title: Mobile Agent Middleware for Mobile Computing

abstract: Mobile computing requires an advanced infrastructure that integrates suitable support protocols, mechanisms, and tools. This mobility middleware should dynamically reallocate and trace mobile users and terminals and permit communication and coordination of mobile entities. In addition, open and untrusted environments must overcome system heterogeneity and grant the appropriate security level. Solutions to these issues require compliance with standards to interoperate with different systems and legacy components and a reliable security infrastructure based on standard cryptographic mechanisms and tools. Many proposals suggest using mobile agent technology middleware to address these issues. A mobile agent moves entities in execution together with code and achieved state, making it possible to upgrade distributed computing environments without suspending service. We propose three mobile computing services: user virtual environment (UVE), mobile virtual terminal (MVT), and virtual resource management (VRM). UVE provides users with a uniform view of their working environments independent of current locations and specific terminals. MVT extends traditional terminal mobility by preserving the terminal execution state for restoration at new locations, including active processes and subscribed services. VRM permits mobile users and terminals to maintain access to resources and services by automatically requalifying the bindings and moving specific resources or services to permit load balancing and replication.


title: Deep Learning for Image Denoising: A Survey

abstract: Since the proposal of big data analysis and Graphic Processing Unit (GPU), the deep learning technology has received a great deal of attention and has been widely applied in the field of imaging processing. In this paper, we have an aim to completely review and summarize the deep learning technologies for image denoising proposed in recent years. Morever, we systematically analyze the conventional machine learning methods for image denoising. Finally, we point out some research directions for the deep learning technologies in image denoising.


title: Data Mining Techniques for The Screening of Age-Related Macular Degeneration

abstract: Age related macular degeneration (AMD) is the primary cause of adult blindness. Currently AMD cannot be cured, however early detection does allow the progress of the condition to be inhibited. One of the first symptoms of AMD is the presence of fatty deposits, called drusen, on the retina. The presence of drusen may be identified through the manual inspection/screening of retinal images. This task, however, requires recourse to domain experts and is therefore resource intensive. This paper proposes and compares two data mining techniques to support the automated screening for AMD. The first uses spatial-histograms, that maintain both image colour and spatial information, for the image representation; to which a case based reasoning (CBR) classification technique is applied. The second is founded on a hierarchical decomposition of the image set so that a tree representation is generated. A weighted frequent sub-graph mining technique is then applied to this representation to identify sub-trees that frequently occur across the data set. The identified sub-trees are then encoded in the form of feature vectors to which standard classification techniques can be applied.


title: Identification of patient deterioration in vital-sign data using one-class support vector machines

abstract: Adverse hospital patient outcomes due to deterioration are often preceded by periods of physiological deterioration that is evident in the vital signs, such as heart rate, respiratory rate, etc. Clinical practice currently relies on periodic, manual observation of vital signs, which typically occurs every 2-to-4 hours in most hospital wards, and so patient deterioration may go unidentified. While continuous patient monitoring systems exist for those patients who are confined to a hospital bed, the false alarm rate of conventional systems is typically so high that the alarms generated by them are ignored. This paper explores the use of machine learning methods for automatically identifying patient deterioration, using data acquired from continuous patient monitors. We compare generative and discriminative techniques (a probabilistic method using a mixture model, and a support vector machine, respectively). It is well-known that parameter tuning affects the performance of such methods, and we propose a method to optimise parameter values using “partial AUC”. We demonstrate the performance of the proposed method using both synthetic data and patient vital-sign data collected from a recent observational clinical study.


title: Multiscale Combinatorial Grouping

abstract: We propose a unified approach for bottom-up hierarchical image segmentation and object candidate generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normalized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multiscale information. Finally, we propose a grouping strategy that combines our multiscale regions into highly-accurate object candidates by exploring efficiently their combinatorial space. We conduct extensive experiments on both the BSDS500 and on the PASCAL 2012 segmentation datasets, showing that MCG produces state-of-the-art contours, hierarchical regions and object candidates.


title: 3D Triangular Mesh Parameterization with Semantic Features Based on Competitive Learning Methods

abstract: In 3D computer graphics, mesh parameterization is a key technique for digital geometry processings such as morphing, shape blending, texture mapping, re-meshing and so on. Most of the previous approaches made use of an identical primitive domain to parameterize a mesh model. In recent works of mesh parameterization, more flexible and attractive methods that can create direct mappings between two meshes have been reported. These mappings are called “cross-parameterization” and typically preserve semantic feature correspondences between target meshes. This paper proposes a novel approach for parameterizing a mesh into another one directly. The main idea of our method is to combine a competitive learning and a least-square mesh techniques. It is enough to give some semantic feature correspondences between target meshes, even if they are in different shapes or in different poses.


title: Audit Games with Multiple Defender Resources

abstract: Modern organizations (e.g., hospitals, social networks, government agencies) rely heavily on audit to detect and punish insiders who inappropriately access and disclose confidential information. Recent work on audit games models the strategic interaction between an auditor with a single audit resource and auditees as a Stackelberg game, augmenting associated well-studied security games with a configurable punishment parameter. We significantly generalize this audit game model to account for multiple audit resources where each resource is restricted to audit a subset of all potential violations, thus enabling application to practical auditing scenarios. We provide an FPTAS that computes an approximately optimal solution to the resulting non-convex optimization problem. The main technical novelty is in the design and correctness proof of an optimization transformation that enables the construction of this FPTAS. In addition, we experimentally demonstrate that this transformation significantly speeds up computation of solutions for a class of audit games and security games.


title: Macroflows and Microflows: Enabling Rapid Network Innovation through a Split SDN Data Plane

abstract: In this paper, we empirically quantify inherent limitations of merchant switching silicon, which constrain SDNs'innovation potential. To overcome these limitations, we propose a Split SDN Data Plane (SSDP) architecture -- anew switch architecture which allows rapid network innovation by complementing a cost-effective, yet inflexible, front end merchant silicon switch chip with a deeply programmable co-processor subsystem. We implemented SSDP on a prototype Dell Power Connect platform with a programmable multi-core data plane subsystem. To demonstrate SSDP's potential, we developed diverse real-world cases on the prototype platform. Benchmarking results show that, while delivering on its rapid innovation promise (with significantly shorter turn-around time), a SSDP architecture also provides reasonably high switching rates on deep flow tables.


title: Towards Frequent Subgraph Mining on Single Large Uncertain Graphs

abstract: Uncertainty is intrinsic to a wide spectrum of real-life applications, which inevitably applies to graph data. Representative uncertain graphs are seen in bio-informatics, social networks, etc. This paper motivates the problem of frequent subgraph mining on single uncertain graphs. We present an enumeration-evaluation algorithm to solve the problem. By showing support computation on an uncertain graph is #P-hard, we develop an approximation algorithm with accuracy guarantee for this purpose. To enhance the solution, we devise optimization techniques to achieve better mining performance. Experiment results on real-life data confirm the usability of the algorithm.


title: Quadrotor helicopter flight dynamics and control: Theory and experiment

abstract: Quadrotor helicopters are emerging as a popular platform for unmanned aerial vehicle (UAV) research, due to the simplicity of their construction and maintenance, their ability to hover, and their vertical take o and landing (VTOL) capability. Current designs have often considered only nominal operating conditions for vehicle control design. This work seeks to address issues that arise when deviating significantly from the hover flight regime. Aided by well established research for helicopter flight control, three separate aerodynamic eects are investigated as they pertain to quadrotor flight, due to vehicular velocity, angle of attack, and airframe design. They cause moments that aect attitude control, and thrust variation that aects altitude control. Where possible, a theoretical development is first presented, and is then validated through both thrust test stand measurements and vehicle flight tests using the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC) quadrotor helicopter. The results enabled improved controller performance.


title: A Single-Ended Disturb-Free 9T Subthreshold SRAM With Cross-Point Data-Aware Write Word-Line Structure, Negative Bit-Line, and Adaptive Read Operation Timing Tracing

abstract: This paper presents a novel single-ended disturb-free 9T subthreshold SRAM cell with cross-point data-aware Write word-line structure. The disturb-free feature facilitates bit-interleaving architecture, which can reduce multiple-bit upsets in a single word and enhance soft error immunity by employing Error Checking and Correction (ECC) technique. The proposed 9T SRAM cell is demonstrated by a 72 Kb SRAM macro with a Negative Bit-Line (NBL) Write-assist and an adaptive Read operation timing tracing circuit implemented in 65 nm low-leakage CMOS technology. Measured full Read and Write functionality is error free with VDD down to 0.35 V ( 0.15 V lower than the threshold voltage) with 229 KHz frequency and 4.05 μW power. Data is held down to 0.275 V with 2.29 μW Standby power. The minimum energy per operation is 4.5 pJ at 0.5 V. The 72 Kb SRAM macro has wide operation range from 1.2 V down to 0.35 V, with operating frequency of around 200 MHz for VDD around/above 1.0 V.


title: A simple regression method for mapping quantitative trait loci in line crosses using flanking markers

abstract: The use of flanking marker methods has proved to be a powerful tool for the mapping of quantitative trait loci (QTL) in the segregating generations derived from crosses between inbred lines. Methods to analyse these data, based on maximum-likelihood, have been developed and provide good estimates of QTL effects in some situations. Maximum-likelihood methods are, however, relatively complex and can be computationally slow. In this paper we develop methods for mapping QTL based on multiple regression which can be applied using any general statistical package. We use the example of mapping in an F(2) population and show that these regression methods produce very similar results to those obtained using maximum likelihood. The relative simplicity of the regression methods means that models with more than a single QTL can be explored and we give examples of two lined loci and of two interacting loci. Other models, for example with more than two QTL, with environmental fixed effects, with between family variance or for threshold traits, could be fitted in a similar way. The ease, speed of application and generality of regression methods for flanking marker analysis, and the good estimates they obtain, suggest that they should provide the method of choice for the analysis of QTL mapping data from inbred line crosses.


title: In situ depth maps based feature extraction and tracking

abstract: Parallel numerical simulation is a powerful tool used by scientists to study complex problems. It has been a common practice to save the simulation output to disk and then conduct post-hoc in-depth analyses of the saved data. System I/O capabilities have not kept pace as simulations have scaled up over time, so a common approach has been to output only subsets of the data to reduce I/O. However, as we are entering the era of peta- and exa-scale computing, this sub-sampling approach is no longer acceptable because too much valuable information is lost. In situ visualization has been shown a promising approach to the data problem at extreme-scale. We present a novel in situ solution using depth maps to enable post-hoc image-based visualization and feature extraction and tracking. An interactive interface is provided to allow for fine-tuning the generation of depth maps during the course of a simulation run to better capture the features of interest. We use several applications including one actual simulation run on a Cray XE6 supercomputer to demonstrate the effectiveness of our approach.


title: The critical role of macrophages in the pathogenesis of hidradenitis suppurativa

abstract: Introduction ::: Hidradenitis suppurativa (HS) is a painful chronic inflammatory disease with a prevalence between 1 and 4% of general population. The pathogenesis of HS long eluded scientists, but growing evidence suggests that it is a consequence of inflammatory dysregulation.


title: A Survey of Intrusion Detection Systems in Wireless Sensor Networks

abstract: Wireless Sensor Networking is one of the most promising technologies that have applications ranging from health care to tactical military. Although Wireless Sensor Networks (WSNs) have appealing features (e.g., low installation cost, unattended network operation), due to the lack of a physical line of defense (i.e., there are no gateways or switches to monitor the information flow), the security of such networks is a big concern, especially for the applications where confidentiality has prime importance. Therefore, in order to operate WSNs in a secure way, any kind of intrusions should be detected before attackers can harm the network (i.e., sensor nodes) and/or information destination (i.e., data sink or base station). In this article, a survey of the state-of-the-art in Intrusion Detection Systems (IDSs) that are proposed for WSNs is presented. Firstly, detailed information about IDSs is provided. Secondly, a brief survey of IDSs proposed for Mobile Ad-Hoc Networks (MANETs) is presented and applicability of those systems to WSNs are discussed. Thirdly, IDSs proposed for WSNs are presented. This is followed by the analysis and comparison of each scheme along with their advantages and disadvantages. Finally, guidelines on IDSs that are potentially applicable to WSNs are provided. Our survey is concluded by highlighting open research issues in the field.


Attention Based LSTM for Target Dependent Sentiment Classification.


title: True grit and genetics: Predicting academic achievement from personality.

abstract: Grit—perseverance and passion for long-term goals—has been shown to be a significant predictor of academic success, even after controlling for other personality factors. Here, for the first time, we use a U.K.-representative sample and a genetically sensitive design to unpack the etiology of Grit and its prediction of academic achievement in comparison to well-established personality traits. For 4,642 16-year-olds (2,321 twin pairs), we used the Grit-S scale (perseverance of effort and consistency of interest), along with the Big Five personality traits, to predict grades on the General Certificate of Secondary Education (GCSE) exams, which are administered U.K.-wide at the end of compulsory education. Twin analyses of Grit perseverance yielded a heritability estimate of 37% (20% for consistency of interest) and no evidence for shared environmental influence. Personality, primarily conscientiousness, predicts about 6% of the variance in GCSE grades, but Grit adds little to this prediction. Moreover, multivariate twin analyses showed that roughly two-thirds of the GCSE prediction is mediated genetically. Grit perseverance of effort and Big Five conscientiousness are to a large extent the same trait both phenotypically (r = 0.53) and genetically (genetic correlation = 0.86). We conclude that the etiology of Grit is highly similar to other personality traits, not only in showing substantial genetic influence but also in showing no influence of shared environmental factors. Personality significantly predicts academic achievement, but Grit adds little phenotypically or genetically to the prediction of academic achievement beyond traditional personality factors, especially conscientiousness. (PsycINFO Database Record (c) 2016 APA, all rights reserved)


title: The See-Through System: From implementation to test-drive

abstract: Cooperative awareness in vehicular networks is probably the killer application for vehicle-to-vehicle (V2V) communications that cannot be matched by infrastructure-based alternatives even when disregarding communication costs. New and improved driver assistance systems can be introduced by extending their reach to sensors residing in neighboring vehicles, such as windshield-installed cameras. In previous work, we defined theoretical foundations for a driver assistance system that leverages on V2V communication and windshield-installed cameras to transform vision-obstructing vehicles into transparent tubular objects. We now present an implementation of the actual See-Through System (STS), where we combine the communication aspects with the control and augmented reality components of the system. We present a validation methodology and test the system with multiple vehicles on a closed road segment. This evaluation shows that the STS is able to increase the visibility of drivers intending to overtake, thus increasing the safety of such critical maneuvers. It also shows that Dedicated Short Range Communication (DSRC) provides the required latency for this delay-critical inter-vehicle communication, which could hardly be guaranteed with infrastructure-based communication technologies.


title: Voice Conversion Based on Maximum-Likelihood Estimation of Spectral Parameter Trajectory

abstract: In this paper, we describe a novel spectral conversion method for voice conversion (VC). A Gaussian mixture model (GMM) of the joint probability density of source and target features is employed for performing spectral conversion between speakers. The conventional method converts spectral parameters frame by frame based on the minimum mean square error. Although it is reasonably effective, the deterioration of speech quality is caused by some problems: 1) appropriate spectral movements are not always caused by the frame-based conversion process, and 2) the converted spectra are excessively smoothed by statistical modeling. In order to address those problems, we propose a conversion method based on the maximum-likelihood estimation of a spectral parameter trajectory. Not only static but also dynamic feature statistics are used for realizing the appropriate converted spectrum sequence. Moreover, the oversmoothing effect is alleviated by considering a global variance feature of the converted spectra. Experimental results indicate that the performance of VC can be dramatically improved by the proposed method in view of both speech quality and conversion accuracy for speaker individuality.


title: A Compact Dual-Band Printed Yagi-Uda Antenna for GNSS and CMMB Applications

abstract: A printed Yagi-Uda antenna with a meandered driven dipole and a concave parabolic reflector is proposed for dual-band operations of L1-band Global Navigation Satellite System (GNSS) and S-band China Mobile Multimedia Broadcasting (CMMB). The antenna is designed and fabricated on a thin dielectric substrate, and measured at 1580 MHz in the low band (L1-band) and 2645 MHz in the high band (S-band), respectively, with directivities of 6.7 and 4.9 dBi, front-to-back ratios of 13.1 and 10.3 dB, cross-polarization levels of $- \bm{23.8}$ and $- \bm{21.9}\,\bf{dB}$ , bandwidths of 4.0% and 6.5%, and antenna efficiencies of $- \bm{0.40}\,\bf{dB}$ (91.2%) and $- \bm{0.96}\,\bf{dB}$ (80.2%), which are better than $- \bm{1}\,\bf{dBi}$ in terms of the three-dimensional (3-D) average gain. The occupied area of this dual-band antenna is the same as that of the previously proposed single-band one. With these properties, the proposed antenna is promising for combo applications of L1-band GNSS and S-band CMMB.


title: Malware Detection by HTTPS Traffic Analysis

abstract: In order to evade detection by network-traffic analysis, a growing proportion of malware uses the encrypted HTTPS protocol. We explore the problem of detecting malware on client computers based on HTTPS traffic analysis. In this setting, malware has to be detected based on the host IP address, ports, timestamp, and data volume information of TCP/IP packets that are sent and received by all the applications on the client. We develop a scalable protocol that allows us to collect network flows of known malicious and benign applications as training data and derive a malware-detection method based on a neural networks and sequence classification. We study the method's ability to detect known and new, unknown malware in a large-scale empirical study.


title: Solid-State Lighting: Toward Superior Illumination

abstract: Solid-state lighting technology is now emerging as a cost-competitive,energy-efficient alternative to conventional electrical lighting. We review the history of lighting, discuss the benefits and challenges of the solid-state lighting technologies, and compare two approaches for generating white light from solid-state sources based on phosphor LEDs (which could be considered as solid-state replacement of fluorescent tubes) and multichip LED lamps,which offer many advantages, such as chromaticity control, better light quality,and higher efficiency.


abstract: Probabilistic Reasoning and Decision Making in Sensory-Motor Systems by Pierre Bessiere, Christian Laugier and Roland Siegwart provides a unique collection of a sizable segment of the cognitive systems research community in Europe. It reports on contributions from leading academic institutions brought together within the European projects Bayesian Inspired Brain and Artifact (BIBA) and Bayesian Approach to Cognitive Systems (BACS). This fourteen-chapter volume covers important research along two main lines: new probabilistic models and algorithms for perception and action, new probabilistic methodology and techniques for artefact conception and development. The work addresses key issues concerned with Bayesian programming, navigation, filtering, modelling and mapping, with applications in a number of different contexts.


title: Identifying distinctive subsequences in multivariate time series by clustering

abstract: Most time series comparison algorithms attempt to discover what the members of a set of time series have in common. We investigate a di erent problem, determining what distinguishes time series in that set from other time series obtained from the same source. In both cases the goal is to identify shared patterns, though in the latter case those patterns must be distinctive as well. An e cient incremental algorithm for identifying distinctive subsequences in multivariate, real-valued time series is described and evaluated with data from two very di erent sources: the response of a set of bandpass lters to human speech and the sensors of a mobile robot. Reference Number: 357


title: Dynamic expansion of behaviour trees

abstract: Artificial intelligence in games is typically used for creating player's opponents. Manual edition of intelligent behaviors for Non-Player Characters (NPCs) of games is a cumbersome task that needs experienced designers. Our research aims to assist designers in this task. Behaviours typically use recurring patterns, so that experience and reuse are crucial aspects for behavior design. The use of hierarchical state machines allows working on different abstraction levels, sharing transitions and reusing pieces from the more detailed levels. However, the static nature of the design process does not release the designer from the burden to completely specify each behaviour. Our approach applies Case-Based Reasoning (CBR) techniques to retrieve and reuse stored behaviors represented as hierarchical state machines (actually, behaviour trees). In this paper we focus on dynamic retrieval of behaviours taking into account the world state and the underlying goals to select the most appropriate state machine to guide the NPC behaviour. The global behaviour of the NPC is dynamically built in run time querying the CBR system. We exemplify our approach through a serious game, developed by our research group, with gameplay elements from First-Person Shooter (FPS) games.


title: Intentional behaviour in dog-human communication: an experimental analysis of “showing” behaviour in the dog

abstract: Despite earlier scepticism there is now evidence for simple forms of intentional and functionally referential communication in many animal species. Here we investigate whether dogs engage in functional referential communication with their owners. “Showing” is defined as a communicative action consisting of both a directional component related to an external target and an attention-getting component that directs the attention of the perceiver to the informer or sender. In our experimental situation dogs witness the hiding of a piece of food (or a favourite toy) which they cannot get access to. We asked whether dogs would engage in “showing” in the presence of their owner. To control for the motivational effects of both the owner and the food on the dogs’ behaviour, control observations were also staged where only the food (or the toy) or the owner was present. Dogs’ gazing frequency at both the food (toy) and the owner was greater when only one of these was present. In other words, dogs looked more frequently at their owner when the food (toy) was present, and they looked more at the location of the food (toy) when the owner was present. When both the food (toy) and the owner were present a new behaviour, “gaze alternation”, emerged which was defined as changing the direction of the gaze from the location of the food (toy) to looking at the owner (or vice versa) within 2 s. Vocalisations that occurred in this phase were always associated with gazing at the owner or the location of the food. This behaviour, which was specific to this situation, has also been described in chimpanzees, a gorilla and humans, and has often been interpreted as a form of functionally referential communication. Based on our observations we argue that dogs might be able to engage in functionally referential communication with their owner, and their behaviour could be described as a form of “showing”. The contribution of domestication and individual learning to the well-developed communicative skills in dogs is discussed and will be the subject of further studies.


title: An Intelligent Two-Stage Evolutionary Algorithm for Dynamic Pathway Identification From Gene Expression Profiles

abstract: From gene expression profiles, it is desirable to rebuild cellular dynamic regulation networks to discover more delicate and substantial functions in molecular biology, biochemistry, bioengineering, and pharmaceutics. The S-system model is suitable to characterize biochemical network systems and capable of analyzing the regulatory system dynamics. However, the inference of an S-system model of N-gene genetic networks has 2N(N + 1) parameters in a set of nonlinear differential equations to be optimized. This paper proposes an intelligent two-stage evolutionary algorithm (iTEA) to efficiently infer the S-system models of genetic networks from time-series data of gene expression. To cope with the curse of dimensionality, the proposed algorithm consists of two stages, where each uses a divide-and-conquer strategy. The optimization problem is first decomposed into N subproblems having 2(N + 1) parameters each. At the first stage, each subproblem is solved using a novel intelligent genetic algorithm (IGA) with intelligent crossover based on an orthogonal experimental design (OED). At the second stage, the obtained N solutions to the N subproblems are combined and refined using an OED-based simulated annealing algorithm for handling noisy gene expression profiles. The effectiveness of iTEA is evaluated using simulated expression patterns with and without noise running on a single-processor PC. It is shown that 1) IGA is efficient enough to solve subproblems, 2) IGA is significantly superior to the existing method GA with simplex crossover (SPXGA), and 3) iTEA performs well in inferring S-system models for dynamic pathway identification.


Lateral track control law for aerosonde UAV


title: Culture and the self: Implications for cognition, emotion, and motivation

abstract: People in different cultures have strikingly different construals of the self, of others, and of the interdependence of the 2. These construals can influence, and in many cases determine, the very nature of individual experience, including cognition, emotion, and motivation. Many Asian cultures have distinct conceptions of individuality that insist on the fundamental relatedness of individuals to each other. The emphasis is on attending to others, fitting in, and harmonious interdependence with them. American culture neither assumes nor values such an overt connectedness among individuals. In contrast, individuals seek to maintain their independence from others by attending to the self and by discovering and expressing their unique inner attributes. As proposed herein, these construals are even more powerful than previously imagined. Theories of the self from both psychology and anthropology are integrated to define in detail the difference between a construal of the self as independent and a construal of the self as interdependent. Each of these divergent construals should have a set of specific consequences for cognition, emotion, and motivation; these consequences are proposed and relevant empirical literature is reviewed. Focusing on differences in self-construals enables apparently inconsistent empirical findings to be reconciled, and raises questions about what have been thought to be culture-free aspects of cognition, emotion, and motivation.


title: The Psychological Benefits of Being Authentic on Facebook

abstract: Abstract Having others acknowledge and validate one's true self is associated with better psychological health. Existing research indicates that an individual's true self may be more readily expressed on Facebook than in person. This study brought together these two premises by investigating for the first time the psychosocial outcomes associated with communicating one's true self on Facebook. Participants (n = 164) completed a personality assessment once as their true self and once as the self they present on Facebook (Facebook self), as well as measures of social connectedness, subjective well-being, depression, anxiety, and stress. Euclidean distances quantified the difference between one's true self and the Facebook self. Hypotheses received partial support. Better coherence between the true self and the Facebook self was associated with better social connectedness and less stress. Two models provided evidence of mediation effects. Findings highlight that authentic self-presentation on Facebook can be...


title: Positional relevance model for pseudo-relevance feedback

abstract: Pseudo-relevance feedback is an effective technique for improving retrieval results. Traditional feedback algorithms use a whole feedback document as a unit to extract words for query expansion, which is not optimal as a document may cover several different topics and thus contain much irrelevant information. In this paper, we study how to effectively select from feedback documents those words that are focused on the query topic based on positions of terms in feedback documents. We propose a positional relevance model (PRM) to address this problem in a unified probabilistic way. The proposed PRM is an extension of the relevance model to exploit term positions and proximity so as to assign more weights to words closer to query words based on the intuition that words closer to query words are more likely to be related to the query topic. We develop two methods to estimate PRM based on different sampling processes. Experiment results on two large retrieval datasets show that the proposed PRM is effective and robust for pseudo-relevance feedback, significantly outperforming the relevance model in both document-based feedback and passage-based feedback.


title: EEG-based estimation of mental fatigue by using KPCA-HMM and complexity parameters

abstract: Abstract Two complexity parameters of EEG, i.e. approximate entropy ( ApEn ) and Kolmogorov complexity ( Kc ) are utilized to characterize the complexity and irregularity of EEG data under the different mental fatigue states. Then the kernel principal component analysis (KPCA) and Hidden Markov Model (HMM) are combined to differentiate two mental fatigue states. The KPCA algorithm is employed to extract nonlinear features from the complexity parameters of EEG and improve the generalization performance of HMM. The investigation suggests that ApEn and Kc can effectively describe the dynamic complexity of EEG, which is strongly correlated with mental fatigue. Both complexity parameters are significantly decreased ( P


title: Recommendation for modifying current cytotoxicity testing standards for biodegradable magnesium-based materials

abstract: As one of the most promising medical metal implants, magnesium (Mg) or its alloys have shown significant advantages over other candidates attributed to not only their excellent biodegradability and suitable mechanical properties but also their osteopromotive effects for bone applications. Prior to approval mandated by the governmental regulatory body, the access to the medical market for Mg-based implants requires a series of testing for assurance of their safety and efficacy via preclinical evaluations and clinical tests including phase 1 and 2 evaluations, and phase 3 of multi-center randomized double blind and placebo-controlled clinical trials. However, as the most widely used protocols for biosafety evaluation of medical devices, current ISO 10993 standards should be carefully reevaluated when directly applying them to predict potential health risks of degradable Mg based biomaterials via cytotoxicity tests due to the huge gap between in vitro and in vivo conditions. Therefore, instead of a direct adoption, modification of current ISO standards for in vitro cytotoxicity test is desirable and justified. The differences in sensitivities of cells to in vitro and in vivo Mg ions and the capability of in vivo circulation system to dilute local degradation products were fully considered to propose modification of current ISO standards. This paper recommended a minimal 6 times to a maximal 10 times dilution of extracts for in vitro cytotoxicity test specified in ISO 10993 part 5 for pure Mg developed as potential orthopedic implants based on literature review and our specifically designed in vitro and in vivo tests presented in the study. Our work may contribute to the progress of biodegradable metals involved translational work.


Author ' s personal copy Video gameplay , personality and academic performance


Sex and Gender: Beyond the Binaries


title: MATERNAL BORDERLINE PERSONALITY DISORDER SYMPTOMS AND ADOLESCENT PSYCHOSOCIAL FUNCTIONING

abstract: Borderline personality disorder (BPD) is characterized by severe disruption of interpersonal relationships, yet very little research has examined the relationship between maternal BPD and offspring psychosocial functioning. The present study examined 815 mothers and their 15year-old children from a community-based sample to determine (1) if there is an association between mothers’ BPD symptoms and the interpersonal functioning, attachment cognitions, and depressive symptoms of their offspring, and (2) if the association of maternal BPD and youth outcomes is independent of maternal and youth depression. Measures of youth psychosocial functioning included self, mother, interviewer rated, and teacher reports. Results indicated that there was a significant association between maternal BPD symptoms and youth outcomes, and that this association remained even after controlling for maternal lifetime history of major depression, maternal history of dysthymic disorder, and youth depressive symptoms. This study provides some of the first empirical evidence for a link between mother’s BPD symptoms and youth psychosocial outcomes.


title: CIL: Intermediate language and tools for analysis and transformation of C programs

abstract: This paper describes the C Intermediate Language: a high-level representation along with a set of tools that permit easy analysis and source-to-source transformation of C programs.Compared to C, CIL has fewer constructs. It breaks down certain complicated constructs of C into simpler ones, and thus it works at a lower level than abstract-syntax trees. But CIL is also more high-level than typical intermediate languages (e.g., three-address code) designed for compilation. As a result, what we have is a representation that makes it easy to analyze and manipulate C programs, and emit them in a form that resembles the original source. Moreover, it comes with a front-end that translates to CIL not only ANSI C programs but also those using Microsoft C or GNU C extensions.We describe the structure of CIL with a focus on how it disambiguates those features of C that we found to be most confusing for program analysis and transformation. We also describe a whole-program merger based on structural type equality, allowing a complete project to be viewed as a single compilation unit. As a representative application of CIL, we show a transformation aimed at making code immune to stack-smashing attacks. We are currently using CIL as part of a system that analyzes and instruments C programs with run-time checks to ensure type safety. CIL has served us very well in this project, and we believe it can usefully be applied in other situations as well.


title: A shear gradient-activated microfluidic device for automated monitoring of whole blood haemostasis and platelet function

abstract: Accurate assessment of blood haemostasis is essential for the management of patients who use extracorporeal devices, receive anticoagulation therapy or experience coagulopathies. However, current monitoring devices do not measure effects of haemodynamic forces that contribute significantly to platelet function and thrombus formation. Here we describe a microfluidic device that mimics a network of stenosed arteriolar vessels, permitting evaluation of blood clotting within small sample volumes under pathophysiological flow. By applying a clotting time analysis based on a phenomenological mathematical model of thrombus formation, coagulation and platelet function can be accurately measured in vitro in patient blood samples. When the device is integrated into an extracorporeal circuit in pig endotoxemia or heparin therapy models, it produces real-time readouts of alterations in coagulation ex vivo that are more reliable than standard clotting assays. Thus, this disposable device may be useful for personalized diagnostics and for real-time surveillance of antithrombotic therapy in clinic.


MarineBlue: A low-cost chess robot


title: Evaluation of Multiple-F0 Estimation and Tracking Systems

abstract: Multi-pitch estimation of sources in music is an ongoing research area that has a wealth of applications in music information retrieval systems. This paper presents the systematic evaluations of over a dozen competing methods and algorithms for extracting the fundamental frequencies of pitched sound sources in polyphonic music. The evaluations were carried out as part of the Music Information Retrieval Evaluation eXchange (MIREX) over the course of two years, from 2007 to 2008. The generation of the dataset and its corresponding ground-truth, the methods by which systems can be evaluated, and the evaluation results of the different systems are presented and discussed.


title: Preliminary use of Polygonum minus Linn. leaf extract on growth performance, feed utilization, and some hematological indices of Anabas testudineus (Bloch, 1792)

abstract: The main objectives of the present study were to improve the growth performance, survival rate, and hematological indices of fingerling Anabas testudineus, by using Polygonum minus plant leaf extract, sprayed at four different levels (0 as the control group, 50, 70, and 100 ml/kg fish feed), for 60 days. The growth indices and survival rate were investigated at 15-day intervals, while hematological indices were evaluated at the end of experiment (60 days). The results showed that the weight gain (WG), average daily growth rate (ADG), specific growth rate (SGR), feed efficiency (FE), and protein efficiency ratio (PER) values were significantly different, and that the highest value in the group was found in fish fed 70 ml/kg feed. Meanwhile, feed conversion rate (FCR) was shown to have the lowest value in the group that was fed 70 ml/kg feed. In addition, the survival rate was shown to be higher in all groups except for the control group: a significant difference (p 0.05), there was a trend towards higher concentrations. Overall, our results indicated that P. minus leaf extract supplementation could improve growth performance, especially with regard to feed utilization, and the survival rate was very important for the fingerling nursing step; moreover, the WBC could be improved by using herbal extracts.


title: Saliency Based on Information Maximization

abstract: A model of bottom-up overt attention is proposed based on the principle of maximizing information sampled from a scene. The proposed operation is based on Shannon's self-information measure and is achieved in a neural circuit, which is demonstrated as having close ties with the circuitry existent in die primate visual cortex. It is further shown that the proposed salicney measure may be extended to address issues that currently elude explanation in the domain of saliency based models. Results on natural images are compared with experimental eye tracking data revealing the efficacy of the model in predicting the deployment of overt attention as compared with existing efforts.


Types and Indications of Colostomy and Determinants of Outcomes of Patients After Surgery


title: Placing Virtual Machines to Optimize Cloud Gaming Experience

abstract: Optimizing cloud gaming experience is no easy task due to the complex tradeoff between gamer quality of experience (QoE) and provider net profit. We tackle the challenge and study an optimization problem to maximize the cloud gaming provider’s total profit while achieving just-good-enough QoE. We conduct measurement studies to derive the QoE and performance models. We formulate and optimally solve the problem. The optimization problem has exponential running time, and we develop an efficient heuristic algorithm. We also present an alternative formulation and algorithms for closed cloud gaming services with dedicated infrastructures, where the profit is not a concern and overall gaming QoE needs to be maximized. We present a prototype system and testbed using off-the-shelf virtualization software, to demonstrate the practicality and efficiency of our algorithms. Our experience on realizing the testbed sheds some lights on how cloud gaming providers may build up their own profitable services. Last, we conduct extensive trace-driven simulations to evaluate our proposed algorithms. The simulation results show that the proposed heuristic algorithms: (i) produce close-to-optimal solutions, (ii) scale to large cloud gaming services with 20,000 servers and 40,000 gamers, and (iii) outperform the state-of-the-art placement heuristic, e.g., by up to 3.5 times in terms of net profits.


title: CONGA: distributed congestion-aware load balancing for datacenters

abstract: We present the design, implementation, and evaluation of CONGA, a network-based distributed congestion-aware load balancing mechanism for datacenters. CONGA exploits recent trends including the use of regular Clos topologies and overlays for network virtualization. It splits TCP flows into flowlets, estimates real-time congestion on fabric paths, and allocates flowlets to paths based on feedback from remote switches. This enables CONGA to efficiently balance load and seamlessly handle asymmetry, without requiring any TCP modifications. CONGA has been implemented in custom ASICs as part of a new datacenter fabric. In testbed experiments, CONGA has 5x better flow completion times than ECMP even with a single link failure and achieves 2-8x better throughput than MPTCP in Incast scenarios. Further, the Price of Anarchy for CONGA is provably small in Leaf-Spine topologies; hence CONGA is nearly as effective as a centralized scheduler while being able to react to congestion in microseconds. Our main thesis is that datacenter fabric load balancing is best done in the network, and requires global schemes such as CONGA to handle asymmetry.


title: Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG

abstract: This report summarizes our recent efforts to deliver real-time data extraction, preprocessing, artifact rejection, source reconstruction, multivariate dynamical system analysis (including spectral Granger causality) and 3D visualization as well as classification within the open-source SIFT and BCILAB toolboxes. We report the application of such a pipeline to simulated data and real EEG data obtained from a novel wearable high-density (64-channel) dry EEG system.


title: Self-Training for Enhancement and Domain Adaptation of Statistical Parsers Trained on Small Datasets

abstract: Creating large amounts of annotated data to train statistical PCFG parsers is expensive, and the performance of such parsers declines when training and test data are taken from different domains. In this paper we use selftraining in order to improve the quality of a parser and to adapt it to a different domain, using only small amounts of manually annotated seed data. We report significant improvement both when the seed and test data are in the same domain and in the outof-domain adaptation scenario. In particular, we achieve 50% reduction in annotation cost for the in-domain case, yielding an improvement of 66% over previous work, and a 20-33% reduction for the domain adaptation case. This is the first time that self-training with small labeled datasets is applied successfully to these tasks. We were also able to formulate a characterization of when selftraining is valuable.


title: Comparison of human and computer performance across face recognition experiments

abstract: Since 2005, human and computer performance has been systematically compared as part of face recognition competitions, with results being reported for both still and video imagery. The key results from these competitions are reviewed. To analyze performance across studies, the cross-modal performance analysis (CMPA) framework is introduced. The CMPA framework is applied to experiments that were part of face a recognition competition. The analysis shows that for matching frontal faces in still images, algorithms are consistently superior to humans. For video and difficult still face pairs, humans are superior. Finally, based on the CMPA framework and a face performance index, we outline a challenge problem for developing algorithms that are superior to humans for the general face recognition problem.


title: Standardized Protocol Stack for the Internet of (Important) Things

abstract: We have witnessed the Fixed Internet emerging with virtually every computer being connected today; we are currently witnessing the emergence of the Mobile Internet with the exponential explosion of smart phones, tablets and net-books. However, both will be dwarfed by the anticipated emergence of the Internet of Things (IoT), in which everyday objects are able to connect to the Internet, tweet or be queried. Whilst the impact onto economies and societies around the world is undisputed, the technologies facilitating such a ubiquitous connectivity have struggled so far and only recently commenced to take shape. To this end, this paper introduces in a timely manner and for the first time the wireless communications stack the industry believes to meet the important criteria of power-efficiency, reliability and Internet connectivity. Industrial applications have been the early adopters of this stack, which has become the de-facto standard, thereby bootstrapping early IoT developments with already thousands of wireless nodes deployed. Corroborated throughout this paper and by emerging industry alliances, we believe that a standardized approach, using latest developments in the IEEE 802.15.4 and IETF working groups, is the only way forward. We introduce and relate key embodiments of the power-efficient IEEE 802.15.4-2006 PHY layer, the power-saving and reliable IEEE 802.15.4e MAC layer, the IETF 6LoWPAN adaptation layer enabling universal Internet connectivity, the IETF ROLL routing protocol enabling availability, and finally the IETF CoAP enabling seamless transport and support of Internet applications. The protocol stack proposed in the present work converges towards the standardized notations of the ISO/OSI and TCP/IP stacks. What thus seemed impossible some years back, i.e., building a clearly defined, standards-compliant and Internet-compliant stack given the extreme restrictions of IoT networks, is commencing to become reality.


title: Fabrication of (Co,Mn)3O4/rGO Composite for Lithium Ion Battery Anode by a One-Step Hydrothermal Process with H2O2 as Additive

abstract: Binary transition metal oxides have been regarded as one of the most promising candidates for high-performance electrodes in energy storage devices, since they can offer high electrochemical activity and high capacity. Rational designing nanosized metal oxide/carbon composite architectures has been proven to be an effective way to improve the electrochemical performance. In this work, the (Co,Mn)3O4 spinel was synthesized and anchored on reduced graphene oxide (rGO) nanosheets using a facile and single hydrothermal step with H2O2 as additive, no further additional calcination required. Analysis showed that this method gives a mixed spinel, i.e. (Co,Mn)3O4, having 2+ and 3+ Co and Mn ions in both the octahedral and tetrahedral sites of the spinel structure, with a nanocubic morphology roughly 20 nm in size. The nanocubes are bound onto the rGO nanosheet uniformly in a single hydrothermal process, then the as-prepared (Co,Mn)3O4/rGO composite was characterized as the anode materials for Li-ion battery (LIB). It can deliver 1130.6 mAh g-1 at current density of 100 mA g-1 with 98% of coulombic efficiency after 140 cycles. At 1000 mA g-1, the capacity can still maintain 750 mAh g-1, demonstrating excellent rate capabilities. Therefore, the one-step process is a facile and promising method to fabricate metal oxide/rGO composite materials for energy storage applications.


title: Flattening NTRU for Evaluation Key Free Homomorphic Encryption

abstract: We propose a new FHE scheme F-NTRU that adopts the flattening technique proposed in GSW to derive an NTRU based scheme that (similar to GSW) does not require evaluation keys or key switching. Our scheme eliminates the decision small polynomial ratio (DSPR) assumption but relies only on the standard R-LWE assumption. It uses wide key distributions, and hence is immune to the Subfield Lattice Attack. In practice, our scheme achieves competitive timings compared to the existing schemes. We are able to compute a homomorphic multiplication in 5.8 msec and 17.6 msec for 5 and 30 levels, respectively, without amortization. Furthermore, our scheme features small ciphertexts, e.g. 567 KB for 30 levels, and eliminates the need for storing and managing costly evaluation keys. In addition, we present a slightly modified version of F-NTRU that is capable to support integer operations with a very large message space along with noise analysis for all cases. The assurance gained by using wide key distributions along with the message space flexibility of the scheme, i.e. bits, binary polynomials, and integers with a large message space, allows the use the proposed scheme in a wide array of applications.


title: Knowledge management-enabled health care management systems: capabilities, infrastructure, and decision-support

abstract: Abstract The health care industry is increasingly becoming a knowledge-based community that is connected to hospitals, clinics, pharmacies, and customers for sharing knowledge, reducing administrative costs and improving the quality of care. Thus, the success of health care depends critically on the collection, analysis and seamless exchange of clinical, billing, and utilization information or knowledge within and across the above organizational boundaries. This research envisions a knowledge management-enabled health care management system that would help integrate clinical, administrative, and financial processes in health care through a common technical architecture; and provides a decision support infrastructure for clinical and administrative decision-making. Hence, the objective of this research is to present and describe the knowledge management capabilities, the technical infrastructure, and the decision support architecture for such a health care management system. The research findings would immensely help the health care information technology (IT) managers and knowledge based system developers to identify their IT needs and to plan for and develop the technical infrastructure of the health care management system for their organizations.


title: Biomonitoring of exposure to urban air pollutants: analysis of sister chromatid exchanges and DNA lesions in peripheral lymphocytes of traffic policemen

abstract: Abstract In order to elucidate the health effects of occupational exposure to traffic fumes, a few biomarkers of early genetic effect were investigated in Rome traffic policemen. One hundred and ninety healthy subjects engaged in traffic control (133 subjects) or in office work (57 subjects) participated the study. For all subjects, detailed information on smoking habits and other potential confounders were recorded by questionnaires. Average exposure of the study groups to benzene and other aromatic hydrocarbons was evaluated in a parallel exposure survey. All workers were genotyped for the following metabolic polymorphisms: CYP1A1 ( m1 , m2 , and m4 variants), CYP2E1 ( PstI and RsaI ), NQO1 ( Hinf1 ), GSTM1 and GSTT1 ( null variants). In this paper, the results of the analysis of sister chromatid exchanges (SCE) in peripheral lymphocytes, and DNA damage by alkaline (pH 13) comet assay in mononuclear blood cells are reported. No statistically significant difference in the frequency of SCE or high frequency cells (HFC) was observed between traffic wardens and office workers (controls), despite the significantly higher exposure to benzene of the former (average group exposure 9.5 versus 3.8 μg/m 3 , 7 h TWA). Conversely, both SCE per cell and HFC were highly significantly ( P P P =0.050) and HFC ( P =0.090) was found. This effect was mainly observed in light smokers (


title: TICS-817; No of Pages 7 Review The description–experience gap in risky

abstract: According to a common conception in behavioral decision research, two cognitive processes—overestimation and overweighting—operate to increase the impact of rare events on people's choices. Supportive findings stem primarily from investigations in which people learn about options via descriptions thereof. Recently, a number of researchers have begun to investigate risky choice in settings in which people learn about options by experiential sampling over time. This article reviews work across three experiential paradigms. Converging findings show that when people make decisions based on experience, rare events tend to have less impact than they deserve according to their objective probabilities. Striking similarities in human and animal experience-based choices, ways of modeling these choices, and their implications for risk and precautionary behavior are discussed.


title: Thoracopagus Conjoined Twins: A Case Report

abstract: Objective. Conjoined twin is a rarely seen congenital anomaly together with severe mortality and morbidity. The more common types of conjoined twins include the thoracopagus type, where the fusion is anterior, at the chest, and involves the heart. We are reporting one case of conjoined thoracopagus twins diagnosed by ultrasonography at 11 weeks. Case Report. In a multigravid pregnant woman who has been admitted to our clinic with a diagnosis of conjoined twins, thoracopagus, by ultrasonography at an 11-week gestation, termination of the pregnancy was performed. Conclusion. Making an early diagnosis with ultrasonographic examination gives the parents a chance to elect pregnancy termination.


title: Application of PIV Measurement Technology on Turbomachinery

abstract: PIV is a new measure instrument,which has better merit than others especially for complex flow field.The working rule,disposal flow chart and operation outline of PIV are introduced in detail.Finally,an example is used to measure the instantaneous entire velocity field along blade height and outlet plane with PIV.As a result,it is satisfied and beneficial to optimize the blade flow field.


title: Adoption of electronic health records in the presence of privacy concerns: the elaboration likelihood model and individual persuasion

abstract: Within the emerging context of the digitization of health care, electronic health records (EHRs) constitute a significant technological advance in the way medical information is stored, communicated, and processed by the multiple parties involved in health care delivery. However, in spite of the anticipated value potential of this technology, there is widespread concern that consumer privacy issues may impede its diffusion. In this study, we pose the question: Can individuals be persuaded to change their attitudes and opt-in behavioral intentions toward EHRs, and allow their medical information to be digitized even in the presence of significant privacy concerns? To investigate this question, we integrate an individual's concern for information privacy (CFIP) with the elaboration likelihood model (ELM) to examine attitude change and likelihood of opting-in to an EHR system. We theorize that issue involvement and argument framing interact to influence attitude change, and that concern for information privacy further moderates the effects of these variables. We also propose that likelihood of adoption is driven by concern for information privacy and attitude. We test our predictions using an experiment with 366 subjects where we manipulate the framing of the arguments supporting EHRs. We find that an individual's CFIP interacts with argument framing and issue involvement to affect attitudes toward the use of EHRs. In addition, results suggest that attitude toward EHR use and CFIP directly influence opt-in behavioral intentions. An important finding for both theory and practice is that even when people have high concerns for privacy, their attitudes can be positively altered with appropriate message framing. These results as well as other theoretical and practical implications are discussed.


title: Unsupervised Sentence Representations as Word Information Series: Revisiting TF--IDF

abstract: Sentence representation at the semantic level is a challenging task for Natural Language Processing and Artificial Intelligence. Despite the advances in word embeddings (i.e. word vector representations), capturing sentence meaning is an open question due to complexities of semantic interactions among words. In this paper, we present an embedding method, which is aimed at learning unsupervised sentence representations from unlabeled text. We propose an unsupervised method that models a sentence as a weighted series of word embeddings. The weights of the word embeddings are fitted by using Shannon's word entropies provided by the Term Frequency--Inverse Document Frequency (TF--IDF) transform. The hyperparameters of the model can be selected according to the properties of data (e.g. sentence length and textual gender). Hyperparameter selection involves word embedding methods and dimensionalities, as well as weighting schemata. Our method offers advantages over existing methods: identifiable modules, short-term training, online inference of (unseen) sentence representations, as well as independence from domain, external knowledge and language resources. Results showed that our model outperformed the state of the art in well-known Semantic Textual Similarity (STS) benchmarks. Moreover, our model reached state-of-the-art performance when compared to supervised and knowledge-based STS systems.


title: Control of single-phase cascaded H-bridge multilevel inverter with modified MPPT for grid-connected photovoltaic systems

abstract: This paper presents a control technique of cascaded H-bridge multilevel voltage source inverter (CHB-MLI) for a grid-connected photovoltaic system (GCPVS). The proposed control technique is the modified ripple-correlation control maximum power point tracking (MRCC-MPPT). This algorithm has been developed using the mean function concept to continuously correct the maximum power point (MPP) of power transferring from each PV string and to speedily reach the MPP in rapidly shading irradiance. Additionally, It can reduce a PV voltage harmonic filter in the dc-link voltage controller. In task of injecting the quality current to the utility grid, the current control technique based-on the principle of rotating reference frame is proposed. This method can generate the sinusoidal current and independently control the injection of active and reactive power to the utility grid. Simulation results for two H-bridge cells CHB-MLI 4000W/220V/50Hz GCPVS are presented to validate the proposed control scheme.


Amplitude Incremental Variational Principle for Nonlinear Vibration of Elastic Systems


title: Fusion of Inertial and Depth Sensor Data for Robust Hand Gesture Recognition

abstract: This paper presents the first attempt at fusing data from inertial and vision depth sensors within the framework of a hidden Markov model for the application of hand gesture recognition. The data fusion approach introduced in this paper is general purpose in the sense that it can be used for recognition of various body movements. It is shown that the fusion of data from the vision depth and inertial sensors act in a complementary manner leading to a more robust recognition outcome compared with the situations when each sensor is used individually on its own. The obtained recognition rates for the single hand gestures in the Microsoft MSR data set indicate that our fusion approach provides improved recognition in real-time and under realistic conditions.


title: Efficient IR-Style Keyword Search over Relational Databases

abstract: Applications in which plain text coexists with structured data are pervasive. Commercial relational database management systems (RDBMSs) generally provide querying capabilities for text attributes that incorporate state-of-the-art information retrieval (IR) relevance ranking strategies, but this search functionality requires that queries specify the exact column or columns against which a given list of keywords is to be matched. This requirement can be cumbersome and inflexible from a user perspective: good answers to a keyword query might need to be "assembled" -in perhaps unforeseen ways- by joining tuples from multiple relations. This observation has motivated recent research on free-form keyword search over RDBMSs. In this paper, we adapt IR-style document-relevance ranking strategies to the problem of processing free-form keyword queries over RDBMSs. Our query model can handle queries with both AND and OR semantics, and exploits the sophisticated single-column text-search functionality often available in commercial RDBMSs. We develop query-processing strategies that build on a crucial characteristic of IR-style keyword search: only the few most relevant matches -according to some definition of "relevance"- are generally of interest. Consequently, rather than computing all matches for a keyword query, which leads to inefficient executions, our techniques focus on the top-k matches for the query, for moderate values of k. A thorough experimental evaluation over real data shows the performance advantages of our approach.


title: Faster DB-scan and HDB-scan in Low-Dimensional Euclidean Spaces

abstract: We present a new algorithm for the widely used density-based clustering method DBscan. Our algorithm computes the DBscan-clustering in $O(n\log n)$ time in $\mathbb{R}^2$, irrespective of the scale parameter $\varepsilon$ (and assuming the second parameter MinPts is set to a fixed constant, as is the case in practice). Experiments show that the new algorithm is not only fast in theory, but that a slightly simplified version is competitive in practice and much less sensitive to the choice of $\varepsilon$ than the original DBscan algorithm. We also present an $O(n\log n)$ randomized algorithm for HDBscan in the plane---HDBscan is a hierarchical version of DBscan introduced recently---and we show how to compute an approximate version of HDBscan in near-linear time in any fixed dimension.


title: Uni-Planar Dual-Band Monopole Antenna for 2.4/5 GHz WLAN Operation in the Laptop Computer

abstract: A novel uni-planar dual-band monopole antenna capable of generating two wide bands for 2.4/5 GHz WLAN operation is presented. The antenna has a simple structure consisting of a driven strip and a coupled shorted strip. The antenna occupies a small area of 6 times 20 mm2 on an FR4 substrate. The small area allows the antenna to be easily employed in the narrow space between the top edge of the display panel and the casing of the laptop computer to operate as an internal antenna. It is believed that the size of the antenna is about the smallest among the existing uni-planar internal laptop antennas for 2.4/5 GHz WLAN operation.


title: A Novel Method of Vessel Segmentation for X-ray Coronary Angiography Images

abstract: This paper presents a new automatic region-growing method for vessel segmentation in two-dimensional X-ray coronary angiography images. The method consists of two parts: the feature map extraction based on a novel vesselness function; and the segmentation process which includes automatic seed-point selection, main branch segmentation and vessel detail repair. Both the greyscale and spatial information are extracted for segmentation based on region growing algorithm. The presented method is validated on several clinical X-ray coronary angiography images, and the experimental results show that the method can not only segment large vessels but also small vessels.


Effect of Growth Mindset on School Engagement and Psychological Well-Being of Chinese Primary and Middle School Students: The Mediating Role of Resilience


title: Population pharmacokinetics of abacavir in infants, toddlers and children

abstract: Aims ::: ::: To characterize the pharmacokinetics of abacavir in infants, toddlers and children and to assess the influence of covariates on drug disposition across these populations. ::: ::: ::: ::: Methods ::: ::: Abacavir concentration data from three clinical studies in human immunodeficiency virus-infected children (n = 69) were used for model building. The children received either a weight-normalized dose of 16 mg kg−1 day−1 or the World Health Organization recommended dose based on weight bands. A population pharmacokinetic analysis was performed using nonlinear mixed effects modelling VI. The influence of age, gender, bodyweight and formulation was evaluated. The final model was selected according to graphical and statistical criteria. ::: ::: ::: ::: Results ::: ::: A two-compartmental model with first-order absorption and first-order elimination best described the pharmacokinetics of abacavir. Bodyweight was identified as significant covariate influencing the apparent oral clearance and volume of distribution. Predicted steady-state maximal plasma concentration and area under the concentration–time curve from 0 to 12 h of the standard twice daily regimen were 2.5 mg l−1 and 6.1 mg h l−1 for toddlers and infants, and 3.6 mg l−1 and 8.7 mg h l−1 for children, respectively. Model-based predictions showed that equivalent systemic exposure was achieved after once and twice daily dosing regimens. There were no pharmacokinetic differences between the two formulations (tablet and solution). The model demonstrated good predictive performance for dosing prediction in individual patients and, as such, can be used to support therapeutic drug monitoring in conjunction with sparse sampling. ::: ::: ::: ::: Conclusions ::: ::: The disposition of abacavir in children appears to be affected only by differences in size, irrespective of the patient's age. Maturation processes of abacavir metabolism in younger infants should be evaluated in further studies to demonstrate the potential impact of ontogeny.


title: Natural language question-answering systems: 1969

abstract: Recent experiments in programming natural language question-answering systems are reviewed to summarize the methods that have been developed for syntactic, semantic, and logical analysis of English strings. It is concluded that at least minimally effective techniques have been devised for answering questions from natural language subsets in small scale experimental systems and that a useful paradigm has evolved to guide research efforts in the field. Current approaches to semantic analysis and logical inference are seen to be effective beginnings but of questionable generality with respect either to subtle aspects of meaning or to applications over large subsets of English. Generalizing from current small-scale experiments to language-processing systems based on dictionaries with thousands of entries—with correspondingly large grammars and semantic systems—may entail a new order of complexity and require the invention and development of entirely different approaches to semantic analysis and question answering.


title: Towards flexible automated software architecture erosion diagnosis and treatment

abstract: Uncontrolled software architecture erosion can lead to a degradation of the quality of a software system. It is hence of great importance to repair erosion efficiently. Refactorings can help to systematically reverse software architecture erosion through applying them in the system where architectural violations have been detected. However, existing refactoring approaches do not address architecture erosion holistically. In this paper, we describe and formalize the theoretical problem of repairing eroded software systems by finding optimal repair sequences. Furthermore, we investigate the applicability and limitations of existing refactoring approaches. We argue, true to the motto "more knowledge means less search" that using formalized and explicit knowledge of software engineers---modeled as fault patterns and repair strategies---combined with heuristic search techniques could overcome those limitations. This paper outlines a new approach---analog to a patient history in medicine---we have been starting to investigate in our recent research and also aims at stimulating a discussion about further research challenges in repairing eroded software systems.


title: Constructive Induction of Cartesian Product Attributes

abstract: Constructive induction is the process of changing the representation of examples by creating new attributes from existing attributes. In classification, the goal of constructive induction is to find a representation that facilitates learning a concept description by a particular learning system. Typically, the new attributes are Boolean or arithmetic combinations of existing attributes and the learning algorithms used are decision trees or rule learners. We describe the construction of new attributes that are the Cartesian product of existing attributes. We consider the effects of this operator on three learning algorithms and compare two different methods for determining when to construct new attributes with this operator


title: Modeling Concept Dependencies in a Scientific Corpus

abstract: Our goal is to generate reading lists for students that help them optimally learn technical material. Existing retrieval algorithms return items directly relevant to a query but do not return results to help users read about the concepts supporting their query. This is because the dependency structure of concepts that must be understood before reading material pertaining to a given query is never considered. Here we formulate an information-theoretic view of concept dependency and present methods to construct a “concept graph” automatically from a text corpus. We perform the first human evaluation of concept dependency edges (to be published as open data), and the results verify the feasibility of automatic approaches for inferring concepts and their dependency relations. This result can support search capabilities that may be tuned to help users learn a subject rather than retrieve documents based on a single query.


title: Multiple trace theory of human memory: computational, neuroimaging, and neuropsychological results

abstract: Hippocampal-neocortical interactions in memory have typically been characterized within the “standard model” of memory consolidation. In this view, memory storage initially requires hippocampal linking of dispersed neocortical storage sites, but over time this need dissipates, and the hippocampal component is rendered unnecessary. This change in function over time is held to account for the retorgrade amnesia (RA) gradients often seen in patients with hippocampal damage. Recent evidence, however, calls this standard model into question, and we have recently proposed a new approach, the “multiple memory trace” (MMT) theory. In this view, hippocampal ensembles are always involved in storage and retrieval of episodic information, but semantic (gist) information can be established in neocortex, and will survive damage to the hippocampal system if enough time has elapsed. This approach accounts more readily for the very long RA gradients often observed in amnesia. We report the results of analytic and connectionist simulations that demonstrate the feasibility of MMT. We also report a neuroimaging study showing that retrieval of very remote (25-year-old) memories elicits as much activation in hippocampus as retrieval of quite recent memories. Finally, we report new data from the study of patients with temporal lobe damage, using more sensitive measures than previously the case, showing that deficits in both episodic and spatial detail can bed observed even for very remote memories. Overall, these findings indicate that the standard model of memory consolidation, which views the hippocampus as having only a temporary role in memory, is wrong. Instead, the data support the view that for episodic and spatial detail the hippocampal system is always necessary. Hippocampus 10:352–368, 2000 © 2000 Wiley-Liss, Inc.


title: Discovering User Interest on Twitter with a Modified Author-Topic Model

abstract: This paper focuses on the problem of discovering users' topics of interest on Twitter. While previous efforts in modeling users' topics of interest on Twitter have focused on building a "bag-of-words" profile for each user based on his tweets, they overlooked the fact that Twitter users usually publish noisy posts about their lives or create conversation with their friends, which do not relate to their topics of interest. In this paper, we propose a novel framework to address this problem by introducing a modified author-topic model named twitter-user model. For each single tweet, our model uses a latent variable to indicate whether it is related to its author's interest. Experiments on a large dataset we crawled using Twitter API demonstrate that our model outperforms traditional methods in discovering user interest on Twitter.


title: Selective Refinement Network for High Performance Face Detection

abstract: High performance face detection remains a very challenging problem, especially when there exists many tiny faces. This paper presents a novel single-shot face detector, named Selective Refinement Network (SRN), which introduces novel two-step classification and regression operations selectively into an anchor-based face detector to reduce false positives and improve location accuracy simultaneously. In particular, the SRN consists of two modules: the Selective Two-step Classification (STC) module and the Selective Two-step Regression (STR) module. The STC aims to filter out most simple negative anchors from low level detection layers to reduce the search space for the subsequent classifier, while the STR is designed to coarsely adjust the locations and sizes of anchors from high level detection layers to provide better initialization for the subsequent regressor. Moreover, we design a Receptive Field Enhancement (RFE) block to provide more diverse receptive field, which helps to better capture faces in some extreme poses. As a consequence, the proposed SRN detector achieves state-of-the-art performance on all the widely used face detection benchmarks, including AFW, PASCAL face, FDDB, and WIDER FACE datasets. Codes will be released to facilitate further studies on the face detection problem.


Patient Privacy in Paralinguistic Tasks


title: 3D Inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer's Disease diagnostics

abstract: In the last decade, computer-aided early diagnostics of Alzheimer's Disease (AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been the subject of extensive research. Some recent studies have shown promising results in the AD and MCI determination using structural and functional Magnetic Resonance Imaging (sMRI, fMRI), Positron Emission Tomography (PET) and Diffusion Tensor Imaging (DTI) modalities. Furthermore, fusion of imaging modalities in a supervised machine learning framework has shown promising direction of research. In this paper we first review major trends in automatic classification methods such as feature extraction based methods as well as deep learning approaches in medical image analysis applied to the field of Alzheimer's Disease diagnostics. Then we propose our own design of a 3D Inception-based Convolutional Neural Network (CNN) for Alzheimer's Disease diagnostics. The network is designed with an emphasis on the interior resource utilization and uses sMRI and DTI modalities fusion on hippocampal ROI. The comparison with the conventional AlexNet-based network using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (http://adni.loni.usc.edu) demonstrates significantly better performance of the proposed 3D Inception-based CNN.


title: Lessons From A Sensor Network Expedition

abstract: Habitat monitoring is an important driving application for wireless sensor networks (WSNs). Although researchers anticipate some challenges arising in the real-world deployments of sensor networks, a number of problems can be discovered only through experience. This paper evaluates a sensor network system described in an earlier work and presents a set of experiences from a four month long deployment on a remote island off the coast of Maine. We present an in-depth analysis of the environmental and node health data. The close integration of WSNs with their environment provides biological data at densities previous impossible; however, we show that the sensor data is also useful for predicting system operation and network failures. Based on over one million data and health readings, we analyze the node and network design and develop network reliability profiles and failure models.


title: SecureME: a hardware-software approach to full system security

abstract: With computing increasingly becoming more dispersed, relying on mobile devices, distributed computing, cloud computing, etc. there is an increasing threat from adversaries obtaining physical access to some of the computer systems through theft or security breaches. With such an untrusted computing node, a key challenge is how to provide secure computing environment where we provide privacy and integrity for data and code of the application. We propose SecureME, a hardware-software mechanism that provides such a secure computing environment. SecureME protects an application from hardware attacks by using a secure processor substrate, and also from the Operating System (OS) through memory cloaking, permission paging, and system call protection. Memory cloaking hides data from the OS but allows the OS to perform regular virtual memory management functions, such as page initialization, copying, and swapping. Permission paging extends the OS paging mechanism to provide a secure way for two applications to establish shared pages for inter-process communication. Finally, system call protection applies spatio-temporal protection for arguments that are passed between the application and the OS. Based on our performance evaluation using microbenchmarks, single-program workloads, and multiprogrammed workloads, we found that SecureME only adds a small execution time overhead compared to a fully unprotected system. Roughly half of the overheads are contributed by the secure processor substrate. SecureME also incurs a negligible additional storage overhead over the secure processor substrate.


title: Delayed Immune Mediated Adverse Effects to Hyaluronic Acid Fillers: Report of Five Cases and Review of the Literature

abstract: Hyaluronic acid (HA) fillers in cosmetic medicine have been considered relatively safe, though fillers used in European countries and throughout the world are not necessarily approved by the Food and Drug Administration. As their use continues to expand worldwide, physicians in a wide range of medical specialties are authorized to perform HA injections, including general medicine practitioners and even dentists. An increasing number of reports have appeared regarding side effects to these products. It is now known that reactions to Hyaluronic acid are related not only to technical faults of the injections, but also to immune responses, including delayed hypersensitivity and granulomatous reactions. Herein, we describe five cases treated by a variety of treatment modalities, all with delayed reactions to different brands of hyaluronic acid fillers. As there is currently no standardization of treatment options of adverse effects, these cases accentuate the debate regarding the approach to the individual patient and the possible need for pre-testing in patients with an atopic tendency.


title: A progression model of software engineering goals, challenges, and practices in start-ups

abstract: Context: Software start-ups are emerging as suppliers of innovation and software-intensive products. However, traditional software engineering practices are not evaluated in the context, nor adopted to goals and challenges of start-ups. As a result, there is insufficient support for software engineering in the start-up context.


title: Attacking software-defined networks: a first feasibility study

abstract: In this paper, for the first time we show a new attack to fin- gerprint SDN networks and further launch efficient resource consumption attacks. This attack demonstrates that SDN brings new security issues that may not be ignored. We provide the first feasibility study of such attack and hope to stimulate further studies in SDN security research.


Arithmetic Properties of Apollonian Circle Packings


title: ApkCombiner: Combining Multiple Android Apps to Support Inter-App Analysis

abstract: Android apps are made of components which can leak information between one another using the ICC mechanism. With the growing momentum of Android, a number of research contributions have led to tools for the intra-app analysis of Android apps. Unfortunately, these state-of-the-art approaches, and the associated tools, have long left out the security flaws that arise across the boundaries of single apps, in the interaction between several apps. In this paper, we present a tool called ApkCombiner which aims at reducing an inter-app communication problem to an intra-app inter-component communication problem. In practice, ApkCombiner combines different apps into a single apk on which existing tools can indirectly perform inter-app analysis. We have evaluated ApkCombiner on a dataset of 3,000 real-world Android apps, to demonstrate its capability to support static context-aware inter-app analysis scenarios.


title: Towards multi-layer interoperability of heterogeneous IoT platforms : the INTER-IoT approach

abstract: Open interoperability delivers on the promise of enabling vendors and developers to interact and interoperate, without interfering with anyone’s ability to compete by delivering a superior product and experience. In the absence of global IoT standards, the INTER-IoT voluntary approach will support and make it easy for any IoT stakeholder to design open IoT devices, smart objects, services, and complex systems and get them to be operative and interconnected quickly, thus creating new IoT interoperable ecosystems by using a bottom-up approach. In particular, INTER-IoT is based on hardware/software tools (INTER-Layer) granting multi-layer interoperability among IoT system layers (i.e. device, networking, middleware, application service, data and semantics), on frameworks for open IoT application and system programming and deployment (INTER-FW), and on a full-fledged CASE tool-supported engineering methodology for IoT systems integration (INTER-Meth). The INTER-IoT approach is notably exemplified through two use cases: INTER-LogP, involving interoperability of port logistics ecosystems, and INTER-Health, encompassing integration between e-Health at home and in mobility infrastructures.


title: Speech reconstruction using a deep partially supervised neural network

abstract: Statistical speech reconstruction for larynx-related dysphonia has achieved good performance using Gaussian mixture models and, more recently, restricted Boltzmann machine arrays; however, deep neural network (DNN)-based systems have been hampered by the limited amount of training data available from individual voice-loss patients. The authors propose a novel DNN structure that allows a partially supervised training approach on spectral features from smaller data sets, yielding very good results compared with the current state-of-the-art.


title: CMOS THz Generator With Frequency Selective Negative Resistance Tank

abstract: This paper reports a CMOS terahertz oscillator with a novel frequency selective negative resistance (FSNR) tank to boost its operating frequency. The demonstrated oscillator can operate at a fundamental frequency of about 0.22 THz, exceeding the CMOS device cutoff frequency of fT. The proposed architecture suppresses undesired 2nd and odd harmonics and boosts the fourth-order harmonic (0.87 THz), which radiates through an on-chip patch antenna. The THz oscillator's output spectrum is profiled by using a Michelson interferometer. The oscillator circuit consumes 12 mA from a 1.4 V supply and occupies a 0.045 mm2 die area in a 65 nm CMOS technology.


title: Segmentation-Based Filtering of Airborne LiDAR Point Clouds by Progressive Densification of Terrain Segments

abstract: Abstract: Filtering is one of the core post-processing steps for Airborne Laser Scanning (ALS) point clouds. A segmentation-based filtering (SBF) method is proposed herein. This method is composed of three key steps: point cloud segmentation, multiple echoes analysis, and iterative judgment. Moreover, the third step is our main contribution. Particularly, the iterative judgment is based on the framework of the classic progressive TIN (triangular irregular network) densification (PTD) method, but with basic processing unit being a segment rather than a single point. Seven benchmark datasets provided by ISPRS Working Group III/3 are utilized to test the SBF algorithm and the classic PTD method. Experimental results suggest that, compared with the PTD method, the SBF approach is capable of preserving discontinuities of landscapes and removing the lower parts of large objects attached on the ground surface. As a result, the SBF approach is able to reduce omission errors and total errors by 18.26% and 11.47% respectively, which would significantly decrease the cost of manual operation required in post-processing.


title: A comparison of deep learning and hand crafted features in medical image modality classification

abstract: Modality corresponding to medical images is a vital filter in medical image retrieval systems, as radiologists or physicians are interested in only one of radiology images e.g CT scan, MRI, X-ray. Various handcrafted feature schemes have been proposed for medical image modality classification. On the other hand not enough attempts have been made for deep learned feature extraction. A comparative evaluation of both handcrafted and deep learned features for medical image modality classification is presented in this paper. The experiments are performed on IMAGECLEF 2012 data. After carrying out the experiments it is shown that the handcrafted features outperforms the deep learned features and shows the potential of handcrafted feature extraction models in the medical image field.


title: Time-Triggered Ethernet and IEEE 1588 Clock Synchronization

abstract: The time-triggered Ethernet unifies real-time and non-real-time traffic into a single communication architecture. We have built a prototype implementation of an FPGA TT-Ethernet switch and an FPGA TT Ethernet communication controller supporting a network bandwidth of 100 Mbit/sec. Time-Triggered Ethernet introduces two message classes, i) the standard event-triggered Ethernet messages, denoted as ET messages, and ii) the time-triggered Ethernet messages, denoted as TT messages. All TT messages are transmitted periodically and are scheduled a priori in a way that there are no conflicts on the network. The network handles these messages according to the cut-through paradigm. Computer nodes containing TT Ethernet communication controllers establish and maintain global time base. However nodes containing standard Ethernet controllers can be connected to a TT Ethernet system and can send ET messages without affecting the temporal properties of the TT messages. The global time format of the TT Ethernet deploys the UTC time format which is compatible with the time format of the IEEE 1588 standard. In these work we present how we deploy the IEEE 1588 in order to synchronize the TT Ethernet controllers which require a tight synchronization among them. Additionally the IEEE 1588 clock synchronization based protocol will be implemented at standard Ethernet controllers such that they can be establish and maintain a global time base.


title: An empirical study of discriminative sequence labeling models for Vietnamese text processing

abstract: This paper presents an empirical study of two widely-used sequence prediction models, Conditional Random Fields (CRFs) and Long Short-Term Memory Networks (LSTMs), on two fundamental tasks for Vietnamese text processing, including part-of-speech tagging and named entity recognition. We show that a strong lower bound for labeling accuracy can be obtained by relying only on simple word-based features with minimal hand-crafted feature engineering, of 90.65\% and 86.03\% performance scores on the standard test sets for the two tasks respectively. In particular, we demonstrate empirically the surprising efficiency of word embeddings in both of the two tasks, with both of the two models. We point out that the state-of-the-art LSTMs model does not always outperform significantly the traditional CRFs model, especially on moderate-sized data sets. Finally, we give some suggestions and discussions for efficient use of sequence labeling models in practical applications.


title: Single Image Dehazing Using Color Attenuation Prior

abstract: In this paper, we propose a simple but powerful prior, color attenuation prior, for haze removal from a single input hazy image. By creating a linear model for modelling the scene depth of the hazy image under this novel prior and learning the parameters of the model by using a supervised learning method, the depth information can be well recovered. With the depth map of the hazy image, we can easily remove haze from a single image. Experimental results show that the proposed approach is highly efficient and it outperforms state-of-the-art haze removal algorithms in terms of the dehazing effect as well.


title: CzEngVallex: a Bilingual Czech-English Valency Lexicon

abstract: This paper introduces a new bilingual Czech-English verbal valency lexicon (called CzEngVallex) representing a relatively large empirical database. It includes 20,835 aligned valency frame pairs (i.e., verb senses which are translations of each other) and their aligned arguments. This new lexicon uses data from the Prague Czech-English Dependency Treebank and also takes advantage of the existing valency lexicons for both languages: the PDT-Vallex for Czech and the EngVallex for English. The CzEngVallex is available for browsing as well as for download in the LINDAT/CLARIN repository. The CzEngVallex ismeant to be used not only by traditional linguists, lexicographers, translators but also by computational linguists both for the purposes of enriching theoretical linguistic accounts of verbal valency from a cross-linguistic perspective and for an innovative use in various NLP tasks.


title: Detecting trend and seasonal 1 changes in satellite image time series 2

abstract: Abstract A wealth of remotely sensed image time series covering large areas is now available to the earth science community. Change detection methods are often not capable of detecting land cover changes within time series that are heavily influenced by seasonal climatic variations. Detecting change within the trend and seasonal components of time series enables the classification of different types of changes. Changes occurring in the trend component often indicate disturbances (e.g. fires, insect attacks), while changes occurring in the seasonal component indicate phenological changes (e.g. change in land cover type). A generic change detection approach is proposed for time series by detecting and characterizing Breaks For Additive Seasonal and Trend (BFAST). BFAST integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within time series. BFAST iteratively estimates the time and number of changes, and characterizes change by its magnitude and direction. We tested BFAST by simulating 16-day Normalized Difference Vegetation Index (NDVI) time series with varying amounts of seasonality and noise, and by adding abrupt changes at different times and magnitudes. This revealed that BFAST can robustly detect change with different magnitudes (> 0.1 NDVI) within time series with different noise levels (0.01–0.07 σ ) and seasonal amplitudes (0.1–0.5 NDVI). Additionally, BFAST was applied to 16-day NDVI Moderate Resolution Imaging Spectroradiometer (MODIS) composites for a forested study area in south eastern Australia. This showed that BFAST is able to detect and characterize spatial and temporal changes in a forested landscape. BFAST is not specific to a particular data type and can be applied to time series without the need to normalize for land cover types, select a reference period, or change trajectory. The method can be integrated within monitoring frameworks and used as an alarm system to flag when and where changes occur.


title: An extendible data structure for handling large multidimensional data sets

abstract: Multidimensional array is widely used in large number of scientific research and engineering applications for handling large multidimensional data. There exist many data structures to represent multidimensional data. But most of these data structures are static (such as traditional multidimensional array) and can not handle the dynamic extension or reduction of the array. The Traditional Multidimensional Array (TMA) is efficient in terms of accessing the elements of the array by random computing the addressing function but TMA is not extendible during run time. In this paper we propose a new scheme, Karnaugh Representation of Extendible Array (KEA), to represent the multidimensional data. The main idea of this scheme is to represent n dimensional array by a set of two dimensional extendible arrays. The scheme can be extended in any direction during run time. To evaluate our proposed scheme, we implement and compare with the existing systems for different operations with the Traditional Multidimensional Array (TMA), and Traditional Extendible Array (TEA). Our experimental result shows that the KEA scheme outperforms TMA and TEA.


Flecainide and elevated liver enzymes in α1-antitrypsin deficiency