title: The Association of Methylation in the Promoter ofAPCandMGMTand the Prognosis of Taiwanese CRC Patients

abstract: AIMS ::: The purpose of this study was to investigate the association of methylation in the promoter regions of adenomatous polyposis coli (APC) and O(6)-methylguanine-DNA methyltransferase (MGMT) and the survival of Taiwanese colorectal cancer (CRC) subjects who received 5-fluorouracil (5-FU) adjuvant chemotherapy. ::: ::: ::: RESULTS ::: DNA isolated from tumor tissue of 117 CRC subjects was analyzed for the existence of methylation in the promoter regions of APC and MGMT by methylation-specific PCR. Various characteristics of the 117 subjects were recorded and used in the Cox proportional-hazard model analyses. Methylation in the promoter region is 62.4% (73/117) for APC and 60.7% (71/117) for MGMT in our CRC patients. Subjects presenting methylation in the APC promoter demonstrate significantly lower hazards for all causes of death (hazard ratios=0.378, p=0.011) or CRC deaths (hazard ratios=0.426, p=0.039). However, no significant correlation is found between the methylation of MGMT promoter and the prognosis of CRC subjects. In addition, no interaction between 5-FU adjuvant chemotherapy and methylation of the two genes are observed. ::: ::: ::: CONCLUSIONS ::: Methylation in the APC promoter may serve as a predictor for the prognosis of Taiwanese CRC patients.


title: Application of repulsive force and genetic algorithm to multi-manipulator collision avoidance

abstract: Manipulator collision avoidance using genetic algorithms is presented. Control gains in the collision avoidance control model are selected based on genetic algorithms. A repulsive force is artificially created using the distances between the robot links and obstacles, which are generated by a distance computation algorithm. Real-time manipulator collision avoidance control has achieved. A repulsive force gain is introduced through the approaches for definition of link coordinate frames and kinematics computations. The safety distance between objects is affected by the repulsive force gain. This makes the safety zone adjustable and provides greater intelligence for robotic tasks under the ever-changing environment.


title: A Survey: Smart agriculture IoT with cloud computing

abstract: IoT is a revolutionary technology that represents the future of computing and communications. Most of the people over all worlds depend on agriculture. Because of this reason smart IT technologies are needed to migrate with traditional agriculture methods. Using modern technologies can control the cost, maintenance and monitoring performance. Satellite and aerial imagery play a vital role in modern agriculture. Precision agriculture sensor monitoring network is used greatly to measure agri-related information like temperature, humidity, soil PH, soil nutrition levels, water level etc. so, with IoT farmers can remotely monitor their crop and equipment by phones and computers. In this paper, we surveyed some typical applications of Agriculture IoT Sensor Monitoring Network technologies using Cloud computing as the backbone. This survey is used to understand the different technologies and to build sustainable smart agriculture. Simple IoT agriculture model is addressed with a wireless network.


title: Text Classification Methodologies Applied to Micro-Text in Military Chat

abstract: We propose methods to classify lines of military chat, or posts, which contain items of interest. We evaluated several current text categorization and feature selection methodologies on chat posts. Our chat posts are examples of 'micro-text', or text that is generally very short in length, semi-structured, and characterized by unstructured or informal grammar and language. Although this study focused specifically on tactical updates via chat, we believe the findings are applicable to content of a similar linguistic structure. Completion of this milestone is a significant first step in allowing for more complex categorization and information extraction.


title: Strangers Intrusion Detection - Detecting Spammers and Fake Proles in Social Networks Based on Topology Anomalies

abstract: Today's social networks are plagued by numeroustypes of malicious proles which can range from so-cialbots to sexual predators. We present a novelmethod for the detection of these malicious prolesby using the social network's own topological fea-tures only. Reliance on these features alone ensuresthat the proposed method is generic enough to beapplied to a range of social networks. The algorithmhas been evaluated on several social networks andwas found to be eective in detecting various typesof malicious proles. We believe this method is avaluable step in the increasing battle against socialnetwork spammers, socialbots, and sexual predictors.


title: Tax Aggressiveness and Accounting Fraud

abstract: There are competing arguments and mixed prior evidence on whether firms that are aggressive in their financial reporting exhibit more or less tax aggressiveness. Our research contributes to resolving this issue by examining the association between aggressive tax reporting and the incidence of alleged accounting fraud. Relying on several proxies for tax aggressiveness to triangulate our evidence, we generally find that tax aggressive U.S. public firms are less likely to commit accounting fraud. However, we caution that our results are sensitive to how tax aggressiveness is measured. More specifically, four (two) of the five (three) proxies for firms’ effective tax rates (book-tax differences) load positively (negatively) during the 1981-2001 period, implying that fraud firms are less tax aggressiveness. Our inferences persist when we isolate the 1995-2001 period in which accounting impropriety steeply rose and corporate tax compliance steeply fell. Moreover, we continue to find that tax aggressive firms are less apt to fraudulently manipulate their financial statements when we apply factor analysis to identify tax avoidance with a common factor extracted from the underlying proxies and match on propensity scores to ensure that the fraud and non-fraud samples have very similar non-tax characteristics.


Music Therapy in Palliative Care A Randomized Controlled Trial to Evaluate Effects on Relaxation


title: Wireless Fractal Ultra-Dense Cellular Networks

abstract: With the ever-growing number of mobile devices, there is an explosive expansion in mobile data services. This represents a challenge for the traditional cellular network architecture to cope with the massive wireless traffic generated by mobile media applications. To meet this challenge, research is currently focused on the introduction of a small cell base station (BS) due to its low transmit power consumption and flexibility of deployment. However, due to a complex deployment environment and low transmit power of small cell BSs, the coverage boundary of small cell BSs will not have a traditional regular shape. Therefore, in this paper, we discuss the coverage boundary of an ultra-dense small cell network and give its main features: aeolotropy of path loss fading and fractal coverage boundary. Simple performance analysis is given, including coverage probability and transmission rate, etc., based on stochastic geometry theory and fractal theory. Finally, we present an application scene and discuss challenges in the ultra-dense small cell network.


title: Surgical Anatomy of the Ligamentous Attachments of the Lower Lid and Lateral Canthus

abstract: Description of the surgical anatomy of the superficial fascia of the face must include its deep attachments. These attachments have been mapped out for the forehead, temple, and cheek as retaining ligaments. The deep attachments of the orbicularis oculi of the lower lid and lateral canthus have long


Management Accounting Change in the Financial Services Sector: The Case of Activity-Based Costing in a Portuguese Bank


title: Student experiences using GitHub in software engineering courses: a case study

abstract: GitHub has been embraced by the software development community as an important social platform for managing software projects and to support collaborative development. More recently, educators have begun to adopt it for hosting course content and student assignments. From our previous research, we found that educators leverage GitHub's collaboration and transparency features to create, reuse and remix course materials, and to encourage student contributions and monitor student activity on assignments and projects. However, our previous research did not consider the student perspective. In this paper, we present a case study where GitHub is used as a learning platform for two software engineering courses. We gathered student perspectives on how the use of GitHub in their courses might benefit them and to identify the challenges they may face. The findings from our case study indicate that software engineering students do benefit from GitHub's transparent and open workflow. However, students were concerned that since GitHub is not inherently an educational tool, it lacks key features important for education and poses learning and privacy concerns. Our findings provide recommendations for designers on how tools such as GitHub can be used to improve software engineering education, and also point to recommendations for instructors on how to use it more effectively in their courses.


title: Maximizing Constructivist Learning From Multimedia Communications by Minimizing Cognitive Load

abstract: Students viewed an animation depicting either the process of lightning formation or how car brakes work and listened to a corresponding narration describing the steps. The entire animation and narration were presented at the same time (concurrent), the entire narration was presented before or after the entire animation (successive large bites), or short portions of the narration were presented before or after corresponding short portions of the animation for each successive portion of the presentation (successive small bites). Overall, the concurrent and successive small bites groups performed significantly better than the successive large bites groups on remembering the explanation in words (retention), generating solutions to transfer problems (transfer), and selecting verbal labels for elements in a line drawing (matching), but they did not differ significantly from each other. Results are consistent with a dual-process model of working memory in which learners are more likely to construct connections between words and corresponding pictures when they are held in working memory at the same time. The purpose of this research is to examine theory-based design principles for promoting constructivist learning in multimedia environments. To address this goal, it is necessary to clarify what is meant by multimedia environments, constructivist learning, and theory-based design principles, and to specify predictions concerning ways of designing multimedia environments for constructivist learning.


title: A comprehensive study on APT attacks and countermeasures for future networks and communications: challenges and solutions

abstract: Recently in the connected digital world, targeted attack has become one of the most serious threats to conventional computing systems. Advanced persistent threat (APT) is currently one of the most important threats considering the information security concept. APT persistently collects data from a specific target by exploiting vulnerabilities using diverse attack techniques. Many researchers have contributed to find approaches and solutions to fight against network intrusion and malicious software. However, only a few of these solutions are particularly focused on APT. In this paper, we introduce a structured study on semantic-aware work to find potential contributions that analyze and detect APT in details. We propose modeling phase that discusses the typical steps in APT attacks to collect the desired information by attackers. Our research explores social network and web infrastructure exploitation as well as communication protocols and much more for future networks and communications. The paper also includes some recent Zero-day attacks, use case scenarios and cyber trends in southeastern countries. To overcome these challenges and attacks, we introduce a detailed comprehensive literature evaluation scheme that classifies and provides countermeasures of APT attack behavior. Furthermore, we discuss future research direction of APT defense framework of next-generation threat life cycle.


title: Stabilization of trajectories for systems with nonholonomic constraints

abstract: A technique for stabilizing nonholonomic systems to trajectories is presented. It is well known that such systems cannot be stabilized to a point using smooth static-state feedback. The authors suggest the use of control laws for stabilizing a system about a trajectory, instead of a point. Given a nonlinear system and a desired nominal feasible trajectory, an explicit control law which will locally exponentially stabilize the system to the desired trajectory is given. The theory is applied to several examples, including a car-like robot. >


Organoleptic and Sensory Ontology


title: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration

abstract: All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms ("SPM2-type" and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http://www.mindboggle.info/papers/ website.


title: Analyzing the Performance of Multilayer Neural Networks for Object Recognition

abstract: In the last two years, convolutional neural networks (CNNs) have achieved an impressive suite of results on standard recognition datasets and tasks. CNN-based features seem poised to quickly replace engineered representations, such as SIFT and HOG. However, compared to SIFT and HOG, we understand much less about the nature of the features learned by large CNNs. In this paper, we experimentally probe several aspects of CNN feature learning in an attempt to help practitioners gain useful, evidence-backed intuitions about how to apply CNNs to computer vision problems.


title: Dense Captioning with Joint Inference and Visual Context

abstract: Dense captioning is a newly emerging computer vision topic for understanding images with dense language descriptions. The goal is to densely detect visual concepts (e.g., objects, object parts, and interactions between them) from images, labeling each with a short descriptive phrase. We identify two key challenges of dense captioning that need to be properly addressed when tackling the problem. First, dense visual concept annotations in each image are associated with highly overlapping target regions, making accurate localization of each visual concept challenging. Second, the large amount of visual concepts makes it hard to recognize each of them by appearance alone. We propose a new model pipeline based on two novel ideas, joint inference and context fusion, to alleviate these two challenges. We design our model architecture in a methodical manner and thoroughly evaluate the variations in architecture. Our final model, compact and efficient, achieves state-of-the-art accuracy on Visual Genome [23] for dense captioning with a relative gain of 73% compared to the previous best algorithm. Qualitative experiments also reveal the semantic capabilities of our model in dense captioning.


title: Error-Related EEG Potentials Generated During Simulated Brain–Computer Interaction

abstract: Brain-computer interfaces (BCIs) are prone to errors in the recognition of subject's intent. An elegant approach to improve the accuracy of BCIs consists in a verification procedure directly based on the presence of error-related potentials (ErrP) in the electroencephalogram (EEG) recorded right after the occurrence of an error. Several studies show the presence of ErrP in typical choice reaction tasks. However, in the context of a BCI, the central question is: ldquoAre ErrP also elicited when the error is made by the interface during the recognition of the subject's intent?rdquo We have thus explored whether ErrP also follow a feedback indicating incorrect responses of the simulated BCI interface. Five healthy volunteer subjects participated in a new human-robot interaction experiment, which seem to confirm the previously reported presence of a new kind of ErrP. However, in order to exploit these ErrP, we need to detect them in each single trial using a short window following the feedback associated to the response of the BCI. We have achieved an average recognition rate of correct and erroneous single trials of 83.5% and 79.2%, respectively, using a classifier built with data recorded up to three months earlier.


title: Fast Automatic Single-View 3-d Reconstruction of Urban Scenes

abstract: We consider the problem of estimating 3-d structure from a single still image of an outdoor urban scene. Our goal is to efficiently create 3-d models which are visually pleasant. We chose an appropriate 3-d model structure and formulate the task of 3-d reconstruction as model fitting problem. Our 3-d models are composed of a number of vertical walls and a ground plane, where ground-vertical boundary is a continuous polyline. We achieve computational efficiency by special preprocessing together with stepwise search of 3-d model parameters dividing the problem into two smaller sub-problems on chain graphs. The use of Conditional Random Field models for both problems allows to various cues. We infer orientation of vertical walls of 3-d model vanishing points.


title: Self-Managed Systems: an Architectural Challenge

abstract: Self-management is put forward as one of the means by which we could provide systems that are scalable, support dynamic composition and rigorous analysis, and are flexible and robust in the presence of change. In this paper, we focus on architectural approaches to self-management, not because the language-level or network-level approaches are uninteresting or less promising, but because we believe that the architectural level seems to provide the required level of abstraction and generality to deal with the challenges posed. A self-managed software architecture is one in which components automatically configure their interaction in a way that is compatible with an overall architectural specification and achieves the goals of the system. The objective is to minimise the degree of explicit management necessary for construction and subsequent evolution whilst preserving the architectural properties implied by its specification. This paper discusses some of the current promising work and presents an outline three-layer reference model as a context in which to articulate some of the main outstanding research challenges.


title: ROLES OF ONTOLOGIES OF ENGINEERING ARTIFACTS FOR DESIGN KNOWLEDGE MODELING

abstract: Capturing design knowledge and its modeling are crucial issues for design support systems and design knowledge management. It is important that knowledge models are systematic, consistent, reusable and interoperable. This survey article discusses the roles of ontologies of engineering artifacts for contributing to such design knowledge modeling from a viewpoint of computer science. An ontology of artifacts, in general, consists of systematic and computational definitions of fundamental concepts and relationship which exist in the physical world related to target artifacts, and shows how to capture the artifacts. This article, firstly, discusses needs, types, levels and some examples of ontologies of engineering artifacts. Then, we discuss roles of the ontologies for design knowledge modeling such as modeling specification for consistent modeling, capturing implicit knowledge and basis of knowledge systematization.


title: Globalization and national forms of global strategies

abstract: A recent, very successful historical form of globalization is the Euro-Atlantic model of human integration, so-called Westernization. Yet, the author believes that the future of globalization is not ultimately associated with the Westernization of countries of the second world. The current neoliberal stage of globalization prompts big regional powers to develop their own, culturally different forms of global strategies opposing Westernization.


title: Mirroring others' emotions relates to empathy and interpersonal competence in children

abstract: Abstract The mirror neuron system (MNS) has been proposed to play an important role in social cognition by providing a neural mechanism by which others' actions, intentions, and emotions can be understood. Here functional magnetic resonance imaging was used to directly examine the relationship between MNS activity and two distinct indicators of social functioning in typically-developing children (aged 10.1 years ± 7 months): empathy and interpersonal competence. Reliable activity in pars opercularis, the frontal component of the MNS, was elicited by observation and imitation of emotional expressions. Importantly, activity in this region (as well as in the anterior insula and amygdala) was significantly and positively correlated with established behavioral measures indexing children's empathic behavior (during both imitation and observation) and interpersonal skills (during imitation only). These findings suggest that simulation mechanisms and the MNS may indeed be relevant to social functioning in everyday life during typical human development.


title: An extensive comparative study of cluster validity indices

abstract: The validation of the results obtained by clustering algorithms is a fundamental part of the clustering process. The most used approaches for cluster validation are based on internal cluster validity indices. Although many indices have been proposed, there is no recent extensive comparative study of their performance. In this paper we show the results of an experimental work that compares 30 cluster validity indices in many different environments with different characteristics. These results can serve as a guideline for selecting the most suitable index for each possible application and provide a deep insight into the performance differences between the currently available indices.


title: Cooperative Search Using Agents for Cardinality Constrained Portfolio Selection Problem

abstract: This paper presents an agent-based model to select an investment portfolio with a restriction on the number of stocks in it. Daily movements of all the stocks in the market for the past few years are assumed to be available. The scheme deploys a federally structured consortium of agents in the stock market at the start of the historical period. Each agent starts with a pseudorandom portfolio and follows individual investment strategies as it walks through the past data. The agents are designed to emulate some of the characteristics of human investors-adjusting the weights of the stocks based on its own attitude toward risk, occasionally dropping and adding stocks to the portfolio, etc. Periodically, the agents share information about their performances and can switch portfolios. A final cardinality constrained portfolio is constructed by consolidating individual portfolios arrived at by the agents working on the historical data of the stocks. When tested in real markets of the U.K. and Japan, the model suggested portfolios that were quite competitive to, and frequently better than, the portfolios suggested by the mean-variance models.


title: Automated microaneurysms (MAs) detection in digital colour fundus images using matched filter

abstract: Diabetic retinopathy (DR), one of the most common causes of blindness, is a retinal abnormality caused by high glucose in diabetic patients that leads to micro vascular complications. DR has five levels of severity, i.e. no DR, mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR and proliferative diabetic retinopathy (PDR). Microaneurysms (MAs), the first sign of NPDR, can be used as a pre-indicator of DR. However, a manual assessment on digital colour fundus images conducted by ophthalmologists is time consuming. This paper introduces a new algorithm for the automated microaneurysms (MAs) detection in digital colour fundus images using matched filter. Generally, the algorithm consists of four phases, namely green band extraction, MAs and blood vessels isolation, MAs and blood vessels detection, and blood vessels removal. To validate the developed algorithm, the results are compared with their ground truths and annotations using ROI based validation. This algorithm obtains an average sensitivity, specificity, accuracy, and false positive number of 91.0603%, 99.9752%, 99.9752% and 256.44 pixels, respectively. This indicates that the proposed algorithm successfully detects microaneurysms and is able to be implemented in a system for DR mass screening purposes.


title: The relation between the ROC curve and the CMC

abstract: The cumulative match curve (CMC) is used as a measure of 1: m identification system performance. It judges the ranking capabilities of an identification system. The receiver operating characteristic curve (ROC curve) of a verification system, on the other hand, expresses the quality of a 1:1 matcher. The ROC plots the false accept rate (FAR) of a 1:1 matcher versus the false reject rate (FRR) of the matcher. We show that the CMC is also related to the FAR and FRR of a 1:1 matcher, i.e., the matcher that is used to rank the candidates by sorting the scores. This has as a consequence that when a 1:1 matcher is used for identification, that is, for sorting match scores from high to low, the CMC does not offer any additional information beyond the FAR and FRR curves. The CMC is just another way of displaying the data and can be computed from the FAR and FRR.


title: Best Keyword Cover Search

abstract: It is common that the objects in a spatial database (e.g., restaurants/hotels) are associated with keyword(s) to indicate their businesses/services/features. An interesting problem known as Closest Keywords search is to query objects, called keyword cover, which together cover a set of query keywords and have the minimum inter-objects distance. In recent years, we observe the increasing availability and importance of keyword rating in object evaluation for the better decision making. This motivates us to investigate a generic version of Closest Keywords search called Best Keyword Cover which considers inter-objects distance as well as the keyword rating of objects. The baseline algorithm is inspired by the methods of Closest Keywords search which is based on exhaustively combining objects from different query keywords to generate candidate keyword covers. When the number of query keywords increases, the performance of the baseline algorithm drops dramatically as a result of massive candidate keyword covers generated. To attack this drawback, this work proposes a much more scalable algorithm called keyword nearest neighbor expansion (keyword-NNE). Compared to the baseline algorithm, keyword-NNE algorithm significantly reduces the number of candidate keyword covers generated. The in-depth analysis and extensive experiments on real data sets have justified the superiority of our keyword-NNE algorithm.


title: BIM-based Interoperable Workflow for Energy Improvement of School Buildings over the life cycle

abstract: Simulation and predictive models to verify the energy performance of new or retrofitted existing buildings strongly need tuning procedures based on energy monitoring in the operational phase, thus to avoid performance gap concerns. The value chain of an uninterrupted information for energy models based on design data (e.g. for new buildings) or on paper documents and additional surveys (e.g. for existing buildings) is crucial to perform a reliable design optioneering process. The present research applies a BIM2BEM approach to provide a public administration with a model for facility management (i.e. BIM) and a model to assess energy improvement and check the choices (i.e. BEM), as a pilot project on school buildings renovation. Schools are frequently out-of-date buildings in harsh need of structural and energy refurbishment and public administrations require consistent economic evaluations considering the payback time of the proposed solutions. Additionally, O&M (Operation & Maintenance) costs should be included in a life cycle evaluation of the buildings. Two schools with different layouts and configuration adopted as case studies to compare energy simulation to actual consumption. The workflow empowers the energy simulation results highlighting the accuracy of energy modelling in different situations and it introduces possible tuning strategies to decrease uncertainty.


title: Scan registration for autonomous mining vehicles using 3D-NDT

abstract: Scan registration is an essential sub-task when building maps based on range finder data from mobile robots. The problem is to deduce how the robot has moved between consecutive scans, based on the ...


title: Basing Cryptographic Protocols on Tamper-Evident Seals

abstract: In this paper we attempt to formally study two very intuitive physical models: sealed envelopes and locked boxes, often used as illustrations for common cryptographic operations. We relax the security properties usually required from locked boxes (such as in bit-commitment protocols) and require only that a broken lock or torn envelope be identifiable to the original sender. Unlike the completely impregnable locked box, this functionality may be achievable in real life, where containers having this property are called “tamper-evident seals”. Another physical object with this property is the “scratch-off card”, often used in lottery tickets. We show that scratch-off cards can be used to implement bit-commitment and coin flipping, but not oblivious transfer. Of particular interest, we give a strongly-fair coin flipping protocol with bias bounded by O(1/r) (where r is the number of rounds), beating the best known bias in the standard model even with cryptographic assumptions.


title: Dual Aperture Photography: Image and Depth from a Mobile Camera

abstract: Conventional cameras capture images with limited depth of field and no depth information. Camera systems have been proposed that enable additional depth information to be captured with the image. These systems reduce the resolution of the captured image or result in reduced sensitivity of the lens. We demonstrate a camera that is able to capture extended depth of field images together with depth information at each single frame while requiring minimal impact on the physical design of the camera or its performance. In this paper we show results with a camera for mobile devices, but this technology (named dual aperture to recall the major change in the camera model) can be applied with even greater effect in larger form factor cameras.


title: Big data challenges in railway engineering

abstract: As Big Data becomes part of railroad data analysis, there are many challenges which need to be addressed by the railway industry. This extended abstract highlights some of the challenges from specific examples in railway engineering. This work does not present the challenges of dealing with Big Data in general which is beyond the scope of this paper. The examples provided in this extended abstract cover both the engineering and the management of railroad applications.


Communication Performance Evaluation of Bilateral Teleoperation Systems Implemented over WLAN


title: 42 variability bugs in the linux kernel: a qualitative analysis

abstract: Feature-sensitive verification pursues effective analysis of the exponentially many variants of a program family. However, researchers lack examples of concrete bugs induced by variability, occurring in real large-scale systems. Such a collection of bugs is a requirement for goal-oriented research, serving to evaluate tool implementations of feature-sensitive analyses by testing them on real bugs. We present a qualitative study of 42 variability bugs collected from bug-fixing commits to the Linux kernel repository. We analyze each of the bugs, and record the results in a database. In addition, we provide self-contained simplified C99 versions of the bugs, facilitating understanding and tool evaluation. Our study provides insights into the nature and occurrence of variability bugs in a large C software system, and shows in what ways variability affects and increases the complexity of software bugs.


title: Systematic Model-Based Safety Assessment via Probabilistic Model Checking

abstract: Safety assessment is a well-established process for assuring the safety and reliability of critical (aeronautical) systems. It uses probabilistic (quantitative) analysis to provide precise measures about the safety requirements of a system. Traditionally, quantitative safety assessment uses fault-tree analysis, but certification authorities also allow the use of Markov models. In this paper we propose a strategy for quantitative safety assessment based on the Prism model-checker. Prism models are extracted systematically from a high-level model via the application of translation rules. We illustrate our strategy with a representative system design from the airborne industry.


title: Muta cum Liquida in the Light of Tertenia Sardinian Metathesis and Compensatory Lengthening Lat

abstract: This article is designed to show that muta cum liquida (branching onsets) enclose an empty nucleus (in case they are bipositional). Arguments come from two data sets. A compensatory lengthening is studied that has occurred in the evolution from Latin to Old French within a muta cum liquida: tr dr > rr , i.e., the loss of t d is accompanied by the gemination of r iff the preceding vowel is short ( petra > pierre vs. paatre > pere ). In Tertenia Sardinian, it is argued that metathesis of r from the right to the left of the o in /seso doormendu/ → sro ðorɔmmέndu ; ndu (with ensuing gemination of the m on the position vacated) occurs in order to circumscribe the second of two empty nuclei (o) in a row.


title: Summarizing Scientific Articles: Experiments With Relevance And Rhetorical Status

abstract: In this article we propose a strategy for the summarization of scientific articles that concentrates on the rhetorical status of statements in an article: Material for summaries is selected in such a way that summaries can highlight the new contribution of the source article and situate it with respect to earlier work.We provide a gold standard for summaries of this kind consisting of a substantial corpus of conference articles in computational linguistics annotated with human judgments of the rhetorical status and relevance of each sentence in the articles. We present several experiments measuring our judges' agreement on these annotations.We also present an algorithm that, on the basis of the annotated training material, selects content from unseen articles and classifies it into a fixed set of seven rhetorical categories. The output of this extraction and classification system can be viewed as a single-document summary in its own right; alternatively, it provides starting material for the generation of task-oriented and user-tailored summaries designed to give users an overview of a scientific field.


A Child as a Donor for Hematopoietic Stem Cell Transplantation: Bioethical Justification—A Case Study on Sickle Cell Disease


title: Effective multi-label active learning for text classification

abstract: Labeling text data is quite time-consuming but essential for automatic text classification. Especially, manually creating multiple labels for each document may become impractical when a very large amount of data is needed for training multi-label text classifiers. To minimize the human-labeling efforts, we propose a novel multi-label active learning approach which can reduce the required labeled data without sacrificing the classification accuracy. Traditional active learning algorithms can only handle single-label problems, that is, each data is restricted to have one label. Our approach takes into account the multi-label information, and select the unlabeled data which can lead to the largest reduction of the expected model loss. Specifically, the model loss is approximated by the size of version space, and the reduction rate of the size of version space is optimized with Support Vector Machines (SVM). An effective label prediction method is designed to predict possible labels for each unlabeled data point, and the expected loss for multi-label data is approximated by summing up losses on all labels according to the most confident result of label prediction. Experiments on several real-world data sets (all are publicly available) demonstrate that our approach can obtain promising classification result with much fewer labeled data than state-of-the-art methods.


title: A Review of Distributed Deadlock Detection Techniques based on Diffusion Computation Approach

abstract: is a system state in which every process in some group requests resources from other processes in the group, and then waits indefinitely for these requests to be satisfied. Deadlocks have a very adverse effect on the efficient working of operating system therefore they should be either prevented, avoided or if exist should be detected and resolved. Because distributed systems are more vulnerable to deadlocks, the problems of deadlock detection and resolution have long been considered important problems in such systems. This paper provides a comprehensive review of the some of the existing techniques for deadlock detection in distributed environment.


title: Control-Theoretic Methods for Cyberphysical Security: Geometric Principles for Optimal Cross-Layer Resilient Control Systems

abstract: 1066-033X/15©2015ieee Digital Object Identifier 10.1109/MCS.2014.2364725 Date of publication: 19 January 2015 C yberphysical systems integrate physical processes, computational resources, and communication capabilities. Cyberphysical systems have permeated modern society, becoming prevalent in many domains, including energy production, health care, and telecommunications. Examples of cyberphysical systems include sensor networks, industrial automation systems, and critical infrastructures such as transportation networks, power generation and distribution networks, water and gas distribution networks, and advanced manufacturing systems. The integration of cybertechnologies with physical processes increases system efficiencies and, at the same time, introduces vulnerabilities that undermine the reliability of critical infrastructures. As recently highlighted by the Maroochy water breach in March 2000 [1], multiple recent power blackouts in Brazil [2], the SQL Slammer worm attack on the Davis-Besse nuclear plant in January 2003 [3], the StuxNet computer worm in June 2010 [4], and various industrial security incidents [5], Geometric PrinciPles For oPtimal cross-layer resilient control systems


title: A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques

abstract: The amount of text that is generated every day is increasing dramatically. This tremendous volume of mostly unstructured text cannot be simply processed and perceived by computers. Therefore, efficient and effective techniques and algorithms are required to discover useful patterns. Text mining is the task of extracting meaningful information from text, which has gained significant attentions in recent years. In this paper, we describe several of the most fundamental text mining tasks and techniques including text pre-processing, classification and clustering. Additionally, we briefly explain text mining in biomedical and health care domains.


title: Dynamic performance analysis of 3-level integrated buck converters

abstract: A systematic analysis of 3-level integrated buck converters is conducted in this paper. Detailed derivations of the working principles, voltage conversion ratio and output voltage ripple are given. In particular, the system loop gain of 3-level buck converters is analyzed, which provides guidance on designing fast and robust 3-level converters. The derived loop gain is verified with time-domain small signal injection analysis. The results show a good match between analytical and simulation results.


title: Dynamic shape capture using multi-view photometric stereo

abstract: We describe a system for high-resolution capture of moving 3D geometry, beginning with dynamic normal maps from multiple views. The normal maps are captured using active shape-from-shading (photometric stereo), with a large lighting dome providing a series of novel spherical lighting configurations. To compensate for low-frequency deformation, we perform multi-view matching and thin-plate spline deformation on the initial surfaces obtained by integrating the normal maps. Next, the corrected meshes are merged into a single mesh using a volumetric method. The final output is a set of meshes, which were impossible to produce with previous methods. The meshes exhibit details on the order of a few millimeters, and represent the performance over human-size working volumes at a temporal resolution of 60Hz.


title: Parent Child Interaction Therapy for Children with Disruptive Behavior and Developmental Disabilities.

abstract: Abstract Children with developmental disabilities (DD) often present with comorbid disruptive behavior problems that may negatively affect their education, treatment, and social interactions. Therefore, treatments that reduce disruptive behavior in children with DD are critical. Three characteristics strongly suggest that PCIT is an appropriate treatment choice among children with DD: PCIT shares several features with treatments that have demonstrated efficacy for children with DD in single case design studies; PCIT needs little modification to be effective with children with DD; and PCIT is an evidence-based treatment for disruptive behavior in typically developing children. Techniques for adapting PCIT for children with DD are presented. Further research is suggested to augment anecdotal evidence for the effectiveness of PCIT with this population. ********** Developmental disabilities (DD) refer to any chronic physical or mental condition of childhood that results in the child acquiring cognitive and adaptive skills at a slower rate than same-aged peers (Coleman, 1999). The most common conditions include mental retardation (MR), pervasive developmental disorders (e.g., autism), cerebral palsy, and spina bifida (myelomeningocele). Although children with primary sensory deficits (i.e., blindness or deafness) also may be classified as developmentally disabled, their treatment needs are unique and largely outside the scope of this discussion. Conditions associated with DD display varied etiologies, including genetic syndromes, metabolic disorders, exposure to environmental toxins, and perinatal complications. Some degree of cognitive deficit is associated with all of these conditions, as are difficulties in other areas of functioning (e.g., motor development, social interactions, language; Levy, 1996). Disruptive behaviors are often a concern and source of stress among parents of young children with and without DD. In typically developing children, particularly preschoolers, behaviors such as noncompliance, aggression, rule-breaking, and property destruction, are among the most frequent reasons for referral for psychological treatment. It is estimated that between 2% and 16% of children meet criteria for a disruptive behavior disorder such as Oppositional Defiant Disorder or Conduct Disorder, depending on the population studied (Loeber, Burke, Lahey, Winters, & Zera, 2000). Without treatment, these behaviors can escalate into more serious delinquent and antisocial behavior (Loeber, Green, Lahey, & Kalb, 2000). In contrast, several researchers have shown success in treating these behaviors in typically developing preschoolers. Children with DD are more likely to display disruptive behaviors than their typically developing peers. For example, an estimated 40% of children with MR display some degree of disruptive behavior in childhood. In one study, nearly half of all clinic-referred children and adolescents with MR were referred due to disruptive behavior. The association between DD and disruptive behavior may have several explanations. Disruptive behavior may be due to frustration based on discord among the child's needs, skills, and environmental demands. A primary example of this phenomenon occurs when children become frustrated at their inability to verbally express their needs, resulting in aggression or temper tantrums. Children's disruptive behavior may also have an organic cause, such as seizure activity or thyroid dysfunction. Medication side effects are another potential etiology that must be considered for children with DD and disruptive behavior. Finally, the child's disruptive behavior may be associated with a psychiatric diagnosis. While diagnoses of Oppositional Defiant Disorder, Conduct Disorder, or Attention Deficit Hyperactivity Disorder are most common among children with disruptive behavior, depressive or anxiety disorders may also be present. Disruptive behavior alone is associated with several negative outcomes such as school problems and later delinquency. …


A New Approach for Combining Time-of-Flight and RGB Cameras Based on Depth-Dependent Planar Projective Transformations


Primary central nervous system lymphoma: is absence of intratumoral hemorrhage a characteristic finding on MRI?


#Comparison: An Examination of Social Comparison Orientation on Instagram as It Relates to Self-Esteem and State Anxiety


title: Can cyclosporin A induce permanent remission of atopic dermatitis

abstract: Three adult patients, wbo bad suffered from severe tberapy-resistant atopic dermatitis since childbood, were treated witb long-term (29,44 and 22 months) low-dose (maintenance dose: 0.5–0.7 mg/kg) cyclosporin A (CyA). All responded rapidly witbin 3–5 weeks and exbibited only minor exacerbations during tbe first pbases of therapy. No relapse occurred in any of the patients after drug withdrawal. All patients remained symptom-free, except for occasional very mild and easily manageable localized lesions, for follow-up periods of 34, 13 and 22 months. Side-effects were minimal or tolerable (mild by pertricbosis in two patients, a slight decrease of creatinine clearance in one). In contrast with the clinical improvement, all atopy-related parameters such as blood eosinophilia, elevated IgE levels and specific IgE (RAST) reactivity remained essentially unchanged, as were skin dryness and stigmata of atopy. It is concluded tbat long-term low-dose CyA treatment may lead to permanent or long-term total or subtotal remission of atopic dermatitis.


title: pFind 2.0: a software package for peptide and protein identification via tandem mass spectrometry

abstract: This paper describes the pFind 2.0 software package for peptide and protein identification via tandem mass spectrometry. Firstly, the most important feature of pFind 2.0 is that it offers a modularized and customized platform for third parties to test and compare their algorithms. The developers can create their own modules following the open application programming interface (API) standards and then add it into workflows in place of the default modules. In addition, to accommodate different requirements, the package provides four automated workflows adopting different algorithm modules, executing processes and result reports. Based on this design, pFind 2.0 provides an automated target-decoy database search strategy: The user can just specify a certain false positive rate (FPR) and start searching. Then the system will return the protein identification results automatically filtered by such an estimated FPR. Secondly, pFind 2.0 is also of high accuracy and high speed. Many pragmatic preprocessing, peptide-scoring, validation, and protein inference algorithms have been incorporated. To speed up the searching process, a toolbox for indexing protein databases is developed for high-throughput applications and all modules are implemented under a new architecture designed for large-scale parallel and distributed searching. An experiment on a public dataset shows that pFind 2.0 can identify more peptides than SEQUEST and Mascot at the 1% FPR. It is also demonstrated that this version of pFind 2.0 has better usability and higher speed than its previous versions. The software and more detailed supplementary information can both be accessed at http://pfind.ict.ac.cn/.


title: Cyberterrorism: The Sum of All Fears?

abstract: Cyberterrorism conjures up images of vicious terrorists unleashing catastrophic attacks against computer networks, wreaking havoc, and paralyzing nations. This is a frightening scenario, but how likely is it to occur? Could terrorists cripple critical military, financial, and service computer systems? This article charts the rise of cyberangst and examines the evidence cited by those who predict imminent catastrophe. Psychological, political, and economic forces have combined to promote the fear of cyberterrorism. From a psychological perspective, two of the greatest fears of modern time are combined in the term “cyberterrorism.” The fear of random, violent victimization segues well with the distrust and outright fear of computer technology. Many of these fears, the report contends, are exaggerated: not a single case of cyberterrorism has yet been recorded, hackers are regularly mistaken for terrorists, and cyberdefenses are more robust than is commonly supposed. Even so, the potential threat is undeniabl...


Robust Wrist-Type Wireless Multiple Photo-Interrupter Pulse Sensor


title: Bioavailability, bioactivity and impact on health of dietary flavonoids and related compounds: an update

abstract: There is substantial interest in the role of plant secondary metabolites as protective dietary agents. In particular, the involvement of flavonoids and related compounds has become a major topic in human nutrition research. Evidence from epidemiological and human intervention studies is emerging regarding the protective effects of various (poly)phenol-rich foods against several chronic diseases, including neurodegeneration, cancer and cardiovascular diseases. In recent years, the use of HPLC–MS for the analysis of flavonoids and related compounds in foods and biological samples has significantly enhanced our understanding of (poly)phenol bioavailability. These advancements have also led to improvements in the available food composition and metabolomic databases, and consequently in the development of biomarkers of (poly)phenol intake to use in epidemiological studies. Efforts to create adequate standardised materials and well-matched controls to use in randomised controlled trials have also improved the quality of the available data. In vitro investigations using physiologically achievable concentrations of (poly)phenol metabolites and catabolites with appropriate model test systems have provided new and interesting insights on potential mechanisms of actions. This article will summarise recent findings on the bioavailability and biological activity of (poly)phenols, focusing on the epidemiological and clinical evidence of beneficial effects of flavonoids and related compounds on urinary tract infections, cognitive function and age-related cognitive decline, cancer and cardiovascular disease.


title: On the Mathematical Foundations of Theoretical Statistics

abstract: Centre of Location. That abscissa of a frequency curve for which the sampling errors of optimum location are uncorrelated with those of optimum scaling. (9.)


title: A large scale study of SVM based methods for abstract screening in systematic reviews

abstract: A major task in systematic reviews is abstract screening, i.e., excluding, often hundreds or thousand of, irrelevant citations returned from a database search based on titles and abstracts. Thus, a systematic review platform that can automate the abstract screening process is of huge importance. Several methods have been proposed for this task. However, it is very hard to clearly understand the applicability of these methods in a systematic review platform because of the following challenges: (1) the use of non-overlapping metrics for the evaluation of the proposed methods, (2) usage of features that are very hard to collect, (3) using a small set of reviews for the evaluation, and (4) no solid statistical testing or equivalence grouping of the methods. In this paper, we use feature representation that can be extracted per citation. We evaluate SVM-based methods (commonly used) on a large set of reviews ($61$) and metrics ($11$) to provide equivalence grouping of methods based on a solid statistical test. Our analysis also includes a strong variability of the metrics using $500$x$2$ cross validation. While some methods shine for different metrics and for different datasets, there is no single method that dominates the pack. Furthermore, we observe that in some cases relevant (included) citations can be found after screening only 15-20% of them via a certainty based sampling. A few included citations present outlying characteristics and can only be found after a very large number of screening steps. Finally, we present an ensemble algorithm for producing a $5$-star rating of citations based on their relevance. Such algorithm combines the best methods from our evaluation and through its $5$-star rating outputs a more easy-to-consume prediction.


title: Effects of mindfulness training on body awareness to sexual stimuli: implications for female sexual dysfunction.

abstract: Objectives: Treatments of female sexual dysfunction have been largely unsuccessful because they do not address the psychological factors that underlie female sexuality. Negative self-evaluative processes interfere with the ability to attend and register physiological changes (interoceptive awareness). This study explores the effect of mindfulness meditation training on interoceptive awareness and the three categories of known barriers to healthy sexual functioning: attention, self-judgment, and clinical symptoms. Methods: Forty-four college students (30 women) participated in either a 12-week course containing a "meditation laboratory" or an active control course with similar content or laboratory format. Interoceptive awareness was measured by reaction time in rating physiological response to sexual stimuli. Psychological barriers were assessed with self-reported measures of mindfulness and psychological well-being. Results: Women who participated in the meditation training became significantly faster at registering their physiological responses (interoceptive awareness) to sexual stimuli compared with active controls (F(1,28) = 5.45, p = .03, ηp2 = 0.15). Female meditators also improved their scores on attention (t = 4.42, df = 11, p = .001), self-judgment, (t = 3.1, df = 11, p = .01), and symptoms of anxiety (t = −3.17, df = 11, p = .009) and depression (t = −2.13, df = 11, p Conclusions: Mindfulness-based improvements in interoceptive awareness highlight the potential of mindfulness training as a treatment of female sexual dysfunction.


title: Ray tracing volume densities

abstract: This paper presents new algorithms to trace objects represented by densities within a volume grid, e.g. clouds, fog, flames, dust, particle systems. We develop the light scattering equations, discuss previous methods of solution, and present a new approximate solution to the full three-dimensional radiative scattering problem suitable for use in computer graphics. Additionally we review dynamical models for clouds used to make an animated movie.


title: The motivations and experiences of the on-demand mobile workforce

abstract: On-demand mobile workforce applications match physical world tasks and willing workers. These systems offer to help conserve resources, streamline courses of action, and increase market efficiency for micro- and mid-level tasks, from verifying the existence of a pothole to walking a neighbor's dog. This study reports on the motivations and experiences of individuals who regularly complete physical world tasks posted in on-demand mobile workforce marketplaces. Data collection included semi-structured interviews with members (workers) of two different services. The analysis revealed the main drivers for participating in an on-demand mobile workforce, including desires for monetary compensation and control over schedules and task selection. We also reveal main reasons for task selection, which involve situational factors, convenient physical locations, and task requester profile information. Finally, we discuss the key characteristics of the most worthwhile tasks and offer implications for novel crowdsourcing systems for physical world tasks.


title: Robust guidewire tracking in fluoroscopy

abstract: A guidewire is a medical device inserted into vessels during image guided interventions for balloon inflation. During interventions, the guidewire undergoes non-rigid deformation due to patients' breathing and cardiac motions, and such 3D motions are complicated when being projected onto the 2D fluoroscopy. Furthermore, in fluoroscopy there exist severe image artifacts and other wire-like structures. All these make robust guidewire tracking challenging. To address these challenges, this paper presents a probabilistic framework for robust guidewire tracking. We first introduce a semantic guidewire model that contains three parts, including a catheter tip, a guidewire tip and a guidewire body. Measurements of different parts are integrated into a Bayesian framework as measurements of a whole guidewire for robust guidewire tracking. Moreover, for each part, two types of measurements, one from learning-based detectors and the other from online appearance models, are applied and combined. A hierarchical and multi-resolution tracking scheme is then developed based on kernel-based measurement smoothing to track guidewires effectively and efficiently in a coarse-to-fine manner. The presented framework has been validated on a test set of 47 sequences, and achieves a mean tracking error of less than 2 pixels. This demonstrates the great potential of our method for clinical applications.


title: High-Accuracy Low-Precision Training

abstract: Low-precision computation is often used to lower the time and energy cost of machine learning, and recently hardware accelerators have been developed to support it. Still, it has been used primarily for inference - not training. Previous low-precision training algorithms suffered from a fundamental tradeoff: as the number of bits of precision is lowered, quantization noise is added to the model, which limits statistical accuracy. To address this issue, we describe a simple low-precision stochastic gradient descent variant called HALP. HALP converges at the same theoretical rate as full-precision algorithms despite the noise introduced by using low precision throughout execution. The key idea is to use SVRG to reduce gradient variance, and to combine this with a novel technique called bit centering to reduce quantization error. We show that on the CPU, HALP can run up to $4 \times$ faster than full-precision SVRG and can match its convergence trajectory. We implemented HALP in TensorQuant, and show that it exceeds the validation performance of plain low-precision SGD on two deep learning tasks.


title: Social Media Use and Access to Digital Technology in US Young Adults in 2016

abstract: Background: In 2015, 90% of US young adults with Internet access used social media. Digital and social media are highly prevalent modalities through which young adults explore identity formation, and by extension, learn and transmit norms about health and risk behaviors during this developmental life stage. Objective: The purpose of this study was to provide updated estimates of social media use from 2014 to 2016 and correlates of social media use and access to digital technology in data collected from a national sample of US young adults in 2016. Methods: Young adult participants aged 18-24 years in Wave 7 (October 2014, N=1259) and Wave 9 (February 2016, N=989) of the Truth Initiative Young Adult Cohort Study were asked about use frequency for 11 social media sites and access to digital devices, in addition to sociodemographic characteristics. Regular use was defined as using a given social media site at least weekly. Weighted analyses estimated the prevalence of use of each social media site, overlap between regular use of specific sites, and correlates of using a greater number of social media sites regularly. Bivariate analyses identified sociodemographic correlates of access to specific digital devices. Results: In 2014, 89.42% (weighted n, 1126/1298) of young adults reported regular use of at least one social media site. This increased to 97.5% (weighted n, 965/989) of young adults in 2016. Among regular users of social media sites in 2016, the top five sites were Tumblr (85.5%), Vine (84.7%), Snapchat (81.7%), Instagram (80.7%), and LinkedIn (78.9%). Respondents reported regularly using an average of 7.6 social media sites, with 85% using 6 or more sites regularly. Overall, 87% of young adults reported access or use of a smartphone with Internet access, 74% a desktop or laptop computer with Internet access, 41% a tablet with Internet access, 29% a smart TV or video game console with Internet access, 11% a cell phone without Internet access, and 3% none of these. Access to all digital devices with Internet was lower in those reporting a lower subjective financial situation; there were also significant differences in access to specific digital devices with Internet by race, ethnicity, and education. Conclusions: The high mean number of social media sites used regularly and the substantial overlap in use of multiple social media sites reflect the rapidly changing social media environment. Mobile devices are a primary channel for social media, and our study highlights disparities in access to digital technologies with Internet access among US young adults by race/ethnicity, education, and subjective financial status. Findings from this study may guide the development and implementation of future health interventions for young adults delivered via the Internet or social media sites. [J Med Internet Res 2017;19(6):e196]


title: Generalizing Biomedical Event Extraction

abstract: We present a system for extracting biomedical events (detailed descriptions of biomolecular interactions) from research articles. This system was developed for the BioNLP'11 Shared Task and extends our BioNLP'09 Shared Task winning Turku Event Extraction System. It uses support vector machines to first detect event-defining words, followed by detection of their relationships. The theme of the BioNLP'11 Shared Task is generalization, extending event extraction to varied biomedical domains. Our current system successfully predicts events for every domain case introduced in the BioNLP'11 Shared Task, being the only system to participate in all eight tasks and all of their subtasks, with best performance in four tasks.


title: The Impact Of Social Media On Business Performance

abstract: Social media are gaining popularity and are increasingly used in regular operations of many companies, including start-ups, small, medium-sized, and large organizations. The purpose of this research is to explore the impact of social media and to analyze to what extent social media have impact on organizational capabilities and business performance. We develop a research model and two simple propositions based on the resource based view of the firm. We analyze the impact of six social media applications on six business capabilities and on business performance in SponsorPay, a start-up company since 2009 in the on-line game advertising industry. We use a mixed research method including qualitative analysis based on interviews and quantitative analysis based on a survey among 60 employees. We find that the use of social media enhances business capabilities and business performance. The impact is not due to one (out of six) social media tools only, but due to successfully combining the six social media tools into one effective social media ecosystem that enables coordination between internal and external business processes.


title: Ranking scientific publications: the effect of nonlinearity

abstract: Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to address this problem. In this paper, we introduce nonlinearity to the PageRank algorithm when aggregating resources from different nodes to further enhance the effect of important papers. The validation of our method is performed on the data of American Physical Society (APS) journals. The results indicate that the nonlinearity improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Although the nonlinearity analysis is based on the PageRank algorithm, it can be easily extended to other iterative ranking algorithms and similar improvements are expected.


title: Sparse Neural Networks Topologies

abstract: We propose Sparse Neural Network architectures that are based on random or structured bipartite graph topologies. Sparse architectures provide compression of the models learned and speed-ups of computations, they can also surpass their unstructured or fully connected counterparts. As we show, even more compact topologies of the so-called SNN (Sparse Neural Network) can be achieved with the use of structured graphs of connections between consecutive layers of neurons. In this paper, we investigate how the accuracy and training speed of the models depend on the topology and sparsity of the neural network. Previous approaches using sparcity are all based on fully connected neural network models and create sparcity during training phase, instead we explicitly define a sparse architectures of connections before the training. Building compact neural network models is coherent with empirical observations showing that there is much redundancy in learned neural network models. We show experimentally that the accuracy of the models learned with neural networks depends on expander-like properties of the underlying topologies such as the spectral gap and algebraic connectivity rather than the density of the graphs of connections.


title: Applications of type-2 fuzzy logic in power systems: A literature survey

abstract: In recent years the application of fuzzy systems in real world problems is increasing because of the fact that fuzzy systems can deal with linguistic data along with the numerical data. Although type-1 fuzzy system can handle the uncertainties related to imprecise data, in some cases a higher degree of precision is required. This lead to the origin of type-2 fuzzy systems. Since then the type-2 fuzzy system theory has been applied to different fields of engineering, finance and medical domains. This paper attempts to present a comprehensive set of references on applications of type-2 fuzzy systems in power system.


title: MedEx: a medication information extraction system for clinical narratives.

abstract: Medication information is one of the most important types of clinical data in electronic medical records. It is critical for healthcare safety and quality, as well as for clinical research that uses electronic medical record data. However, medication data are often recorded in clinical notes as free-text. As such, they are not accessible to other computerized applications that rely on coded data. We describe a new natural language processing system (MedEx), which extracts medication information from clinical notes. MedEx was initially developed using discharge summaries. An evaluation using a data set of 50 discharge summaries showed it performed well on identifying not only drug names (F-measure 93.2%), but also signature information, such as strength, route, and frequency, with F-measures of 94.5%, 93.9%, and 96.0% respectively. We then applied MedEx unchanged to outpatient clinic visit notes. It performed similarly with F-measures over 90% on a set of 25 clinic visit notes.


title: A self-steering I/Q receiver array in 45-nm CMOS SOI

abstract: A novel I/Q receiver array is demonstrated that adapts phase shifts in each receive channel to point a receive beam toward an incident RF signal. The measured array operates at 8.1 GHz and covers steering angles of +/-35 degrees for a four element array. Additionally, the receiver incorporates an I/Q down-converter and demodulates 64QAM with EVM less than 4%. The chip is fabricated in 45 nm CMOS SOI process and occupies an area of 3.45 mm2 while consuming 143 mW dc power.


title: Thermoelectric Microconverter for Energy Harvesting Systems

abstract: This paper presents a solution for energy microgeneration through energy harvesting by taking advantage of temperature differences that are converted into electrical energy using the Seebeck effect. A thermoelectric microconverter for energy scavenging systems that can supply low-power electronics was fabricated using thin films of bismuth and antimony tellurides. Thin films of n-type bismuth (Bi2Te3) and p-type antimony (Sb2Te3) tellurides were obtained by thermal coevaporation with thermoelectric figures of merit (ZT) at room temperature of 0.84 and 0.5 and power factors (PF × 10-3 [W · K-1 ·m-2]) of 4.87 and 2.81, respectively. The films were patterned by photolithography and wet-etching techniques. The goal for this thermoelectric microconverter is to supply individual electroencephalogram (EEG) modules composed by an electrode, processing electronics, and an antenna, where the power consumption ranges from hundredths of microwatts to a few milliwatts. Moreover, these wireless EEG modules allow patients to maintain their mobility while simultaneously having their electrical brain activity monitored.


Deep Learning Lane Detection for Autonomous Vehicle Localization


title: Multi-Class Active Learning by Uncertainty Sampling with Diversity Maximization

abstract: As a way to relieve the tedious work of manual annotation, active learning plays important roles in many applications of visual concept recognition. In typical active learning scenarios, the number of labelled data in the seed set is usually small. However, most existing active learning algorithms only exploit the labelled data, which often suffers from over-fitting due to the small number of labelled examples. Besides, while much progress has been made in binary class active learning, little research attention has been focused on multi-class active learning. In this paper, we propose a semi-supervised batch mode multi-class active learning algorithm for visual concept recognition. Our algorithm exploits the whole active pool to evaluate the uncertainty of the data. Considering that uncertain data are always similar to each other, we propose to make the selected data as diverse as possible, for which we explicitly impose a diversity constraint on the objective function. As a multi-class active learning algorithm, our algorithm is able to exploit uncertainty across multiple classes. An efficient algorithm is used to optimize the objective function. Extensive experiments on action recognition, object classification, scene recognition, and event detection demonstrate its advantages.


A Review of Customer Relationship Management , Implementation and Procedures from the Rich Nation and Emerging Market Perspectives


title: Adaptive and generic corner detection based on the accelerated segment test

abstract: The efficient detection of interesting features is a crucial step for various tasks in Computer Vision. Corners are favored cues due to their two dimensional constraint and fast algorithms to detect them. Recently, a novel corner detection approach, FAST, has been presentedwhich outperforms previous algorithms in both computational performance and repeatability. We will show how the accelerated segment test, which underlies FAST, can be significantly improved by making it more generic while increasing its performance.We do so by finding the optimal decision tree in an extended configuration space, and demonstrating how specialized trees can be combined to yield an adaptive and generic accelerated segment test. The resulting method provides high performance for arbitrary environments and so unlike FAST does not have to be adapted to a specific scene structure. We will also discuss how different test patterns affect the corner response of the accelerated segment test.


title: Shifting Paradigms: From Flexner to Competencies

abstract: Realizing medical education is on the brink of a major paradigm shift from structure- and process-based to competency-based education and measurement of outcomes, the authors reviewed the existing medical literature to provide practical insight into how to accomplish full implementation and evaluation of this new paradigm. They searched Medline and the Educational Resource Information Clearinghouse from the 1960s until the present, reviewed the titles and abstracts of the 469 articles the search produced, and chose 68 relevant articles for full review. The authors found that in the 1970s and 1980s much attention was given to the need for and the development of professional competencies for many medical disciplines. Little attention, however, was devoted to defining the benchmarks of specific competencies, how to attain them, or the evaluation of competence. Lack of evaluation strategies was likely one of the forces responsible for the threedecade lag between initiation of the movement and widespread adoption. Lessons learned from past experiences include the importance of strategic planning and faculty and learner buy-in for defining competencies. In addition, the benchmarks for defining competency and the thresholds for attaining competence must be clearly delineated. The development of appropriate assessment tools to measure competence remains the challenge of this decade, and educators must be responsible for studying the impact of this paradigm shift to determine whether its ultimate effect is the production of more competent physicians. Acad. Med. 2002;77:361‐367.


title: Lane detection algorithm based on local feature extraction

abstract: An effective local feature extraction algorithm for lane detection is proposed in this paper. First, a lane region of interest (ROI) is determined by the location of road surface appeared in an image. Then, the light intensity and width of lane markings are taken as the local feature. A local threshold segmentation algorithm is utilized to extract lane-marking candidates followed by a morphological operation to obtain the accurate lane. An edge refining procedure is used to eliminate the interference and reduce computational cost. Finally, the lane marking is detected using Hough transform with some subsidiary conditions. With the proposed method, the lane can be accurately detected in conditions of fluctuating and poor illumination, as well as the interference from reflected light can be avoided effectively. The experimental results demonstrate the efficiency of the proposed method.


title: Introducing Entity-Based Concepts to Business Process Modeling

abstract: The so-called Internet of Things (IoT) that comprises interconnected physical devices such as sensor networks and its technologies like Radio Frequency Identification (RFID) is increasingly adopted in many industries and thus becomes highly relevant for process modeling and execution. As BPMN 2.0 does not yet consider the idiosyncrasies of real-world entities we suggest new modeling concepts for a physical entity as well as a sensing task and an actuation task to make BPMN IoT-aware.


title: Maximum inner-product search using cone trees

abstract: The problem of efficiently finding the best match for a query in a given set with respect to the Euclidean distance or the cosine similarity has been extensively studied. However, the closely related problem of efficiently finding the best match with respect to the inner-product has never been explored in the general setting to the best of our knowledge. In this paper we consider this problem and contrast it with the previous problems considered. First, we propose a general branch-and-bound algorithm based on a (single) tree data structure. Subsequently, we present a dual-tree algorithm for the case where there are multiple queries. Our proposed branch-and-bound algorithms are based on novel inner-product bounds. Finally we present a new data structure, the cone tree, for increasing the efficiency of the dual-tree algorithm. We evaluate our proposed algorithms on a variety of data sets from various applications, and exhibit up to five orders of magnitude improvement in query time over the naive search technique in some cases.


title: Risk-Averse Approximate Dynamic Programming with Quantile-Based Risk Measures

abstract: In this paper, we consider a finite-horizon Markov decision process (MDP) for which the objective at each stage is to minimize a quantile-based risk measure (QBRM) of the sequence of future costs; we call the overall objective a dynamic quantile-based risk measure (DQBRM). In particular, we consider optimizing dynamic risk measures where the one-step risk measures are QBRMs, a class of risk measures that includes the popular value at risk (VaR) and the conditional value at risk (CVaR). Although there is considerable theoretical development of risk-averse MDPs in the literature, the computational challenges have not been explored as thoroughly. We propose data-driven and simulation-based approximate dynamic programming (ADP) algorithms to solve the risk-averse sequential decision problem. We address the issue of inefficient sampling for risk applications in simulated settings and present a procedure, based on importance sampling, to direct samples toward the “risky region” as the ADP algorithm progresses. ...


title: Learning to Follow Navigational Route Instructions

abstract: There have been many proposals to compute similarities between words based on their distributions in contexts. However, these approaches do not distinguish between synonyms and antonyms. We present two methods for identifying synonyms among distributionally similar words.


title: Dual-$V_{th}$ Independent-Gate FinFETs for Low Power Logic Circuits

abstract: This paper describes the electrode work-function, oxide thickness, gate-source/drain underlap, and silicon thick ness optimization required to realize dual-Vth independent-gate FinFETs. Optimum values for these FinFET design parameters are derived using the physics-based University of Florida SPICE model for double-gate devices, and the optimized FinFETs are simulated and validated using Sentaurus TCAD simulations. Dual-Vth FinFETs with independent gates enable series and parallel merge transformations in logic gates, realizing compact low power alternative gates with competitive performance and reduced input capacitance in comparison to conventional FinFET gates. Furthermore, they also enable the design of a new class of compact logic gates with higher expressive power and flexibility than conventional CMOS gates, e.g., implementing 12 unique Boolean functions using only four transistors. Circuit designs that balance and improve the performance of the novel gates are described. The gates are designed and calibrated using the University of Florida double-gate model into conventional and enhanced technology libraries. Synthesis results for 16 benchmark circuits from the ISCAS and OpenSPARC suites indicate that on average at 2 GHz, the enhanced library reduces total power and the number of fins by 36% and 37%, respectively, over a conventional library designed using shorted-gate FinFETs in 32 nm technology.


title: Interventions to improve social determinants of health among elderly ethnic minority groups: a review

abstract: Background ::: Like the European general population, ethnic minorities are aging. In this group, important social determinants of health (social participation, social isolation and loneliness) that lead to negative health outcomes frequently occur. Interventions targeting these determinants may decrease negative health outcomes. The goal of this article was to identify effective interventions that improve social participation, and minimise social isolation and loneliness in community dwelling elderly ethnic minorities. ::: ::: ::: Methods ::: An electronic database (PubMed) was systematically searched using an extensive search strategy, for intervention studies in English, French, Dutch of German, without time limit. Additional articles were found using references. Articles were included if they studied an intervention aimed to improve social participation or minimise social isolation or loneliness and were focusing on community dwelling elderly ethnic minorities. Data regarding studies characteristics and results were extracted. ::: ::: ::: Results ::: Six studies (three randomized controlled trials, three non-controlled intervention studies) were included in the review. All studies were group-based interventions and had a theoretical basis. Five out of six studies showed improvement on a social participation, -isolation or loneliness outcome. Type of intervention included volunteering-, educational- and physical activities. In three studies active participation of the participant was required, these interventions were not more effective than other interventions. ::: ::: ::: Conclusion ::: Some interventions improved the included social determinants of health in community dwelling elderly ethnic minorities. Investment in further development and implementation of these interventions may help to improve social determinants of health in these populations. It is necessary to evaluate these interventions in the European setting.


title: Model Selection for Anomaly Detection

abstract: Anomaly detection based on one-class classification algorithms is broadly used in many applied domains like image processing (e.g. detection of whether a patient is “cancerous” or “healthy” from mammography image), network intrusion detection, etc. Performance of an anomaly detection algorithm crucially depends on a kernel, used to measure similarity in a feature space. The standard approaches (e.g. cross-validation) for kernel selection, used in two-class classification problems, can not be used directly due to the specific nature of a data (absence of a second, abnormal, class data). In this paper we generalize several kernel selection methods from binary-class case to the case of one-class classification and perform extensive comparison of these approaches using both synthetic and real-world data.


Why Deep Neural Networks?


title: How we experience immersive virtual environments: the concept of presence and its measurement *

abstract: This paper reviews the concept of presence in immersive virtual environments, the sense of being there signalled by people acting and responding realistically to virtual situations and events. We argue that presence is a unique phenomenon that must be distinguished from the degree of engagement, involvement in the portrayed environment. We argue that there are three necessary conditions for presence: the (a) consistent low latency sensorimotor loop between sensory data and proprioception; (b) statistical plausibility: images must be statistically plausible in relation to the probability distribution of images over natural scenes. A constraint on this plausibility is the level of immersion; (c) behaviour-response correlations: Presence may be enhanced and maintained over time by appropriate correlations between the state and behaviour of participants and responses within the environment, correlations that show appropriate responses to the activity of the participants. We conclude with a discussion of methods for assessing whether presence occurs, and in particular recommend the approach of comparison with ground truth and give some examples of this.


title: Customer Data Clustering using Data Mining Technique

abstract: Classification and patterns extraction from customer data is very important for business support and decision making. Timely identification of newly emerging trends is very important in business process. Large companies are having huge volume of data but starving for knowledge. To overcome the organization current issue, the new breed of technique is required that has intelligence and capability to solve the knowledge scarcity and the technique is called Data mining. The objectives of this paper are to identify the high-profit, high-value and low-risk customers by one of the data mining technique - customer clustering. In the first phase, cleansing the data and developed the patterns via demographic clustering algorithm using IBM I-Miner. In the second phase, profiling the data, develop the clusters and identify the high-value low-risk customers. This cluster typically represents the 10-20 percent of customers which yields 80% of the revenue.


title: Cortical hypoperfusion in the B6.PS2APP mouse model for Alzheimer's disease: Comprehensive phenotyping of vascular and tissular parameters by MRI

abstract: Function and morphology of the cerebral vasculature were studied in the amyloid (Abeta) plaque-containing double-transgenic (TG) B6.PS2APP Alzheimer's disease (AD) mouse model with MRI at an age range of 10 to 17 months. Perfusion, blood volume, and average vessel geometry were assessed in the brain and compared to age-matched controls (wild-type [WT] C57Bl/6). Additionally, the MR relaxation times T(1), T(2), and T(2)* were measured to detect potential pathological changes that might be associated with Abeta plaque depositions. Both decreased perfusion and decreased blood volume were observed in the occipital cortex in B6.PS2APP mice as compared to controls. A significant decrease in T(1) and T(2) was found in the frontal cortex and in the subiculum/parasubiculum. Immunohistochemistry confirmed plaque depositions in the cortex and in the subiculum/parasubiculum. In summary, our data indicate a reduced blood supply of B6.PS2APP mice in the occipital cortex that parallels the findings in cortical regions of patients with AD.


title: Overlapping and segregated resting-state functional connectivity in patients with major depressive disorder with and without childhood neglect

abstract: Many studies have suggested that childhood maltreatment increase risk of adulthood major depressive disorder (MDD) and predict its unfavorable treatment outcome, yet the neural underpinnings associated with childhood maltreatment in MDD remain poorly understood. Here, we seek to investigate the whole-brain functional connectivity patterns in MDD patients with childhood maltreatment. Resting-state functional magnetic resonance imaging was used to explore intrinsic or spontaneous functional connectivity networks of 18 MDD patients with childhood neglect, 20 MDD patients without childhood neglect, and 20 healthy controls. Whole-brain functional networks were constructed by measuring the temporal correlations of every pairs of brain voxels and were further analyzed by using graph-theory approaches. Relative to the healthy control group, the two MDD patient groups showed overlapping reduced functional connectivity strength in bilateral ventral medial prefrontal cortex/ventral anterior cingulate cortex. However, compared with MDD patients without a history of childhood maltreatment, those patients with such a history displayed widespread reduction of functional connectivity strength primarily in brain regions within the prefrontal-limbic-thalamic-cerebellar circuitry, and these reductions significantly correlated with measures of childhood neglect. Together, we showed that the MDD groups with and without childhood neglect exhibited overlapping and segregated functional connectivity patterns in the whole-brain networks, providing empirical evidence for the contribution of early life stress to the pathophysiology of MDD. Hum Brain Mapp 35:1154–1166, 2014. © 2013 Wiley Periodicals, Inc.


title: A Modular Multilevel HVDC Buck–Boost Converter Derived From Its Switched-Mode Counterpart

abstract: This paper begins by presenting a generalized methodology for conceptualizing modular multilevel converter (MMC)-based dc–dc topologies, which is predicated on the concept of harmonic power balance. A compelling implication is that MMC-based variants of conventional switched-mode converter structures can be realized. As an example case study, this paper introduces a new dc–dc MMC for HVdc applications, which is derived from the classical buck–boost dc–dc converter. This new topology, which is revealed to be an alternative option to the well-known dual active bridge (DAB) converter with an intermediate transformer, offers buck–boost functionality and bidirectional dc fault blocking, using only two quadrant switching cells. Comparative analysis shows the proposed topology has lower operating losses and a lower total magnetics rating in comparison to an MMC-based DAB solution for dc stepping ratios around unity. A dynamic controller is developed that regulates the converter dc power throughput while maintaining balanced capacitor voltages. The converter operating principle, dynamic controller performance, and dc fault blocking are verified by simulation.


title: Compact Substrate Integrated Waveguide (SIW) Filter With Defected Ground Structure

abstract: A compact substrate integrated waveguide (SIW) filter with defected ground structure (DGS) is proposed. The DGS is etched on the ground plane of the SIW cavity, and it behaves as a resonator. Under such condition, better frequency selectivity contributed to three transmission zeros is obtained, and two of them are located at the lower stopband. The proposed filter, fabricated using standard printed circuit board technology, has the advantages of compactness, good spurious suppression characteristics and low cost. Its measured S-parameters agree well with the simulated ones.


title: The steps to health employee weight management randomized control trial: rationale, design and baseline characteristics.

abstract: BACKGROUND ::: The workplace can be an important setting for addressing obesity. An increasing number of employers offer weight management programs. ::: ::: ::: PURPOSE ::: Present the design, rationale and baseline characteristics of the Steps to Health study (STH), a randomized trial to evaluate the effectiveness of two preexisting employee weight management programs offered at Duke University and Medical Center. ::: ::: ::: METHODS ::: 550 obese (BMI ≥30) employee volunteers were randomized 1:1 to two programs. Baseline data, collected between January 2011 and July 2012, included height/weight, accelerometry, workplace injuries, health care utilization, and questionnaires querying socio-cognitive factors, perceptions of health climate, physical activity, and dietary intake. In secondary analyses participants in the two programs will also be compared to a non-randomized observational control group of obese employees. ::: ::: ::: RESULTS ::: At baseline, the mean age was 45 years, 83% were female, 41% white, and 53% black. Mean BMI was 37.2. Participants consumed a mean of 2.37 servings of fruits and vegetables per day (in the past week), participated in 11.5 min of moderate-to-vigorous physical activity, and spent 620 min being sedentary. ::: ::: ::: CONCLUSION ::: STH addresses the need for evaluation of worksite interventions to promote healthy weight. In addition to having direct positive effects on workers' health, worksite programs have the potential to increase productivity and reduce health care costs.


title: Late time response analysis in UWB radar for concealed weapon detection: feasibility study

abstract: Remote detection of body-worn concealed weapons or explosives (CWE) is a field of ongoing research. In this Thesis the feasibility of CWE detection by using the UWB radar is explored. ::: The CWE detection is based on the analysis of the Late Time Response (LTR) of the human which has been illuminated by the UWB signal. A specific set of LTR parameters characterizes the target signature. Therefore the existence of a CWE attached on the human body will influence the LTR characteristics and give the composite object i.e. human-CWE a different signature than the simple object i.e. human. ::: The CWE detection methodology is verified by theoretical analysis, modelling and extensive laboratory experimentation. Investigation of the way the LTR parameters are influenced by the existence of the CWE signifies the differences of the LTR signature between the human and human-CWE. So the resolution of the differences in the LTR of a human with and without a CWE as the main objective of the research, are presented in the Thesis. The results verify that CWE detection with the use of LTR is feasible under the experimental conditions presented. Furthermore consideration of all possible detection scenarios is out of the scope of this Thesis.


title: Optical properties of normal and cancerous human skin in the visible and near-infrared spectral range

abstract: Differences in absorption and/or scattering of cancerous and normal skin have the potential to provide a basis for noninvasive cancer detection. In this study, we have determined and compared the in vitro optical properties of human epidermis, dermis, and subcutaneous fat with those of nonmelanoma skin cancers in the spectral range from 370 to 1600 nm. Fresh specimens of normal and cancerous human skin were obtained from surgeries. The samples were rinsed in saline solution and sectioned. Diffuse reflectance and total transmittance were measured using an integrating sphere spectrophotometer. Absorption and reduced scattering coefficients were calculated from the measured quantities using an inverse Monte Carlo technique. The differences between optical properties of each normal tissue-cancer pair were statistically analyzed. The results indicate that there are significant differences in the scattering of cancerous and healthy tissues in the spectral range from 1050 to 1400 nm. In this spectral region, the scattering of cancerous lesions is consistently lower than that of normal tissues, whereas absorption does not differ significantly, with the exception of nodular basal cell carcinomas (BCC). Nodular BCCs exhibit significantly lower absorption as compared to normal skin. Therefore, the spectral range between 1050 and 1400 nm appears to be optimal for nonmelanoma skin cancer detection.


title: Predicting patient revisits at the University of Virginia Health System Emergency Department

abstract: This study focuses on the predictive identification of patients frequently revisiting the University of Virginia Health System Emergency Department. Identifying these patients can help the Emergency Department formulate strategies that improve patient care and decrease excess Emergency Department utilization. The Health System in particular faces a number of unique challenges in its ongoing mission to reduce extraneous patient revisits. In addition to its status as an academic hospital, it serves a broad geographic region as one of five level-I trauma centers in the Commonwealth of Virginia. In this study we utilized 5 years of data from the University of Virginia Health System data warehouse. These data contain information on 91,297 patients and 196,902 unique encounters, including details on patient demographics, diagnoses and hospital departments visited. From these raw data we engineered features, trained gradient boosted decision trees, and experimented with unsupervised clustering techniques to best approximate 30-day Emergency Department revisit risk at the conclusion of each patient encounter. Our best model for revisit risk resulted in a Receiver Operator Characteristic Area Under the Curve of 0.75. Furthermore, we exhibit the real-time performance of our model as a tool to rank which at-risk patients should receive priority for Emergency Department resources. This test demonstrated a significant improvement over the current allocation of Emergency Department social worker resources with a daily Mean Average Precision of 0.83. The methodologies proposed in this paper exhibit an end-to-end framework to transform raw administrative claims and limited clinical data into predictive models that help the Emergency Department better manage resources and target interventions.