title: Chunking during human visuomotor sequence learning

abstract: Motor sequence learning is a process whereby a series of elementary movements is re-coded into an efficient representation for the entire sequence. Here we show that human subjects learn a visuomotor sequence by spontaneously chunking the elementary movements, while each chunk acts as a single memory unit. The subjects learned to press a sequence of 10 sets of two buttons through trial and error. By examining the temporal patterns with which subjects performed a visuomotor sequence, we found that the subjects performed the 10 sets as several clusters of sets, which were separated by long time gaps. While the overall performance time decreased by repeating the same sequence, the clusters became clearer and more consistent. The cluster pattern was uncorrelated with the distance of hand movements and was different across subjects who learned the same sequence. We then split a learned sequence into three segments, while preserving or destroying the clusters in the learned sequence, and shuffled the segments. The performance on the shuffled sequence was more accurate and quicker when the clusters in the original sequence were preserved than when they were destroyed. The results suggest that each cluster is processed as a single memory unit, a chunk, and is necessary for efficient sequence processing. A learned visuomotor sequence is hierarchically represented as chunks that contain several elementary movements. We also found that the temporal patterns of sequence performance transferred from the nondominant to dominant hand, but not vice versa. This may suggest a role of the dominant hemisphere in storage of learned chunks. Together with our previous unit-recording and imaging studies that used the same learning paradigm, we predict specific roles of the dominant parietal area, basal ganglia, and presupplementary motor area in the chunking.


title: Quad-mode and dual-mode dielectric resonator filters

abstract: This paper introduces for the first time a quad-mode dielectric resonator filter, using a simple cylinder resonator. A four-pole single cavity filter is designed, simulated, and fabricated based on this quadruple mode resonator. Additionally, a new type of dual-mode dielectric resonator filters is introduced, using the same cylindrical resonator cut in half along its axis. Center frequency control, intra/inter/input-coupling mechanisms, tuning, and spurious improvement methods are discussed, showing versatility of the proposed structures to realize different filtering functions and specifications. Measured results are presented for practical filters, employing the proposed quad-mode and dual-mode resonators. The dielectric resonator filters presented in this paper offer a significant size and mass reduction in comparison with conventional dielectric resonator filters. They promise to be useful for both wireless and satellite applications.


title: Foreign Body in Scrotum Following a Boat Engine Blast Accident

abstract: Male genital injuries, demand prompt management to prevent long-term sexual and psychological damage. Injuries to the scrotum and contents may produce impaired fertility.We report our experience in diagnosing and managing a case of a foreign body in the scrotum following a boat engine blast accident. This case report highlights the need for a good history and thorough general examination to establish the mechanism of injury in order to distinguish between an embedded penetrating projectile injury and an injury with an exit wound. Prompt surgical exploration with hematoma evacuation limits complications.


Influence of cross-link structure, density and mechanical properties in the mesoscale deformation mechanisms of collagen fibrils


title: A Frequency Shift Dielectric Soil Moisture Sensor

abstract: Field implantable and laboratory sensors for the measurement of moisture in soil based on the increase of soil dielectric permittivity with volume fraction of water were developed. A typical sensor consists of a case containing a high-frequency (31-MHz) oscillator whose frequency determining resonance LC network is coupled to the built-in electrode via a capacitor T network. Increases in moisture cause a shift in the resonance frequency, which is detected by an external differential frequency counter or readout. The T network and electrode electronic switches reduce the undesirable influence of soil conductivity on the frequency shift. Although the sensor contains active elements, it acts as a passive transducer since power is supplied to the sensor from the readout unit through the two-conductor cable which transmits the RF.


Inhalation Techniques Used in Patients with Respiratory Failure Treated with Noninvasive Mechanical Ventilation


title: Using Mobile Phones for Vocabulary Activities: Examining the Effect of the Platform.

abstract: While problems such as small screens and inconvenient keypads have been pointed out by researchers (e.g., Thornton & Houser, 2002), we still have little knowledge of how the mobile platform affects the way in which activities are completed and how learners make decisions about using mobile phones. Stockwell (2007b) provided preliminary evidence that learners generally require more time to complete vocabulary activities and achieved slightly lower scores on mobile phones when compared to completing the same activities on desktop computers, but data in the study were limited. The current study examines 175 pre-intermediate learners of English who could choose to complete vocabulary activities on either a mobile phone or a desktop computer to identify the effect of the mobile platform. Data were collected from three cohorts of learners over a three-year period, and learner activity was analysed for the amount of time required to complete activities on both platforms and the scores they achieved for the activities. The results of the study are discussed in terms of how the platform affects learners’ ability to complete tasks, whether continued usage contributes to improved performance or sustained usage of the mobile platform over time. Trends across the yearly cohorts were also identified.


abstract: VDTN is the application of mobile DTN having irregular/disruptive connections between the nodes. The paper discusses and simulates a methodology for Improving Security and Performance Parameter using Social Skeleton in heterogeneous VDTN. The scenario of Katra Town with predefined different Social Skeletons (nodes/linkages) with heterogeneous characteristics is analyzed against predefined standard protocols. The performance parameters which are analyzed are Over Head Ratio, Average Latency and Delivery Probability. The results shows improved and/comparable results with improved security feature.


title: Production planning and scheduling integration through augmented Lagrangian optimization

abstract: Abstract To improve the quality of decision making in the process operations, it is essential to implement integrated planning and scheduling optimization. Major challenge for the integration lies in that the corresponding optimization problem is generally hard to solve because of the intractable model size. In this paper, augmented Lagrangian method is applied to solve the full-space integration problem which takes a block angular structure. To resolve the non-separability issue in the augmented Lagrangian relaxation, we study the traditional method which approximates the cross-product term through linearization and also propose a new decomposition strategy based on two-level optimization. The results from case study show that the augmented Lagrangian method is effective in solving the large integration problem and generating a feasible solution. Furthermore, the proposed decomposition strategy based on two-level optimization can get better feasible solution than the traditional linearization method.


title: Computer-supported team-based learning: The impact of motivation, enjoyment and team contributions on learning outcomes

abstract: The benefits of teamwork and collaboration have long been advocated by many educational theories, such as constructivist and social learning models. Among the various applications of collaborative learning, the iterative team-based learning (TBL) process proposed by Michaelsen, Fink, and Knight (2002) has been successfully used in the classroom without computer support. This paper describes the implementation and evaluation results of a classroom application of the TBL process, which was modified to include computer mediation. We call this process computer-supported team-based learning (CS-TBL). This work extends learning in small teams from the traditional classroom to the hybrid classroom where students meet both face-to-face and online by emphasizing the importance of online team interactions. The outcomes are assessed through an evaluation model that considers the impact of motivation, enjoyment and team contributions on learning outcomes. The study results indicate that motivation influences the relationship between team interactions and perceived learning. Enjoyment is affected by motivation and perceptions of team members' contributions, with the implication that students who perceive that the team interactions are adding value to their education will better enjoy learning and will experience higher-level learning outcomes.


title: Reliability and Performance Trade-off Study of Heterogeneous Memories

abstract: Heterogeneous memories, organized as die-stacked in-package and off-package memory, have been a focus of attention by the computer architects to improve memory bandwidth and capacity. Researchers have explored methods and organizations to optimize performance by increasing the access rate to faster die-stacked memory. Unfortunately, reliability of such arrangements has not been studied carefully thus making them less attractive for data centers and mission-critical systems. Field studies show memory reliability depends on device physics as well as on error correction codes (ECC). Due to the capacity, latency, and energy costs of ECC, the performance-critical in-package memories may favor weaker ECC solutions than off-chip. Moreover, these systems are optimized to run at peak performance by increasing access rate to high-performance in-package memory. In this paper, authors use the real-world DRAM failure data to conduct a trade-off study on reliability and performance of Heterogeneous Memory Architectures (HMA). This paper illustrates the problem that an HMA system which only optimizes for performance may suffer from impaired reliability over time. This work also proposes an age-aware access rate control algorithm to ensure reliable operation of long-running systems.


title: Demonstration of Adaptive Overcurrent Protection using IEC 61850 Communications

abstract: This paper contains a description of an adaptive protection scheme that has been implemented and demonstrated in a hardware in the loop simulation environment using commercially available protection hardware and IEC 61850 communications. The implementation is based on an actual 11kV system which includes distributed generation and network automation. IEC 61850 communications offers several benefits for the implementation of adaptive protection, but also presents some limitations which are discussed in the paper. An alternative approach to overcome a number of the limitations is also presented.


title: Approximating the Solution of Optimal Control Problems by Fuzzy Systems

abstract: In this paper, the ability of fuzzy systems is used to estimate the solution of crisp optimal control problems. To solve an optimal control problem, first the well-known Euler---Lagrange conditions are obtained and then, the solution of these conditions is approximated by defining a trial solution based on fuzzy systems. The parameters of fuzzy systems are adjusted by an optimization algorithm. Numerical examples and comparisons with exact solutions reveal the capability and accuracy of proposed method.


Qualitative Analysis of Content by


Knowledge sharing through online communities of practice: the case of linux ports to embedded processors


title: One pot synthesis of nickel foam supported self-assembly of NiWO4 and CoWO4 nanostructures that act as high performance electrochemical capacitor electrodes

abstract: In this work, we report a facile one-step hydrothermal approach to synthesize NiWO4 and CoWO4 nanostructures on nickel foam as binder-free electrodes for use as supercapacitors. The as-synthesized materials showed excellent electrochemical performance, with a high specific capacitance of 797.8 F g−1 and 764.4 F g−1 at a current density of 1 A g−1 after 3000 cycles. On increasing the current density by 20 times, the rate capabilities still maintained 55.6% and 50.6% of the original value for NiWO4/Ni foam and CoWO4/Ni foam, respectively. Moreover, both of these materials exhibited outstanding cycling stability, the 6000th cycle at 50 mV s−1 demonstrated 2.06 and 2.81 times better capacitance than the initial cycles for NiWO4/Ni foam and CoWO4/Ni foam, respectively. To our knowledge, this capacitance performance is better than any previously reported value for these materials and is a consequence of the highly evolved surface area/microstructure of the materials formed by this technique.


title: An overview and management of osteoporosis

abstract: Osteoporosis -related to various factors including menopause and aging- is the most common chronic metabolic bone disease, which is characterized by increased bone fragility. Although it is seen in all age groups, gender, and races, it is more common in Caucasians (white race), older people, and women. With an aging population and longer life span, osteoporosis is increasingly becoming a global epidemic. Currently, it has been estimated that more than 200 million people are suffering from osteoporosis. According to recent statistics from the International Osteoporosis Foundation, worldwide, 1 in 3 women over the age of 50 years and 1 in 5 men will experience osteoporotic fractures in their lifetime. Every fracture is a sign of another impending one. Osteoporosis has no clinical manifestations until there is a fracture. Fractures cause important morbidity; in men, in particular, they can cause mortality. Moreover, osteoporosis results in a decreased quality of life, increased disability-adjusted life span, and big financial burden to health insurance systems of countries that are responsible for the care of such patients. With an early diagnosis of this disease before fractures occur and by assessing the bone mineral density and with early treatment, osteoporosis can be prevented. Therefore, increasing awareness among doctors, which, in turn, facilitates increase awareness of the normal populace, will be effective in preventing this epidemic.


title: Ambient Intelligence in Assisted Living : Enable Elderly People to Handle Future Interfaces

abstract: Ambient Assisted Living is currently one of the important research and development areas, where accessibility, usability and learning plays a major role and where future interfaces are an important concern for applied engineering. The general goal of ambient assisted living solutions is to apply ambient intelligence technology to enable people with specific demands, e.g. handicapped or elderly, to live in their preferred environment longer. Due to the high potential of emergencies, a sound emergency assistance is required, for instance assisting elderly people with comprehensive ambient assisted living solutions sets high demands on the overall system quality and consequently on software and system engineering - user acceptance and support by various userinterfaces is an absolute necessity. In this article, we present an Assisted Living Laboratory that is used to train elderly people to handle modern interfaces for Assisted Living and evaluate the usability and suitability of these interfaces in specific situations, e.g., emergency cases.


A Fluid-Limit Model Criterion for Instability of Multiclass Queueing Networks


title: An MRF Model-Based Active Learning Framework for the Spectral-Spatial Classification of Hyperspectral Imagery

abstract: Hyperspectral image classification has attracted extensive research efforts in the recent decades. The main difficulty lies in the few labeled samples versus high dimensional features. The spectral-spatial classification method using Markov random field (MRF) has been shown to perform well in improving the classification performance. Moreover, active learning (AL), which iteratively selects the most informative unlabeled samples and enlarges the training set, has been widely studied and proven useful in remotely sensed data. In this paper, we focus on the combination of MRF and AL in the classification of hyperspectral images, and a new MRF model-based AL (MRF-AL) framework is proposed. In the proposed framework, the unlabeled samples whose predicted results vary before and after the MRF processing step is considered as uncertain. In this way, subset is firstly extracted from the entire unlabeled set, and AL process is then performed on the samples in the subset. Moreover, hybrid AL methods which combine the MRF-AL framework with either the passive random selection method or the existing AL methods are investigated. To evaluate and compare the proposed AL approaches with other state-of-the-art techniques, experiments were conducted on two hyperspectral data sets. Results demonstrated the effectiveness of the hybrid AL methods, as well as the advantage of the proposed MRF-AL framework.


title: A 5GHz Digital Fractional- $N$ PLL Using a 1-bit Delta–Sigma Frequency-to-Digital Converter in 65 nm CMOS

abstract: A highly digital two-stage fractional- $N$ phase-locked loop (PLL) architecture utilizing a first-order 1-bit $ \Delta \!\Sigma $ frequency-to-digital converter (FDC) is proposed and implemented in a 65nm CMOS process. Performance of the first-order 1-bit $ \Delta \!\Sigma $ FDC is improved by using a phase interpolator-based fractional divider that reduces phase quantizer input span and by using a multiplying delay-locked loop that increases its oversampling ratio. We also describe an analogy between a time-to-digital converter (TDC) and a $ \Delta \!\Sigma $ FDC followed by an accumulator that allows us to leverage the TDC-based PLL analysis techniques to study the impact of $ \Delta \!\Sigma $ FDC characteristics on $ \Delta \!\Sigma $ FDC-based fractional- $N$ PLL (FDCPLL) performance. Utilizing proposed techniques, a prototype PLL achieves 1 MHz bandwidth, −101.6 dBc/Hz in-band phase noise, and $ {\textrm {1.22 ps}_{\textrm {rms}}}$ (1 kHz–40 MHz) jitter while generating 5.031GHz output from 31.25MHz reference clock input. For the same output frequency, the stand-alone second-stage fractional- $N$ FDCPLL achieves 1MHz bandwidth, −106.1dBc/Hz in-band phase noise, and $ {\textrm {403 fs}_{\textrm {rms}}}$ jitter with a 500MHz reference clock input. The two-stage PLL consumes 10.1mW power from a 1V supply, out of which 7.1 mW is consumed by the second-stage FDCPLL.


title: The potentiality of computer-assisted instruction towards ameliorating Iranian EFL learners' reading level

abstract: The fundamental objective behind the present study is to demonstrate the visible effect of Computer-Assisted Instruction upon Iranian EFL learners' reading performance, and to see if it has any impact upon this skill in the Iranian EFLeducational settings. To this end, a sample of 50 male and female EFL learners was drawn from an English language institute in Iran. After participating in a proficiency pretest, these participants were assigned into two experimental and control groups, 25 and 25, respectively. An independent sample t-test was administered to find out if there were salient differences between the findings of the two selected groups in their reading test. The key research question was to see providing learners with computer-assisted instruction during the processes of learning and instruction for learners would have an affirmative influence upon the improvement and development of their reading skill. The results pinpointed computer-assisted instruction users' performance was meaningfully higher than that of nonusers (DF?=?48, P?<?05). The consequences revealed that computer-assisted language learning and computer technology application have resulted in a greater promotion of students' reading improvement. In other words, computer-assisted instruction users outperformed the nonusers. The research, therefore, highlights the conclusion that EFL learners' use of computer-assisted instruction has the potential to promote more effective reading ability. Computer-assisted instruction can increase the efficiency of students' reading comprehension.Using technology, teachers can provide more suitable texts for learners, based on their needs.CALL can motivate learners more to choose those texts who are more interested in.CAI is more learner-centered than traditional classes.Using CALL, learners are more error-free and more accurate in reading practices.


title: Ultra-compact Power Pulsation Buffer for single-phase DC/AC converter systems

abstract: In single-phase power conversion systems, typically bulky electrolytic capacitors are installed in order to cope with the intrinsic double-line frequency power pulsation. However, since the voltage ripple at the dc bus is typ. limited to just a few percent of the nominal voltage, only a small fraction of the actually stored energy in the capacitors is used for the power decoupling. In this paper an auxiliary buffer converter is employed, shifting the double-line frequency power pulsation away from dc bus to a buffer capacitor. Being relieved from strict voltage ripple requirements, a larger voltage ripple is allowed across the buffer capacitor, significantly reducing the capacitance requirement. In this paper an ultra compact Power Pulsation Buffer (PPB) is designed for a 2kW PV-inverter application by means of a comprehensive Pareto optimization. Besides compensating the power pulsation, the PPB must be able to quickly stabilize the dc bus in case of abrupt load variations and maintain an average buffer capacitor voltage. In this paper, a novel cascaded control structure is presented, meeting all aforementioned control objectives. A constructed prototype of the optimized PPB is presented in the paper and experimental measurements verify the outstanding performance of the proposed control system.


title: Pin-Loaded Circularly-Polarized Patch Antennas With Wide 3-dB Axial Ratio Beamwidth

abstract: Pin-loaded circularly polarized (CP) patch antennas with wide 3-dB axial ratio beamwidth (ARBW) are proposed in this paper. The ARBW of a CP patch antenna is mainly dependent on its electrical width at a frequency, while the patch size at resonance is fully controlled by the shunt inductive load. First of all, four shorting pins are introduced along the diagonals of the dual-fed square patch, so as to exhibit that the 3-dB ARBW can be significantly extended if the shorting position and pin radius are properly chosen. Next, the two degenerate modes in a single-fed CP patch antenna are slightly separated in resonant frequencies by loading another pair of shorting pins along the central line of the patch, so as to excite CP radiation. After extensive analysis is executed, two patch antennas with and without shorting pins are fabricated and measured. Simulated and measured results show good agreement, and both of them demonstrate that the 3-dB ARBW is tremendously improved from 50° to 140° or gets nearly 90° increment by means of the proposed method.


title: Change detection techniques

abstract: Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature.


title: Analysis of Multiresolution Image Denoising Schemes Using Generalized Gaussian and Complexity Priors

abstract: Research on universal and minimax wavelet shrinkage and thresholding methods has demonstrated near-ideal estimation performance in various asymptotic frameworks. However, image processing practice has shown that universal thresholding methods are outperformed by simple Bayesian estimators assuming independent wavelet coefficients and heavy-tailed priors such as generalized Gaussian distributions (GGDs). In this paper, we investigate various connections between shrinkage methods and maximum a posteriori (MAP) estimation using such priors. In particular, we state a simple condition under which MAP estimates are sparse. We also introduce a new family of complexity priors based upon Rissanen's universal prior on integers. One particular estimator in this class outperforms conventional estimators based on earlier applications of the minimum description length (MDL) principle. We develop analytical expressions for the shrinkage rules implied by GGD and complexity priors. This allows us to show the equivalence between universal hard thresholding, MAP estimation using a very heavy-tailed GGD, and MDL estimation using one of the new complexity priors. Theoretical analysis supported by numerous practical experiments shows the robustness of some of these estimates against mis-specifications of the prior-a basic concern in image processing applications.


title: Archaisms and neologisms identification in texts

abstract: In this paper we present an application for identifying archaisms and neologisms in texts. The application also provides the ability to view graphically the evolution trends of these words for a better interpretation of the results. The presented solution consists of two phases: the learning phase in which we identify the general evolution trends of three categories of words (archaisms, neologisms and common words) and the classification phase in which we label new words with their corresponding category. For both phases, the application requires Internet access because it is using the Google Books N-gram Viewer to generate the images that back up the decisions.


title: A Large Scale Arabic Sentiment Lexicon for Arabic Opinion Mining

abstract: Most opinion mining methods in English rely successfully on sentiment lexicons, such as English SentiWordnet (ESWN). While there have been efforts towards building Arabic sentiment lexicons, they suffer from many deficiencies: limited size, unclear usability plan given Arabic’s rich morphology, or nonavailability publicly. In this paper, we address all of these issues and produce the first publicly available large scale Standard Arabic sentiment lexicon (ArSenL) using a combination of existing resources: ESWN, Arabic WordNet, and the Standard Arabic Morphological Analyzer (SAMA). We compare and combine two methods of constructing this lexicon with an eye on insights for Arabic dialects and other low resource languages. We also present an extrinsic evaluation in terms of subjectivity and sentiment analysis.


title: An Evolutionary Multi-objective Approach for Modelling Network Security

abstract: Security is the most important issue in a network system. Administrators can more easily understand threats to the network by using a model. In this paper, we present an approach for modelling a network that considers the benefits of the network as well as its limitations. In our approach, we model the system as an optimization problem, which is solved using three algorithms. As the proposed approach is stochastic, it works very efficiently in a network environment. This paper, presents a mathematical model of the system. Model provides easy comprehend of system. Presented model is based on multi-objective optimization problem. One parameter in the presented model is security and another parameter is user productivity. Security is the most important issue in a network system. Administrators can more easily understand threats to the network by using a model. In this paper, we present an approach for modelling a network that considers the benefits of the network as well as its limitations.


title: Jointly Learning Word Representations and Composition Functions Using Predicate-Argument Structures

abstract: We introduce a novel compositional language model that works on PredicateArgument Structures (PASs). Our model jointly learns word representations and their composition functions using bagof-words and dependency-based contexts. Unlike previous word-sequencebased models, our PAS-based model composes arguments into predicates by using the category information from the PAS. This enables our model to capture longrange dependencies between words and to better handle constructs such as verbobject and subject-verb-object relations. We verify this experimentally using two phrase similarity datasets and achieve results comparable to or higher than the previous best results. Our system achieves these results without the need for pretrained word vectors and using a much smaller training corpus; despite this, for the subject-verb-object dataset our model improves upon the state of the art by as much as ∼10% in relative performance.


title: Genital trauma and vaginal bleeding: is it a lapse of time issue? A case report of a prepubertal girl and review of the literature

abstract: Child victims of sexual abuse may present with physical findings whose interpretation requires the most exhaustive evaluation and an accurate collection of a detailed history. Genital bleeding is usually considered as an acute sign, related to a trauma that occurred shortly before its appearance. We report a case of a 34-month-old child who was referred to the emergency room with a significant vaginal hemorrhage, originating from a wide laceration of the posterior fourchette, and a negative history for accidental trauma. The characteristics of the lesion, compared to the temporal evolution of the healing process, and the witnesses’ depositions led us to assume that the time elapsed between the abusive event and the physical examination was longer in respect to what had appeared at the first sight. The judicial reconstruction of the events confirmed our assumption, allowing the charge of the right abuse perpetrator. As the literature regarding this eventuality is very poor, we report this case to stress the importance for physicians to consider that an active bleeding may be the manifestation of a trauma that occurred very long before.


title: Group topic modeling for academic knowledge discovery

abstract: Conference mining and expert finding are useful academic knowledge discovery problems from an academic recommendation point of view. Group level (GL) topic modeling can provide us with richer text semantics and relationships, which results in denser topics. And denser topics are more useful for academic discovery issues in contrast to Element level (EL) or Document level (DL) topic modeling, which produces sparser topics. Previous methods performed academic knowledge discovery by using network connectivity (only links not text of documents), keywords-based matching (no semantics) or by using semantics-based intrinsic structure of the words presented between documents (semantics at DL), while ignoring semantics-based intrinsic structure of the words and relationships between conferences (semantics at GL). In this paper, we consider semantics-based intrinsic structure of words and relationships presented in conferences (richer text semantics and relationships) by modeling from GL. We propose group topic modeling methods based on Latent Dirichlet Allocation (LDA). Detailed empirical evaluation shows that our proposed GL methods significantly outperformed DL methods for conference mining and expert finding problems.


title: Chunks in expert memory : Evidence for the magical number four ... or is it two ?

abstract: This study aims to test the divergent predictions of the chunking theory (Chase & Simon, 1973) and template theory (Gobet & Simon, 1996a, 2000) with respect to the number of chunks held in visual short-term memory and the size of chunks used by experts. We presented game and random chessboards in both a copy and a recall task. In a within-subject design, the stimuli were displayed using two presentation media: (a) physical board and pieces, as in Chase and Simon's (1973) study; and (b) a computer display, as in Gobet and Simon's (1998) study. Results show that, in most cases, no more than three chunks were replaced in the recall task, as predicted by template theory. In addition, with game positions in the computer condition, chess Masters replaced very large chunks (up to 15 pieces), again in line with template theory. Overall, the results suggest that the original chunking theory overestimated short-term memory capacity and underestimated the size of chunks used, in particular with Masters. They also su...


title: Bayesian inference for psychology, part IV: parameter estimation and Bayes factors

abstract: In the psychological literature, there are two seemingly different approaches to inference: that from estimation of posterior intervals and that from Bayes factors. We provide an overview of each method and show that a salient difference is the choice of models. The two approaches as commonly practiced can be unified with a certain model specification, now popular in the statistics literature, called spike-and-slab priors. A spike-and-slab prior is a mixture of a null model, the spike, with an effect model, the slab. The estimate of the effect size here is a function of the Bayes factor, showing that estimation and model comparison can be unified. The salient difference is that common Bayes factor approaches provide for privileged consideration of theoretically useful parameter values, such as the value corresponding to the null hypothesis, while estimation approaches do not. Both approaches, either privileging the null or not, are useful depending on the goals of the analyst.


title: Kinematics and workspace of 3-RUU parallel manipulator

abstract: Kinematics design of a lower-DOF parallel robot parallel, a 3-UUR pure rotational parallel manipulator, is presented. We first analyzed the instantaneous motions of 3-RUU parallel robot at initial configuration by reciprocal screw, after the inverse kinematics and forward kinematics are analyzed. At the basis on the kinematics, the workspace of parallel robot is presented in the paper. The main influences for the workspace of parallel robot consist of circumscribed circle radius of fixed platform Ra, circumscribed circle radius of moving fixed platform R b , length of connecting rod l and the angles Θ among connecting rod. The performances of such 3-RUU mechanisms are very different for different configurations. The curve of simulation which prove the academic analysis is given by Adams.


Physiological Variables Related to 20 km Race Walk Performance


title: Study of various slanted air-gap structures of interior permanent magnet synchronous motor with brushless field excitation

abstract: This paper shows how to maximize the effect of the slanted air-gap structure of an interior permanent magnet synchronous motor with brushless field excitation (BFE) for application in a hybrid electric vehicle. The BFE structure offers high torque density at low speed and weakened flux at high speed. The unique slanted air-gap is intended to increase the output torque of the machine as well as to maximize the ratio of the back-emf of a machine that is controllable by BFE. This irregularly shaped air-gap makes a flux barrier along the d-axis flux path and decreases the d-axis inductance; as a result, the reluctance torque of the machine is much higher than a uniform air-gap machine, and so is the output torque. Also, the machine achieves a higher ratio of the magnitude of controllable back-emf. The determination of the slanted shape was performed by using magnetic equivalent circuit analysis and finite element analysis (FEA).


title: Innovation at the Intersection of Clinical Trials and Real‐World Data Science to Advance Patient Care

abstract: While efficacy and safety data collected from randomized clinical trials are the evidentiary standard for determining market authorization, this alone may no longer be sufficient to address the needs of key stakeholders (regulators, providers, and payers) and guarantee long-term success of pharmaceutical products. There is a heightened interest from stakeholders on understanding the use of real-world evidence (RWE) to substantiate benefit-risk assessment and support the value of a new drug. This review provides an overview of real-world data (RWD) and related advances in the regulatory framework, and discusses their impact on clinical research and development. A framework for linking drug development decisions with the value proposition of the drug, utilizing pharmacokinetic-pharmacodynamic-pharmacoeconomic models, is introduced. The summary presented here is based on the presentations and discussion at the symposium entitled Innovation at the Intersection of Clinical Trials and Real-World Data to Advance Patient Care at the American Society for Clinical Pharmacology and Therapeutics (ASCPT) 2017 Annual Meeting.


title: The Creative Benefits Scale: Connecting Generativity to Life Satisfaction.

abstract: Long-term participation in creative activities has benefits for middle-aged and older people that may improve their adaptation to later life. We first investigated the factor structure of the Creat...


title: Periodontal regeneration compared with access flap surgery in human intra‐bony defects 20‐year follow‐up of a randomized clinical trial: tooth retention, periodontitis recurrence and costs

abstract: AIM ::: Compare the long-term outcomes and costs of three treatment modalities in intra-bony defects. ::: ::: ::: MATERIALS AND METHODS ::: Forty-five intra-bony defects in 45 patients had been randomly allocated to receive: modified papilla preservation technique with titanium-reinforced expanded-polytetrafluoroethylene (ePTFE) membranes (MPPT Tit, N = 15); access flap with expanded-PTFE membranes (Flap-ePTFE, N = 15) and access flap alone (Flap, N = 15). Supportive periodontal care (SPC) was provided monthly for 1 year, then every 3 months for 20 years. Periodontal therapy was delivered to sites showing recurrences. ::: ::: ::: RESULTS ::: Forty-one patients complied with SPC. Four subjects were lost to follow-up. Clinical attachment-level differences between 1 and 20 years were -0.1 ± 0.3 mm (p = 0.58) in the MPPT Tit; -0.5 ± 0.1 mm (p = 0.003) in the Flap-ePTFE and -1.7 ± 0.4 mm (p < 0.001) in the Flap. At 20 years, sites treated with Flap showed greater attachment loss compared to MPPT Tit (1.4 ± 0.4 mm; p = 0.008) and to Flap-ePTFE (1.1 ± 0.4 mm; p = 0.03). Flap group lost two treated teeth. Five episodes of recurrences occurred in the MPPT Tit, six in the Flap-ePTFE and fifteen in the Flap group. Residual pocket depth at 1-year was significantly correlated with the number of recurrences (p = 0.002). Sites treated with flap had greater OR for recurrences and higher costs of re-intervention than regenerated sites over a 20-year follow-up period with SPC. ::: ::: ::: CONCLUSIONS ::: Regeneration provided better long-term benefits than Flap: no tooth loss, less periodontitis progression and less expense from re-intervention over a 20-year period. These benefits need to be interpreted in the context of higher immediate costs associated with regenerative treatment. These initial observations need to be extended to larger groups and broader clinical settings.


title: Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering

abstract: We address the problem of Visual Question Answering (VQA), which requires joint image and language understanding to answer a question about a given photograph. Recent approaches have applied deep image captioning methods based on convolutional-recurrent networks to this problem, but have failed to model spatial inference. To remedy this, we propose a model we call the Spatial Memory Network and apply it to the VQA task. Memory networks are recurrent neural networks with an explicit attention mechanism that selects certain parts of the information stored in memory. Our Spatial Memory Network stores neuron activations from different spatial regions of the image in its memory, and uses the question to choose relevant regions for computing the answer, a process of which constitutes a single "hop" in the network. We propose a novel spatial attention architecture that aligns words with image patches in the first hop, and obtain improved results by adding a second attention hop which considers the whole question to choose visual evidence based on the results of the first hop. To better understand the inference process learned by the network, we design synthetic questions that specifically require spatial inference and visualize the attention weights. We evaluate our model on two published visual question answering datasets, DAQUAR [1] and VQA [2], and obtain improved results compared to a strong deep baseline model (iBOWIMG) which concatenates image and question features to predict the answer [3].


abstract: A graduated assignment algorithm for graph matching is presented which is fast and accurate even in the presence of high noise. By combining graduated nonconvexity, two-way (assignment) constraints, and sparsity, large improvements in accuracy and speed are achieved. Its low order computational complexity [O(lm), where l and m are the number of links in the two graphs] and robustness in the presence of noise offer advantages over traditional combinatorial approaches. The algorithm, not restricted to any special class of graph, is applied to subgraph isomorphism, weighted graph matching, and attributed relational graph matching. To illustrate the performance of the algorithm, attributed relational graphs derived from objects are matched. Then, results from twenty-five thousand experiments conducted on 100 mode random graphs of varying types (graphs with only zero-one links, weighted graphs, and graphs with node attributes and multiple link types) are reported. No comparable results have been reported by any other graph matching algorithm before in the research literature. Twenty-five hundred control experiments are conducted using a relaxation labeling algorithm and large improvements in accuracy are demonstrated.


How to Parry Goodman's "Seven Strictures on Similarity"


title: Brain computer interface systems using non-invasive electroencephalogram signal: A literature review

abstract: Systems based on Brain Computer Interface (BCI) have been developed from the past three decades for assisting locked-in state patients. Researchers across the globe are developing new techniques to increase the BCI accuracy. In 1924 Dr. Hans Berger recorded the first EEG signal. The number of experimental measurements of brain activity has been done using human control commands. The main function of BCI is to convert and transmit human intentions into appropriate motion commands for the wheelchairs, robots, devices, and so forth. BCI allows improving the quality of life of disabled patients and letting them interact with their environment. Since the BCI signals are non-stationary, the main challenges in the non-invasive BCI system are to accurately detect and classify the signals. This paper reviews the State of Art of BCI and techniques used for feature extraction and classification using electroencephalogram (EEG) and highlights the need of adaptation concept.


title: Acoustic scene classification using convolutional neural network and multiple-width frequency-delta data augmentation

abstract: In recent years, neural network approaches have shown superior performance to conventional hand-made features in numerous application areas. In particular, convolutional neural networks (ConvNets) exploit spatially local correlations across input data to improve the performance of audio processing tasks, such as speech recognition, musical chord recognition, and onset detection. Here we apply ConvNet to acoustic scene classification, and show that the error rate can be further decreased by using delta features in the frequency domain. We propose a multiple-width frequency-delta (MWFD) data augmentation method that uses static mel-spectrogram and frequency-delta features as individual input examples. In addition, we describe a ConvNet output aggregation method designed for MWFD augmentation, folded mean aggregation, which combines output probabilities of static and MWFD features from the same analysis window using multiplication first, rather than taking an average of all output probabilities. We describe calculation results using the DCASE 2016 challenge dataset, which shows that ConvNet outperforms both of the baseline system with hand-crafted features and a deep neural network approach by around 7%. The performance was further improved (by 5.7%) using the MWFD augmentation together with folded mean aggregation. The system exhibited a classification accuracy of 0.831 when classifying 15 acoustic scenes.


title: Managing water on heat transfer surfaces: A critical review of techniques to modify surface wettability for applications with condensation or evaporation

abstract: Abstract Most materials of practical interest are neither completely wetting nor completely non-wetting. “Surface wettability” then refers to the degree that a surface is hydrophilic (i.e. water-loving) or hydrophobic (i.e. water-fearing). Through careful design, it is possible to alter the natural wettability of a surface to be more water-loving or water-fearing. This is principally achieved by modifying the surface chemistry and/or surface roughness. In some cases, modifying the surface may bring operational benefit or advantage. For example, aluminum and copper (which are used in the construction of heat exchangers) tend to retain water in application, which can degrade performance. Modifying the surface however to be superhydrophilic can help to spread out the condensate, reduce the air-side pressure drop, and facilitate drainage. Moreover, by creating a wettability pattern or gradient, it is possible to predetermine the initiating sites for condensation on a surface as well as facilitate droplet motion and/or control the water droplet movement path. In the first part of this review, the current state of the art of surface wettability modification and control techniques are presented, which includes topographical manipulation, chemical modification, as well as methods for creating gradient surfaces and patterned wettability. In the second part of this review, possible applications and the potential impact of these methodologies in energy systems are discussed with a special focus on heating, ventilation, air conditioning, and refrigeration (HVAC&R) systems and components.


title: Osmotic Computing: A New Paradigm for Edge/Cloud Integration

abstract: Osmotic computing is a new paradigm to support the efficient execution of Internet of Things (IoT) services and applications at the network edge. This paradigm is founded on the need for a holistic distributed system abstraction enabling the deployment of lightweight microservices on resource-constrained IoT platforms at the network edge, coupled with more complex microservices running on large-scale datacenters. This paradigm is driven by the significant increase in resource capacity/capability at the network edge, along with support for data transfer protocols that enable such resources to interact more seamlessly with datacenter-based services. This installment of "Blue Skies" discusses osmotic computing features, challenges, and future directions.


Sociolinguistic Contours in the Verbal Art of Chicano Children.


title: CEDAR: a core-extraction distributed ad hoc routing algorithm

abstract: CEDAR is an algorithm for QoS routing in ad hoc network environments. It has three key components: (a) the establishment and maintenance of a self-organizing routing infrastructure called the core for performing route computations, (b) the propagation of the link-state of stable high-bandwidth links in the core through increase/decrease waves, and (c) a QoS route computation algorithm that is executed at the core nodes using only locally available state. But preliminary performance evaluation shows that CEDAR is a robust and adaptive QoS routing algorithm that reacts effectively to the dynamics of the network while still approximating link-state performance for stable networks.


title: NoSQL database systems: a survey and decision guidance

abstract: Today, data is generated and consumed at unprecedented scale. This has lead to novel approaches for scalable data management subsumed under the term "NoSQL" database systems to handle the ever-increasing data volume and request loads. However, the heterogeneity and diversity of the numerous existing systems impede the well-informed selection of a data store appropriate for a given application context. Therefore, this article gives a top-down overview of the field: instead of contrasting the implementation specifics of individual representatives, we propose a comparative classification model that relates functional and non-functional requirements to techniques and algorithms employed in NoSQL databases. This NoSQL Toolbox allows us to derive a simple decision tree to help practitioners and researchers filter potential system candidates based on central application requirements.


title: Worth its Weight in Likes: Towards Detecting Fake Likes on Instagram

abstract: Instagram is a significant platform for users to share media; reflecting their interests. It is used by marketers and brands to reach their potential audience for advertisement. The number of likes on posts serves as a proxy for social reputation of the users, and in some cases, social media influencers with an extensive reach are compensated by marketers to promote products. This emerging market has led to users artificially bolstering the likes they get to project an inflated social worth. In this study, we enumerate the potential factors which contribute towards a genuine like on Instagram. Based on our analysis of liking behaviour, we build an automated mechanism to detect fake likes on Instagram which achieves a high precision of 83.5%. Our work serves an important first step in reducing the effect of fake likes on Instagram influencer market.


title: Enacting Rituals to Improve Self-Control

abstract: Rituals are predefined sequences of actions characterized by rigidity and repetition. We propose that enacting ritualized actions can enhance subjective feelings of self-discipline, such that rituals can be harnessed to improve behavioral self-control. We test this hypothesis in 6 experiments. A field experiment showed that engaging in a pre-eating ritual over a 5-day period helped participants reduce calorie intake (Experiment 1). Pairing a ritual with healthy eating behavior increased the likelihood of choosing healthy food in a subsequent decision (Experiment 2), and enacting a ritual before a food choice (i.e., without being integrated into the consumption process) promoted the choice of healthy food over unhealthy food (Experiments 3a and 3b). The positive effect of rituals on self-control held even when a set of ritualized gestures were not explicitly labeled as a ritual, and in other domains of behavioral self-control (i.e., prosocial decision-making; Experiments 4 and 5). Furthermore, Experiments 3a, 3b, 4, and 5 provided evidence for the psychological process underlying the effectiveness of rituals: heightened feelings of self-discipline. Finally, Experiment 5 showed that the absence of a self-control conflict eliminated the effect of rituals on behavior, demonstrating that rituals affect behavioral self-control specifically because they alter responses to self-control conflicts. We conclude by briefly describing the results of a number of additional experiments examining rituals in other self-control domains. Our body of evidence suggests that rituals can have beneficial consequences for self-control.


title: FH-SCMA: Frequency-Hopping Based Sparse Code Multiple Access for Next Generation Internet of Things

abstract: The next generation Internet of Things (IoT) is expected to support extremely diverse applications and terminals, which requires the massive connectivity. Unfortunately, current mobile communication systems with orthogonal multiple access technique can hardly satisfy this demand. On the other hand, the narrow band property has become the trend of the next generation IoT, however, the problem of interference alleviation in the narrow band system is difficult to be resolved. Recently, the sparse code multiple access (SCMA), a non- orthogonal multiple access technique for 5G, shows the obvious improvement in user connectivity and system capacity. After carrier aggregation brought into the future network, The IoT could offer the frequency hopping mechanism larger bandwidth resources. In this paper, by introducing frequency hopping mechanism to the SCMA, a frequency-hopping based sparse code multiple access (FH-SCMA) multiple access technique is proposed to meet the demand of both massive connectivity and the improvement in the interference alleviation. Simulation results show that the FH-SCMA obtains a spectrum efficiency gain of almost 300% compared with the frequency division multiple access (FDMA) based IoT system with frequency-hopping, and the performance of interference alleviation can also be significantly improved by about 2 to 5 times over the FDMA with frequency hopping.


title: ASSOCIATION RULE MINING ON STREAMS

abstract: Let I = {i1, · · · , im} be a set of items. Let S be a stream of transactions in a sequential order, where each transaction is a subset of I. For an itemset X, which is a subset of I, a transaction T in S is said to contain the itemset X if X ⊆ T . The support of X is defined as the fraction of transactions in S that contain X. For a given support threshold s, X is frequent if the support of X is greater than or equal to s%, i.e., if at least s% transactions in S contain X. For a given confidence threshold c, an association rule X ⇒ Y holds if X ∪ Y is frequent and at least c% of transactions in S that contain X also contain Y . The problem of association rule mining on streams is to discover all association rules that hold in a stream of transactions.


title: Therapeutic Use of Hyaluronan‐Based Products

abstract: Publisher Summary The development of hyaluronan preparations suitable for parenteral application has a major impact on clinical practice in multiple medical specialties, enabling the introduction of entirely new therapeutic procedures. Hyaluronan-based products are firmly established for ophthalmic surgery (viscosurgery), intra-articular treatment of arthritis (viscosupplementation), and tissue augmentation (viscoaugmentation). Hyaluronan is also used to control postsurgical adhesions and scar formation, to promote wound healing, and for cell-based tissue repair and drug delivery, though these applications have less impact on the medical needs they address. After being purified from proteins, nucleic acids, endotoxin, and other inflammatory agents, hyaluronan solutions can be used in various surgical procedures, provided the solution has certain viscous and elastic properties. The clinical studies established the usefulness of the highly elastoviscous hyaluronan solutions as vitreous replacements in retinal detachment surgery and as viscoelastic tools for surgical manipulations. The rationale for using these highly elastoviscous hyaluronan solutions was that they should serve as a “mechanical buffer” between the retina and the gel vitreous, the cornea and lens, and the cornea and the vitreous in aphakic eyes. The same mechanical properties of the elastoviscous fluid made it a “soft tool” to manipulate sensitive tissues during surgery. An equally important reason for using hyaluronan in eye surgery was its effect on wound healing. Animal studies with this hyaluronan preparation showed that it prevented excessive fibrous tissue formation during wound healing.


title: Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI

abstract: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder. Finding the biomarkers associated with ASD is extremely helpful to understand the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment. Although Deep Neural Networks (DNNs) have been applied in functional magnetic resonance imaging (fMRI) to identify ASD, understanding the data-driven computational decision making procedure has not been previously explored. Therefore, in this work, we address the problem of interpreting reliable biomarkers associated with identifying ASD; specifically, we propose a 2-stage method that classifies ASD and control subjects using fMRI images and interprets the saliency features activated by the classifier. First, we trained an accurate DNN classifier. Then, for detecting the biomarkers, different from the DNN visualization works in computer vision, we take advantage of the anatomical structure of brain fMRI and develop a frequency-normalized sampling method to corrupt images. Furthermore, in the ASD vs. control subjects classification scenario, we provide a new approach to detect and characterize important brain features into three categories. The biomarkers we found by the proposed method are robust and consistent with previous findings in the literature. We also validate the detected biomarkers by neurological function decoding and comparing with the DNN activation maps.


title: Three-phase LLC resonant converter with integrated magnetics

abstract: Recently, Electric Vehicles (EVs) have required high power density and high efficiency systems in order to save energy and costs. Specifically, in the DC-DC converter that feeds the non-propulsive loads in these vehicles, where the output voltage is much lower than the one of the energy storage unit. Therefore, the output current becomes quite high, and the efficiency and power density are reduced due to the high current ratings. Furthermore, magnetic components usually are the biggest contributors to the mass and volume in these converters. This paper proposes a Three-phase LLC resonant converter with one integrated transformer where all the windings of the three independent transformers are installed into only one core. Using this technique, a high reduction in the core size and thereby an increment in the power density and a reduction of the production cost are obtained. In addition, this integrated transformer is intended to be applied in the novel Three-phase LLC resonant converter with Star connection that is expected to offer reduction of the imbalanced output current, which is produced by tolerances between the phase components. Finally, the proposed converter with the novel integrated transformer is discussed and evaluated from the experimental point of view. As a result, a 70% reduction in the mass of the magnetic cores was achieved.


title: Disrupting developer productivity one bot at a time

abstract: Bots are used to support different software development activities, from automating repetitive tasks to bridging knowledge and communication gaps in software teams. We anticipate the use of Bots will increase and lead to improvements in software quality and developer and team productivity, but what if the disruptive effect is not what we expect? Our goal in this paper is to provoke and inspire researchers to study the impact (positive and negative) of Bots on software development. We outline the modern Bot landscape and use examples to describe the common roles Bots occupy in software teams. We propose a preliminary cognitive support framework that can be used to understand these roles and to reflect on the impact of Bots in software development on productivity. Finally, we consider challenges that Bots may bring and propose some directions for future research.


GeoSQL: A Sketch-Based Query Language for Geographical Data.


title: Non-Invasive Blood Glucose Measurement

abstract: 3 Abstract: Diabetes has emerged as a major healthcare problem in India. Today Approximately 8.3 % of global adult population is suffering from Diabetes. India is one of the most diabetic populated country in the world. Today the technologies available in the market are invasive methods. Since invasive methods cause pain, time consuming, expensive and there is a potential risk of infectious diseases like Hepatitis & HIV spreading and continuous monitoring is therefore not possible. Now a days there is a tremendous increase in the use of electrical and electronic equipment in the medical field for clinical and research purposes. Thus biomedical equipment's have a greater role in solving medical problems and enhance quality of life. Hence there is a great demand to have a reliable, instantaneous, cost effective and comfortable measurement system for the detection of blood glucose concentration. Non-invasive blood glucose measurement device is one such which can be used for continuous monitoring of glucose levels in human body.


title: A Coplanar Vivaldi-Style Launcher for Goubau Single-Wire Transmission Lines

abstract: In this letter, we present a coplanar Vivaldi-style launcher for single-wire transmission lines (SWTLs), sometimes called Goubau lines. The launcher converts a transverse electromagnetic mode (TEM) mode from a coaxial feed port to the transverse magnetic (TM $_{01}$ ) surface-wave mode supported by the SWTL. The original Goubau design for SWTL launchers uses large horns to enforce the mode conversion. In contrast, the proposed coplanar Vivaldi-type launcher comprises a planar structure with dimensions of 100 mm $\times$ 150 mm, which is more convenient to install on existing infrastructure, such as power lines, communication wires, structural steel cables, or pipes. The proposed launcher can be used to provide a broadband communication channel for ultrahigh frequency and microwave radio-frequency signals from 0.81 $\text{to}$ 2.29 GHz. It has a measured 10 dB return loss bandwidth of 1.39 GHz. The measured line loss with an 8 mm diameter SWTL conductor is approximately 0.19 dB/m at 1.5 GHz, which is comparable to high-quality coaxial cables. We have also shown that multiple launcher pairs can be used back-to-back on the same SWTL conductor to provide isolated communication links. With a launcher separation distance of 250 mm, the measured back-to-back isolation exceeds 30 dB.


title: Item and test analysis to identify quality multiple choice questions (MCQS) from an assessment of medical students of Ahmedabad, Gujarat

abstract: Background: Multiple choice questions (MCQs) are frequently used to assess students in different educational streams for their objectivity and wide reach of coverage in less time. However, the MCQs to be used must be of quality which depends upon its difficulty index (DIF I), discrimination index (DI) and distracter efficiency (DE). Objective: To evaluate MCQs or items and develop a pool of valid items by assessing with DIF I, DI and DE and also to revise/ store or discard items based on obtained results. Settings: Study was conducted in a medical school of Ahmedabad. Materials and Methods: An internal examination in Community Medicine was conducted after 40 hours teaching during 1 st MBBS which was attended by 148 out of 150 students. Total 50 MCQs or items and 150 distractors were analyzed. Statistical Analysis: Data was entered and analyzed in MS Excel 2007 and simple proportions, mean, standard deviations, coefficient of variation were calculated and unpaired t test was applied. Results: Out of 50 items, 24 had good to excellent DIF I (31 - 60%) and 15 had good to excellent DI (> 0.25). Mean DE was 88.6% considered as ideal/ acceptable and non functional distractors (NFD) were only 11.4%. Mean DI was 0.14. Poor DI (< 0.15) with negative DI in 10 items indicates poor preparedness of students and some issues with framing of at least some of the MCQs. Increased proportion of NFDs (incorrect alternatives selected by < 5% students) in an item decrease DE and makes it easier. There were 15 items with 17 NFDs, while rest items did not have any NFD with mean DE of 100%. Conclusion: Study emphasizes the selection of quality MCQs which truly assess the knowledge and are able to differentiate the students of different abilities in correct manner.


title: A CONCEPT FOR AUTOMATED CONSTRUCTION PROGRESS MONITORING USING BIM-BASED GEOMETRIC CONSTRAINTS AND PHOTOGRAMMETRIC POINT CLOUDS

abstract: On-site progress monitoring is essential for keeping track of the ongoing work on construction sites. Currently, this task is a manual, time-consuming activity. The research presented here, describes a concept for an automated comparison of the actual state of construction with the planned state for the early detection of deviations in the construction process. The actual state of the construction site is detected by photogrammetric surveys. From these recordings, dense point clouds are generated by the fusion of disparity maps created with semi-global-matching (SGM). These are matched against the target state provided by a 4D Building Information Model (BIM). For matching the point cloud and the BIM, the distances between individual points of the cloud and a componentOs surface are aggregated using a regular cell grid. For each cell, the degree of coverage is determined. Based on this, a confidence value is computed which serves as basis for the existence decision concerning the respective component. Additionally, process- and dependency-relations are included to further enhance the detection process. Experimental results from a real-world case study are presented and discussed.


3 D DATA ACQUISITION BASED ON OPENCV FOR CLOSE-RANGE PHOTOGRAMMETRY APPLICATIONS


title: Big data: the next frontier for innovation in therapeutics and healthcare

abstract: Advancements in genomics and personalized medicine not only effect healthcare delivery from patient and provider standpoints, but also reshape biomedical discovery. We are in the era of the ‘-omics’, wherein an individual’s genome, transcriptome, proteome and metabolome can be scrutinized to the finest resolution to paint a personalized biochemical fingerprint that enables tailored treatments, prognoses, risk factors, etc. Digitization of this information parlays into ‘big data’ informatics-driven evidence-based medical practice. While individualized patient management is a key beneficiary of next-generation medical informatics, this data also harbors a wealth of novel therapeutic discoveries waiting to be uncovered. ‘Big data’ informatics allows for networks-driven systems pharmacodynamics whereby drug information can be coupled to cellular- and organ-level physiology for determining whole-body outcomes. Patient ‘-omics’ data can be integrated for ontology-based data-mining for the discovery of new biolo...


title: Narasin, a novel antiviral compound that blocks dengue virus protein expression.

abstract: BACKGROUND ::: Dengue virus (DENV) is a mosquito-borne virus that causes a spectrum of human diseases ranging from mild dengue fever to dengue haemorrhagic fever and dengue shock syndrome in severe cases. Currently, there is no effective antiviral therapy or vaccine against DENV infection. ::: ::: ::: METHODS ::: In order to identify potential antiviral agents against DENV, we performed high-throughput cell-based screening on a highly purified natural products library. Among the screening hits, selected compounds which displayed 50-75% inhibition against DENV2 were validated using secondary assays. Time-of-addition studies, dose-dependent assays, real time quantitative reverse transcriptase (RT)-PCR, Western blot and ultrastructural imaging were conducted in an attempt to elucidate the potential antiviral mechanisms of narasin. ::: ::: ::: RESULTS ::: In this study, an ionophore, narasin was selected for detailed analysis due to its strong inhibitory profile against DENV infection with minimal cytotoxicity (50% cytotoxic concentration >1,000 μM). A dose-dependent study revealed narasin to have an 50% inhibitory concentration of less than 1 μM against all four serotypes of DENV. Time-of-addition studies of narasin-treated, DENV2-infected Huh-7 cells suggested narasin to be involved in inhibiting the post-entry stages of viral replication during DENV infection. Proteomic and ultrastructural analyses revealed the antiviral mechanism of narasin as likely to be associated with the disruption of viral protein synthesis. In addition, quantitative RT-PCR studies showed no differences in viral RNA levels between narasin-treated and control DENV2-infected cells. ::: ::: ::: CONCLUSIONS ::: Narasin was identified and characterized as a novel agent that inhibits DENV replication in vitro through non-cytotoxic mechanisms, thus indicating its potential to be further developed as a therapeutic anti-DENV agent.


title: A self-organizing semantic map for information retrieval

abstract: Self-organizing Semantic Map for Information Retrieval Xia Lin Dagobert Soergel Gary Marchionini College of Library and Information Services University of Maryland College Park, A neural network’s unsupervised learning algorithm, Kohonen’s feature map, is applied to constructing a selforganizing semantic map for information retrieval. The semantic map visualizes semantic relationships between input documents, and has properties of economic representation of data with their interrelationships. The potentials of the semantic map include using the map as a retrieval interface for an online bibliographic system. A prototype system that demonstrates this potential is described.


title: A Bidirectional Hierarchical Skip-Gram model for text topic embedding

abstract: Taking advantage of the large scale corpus on the web to effectively and efficiently mine the topics within texts is an essential problem in the era of big data. We focus on the problem of learning text topic embedding in an unsupervised manner, which enjoys the properties of efficiency and scalability. Text topic embedding represents words and documents in a semantic topic space, in which the words and documents with similar topic will be embedded close to each other. When compared with conventional topic models, which implicitly capture the document-level word co-occurrence patterns, text topic embedding alleviates the data sparsity problem and captures the semantic relevance between different words and documents. To model text topic embedding, we propose a Bidirectional Hierarchical Skip-Gram model (BHSG) based on skip-gram model. BHSG includes two components: semantic generation module to learn semantic relevance between texts and topic enhance module to produce the text topic embedding based on text embedding learned in the former module. We evaluated our method on two kinds of topic-related tasks: text classification and information retrieval. The experimental results on four public datasets and one dataset we provide all demonstrate that our proposed method can achieve a better performance.


title: iVizTRANS: Interactive visual learning for home and work place detection from massive public transportation data

abstract: Using transport smart card transaction data to understand the homework dynamics of a city for urban planning is emerging as an alternative to traditional surveys which may be conducted every few years are no longer effective and efficient for the rapidly transforming modern cities. As commuters travel patterns are highly diverse, existing rule-based methods are not fully adequate. In this paper, we present iVizTRANS - a tool which combines an interactive visual analytics (VA) component to aid urban planners to analyse complex travel patterns and decipher activity locations for single public transport commuters. It is coupled with a machine learning component that iteratively learns from the planners classifications to train a classifier. The classifier is then applied to the city-wide smart card data to derive the dynamics for all public transport commuters. Our evaluation shows it outperforms the rule-based methods in previous work.


title: Does team training work? Principles for health care.

abstract: Teamwork is integral to a working environment conducive to patient safety and care. Team training is one methodology designed to equip team members with the competencies necessary for optimizing teamwork. There is evidence of team training's effectiveness in highly complex and dynamic work environments, such as aviation and health care. However, most quantitative evaluations of training do not offer any insight into the actual reasons why, how, and when team training is effective. To address this gap in understanding, and to provide guidance for members of the health care community interested in implementing team training programs, this article presents both quantitative results and a specific qualitative review and content analysis of team training implemented in health care. Based on this review, we offer eight evidence-based principles for effective planning, implementation, and evaluation of team training programs specific to health care.


title: Layout-Dependent Effects-Aware Analytical Analog Placement

abstract: Layout-dependent effects (LDEs) have become a critical issue in modern analog and mixed-signal circuit designs. The three major sources of LDEs, well proximity, length of oxide diffusion, and oxide-to-oxide spacing, significantly affect the threshold voltage and mobility of devices in advanced technology nodes. In this paper, we propose the first work to consider the three major sources of LDEs during analog placement. We first transform the three LDE models into nonlinear analytical placement models. Then an LDE-aware analytical analog placement algorithm is presented to mitigate the influence of the LDEs while improving circuit performance. Experimental results show that our placement algorithm can effectively and efficiently reduce the LDE-induced variations and improve circuit performance.


title: Linguistic-based emotion analysis and recognition for measuring consumer satisfaction: an application of affective computing

abstract: A growing body of research suggests that affective computing has many valuable applications in enterprise systems research and e-businesses. This paper explores affective computing techniques for a vital sub-area in enterprise systems—consumer satisfaction measurement. We propose a linguistic-based emotion analysis and recognition method for measuring consumer satisfaction. Using an annotated emotion corpus (Ren-CECps), we first present a general evaluation of customer satisfaction by comparing the linguistic characteristics of emotional expressions of positive and negative attitudes. The associations in four negative emotions are further investigated. After that, we build a fine-grained emotion recognition system based on machine learning algorithms for measuring customer satisfaction; it can detect and recognize multiple emotions using customers’ words or comments. The results indicate that blended emotion recognition is able to gain rich feedback data from customers, which can provide more appropriate follow-up for customer relationship management.


title: Learning Representations by Stochastic Meta-Gradient Descent in Neural Networks

abstract: Representations are fundamental to artificial intelligence. The performance of a learning system depends on the type of representation used for representing the data. Typically, these representations are hand-engineered using domain knowledge. More recently, the trend is to learn these representations through stochastic gradient descent in multi-layer neural networks, which is called backprop. Learning the representations directly from the incoming data stream reduces the human labour involved in designing a learning system. More importantly, this allows in scaling of a learning system for difficult tasks. In this paper, we introduce a new incremental learning algorithm called crossprop, which learns incoming weights of hidden units based on the meta-gradient descent approach, that was previously introduced by Sutton (1992) and Schraudolph (1999) for learning step-sizes. The final update equation introduces an additional memory parameter for each of these weights and generalizes the backprop update equation. From our experiments, we show that crossprop learns and reuses its feature representation while tackling new and unseen tasks whereas backprop relearns a new feature representation.


title: Real-Time Inverse Kinematics Techniques for Anthropomorphic Limbs

abstract: In this paper we develop a set of inverse kinematics algorithms suitable for an anthropomorphic arm or leg. We use a combination of analytical and numerical methods to solve generalized inverse kinematics problems including position, orientation, and aiming constraints. Our combination of analytical and numerical methods results in faster and more reliable algorithms than conventional inverse Jacobian and optimization-based techniques. Additionally, unlike conventional numerical algorithms, our methods allow the user to interactively explore all possible solutions using an intuitive set of parameters that define the redundancy of the system.


title: Eye localization from thermal infrared images

abstract: By using the knowledge of facial structure and temperature distribution, this paper proposes an automatic eye localization method from long wave infrared thermal images both with eyeglasses and without eyeglasses. First, with the help of support vector machine classifier, three gray-projection features are defined to determine whether a subject is with eyeglasses. For subjects with eyeglasses, the locations of valleys in the projection curve are used to perform eye localization. For subjects without eyeglasses, a facial structure consisting of 15 sub-regions is proposed to extract Haar-like features. Eight classifiers are learned from the features selected by Adaboost algorithm for left and right eye, respectively. A vote strategy is employed to find the most likely eyes. To evaluate the effectiveness of our approach, experiments are performed on NVIE and Equinox databases. The eyeglass detection results on NVIE database and Equinox database are 99.36% and 95%, respectively, which demonstrate the effectiveness and robustness of our eyeglass detection method. Eye localization results of within-database experiments and cross-database experiments on these two databases are very comparable with the previous results in this field, verifying the effectiveness and the generalization ability of our approach.


title: Learner Engagement Measurement and Classification in 1:1 Learning

abstract: We explore the feasibility of measuring learner engagement and classifying the engagement level based on machine learning applied on data from 2D/3D camera sensors and eye trackers in a 1:1 learning setting. Our results are based on nine pilot sessions held in a local high school where we recorded features related to student engagement while consuming educational content. We label the collected data as Engaged or NotEngaged while observing videos of the students and their screens. Based on the collected data, perceptual user features (e.g., body posture, facial points, and gaze) are extracted. We use feature selection and classification methods to produce classifiers that can detect whether a student is engaged or not. Accuracies of up to 85-95% are achieved on the collected dataset. We believe our work pioneers in the successful classification of student engagement based on perceptual user features in a 1:1 authentic learning setting.


title: Intrinsically Safe Robot Arm: Adjustable Static Balancing and Low Power Actuation

abstract: We present a design for a manipulator that is intrinsically mechanically safe, i.e. it can not cause pain (let alone damage) to a human being even if the control system has a failure. Based on the pressure pain thresholds for human skin, we derive a pinching safety constraint that limits the actuator torque, and an impact safety constraint that results in a trade-off between mass and velocity. To fulfill all constraints, the manipulator requires a spring balancing system that counteracts gravity in all configurations of the manipulator. This allows the use of extremely low-power DC motors (only 4.5 W). Thanks to the torque and speed limitations of these motors the manipulator is indeed intrinsically safe, yet still capable of moving a useful payload of 1.2 kg over a distance of 0.8 m in 1.5 s.


Impact of Dynamic Loading on the Implant-abutment Interface Using a Gas-enhanced Permeation Test In Vitro


title: The importance of is stakeholder perspectives and perceptions to requirements negotiation

abstract: Project managers and developers need to acknowledge the influence of IS stakeholders’ perspectives and perceptions on the outcome of requirements negotiation - the essential component of requirements elicitation. This paper describes a conceptual study, which demonstrates such an influence and asserts that stakeholder perspectives, goals and issues are the key to negotiators’ perceptions of the system requirements. Analysis of two seminal IS case studies further supports this assertion and indicates that goals, perspectives and prior experience with negotiation techniques can guide stakeholders bargaining behaviour during requirements negotiation. Our findings also show that to achieve consensus on requirements, stakeholder perspectives must be aligned or accepted by negotiating parties. Achieving alignment of perspectives, however, is quite difficult because during requirements elicitation stakeholders’ goals continually alter due to their acquisition of technical and business knowledge, development of inter-personal relationships and creation of new perceptions of issues relevant to requirements negotiation.


title: Detection, Tracking and Classification of Road Signs in Adverse Conditions

abstract: In this paper a complete automatic system for road-sign detection, tracking, and classification is presented and evaluated. The processing of video frames in the L*a*b color space improves significantly the sign detection rate by processing the same frame in different normalized color spaces. The tracking module reduces significantly the processing time by transferring the sign detection information in the next frames and processing different radii signs in parallel. The proposed system is evaluated in normal, raining and night driving conditions. In a total number of 266 road-sign recordings, the complete system track and recognize successfully 216. The main source of system fault appears in city night driving due to the presence of a great number of light sources


title: Earthquake shakes Twitter users: real-time event detection by social sensors

abstract: Twitter, a popular microblogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. For example, when an earthquake occurs, people make many Twitter posts (tweets) related to the earthquake, which enables detection of earthquake occurrence promptly, simply by observing the tweets. As described in this paper, we investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location. We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering, which are widely used for location estimation in ubiquitous/pervasive computing. The particle filter works better than other comparable methods for estimating the centers of earthquakes and the trajectories of typhoons. As an application, we construct an earthquake reporting system in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (96% of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and sends e-mails to registered users. Notification is delivered much faster than the announcements that are broadcast by the JMA.


title: Generate, Segment and Refine: Towards Generic Manipulation Segmentation

abstract: Detecting manipulated images has become a significant emerging challenge. The advent of image sharing platforms and the easy availability of advanced photo editing software have resulted in a large quantities of manipulated images being shared on the internet. While the intent behind such manipulations varies widely, concerns on the spread of fake news and misinformation is growing. Current state of the art methods for detecting these manipulated images suffers from the lack of training data due to the laborious labeling process. We address this problem in this paper, for which we introduce a manipulated image generation process that creates true positives using currently available datasets. Drawing from traditional work on image blending, we propose a novel generator for creating such examples. In addition, we also propose to further create examples that force the algorithm to focus on boundary artifacts during training. Strong experimental results validate our proposal.


title: Three-Dimensional Mathematical Model for Deformation of Human Fasciae in Manual Therapy

abstract: CONTEXT ::: Although mathematical models have been developed for the bony movement occurring during chiropractic manipulation, such models are not available for soft tissue motion. ::: ::: ::: OBJECTIVE ::: To develop a three-dimensional mathematical model for exploring the relationship between mechanical forces and deformation of human fasciae in manual therapy using a finite deformation theory. ::: ::: ::: METHODS ::: The predicted stresses required to produce plastic deformation were evaluated for a volunteer subject's fascia lata, plantar fascia, and superficial nasal fascia. These stresses were then compared with previous experimental findings for plastic deformation in dense connective tissues. Using the three-dimensional mathematical model, the authors determined the changing amounts of compression and shear produced in fascial tissue during 20 seconds of manual therapy. ::: ::: ::: RESULTS ::: The three-dimensional model's equations revealed that very large forces, outside the normal physiologic range, are required to produce even 1% compression and 1% shear in fascia lata and plantar fascia. Such large forces are not required to produce substantial compression and shear in superficial nasal fascia, however. ::: ::: ::: CONCLUSION ::: The palpable sensations of tissue release that are often reported by osteopathic physicians and other manual therapists cannot be due to deformations produced in the firm tissues of plantar fascia and fascia lata. However, palpable tissue release could result from deformation in softer tissues, such as superficial nasal fascia.


title: Stochastic-deterministic and sensitivity analysis of the transient field generated by GPR dipole antenna and transmitted into a lossy ground

abstract: The paper deals with a stochastic-deterministic modeling and sensitivity analysis of the transient field generated by a ground penetrating radar (GPR) dipole antenna and transmitted into a lossy ground in time domain. Uncertainties are inherent to the environmental and geometrical parameters pertaining to GPR applications. Such settings include dielectric properties of a soil, the terrain inhomogeneity, surface roughness, uncertainty in detected depth of buried object etc. The stochastic modelling is carried out by means of Lagrange's stochastic collocation (SC) method while deterministic model is based on the Time Domain Integral Equation (TDIE) approach and related numerical solution via the space-time variant of Galerkin Bubnov's Indirect Boundary Element Method (GB-IBEM). The presented model provides a mean to rank the input parameters from the most to least influential one and to obtain statistical moments for the transient field values. The paper also aims to combine the time domain techniques with the stochastic method to provide a more complete description of GPR antenna generated fields.


title: Antenna design challenges for 4G

abstract: The trend in mobile wireless devices has been to provide faster access, improved processors, more memory, brighter and higher resolution screens, additional connectivity with Wi-Fi, GPS, third generation (3G), and fourth generation (4G) world access - all with longer battery life in thinner, sleeker packages. Compound this with the desire of mobile operators to expand their available band allocations, and what results is a difficult industrial design arena, where suppliers are vying afor physical space within the confines of a smartphone or similar device to accommodate necessary components.


title: Engineering highway hierarchies

abstract: Highway hierarchies exploit hierarchical properties inherent in real-world road networks to allow fast and exact point-to-point shortest-path queries. A fast preprocessing routine iteratively performs two steps: First, it removes edges that only appear on shortest paths close to source or target; second, it identifies low-degree nodes and bypasses them by introducing shortcut edges. The resulting hierarchy of highway networks is then used in a Dijkstra-like bidirectional query algorithm to considerably reduce the search space size without losing exactness. The crucial fact is that ‘far away’ from source and target it is sufficient to consider only high-level edges. Experiments with road networks for a continent show that using a preprocessing time of around 15 min, one can achieve a query time of around 1ms on a 2.0GHz AMD Opteron. Highway hierarchies can be combined with goal-directed search, they can be extended to answer many-to-many queries, and they can be used as a basis for other speed-up techniques (e.g., for transit-node routing and highway-node routing).


title: Design and Analysis of Low Flicker-Noise CMOS Mixers for Direct-Conversion Receivers

abstract: This paper presents the design and analysis of low flicker-noise RF mixers in a 0.18-mum CMOS process for C-band direct-conversion receivers. The low flicker-noise mixers are implemented by incorporating a double-balanced Gilbert-type configuration, the RF leakageless current bleeding technique, and the resonating technique for the tail capacitance. First, a double-balanced Gilbert-type mixer using the current bleeding technique has been fabricated and measured for lowering flicker corner frequency. Second, a double-balanced Gilbert-type mixer using the current bleeding technique with one resonating inductor has been designed to improve conversion gain and flicker-noise performance. Third, by using two separate inductors at the node between the current bleeding device and local oscillator switches, conversion gain and flicker-noise performance are significantly improved. A conventional Gilbert-type mixer without any technique has also been fabricated and measured for comparative purposes. The Gilbert-type mixer using the current bleeding technique with two resonating inductors has a measured conversion gain of 16.1 dB, a measured input third-order intercept point of -5 dBm, a measured noise figure of 9.8 dB at 1 MHz, and a flicker corner frequency of 125 kHz while consuming only 7 mW of dc power. To the best of our knowledge, the proposed mixer shows the lowest flicker corner frequency (125 kHz) with more than 15 dB of conversion gain in the CMOS process


title: The Measurement of Self-Esteem: Refining Our Methods

abstract: A review of the literature indicates that (a) very little attention has been devoted to measurement problems plaguing the study of self-esteem and (b) few studies employ more than one type of self-esteem instrument. This study addresses these issues by using eight measures of self-esteem involving self-reports, ratings by others, and a projective instrument. Their intercorrelations are examined to provide preliminary validational evidence; then, confirmatory factor analysis is used to construct measurement models and further assess the validity of the measures. The results suggest that two traditional questionnaires and a personal interview are valid in measuring experienced self-esteem, and three measures involving ratings by others are valid in measuring presented self-esteem These findings are consistent with previous multidimensional conceptualizations of self-esteem, indicating that a variety of methods is necessary to adequately measure self-concept.


An RNN-Based Quantized F0 Model with Multi-Tier Feedback Links for Text-to-Speech Synthesis


title: EFFECTS OF INTERNET USE AND SOCIAL RESOURCES ON CHANGES IN DEPRESSION

abstract: We examine how people's different uses of the Internet predict their later scores on a standard measure of depression, and how their existing social resources moderate these effects. In a longitudinal US survey conducted in 2001 and 2002, almost all respondents reported using the Internet for information, and entertainment and escape; these uses of the Internet had no impact on changes in respondents' level of depression. Almost all respondents also used the Internet for communicating with friends and family, and they showed lower depression scores six months later. Only about 20 percent of this sample reported using the Internet to meet new people and talk in online groups. Doing so changed their depression scores depending on their initial levels of social support. Those having high or medium levels of social support showed higher depression scores; those with low levels of social support did not experience these increases in depression. Our results suggest that individual differences in social resources and people's choices of how they use the Internet may account for the different outcomes reported in the literature.


Item based Collaborative filtering approach for Big Data application


title: Randomized Block Coordinate Descent for Online and Stochastic Optimization

abstract: Two types of low cost-per-iteration gradient descent methods have been extensively studied in parallel. One is online or stochastic gradient descent (OGD/SGD), and the other is randomzied coordinate descent (RBCD). In this paper, we combine the two types of methods together and propose online randomized block coordinate descent (ORBCD). At each iteration, ORBCD only computes the partial gradient of one block coordinate of one mini-batch samples. ORBCD is well suited for the composite minimization problem where one function is the average of the losses of a large number of samples and the other is a simple regularizer defined on high dimensional variables. We show that the iteration complexity of ORBCD has the same order as OGD or SGD. For strongly convex functions, by reducing the variance of stochastic gradients, we show that ORBCD can converge at a geometric rate in expectation, matching the convergence rate of SGD with variance reduction and RBCD.


Automatic Camera Calibration for Traffic Understanding.


title: Improving the Deductive System DES with Persistence by Using SQL DBMS's

abstract: This work presents how persistent predicates have been included in the in-memory deductive system DES by relying on external SQL database management systems. We introduce how persistence is supported from a user-point of view and the possible applications the system opens up, as the deductive expressive power is projected to relational databases. Also, we describe how it is possible to intermix computations of the deductive engine and the external database, explaining its implementation and some optimizations. Finally, a performance analysis is undertaken, comparing the system with current relational database systems.


title: Practical GPU and Voxel-Based Indirect Illumination for Real Time Computer Games

abstract: Indirect illumination techniques greatly improve the realism of computer generated 3D scenes. However, most of these techniques are used offline and rely on pre-baked solutions which do not support dynamic updates. There are some real time solutions but usually the degree of realism that they achieve is not convincing enough. We present a real time solution based on a voxel representation of the scene generated from the mesh representation paired with an approximate voxel cone tracing technique which achieves good visual results and interactive frame rates. We also present an integration of this solution into a custom game engine to demonstrate the usage viability of the method in real world scenarios.