title: Scene management models and overlap tests for tile-based rendering

abstract: Tile-based rendering (also called chunk rendering or bucket rendering) is a promising technique for low-power, 3D graphics platforms. This technique decomposes a scene into smaller regions called tiles and renders the tiles one-by-one. The advantage of this scheme is that a small memory integrated on the graphics accelerator can be used to store the color components and z values of one tile, so that accesses to the frame and z buffer are local, on-chip accesses which consume significantly less power than off-chip accesses. Tile-based rendering, however, requires that the primitives (commonly triangles) are sorted into bins corresponding to the tiles. This paper describes several algorithms for sorting the primitives into bins and evaluates their computational complexity and memory requirements. In addition, we present and evaluate several tests for determining if a triangle and a tile overlap. Experimental results obtained using several suitable 3D graphics workloads show that various trade-offs can be made and that, usually, better performance can be obtained by trading it for memory. This information allows the designer to select the appropriate method depending on the amount of memory available and the computational power.


title: Path following control for tracked vehicles based on slip-compensating odometry

abstract: Tracked vehicles have the advantage of stable locomotion on uneven terrain, and, as a result, such mechanisms are used for locomotion on outdoor robots, including those used for search and rescue. However, such mechanisms always slip when a tracked vehicle follows a curve, and the slippage generates large accumulated positioning errors in the vehicle compared with conventional wheeled mobile robots. To improve the accuracy of the odometry and enable a path-following control, the estimation of the track slippage is essential. In this paper, we propose an improved method of odometry for tracked vehicles to follow a straight line or a curve. In this method, the vehicle estimates the slip ratios using two encoders (attached to the actuators) and a gyro-sensor. Based on the improved odometry, the path-following control of tracked vehicles is significantly improved. The validity of the method was confirmed with experiments involving our tracked vehicle on several types of surfaces.


Risk Management Analysis in Software Projects which Use the Scrum Framework.


title: 9.3.2 Simulation-Based Design Using SysML Part 1: A Parametrics Primer

abstract: OMG SysML™ is a modeling language for specifying, analyzing, designing, and verifying complex systems. It is a general-purpose graphical modeling language with computer-sensible semantics. This Part 1 paper and its Part 2 companion show how SysML supports simulation-based design (SBD) via tutorial-like examples. Our target audience is end users wanting to learn about SysML parametrics in general and its applications to engineering design and analysis in particular. We include background on the development of SysML parametrics that may also be useful for other stakeholders (e.g, vendors and researchers). In Part 1 we walk through models of simple objects that progressively introduce SysML parametrics concepts. To enhance understanding by comparison and contrast, we present corresponding models based on composable objects (COBs). The COB knowledge representation has provided a conceptual foundation for SysML parametrics, including executability and validation. We end with sample analysis building blocks (ABBs) from mechanics of materials showing how SysML captures engineering knowledge in a reusable form. Part 2 employs these ABBs in a high diversity mechanical example that integrates computer-aided design and engineering analysis (CAD/CAE). The object and constraint graph concepts embodied in SysML parametrics and COBs provide modular analysis capabilities based on multi-directional constraints. These concepts and capabilities provide a semantically rich way to organize and reuse the complex relations and properties that characterize SBD models. Representing relations as non-causal constraints, which generally accept any valid combination of inputs and outputs, enhances modeling flexibility and expressiveness. We envision SysML becoming a unifying representation of domain-specific engineering analysis models that include fine-grain associativity with other domain- and system-level models, ultimately providing fundamental capabilities for next-generation systems lifecycle management.


title: The role of social cognition in emotion

abstract: Although recent research has shown that social cognition and emotion engage overlapping regions of the brain, few accounts of this overlap have been offered. What systems might be commonly or distinctively involved in each? The close functional relationship between social cognition and emotion might be understood in terms of a central role for mental state attribution in the understanding, learning and regulation of emotion. In each of these cases, mental state attributions might be supported by either stimulus-driven or more reflective processes.


title: Leadership for primary health care research.

abstract: Over the last decade, I have put together a new theory of leadership. This paper describes its four propositions, which are consistent with the research literature but which lead to conclusions that are not commonly held and seldom put into practice. The first proposition is a model describing the territory of leadership that is different from either the Leadership Qualities Framework, 2006 or the Medical Leadership Competency Framework, 2010, both of which have been devised specifically for the NHS (National Health Service). The second proposition concerns the ill-advised attempt of individuals to become expert in all aspects of leadership: complete in themselves. The third suggests how personality and capability are related. The fourth embraces and recommends the notion of complementary differences among leaders. As the NHS seeks increasing leadership effectiveness, these propositions may need to be considered and their implications woven into the fabric of NHS leader selection and development. Primary Health Care research, like all fields of collective human endeavour, is eminently in need of sound leadership and the same principles that facilitate sound leadership in other fields is likely to be relevant to research teams.


title: Smooth contact-aware facial blendshapes transfer

abstract: In this paper, we propose a method to improve the quality of the facial blendshapes synthesized using deformation transfer by introducing the idea of contact and smoothness awareness. Simply applying deformation transfer for face models easily results in penetrations and separations which do not exist in the original data. As there is no concept of smoothness, crumpling artifacts appear where the mesh is extremely concentrated, e.g. eyelids and mouth corners. We solve these issues by first introducing the concept of virtual triangles and then adding a Laplacian energy term into the linear problem to be solved for the deformation transfer. Experimental results show that our system reduces the amount of artifacts caused by the original deformation transfer, improving the efficiency of the animation pipeline.


title: Addressing Training Bias via Automated Image Annotation

abstract: Build accurate DNN models requires training on large labeled, context specific datasets, especially those matching the target scenario. We believe advances in wireless localization, working in unison with cameras, can produce automated annotation of targets on images and videos captured in the wild. Using pedestrian and vehicle detection as examples, we demonstrate the feasibility, benefits, and challenges of an automatic image annotation system. Our work calls for new technical development on passive localization, mobile data analytics, and error-resilient ML models, as well as design issues in user privacy policies.


title: Generative Deep Neural Networks for Dialogue: A Short Review

abstract: Researchers have recently started investigating deep neural networks for dialogue applications. In particular, generative sequence-to-sequence (Seq2Seq) models have shown promising results for unstructured tasks, such as word-level dialogue response generation. The hope is that such models will be able to leverage massive amounts of data to learn meaningful natural language representations and response generation strategies, while requiring a minimum amount of domain knowledge and hand-crafting. An important challenge is to develop models that can effectively incorporate dialogue context and generate meaningful and diverse responses. In support of this goal, we review recently proposed models based on generative encoder-decoder neural network architectures, and show that these models have better ability to incorporate long-term dialogue history, to model uncertainty and ambiguity in dialogue, and to generate responses with high-level compositional structure.


PolyFuse: A Large-Scale Hybrid Data Fusion System


title: Weakly-Supervised Video Object Grounding from Text by Loss Weighting and Object Interaction

abstract: We study weakly-supervised video object grounding: given a video segment and a corresponding descriptive sentence, the goal is to localize objects that are mentioned from the sentence in the video. During training, no object bounding boxes are available, but the set of possible objects to be grounded is known beforehand. Existing approaches in the image domain use Multiple Instance Learning (MIL) to ground objects by enforcing matches between visual and semantic features. A naive extension of this approach to the video domain is to treat the entire segment as a bag of spatial object proposals. However, an object existing sparsely across multiple frames might not be detected completely since successfully spotting it from one single frame would trigger a satisfactory match. To this end, we propagate the weak supervisory signal from the segment level to frames that likely contain the target object. For frames that are unlikely to contain the target objects, we use an alternative penalty loss. We also leverage the interactions among objects as a textual guide for the grounding. We evaluate our model on the newly-collected benchmark YouCook2-BoundingBox and show improvements over competitive baselines.


title: Mapping ground surface deformation using temporarily coherent point SAR interferometry: Application to Los Angeles Basin

abstract: Abstract Multi-temporal interferometric synthetic aperture radar (InSAR) is an effective tool to detect long-term seismotectonic motions by reducing the atmospheric artifacts, thereby providing more precise deformation signal. The commonly used approaches such as persistent scatterer InSAR (PSInSAR) and small baseline subset (SBAS) algorithms need to resolve the phase ambiguities in interferogram stacks either by searching a predefined solution space or by sparse phase unwrapping methods; however the efficiency and the success of phase unwrapping cannot be guaranteed. We present here an alternative approach – temporarily coherent point (TCP) InSAR (TCPInSAR) – to estimate the long term deformation rate without the need of phase unwrapping. The proposed approach has a series of innovations including TCP identification, TCP network and TCP least squares estimator. We apply the proposed method to the Los Angeles Basin in southern California where structurally active faults are believed capable of generating damaging earthquakes. The analysis is based on 55 interferograms from 32 ERS-1/2 images acquired during Oct. 1995 and Dec. 2000. To evaluate the performance of TCPInSAR on a small set of observations, a test with half of interferometric pairs is also performed. The retrieved TCPInSAR measurements have been validated by a comparison with GPS observations from Southern California Integrated GPS Network. Our result presents a similar deformation pattern as shown in past InSAR studies but with a smaller average standard deviation (4.6 mm) compared with GPS observations, indicating that TCPInSAR is a promising alternative for efficiently mapping ground deformation even from a relatively smaller set of interferograms.


title: Centralised supply chain planning at IKEA

abstract: Purpose - The purpose is to analyse the prerequisites and effects of centralised supply chain planning at IKEA, and to explore how the planning process, planning system, and planning organization make up a centralised planning approach. ::: ::: Design/methodology/approach - The paper is based on a longitudinal case study of IKEA's implementation of global supply chain planning. The literature review generated a framework which identifies prerequisites for, approaches to, and the effects of and obstacles to centralised supply chain planning. This framework was used to analyse IKEA’s supply chain planning before and after the implementation. Finally, we reflected upon the learning from IKEA and refined the framework. ::: ::: Findings - A number of prerequisites for centralised supply chain planning were identified: functional products, vertical integration, a dominating organization possessing the power and competence to enforce the implementation, and the use of one planning domain possessing all critical planning information. The direct effects of centralised supply chain planning were related to supply chain integration, standardisation, specialisation, and learning effects. Implementing centralised supply chain planning in an appropriate planning context led to several operational performance improvements. Obstacles were mainly related to human and organizational, as well as to software and data issues. ::: ::: Research limitations/implications - This is a first approach towards development of a framework of how to design, use and benefit from centralised supply chain planning. The developed conceptual model, which is refined through the case study, offers some generalizability in researching centralised supply chain planning. ::: ::: Practical implications - The findings show that centralised supply chain planning is a necessity for a large and growing, global supply chain striving for low-cost production and efficiency. ::: ::: Originality/value - IKEA is a unique case with its supply chain characteristics and recently implemented planning concept.


title: Mechanisms and consequences of intestinal dysbiosis

abstract: The composition of the gut microbiota is in constant flow under the influence of factors such as the diet, ingested drugs, the intestinal mucosa, the immune system, and the microbiota itself. Natural variations in the gut microbiota can deteriorate to a state of dysbiosis when stress conditions rapidly decrease microbial diversity and promote the expansion of specific bacterial taxa. The mechanisms underlying intestinal dysbiosis often remain unclear given that combinations of natural variations and stress factors mediate cascades of destabilizing events. Oxidative stress, bacteriophages induction and the secretion of bacterial toxins can trigger rapid shifts among intestinal microbial groups thereby yielding dysbiosis. A multitude of diseases including inflammatory bowel diseases but also metabolic disorders such as obesity and diabetes type II are associated with intestinal dysbiosis. The characterization of the changes leading to intestinal dysbiosis and the identification of the microbial taxa contributing to pathological effects are essential prerequisites to better understand the impact of the microbiota on health and disease.


title: A dissimilarity-based approach to predictive maintenance with application to HVAC systems

abstract: The goal of predictive maintenance is to forecast the occurrence of faults of an appliance, in order to proactively take the necessary actions to ensure its availability. In many application scenarios, predictive maintenance is applied to a set of homogeneous appliances. In this paper, we firstly review taxonomies and main methodologies currently used for condition-based maintenance; secondly, we argue that the mutual dissimilarities of the behaviours of all appliances of this set (the"cohort") can be exploited to detect upcoming faults. Specifically, inspired by dissimilarity-based representations, we propose a novel machine learning approach based on the analysis of concurrent mutual differences of the measurements coming from the cohort. We evaluate our method over one year of historical data from a cohort of 17 HVAC (Heating, Ventilation and Air Conditioning) systems installed in an Italian hospital. We show that certain kinds of faults can be foreseen with an accuracy, measured in terms of area under the ROC curve, as high as 0.96.


title: Data analysis and visualization for the bridge deck inspection and evaluation robotic system

abstract: Bridge deck inspection is essential task to monitor the health of the bridges. Condition monitoring and timely implementation of maintenance and rehabilitation procedures are needed to reduce future costs associated with bridge management. A number of Nondestructive Evaluation (NDE) technologies are currently used in bridge deck inspection and evaluation, including impact-echo (IE), ground penetrating radar (GPR), electrical resistivity (ER), ultrasonic surface waves (USW) testing, and visual inspection. However, current NDE data collection is manually conducted and thus faces with several problems such as prone to human errors, safety risks due to open traffic, and high cost process. This paper reports the automated data collection and analysis for bridge decks based on our novel robotic system which can autonomously and accurately navigate on the bridge. The developed robotic system can lessen the cost and time of the bridge deck data collection and risks of human inspections. The advanced software is developed to allow the robot to collect visual images and conduct NDE measurements. The image stitching algorithm to build a whole bridge deck image from individual images is presented in detail. The ER, IE and USW data collected by the robot are analyzed to generate the corrosion, delamination and concrete elastic modulus maps of the deck, respectively. These condition maps provide detail information of the bridge deck quality. The automated bridge deck data collection and analysis is developed. The image stitching algorithm allowed to generate a very high resolution image of the whole bridge deck, and the bridge viewer software allows to calibrate the stitched image to the bridge coordinate. The corrosion, delamination and elastic modulus maps were built based on ER, IE and USW data collected by the robot to provide easy evaluation and condition monitoring of bridge decks.


title: A comparison of feed-forward and recurrent neural networks in time series forecasting

abstract: Forecasting performances of feed-forward and recurrent neural networks (NN) trained with different learning algorithms are analyzed and compared using the Mackey-Glass nonlinear chaotic time series. This system is a known benchmark test whose elements are hard to predict. Multi-layer Perceptron NN was chosen as a feed-forward neural network because it is still the most commonly used network in financial forecasting models. It is compared with the modified version of the so-called Dynamic Multi-layer Perceptron NN characterized with a dynamic neuron model, i.e., Auto Regressive Moving Average filter built into the hidden layer neurons. Thus, every hidden layer neuron has the ability to process previous values of its own activity together with new input signals. The obtained results indicate satisfactory forecasting characteristics of both networks. However, recurrent NN was more accurate in practically all tests using less number of hidden layer neurons than the feed-forward NN. This study once again confirmed a great effectiveness and potential of dynamic neural networks in modeling and predicting highly nonlinear processes. Their application in the design of financial forecasting models is therefore most recommended.


title: Thermoresistive Effect for Advanced Thermal Sensors: Fundamentals, Design Considerations, and Applications

abstract: Microelectromechanical systems sensors have been intensively developed utilizing various physical concepts, such as piezoresistive, piezoelectric, and thermoresistive effects. Among these sensing concepts, the thermoresistive effect is of interest for a wide range of thermal sensors and devices, thanks to its simplicity in implementation and high sensitivity. The effect of temperature on the electrical resistance of some metals and semiconductors has been thoroughly investigated, leading to the significant growth and successful demonstration of thermal-based sensors, such as temperature sensors, convective accelerometers and gyroscopes, and thermal flow sensors. In this paper, we review the fundamentals of the thermoresistive effect in metals and semiconductors. We also discuss the influence of design and fabrication parameters on the thermoresistive sensitivity. This paper includes several desirable features of thermoresistive sensors and recent developments in these sensors are summarized. This review provides insights into how it is affected by various parameters, and useful guidance for industrial designers in terms of high sensitivity and linearity and fast response. [2017-0022]


title: Loitering detection using an associating pedestrian tracker in crowded scenes

abstract: This paper presents a loitering detection method using an associating pedestrian tracker in public areas. We analyze the spatio-temporal characteristics to perform monitoring of people and generate alerts when loitering persons are detected. To determine and adjust a time threshold for raising an alarm, we obtain the mean time of stay for normal and abnormal situation. In addition, we consider an optimal threshold for staying time and escaping time to deal with various conditions. For object identification, we measure the mean square error and histogram of oriented gradients. In order to trace moving objects continuously, the HSI color model and a combination of Euclidean distance, color difference, and shape difference are measured based on consistent labeling tracking. To evaluate the performance of our method, we showed detection results of the PETS2007 dataset using thresholds obtained by our proposed methods. Our experiments show promising results with 75.45 % averaged recall rate and 87.12 % averaged precision rate were obtained in loitering objects. We also compared the proposed method to other reported methods. The experimental results showed a significant improvement on precision.


title: A thin slice perspective on the accuracy of first impressions

abstract: Abstract The accuracy of first impressions was examined by investigating judged construct (negative affect, positive affect, the Big five personality variables, intelligence), exposure time (5, 20, 45, 60, and 300 s), and slice location (beginning, middle, end). Three hundred and thirty four judges rated 30 targets. Accuracy was defined as the correlation between a judge’s ratings and the target’s criterion scores on the same construct. Negative affect, extraversion, conscientiousness, and intelligence were judged moderately well after 5-s exposures; however, positive affect, neuroticism, openness, and agreeableness required more exposure time to achieve similar levels of accuracy. Overall, accuracy increased with exposure time, judgments based on later segments of the 5-min interactions were more accurate, and 60 s yielded the optimal ratio between accuracy and slice length. Results suggest that accuracy of first impressions depends on the type of judgment made, amount of exposure, and temporal location of the slice of judged social behavior.


title: Fuzzy logic controller implementation on a FPGA using VHDL

abstract: Fuzzy-logic-based control (FLC) systems have emerged as one of the most promising areas for research in Applied Computational Intelligence. These systems operate with knowledge represented in a linguistic form (IF-THEN rules) that describes relations which are not precisely known, but those effects are intuitively understood by humans. This fundamental feature makes FLC a powerful tool for industrial applications, since complex systems can be controlled by easily comprehensive rules. The growth in number of fuzzy logic applications led to the need of finding efficient and economic ways to implement them. The Field Programmable Gate Arrays (FPGAs) devices, with their reconfigurable logic, practicality, portability, low consumption of energy, high operation speedy and large datastorage capacity, are a great choice for FLC embedded systems project development and prototyping. In this paper, the design and implementation of a Mamdani-type Fuzzy controller is demonstrated using VHDL programming language.


title: Evaluation of candidate genes MAP2K4, MADH4, ACVR1B, and BRCA2 in familial pancreatic cancer: deleterious BRCA2 mutations in 17%.

abstract: It is estimated that familial aggregation and genetic susceptibility play a role in as many as 10% of pancreatic ductal adenocarcinomas. To investigate the role of germ-line mutations in the etiology of pancreatic cancer, we have analyzed samples from patients with pancreatic cancer enrolled in the NFPTR for mutations in four tumor suppressor candidate genes: (a) MAP2K4; (b) MADH4; (c) ACVR1B; and (d) BRCA2 by direct sequencing of constitutional DNA. These genes are mutated in clinically sporadic pancreatic cancer, but germ-line mutations are either not reported or anecdotal in familial pancreatic cancer. Pancreatic cancer patient samples were selected from kindreds in which three or more family members were affected with pancreatic cancer, at least two of which were first-degree relatives. No mutations were identified in mitogen-activated protein kinase kinase 4 (0 of 22), MADH4 (0 of 22), or ACVR1B (0 of 29), making it unlikely that germ-line mutations in these genes account for a significant number of inherited pancreatic cancers. BRCA2 gene sequencing identified five mutations (5 of 29, 17.2%) that are believed to be deleterious and one point mutation (M192T) unreported previously. Three patients harbored the common 6174delT frameshift mutation, one had the splice site mutation IVS 16-2A > G, and one had the splice site mutation IVS 15-1G > A. Two of the five BRCA2 mutation carriers reported a family history of breast cancer, and none reported a family history of ovarian cancer. These findings confirm the increased risk of pancreatic cancer in individuals with BRCA2 mutations and identify germ-line BRCA2 mutations as the most common inherited genetic alteration yet identified in familial pancreatic cancer.


title: HF, VHF, and UHF systems and technology

abstract: A wide variety of unique systems and components inhabits the HF, VHF, and UHF bands. Many communication systems (ionospheric, meteor-burst, and troposcatter) provide beyond-line-of-sight coverage and operate independently of external infrastructure. Broadcasting and over-the-horizon radar also operate in these bands. Magnetic-resonance imaging uses HF/VHF signals to see the interior of a human body, and RF heating is used in a variety of medical and industrial applications. Receivers typically employ a mix of analog and digital-signal-processing techniques. Systems for these frequencies make use of RF-power MOSFETs, p-i-n diodes, and ferrite-loaded transmission-line transformers.


title: Malware detection using adaptive data compression

abstract: A popular approach in current commercial anti-malware software detects malicious programs by searching in the code of programs for scan strings that are byte sequences indicative of malicious code. The scan strings, also known as the signatures of existing malware, are extracted by malware analysts from known malware samples, and stored in a database often referred to as a virus dictionary. This process often involves a significant amount of human efforts. In addition, there are two major limitations in this technique. First, not all malicious programs have bit patterns that are evidence of their malicious nature. Therefore, some malware is not recorded in the virus dictionary and can not be detected through signature matching. Second, searching for specific bit patterns will not work on malware that can take many forms--obfuscated malware. Signature matching has been shown to be incapable of identifying new malware patterns and fails to recognize obfuscated malware. This paper presents a malware detection technique that discovers malware by means of a learning engine trained on a set of malware instances and a set of benign code instances. The learning engine uses an adaptive data compression model--prediction by partial matching (PPM)--to build two compression models, one from the malware instances and the other from the benign code instances. A code instance is classified, either as "malware" or "benign", by minimizing its estimated cross entropy. Our preliminary results are very promising. We achieved about 0.94 true positive rate with as low as 0.016 false positive rate. Our experiments also demonstrate that this technique can effectively detect unknown and obfuscated malware.


title: Like parent, like child? Dietary resemblance in families

abstract: Studies investigating dietary resemblance between parents and their children have gained mixed results, and the resemblance seems to vary across nutrients, foods, dietary-assessment tools used, and parent-child pairs. We investigated parent-child dietary resemblance using a novel approach in applying statistical analysis, which allowed the comparison of ‘whole-diet’ between parents and their children. Additionally, we sought to establish whether sociodemographic factors or family meals were associated with dietary resemblance and whether parent-child dietary resemblance was dependent on the parent providing food consumption data on behalf of the child (father or mother, “the respondent”). The DAGIS study investigated health behaviors among Finnish preschoolers using a cross-sectional design. One parent filled in a food frequency questionnaire (FFQ) measuring the child’s food consumption outside preschool hours during the last week. In addition, we instructed both parents or legal guardians, should the child have two, to fill in a similar FFQ regarding their own food use. Parents also reported their educational level, the number of children living in the same household, and the number of family meals. As a measure of dietary resemblance between a parent and a child, we computed Spearman correlations ranging mostly from no resemblance (0) to complete resemblance (+ 1) between parent-child pairs over the ‘whole-diet’ (excluding preschool hours). These resemblance measures were further investigated using linear mixed models. We obtained 665 father-child and 798 mother-child resemblance measures. Mother-child resemblance was on average 0.57 and stronger than father-child resemblance (0.50, p < 0.0001), which was explained by a parent-respondent interaction: the diet of the child resembled more the diet of the parent who provided food consumption data for the child. In univariate models, father- and mother-reported number of family meals were positively associated with father-child and mother-child resemblances. Mother-reported number of family meals was positively associated with mother-child resemblance in a full model. The diet of the child seems to resemble more the diet of the parent responsible for the reporting of food consumption. Studies should report who provided the food consumption data for the child and take this into account in analyses, since reporter-bias can influence the results.


title: Mining Android App Descriptions for Permission Requirements Recommendation

abstract: During the development or maintenance of an Android app, the app developer needs to determine the app's security and privacy requirements such as permission requirements. Permission requirements include two folds. First, what permissions (i.e., access to sensitive resources, e.g., location or contact list) the app needs to request. Second, how to explain the reason of permission usages to users. In this paper, we focus on the multiple challenges that developers face when creating permission-usage explanations. We propose a novel framework, CLAP, that mines potential explanations from the descriptions of similar apps. CLAP leverages information retrieval and text summarization techniques to find frequent permission usages. We evaluate CLAP on a large dataset containing 1.4 million Android apps. The evaluation results outperform existing state-of-the-art approaches, showing great promise of CLAP as a tool for assisting developers and permission requirements discovery.


title: (MP) 2 T: Multiple People Multiple Parts Tracker

abstract: We present a method for multi-target tracking that exploits the persistence in detection of object parts. While the implicit representation and detection of body parts have recently been leveraged for improved human detection, ours is the first method that attempts to temporally constrain the location of human body parts with the express purpose of improving pedestrian tracking. We pose the problem of simultaneous tracking of multiple targets and their parts in a network flow optimization framework and show that parts of this network need to be optimized separately and iteratively, due to inter-dependencies of node and edge costs. Given potential detections of humans and their parts separately, an initial set of pedestrian tracklets is first obtained, followed by explicit tracking of human parts as constrained by initial human tracking. A merging step is then performed whereby we attempt to include part-only detections for which the entire human is not observable. This step employs a selective appearance model, which allows us to skip occluded parts in description of positive training samples. The result is high confidence, robust trajectories of pedestrians as well as their parts, which essentially constrain each other's locations and associations, thus improving human tracking and parts detection. We test our algorithm on multiple real datasets and show that the proposed algorithm is an improvement over the state-of-the-art.


title: Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources

abstract: The quality of web sources has been traditionally evaluated using exogenous signals such as the hyperlink structure of the graph. We propose a new approach that relies on endogenous signals, namely, the correctness of factual information provided by the source. A source that has few false facts is considered to be trustworthy. ::: ::: The facts are automatically extracted from each source by information extraction methods commonly used to construct knowledge bases. We propose a way to distinguish errors made in the extraction process from factual errors in the web source per se, by using joint inference in a novel multi-layer probabilistic model. ::: ::: We call the trustworthiness score we computed Knowledge-Based Trust (KBT). On synthetic data, we show that our method can reliably compute the true trustworthiness levels of the sources. We then apply it to a database of 2.8B facts extracted from the web, and thereby estimate the trustworthiness of 119M webpages. Manual evaluation of a subset of the results confirms the effectiveness of the method.


title: Cloud Armor: a platform for credibility-based trust management of cloud services

abstract: Trust management of cloud services is emerging as an important research issue in recent years, which poses significant challenges because of the highly dynamic, distributed, and non-transparent nature of cloud services. This paper describes Cloud Armor, a platform for credibility-based trust management of cloud services. The platform provides a crawler for automatic cloud services discovery, an adaptive and robust credibility model for measuring the credibility of feedbacks, and a trust-based recommender to recommend the most trustworthy cloud services to users. This paper presents the motivation, system design, implementation, and a demonstration of the Cloud Armor platform.


title: Stabilizing Dynamic Controllers for Hybrid Systems: A Hybrid Control Lyapunov Function Approach

abstract: This paper proposes a dynamic controller structure and a systematic design procedure for stabilizing discrete-time hybrid systems. The proposed approach is based on the concept of control Lyapunov functions (CLFs), which, when available, can be used to design a stabilizing state-feedback control law. In general, the construction of a CLF for hybrid dynamical systems involving both continuous and discrete states is extremely complicated, especially in the presence of non-trivial discrete dynamics. Therefore, we introduce the novel concept of a hybrid control Lyapunov function, which allows the compositional design of a discrete and a continuous part of the CLF, and we formally prove that the existence of a hybrid CLF guarantees the existence of a classical CLF. A constructive procedure is provided to synthesize a hybrid CLF, by expanding the dynamics of the hybrid system with a specific controller dynamics. We show that this synthesis procedure leads to a dynamic controller that can be implemented by a receding horizon control strategy, and that the associated optimization problem is numerically tractable for a fairly general class of hybrid systems, useful in real world applications. Compared to classical hybrid receding horizon control algorithms, the proposed approach typically requires a shorter prediction horizon to guarantee asymptotic stability of the closed-loop system, which yields a reduction of the computational burden, as illustrated through two examples.


title: K-means clustering for efficient and robust registration of multi-view point sets

abstract: Efficiency and robustness are the important performance for the registration of multi-view point sets. To address these two issues, this paper casts the multi-view registration into a clustering problem, which can be solved by the extended K-means clustering algorithm. Before the clustering, all the centroids are uniformly sampled from the initially aligned point sets involved in the multi-view registration. Then, two standard K-means steps are utilized to assign all points to one special cluster and update each clustering centroid. Subsequently, the shape comprised by all cluster centroids can be used to sequentially estimate the rigid transformation for each point set. These two standard K-means steps and the step of transformation estimation constitute the extended K-means clustering algorithm, which can achieve the clustering as well as the multi-view registration by iterations. To show its superiority, the proposed approach has tested on some public data sets and compared with the-state-of-art algorithms. Experimental results illustrate its good efficiency and robustness for the registration of multi-view point sets.


title: Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks

abstract: Sentence-level classification and sequential labeling are two fundamental tasks in language understanding. While these two tasks are usually modeled separately, in reality, they are often correlated, for example in intent classification and slot filling, or in topic classification and named-entity recognition. In order to utilize the potential benefits from their correlations, we propose a jointly trained model for learning the two tasks simultaneously via Long Short-Term Memory (LSTM) networks. This model predicts the sentence-level category and the word-level label sequence from the stepwise output hidden representations of LSTM. We also introduce a novel mechanism of "sparse attention" to weigh words differently based on their semantic relevance to sentence-level classification. The proposed method outperforms baseline models on ATIS and TREC datasets.


title: A broadband SiGe power amplifier in an efficient polar transmitter using envelope-tracking for mobile WiMAX

abstract: This paper presents a broadband cascode SiGe power amplifier (PA) in the polar transmitter (TX) system using the envelope-tacking (ET) technique. The cascode PA achieves the power-added efficiency (PAE) of >30% across the frequency range of 0.6∼2.4 GHz in continuous wave (CW) mode. The ET-based polar TX system using this cascode PA is evaluated and compared with the conventional stand-alone cascode PA. The experimental data shows that the cascode PA is successfully linearized by the ET scheme, passing the stringent WiMAX spectral mask and the required error vector magnitude (EVM). The entire polar TX system reaches the PAE of 30%/36% at the average output power of 18/17 dBm at 2.3/0.7 GHz for WiMAX 16QAM 3.5 MHz signals. These measurement results suggest that our saturated cascode SiGe PA can be attractive for dual-mode WiMAX applications.


title: MIKE: Keyphrase Extraction by Integrating Multidimensional Information

abstract: Traditional supervised keyphrase extraction models depend on the features of labelled keyphrases while prevailing unsupervised models mainly rely on structure of the word graph, with candidate words as nodes and edges capturing the co-occurrence information between words. However, systematically integrating all these multidimensional heterogeneous information into a unified model is relatively unexplored. In this paper, we focus on how to effectively exploit multidimensional information to improve the keyphrase extraction performance (MIKE). Specifically, we propose a random-walk parametric model, MIKE, that learns the latent representation for a candidate keyphrase that captures the mutual influences among all information, and simultaneously optimizes the parameters and ranking scores of candidates in the word graph. We use the gradient-descent algorithm to optimize our model and show the comprehensive experiments with two publicly-available WWW and KDD datasets in Computer Science. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art graph-based keyphrase extraction approaches.


title: Marrying uncertainty and time in knowledge graphs

abstract: The management of uncertainty is crucial when harvesting structured content from unstructured and noisy sources. Knowledge Graphs ( KGs ) are a prominent example. KGs maintain both numerical and non-numerical facts, with the support of an underlying schema. These facts are usually accompanied by a confidence score that witnesses how likely is for them to hold. Despite their popularity, most of existing KGs focus on static data thus impeding the availabilityof timewise knowledge. What is missing is a comprehensive solution for the management of uncertain and temporal data in KGs . The goal of this paper is to fill this gap. We rely on two main ingredients. The first is a numerical extension of Markov Logic Networks (MLNs) that provide the necessary underpinning to formalize the syntax and semantics of uncertain temporal KGs . The second is a set of Datalog constraints with inequalities that extend the underlying schema of the KGs and help to detect inconsistencies. From a theoretical point of view, we discuss the complexity of two important classes of queries for uncertain temporal KGs: maximuma-posteriori and conditional probability inference. Due to the hardness of these problems and the fact that MLN solvers do not scale well, we also explore the usage of Probabilistic Soft Logics (PSL) as a practical tool to support our reasoning tasks. We report on an experimental evaluation comparing the MLN and PSL approaches.


title: High-resolution magnetic resonance-guided posterior femoral cutaneous nerve blocks

abstract: OBJECTIVE ::: To assess the feasibility, technical success, and effectiveness of high-resolution magnetic resonance (MR)-guided posterior femoral cutaneous nerve (PFCN) blocks. ::: ::: ::: MATERIALS AND METHODS ::: A retrospective analysis of 12 posterior femoral cutaneous nerve blocks in 8 patients [6 (75%) female, 2 (25%) male; mean age, 47 years; range, 42-84 years] with chronic perineal pain suggesting PFCN neuropathy was performed. Procedures were performed with a clinical wide-bore 1.5-T MR imaging system. High-resolution MR imaging was utilized for visualization and targeting of the PFCN. Commercially available, MR-compatible 20-G needles were used for drug delivery. Variables assessed were technical success (defined as injectant surrounding the targeted PFCN on post-intervention MR images) effectiveness, (defined as post-interventional regional anesthesia of the target area innervation downstream from the posterior femoral cutaneous nerve block), rate of complications, and length of procedure time. ::: ::: ::: RESULTS ::: MR-guided PFCN injections were technically successful in 12/12 cases (100%) with uniform perineural distribution of the injectant. All blocks were effective and resulted in post-interventional regional anesthesia of the expected areas (12/12, 100%). No complications occurred during the procedure or during follow-up. The average total procedure time was 45 min (30-70) min. ::: ::: ::: CONCLUSIONS ::: Our initial results demonstrate that this technique of selective MR-guided PFCN blocks is feasible and suggest high technical success and effectiveness. Larger studies are needed to confirm our initial results.


title: Influence of Privacy Attitude and Privacy Cue Framing on Android App Choices

abstract: Transmission of personally identifiable information from smartphone apps has become ubiquitous as smartphones themselves. Privacy controls provided in the form of permissions warnings falls insu cient especially for communicating risk during app installation. Presenting easy to understand privacy risk icons/cues would help people make low risk app choices. However, the human factor requirements for designing such privacy risk icons are largely unknown. Towards this, we conducted a user experiment with 480 participants who made a series of app choices with/without privacy priming and with/without privacy risk communicating icons. Overall, presenting risk communicating icons along with app benefit icons had a significant e ect on user app choices in terms of riskbenefit trade-o . We found that one type of privacy icon framing led to mediocre app choices under particular conditions. We found that priming for privacy led to increased concern while choosing apps but did not have an augmenting e ect on final app choices when combined with certain type of privacy framing. We conclude by proposing human factor based recommendations for designing privacy risk communicating icons.


title: Factorization machines with follow-the-regularized-leader for CTR prediction in display advertising

abstract: Predicting ad click-through rates is the core problem in display advertising, which has received much attention from the machine learning community in recent years. In this paper, we present an online learning algorithm for click-though rate prediction, namely Follow-The-Regularized-Factorized-Leader (FTRFL), which incorporates the Follow-The-Regularized-Leader (FTRL-Proximal) algorithm with per-coordinate learning rates into Factorization machines. Experiments on a real-world advertising dataset show that the FTRFL method outperforms the baseline with stochastic gradient descent, and has a faster rate of convergence.


title: Silhouette Extraction in Hough Space

abstract: Object-space silhouette extraction is an important problem in fields ranging fr om non-photorealistic computer graphics to medical robotics. We present an efficient silhouette extracto r for triangle meshes under perspective projection and make three contributions. First, we describe a novel applic ation of 3D Hough transforms, which allows us to organize mesh data more effectively for silhouette computations tha n the traditional dual transform. Next, we introduce an incremental silhouette update algorithm which operate s on an octree augmented with neighbour information and optimized for efficient low-level traversal. Finally, wepresent a method for initial extraction of silhouette, using the same data structure, whose performance is linear in the size of the extracted silhouette. We demonstrate significant performance improvements given by our appro ach over the current state of the art.


title: ACME: adaptive compilation made efficient

abstract: Research over the past five years has shown significant performance improvements using a technique called adaptive compilation. An adaptive compiler uses a compile-execute-analyze feedback loop to find the combination of optimizations and parameters that minimizes some performance goal, such as code size or execution time.Despite its ability to improve performance, adaptive compilation has not seen widespread use because of two obstacles: the large amounts of time that such systems have used to perform the many compilations and executions prohibits most users from adopting these systems, and the complexity inherent in a feedback-driven adaptive system has made it difficult to build and hard to use.A significant portion of the adaptive compilation process is devoted to multiple executions of the code being compiled. We have developed a technique called virtual execution to address this problem. Virtual execution runs the program a single time and preserves information that allows us to accurately predict the performance of different optimization sequences without running the code again. Our prototype implementation of this technique significantly reduces the time required by our adaptive compiler.In conjunction with this performance boost, we have developed a graphical-user interface (GUI) that provides a controlled view of the compilation process. By providing appropriate defaults, the interface limits the amount of information that the user must provide to get started. At the same time, it lets the experienced user exert fine-grained control over the parameters that control the system.


title: Question Type Guided Attention in Visual Question Answering

abstract: Visual Question Answering (VQA) requires integration of feature maps with drastically different structures. Image descriptors have structures at multiple spatial scales, while lexical inputs inherently follow a temporal sequence and naturally cluster into semantically different question types. A lot of previous works use complex models to extract feature representations but neglect to use high-level information summary such as question types in learning. In this work, we propose Question Type-guided Attention (QTA). It utilizes the information of question type to dynamically balance between bottom-up and top-down visual features, respectively extracted from ResNet and Faster R-CNN networks. We experiment with multiple VQA architectures with extensive input ablation studies over the TDIUC dataset and show that QTA systematically improves the performance by more than 5% across multiple question type categories such as “Activity Recognition”, “Utility” and “Counting” on TDIUC dataset compared to the state-of-art. By adding QTA on the state-of-art model MCB, we achieve 3% improvement in overall accuracy. Finally, we propose a multi-task extension to predict question types which generalizes QTA to applications that lack question type, with a minimal performance loss.


title: Threat-Driven Architectural Design of Secure Information Systems

abstract: To deal with software security issues in the early stages of system development, this paper presents a threatdriven approach to the architectural design and analysis of secure information systems. In this approach, we model security threats to systems with misuse cases and mitigation requirements with mitigation use cases at the requirements analysis phase. Then we drive system architecture design (including the identification of architectural components and their connections) by use cases, misuse cases, and mitigation use cases. According to the misuse case-based threat model, we analyze whether or not a candidate architecture is resistant to the identified security threats and what constraints must be imposed on the choices of system implementation. This provides a smooth transition from requirements specification to high-level design and greatly improves the traceability of security concerns in high assurance information systems. We demonstrate our approach through a case study on a security-intensive payroll information system.


title: Designing the Mind of a Social Robot

abstract: Humans have an innate tendency to anthropomorphize surrounding entities and have always been fascinated by the creation of machines endowed with human-inspired capabilities and traits. In the last few decades, this has become a reality with enormous advances in hardware performance, computer graphics, robotics technology, and artificial intelligence. New interdisciplinary research fields have brought forth cognitive robotics aimed at building a new generation of control systems and providing robots with social, empathetic and affective capabilities. This paper presents the design, implementation, and test of a human-inspired cognitive architecture for social robots. State-of-the-art design approaches and methods are thoroughly analyzed and discussed, cases where the developed system has been successfully used are reported. The tests demonstrated the system’s ability to endow a social humanoid robot with human social behaviors and with in-silico robotic emotions.


title: Brain-computer communication: unlocking the locked in.

abstract: With the increasing efficiency of life-support systems and better intensive care, more patients survive severe injuries of the brain and spinal cord. Many of these patients experience locked-in syndrome: The active mind is locked in a paralyzed body. Consequently, communication is extremely restricted or impossible. A muscle-independent communication channel overcomes this problem and is realized through a brain-computer interface, a direct connection between brain and computer. The number of technically elaborated brain-computer interfaces is in contrast with the number of systems used in the daily life of locked-in patients. It is hypothesized that a profound knowledge and consideration of psychological principles are necessary to make brain-computer interfaces feasible for locked-in patients.


title: Claytronics: highly scalable communications, sensing, and actuation networks

abstract: We propose a demonstration of extremely scalable modular robotics algorithms developed as part of the Claytronics Project (http://www-2.cs.cmu.edu/~claytronics/), as well as a demonstration of proof-of-concept prototypes. Our effort envisions multi-million-module robot ensembles able to morph into three-dimensional scenes, eventually with sufficient fidelity so as to convince a human observer the scenes are real. Although this work is potentially revolutionary in the sense that it holds out the possibility of radically altering the relationship between computation, humans, and the physical world, many of the research questions involved are similar in flavor to more mainstream systems research, albeit larger in scale. For instance, as in sensor networks, each robot will incorporate sensing, computation, and communications components. However, unlike most sensor networks each robot will also include mechanisms for actuation and motion. Many of the key challenges in this project involve coordination and communication of sensing and actuation across such large ensembles of independent units.


title: Influence of Reaction Time on Morphology and Optical Property of ZnWO_4 Nanocrystallites Prepared by Microwave Hydrothermal Process

abstract: The wolframite zinc tungstate nanocrystallites were prepared by a microwave hydrothermal method in the absence of template with Na2WO4?2H2O and Zn(COOCH3)2?2H2O as start materials.The phase,morphology and optical properties of the as-prepared samples were characterized by X-ray diffraction,transmission electron microscopy and UV-Vis absorption spectra,respectively.The results indicated that the wolframite ZnWO4 nanocrystallites can be obtained by the microwave hydrothermal method at 180 ℃ for only 10 minutes.The morphology of ZnWO4 nanocrystallites changed from short to elongated rod-like with increasing microwave hydrothermal time.The as-prepared ZnWO4 nanocrystallites have a strong absorption in ultraviolet band.The corresponding band-gap of the ZnWO4 nanocrystallites increased with the increase of reaction time,which can be controlled in the range of 3.77-3.89 eV.


title: Ego-Dissolution and Psychedelics: Validation of the Ego-Dissolution Inventory (EDI)

abstract: Aims: The experience of a compromised sense of ‘self’, termed ego-dissolution, is a key feature of the psychedelic experience and acute psychosis. This study aimed to validate the Ego-Dissolution Inventory (EDI), a new 8-item self-report scale designed to measure ego-dissolution. Additionally, we aimed to investigate the specificity of the relationship between psychedelics and ego-dissolution. Method: Sixteen items relating to altered ego-consciousness were included in an internet questionnaire; 8 relating to the experience of ego-dissolution (comprising the EDI), and 8 relating to the antithetical experience of increased self-assuredness. Items were rated using a visual analogue scale. Participants answered the questionnaire for experiences with classical psychedelic drugs, cocaine or alcohol. They also answered the 7 questions from the Mystical Experiences Questionnaire (MEQ) relating to the experience of unity with one’s surroundings. Results: 691 participants completed the questionnaire, providing data for 1828 drug experiences (1043 psychedelics. 377 cocaine. 408 alcohol). Exploratory factor analysis demonstrated that the 8 EDI items loaded exclusively onto a single common factor, which was orthogonal to a second factor comprised of the items relating to increased self-assuredness (rho= -.110), demonstrating discriminant validity. The EDI correlated strongly with our measure of unitive experience (rho = .735), demonstrating convergent validity. EDI internal consistency was excellent (Cronbach’s alpha 0.93). Three analyses confirmed the specificity of ego-dissolution for experiences occasioned by psychedelic drugs. Firstly, EDI score correlated with drug-dose for psychedelic drugs (rho=.371), but not for cocaine (rho=.115) or alcohol (rho=-0.055). Secondly, the linear regression line relating the subjective intensity of the experience to EDI was significantly steeper for psychedelics (unstandardized B coefficient= 0.701) compared with cocaine (0.135) or alcohol (0.144). Finally, a binary support vector machine classifier identified experiences occasioned by psychedelic drugs vs. cocaine or alcohol with over 85% accuracy using ratings of ego-dissolution and ego-inflation alone. Conclusions: Our results demonstrate the psychometric structure, internal consistency and construct validity of the EDI. Moreover, we demonstrate the close relationship between ego-dissolution and the psychedelic experience. The EDI will facilitate the study of the neuronal correlates of ego-dissolution, which is relevant for psychedelic-assisted psychotherapy and our understanding of psychosis.


Pro Single Page Application Development


title: A survey on question answering technology from an information retrieval perspective

abstract: This article provides a comprehensive and comparative overview of question answering technology. It presents the question answering task from an information retrieval perspective and emphasises the importance of retrieval models, i.e., representations of queries and information documents, and retrieval functions which are used for estimating the relevance between a query and an answer candidate. The survey suggests a general question answering architecture that steadily increases the complexity of the representation level of questions and information objects. On the one hand, natural language queries are reduced to keyword-based searches, on the other hand, knowledge bases are queried with structured or logical queries obtained from the natural language questions, and answers are obtained through reasoning. We discuss different levels of processing yielding bag-of-words-based and more complex representations integrating part-of-speech tags, classification of the expected answer type, semantic roles, discourse analysis, translation into a SQL-like language and logical representations.


title: Predicting Future Crowd Motion Including Event Treatment

abstract: Crowd simulation has become an important area, mainly in entertainment and security applications. In particular, this area has been explored in safety systems to evaluate environments in terms of people comfort and security. In general, the evaluation involves the execution of one or more simulations in order to provide statistical information about the crowd behavior in a certain environment. Real-time applications can also be desirable, for instance in order to estimate the crowd behavior in a near future knowing the current crowd state, aiming to anticipate a potential problem and prevent it. This paper presents a model to estimate crowd behaviors in a future time, presenting a good compromise between accuracy and running time. It also presents a new error measure to compare two crowds based on local density.


title: Enriching Phrase Tables for Statistical Machine Translation Using Mixed Embeddings

abstract: The phrase table is considered to be the main bilingual resource for the phrase-based statistical machine translation (PBSMT) model. During translation, a source sentence is decomposed ::: into several phrases. The best match of each source phrase is selected among several target-side ::: counterparts within the phrase table, and processed by the decoder to generate a sentence-level ::: translation. The best match is chosen according to several factors, including a set of bilingual ::: features. PBSMT engines by default provide four probability scores in phrase tables which are ::: considered as the main set of bilingual features. Our goal is to enrich that set of features, as a ::: better feature set should yield better translations. We propose new scores generated by a Convolutional Neural Network (CNN) which indicate the semantic relatedness of phrase pairs. We ::: evaluate our model in different experimental settings with different language pairs. We observe ::: significant improvements when the proposed features are incorporated into the PBSMT pipeline.


title: Effects of storage time and temperature on coagulation tests and factors in fresh plasma

abstract: Coagulation tests and factors measurements have been widely applied in clinical practice. Pre-analytical conditions are very important in laboratory assessment.Here,we aim to determine the effects of storage time and temperature on activated partial thromboplastin time (APTT), fibrinogen (Fbg), prothrombin time (PT), the international normalized ratio (INR), thrombin time (TT), factor VIII activity (FVIII:C), and factor IX activity (FIX:C) in fresh plasma. Seventy-two blood samples were tested after storage for 0 (baseline), 2, 4, 6, 8, 12, and 24 h at 25°C (room temperature) and 4°C (refrigeration) in two centers. The mean percentage change of greater than 10% and the numbers of samples with greater than 10% percentage changes more than 25% were used to determine clinically relevant difference. We demonstrated that samples for Fbg, PT/INR, and TT could be safely stored for ≤24 h; FVIII:C for ≤2 h; FIX:C for ≤4 h both at 4°C and 25°C; and APTT for ≤12 h at 4°C and ≤8 h at 25°C.


title: Persistent post-traumatic headache vs. migraine: an MRI study demonstrating differences in brain structure

abstract: BACKGROUND ::: The majority of individuals with post-traumatic headache have symptoms that are indistinguishable from migraine. The overlap in symptoms amongst these individuals raises the question as to whether post-traumatic headache has a unique pathophysiology or if head trauma triggers migraine. The objective of this study was to compare brain structure in individuals with persistent post-traumatic headache (i.e. headache lasting at least 3 months following a traumatic brain injury) attributed to mild traumatic brain injury to that of individuals with migraine. ::: ::: ::: METHODS ::: Twenty-eight individuals with persistent post-traumatic headache attributed to mild traumatic brain injury and 28 individuals with migraine underwent brain magnetic resonance imaging on a 3 T scanner. Regional volumes, cortical thickness, surface area and curvature measurements were calculated from T1-weighted sequences and compared between subject groups using ANCOVA. MRI data from 28 healthy control subjects were used to interpret the differences in brain structure between migraine and persistent post-traumatic headache. ::: ::: ::: RESULTS ::: Differences in regional volumes, cortical thickness, surface area and brain curvature were identified when comparing the group of individuals with persistent post-traumatic headache to the group with migraine. Structure was different between groups for regions within the right lateral orbitofrontal lobe, left caudal middle frontal lobe, left superior frontal lobe, left precuneus and right supramarginal gyrus (p < .05). Considering these regions only, there were differences between individuals with persistent post-traumatic headache and healthy controls within the right lateral orbitofrontal lobe, right supramarginal gyrus, and left superior frontal lobe and no differences when comparing the migraine cohort to healthy controls. ::: ::: ::: CONCLUSIONS ::: In conclusion, persistent post-traumatic headache and migraine are associated with differences in brain structure, perhaps suggesting differences in their underlying pathophysiology. Additional studies are needed to further delineate similarities and differences in brain structure and function that are associated with post-traumatic headache and migraine and to determine their specificity for each of the headache types.


title: A Ride-Sharing Problem with Meeting Points and Return Restrictions

abstract: Ride sharing has been widely acknowledged as an effective solution for reducing travel costs, congestion, and pollution. This paper considers the ride-sharing problem of the scheduled commuter and ...


title: Smartphone addiction proneness in relation to sleep and morningness–eveningness in German adolescents

abstract: BackgroundMobile phones are an important part of adolescents’ life. In this study, the relationships among smartphone addiction, age, gender, and chronotype of German adolescents were examined.Materials and methodsTwo studies focused on two different measures of smartphone addiction. The Smartphone Addiction Proneness Scale (SAPS) was applied to 342 younger adolescents (13.39 ± 1.77; 176 boys, 165 girls, and 1 not indicated) in Study 1 and the Smartphone Addiction Scale was applied to 208 older adolescents (17.07 ± 4.28; 146 girls and 62 boys) in Study 2, both samples in southwest Germany. In addition, a demographic questionnaire and the Composite Scale of Morningness (CSM) and sleep measures were implemented.ResultsThe most remarkable result of this study was that morningness–eveningness (as measured by CSM scores) is an important predictor for smartphone addiction; even stronger than sleep duration. Evening oriented adolescents scored higher on both smartphone addiction scales. In addition, gender is an...


title: Supersonic-inlet boundary-layer bleed flow

abstract: Boundary-layer bleed in supersonic inlets is typically used to avoid separation from adverse shock-wave-boundary-layer interactions and subsequent total-pressure losses in the subsonic diffuser and to improve normal-shock stability. Methodologies used to determine bleed requirements are reviewed. Empirical sonic flow coefficients are currently used to determine the bleed-hole pattern. These coefficients depend on local Mach number, pressure ratio, hole geometry, etc. A new analytical bleed method is presented to compute sonic flow coefficients for holes and narrow slots, and predictions are compared with published data to illustrate the accuracy of the model. The model can be used by inlet designers and as a bleed boundary condition for computational fluid-dynamic studies.


A review of factors that affect the static load-bearing capacity of urban trees


title: Software reverse engineering process: Factors, elements and features

abstract: The reverse engineering presents the system artifacts at higher levels of abstraction for maintenance activities. This paper presents an overview of the case studies on various types of existing software system to recover the different artifacts existing at implementation, structural, functional and domain levels. As a result of these case studies; the factors on which reverse engineering process depends, features and elements required by the reverse engineering process to recover the artifacts for maintenance at domain, functional, structural and implementation abstraction levels in varying details for reverse engineering are identified and presented in this paper. ::: ::: ::: ::: Key words: Case studies, software maintenance, re-engineering, reverse engineering, design recovery.


title: A high efficiency CMOS rectifier with ON-OFF response compensation for wireless power transfer in biomedical applications

abstract: This paper presents a high efficiency comparator-based CMOS rectifier for wireless power transfer in biomedical implant devices. By using a novel ON-OFF compensation technology, the conduction time of the switch can be optimized and the reverse current has been reduced efficiently. The proposed rectifier was verified using a standard 0.18μm CMOS process. Results shows that the rectifier can achieve a maximum voltage conversion efficiency of 96% and power efficiency near to 91%.


title: On algebraic construction of Gallager and circulant low-density parity-check codes

abstract: This correspondence presents three algebraic methods for constructing low-density parity-check (LDPC) codes. These methods are based on the structural properties of finite geometries. The first method gives a class of Gallager codes and a class of complementary Gallager codes. The second method results in two classes of circulant-LDPC codes, one in cyclic form and the other in quasi-cyclic form. The third method is a two-step hybrid method. Codes in these classes have a wide range of rates and minimum distances, and they perform well with iterative decoding.


title: The Comprehensive Antibiotic Resistance Database

abstract: ABSTRACT The field of antibiotic drug discovery and the monitoring of new antibiotic resistance elements have yet to fully exploit the power of the genome revolution. Despite the fact that the first genomes sequenced of free living organisms were those of bacteria, there have been few specialized bioinformatic tools developed to mine the growing amount of genomic data associated with pathogens. In particular, there are few tools to study the genetics and genomics of antibiotic resistance and how it impacts bacterial populations, ecology, and the clinic. We have initiated development of such tools in the form of the Comprehensive Antibiotic Research Database (CARD; http://arpcard.mcmaster.ca). The CARD integrates disparate molecular and sequence data, provides a unique organizing principle in the form of the Antibiotic Resistance Ontology (ARO), and can quickly identify putative antibiotic resistance genes in new unannotated genome sequences. This unique platform provides an informatic tool that bridges antibiotic resistance concerns in health care, agriculture, and the environment.


Scalable Construction and Reasoning of Massive Knowledge Bases


Smart Electronic Wheelchair Using Arduino and Bluetooth Module


title: Mapping virtual and physical reality

abstract: Real walking offers higher immersive presence for virtual reality (VR) applications than alternative locomotive means such as walking-in-place and external control gadgets, but needs to take into consideration different room sizes, wall shapes, and surrounding objects in the virtual and real worlds. Despite perceptual study of impossible spaces and redirected walking, there are no general methods to match a given pair of virtual and real scenes. We propose a system to match a given pair of virtual and physical worlds for immersive VR navigation. We first compute a planar map between the virtual and physical floor plans that minimizes angular and distal distortions while conforming to the virtual environment goals and physical environment constraints. Our key idea is to design maps that are globally surjective to allow proper folding of large virtual scenes into smaller real scenes but locally injective to avoid locomotion ambiguity and intersecting virtual objects. From these maps we derive altered rendering to guide user navigation within the physical environment while retaining visual fidelity to the virtual environment. Our key idea is to properly warp the virtual world appearance into real world geometry with sufficient quality and performance. We evaluate our method through a formative user study, and demonstrate applications in gaming, architecture walkthrough, and medical imaging.


title: Symmetry building and symmetry breaking in synchronized movement

abstract: Mirror symmetry between individuals is a behavioral phenomenon that is commonly observed in conversation: individuals tend to mimic each other’s postures and gestures as a part of a shared dialog. The present work studies the process of symmetry building and symmetry breaking in the movements of pairs of individuals while imitating each others’ movements in dance. Spatial and temporal symmetries are found in the overall velocities from the results of full body motion tracking. This work was supported in part by NIA grant R29 AG14983-01 and a grant from the University of Notre Dame. We wish to thank Elizabeth Marsh who assisted in gathering these data. Corresponding author: Steven M. Boker, Department of Psychology, The University of Notre Dame, Notre Dame Indiana 46556, USA; sboker@nd.edu; http://www.nd.edu/ sboker. SYMMETRY IN SYNCHRONIZED MOVEMENT 2


title: Replicating and Scaling up Qualitative Analysis using Crowdsourcing: A Github-based Case Study

abstract: Due to the difficulties in replicating and scaling up qualitative studies, such studies are rarely verified. Accordingly, in this paper, we leverage the advantages of crowdsourcing (low costs, fast speed, scalable workforce) to replicate and scale-up one state-of-the-art qualitative study. That qualitative study explored 20 GitHub pull requests to learn factors that influence the fate of pull requests with respect to approval and merging. As a secondary study, using crowdsourcing at a cost of $200, we studied 250 pull requests from 142 GitHub projects. The prior qualitative findings are mapped into questions for crowds workers. Their answers were converted into binary features to build a predictor which predicts whether code would be merged with median F1 scores of 68%. For the same large group of pull requests, the median F1 scores could achieve 90% by a predictor built with additional features defined by prior quantitative results. Based on this case study, we conclude that there is much benefit in combining different kinds of research methods. While qualitative insights are very useful for finding novel insights, they can be hard to scale or replicate. That said, they can guide and define the goals of scalable secondary studies that use (e.g.) crowdsourcing+data mining. On the other hand, while data mining methods are reproducible and scalable to large data sets, their results may be spectacularly wrong since they lack contextual information. That said, they can be used to test the stability and external validity, of the insights gained from a qualitative analysis.


title: Shape from shading: a well-posed problem?

abstract: Shape from shading is known to be an ill-posed problem. We show in this paper that if we model the problem in a different way than it is usually done, more precisely by taking into account the 1/r/sup 2/ attenuation term of the illumination, shape from shading becomes completely well-posed. Thus the shading allows to recover (almost) any surface from only one image (of this surface) without any additional data (in particular, without the knowledge of the heights of the solution at the local intensity "minima", contrary to [P. Dupuis et al. (1994), E. Prados et al. (2004), B. Horn (1986), E. Rouy et al. (1992), R. Kimmel et al. (2001)]) and without regularity assumptions (contrary to [J. Oliensis et al. (1993), R. Kimmel et al. (1995)], for example). More precisely, we formulate the problem as that of solving a new partial differential equation (PDE), we develop a complete mathematical study of this equation and we design a new provably convergent numerical method. Finally, we present results of our new shape from shading method on various synthetic and real images.


title: Understanding Human Language: Can NLP and Deep Learning Help?

abstract: There is a lot of overlap between the core problems of information retrieval (IR) and natural language processing (NLP). An IR system gains from understanding a user need and from understanding documents, and hence being able to determine whether a document has information that satisfies the user need. Much of NLP is about the same thing: Natural language understanding aims to understand the meaning of questions and documents and meaning relationships. The exciting recent application of deep learning approaches in NLP has brought new tools for effectively understanding language semantics. In principle, there should be a lot of synergy, though in practice the concerns of IR on large systems and macro-scale understanding have tended to contrast with the emphasis in NLP on language structure and micro-scale understanding. My talk will emphasize the two topics of how NLP can contribute to understanding textual relationships and how deep learning approaches substantially aid in this goal. One basic -- and very successful tool -- has been the new generation of distributed word representations: neural word embeddings. However, beyond just word meanings, we need to understand how to compose the meanings of larger pieces of text. Two requirements for that are good ways to understand the structure of human language utterances and ways to compose their meanings. Deep learning methods can help for both tasks. Finally, we need to understand relationships between pieces of text, to be able to do tasks such as Natural Language Inference (or Recognizing Textual Entailment) and Question Answering, and I will look at some of our recent work in these areas, both with and without the help of neural networks


title: B Vitamins and the Brain: Mechanisms, Dose and Efficacy—A Review

abstract: The B-vitamins comprise a group of eight water soluble vitamins that perform essential, closely inter-related roles in cellular functioning, acting as co-enzymes in a vast array of catabolic and anabolic enzymatic reactions. Their collective effects are particularly prevalent to numerous aspects of brain function, including energy production, DNA/RNA synthesis/repair, genomic and non-genomic methylation, and the synthesis of numerous neurochemicals and signaling molecules. However, human epidemiological and controlled trial investigations, and the resultant scientific commentary, have focused almost exclusively on the small sub-set of vitamins (B9/B12/B6) that are the most prominent (but not the exclusive) B-vitamins involved in homocysteine metabolism. Scant regard has been paid to the other B vitamins. This review describes the closely inter-related functions of the eight B-vitamins and marshals evidence suggesting that adequate levels of all members of this group of micronutrients are essential for optimal physiological and neurological functioning. Furthermore, evidence from human research clearly shows both that a significant proportion of the populations of developed countries suffer from deficiencies or insufficiencies in one or more of this group of vitamins, and that, in the absence of an optimal diet, administration of the entire B-vitamin group, rather than a small sub-set, at doses greatly in excess of the current governmental recommendations, would be a rational approach for preserving brain health.


A New Collision Differential For MD5 With Its Full Differential Path


title: Low Impact Artificial Intelligences

abstract: There are many goals for an AI that could become dangerous if the AI becomes superintelligent or otherwise powerful. Much work on the AI control problem has been focused on constructing AI goals that are safe even for such AIs. This paper looks at an alternative approach: defining a general concept of `low impact'. The aim is to ensure that a powerful AI which implements low impact will not modify the world extensively, even if it is given a simple or dangerous goal. The paper proposes various ways of defining and grounding low impact, and discusses methods for ensuring that the AI can still be allowed to have a (desired) impact despite the restriction. The end of the paper addresses known issues with this approach and avenues for future research.


title: Object detection by labeling superpixels

abstract: Object detection is often conducted by object proposal generation and classification sequentially. This paper handles object detection in a superpixel oriented manner instead of the proposal oriented. Specially, this paper takes object detection as a multi-label superpixel labeling problem by minimizing an energy function. It uses the data cost term to capture the appearance, smooth cost term to encode the spatial context and label cost term to favor compact detection. The data cost is learned through a convolutional neural network and the parameters in the labeling model are learned through a structural SVM. Compared with proposal generation and classification based methods, the proposed superpixel labeling method can naturally detect objects missed by proposal generation step and capture the global image context to infer the overlapping objects. The proposed method shows its advantage in Pascal VOC and ImageNet. Notably, it performs better than the ImageNet ILSVRC2014 winner GoogLeNet (45.0% V.S. 43.9% in mAP) with much shallower and fewer CNNs.


title: Navigation of an omni-directional mobile robot with active caster wheels

abstract: This work deals with navigation of an omni-directional mobile robot with active caster wheels. Initially, the posture of the omni-directional mobile robot is calculated by using the odometry information. Next, the position accuracy of the mobile robot is measured through comparison of the odometry information and the external sensor measurement. Finally, for successful navigation of the mobile robot, a motion planning algorithm that employs kinematic redundancy resolution method is proposed. Through experiments for multiple obstacles and multiple moving obstacles, the feasibility of the proposed navigation algorithm was verified.


title: POLYGLOT: Multilingual Semantic Role Labeling with Unified Labels.

abstract: Semantic role labeling (SRL) identifies the predicate-argument structure in text with semantic labels. It plays a key role in understanding natural language. In this paper, we present POLYGLOT, a multilingual semantic role labeling system capable of semantically parsing sentences in 9 different languages from 4 different language groups. The core of POLYGLOT are SRL models for individual languages trained with automatically generated Proposition Banks (Akbik et al., 2015). The key feature of the system is that it treats the semantic labels of the English Proposition Bank as “universal semantic labels”: Given a sentence in any of the supported languages, POLYGLOT applies the corresponding SRL and predicts English PropBank frame and role annotation. The results are then visualized to facilitate the understanding of multilingual SRL with this unified semantic representation.


title: Influence of long-term Sahaja Yoga meditation practice on emotional processing in the brain: An ERP study

abstract: Abstract Despite growing interest in meditation as a tool for alternative therapy of stress-related and psychosomatic diseases, brain mechanisms of beneficial influences of meditation practice on health and quality of life are still unclear. We propose that the key point is a persistent change in emotional functioning, specifically the modulation of the early appraisal of motivational significance of events. The main aim was to study the effects of long-term meditation practice on event-related brain potentials (ERPs) during affective picture viewing. ERPs were recorded in 20 long-term Sahaja Yoga meditators and 20 control subjects without prior experience in meditation. The meditators’ mid-latency (140–400 ms) ERPs were attenuated for both positive and negative pictures (i.e. there were no arousal-related increases in ERP positivity) and this effect was more prominent over the right hemisphere. However, we found no differences in the long latency (400–800 ms) responses to emotional images, associated with meditation practice. In addition we found stronger ERP negativity in the time window 200–300 ms for meditators compared to the controls, regardless of picture valence. We assume that long-term meditation practice enhances frontal top-down control over fast automatic salience detection, based on amygdala functions.


title: Surface reconstruction with the photometric method in SEM

abstract: Abstract The authors present an improved method for three-dimensional reconstruction of the surface topography, which evolved from the group of photometric methods. It consists in directional acquisition of secondary electrons generated with a known angular distribution, in a four-detector system. Thanks to proper signal processing, the topography can be obtained and displayed in a form of the fully three-dimensional axonometric view. The authors propose new numerical procedures of the signal processing, which may curb the errors inherent in the method. The presented method is particularly useful for the reconstruction of relatively smooth objects, without surface details numerous enough to be identified in the “stereo pair” used in a more popular “stereoscopic method”.


title: Half-Loop Frame Antenna for the LTE Metal-Casing Tablet Device

abstract: The use of metal casing is attractive to achieve robustness of modern slim tablet devices. The metal casing includes the metal back cover and the metal frame around the edges thereof. For such metal-casing tablet devices, the frame antenna that uses a part of the metal frame as an antenna’s radiator is promising to achieve wide bandwidths for mobile communications. In this paper, the frame antenna based on the simple half-loop antenna structure to cover the long-term evolution 746–960 and 1710–2690 MHz bands is presented. The half-loop structure for the frame antenna is easy for manufacturing and increases the robustness of the metal casing. The dual-wideband operation of the half-loop frame antenna is obtained by using an elevated feed network supported by a thin feed substrate. The measured antenna efficiencies are, respectively, 45%–69% and 60%–83% in the low and high bands. By selecting different feed circuits, the antenna’s low band can also be shifted from 746–960 MHz to lower frequencies such as 698–840 MHz, with the antenna’s high-band coverage very slightly varied. The working principle of the antenna with the elevated feed network is discussed. The antenna is also fabricated and tested, and experimental results are presented.


title: A Hybrid PBIL-Based Krill Herd Algorithm

abstract: When krill herd (KH) is used to solve complicated multimodal functions, sometimes it fails to find the best solutions and cannot converge fast. Herein, we propose a hybrid KH method, called PBILKH, by integrating the KH with the population-based incremental learning (PBIL). In addition, a type of elitism is applied to memorize the krill with the best fitness when finding the best solution. The effectiveness of the PBILKH is verified by various benchmarks and experimental results demonstrate that our PBILKH is well capable of overtaking the KH algorithm and other optimization methods in solving nonlinear problems.


title: From acute musculoskeletal pain to chronic widespread pain and fibromyalgia: application of pain neurophysiology in manual therapy practice.

abstract: During the past decade, scientific research has provided new insight into the development from an acute, localised musculoskeletal disorder towards chronic widespread pain/fibromyalgia (FM). Chronic widespread pain/FM is characterised by sensitisation of central pain pathways. An in-depth review of basic and clinical research was performed to design a theoretical framework for manual therapy in these patients. It is explained that manual therapy might be able to influence the process of chronicity in three different ways. (I) In order to prevent chronicity in (sub)acute musculoskeletal disorders, it seems crucial to limit the time course of afferent stimulation of peripheral nociceptors. (II) In the case of chronic widespread pain and established sensitisation of central pain pathways, relatively minor injuries/trauma at any locations are likely to sustain the process of central sensitisation and should be treated appropriately with manual therapy accounting for the decreased sensory threshold. Inappropriate pain beliefs should be addressed and exercise interventions should account for the process of central sensitisation. (III) However, manual therapists ignoring the processes involved in the development and maintenance of chronic widespread pain/FM may cause more harm then benefit to the patient by triggering or sustaining central sensitisation.


title: Towards a Conceptualization of Sociomaterial Entanglement

abstract: In knowledge representation, socio-technical systems can be modeled as multiagent systems in which the local knowledge of each individual agent can be seen as a context. In this paper we propose formal ontologies as a means to describe the assumptions driving the construction of contexts as local theories and to enable interoperability among them. In particular, we present two alternative conceptualizations of the notion of sociomateriality (and entanglement), which is central in the recent debates on socio-technical systems in the social sciences, namely critical and agential realism.


Uncorrelated Patient Similarity Learning


title: SieveQ: A Layered BFT Protection System for Critical Services

abstract: Firewalls play a crucial role in assuring the security of today's critical infrastructures, forming a first line of defense by being placed strategically at the front-end of the networks. Sometimes, however, they have exploitable weaknesses, allowing an adversary to bypass them in different ways. Therefore, their design should include improved resilience capabilities to allow them to operate correctly in highly adverse environments. This paper proposes SieveQ, a message queue service that protects and regulates the access to critical systems, in a way similar to an application-level firewall. SieveQ achieves fault and intrusion tolerance by employing an architecture based on two filtering layers, enabling efficient removal of invalid messages at early stages and decreasing the costs associated with Byzantine Fault-Tolerant (BFT) replication of previous solutions. Our experimental evaluation shows that SieveQ improves existing replicated-firewalls resilience in the presence of corrupted messages by faulty nodes. Furthermore, it accommodates high loads, as it is able to handle sixteen times more security events per second than what was processed by the Security Information and Event Management (SIEM) infrastructure employed in the 2012 Summer Olympic Games.


title: An application of pretrained deep neural networks to large vocabulary speech recognition

abstract: The use of Deep Belief Networks (DBN) to pretrain Neural Networks has recently led to a resurgence in the use of Artificial Neural Network Hidden Markov Model (ANN/HMM) hybrid systems for Automatic Speech Recognition (ASR). In this paper we report results of a DBN-pretrained context-dependent ANN/HMM system trained on two datasets that are much larger than any reported previously with DBN-pretrained ANN/HMM systems 5870 hours of Voice Search and 1400 hours of YouTube data. On the first dataset, the pretrained ANN/HMM system outperforms the best Gaussian Mixture Model Hidden Markov Model (GMM/HMM) baseline, built with a much larger dataset by 3.7% absolute WER, while on the second dataset, it outperforms the GMM/HMM baseline by 4.7% absolute. Maximum Mutual Information (MMI) fine tuning and model combination using Segmental Conditional Random Fields (SCARF) give additional gains of 0.1% and 0.4% on the first dataset and 0.5% and 0.9% absolute on the second dataset.


title: DESIGN OF AN ULTRA-WIDEBAND POWER DIVIDER VIA THE COARSE-GRAINED PARALLEL MICRO-GENETIC ALGORITHM

abstract: An ultra-wideband (UWB) power divider is designed in this paper. The UWB performance of this power divider is obtained by using a tapered microstrip line that consists of exponential and elliptic sections. The coarse grained parallel micro-genetic algorithm (PMGA) and CST Microwave Studio are combined to achieve an automated parallel design process. The method is applied to optimize the UWB power divider. The optimized power divider is fabricated and measured. The measured results show relatively low insertion loss, good return loss, and high isolation between the output ports across the whole UWB (3.1{10.6GHz).


title: Detection and Inpainting of Facial Wrinkles Using Texture Orientation Fields and Markov Random Field Modeling

abstract: Facial retouching is widely used in media and entertainment industry. Professional software usually require a minimum level of user expertise to achieve the desirable results. In this paper, we present an algorithm to detect facial wrinkles/imperfection. We believe that any such algorithm would be amenable to facial retouching applications. The detection of wrinkles/imperfections can allow these skin features to be processed differently than the surrounding skin without much user interaction. For detection, Gabor filter responses along with texture orientation field are used as image features. A bimodal Gaussian mixture model (GMM) represents distributions of Gabor features of normal skin versus skin imperfections. Then, a Markov random field model is used to incorporate the spatial relationships among neighboring pixels for their GMM distributions and texture orientations. An expectation-maximization algorithm then classifies skin versus skin wrinkles/imperfections. Once detected automatically, wrinkles/imperfections are removed completely instead of being blended or blurred. We propose an exemplar-based constrained texture synthesis algorithm to inpaint irregularly shaped gaps left by the removal of detected wrinkles/imperfections. We present results conducted on images downloaded from the Internet to show the efficacy of our algorithms.


title: Closed-loop force control for haptic simulation of virtual environments

abstract: Force feedback control is investigated for improving the quality of the haptic feedback in virtual reality applications. Advanced control design can increase the transparency of the haptic device at the haptic interface thereby increasing the realism of the simulation. Force feedback also enables the implementation of admittance control approaches heretofore considered the domain of large robotic platforms. The quality of the haptic interface is "rst quanti"ed, and the standard open-loop impedance approach to haptic control is reviewed. Dual-input control schemes with sensory force feedback are then introduced, and the resulting quality of the haptic interface is derived. A four-bar linkage example is used to illustrate the improvement in "delity realized with the various approaches. The tradeoffs encountered in moving to force feedback controllers for haptic applications are also discussed.


title: Fibrinogen/fibrin deposits in mesenteric lymphadenitis due to Yersinia pseudotuberculosis type I: morphologic, immunohistochemical and electron microscopic studies of one case.

abstract: A case of mesenteric lymphadenitis due to Yersinia pseudotuberculosis type I is described in a young adult. The diagnosis is suggested by histological appearance and confirmed by serological tests. Histological and semithin sections reveal granulomas with central microabscesses and concentric coagulative necrosis. By histochemical and immunohistochemical techniques, fibrinogen/fibrin deposits are observed as a prominent intercellular meshwork around microabscesses or as loosely intertwining strands in small granulomas. Electron microscopy shows fibrinogen/fibrin deposits closely associated both fibrous long-spacing (FLS) fibers and thin collagen fibers. The pathogenesis and type of granuloma as well as the significance of fibrinogen/fibrin deposits are discussed with reference to current literature.


title: A computer vision-aided motion sensing algorithm for mobile robot's indoor navigation

abstract: This paper presents the design and analysis of a computer vision-aided motion sensing algorithm for wheeled mobile robot's indoor navigation. The algorithm is realized using two vision cameras attached on a wheeled mobile robot. The first camera is positioned at front-looking direction while the second camera is positioned at downward-looking direction. An algorithm is developed to process the images acquired from the cameras to yield the mobile robot's positions and orientations. The proposed algorithm is implemented on a wheeled mobile robot for real-world effectiveness testing. Results are compared and shown the accuracy of the proposed algorithm. At the end of the paper, an artificial landmark approach is introduced to improve the navigation efficiency. Future work involved implementing the proposed artificial landmark for indoor navigation applications with minimized accumulated errors.