title: Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization

abstract: Differential evolution (DE) is an efficient and powerful population-based stochastic search technique for solving optimization problems over continuous space, which has been widely applied in many scientific and engineering fields. However, the success of DE in solving a specific problem crucially depends on appropriately choosing trial vector generation strategies and their associated control parameter values. Employing a trial-and-error scheme to search for the most suitable strategy and its associated parameter settings requires high computational costs. Moreover, at different stages of evolution, different strategies coupled with different parameter settings may be required in order to achieve the best performance. In this paper, we propose a self-adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, a more suitable generation strategy along with its parameter settings can be determined adaptively to match different phases of the search process/evolution. The performance of the SaDE algorithm is extensively evaluated (using codes available from P. N. Suganthan) on a suite of 26 bound-constrained numerical optimization problems and compares favorably with the conventional DE and several state-of-the-art parameter adaptive DE variants.


Knowledge-System Technology : Ontologies and Problem-Solving Methods


title: Real-time scheduling theory and Ada

abstract: Rate monotonic scheduling theory puts real-time software engineering on a sound analytical footing. The authors review the theory and its implications for Ada. >


title: Recipe recognition with large multimodal food dataset

abstract: This paper deals with automatic systems for image recipe recognition. For this purpose, we compare and evaluate leading vision-based and text-based technologies on a new very large multimodal dataset (UPMC Food-101) containing about 100,000 recipes for a total of 101 food categories. Each item in this dataset is represented by one image plus textual information. We present deep experiments of recipe recognition on our dataset using visual, textual information and fusion. Additionally, we present experiments with text-based embedding technology to represent any food word in a semantical continuous space. We also compare our dataset features with a twin dataset provided by ETHZ university: we revisit their data collection protocols and carry out transfer learning schemes to highlight similarities and differences between both datasets. Finally, we propose a real application for daily users to identify recipes. This application is a web search engine that allows any mobile device to send a query image and retrieve the most relevant recipes in our dataset.


title: Human age estimation using bio-inspired features

abstract: We investigate the biologically inspired features (BIF) for human age estimation from faces. As in previous bio-inspired models, a pyramid of Gabor filters are used at all positions of the input image for the S1 units. But unlike previous models, we find that the pre-learned prototypes for the S2 layer and then progressing to C2 cannot work well for age estimation. We also propose to use Gabor filters with smaller sizes and suggest to determine the number of bands and orientations in a problem-specific manner, rather than using a predefined number. More importantly, we propose a new operator “STD” to encode the aging subtlety on faces. Evaluated on the large database YGA with 8,000 face images and the public available FG-NET database, our approach achieves significant improvements in age estimation accuracy over the state-of-the-art methods. By applying our system to some Internet face images, we show the robustness of our method and the potential of cross-race age estimation, which has not been explored by any studies before.


title: Cesium Lead Halide Perovskites with Improved Stability for Tandem Solar Cells.

abstract: A semiconductor that can be processed on a large scale with a bandgap around 1.8 eV could enable the manufacture of highly efficient low cost double-junction solar cells on crystalline Si. Solution-processable organic-inorganic halide perovskites have recently generated considerable excitement as absorbers in single-junction solar cells, and though it is possible to tune the bandgap of (CH3NH3)Pb(BrxI1-x)3 between 2.3 and 1.6 eV by controlling the halide concentration, optical instability due to photoinduced phase segregation limits the voltage that can be extracted from compositions with appropriate bandgaps for tandem applications. Moreover, these materials have been shown to suffer from thermal degradation at temperatures within the processing and operational window. By replacing the volatile methylammonium cation with cesium, it is possible to synthesize a mixed halide absorber material with improved optical and thermal stability, a stabilized photoconversion efficiency of 6.5%, and a bandgap of 1.9 eV.


title: Design and implementation of digital energy meter with data sending capability using GSM network

abstract: Efficient use of energy becomes more crucial when increase in the cost of energy is observed. Since energy management is required to define the amount of consumed energy in a specific period of time, utilization of Energy Meters is essential. It is possible to measure the consumed energy by using a simple energy meter. But sometimes the limited functionality of these meters restricts their area of application; especially in inaccessible positions or in the situations where visibility of the meter is poor, it is not possible to use such an appliance. A possible solution is a Wireless Energy Meter (WEM) which is able to send its data via wireless communication to a central server where monitoring and analysis of the data will be easily made. This paper describes the design and implementation of a Digital Energy Meter based on PIC microcontroller. Calculation of the phase difference between current and voltage using Zero Cross Detection capability of op-amp is a distinct feature of this project. Current transformer is used to measure the current signal and a step down potential transformer is used to measure the voltage. SIM900 based GSM module is chosen as a possible wireless solution to this issue.


title: The Impact of Corporate Social Responsibility on Firm Value: The Role of Customer Awareness

abstract: This paper shows that corporate social responsibility (CSR) and firm value are positively related for firms with high customer awareness, as proxied by advertising expenditures. For firms with low customer awareness, the relation is either negative or insignificant. In addition, we find that the effect of awareness on the CSR–value relation is reversed for firms with a poor prior reputation as corporate citizens. This evidence is consistent with the view that CSR activities can add value to the firm but only under certain conditions. This paper was accepted by Bruno Cassiman, business strategy.


title: Xylo-oligosaccharide (XOS) in combination with inulin modulates both the intestinal environment and immune status in healthy subjects, while XOS alone only shows prebiotic properties

abstract: The purpose of the present study was to establish the prebiotic effect of a new xylo-oligosaccharide (XOS) and of an inulin-and-XOS mixture (INU-XOS) and to determine their effect on endotoxaemia (lipopolysaccharides (LPS)) and immune parameters. In this randomised, parallel, placebo-controlled, double-blind study, sixty healthy volunteers were randomly assigned to three groups, receiving either 5 g XOS, INU-XOS (3 g inulin +1 g XOS) or an equivalent weight of wheat maltodextrin (placebo) during 4 weeks. Faecal samples were collected to assess the effects of these products on microbiota, as well as SCFA composition, enzymatic activities and secretory IgA production. Circulating LPS was measured in plasma samples, and whole blood was incubated with LPS to measure cytokine expression. Consumption of XOS alone increased the faecal concentrations of Bifidobacterium and butyrate and activities of α-glucosidase and β-glucuronidase, while decreasing the concentrations of acetate and p-cresol. Consumption of XOS in combination with inulin did not decrease the concentrations of acetate and p-cresol, but increased in addition the faecal concentrations of total SCFA and propionate. Furthermore, consumption of XOS in combination with inulin decreased LPS concentrations in blood and attenuated LPS-induced increases in gene expression in IL-1β and LPS-induced decreases in gene expression in IL-13 in blood. In conclusion, consumption of XOS alone or in combination with inulin results in beneficial albeit different changes in the intestinal microbiome on a high-fat diet. In addition, consumption of XOS in combination with inulin attenuates the proinflammatory effects of a high-fat diet in the blood of healthy subjects.


title: Deep learning in intrusion detection perspective: Overview and further challenges

abstract: Deep learning techniques are famous due to Its capability to cope with large-scale data these days. They have been investigated within various of applications e.g., language, graphical modeling, speech, audio, image recognition, video, natural language and signal processing areas. In addition, extensive researches applying machine-learning methods in Intrusion Detection System (IDS) have been done in both academia and industry. However, huge data and difficulties to obtain data instances are hot challenges to machine-learning-based IDS. We show some limitations of previous IDSs which uses classic machine learners and introduce feature learning including feature construction, extraction and selection to overcome the challenges. We discuss some distinguished deep learning techniques and its application for IDS purposes. Future research directions using deep learning techniques for IDS purposes are briefly summarized.


title: A Comparative Experiment of Several Shape Methods in Recognizing Plants

abstract: Shape is an important aspects in recognizing plants. Several approaches have been introduced to identify objects, including plants. Combination of geometric features such as aspect ratio, compactness, and dispersion, or moments such as moment invariants were usually used toidentify plants. In this research, a comparative experiment of 4 methods to identify plants using shape features was accomplished. Two approaches have never been used in plants identification yet, Zernike moments and Polar Fourier Transform (PFT), were incorporated. The experimental comparison was done on 52 kinds of plants with various shapes. The result, PFT gave best performance with 64% in accuracy and outperformed the other methods.


title: Representing words as regions in vector space

abstract: Vector space models of word meaning typically represent the meaning of a word as a vector computed by summing over all its corpus occurrences. Words close to this point in space can be assumed to be similar to it in meaning. But how far around this point does the region of similar meaning extend? In this paper we discuss two models that represent word meaning as regions in vector space. Both representations can be computed from traditional point representations in vector space. We find that both models perform at over 95% F-score on a token classification task.


title: Multivariate Survival Analysis (I): Shared Frailty Approaches to Reliability and Dependence Modeling

abstract: The latest advances in survival analysis have been centered on multivariate systems. Multivariate survival analysis has two major categories of models: one is multi-state modeling; the other is shared frailty modeling. Multi-state models, although formulated differently in both fields, have been extensively studied in reliability analysis in the context of Markov chain analysis. In contrast, shared frailty modeling seems little known in reliability analysis and computer science. In this article, we focus exclusively on shared frailty modeling. Shared frailty refers to the often-unobserved factors or risks responsible for the common risks dependence between multiple events. It is well recognized as the most effective modeling approach to address common risks dependence and, more recently, the event-related dependence. The only exclusion of dependence modeling for the frailty approach is the common events type, which is best addressed by multi-state modeling. We argue that shared frailty modeling not only is perfectly applicable for engineering reliability, but also is of significant potential in other fields of computer science, such as networking and software reliability and survivability, machine learning, and prognostics and health management (PHM).


title: Reasoning about temporal relations: a maximal tractable subclass of Allen's interval algebra

abstract: We introduce a new subclass of Allen's interval algebra we call "ORD-Horn subclass," which is a strict superset of the "pointisable subclass." We prove that reasoning in the ORD-Horn subclass is a polynomial-time problem and show that the path-consistency method is sufficient for deciding satisfiability. Further, using an extensive machine-generated case analysis, we show that the ORD-Horn subclass is a maximal tractable subclass of the full algebra (assuming P≠NP). In fact, it is the unique greatest tractable subclass amongst the subclasses that contain all basic relations.


A Steady-State Evolutionary Algorithm for Building Collaborative Learning Teams in Educational Environments Considering the Understanding Levels and Interest Levels of the Students.


title: The Foams and Emulsions Ontology Model

abstract: Creation of the intelligent system for highly structured domain is possible with determining the whole system of concepts included in the ontology of this area. Presentation by means of formal ontology model provides a uniquely interpreted terms. Based on the model of ontology the intelligent system will integrate their knowledge and information components of all sections of the data area. The paper considers the unification of nanomaterials subdomain with other subdomains that are need to be used for intelligent system for nanotechnology. The paper also proposes mathematical ontology model of one of the component - that is ontology model of foams and emulsions class.


title: Relevance feedback in image retrieval: A comprehensive review

abstract: We analyze the nature of the relevance feedback problem in a continuous representation space in the context of content-based image retrieval. Emphasis is put on exploring the uniqueness of the problem and comparing the assumptions, implementations, and merits of various solutions in the literature. An attempt is made to compile a list of critical issues to consider when designing a relevance feedback algorithm. With a comprehensive review as the main portion, this paper also offers some novel solutions and perspectives throughout the discussion.


title: Cleft Lip – A Comprehensive Review

abstract: Inauguration article/Review, I didn't put in an abstract because I noted similar articles in the journal didn't have an abstract. if one is needed then will do that.


title: A digitally controlled oscillator in a 90 nm digital CMOS process for mobile phones

abstract: We propose and demonstrate the first RF digitally controlled oscillator (DCO) for cellular mobile phones. The DCO is part of a single-chip quad-band fully compliant GSM transceiver realized in a 90 nm digital CMOS process. Wide and precise linear frequency tuning is achieved through digital control of a large array of standard n-poly/n-well MOSCAP devices that operate in flat regions of their C- V curves. The varactors are partitioned into binary-weighted and unit-weighted banks that are sequentially activated during frequency locking and tracking. The finest varactor step size is 12 kHz at the 1.6-2.0 GHz high-band output. To attenuate the quantization noise to below the natural oscillator phase noise, the varactors undergo high-speed second-order /spl Sigma//spl Delta/ dithering. We analyze the effect of the /spl Sigma//spl Delta/ dithering on the phase noise and show that it can be made sufficiently small. The measured phase noise at 20 MHz offset in the GSM900 band is -165 dBc/Hz and shows no degradation due to the /spl Sigma//spl Delta/ dithering. The 3.6 GHz DCO core consumes 18.0 mA from a 1.4 V supply and has a very wide tuning range of 900 MHz to support the quad-band operation.


Coordination Guidelines for Virtual Design and Construction


title: Symbol Grounding via Chaining of Morphisms

abstract: A new model of symbol grounding is presented, in which the structures of natural language, logical semantics, perception and action are represented categorically, and symbol grounding is modeled via the composition of morphisms between the relevant categories. This model gives conceptual insight into the fundamentally systematic nature of symbol grounding, and also connects naturally to practical real-world AI systems in current research and commercial use. Specifically, it is argued that the structure of linguistic syntax can be modeled as a certain asymmetric monoidal category, as e.g. implicit in the link grammar formalism; the structure of spatiotemporal relationships and action plans can be modeled similarly using "image grammars" and "action grammars"; and common-sense logical semantic structure can be modeled using dependently-typed lambda calculus with uncertain truth values. Given these formalisms, the grounding of linguistic descriptions in spatiotemporal perceptions and coordinated actions consists of following morphisms from language to logic through to spacetime and body (for comprehension), and vice versa (for generation). The mapping is indicated between the spatial relationships in the Region Connection Calculus and Allen Interval Algebra and corresponding entries in the link grammar syntax parsing dictionary. Further, the abstractions introduced here are shown to naturally model the structures and systems currently being deployed in the context of using the OpenCog cognitive architecture to control Hanson Robotics humanoid robots.


title: Automatic Mutation Test Case Generation via Dynamic Symbolic Execution

abstract: The automatic test case generation is the principal issue of the software testing activity. Dynamic symbolic execution appears to be a promising approach to this matter as it has been shown to be quite powerful in producing the sought tests. Despite its power, it has only been effectively applied to the entry level criteria of the structural criteria hierarchy such as branch testing. In this paper an extension of this technique is proposed in order to effectively generate test data based on mutation testing. The proposed approach conjoins program transformation and dynamic symbolic execution techniques in order to successfully automate the test generation process. The propositions made in this paper have been incorporated into an automated framework for producing mutation based test cases. Its evaluation on a set of benchmark programs suggests that it is able to produce tests capable of killing most of the non equivalent introduced mutants. The same study also provides some evidence that by employing efficient heuristics it can be possible to perform mutation with reasonable resources.


title: Defending Against Model Stealing Attacks Using Deceptive Perturbations

abstract: Machine learning models are vulnerable to simple model stealing attacks if the adversary can obtain output labels for chosen inputs. To protect against these attacks, it has been proposed to limit the information provided to the adversary by omitting probability scores, significantly impacting the utility of the provided service. In this work, we illustrate how a service provider can still provide useful, albeit misleading, class probability information, while significantly limiting the success of the attack. Our defense forces the adversary to discard the class probabilities, requiring significantly more queries before they can train a model with comparable performance. We evaluate several attack strategies, model architectures, and hyperparameters under varying adversarial models, and evaluate the efficacy of our defense against the strongest adversary. Finally, we quantify the amount of noise injected into the class probabilities to mesure the loss in utility, e.g., adding 1.74 nats per query on CIFAR-10 and 3.27 on MNIST. Our extensive evaluation shows our defense can degrade the accuracy of the stolen model at least 20%, or require 4x more queries while keeping the accuracy of the protected model almost intact.


title: Current Issues and Future Challenges

abstract: Business process modeling has undoubtedly emerged as a popular and relevant practice in Information Systems. Despite being an actively researched field, anecdotal evidence and experiences suggest that the focus of the research community is not always well aligned with the needs of industry. The main aim of this paper is, accordingly, to explore the current issues and the future challenges in business process modeling, as perceived by three key stakeholder groups (academics, practitioners, and tool vendors). We present the results of a global Delphi study with these three groups of stakeholders, and discuss the findings and their implications for research and practice. Our findings suggest that the critical areas of concern are standardization of modeling approaches, identification of the value proposition of business process modeling, and model-driven process execution. These areas are also expected to persist as business process modeling roadblocks in the future.


title: Design on mixed-voltage-tolerant I/O interface with novel tracking circuits in a 0.13-/spl mu/m CMOS technology

abstract: This paper presents a 1.2 V/2.5 V tolerant I/O buffer design with only thin gate-oxide devices. The novel floating N-well and gate-tracking circuits in mixed-voltage I/O buffer are proposed to overcome the problem of leakage current, which will occur in the conventional CMOS I/O buffer when using in the mixed-voltage I/O interfaces. The new proposed 1.2 V/2.5 V tolerant I/O buffer design has been successfully verified in a 0.13-/spl mu/m salicided CMOS process, which can be also applied in other CMOS processes to serve different mixed-voltage I/O interfaces.


title: Power Limits of High-Speed Permanent-Magnet Electrical Machines for Compressor Applications

abstract: The maximum-power limits for high-speed permanent-magnet (PM) electrical machines for air compressor applications are determined in the speed range 20000-100000 r/min. For this purpose, five PM machines are designed and the electromagnetic, thermal, and mechanical designs of each machine are simultaneously performed. The critical values of the thermal and mechanical constraints are considered in order to obtain the maximum powers of the electrical machines. The electromagnetic losses generated in the machine are the output parameters of the electromagnetic design and input parameters for the thermal design. The thermal design is performed using a multiphysics method, which couples computational-fluid-dynamics equations with heat-transfer equations. The mechanical design considers the retention of the rotor elements against the huge centrifugal forces that arise during the high-speed operation and also the rotor dynamics properties of the rotor. The reliability of these design techniques is experimentally validated in the paper. The obtained maximum-power limit defines the speed-power region, in which the high-speed PM electrical machines intended for compressor applications can have a safe operation.


title: Optimization of ATM cash replenishment with group-demand forecasts

abstract: We decide on the time intervals between two replenishments first.We make the forecasts for time intervals.We propose grouping ATMs into nearby-location clusters, to improve forecast accuracy.Group forecast results are loaded to the ATMs.Example studies show that the proposed framework result in lower costs. In ATM cash replenishment banks want to use less resources (e.g., cash kept in ATMs, trucks for loading cash) for meeting fluctuated customer demands. Traditionally, forecasting procedures such as exponentially weighted moving average are applied to daily cash withdraws for individual ATMs. Then, the forecasted results are provided to optimization models for deciding the amount of cash and the trucking logistics schedules for replenishing cash to all ATMs. For some situations where individual ATM withdraws have so much variations (e.g., data collected from Istanbul ATMs) the traditional approaches do not work well. This article proposes grouping ATMs into nearby-location clusters and also optimizing the aggregates of daily cash withdraws (e.g., replenish every week instead of every day) in the forecasting process. Example studies show that this integrated forecasting and optimization procedure performs better for an objective in minimizing costs of replenishing cash, cash-interest charge and potential customer dissatisfaction.


title: On a Fuzzy Algebra for Querying Graph Databases

abstract: This paper proposes a notion of fuzzy graph database and describes a fuzzy query algebra that makes it possible to handle such database, which may be fuzzy or not, in a flexible way. The algebra, based on fuzzy set theory and the concept of a fuzzy graph, is composed of a set of operators that can be used to express preference queries on fuzzy graph databases. The preferences concern i) the content of the vertices of the graph and ii) the structure of the graph. In a similar way as relational algebra constitutes the basis of SQL, the fuzzy algebra proposed here underlies a user-oriented query language and an associated tool implementing this language that are also presented in the paper.


title: GEOMETRIC MEANS IN A NOVEL VECTOR SPACE STRUCTURE ON SYMMETRIC POSITIVE-DEFINITE

abstract: In this work we present a new generalization of the geometric mean of positive numbers on symmetric positive‐definite matrices, called Log‐Euclidean. The approach is based on two novel algebraic structures on symmetric positive‐definite matrices: first, a lie group structure which is compatible with the usual algebraic properties of this matrix space; second, a new scalar multiplication that smoothly extends the Lie group structure into a vector space structure. From bi‐invariant metrics on the Lie group structure, we define the Log‐Euclidean mean from a Riemannian point of view. This notion coincides with the usual Euclidean mean associated with the novel vector space structure. Furthermore, this means corresponds to an arithmetic mean in the domain of matrix logarithms. We detail the invariance properties of this novel geometric mean and compare it to the recently introduced affine‐invariant mean. The two means have the same determinant and are equal in a number of cases, yet they are not identical in g...


title: A Frequency Agile Microstrip Patch Phased Array Antenna With Polarization Reconfiguration

abstract: A novel multifunctional $1 \times 4$ phased array antenna employing wideband frequency agile microstrip patches with simultaneous polarization reconfiguration has been designed and experimentally verified. Each radiating element consists of a circular microstrip patch connected to an annular microstrip ring via four varactor diodes for achieving frequency agility between 1.5 and 2.4 GHz (frequency agility bandwidth $\approx 46$ %, $S_{11} \leq -10$ dB). Employing two feed points per radiator enables switching among four polarization senses (two linear and two circular polarizations) using a polarization feed network (PFN). For realizing beam steering, the optimum amplitude and phase excitation coefficients for each radiating element were calculated using projection matrix method based on active element pattern, which is then applied to each corresponding radiating element using programmable digital phase shifters, low noise amplifiers, and attenuators arranged in a beam forming network. Measurement shows ±52° beam peak steering at 1.5 GHz and ±28° beam peak steering at 2.4 GHz based on 3 dB gain variation criteria for both the linear and circular polarizations. The simulated and measured results agree reasonably well.


title: Unconscious processes improve lie detection.

abstract: The capacity to identify cheaters is essential for maintaining balanced social relationships, yet humans have been shown to be generally poor deception detectors. In fact, a plethora of empirical findings holds that individuals are only slightly better than chance when discerning lies from truths. Here, we report 5 experiments showing that judges' ability to detect deception greatly increases after periods of unconscious processing. Specifically, judges who were kept from consciously deliberating outperformed judges who were encouraged to do so or who made a decision immediately; moreover, unconscious thinkers' detection accuracy was significantly above chance level. The reported experiments further show that this improvement comes about because unconscious thinking processes allow for integrating the particularly rich information basis necessary for accurate lie detection. These findings suggest that the human mind is not unfit to distinguish between truth and deception but that this ability resides in previously overlooked processes. (PsycINFO Database Record (c) 2013 APA, all rights reserved)


title: HIGH RESOLUTION DOA ESTIMATION IN FULLY COHERENT ENVIRONMENTS

abstract: A novel method for direction-of-arrival (DOA) estimation is proposed. This technique employs the excellent performance of Bartlett method in coherent environments as well as high resolution and low computational complexity of Beamspace MUSIC. Simulation results show that the use of Beamspace MUSIC with Bartlett yields significantly improved performance compared to the original MUSIC especially in highly correlated situations.


title: Personalized Effect of Health Behavior on Blood Pressure: Machine Learning Based Prediction and Recommendation

abstract: Blood pressure (BP) is one of the most important indicator of human health. In this paper, we investigate the relationship between BP and health behavior (e.g. sleep and exercise). Using the data collected from off-the-shelf wearable devices and wireless home BP monitors, we propose a data driven personalized model to predict daily BP level and provide actionable insight into health behavior and daily BP. In the proposed machine learning model using Random Forest (RF), trend and periodicity features of BP time-series are extracted to improve prediction. To further enhance the performance of the prediction model, we propose RF with Feature Selection (RFFS), which performs RF-based feature selection to filter out unnecessary features. Our experimental results demonstrate that the proposed approach is robust to different individuals and has smaller prediction error than existing methods. We also validate the effectiveness of personalized recommendation of health behavior generated by RFFS model.


Mobile Intercloud System with Blockchain Yik


title: Design of Sleeve Dipole Antenna for Suppressing Leakage Current on a Coaxial Cable

abstract: A small sleeve dipole antenna (SDA) for a suppressing leakage current on a coaxial cable is presented in this letter. The upper radiating element of the proposed antenna is modified into a helical shape to reduce the antenna size. For good impedance matching performance, a coaxial-type short stub is inserted inside the sleeve. Leakage current on the outer conductor of a coaxial cable due to miniaturization and the sleeve structure is suppressed by using an L-shaped open stub inside the sleeve. The designed small SDA is miniaturized by up to 34.8% compared to a normal SDA. Input impedance is matched below S11 of -20 dB at 1.5 GHz, and the measured -10-dB bandwidth is 70 MHz (4.6%). The average leakage current on the outer conductor of the coaxial cable is 5.1 dB lower than a normal.


title: Learning deep convolutional neural networks for X-ray protein crystallization image analysis

abstract: Obtaining a protein's 3D structure is crucial to the understanding of its functions and interactions with other proteins. It is critical to accelerate the protein crystallization process with improved accuracy for understanding cancer and designing drugs. Systematic high-throughput approaches in protein crystallization have been widely applied, generating a large number of protein crystallization-trial images. Therefore, an efficient and effective automatic analysis for these images is a top priority. In this paper, we present a novel system, CrystalNet, for automatically labeling outcomes of protein crystallization-trial images. CrystalNet is a deep convolutional neural network that automatically extracts features from X-ray protein crystallization images for classification. We show that (1) CrystalNet can provide real-time labels for crystallization images effectively, requiring approximately 2 seconds to provide labels for all 1536 images of crystallization microassay on each plate; (2) compared with the state-of-the-art classification systems in crystallization image analysis, our technique demonstrates an improvement of 8% in accuracy, and achieve 90.8% accuracy in classification. As a part of the high-throughput pipeline which generates millions of images a year, CrystalNet can lead to a substantial reduction of labor-intensive screening.


title: Soft-Switching SEPIC Converter With Ripple-Free Input Current

abstract: A soft-switching single-ended primary inductor converter (SEPIC) is presented in this paper. An auxiliary switch and a clamp capacitor are connected. A coupled inductor and an auxiliary inductor are utilized to obtain ripple-free input current and achieve zero-voltage-switching (ZVS) operation of the main and auxiliary switches. The voltage multiplier technique and active clamp technique are applied to the conventional SEPIC converter to increase the voltage gain, reduce the voltage stresses of the power switches and diode. Moreover, by utilizing the resonance between the resonant inductor and the capacitor in the voltage multiplier circuit, the zero-current-switching operation of the output diode is achieved and its reverse-recovery loss is significantly reduced. The proposed converter achieves high efficiency due to soft-switching commutations of the power semiconductor devices. The presented theoretical analysis is verified by a prototype of 100 kHz and 80 W converter. Also, the measured efficiency of the proposed converter has reached a value of 94.8% at the maximum output power.


Multi-Physical Domain Modeling of a DFIG Wind Turbine System using PLECS ®


title: Detection of human brain tumour using MRI image segmentation and morphological operators

abstract: Image Segmentation is an important and challenging factor in the field of medical sciences. It is widely used for the detection of tumours. This paper deals with detection of brain tumour from MR images of the brain. The brain is the anterior most part of the nervous system. Tumour is a rapid uncontrolled growth of cells. Magnetic Resonance Imaging (MRI) is the device required to diagnose brain tumour. The normal MR images are not that suitable for fine analysis, so segmentation is an important process required for efficiently analyzing the tumour images. Clustering is suitable for biomedical image segmentation as it uses unsupervised learning. This paper work uses K-Means clustering where the detected tumour shows some abnormality which is then rectified by the use of morphological operators along with basic image processing techniques to meet the goal of separating the tumour cells from the normal cells.


title: Optimizing EEG energy-based seizure detection using genetic algorithms

abstract: Epilepsy is one of the most common neurological conditions, affecting 2.2 million people only in the U.S., causing seizures that can have a very serious impact in affected people's lives, including death. Because of this, there is a remarkable research interest in detecting epilepsy as it occurs, so that it effects and consequences can be mitigated immediately.


title: Microbial ecology: Human gut microbes associated with obesity

abstract: Our gut microbes do us a service by performing metabolic chores that we have not evolved to do for ourselves. In a sense their genes are part of the 'metagenome' that is Homo sapiens. That is illustrated by two related papers in this issue that present evidence for a microbial component to obesity. A study of the abundance of the two dominant groups of bacteria in the gut of obese individuals shows that increased numbers of Bacteroidetes bacteria correlate with weight loss. And a study of genetically obese mice reveals that their gut microbial community has a greater capacity for harvesting energy than that of lean littermates: the trait is transmissible by transplanting the community into germ-free mice. This work suggests that the gut microbiome associated with obesity might be a biomarker and possibly a therapeutic target. Two groups of beneficial bacteria are dominant in the human gut, the Bacteroidetes and the Firmicutes. Here we show that the relative proportion of Bacteroidetes is decreased in obese people by comparison with lean people, and that this proportion increases with weight loss on two types of low-calorie diet. Our findings indicate that obesity has a microbial component, which might have potential therapeutic implications.


title: PMHI: Proposals From Motion History Images for Temporal Segmentation of Long Uncut Videos

abstract: This letter proposes a method for the generation of temporal action proposals for the segmentation of long uncut video sequences. The presence of consecutive multiple actions in video sequences makes the temporal segmentation a challenging problem due to the unconstrained nature of actions in space and time. To address this issue, we exploit the nonaction segments present between the actual human actions in uncut videos. From the long uncut video, we compute the energy of consecutive nonoverlapping motion history images (MHIs), which provides spatiotemporal information of motion. Our proposals from MHIs (PMHI) are based on clustering the MHIs into actions and nonaction segments by detecting minima from the energy of MHIs. PMHI efficiently segments the long uncut videos into a small number of nonoverlapping temporal action proposals. The strength of PMHI is that it is unsupervised, which alleviates the requirement for any training data. Our temporal action proposal method outperforms the existing proposal methods on the Multi-view Human Action video (MuHAVi)-uncut and Computer Vision and Pattern recognition (CVPR) 2012 Change Detection datasets with an average recall rate of 86.1% and 86.0%, respectively.


title: Dual-Band Spiral Patch-Slot Antenna With Omnidirectional CP and Unidirectional CP Properties

abstract: A novel dual-band microstrip antenna with omnidirectional circularly polarized (CP) and unidirectional CP characteristic for each band is proposed in this communication. Function of dual-band dual-mode is realized based on loading with metamaterial structure. Since the fields of the fundamental modes are most concentrated on the fringe of the radiating patch, modifying the geometry of the radiating patch has little effect on the radiation patterns of the two modes ( $n = 0, + 1$ mode). CP property for the omnidirectional zeroth-order resonance ( $n = 0$ mode) is achieved by employing curved branches in the radiating patch. Then a 45 $^{\circ}$ inclined rectangular slot is etched in the center of the radiating patch to excite the CP property for the $n = + 1$ mode. A prototype is fabricated to verify the properties of the antenna. Both simulation and measurement results illustrate that this single-feed antenna is valuable in wireless communication for its low-profile, radiation pattern selectivity and CP characteristic.


Improving burden and coping skills in frontotemporal dementia caregivers: a pilot study.


title: A survey of automated material handling systems in 300-mm SemiconductorFabs

abstract: The fast-paced developments and technological breakthroughs in the semiconductor manufacturing industry elevates the importance of optimum utilization of resources. The newer 300-mm wafers fabs place a high level of emphasis on increasing yield and reducing cycle times. Automated material handling systems are importanttools that help us achieve these objectives. In addition, due to the increased weight and size of 300-mm wafers, an automated material handling system isa must for a 300-mm manufacturing facility. This paper discusses various approaches for automated materials handling in semiconductor manufacturing industries.


Automatic Scoring of Shadowing Speech Based on DNN Posteriors and Their DTW.


title: UHF ceramic chip antenna designs for wildlife applications

abstract: In wildlife scenarios UHF transponder can be used to mark and localize animals over a large distance. Therefore, the size of the marking device has to be appropriate besides the wavelength is much bigger than it. Ceramic chip antennae enable a compact design in UHF applications. Due to the unknown position of the transponder in wildlife localization applications an omni-directional radiation pattern is desired in co- and cross-polarization to compensate polarization losses. For this investigation, several linear and circular ceramic chip antenna designs were established. A low co- to cross-polarization suppression show the circular antenna designs. The close proximity of the antenna to the animal body influences the electromagnetic characteristics significantly. A full sphere radiation measurement was performed in an anechoic chamber with and without muscle-like phantom model to characterize the effects of the animal body to the antenna properties. The reflected energy at the dielectric volume is more determent than the absorbed as the antenna was positioned in a cm distance to the phantom model.


title: Concepts of real-time photogrammetry

abstract: Abstract The paper briefly outlines the concepts of digital photogrammetry under special consideration of real-time aspects, including subpixel accuracy measurements in image space, techniques for the robust establishment of multi-image correspondences, strict geometric modeling for 3-D coordinate determination, CCD camera calibration issues and possibilities of statistical data analysis.


title: Extracting information networks from the blogosphere

abstract: We study the problem of automatically extracting information networks formed by recognizable entities as well as relations among them from social media sites. Our approach consists of using state-of-the-art natural language processing tools to identify entities and extract sentences that relate such entities, followed by using text-clustering algorithms to identify the relations within the information network. We propose a new term-weighting scheme that significantly improves on the state-of-the-art in the task of relation extraction, both when used in conjunction with the standard tf ċ idf scheme and also when used as a pruning filter. We describe an effective method for identifying benchmarks for open information extraction that relies on a curated online database that is comparable to the hand-crafted evaluation datasets in the literature. From this benchmark, we derive a much larger dataset which mimics realistic conditions for the task of open information extraction. We report on extensive experiments on both datasets, which not only shed light on the accuracy levels achieved by state-of-the-art open information extraction tools, but also on how to tune such tools for better results.


title: A hybrid powered ankle exoskeleton for walking

abstract: We proposed a hybrid powered ankle exoskeleton. It can be able to supply positive mechanical power to the user during the swing phase of gait. The hybrid powered ankle exoskeleton was composed of a pair of compressed springs and a cable-driven actuator fasted to the shin. It was lightweight, and permitted the cable-driven actuator to provide the forces at the biological level to the ankle joint. Meanwhile the compressed spring also provide power (the power is different according to ankle angles.) to the ankle joint. The hybrid powered ankle exoskeleton was driven by the lithium battery attached to the waist and controlled by motor controller fixed on the transmission. By analyzing the characteristic of the lower limbs motion, the kinematics of the exoskeleton is analyzed and the dynamic model of the lower limb is built. Velocity-force control strategy aims at the change of the angle position. Based on the normal gait, the kinematics simulation of the ankle was built in MATLAB/Simulink.


title: TREM2, Microglia, and Neurodegenerative Diseases.

abstract: Alzheimer's disease (AD) is the most common form of dementia and the 6th leading cause of death in the US. The neuropathological hallmarks of the disease are extracellular amyloid-β (Aβ) plaques and intraneuronal hyperphosphorylated tau aggregates. Genetic variants of TREM2 (triggering receptor expressed on myeloid cells 2), a cell-surface receptor expressed selectively in myeloid cells, greatly increase the risk of AD, implicating microglia and the innate immune system as pivotal factors in AD pathogenesis. Recent studies have advanced our understanding of TREM2 biology and microglial activities in aging and neurodegenerative brains, providing new insights into TREM2 functions in amyloid plaque maintenance, microglial envelopment of plaque, microglia viability, and the identification of novel TREM2 ligands. Our increased understanding of TREM2 and microglia has opened new avenues for therapeutic intervention to delay or prevent the progression of AD.


title: Scaling Nakamoto Consensus to Thousands of Transactions per Second

abstract: This paper presents Conflux, a fast, scalable and decentralized blockchain system that optimistically process concurrent blocks without discarding any as forks. The Conflux consensus protocol represents relationships between blocks as a direct acyclic graph and achieves consensus on a total order of the blocks. Conflux then, from the block order, deterministically derives a transaction total order as the blockchain ledger. We evaluated Con- flux on Amazon EC2 clusters with up to 20k full nodes. Conflux achieves a transaction throughput of 5.76GB/h while confirming transactions in 4.5-7.4 minutes. The throughput is equivalent to 6400 transactions per second for typical Bitcoin transactions. Our results also indicate that when running Conflux, the consensus protocol is no longer the throughput bottleneck. The bottleneck is instead at the processing capability of individual nodes.


Path planning for a Parking Assistance System: Implementation and Experimentation


title: Multiple Auxiliary Information Based Deep Model for Collaborative Filtering

abstract: With the ever-growing dynamicity, complexity, and volume of information resources, the recommendation technique is proposed and becomes one of the most effective techniques for solving the so-called problem of information overload. Traditional recommendation algorithms, such as collaborative filtering based on the user or item, only measure the degree of similarity between users or items with single criterion, i.e., ratings. According to the experience of previous studies, single criterion cannot accurately measure the similarity between user preferences or items. In recent years, the application of deep learning techniques has gained significant momentum in recommender systems for better understanding of user preferences, item characteristics, and historical interactions. In this work, we integrate plot information as auxiliary information into the denoising autoencoder (DAE), called SemRe-DCF, which aims at learning semantic representations of item descriptions and succeeds in capturing fine-grained semantic regularities by using vector arithmetic to get better rating prediction. The results manifest that the proposed method can effectively improve the accuracy of prediction and solve the cold start problem.


title: A Survey of Adaptive Optimization in Virtual Machines

abstract: Virtual machines face significant performance challenges beyond those confronted by traditional static optimizers. First, portable program representations and dynamic language features, such as dynamic class loading, force the deferral of most optimizations until runtime, inducing runtime optimization overhead. Second, modular program representations preclude many forms of whole-program interprocedural optimization. Third, virtual machines incur additional costs for runtime services such as security guarantees and automatic memory management. To address these challenges, vendors have invested considerable resources into adaptive optimization systems in production virtual machines. Today, mainstream virtual machine implementations include substantial infrastructure for online monitoring and profiling, runtime compilation, and feedback-directed optimization. As a result, adaptive optimization has begun to mature as a widespread production-level technology. This paper surveys the evolution and current state of adaptive optimization technology in virtual machines.


title: Reminder-induced recovery from blocking as a function of the number of compound trials☆

abstract: Abstract Conditioned lick suppression in rats was employed to examine changes in the associative status of a blocked stimulus as a function of the number of compound stimulus conditioning trials. In each of two experiments, prior tone-footshock pairings produced similar blocking of conditioned responding to the light element of a tone-light compound when either two or six compound trials were used. In experiment 1, two exposures to the light alone in a dissimilar context during the retention interval, i.e., a reminder treatment, resulted in a restoration of responding to the light which was complete and similar for animals receiving two or six compound trials. This indicated that latent acquisition with respect to the blocked stimulus was largely complete after two compound trials. Because of a potential ceiling effect for reminder-induced recovery from blocking, Experiment 2 employed an attenuated reminder (one light exposure). This reminder reversed the blocking when six, but not when two, compound trials were used. These results suggest that, after latent acquisition to the blocked stimulus is complete, the blocked stimulus continues to be processed during additional compound stimulus conditioning trials, with a consequent latent facilitation of retrieval of associations to the blocked stimulus.


title: A comprehensive taxonomy for multi-robot task allocation

abstract: Task allocation is an important aspect of many multi-robot systems. The features and complexity of multi-robot task allocation (MRTA) problems are dictated by the requirements of the particular domain under consideration. These problems can range from those involving instantaneous distribution of simple, independent tasks among members of a homogenous team, to those requiring the time-extended scheduling of complex interrelated multi-step tasks for members of a heterogenous team related by several constraints. The existing widely used taxonomy for task allocation in multi-robot systems was designed for problems with independent tasks and does not deal with problems with interrelated utilities and constraints. While that taxonomy was a ground-breaking contribution to the MRTA literature, a survey of recent work in MRTA reveals that it is no longer a sufficient taxonomy, due to the increasing importance of interrelated utilities and constraints in realistic MRTA problems under consideration. Thus, in this paper, we present a new, comprehensive taxonomy, iTax, that explicitly takes into consideration the issues of interrelated utilities and constraints. Our taxonomy maps categories of MRTA problems to existing mathematical models from combinatorial optimization and operations research, and hence draws important parallels between robotics and these fields.


title: Sinusoidal pulse width modulation (SPWM) with variable carrier synchronization for multilevel inverter controllers

abstract: Voltage or current converters generate discrete output waveforms, which require large inductances connected in series with the respective load to generate the desired current waveform. Mostly, neither the voltage nor the current waveforms are as expected and also have distorted voltages and currents waveforms produces harmonic contamination, additional power losses, and high frequency noise. In this paper a method of minimization of THD with near to reference current generation is proposed based on multilevel inverter. A sinusoidal pulse width modulation scheme is developed for the multilevel inverter.


Assessment of the Current Practices of e-Waste Management in Alexandria, Egypt (Case Study: Waste from Personal Computers)


title: An Improved Maximum Power Point Tracking for Photovoltaic Grid-Connected Inverter Based on Voltage-Oriented Control

abstract: In this paper, an improved maximum power point (MPP) tracking (MPPT) with better performance based on voltage-oriented control (VOC) is proposed to solve a fast-changing irradiation problem. In VOC, a cascaded control structure with an outer dc link voltage control loop and an inner current control loop is used. The currents are controlled in a synchronous orthogonal d,q frame using a decoupled feedback control. The reference current of proportional-integral (PI) d-axis controller is extracted from the dc-side voltage regulator by applying the energy-balancing control. Furthermore, in order to achieve a unity power factor, the q-axis reference is set to zero. The MPPT controller is applied to the reference of the outer loop control dc voltage photovoltaic (PV). Without PV array power measurement, the proposed MPPT identifies the correct direction of the MPP by processing the d-axis current reflecting the power grid side and the signal error of the PI outer loop designed to only represent the change in power due to the changing atmospheric conditions. The robust tracking capability under rapidly increasing and decreasing irradiance is verified experimentally with a PV array emulator. Simulations and experimental results demonstrate that the proposed method provides effective, fast, and perfect tracking.


title: Modified differential box-counting method using weighted triangle-box partition

abstract: Differential box-counting (DBC) is one of the commonly used methods to estimate fractal dimension (FD) for gray scale images. It has been successfully applied in many applications such as image segmentation, pattern recognition, texture analysis and medical signal analysis. However, the accuracy improvement of FD estimation is still a grand challenge. This paper proposes a modified differential box-counting method using weighted triangle-box partition (MDBC) to reduce the estimation error caused by an undercounting problem. The proposed method is derived from two assumptions: (i) increasing the precision of box-counts by using unequally triangle box partition, and (ii) weighting the box-counts in proportion to the size of triangle-box partition. Based on these assumptions, on each grid a square box is divided into four asymmetric triangle-box patterns. Each pattern is calculated the box-counts by a weighted box-counting technique. The maximum number of box-counts represents the better estimation. In this way, the experimental results show that MDBC outperforms the baseline methods in terms of fitting error. Furthermore, the proposed method applies to finger-knuckle-print recognition in order to test its efficiency. The results illustrate that it significantly enhances the recognition rate when compared with the conventional differential box-counting (DBC) and improved triangle box-counting in combination with DBC (ITBC-DBC) methods.


title: Antibacterial Activity of Long-Chain Primary Alcohols from Solena amplexicaulis Leaves

abstract: Extraction, thin layer chromatography and gas chromatography–mass spectrometry of Solena amplexicaulis (Lam.) Gandhi, commonly known as creeping cucumber, (Cucurbitaceae) leaves revealed 21 long-chain primary alcohols, and 100 g leaves indicated presence of 3651.59 ± 327.18 SE µg long-chain primary alcohols. 1-Heptadecanol and 1-triacontanol were the predominant and least abundant primary alcohols, representing for 780.44 ± 42.59 and 3.28 ± 0.55 SE μg, respectively. Antibacterial property of the complete synthetic blend (0.1%), comparable to long-chain alcohols as detected by GC-FID of 100 g S. amplexicaulis leaf extracts was evaluated on the pathogenic bacteria Salmonella gallinarum by agar well diffusion method, and exhibited 20.4, 26.7 and 38.2 mm zone of inhibition at 25, 50 and 100 μl doses, respectively. One hundred µl dose of 6 individual pure synthetic compounds, 1-tridecanol, 1-pentadecanol, 1-heptadecanol, 1-nonadecanol, 1-eicosanol and 1-tricosanol comparable to the amounts present in 0.1% solution of pure isolated alcohols from S. amplexicaulis leaves displayed 16.2, 17.7, 18.6, 22.8, 15.8 and 14.5 mm zone of inhibition against this bacterium, respectively. Hundred µl dose from a synthetic blend of above 6 compounds (comparable to the proportions as present in 0.1% solution of pure isolated alcohols from 100 g S. amplexicaulis leaves) exhibited 38.1 mm zone of inhibition against this bacterium. Furthermore, 100 μl dose from a mixture (1:1) comprising of chloramphenicol (1 µg/ml) and a synthetic blend of above 6 compounds displayed 38.8 mm inhibition zone against S. gallinarum, and hence, this combination might be used against this pathogenic bacteria.


title: Semantic Feature Selection for Text with Application to Phishing Email Detection

abstract: In a phishing attack, an unsuspecting victim is lured, typically via an email, to a web site designed to steal sensitive information such as bank/credit card account numbers, login information for accounts, etc. Each year Internet users lose billions of dollars to this scourge. In this paper, we present a general semantic feature selection method for text problems based on the statistical t-test and WordNet, and we show its effectiveness on phishing email detection by designing classifiers that combine semantics and statistics in analyzing the text in the email. Our feature selection method is general and useful for other applications involving text-based analysis as well. Our email body-text-only classifier achieves more than 95 % accuracy on detecting phishing emails with a false positive rate of 2.24 %. Due to its use of semantics, our feature selection method is robust against adaptive attacks and avoids the problem of frequent retraining needed by machine learning classifiers.


title: Marine fish spermatozoa: racing ephemeral swimmers

abstract: After a long period of spermatogenesis (several weeks to months), marine fish spermatozoa are delivered at male spawning in seawater (SW) at the same time as ova. In some fish species, as the ova micropyle closes quickly after release, these minute unicells, the spermatozoa, have to accomplish their task of reaching the micropyle within a very brief period (several seconds to minutes), for delivery of the haploid male genetic information to the ova. To achieve this goal, their high-performance motile equipment, the flagellum, must fully activate immediately on contact with the SW and then propel the sperm cell at an unusually high initial velocity. The cost of such 'hyperactivity' is a very rapid consumption of intracellular ATP that outstrips the supply. The spermatozoa become rapidly exhausted because mitochondria cannot compensate for this very fast flagellar energy consumption. Therefore, any spermatozoon ends up with two possibilities: either becoming exhausted and immotile or reaching the egg micropyle within its very short period of forward motility (in the range of tens of seconds) before micropyle closure in relation to both contact of SW and cortical reaction. The aim of the present review is to present step by step the successive events occurring in marine fish spermatozoa from activation until their full arrest of motility. The present knowledge of activation mechanisms is summarized, as well as a description of the motility parameters characterizing the motility period. As a complement, in vitro results on axonemal motility obtained after demembranation of flagella bring further understanding. The description of the sperm energetic content (ATP and other high energy compounds) and its evolution during the swimming period is also discussed. A general model aiming to explain all the successive cellular events occurring immediately after the activation is presented. This model is proposed as a guideline for understanding the events governing the sperm lifespan in the marine fish species that reproduce through external fertilization.


title: UWB Band-Notched Monopole Antenna Design Using Electromagnetic-Bandgap Structures

abstract: A new approach is proposed to reject certain bands within the passband of an ultra-wideband planar monopole antenna. The proposed approach that utilizes a mushroom-type electromagnetic-bandgap (EBG) structure is proven to be an effective way for band-notched designs. The approach has many advantages, such as notch-frequency tunability, notch-band width controllable capacity, efficient dual-notch design, and stable radiation patterns. Several design examples using conventional mushroom-type EBG and edge-located vias mushroom-type EBG are presented. The examples exhibit good bandstop characteristics to reject the wireless local-area network interference bands (5.2- and 5.8-GHz bands). Besides, the causes that lead to the discrepancies between the simulations and measurements are discussed.


title: Large-Scale Electron Microscopy Image Segmentation in Spark

abstract: The emerging field of connectomics aims to unlock the mysteries of the brain by understanding the connectivity between neurons. To map this connectivity, we acquire thousands of electron microscopy (EM) images with nanometer-scale resolution. After aligning these images, the resulting dataset has the potential to reveal the shapes of neurons and the synaptic connections between them. However, imaging the brain of even a tiny organism like the fruit fly yields terabytes of data. It can take years of manual effort to examine such image volumes and trace their neuronal connections. One solution is to apply image segmentation algorithms to help automate the tracing tasks. In this paper, we propose a novel strategy to apply such segmentation on very large datasets that exceed the capacity of a single machine. Our solution is robust to potential segmentation errors which could otherwise severely compromise the quality of the overall segmentation, for example those due to poor classifier generalizability or anomalies in the image dataset. We implement our algorithms in a Spark application which minimizes disk I/O, and apply them to a few large EM datasets, revealing both their effectiveness and scalability. We hope this work will encourage external contributions to EM segmentation by providing 1) a flexible plugin architecture that deploys easily on different cluster environments and 2) an in-memory representation of segmentation that could be conducive to new advances.


title: A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units

abstract: The management of invasive mechanical ventilation, and the regulation of sedation and analgesia during ventilation, constitutes a major part of the care of patients admitted to intensive care units. Both prolonged dependence on mechanical ventilation and premature extubation are associated with increased risk of complications and higher hospital costs, but clinical opinion on the best protocol for weaning patients off of a ventilator varies. This work aims to develop a decision support tool that uses available patient information to predict time-to-extubation readiness and to recommend a personalized regime of sedation dosage and ventilator support. To this end, we use off-policy reinforcement learning algorithms to determine the best action at a given patient state from sub-optimal historical ICU data. We compare treatment policies from fitted Q-iteration with extremely randomized trees and with feedforward neural networks, and demonstrate that the policies learnt show promise in recommending weaning protocols with improved outcomes, in terms of minimizing rates of reintubation and regulating physiological stability.


title: A Qualitative Review on 3D Coarse Registration Methods

abstract: 3D registration or matching is a crucial step in 3D model reconstruction. Registration applications span along a variety of research fields, including computational geometry, computer vision, and geometric modeling. This variety of applications produces many diverse approaches to the problem but at the same time yields divergent notations and a lack of standardized algorithms and guidelines to classify existing methods. In this article, we review the state of the art of the 3D rigid registration topic (focused on Coarse Matching) and offer qualitative comparison between the most relevant approaches. Furthermore, we propose a pipeline to classify the existing methods and define a standard formal notation, offering a global point of view of the literature. Our discussion, based on the results presented in the analyzed papers, shows how, although certain aspects of the registration process still need to be tested further in real application situations, the registration pipeline as a whole has progressed steadily. As a result of this progress in all registration aspects, it is now possible to put together algorithms that are able to tackle new and challenging problems with unprecedented data sizes and meeting strict precision criteria.


title: METHONTOLOGY: From Ontological Art Towards Ontological Engineering

abstract: This paper does not pretend either to transform completely the ontological art in engineering or to enumerate exhaustively the complete set of works that has been reported in this area. Its goal is to clarify to readers interested in building ontologies from scratch, the activities they should perform and in which order, as well as the set of techniques to be used in each phase of the methodology. This paper only presents a set of activities that conform the ontology developmentp rocess, a life cycle to build ontologies based in evolving prototypes, and METHONTOLOGY, a well-structured methodology used to build ontologies from scratch. This paper gathers the experience of the authors on building an ontology in the domain of chemicals.


title: Mutual authentication for mobile cloud computing: Review and suggestion

abstract: With the combination of mobile computing technology and cloud computing, mobile users have the advantage of mobility, resources, and access availability to the Internet to communicate data seamlessly anywhere at any time. However, the transfer of data through vulnerable channels poses security threats such as man-in-middle and sniffing. This paper explores, reviews, and analyses several existing works related to authentication in mobile cloud computing. This paper also aims to propose an authentication scheme to secure data communication in mobile cloud computing. The proposed scheme is based on multi-factor authentication that uses usernames, passwords, and a One-Time Password. The One-Time Password is encrypted with a Diffie-Hellman key exchange algorithm to achieve mutual authentication between the mobile device and the cloud server. The proposed scheme will be implemented and tested using Java.


title: Split-parallel through-the-road hybrid electric vehicle: Operation, power flow and control modes

abstract: A split-parallel hybrid electric vehicle (HEV) is a particular sub-type of the HEV in which the internal combustion engine (ICE) and electric motor provide propulsion power to different axles. Due to the coupling of power through the vehicle itself, its wheels and the road on which it moves - rather than through some mechanical device - the vehicle is referred to as a `through-the-road' (TTR) hybrid. A variant of the TTR hybrid has the motors in the hub of the otherwise non-driven wheels, called in-wheel motors (IWM). This configuration enables an existing vehicle to be converted into a hybrid vehicle with minimal modification. The present work identifies specific operating modes and power flow of the TTR-IWM hybrid, specifically a retrofit-conversion hybrid, in comparison to the ordinary parallel hybrid vehicle. It discusses control architecture, control objectives and system parameters, along with specific control issues of the retrofit TTR-IWM hybrid vehicle.


Operation of Hybrid Electric Vehicle using Bidirectional DC-DC Converter


title: Virtualization at the network edge: A performance comparison

abstract: The rapid growth of the Internet of Things (IoT) is driving the evolution of Cloud technologies towards resource distribution across multi-clouds and the inclusion of various heterogeneous devices. IoT specific applications may require the deployment of gateways at the network edge to enable its interaction with physical sensors, pre-processing data from these sensors, and synchronizing it with the cloud. The orchestration, deployment, and maintenance of the software running on the gateways in large-scale deployments is known to be challenging. In this paper we analyze two virtualization techniques commonly used for deployment and management of applications at the cloud, but on the context of IoT and network edge. A study was conducted to evaluate the performance of the container-based approach compared to a hypervisor-based virtualization when running on devices typically used at the network edge. The study was performed through the execution of several synthetic benchmarks providing an insight in the performance overhead introduced by Docker containers (lightweight-virtualization) and KVM VMs (hypervisor-virtualization) running at network edge devices.


title: Read-log-update: a lightweight synchronization mechanism for concurrent programming

abstract: This paper introduces read-log-update (RLU), a novel extension of the popular read-copy-update (RCU) synchronization mechanism that supports scalability of concurrent code by allowing unsynchronized sequences of reads to execute concurrently with updates. RLU overcomes the major limitations of RCU by allowing, for the first time, concurrency of reads with multiple writers, and providing automation that eliminates most of the programming difficulty associated with RCU programming. At the core of the RLU design is a logging and coordination mechanism inspired by software transactional memory algorithms. In a collection of micro-benchmarks in both the kernel and user space, we show that RLU both simplifies the code and matches or improves on the performance of RCU. As an example of its power, we show how it readily scales the performance of a real-world application, Kyoto Cabinet, a truly difficult concurrent programming feat to attempt in general, and in particular with classic RCU.


title: Innovations in Parallel Corpus Search Tools

abstract: Recent years have seen an increased interest in and availability of parallel corpora. Large corpora from international organizations (e.g. European Union, United Nations, European Patent Office), or from multilingual Internet sites (e.g. OpenSubtitles) are now easily available and are used for statistical machine translation but also for online search by different user groups. This paper gives an overview of different usages and different types of search systems. In the past, parallel corpus search systems were based on sentence-aligned corpora. We argue that automatic word alignment allows for major innovations in searching parallel corpora. Some online query systems already employ word alignment for sorting translation variants, but none supports the full query functionality that has been developed for parallel treebanks. We propose to develop such a system for efficiently searching large parallel corpora with a powerful query language.


title: GeneSIS-Rt: Generating Synthetic Images for Training Secondary Real-World Tasks

abstract: We propose a novel approach for generating high-quality, synthetic data for domain-specific learning tasks, for which training data may not be readily available. We leverage recent progress in image-to-image translation to bridge the gap between simulated and real images, allowing us to generate realistic training data for real-world tasks using only unlabeled real-world images and a simulation. GeneSIS-RT ameliorates the burden of having to collect labeled real-world images and is a promising candidate for generating high-quality, domain-specific, synthetic data. To show the effectiveness of using GeneSIS-RT to create training data, we study two tasks: semantic segmentation and reactive obstacle avoidance. We demonstrate that learning algorithms trained using data generated by GeneSIS-RT make high-accuracy predictions and outperform systems trained on raw simulated data alone, and as well or better than those trained on real data. Finally, we use our data to train a quadcopter to fly 60 meters at speeds up to 3.4 m/s through a cluttered environment, demonstrating that our GeneSIS-RT images can be used to learn to perform mission-critical tasks.


title: High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities

abstract: The ability to perturb genes in human cells is crucial for elucidating gene function and holds great potential for finding therapeutic targets for diseases such as cancer. To extend the catalog of human core and context-dependent fitness genes, we have developed a high-complexity second-generation genome-scale CRISPR-Cas9 gRNA library and applied it to fitness screens in five human cell lines. Using an improved Bayesian analytical approach, we consistently discover 5-fold more fitness genes than were previously observed. We present a list of 1,580 human core fitness genes and describe their general properties. Moreover, we demonstrate that context-dependent fitness genes accurately recapitulate pathway-specific genetic vulnerabilities induced by known oncogenes and reveal cell-type-specific dependencies for specific receptor tyrosine kinases, even in oncogenic KRAS backgrounds. Thus, rigorous identification of human cell line fitness genes using a high-complexity CRISPR-Cas9 library affords a high-resolution view of the genetic vulnerabilities of a cell.


title: Robust Beamforming for Cache-Enabled Cloud Radio Access Networks

abstract: Popular content caching is expected to play a major role in efficiently reducing backhaul congestion and achieving user satisfaction in next generation mobile radio systems. Consider the downlink of a cache-enabled cloud radio access network (CRAN), where each cache-enabled base station (BS) is equipped with limited-size local cache storage. The central computing unit (cloud) is connected to the BSs via a limited capacity backhaul link and serves a set of single-antenna mobile users (MUs). This paper assumes that only imperfect channel state information (CSI) is available at the cloud. It focuses on the problem of minimizing the total network power and backhaul cost so as to determine the beamforming vector of each user across the network, the quantization noise covariance matrix, and the BS clustering subject to imperfect channel state information and fixed cache placement assumptions. The paper suggests solving such a difficult, non-convex optimization problem using the semidefinite relaxation (SDR). The paper then uses the $\ell_0$-norm approximation to provide a feasible, sub-optimal solution using the majorization-minimization (MM) approach. Simulation results particularly show how the cache-enabled network significantly improves the backhaul cost especially at high signal-to-interference-plus-noise ratio (SINR) values as compared to conventional cache-less CRANs.


title: LDA Revisited: Entropy, Prior and Convergence

abstract: Inference algorithms of latent Dirichlet allocation (LDA), either for small or big data, can be broadly categorized into expectation-maximization (EM), variational Bayes (VB) and collapsed Gibbs sampling (GS). Looking for a unified understanding of these different inference algorithms is currently an important open problem. In this paper, we revisit these three algorithms from the entropy perspective, and show that EM can achieve the best predictive perplexity (a standard performance metric for LDA accuracy) by minimizing directly the cross entropy between the observed word distribution and LDA's predictive distribution. Moreover, EM can change the entropy of LDA's predictive distribution through tuning priors of LDA, such as the Dirichlet hyperparameters and the number of topics, to minimize the cross entropy with the observed word distribution. Finally, we propose the adaptive EM (AEM) algorithm that converges faster and more accurate than the current state-of-the-art SparseLDA [20] and AliasLDA [12] from small to big data and LDA models. The core idea is that the number of active topics, measured by the residuals between E-steps at successive iterations, decreases significantly, leading to the amortized σ(1) time complexity in terms of the number of topics. The open source code of AEM is available at GitHub.


title: Pronunciation error detection using DNN articulatory model based on multi-lingual and multi-task learning

abstract: Aiming at detecting pronunciation errors produced by second language learners and providing corrective feedbacks related with articulation, we address effective articulatory models based on deep neural network (DNN). Articulatory attributes are defined for manner and place of articulation. In order to efficiently train these models of non-native speech without using such data, which is difficult to collect in a large scale, we propose a multi-lingual learning method, in which the speech database of the target language (L2) and the native language (L1) of the learners are combined. We also investigate multi-task learning methods by tuning the weights of the secondary task. These methods are applied to Mandarin Chinese pronunciation learning by Japanese native speakers. Effects of the multi-lingual and multi-task learning methods are confirmed in the attribute classification and pronunciation error detection.


title: Fast non-intrusive memory reclamation for highly-concurrent data structures

abstract: Current memory reclamation mechanisms for highly-concurrent data structures present an awkward trade-off. Techniques such as epoch-based reclamation perform well when all threads are running on dedicated processors, but the delay or failure of a single thread will prevent any other thread from reclaiming memory. Alternatives such as hazard pointers are highly robust, but they are expensive because they require a large number of memory barriers. This paper proposes three novel ways to alleviate the costs of the memory barriers associated with hazard pointers and related techniques. These new proposals are backward-compatible with existing code that uses hazard pointers. They move the cost of memory management from the principal code path to the infrequent memory reclamation procedure, significantly reducing or eliminating memory barriers executed on the principal code path. These proposals include (1) exploiting the operating system's memory protection ability, (2) exploiting certain x86 hardware features to trigger memory barriers only when needed, and (3) a novel hardware-assisted mechanism, called a hazard lookaside buffer (HLB) that allows a reclaiming thread to query whether there are hazardous pointers that need to be flushed to memory. We evaluate our proposals using a few fundamental data structures (linked lists and skiplists) and libcuckoo, a recent high-throughput hash-table library, and show significant improvements over the hazard pointer technique.


title: Creating an Ontology for the User Profile: Method and Applications

abstract: User profiling is commonly employed nowadays to enhance usability as well as to support personalization, adaptivity and other user-centric features. Insofar, application designers model user profiles mainly in an ad-hoc manner, hindering thus application interoperability at the user profile level, increasing the amount of work to be done and the possibility of errors or omissions in the profile model. This work aims at creating a user profile ontology that incorporates concepts and properties used to model the user profile. Existing literature, applications and ontologies related to the domain of user context and profiling have been taken into account in order to create a general, comprehensive and extensible user model. This ontology can be used as a reference model, in order to alleviate the aforementioned issues. The model, available for download, is exemplified through its application in two different areas, personal information management and adaptive visualization.


title: Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese

abstract: Sequence-to-sequence attention-based models have recently ::: shown very promising results on automatic speech recognition ::: (ASR) tasks, which integrate an acoustic, pronunciation and ::: language model into a single neural network. In these models, ::: the Transformer, a new sequence-to-sequence attentionbased ::: model relying entirely on self-attention without using ::: RNNs or convolutions, achieves a new single-model state-ofthe- ::: art BLEU on neural machine translation (NMT) tasks. Since ::: the outstanding performance of the Transformer, we extend ::: it to speech and concentrate on it as the basic architecture of ::: sequence-to-sequence attention-based model on Mandarin Chinese ::: ASR tasks. Furthermore, we investigate a comparison between ::: syllable based model and context-independent phoneme ::: (CI-phoneme) based model with the Transformer in Mandarin ::: Chinese. Additionally, a greedy cascading decoder with the ::: Transformer is proposed for mapping CI-phoneme sequences ::: and syllable sequences into word sequences. Experiments on ::: HKUST datasets demonstrate that syllable based model with ::: the Transformer performs better than CI-phoneme based counterpart, ::: and achieves a character error rate (CER) of 28.77%, ::: which is competitive to the state-of-the-art CER of 28.0% by ::: the joint CTC-attention based encoder-decoder network.


title: Smart Cities at Risk!: Privacy and Security Borderlines from Social Networking in Cities

abstract: As smart cities infrastructures mature, data becomes a valuable asset which can radically improve city services and tools. Registration, acquisition and utilization of data, which will be transformed into smart services, are becoming more necessary than ever. Online social networks with their enormous momentum are one of the main sources of urban data offering heterogeneous real-time data at a minimal cost. However, various types of attacks often appear on them, which risk users' privacy and affect their online trust. The purpose of this article is to investigate how risks on online social networks affect smart cities and study the differences between privacy and security threats with regard to smart people and smart living dimensions.


title: Ultrafast 3-D shape measurement with an off-the-shelf DLP projector

abstract: This paper presents a technique that reaches 3-D shape measurement speed beyond the digital-light-processing (DLP) projector's projection speed. In particular, a "solid-state" binary structured pattern is generated with each micro-mirror pixel always being at one status (ON or OFF). By this means, any time segment of projection can represent the whole signal, thus the exposure time can be shorter than the projection time. A sinusoidal fringe pattern is generated by properly defocusing a binary one, and the Fourier fringe analysis means is used for 3-D shape recovery. We have successfully reached 4,000 Hz rate (80 μs exposure time) 3-D shape measurement speed with an off-the-shelf DLP projector.


title: Development of thai question answering system

abstract: Automatic question answering systems play an important role nowadays. In this paper, we developed a Thai question answering system. From an input question, the system analyzes an expected type of answer (e.g. human, place) then searches for all possible answers from source documents. In order to select the most relevance answer, a word order consistency and a relevance score are proposed to measure similarity of the question and the sentences surrounding the possible answers. Based on an experiment from 210 questions and 50 articles, the proposed system can provide a mean reciprocal answer rank of 0.657.


title: Compositional-ly Derived Representations of Morphologically Complex Words in Distributional Semantics

abstract: Speakers of a language can construct an unlimited number of new words through morphological derivation. This is a major cause of data sparseness for corpus-based approaches to lexical semantics, such as distributional semantic models of word meaning. We adapt compositional methods originally developed for phrases to the task of deriving the distributional meaning of morphologically complex words from their parts. Semantic representations constructed in this way beat a strong baseline and can be of higher quality than representations directly constructed from corpus data. Our results constitute a novel evaluation of the proposed composition methods, in which the full additive model achieves the best performance, and demonstrate the usefulness of a compositional morphology component in distributional semantics.


title: USABILITY EVALUATION OF ONTOLOGY EDITORS

abstract: Ontology editors are software tools that allow the creation and maintenance of ontologies through a graphical user interface. As the semantic web effort grows, a larger community of users for this kind of tool is expected. New users include people not specifically skilled in the use of ontology formalisms. In consequence, the usability of ontology editors can be viewed as a key adoption precondition for semantic web technologies. In this paper, the usability evaluation of several representative ontology editors is described. This evaluation is carried out by combining a heuristic pre-assessment with a subsequent user-testing phase. The target population is comprised of people with no specific ontology-creation skills that have a general knowledge about domain modelling. For this kind of user, current editors are adequate for the creation and maintenance of simple ontologies. Also, there is room for improvement, especially in browsing mechanisms, help systems, and visualization metaphors.


Indri: a language-model based search engine for complex queries


title: Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances

abstract: Breast cancer is the second-most common and leading cause of cancer death among women. It has become a major health issue in the world over the past 50 years, and its incidence has increased in recent years. Early detection is an effective way to diagnose and manage breast cancer. Computer-aided detection or diagnosis (CAD) systems can play a key role in the early detection of breast cancer and can reduce the death rate among women with breast cancer. The purpose of this paper is to provide an overview of recent advances in the development of CAD systems and related techniques. We begin with a brief introduction to some basic concepts related to breast cancer detection and diagnosis. We then focus on key CAD techniques developed recently for breast cancer, including detection of calcifications, detection of masses, detection of architectural distortion, detection of bilateral asymmetry, image enhancement, and image retrieval.


title: Containers and Virtual Machines at Scale: A Comparative Study

abstract: Virtualization is used in data center and cloud environments to decouple applications from the hardware they run on. Hardware virtualization and operating system level virtualization are two prominent technologies that enable this. Containers, which use OS virtualization, have recently surged in interest and deployment. In this paper, we study the differences between the two virtualization technologies. We compare containers and virtual machines in large data center environments along the dimensions of performance, manageability and software development. We evaluate the performance differences caused by the different virtualization technologies in data center environments where multiple applications are running on the same servers (multi-tenancy). Our results show that co-located applications can cause performance interference, and the degree of interference is higher in the case of containers for certain types of workloads. We also evaluate differences in the management frameworks which control deployment and orchestration of containers and VMs. We show how the different capabilities exposed by the two virtualization technologies can affect the management and development of applications. Lastly, we evaluate novel approaches which combine hardware and OS virtualization.


title: Predicting Rich Drug-Drug Interactions via Biomedical Knowledge Graphs and Text Jointly Embedding

abstract: Minimizing adverse reactions caused by drug-drug interactions has always been a momentous research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is opening up new approaches to discover various drug-drug interactions. However, these discoveries contain a huge amount of noise and provide knowledge bases far from complete and trustworthy ones to be utilized. Most existing studies focus on predicting binary drug-drug interactions between drug pairs but ignore other interactions. In this paper, we propose a novel framework, called PRD, to predict drug-drug interactions. The framework uses the graph embedding that can overcome data incompleteness and sparsity issues to achieve multiple DDI label prediction. First, a large-scale drug knowledge graph is generated from different sources. Then, the knowledge graph is embedded with comprehensive biomedical text into a common low dimensional space. Finally, the learned embeddings are used to efficiently compute rich DDI information through a link prediction process. To validate the effectiveness of the proposed framework, extensive experiments were conducted on real-world datasets. The results demonstrate that our model outperforms several state-of-the-art baseline methods in terms of capability and accuracy.


title: Deconstructing the Neuropathic Pain Phenotype to Reveal Neural Mechanisms

abstract: After nerve injury maladaptive changes can occur in injured sensory neurons and along the entire nociceptive pathway within the CNS, which may lead to spontaneous pain or pain hypersensitivity. The resulting neuropathic pain syndromes present as a complex combination of negative and positive symptoms, which vary enormously from individual to individual. This variation depends on a diversity of underlying pathophysiological changes resulting from the convergence of etiological, genotypic, and environmental factors. The pain phenotype can serve therefore, as a window on underlying pathophysiological neural mechanisms and as a guide for developing personalized pain medicine.


title: Adaptive Model-Driven User Interface Development Systems

abstract: Adaptive user interfaces (UIs) were introduced to address some of the usability problems that plague many software applications. Model-driven engineering formed the basis for most of the systems targeting the development of such UIs. An overview of these systems is presented and a set of criteria is established to evaluate the strengths and shortcomings of the state of the art, which is categorized under architectures, techniques, and tools. A summary of the evaluation is presented in tables that visually illustrate the fulfillment of each criterion by each system. The evaluation identified several gaps in the existing art and highlighted the areas of promising improvement.


title: Bin Ratio-Based Histogram Distances and Their Application to Image Classification

abstract: Large variations in image background may cause partial matching and normalization problems for histogram-based representations, i.e., the histograms of the same category may have bins which are significantly different, and normalization may produce large changes in the differences between corresponding bins. In this paper, we deal with this problem by using the ratios between bin values of histograms, rather than bin values' differences which are used in the traditional histogram distances. We propose a bin ratio-based histogram distance (BRD), which is an intra-cross-bin distance, in contrast with previous bin-to-bin distances and cross-bin distances. The BRD is robust to partial matching and histogram normalization, and captures correlations between bins with only a linear computational complexity. We combine the BRD with the ℓ1 histogram distance and the χ(2) histogram distance to generate the ℓ1 BRD and the χ(2) BRD, respectively. These combinations exploit and benefit from the robustness of the BRD under partial matching and the robustness of the ℓ1 and χ(2) distances to small noise. We propose a method for assessing the robustness of histogram distances to partial matching. The BRDs and logistic regression-based histogram fusion are applied to image classification. The experimental results on synthetic data sets show the robustness of the BRDs to partial matching, and the experiments on seven benchmark data sets demonstrate promising results of the BRDs for image classification.


title: Sensor-Based Mobile Web Fingerprinting and Cross-Site Input Inference Attacks

abstract: Smartphone motion sensor data are not only accessible to native mobile apps, but have also become accessible to the webpages rendered in either mobile browsers or the WebView components of mobile apps. In this position paper, we highlight four types of broad and severe user fingerprinting and cross-site input inference attacks that can exploit the smartphone motion sensor data to compromise mobile web users' privacy and security, we also discuss some research topics for further investigating the effectiveness of these attacks and designing usable defense mechanisms.


title: Sweetening Ontologies with DOLCE

abstract: In this paper we introduce the DOLCE upper level ontology, the first module of a Foundational Ontologies Library being developed within the WonderWeb project. DOLCE is presented here in an intuitive way; the reader should refer to the project deliverable for a detailed axiomatization. A comparison with WordNet's top-level taxonomy of nouns is also provided, which shows how DOLCE, used in addition to the OntoClean methodology, helps isolating and understanding some major WordNet?s semantic limitations. We suggest that such analysis could hopefully lead to an ?ontologically sweetened? WordNet, meant to be conceptually more rigorous, cognitively transparent, and efficiently exploitable in several applications.


Using PCA to Recognize Characters in Vehicle License Plates


title: Early adolescent Internet game addiction in context: How parents, school, and peers impact youth

abstract: A direct pathway from PAR to IGA was not evident in the final model.A fully indirect effect of PAR on IGA highlighted the protective role of SC.DPA fully mediated the link between PAR and IGA.A sequential mediation path appeared in the final model.The impacts of factors in various contexts on IGA were intertwined with each other. The aim of this study was to identify the underlying mediating mechanisms between the parent-adolescent relationship and Internet game addiction (IGA). A sample of 833 adolescents initially in the 7th grade completed anonymous questionnaires regarding the parent-adolescent relationship, school connectedness, deviant peer affiliation, and IGA during a one year period. Structural equation models showed that both school connectedness and deviant peer affiliation fully mediated the association between the parent-adolescent relationship and adolescent IGA. School connectedness also significantly predicted deviant peer affiliation, forming a sequential mediation model. In general, the results indicated that a low quality parent-adolescent relationship predicted IGA by way of diminishing school connectedness and enhancing affiliations with deviant peers. The non-significant pathway from parent-adolescent relationship to adolescent IGA supports the idea that a distal parent-adolescent relationship still retains a substantial influence on the development of adolescents' IGA, but through the more important proximal variables of school connectedness and deviant peer affiliation. Identifying the processes by which the parent-adolescent relationship is associated with adolescents' IGA has important implications for developing an integrative framework of theory and prevention.