipational collapse and weakened by merging, these color gradients support a picture where the inner GC systems of most intermediate-mass and massive galaxies formed early and rapidly with the most massive galaxies having experienced greater merging. The lack of strong gradients in the GC systems of dwarfs, which probably have not experienced many recent major mergers, suggests that low mass halos were inefficient at retaining and mixing metals during the epoch of GC formation.


title: Dietary treatment of iron deficiency in women of childbearing age

abstract: BACKGROUND ::: The Australian Iron Status Advisory Panel advocates dietary intervention as the first treatment option for mild iron deficiency [serum ferritin (SF) = 10-15 microg/L]. However, there appear to be no studies on the efficacy of dietary treatment for iron deficiency. ::: ::: ::: OBJECTIVE ::: We compared the effects of iron supplementation and of a high-iron diet on serum ferritin (SF) and hemoglobin in iron-deficient women of childbearing age. ::: ::: ::: DESIGN ::: Forty-four iron-deficient women (SF <15 microg/L or SF = 15-20 microg/L plus serum iron <10 micromol/L and total-iron-binding capacity >68 micromol/L) and 22 iron-replete women (hemoglobin > or =120 g/L and SF >20 microg/L) matched for age and parity categories were enrolled and completed 7-d weighed food records at baseline. The iron-deficient women were randomly allocated to receive iron supplementation (105 mg/d; supplement group) or a high-iron diet (recommended intake of absorbable iron: 2.25 mg/d; diet group) for 12 wk. Hematologic and dietary assessments were repeated at the end of the intervention and again after a 6-mo follow-up. ::: ::: ::: RESULTS ::: Mean SF in the supplement group increased from 9.0 +/- 3.9 microg/L at baseline to 24.8 +/- 10.0 microg/L after the intervention and remained stable during follow-up (24.2 +/- 9.8 microg/L), whereas the diet group had smaller increases during the intervention (8.9 +/- 3.1 to 11.0 +/- 5.9 microg/L) but continued to improve during follow-up (to 15.2 +/- 9.5 microg/L). Mean hemoglobin tended to improve in both intervention groups, but the change was only significant in the supplement group. ::: ::: ::: CONCLUSIONS ::: In iron-deficient women of childbearing age, a high-iron diet produced smaller increases in SF than did iron supplementation but resulted in continued improvements in iron status during a 6-mo. follow-up.


title: Computers in Primary Care

abstract: General practitioners are increasingly using computers for practice administration but information technology has had little impact on clinical practice. There is now increasing emphasis in primary health care on screening and prevention and this suggests potential clinical applications for computers. Self-administered computerized assessments for psychiatric disorder have been developed and they could have a role in screening and in providing standardized assessments, which in turn could provide the basis for clinical guidelines for the management of neurotic disorder in primary care.


title: The Micro-Dynamics of Intraorganizational and Individual Behavior and Their Role in Organizational Ambidexterity Boundaries

abstract: Organizational ambidexterity has emerged as a valuable contemporary lens on organizational design and action, examining the dynamic relationships between exploitative (extant) and explorative (evolving) resources within organizational contexts and environments. This article analyzes the literature pertaining to ambidexterity and underlines a number of recurrent preoccupations including definition of the nature, characteristics, and normative boundaries of organizational ambidexterity; a predilection toward considering interfirm/unit comparisons of large-scale corporate organizations; and a concentration on the significance of the managerialistic role of the senior management team’s disposition and action-orientations. While a few calls have been made for a focus on the micro-aspects, predominant attention has remained on the macro-aspects of organizational ambidexterity. The aim of the article, therefore, is to conduct a complementary study that considers the boundaries and transitions between exploitative and explorative modes at the intraorganizational, individual micro-behavioral level. To facilitate this, the article surfaces and underscores the paradigmatic modernistic characterization of large areas of the current organizational ambidexterity literature and the implications of this. Moreover, it explores alternative potentially useful critical paradigms that assist in providing tools with which to examine the “micro.” The research conducts an ethnographic-style study of a quasi-public training and development organization to illustrate the above background contexts and the micro-interface and boundary of explorative and exploitative modes of organizational ambidexterity in the intraorganizational situation. Within this, the study points up the significance of the role of sense-making in operational micro-moment individual and small-group situations, and their vital influence in ultimately underpinning, and contributing to, macroorganizational ambidextrous contexts.


title: Dynamic modeling, simulation & analysis of induction motor drives

abstract: The induction motor plays very important role in industrial sectors, primarily due to its robustness and lowcost. When the mechanical load is applied to induction motor which requires speed control, some of the drive and controlstrategies are based on the estimated axis speed of the motor. The speed measurement directly reduces its stability and increases the cost of drive implementation. This paper proposes an alternative methodology for estimating the speed of a three phase induction motor driven by a voltage source inverter, using space vector modulation under the scalar control strategy and based on artificial intelligent controller such as fuzzy, neural and adaptive neuro fuzzy system. To validate the performance of the proposed method under motor load torque and speed reference set point variations, simulation results are presented. The simulation results validates under no load, constant load as well as variable load of the proposed method and confirms ability to use induction motor speed control inpractice. The dynamic modeling, simulation and analysis of induction motor using Conventional, PI, Fuzzy Controller, ANN and Adaptive neuro fuzzy controller in open loop and closed loop have been presented.


title: MICA: a holistic approach to fast in-memory key-value storage

abstract: MICA is a scalable in-memory key-value store that handles 65.6 to 76.9 million key-value operations per second using a single general-purpose multi-core system. MICA is over 4-13.5x faster than current state-of-the-art systems, while providing consistently high throughput over a variety of mixed read and write workloads. ::: ::: MICA takes a holistic approach that encompasses all aspects of request handling, including parallel data access, network request handling, and data structure design, but makes unconventional choices in each of the three domains. First, MICA optimizes for multi-core architectures by enabling parallel access to partitioned data. Second, for efficient parallel data access, MICA maps client requests directly to specific CPU cores at the server NIC level by using client-supplied information and adopts a light-weight networking stack that bypasses the kernel. Finally, MICA's new data structures--circular logs, lossy concurrent hash indexes, and bulk chaining--handle both read-and write-intensive workloads at low overhead.


title: BiTouch and BiPad: designing bimanual interaction for hand-held tablets

abstract: Despite the demonstrated benefits of bimanual interaction, most tablets use just one hand for interaction, to free the other for support. In a preliminary study, we identified five holds that permit simultaneous support and interaction, and noted that users frequently change position to combat fatigue. We then designed the BiTouch design space, which introduces a support function in the kinematic chain model for interacting with hand-held tablets, and developed BiPad, a toolkit for creating bimanual tablet interaction with the thumb or the fingers of the supporting hand. We ran a controlled experiment to explore how tablet orientation and hand position affect three novel techniques: bimanual taps, gestures and chords. Bimanual taps outperformed our one-handed control condition in both landscape and portrait orientations; bimanual chords and gestures in portrait mode only; and thumbs outperformed fingers, but were more tiring and less stable. Together, BiTouch and BiPad offer new opportunities for designing bimanual interaction on hand-held tablets.


title: Compact Conformal Map for Greedy Routing in Wireless Mobile Sensor Networks

abstract: Motivated by mobile sensor networks as in participatory sensing applications, we are interested in developing a practical, lightweight solution for routing in a mobile network. While greedy routing is robust to mobility, location errors and link dynamics, it may get stuck in a local minimum, which then requires non-trivial recovery methods. We follow the approach taken by Sarkar et. al. [24] to find an embedding of the network such that greedy routing using the virtual coordinates guarantees delivery, thus eliminating the necessity of any recovery methods. Our new contribution is to replace the in-network computation of the embedding by a preprocessing of the domain before network deployment and encode the map of network domain to virtual coordinate space by using a small number of parameters which can be pre-loaded to all sensor nodes. As a result, the map is only dependent on the network domain and is independent of the network connectivity. Each node can directly compute or update its virtual coordinates by applying the locally stored map on its geographical coordinates. This represents the first practical solution for using virtual coordinates for greedy routing in a sensor network and could be easily extended to the case of a mobile network. Being extremely light-weight, greedy routing on the virtual coordinates is shown to be very robust to mobility, link dynamics and non-unit disk graph connectivity models.


A Review on Li-Ion Battery Charger Techniques and Optimize Battery Charger Performance by Fuzzy Logic


title: The Reuters Corpus Volume 1 -from Yesterday's News to Tomorrow's Language Resources

abstract: Reuters, the global information, news and technology group, has for the first time made available free of charge, large quantities of archived Reuters news stories for use by research communities around the world. The Reuters Corpus Volume 1 (RCV1) includes over 800,000 news stories typical of the annual English language news output of Reuters. This paper describes the origins of RCV1, the motivations behind its creation, and how it differs from previous corpora. In addition we discuss the system of category coding, whereby each story is annotated for topic, region and industry sector. We also discuss the process by which these codes were applied, and examine the issues involved in maintaining quality and consistency of coding in an operational, commercial environment.


An Efficient High-Step-Up Interleaved DC – DC Converter with a Common Active Clamp


title: Towards Ontology-Based Program Analysis

abstract: Program analysis is fundamental for program optimizations, debugging, ::: and many other tasks. But developing program analyses has been a ::: challenging and error-prone process for general users. Declarative ::: program analysis has shown the promise to dramatically improve the ::: productivity in the development of program analyses. Current ::: declarative program analysis is however subject to some major ::: limitations in supporting cooperations among analysis tools, guiding ::: program optimizations, and often requires much effort for repeated ::: program preprocessing. ::: ::: In this work, we advocate the integration of ontology into declarative ::: program analysis. As a way to standardize the definitions of concepts ::: in a domain and the representation of the knowledge in the domain, ::: ontology offers a promising way to address the limitations of current ::: declarative program analysis. We develop a prototype framework named ::: PATO for conducting program analysis upon ontology-based program ::: representation. Experiments on six program analyses confirm the ::: potential of ontology for complementing existing declarative program ::: analysis. It supports multiple analyses without separate program ::: preprocessing, promotes cooperative Liveness analysis between two ::: compilers, and effectively guides a data placement optimization for ::: Graphic Processing Units (GPU).


title: Diagnostic accuracy of scapular physical examination tests for shoulder disorders: a systematic review

abstract: OBJECTIVE ::: To systematically review and critique the evidence regarding the diagnostic accuracy of physical examination tests for the scapula in patients with shoulder disorders. ::: ::: ::: METHODS ::: A systematic, computerised literature search of PubMED, EMBASE, CINAHL and the Cochrane Library databases (from database inception through January 2012) using keywords related to diagnostic accuracy of physical examination tests of the scapula. The Quality Assessment of Diagnostic Accuracy Studies tool was used to critique the quality of each paper. ::: ::: ::: RESULTS ::: Eight articles met the inclusion criteria; three were considered to be of high quality. Of the three high-quality studies, two were in reference to a 'diagnosis' of shoulder pain. Only one high-quality article referenced specific shoulder pathology of acromioclavicular dislocation with reported sensitivity of 71% and 41% for the scapular dyskinesis and SICK scapula test, respectively. ::: ::: ::: CONCLUSIONS ::: Overall, no physical examination test of the scapula was found to be useful in differentially diagnosing pathologies of the shoulder.


title: The state of the art in end-user software engineering

abstract: Most programs today are written not by professional software developers, but by people with expertise in other domains working towards goals for which they need computational support. For example, a teacher might write a grading spreadsheet to save time grading, or an interaction designer might use an interface builder to test some user interface design ideas. Although these end-user programmers may not have the same goals as professional developers, they do face many of the same software engineering challenges, including understanding their requirements, as well as making decisions about design, reuse, integration, testing, and debugging. This article summarizes and classifies research on these activities, defining the area of End-User Software Engineering (EUSE) and related terminology. The article then discusses empirical research about end-user software engineering activities and the technologies designed to support them. The article also addresses several crosscutting issues in the design of EUSE tools, including the roles of risk, reward, and domain complexity, and self-efficacy in the design of EUSE tools and the potential of educating users about software engineering principles.


title: Unusual Case of Suicide With a Modified Trap Gun

abstract: INTRODUCTION ::: Trap gun is an illegal, locally manufactured gun with a basic trip system used to hunt wild animals. ::: ::: ::: CASE REPORT ::: The body of a 28-year-old man was found in the jungle in supine position with both legs apart. A trap gun was between the legs pointing toward the cranial side of the body. It had 2 free wires that were not connected together. There was no evidence of foul play.The body had a single-entry wound (2.5-cm diameter) in the anterior chest, with blackening, burning, and tattooing. Six metal particles and nylon clothing material were embedded into soft tissue. No exit wound was found. Toxicology analysis reported an alcohol level of 72 mg/dL. The cause of death was multiple shrapnel injury to the chest at close to intermediate range by a single discharge from a trap gun. Circumstance was concluded as suicide.Ballistic and firearm experts opined that an illegal, manually operated, battery-powered ignition device was used to ignite the gun powder. ::: ::: ::: CONCLUSION ::: We report the first case of suicide by a modified trap gun in literature.


title: AutoSense Model for Word Sense Induction

abstract: Word sense induction (WSI), or the task of automatically discovering multiple senses or meanings of a word, has three main challenges: domain adaptability, novel sense detection, and sense granularity flexibility. While current latent variable models are known to solve the first two challenges, they are not flexible to different word sense granularities, which differ very much among words, from aardvark with one sense, to play with over 50 senses. Current models either require hyperparameter tuning or nonparametric induction of the number of senses, which we find both to be ineffective. Thus, we aim to eliminate these requirements and solve the sense granularity problem by proposing AutoSense, a latent variable model based on two observations: (1) senses are represented as a distribution over topics, and (2) senses generate pairings between the target word and its neighboring word. These observations alleviate the problem by (a) throwing garbage senses and (b) additionally inducing fine-grained word senses. Results show great improvements over the stateof-the-art models on popular WSI datasets. We also show that AutoSense is able to learn the appropriate sense granularity of a word. Finally, we apply AutoSense to the unsupervised author name disambiguation task where the sense granularity problem is more evident and show that AutoSense is evidently better than competing models. We share our data and code here: https://github.com/rktamplayo/AutoSense.


title: Fast Object Class Labelling via Speech

abstract: Object class labelling is the task of annotating images with labels on the presence or absence of objects from a given class vocabulary. Simply asking one yes-no question per class, however, has a cost that is linear in the vocabulary size and is thus inefficient for large vocabularies. Modern approaches rely on a hierarchical organization of the vocabulary to reduce annotation time, but remain expensive (several minutes per image for the 200 classes in ILSVRC). Instead, we propose a new interface where classes are annotated via speech. Speaking is fast and allows for direct access to the class name, without searching through a list or hierarchy. As additional advantages, annotators can simultaneously speak and scan the image for objects, the interface can be kept extremely simple, and using it requires less mouse movement. As annotators using our interface should only say words from a given class vocabulary, we propose a dedicated task to train them to do so. Through experiments on COCO and ILSVRC, we show our method yields high-quality annotations at 2.3x −14.9x less annotation time than existing methods.


Dialogue State Tracking using Long Short Term Memory Neural Networks


title: A − 137 dBm/Hz Noise, 82% Efficiency AC-Coupled Hybrid Supply Modulator With Integrated Buck-Boost Converter

abstract: This paper presents a hybrid supply modulator (SM) with a comprehensive analysis of receiver (RX) band noise in an envelope tracking power amplifier (ET-PA). The designed SM supports both ET mode and average power tracking (APT) mode depending on the PA output power level. In the APT mode, an integrated buck-boost (BB) converter with hysteretic control generates dc supply voltage and its average switching frequency ranges from 1 to 2 MHz. In the ET mode, the bandwidth of SM is determined by a linear amplifier, which has about 30 MHz of signal bandwidth. To improve the ET efficiency, an ac coupling capacitor, with an adaptive offset control, and scaled linear supply are used. The scaled linear supply is provided by the BB converter. To lower the output noise of the SM, a parallel class-AB output buffer and resonance frequency tuning schemes are applied in this paper. For long term evolution 10 MHz with quadrature phase shift keying modulation and 5.8 dB peak-to-average power ratio, the designed SM achieves 82% efficiency at 800 mW output power with a fixed 8 $\Omega $ resistor. Adapting the proposed SM to a PA, a 10 dB ET operation dynamic range is achieved while achieving a power added efficiency of 42.6% at 27 dBm PA output power. The measured SM output noise is −137 dBm/Hz at 95 MHz offset with the PA load, and the PA RX band noise is −124 dBm/Hz, which is dominated by the stand-alone PA itself. The chip is implemented with a 130 nm CMOS process and the die size is 5.0 mm2.


Privacy and Security issues in Cloud Computing


title: A statistical model for scientific readability

abstract: In this paper, we present a new method of using statistical models to estimate readability [1]. Language Model is used to capture the content information. It is combined with linguistic feature model by a linear form. Experiments show that this new method has a better performance than the widely used Flesch-Kincaid readability formula.


title: Investigations of DDDAS for command and control of UAV swarms with agent-based modeling

abstract: The application of Dynamic Data Driven Application Systems (DDDAS) to the command and control of swarms of Unmanned Aerial Vehicles (UAVs) is being investigated. Swarm intelligent systems are not only efficient at solving group-level problems, but also decentralized, controllable by few simple parameters, making possible the command and control of UAV swarms by a single operator. Four separate but related projects are surveyed that explore the command and control of UAV swarms. Each project employs the DDDAS paradigm, entailing the ability of an executing application to incorporate dynamic data into the decision process, and conversely, to steer the measurement process via a central application system. By providing an overview of DDDAS approaches to UAV swarm mission scheduling, UAV swarm communication, UAV swarm formation planning, and flocking applications, general principles of UAV swarms and DDDAS architecture may be observed.


title: A Novel Offloading Partitioning Algorithm in Mobile Cloud Computing

abstract: Mobile cloud offloading that migrates computation-intensive parts of applications from resource-constrained mobile devices onto remote resource-rich servers, is an effective way to shorten response time and extend battery life of mobile devices. Application partitioning plays a critical role in high-performance offloading systems, which involves splitting the execution of applications between the mobile side and cloud side so that the total execution cost is minimized. Through partitioning, the mobile device can have the most benefit from offloading the application to a remote cloud. In this paper, we study how to effectively and dynamically partition a given application into local and remote parts while keeping the total cost as small as possible. For general tasks (i.e., arbitrary topological consumption graphs), we propose a novel min-cost offloading partitioning (MCOP) algorithm that aims at finding the optimal partitioning plan (determining which portions of the application to run on mobile devices and which portions on cloud servers) under different cost models and mobile environments. The simulation results show that the proposed algorithm provides a stably low time complexity method and can significantly reduce execution time and energy consumption by optimally distributing tasks between mobile devices and cloud servers, and in the meantime, it can well adapt to environment changes.


title: Two-Hop Relay-Assisted Cooperative Communication in Wireless Body Area Networks: An Empirical Study

abstract: The pervasive use of wireless body area networks (BANs) has incurred potential inter-BAN interference, which can cause severe performance degradation. In this article, the coexistence of BANs is experimentally performed. A relay-assisted cooperative communications scheme is implemented in a real IEEE 802.15.4-based BAN system with a beacon-enabled mode and guaranteed time slot (GTS) scheduling. As far as we know, it is the first experimental work that enables real-time investigation of the effectiveness of cooperative communications in BANs for co-channel radio interference mitigation. First- and second-order statistics, including outage probability, level crossing rate (LCR), and average fade/nonfade duration, are calculated from the measured effective channel gains of the device-to-coordinator links across all superframes. Empirical results demonstrate significant advantages of using two-hop relay-assisted communications over traditional star topology BAN. Advantages include a maximum of a 10dB increase in channel gain threshold at an outage probability of 10p, which corresponds to a guideline for a 10p maximum packet error rate as specified in the IEEE BAN standard; a reduction in the level crossing rate by a factor of 5 at a channel gain threshold of − 100dB; and an average nonfade duration prolonged by a factor of 5 at the same threshold.


SPATIAL TRANSFORMATIONS


title: Phase- and Self-Injection-Locked Radar for Detecting Vital Signs with Efficient Elimination of DC Offsets and Null Points

abstract: A phase- and self-injection-locked radar is presented for robust vital-sign detection. The dc offset from the clutter is eliminated by using a dual-tuning voltage-controlled oscillator without complex clutter cancellation techniques. Phase noise analysis shows that the proposed radar has the advantages of both phase-locked oscillators and self-injection-locked oscillators to achieve high power spectral signal-to-noise ratio. Path-diversity transmission is employed to eliminate null points and reduce average transmitted power. Experiments demonstrate successful detection of breathing and heartbeat from a distance of 4 m with -22 dBm (6 μW ) average transmitted power.


Inexpensive computer analysis of cell and tissue stainings


title: A Failure-Friendly Design Principle for Hash Functions

abstract: This paper reconsiders the established Merkle-Damgard design principle for iterated hash functions. The internal state size w of an iterated n-bit hash function is treated as a security parameter of its own right. In a formal model, we show that increasing w quantifiably improves security against certain attacks, even if the compression function fails to be collision resistant. We propose the wide-pipe hash, internally using a w-bit compression function, and the double-pipe hash, with w = 2n and an n-bit compression function used twice in parallel.


A SMOOTH SPIRAL-LIKE TOOL PATH WITH CONTROLLED CUTTING WIDTH FOR POCKET MACHINING


title: A Systematic Review of Cybersickness

abstract: The uptake of new interface technologies, such as the Oculus Rift have generated renewed interest in virtual reality especially for private entertainment use. However, long standing issues with unwanted side effects, such as nausea from cybersickness, continue to impact on the general use of devices such as head mounted displays. This in turn has slowed the uptake of more immersive interfaces for computer gaming and indeed more serious applications in training and health. In this paper we report a systematic review in the area of cybersickness with a focus on measuring the diverse symptoms experienced. Indeed the related conditions of simulator sickness and motion sickness have previously been well studied and yet many of the issues are unresolved. Here we report on these issues along with a number of measures, both subjective and objective in nature, using either questionnaires or psychophysiological measures that have been used to study cybersickness. We also report on the factors, individual, device related and task dependent that impact on the condition. We conclude that there remains a need to develop more cost-effective and objective physiological measures of both the impact of cybersickness and a person's susceptibility to the condition.


title: Computational Depth Sensing : Toward high-performance commodity depth cameras

abstract: Depth information plays an important role in a variety of applications, including manufacturing, medical imaging, computer vision, graphics, and virtual/augmented reality (VR/AR). Depth sensing has thus attracted sustained attention from both academia and industry communities for decades. Mainstream depth cameras can be divided into three categories: stereo, time of flight (ToF), and structured light. Stereo cameras require no active illumination and can be used outdoors, but they are fragile for homogeneous surfaces. Recently, off-the-shelf light field cameras have demonstrated improved depth estimation capability with a multiview stereo configuration. ToF cameras operate at a high frame rate and fit time-critical scenarios well, but they are susceptible to noise and limited to low resolution [3]. Structured light cameras can produce high-resolution, high-accuracy depth, provided that a number of patterns are sequentially used. Due to its promising and reliable performance, the structured light approach has been widely adopted for three-dimensional (3-D) scanning purposes. However, achieving real-time depth with structured light either requires highspeed (and thus expensive) hardware or sacrifices depth resolution and accuracy by using a single pattern instead.


Bamboo: Ball-Shape Data Augmentation Against Adversarial Attacks from All Directions


title: NCRF++: An Open-source Neural Sequence Labeling Toolkit

abstract: This paper describes NCRF++, a toolkit for neural sequence labeling. NCRF++ is designed for quick implementation of different neural sequence labeling models with a CRF inference layer. It provides users with an inference for building the custom model structure through configuration file with flexible neural feature design and utilization. Built on PyTorch, the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU. It also includes the implementations of most state-of-the-art neural sequence labeling models such as LSTM-CRF, facilitating reproducing and refinement on those methods.


title: Design and Fabrication of Full Wheatstone-Bridge-Based Angular GMR Sensors

abstract: Since the discovery of the giant magnetoresistive (GMR) effect, GMR sensors have gained much attention in last decades due to their high sensitivity, small size, and low cost. The full Wheatstone-bridge-based GMR sensor is most useful in terms of the application point of view. However, its manufacturing process is usually complex. In this paper, we present an efficient and concise approach to fabricate a full Wheatstone-bridge-based angular GMR sensor by depositing one GMR film stack, utilizing simple patterned processes, and a concise post-annealing procedure based on a special layout. The angular GMR sensor is of good linear performance and achieves a sensitivity of 0.112 mV/V/Oe at the annealing temperature of 260 °C in the magnetic field range from -50 to +50 Oe. This work provides a design and method for GMR-sensor manufacturing that is easy for implementation and suitable for mass production.


title: Application of Grey Wolf Optimization for Solving Combinatorial Problems: Job Shop and Flexible Job Shop Scheduling Cases

abstract: Grey wolf optimization (GWO) algorithm is a new population-oriented intelligence algorithm, which is originally proposed to solve continuous optimization problems inspired from the social hierarchy and hunting behaviors of grey wolves. It has been proved that GWO can provide competitive results compared with some well-known meta-heuristics. This paper aims to employ the GWO to deal with two combinatorial optimization problems in the manufacturing field: job shop and flexible job shop scheduling cases. The effectiveness of GWO algorithm on the two problems can give an idea about its possible application on solving other scheduling problems. For the discrete characteristics of the scheduling solutions, we developed a kind of discrete GWO algorithm with the objective of minimizing the maximum completion time (makespan). In the proposed algorithm, searching operator is designed based on the crossover operation to maintain the algorithm work directly in a discrete domain. Then an adaptive mutation method is introduced to keep the population diversity and avoid premature convergence. In addition, a variable neighborhood search method is embedded to further enhance the exploration. To evaluate the effectiveness, the discrete GWO algorithm is compared with other published algorithms in the literature for the two scheduling cases. Experimental results demonstrate that our algorithm outperforms other algorithms for the scheduling problems under study.


title: Enhancing Interior and Exterior Deep Facial Features for Face Detection in the Wild

abstract: Although face detection has been intensely studied for decades, it is still a challenging topic due to numerous conditions, e.g. heavy occlusions, low resolutions, extreme poses, non-face patterns that look like human faces, etc. This paper proposes a novel region-based ConvNet to address these issues. Our approach enhances the interior deep facial features and explicitly incorporates the exterior deep features. The enhanced interior features provide fine details for small faces. The exterior features capture the local information surrounding the face, supporting the detection under challenging conditions. Experiments show that our proposed components improve the baseline method significantly. Additionally, our approach consistently achieves competitive performance in four challenging databases, i.e. Wider Face, AFW, PASCAL Faces, and FDDB. We also introduce a new challenging non-face dataset 1 of 6,000 images to benchmark false positive rates for future research.


title: Spanish Multicenter Normative Studies (NEURONORMA Project): Normative Data and Equivalence of Four BNT Short-Form Versions

abstract: The application of the Boston Naming Test (BNT) is time-consuming and shortened versions need to be developed for screening purposes. The aims of this study were to develop four equivalent 15-item forms of a Spanish adaptation of the BNT, to test the equivalence of the new versions in a clinical sample, and to provide normative data. The normative sample consisted of 340 subjects. The clinical sample included 172 patients (76 Mild Cognitive Impairment and 96 Alzheimer's disease). An empirical procedure was used to develop the shortened versions. All new versions demonstrated satisfactory internal consistency. Pearson's coefficient analysis showed strong relationships among the four short-form versions as well as between each of them and the 60-item test. The inferential confidence interval method demonstrated the equivalence between the four shortened versions. Age and education affected the score of all short-form versions, but sex was found to be unrelated to the performance. Normative data were calculated for midpoint age groups. This paper proposes four 15-item equivalent versions that could be useful and time-saving tools for screening purposes.


title: Cumulon: optimizing statistical data analysis in the cloud

abstract: We present Cumulon, a system designed to help users rapidly develop and intelligently deploy matrix-based big-data analysis programs in the cloud. Cumulon features a flexible execution model and new operators especially suited for such workloads. We show how to implement Cumulon on top of Hadoop/HDFS while avoiding limitations of MapReduce, and demonstrate Cumulon's performance advantages over existing Hadoop-based systems for statistical data analysis. To support intelligent deployment in the cloud according to time/budget constraints, Cumulon goes beyond database-style optimization to make choices automatically on not only physical operators and their parameters, but also hardware provisioning and configuration settings. We apply a suite of benchmarking, simulation, modeling, and search techniques to support effective cost-based optimization over this rich space of deployment plans.


title: Cross Language Text Classification via Subspace Co-Regularized Multi-View Learning

abstract: In many multilingual text classification problems, the documents in different languages often share the same set of categories. To reduce the labeling cost of training a classification model for each individual language, it is important to transfer the label knowledge gained from one language to another language by conducting cross language classification. In this paper we develop a novel subspace co-regularized multi-view learning method for cross language text classification. This method is built on parallel corpora produced by machine translation. It jointly minimizes the training error of each classifier in each language while penalizing the distance between the subspace representations of parallel documents. Our empirical study on a large set of cross language text classification tasks shows the proposed method consistently outperforms a number of inductive methods, domain adaptation methods, and multi-view learning methods.


title: NLMF: NonLinear Matrix Factorization Methods for Top-N Recommender Systems

abstract: Many existing state-of-the-art top-N recommendation methods model users and items in the same latent space and the recommendation scores are computed via the dot product between those vectors. These methods assume that the user preference is consistent across all the items that he/she has rated. This assumption is not necessarily true, since many users can have multiple personas/interests and their preferences can vary with each such interest. To address this, a recently proposed method modeled the users with multiple interests. In this paper, we build on this approach and model users using a much richer representation. We propose a method which models the user preference as a combination of having global preference and interest-specific preference. The proposed method uses a nonlinear model for predicting the recommendation score, which is used to perform top-N recommendation task. The recommendation score is computed as a sum of the scores from the components representing global preference and interest-specific preference. A comprehensive set of experiments on multiple datasets show that the proposed model outperforms other state-of-the-art methods for top-N recommendation task.


A novel framework for automatic chinese question generation based on multi-feature neural network model


title: ReBucket: a method for clustering duplicate crash reports based on call stack similarity

abstract: Software often crashes. Once a crash happens, a crash report could be sent to software developers for investigation upon user permission. To facilitate efficient handling of crashes, crash reports received by Microsoft's Windows Error Reporting (WER) system are organized into a set of "buckets". Each bucket contains duplicate crash reports that are deemed as manifestations of the same bug. The bucket information is important for prioritizing efforts to resolve crashing bugs. To improve the accuracy of bucketing, we propose ReBucket, a method for clustering crash reports based on call stack matching. ReBucket measures the similarities of call stacks in crash reports and then assigns the reports to appropriate buckets based on the similarity values. We evaluate ReBucket using crash data collected from five widely-used Microsoft products. The results show that ReBucket achieves better overall performance than the existing methods. In average, the F-measure obtained by ReBucket is about 0.88.


title: Unsupervised Nearest Neighbors Clustering With Application to Hyperspectral Images

abstract: We address the problem of unsupervised clustering of multidimensional data when the number of clusters is not known a priori. The proposed iterative approach is a stochastic extension of the $k {\rm NN}$ density-based clustering (knnclust) method which randomly assigns objects to clusters by sampling a posterior class label distribution. In our approach, contextual class-conditional distributions are estimated based on a $k$ nearest neighbors graph, and are iteratively modified to account for current cluster labeling. Posterior probabilities are also slightly reinforced to accelerate convergence to a stationary labeling. A stopping criterion based on the measure of clustering entropy is defined thanks to the Kozachenko-Leonenko differential entropy estimator, computed from current class-conditional entropies. One major advantage of our approach relies in its ability to provide an estimate of the number of clusters present in the data set. The application of our approach to the clustering of real hyperspectral image data is considered. Our algorithm is compared with other unsupervised clustering approaches, namely affinity propagation (ap), knnclust and Non Parametric Stochastic Expectation Maximization (npsem), and is shown to improve the correct classification rate in most experiments.


title: Adapting maize production to climate change in sub-Saharan Africa

abstract: Given the accumulating evidence of climate change in sub-Saharan Africa, there is an urgent need to develop more climate resilient maize systems. Adaptation strategies to climate change in maize systems in sub-Saharan Africa are likely to include improved germplasm with tolerance to drought and heat stress and improved management practices. Adapting maize systems to future climates requires the ability to accurately predict future climate scenarios in order to determine agricultural responses to climate change and set priorities for adaptation strategies. Here we review the projected climate change scenarios for Africa’s maize growing regions using the outputs of 19 global climate models. By 2050, air temperatures are expected to increase throughout maize mega- environments within sub-Saharan Africa by an average of 2.1°C. Rainfall changes during the maize growing season varied with location. Given the time lag between the development of improved cultivars until the seed is in the hands of farmers and adoption of new management practices, there is an urgent need to prioritise research strategies on climate change resilient germplasm development to offset the predicted yield declines.


The Gibbard Satterthwaite Theorem and Arrow’s Impossibility Theorem


title: A Study on Optimal Design of the Triangle Type Permanent Magnet in IPMSM Rotor by Using the Box–Behnken Design

abstract: This paper proposes an optimal rotor design method of an interior permanent magnet synchronous motor (IPMSM) by using a permanent magnet (PM) shape. An IPMSM is a structure in which PMs are buried in an inner rotor. The torque, torque ripple, and safety factor of IPMSM can vary depending on the position of the inserted PMs. To determine the optimal design variables according to the placement of the inserted PMs, parameter analysis was performed. Therefore, a response surface methodology, which is one of the statistical analysis design methods, was used. Among many other response surface methodologies, Box–Behnken design is the most commonly used. For the purpose of this research, Box–Behnken design was used to find the design parameter that can achieve minimum experimental variables of objective function. This paper determines the insert position of the PM to obtain high-torque, low-torque ripple by using a finite-element-method, and this paper obtains an optimal design by using a mechanical stiffness method in which a safety factor is considered.


title: Gender Classification From the Same Iris Code Used for Recognition

abstract: Previous researchers have explored various approaches for predicting the gender of a person based on the features of the iris texture. This paper is the first to predict gender directly from the same binary iris code that could be used for recognition. We found that the information for gender prediction is distributed across the iris, rather than localized in particular concentric bands. We also found that using selected features representing a subset of the iris region achieves better accuracy than using features representing the whole iris region. We used the measures of mutual information to guide the selection of bits from the iris code to use as features in gender prediction. Using this approach, with a person-disjoint training and testing evaluation, we were able to achieve 89% correct gender prediction using the fusion of the best features of iris code from the left and right eyes.


title: Hidden technical debt in Machine learning systems

abstract: Machine learning offers a fantastically powerful toolkit for building useful complex prediction systems quickly. This paper argues it is dangerous to think of these quick wins as coming for free. Using the software engineering framework of technical debt, we find it is common to incur massive ongoing maintenance costs in real-world ML systems. We explore several ML-specific risk factors to account for in system design. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, configuration issues, changes in the external world, and a variety of system-level anti-patterns.


title: E-LAMP: integration of innovative ideas for multimedia event detection

abstract: Detecting multimedia events in web videos is an emerging hot research area in the fields of multimedia and computer vision. In this paper, we introduce the core methods and technologies of the framework we developed recently for our Event Labeling through Analytic Media Processing (E-LAMP) system to deal with different aspects of the overall problem of event detection. More specifically, we have developed efficient methods for feature extraction so that we are able to handle large collections of video data with thousands of hours of videos. Second, we represent the extracted raw features in a spatial bag-of-words model with more effective tilings such that the spatial layout information of different features and different events can be better captured, thus the overall detection performance can be improved. Third, different from widely used early and late fusion schemes, a novel algorithm is developed to learn a more robust and discriminative intermediate feature representation from multiple features so that better event models can be built upon it. Finally, to tackle the additional challenge of event detection with only very few positive exemplars, we have developed a novel algorithm which is able to effectively adapt the knowledge learnt from auxiliary sources to assist the event detection. Both our empirical results and the official evaluation results on TRECVID MED'11 and MED'12 demonstrate the excellent performance of the integration of these ideas.


title: EMG-Controlled Hand Teleoperation Using a Continuous Teleoperation Subspace

abstract: We present a method for EMG-driven teleoperation of non-anthropomorphic robot hands. EMG sensors are appealing as a wearable, inexpensive and unobtrusive way to gather information about the teleoperator's hand pose. However, mapping from EMG signals to the pose space of a non-anthropomorphic hand presents multiple challenges. We present a method that first projects from forearm EMG into a subspace relevant to teleoperation. To increase robustness, we use a model which combines continuous and discrete predictors along different dimensions of this subspace. We then project from the teleoperation subspace into the pose space of the robot hand. We show that our method is effective and intuitive, as it enables novice users to teleoperate pick and place tasks faster and more robustly than state-of-the-art EMG teleoperation methods when applied to a non-anthropomorphic, multi-DOF robot hand.


title: Maximum Correntropy Kalman Filter

abstract: Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate seriously. To improve the robustness of KF against impulsive noises, we propose in this work a new Kalman filter, called the maximum correntropy Kalman filter (MCKF), which adopts the robust maximum correntropy criterion (MCC) as the optimality criterion, instead of using the MMSE. Similar to the traditional KF, the state mean and covariance matrix propagation equations are used to give prior estimations of the state and covariance matrix in MCKF. A novel fixed-point algorithm is then used to update the posterior estimations. A sufficient condition that guarantees the convergence of the fixed-point algorithm is given. Illustration examples are presented to demonstrate the effectiveness and robustness of the new algorithm.


title: Human Creativity: Its Cognitive Basis, its Evolution, and its Connections with Childhood Pretence

abstract: This paper defends two initial claims. First, it argues that essentially the same cognitive resources are shared by adult creative thinking and problem-solving, on the one hand, and by childhood pretend play, on the other—namely, capacities to generate and to reason with suppositions (or imagined possibilities). Second, it argues that the evolutionary function of childhood pretence is to practice and enhance adult forms of creativity. The paper goes on to show how these proposals can provide a smooth and evolutionarily-plausible explanation of the gap between the first appearance of our species in Southern Africa some 100,000 years ago, and the ‘creative explosion’ of cultural, technological and artistic change which took place within dispersed human populations some 60,000 years later. The intention of the paper is to sketch a proposal which might serve as a guide for future interdisciplinary research.