Query: LTEV2Vsim: An LTE-V2V simulator for the investigation of resource allocation for cooperative awareness

Relevant passages:
Simulating LTE Cellular Systems: An Open-Source Framework <=SEP=> Long-term evolution (LTE) represents an emerging and promising technology for providing broadband ubiquitous Internet access. For this reason, several research groups are trying to optimize its performance. Unfortunately, at present, to the best of our knowledge, no open-source simulation platforms, which the scientific community can use to evaluate the performance of the entire LTE system, are freely available. The lack of a common reference simulator does not help the work of researchers and poses limitations on the comparison of results claimed by different research groups. To bridge this gap, herein, the open-source framework LTE-Sim is presented to provide a complete performance verification of LTE networks. LTE-Sim has been conceived to simulate uplink and downlink scheduling strategies in multicell/multiuser environments, taking into account user mobility, radio resource optimization, frequency reuse techniques, the adaptive modulation and coding module, and other aspects that are very relevant to the industrial and scientific communities. The effectiveness of the proposed simulator has been tested and verified considering 1) the software scalability test, which analyzes both memory and simulation time requirements; and 2) the performance evaluation of a realistic LTE network providing a comparison among well-known scheduling strategies.

Enhanced autonomous resource selection for LTE-based V2V communication <=SEP=> Vehicle-to-vehicle (V2V) communication has the potential to improve road safety significantly by providing connectivity between vehicles to exchange information messages. V2V has recently gained considerable attention in part due to the standardization work that has started for Long Term Evolution (LTE) under the Third Generation Partnership Project (3GPP). Efficient and reliable distributed allocation of time-frequency resources among different vehicle user equipments (VUEs) remains a challenge particularly in dense urban environments. In this work, a new autonomous resource selection scheme for urban V2V communication scenario is proposed. Specifically, the proposed scheme relies on resource partitioning based on VUE heading direction along with a sensing-based collision avoidance mechanism, which aims to alleviate the potential interference between VUEs due to resource collision and in-band emission (IBE). The performance of the two-stage autonomous resource selection scheme is evaluated by system-level simulations in urban scenarios, where the results show a significant performance gain in comparison to existing approaches.

Support for vehicle-to-everything services based on LTE <=SEP=> Vehicular applications and communications technologies are often referred to as vehicle-to-everything (V2X), which is classified into four different types: vehicle-to-vehicle (V2V), vehicle- to-infrastructure (V2I), vehicle-to-network (V2N), and vehicle-to-pedestrian (V2P) [1]. V2X related research projects, field tests, and regulatory work have been promoted in different countries and regions. In May 2015, the Ministry of Industry and Information Technology (MIIT) of China explained national strategies, "Made in China 2025," about intelligent connected vehicles. In 2020 and 2025, the overall technology and key technologies for intelligent driver assistance and automatic driving will be available in China, respectively [2]. V2X solutions are the critical technologies to support the realization of such visions. Although IEEE 802.11p has been selected as the technology for V2X communications in some countries such as the United States and in Europe, the intrinsic characteristics of IEEE 802.11p have confined the technology to support low latency with high reliability [3, 4]. Standardization of Long Term Evolution (LTE)-based V2X is being actively conducted by the Third Generation Partnership Project (3GPP) to provide the solution for V2X communications that benefit from the global deployment and fast commercialization of LTE systems. Because of the wide deployment of LTE networks, V2I and V2N services can be provided with high data rate, comprehensive quality of service (QoS) support, ubiquitous coverage, and high penetration rate [5]. Meanwhile, LTE can be extended to support V2V direct communications based on device-to-device (D2D) sidelink design to satisfy the QoS requirements, such as low latency, high reliability, and high speed in the case of high vehicle density [6].

LTE evolution for vehicle-to-everything services <=SEP=> Wireless communication has become a key technology for competitiveness of next generation vehicles. Recently, the 3GPP has initiated standardization activities for LTE-based V2X services composed of vehicle-to-vehicle, vehicle- to-pedestrian, and vehicle-to-infrastructure/network. The goal of these 3GPP activities is to enhance LTE systems to enable vehicles to communicate with other vehicles, pedestrians, and infrastructure in order to exchange messages for aiding in road safety, controlling traffic flow, and providing various traffic notifications. In this article, we provide an overview of the service flow and requirements of the V2X services LTE systems are targeting. This article also discusses the scenarios suitable for operating LTE-based V2X services, and addresses the main challenges of high mobility and densely populated vehicle environments in designing technical solutions to fulfill the requirements of V2X services. Leveraging the spectral-efficient air interface, the cost-effective network deployment, and the versatile nature of supporting different communication types, LTE systems along with proper enhancements can be the key enabler of V2X services.

An improved IEEE 802.16 WiMAX module for the ns-3 simulator <=SEP=> IEEE 802.16 WiMAX is a promising new wireless technology for providing broadband ubiquitous network access. As more and more researchers and industrials are interested in simulating such networks, a number of WiMAX simulators have been emerged in the networking community. One of the most recent WiMAX simulator is the one developed for ns-3. This module provides a standard compliant and well designed implementation of the standard and benefits from the major enhancements provided by ns-3 (compared to other network simulators) which has all the capabilities of becoming the leading network simulator in near future. However, this WiMAX module still lacks some important features which motivated this work. In this paper, we first provide a snapshot of existing WiMAX simulators available in the public domain, while highlighting their limitations. Then, we describe the new features and enhancements we have integrated within the ns-3 WiMAX module, and in particular: a realistic and scalable physical model, an IP packet classifier for the convergence sub-layer, efficient uplink and downlink schedulers, support for multicast traffic and pcap packet tracing functionality. The new design of the physical layer has improved the simulation time by several magnitude orders while still providing a realistic implementation of the standard. Furthermore, the IP classifier has enabled the simulation of an unlimited number of service flows per subscriber station, while the proposed schedulers improve the management of the QoS requirements for the different service flows.

Use of the scored Patient-Generated Subjective Global Assessment (PG-SGA) as a nutrition assessment tool in patients with cancer <=SEP=> Objective: To evaluate the use of the scored Patient-Generated Subjective Global Assessment (PG-SGA) as a nutrition assessment tool in patients with cancer.Design: An observational study assessing the nutritional status of patients with cancer.Setting: Oncology ward of a private tertiary Australian hospital.Subjects: Seventy-one cancer patients aged 18–92 y.Intervention: Scored PG-SGA questionnaire, comparison of scored PG-SGA with subjective global assessment (SGA), sensitivity, specificity.Results: Some 24% (17) of 71 patients were well nourished, 59% (42) of patients were moderately or suspected of being malnourished and 17% (12) of patients were severely malnourished according to subjective global assessment (SGA). The PG-SGA score had a sensitivity of 98% and a specificity of 82% at predicting SGA classification. There was a significant difference in the median PG-SGA scores for each of the SGA classifications (P<0.001), with the severely malnourished patients having the highest scores. Re-admission within 30 days of discharge was significantly different between SGA groups (P=0.037). The mortality rate within 30 days of discharge was not significantly different between SGA groups (P=0.305). The median length of stay of well nourished patients (SGA A) was significantly lower than that of the malnourished (SGA B+C) patients (P=0.024).Conclusion: The scored PG-SGA is an easy to use nutrition assessment tool that allows quick identification and prioritisation of malnutrition in hospitalised patients with cancer.Sponsors: The Wesley Research Institute.

Grid Cells and Neural Coding in High-End Cortices <=SEP=> An ultimate goal of neuroscience is to understand the mechanisms of mammalian intellectual functions, many of which are thought to depend extensively on the cerebral cortex. While this may have been considered a remote objective when Neuron was launched in 1988, neuroscience has now evolved to a stage where it is possible to decipher neural-circuit mechanisms in the deepest parts of the cortex, far away from sensory receptors and motoneurons. In this review, we show how studies of place cells in the hippocampus and grid cells in the entorhinal cortex may provide some of the first glimpses into these mechanisms. We shall review the events that led up to the discovery of grid cells and a functional circuit in the entorhinal cortex and highlight what we currently see as the big questions in this field--questions that, if resolved, will add to our understanding of cortical computation in a general sense.

A Miniaturized, Dual-Band, Circularly Polarized Microstrip Antenna for Installation Into Satellite Mobile Phones <=SEP=> A miniaturized, dual-band, circularly polarized microstrip antenna (CPMA) for satellite mobile phones was designed. Two miniaturized radiation elements using folded structures are stacked vertically to achieve dual bands. Each radiation element shows left-handed circular polarization. The total volume of this dual-band, circularly polarized, microstrip antenna is 46 mm (length) x 24 mm (width) x 13 mm (height). The overall antenna is mounted on a phone-shaped ground plane to fit inside satellite mobile phones.

Learning Deep Features for DNA Methylation Data Analysis <=SEP=> Many studies demonstrated that the DNA methylation, which occurs in the context of a CpG, has strong correlation with diseases, including cancer. There is a strong interest in analyzing the DNA methylation data to find how to distinguish different subtypes of the tumor. However, the conventional statistical methods are not suitable for analyzing the highly dimensional DNA methylation data with bounded support. In order to explicitly capture the properties of the data, we design a deep neural network, which composes of several stacked binary restricted Boltzmann machines, to learn the low-dimensional deep features of the DNA methylation data. Experimental results show that these features perform best in breast cancer DNA methylation data cluster analysis, compared with some state-of-the-art methods.

SeriesNet:A Generative Time Series Forecasting Model <=SEP=> Time series forecasting is emerging as one of the most important branches of big data analysis. However, traditional time series forecasting models can not effectively extract good enough sequence data features and often result in poor forecasting accuracy. In this paper, a novel time series forecasting model, named SeriesNet, which can fully learn features of time series data in different interval lengths. The SeriesNet consists of two networks. The LSTM network aims to learn holistic features and to reduce dimensionality of multi-conditional data, and the dilated causal convolution network aims to learn different time interval. This model can learn multi-range and multi-level features from time series data, and has higher predictive accuracy compared those models using fixed time intervals. Moreover, this model adopts residual learning and batch normalization to improve generalization. Experimental results show our model has higher forecasting accuracy and has greater stableness on several typical time series data sets.

Goal-Oriented Requirements Engineering: A Guided Tour <=SEP=> Goals capture, at different levels of abstraction, the various objectives the system under consideration should achieve. Goal-oriented requirements engineering is concerned with the use of goals for eliciting, elaborating, structuring, specifying, analyzing, negotiating, documenting, and modifying requirements. This area has received increasing attention over the past few years. The paper reviews various research efforts undertaken along this line of research. The arguments in favor of goal orientation are first briefly discussed. The paper then compares the main approaches to goal modeling, goal specification and goal-based reasoning in the many activities of the requirements engineering process. To make the discussion more concrete, a real case study is used to suggest what a goal-oriented requirements engineering method may look like. Experience with such approaches and tool support are briefly discussed as well.

Discrete Dynamic Shortest Path Problems in Transportation Applications: Complexity and Algorithms with Optimal Run Time <=SEP=> This paper solves what appears to be a 30 years old problem dealing with the discovery of most effi algorithms possible to compute all-to-one shortest paths in discrete dynamic networks. This problem lies at th of efficient solution approaches to dynamic network models that arise in dynamic transportation systems, suc Intelligent Transportation Systems (ITS) applications. While the main objective of this paper is the study of the one dynamic shortest paths problem, one-to-all fastest paths problems are studied as well. Early results are and new properties are established. We establish the exact complexity of these problems and develop optima run time sense, solution algorithms. A new and simple solution algorithm is proposed for all-to-one all departur intervals shortest path problems. It is proved, theoretically, that the new solution algorithm has an optimal run complexity that equals the complexity of the problem. Computer implementations and experimental evaluatio various solution algorithms support the theoretical findings and demonstrate the efficiency of the proposed so algorithm. We expect our findings to be of major benefit to research and development activities in the field of dynamic, in particular real-time, management and control of large-scale Intelligent Transportation Systems (I

Developing an Air Hockey Game in LabVIEW <=SEP=> Air Hockey consists of two players volleying a puck, using mallets, across a low friction table surface. Points are scored when a player lands the puck in the opponent's goal. The game is won by the player with the higher score at the end of the match. Virtual versions of air hockey have been implemented using various game development software. This paper details the implementation of the air hockey game in LabVIEW. This version of the game uses a physics engine and an Artificial Intelligence (AI) player. This effort discusses the steps followed to design the game and the effectiveness of the AI player versus a human player.

Social Capital and Knowledge Integration in Digitally Enabled Teams <=SEP=> T understand the impact of social capital on knowledge integration and performance within digitally enabled teams, we studied 46 teams who had a history and a future working together. All three dimensions of their social capital (structural, relational, and cognitive) were measured prior to the team performing two tasks in a controlled setting, one face-to-face and the other through a lean digital network. Structural and cognitive capital were more important to knowledge integration when teams communicated through lean digital networks than when they communicated face-to-face; relational capital directly impacted knowledge integration equally, regardless of the communication media used by the team. Knowledge integration, in turn, impacted team decision quality, suggesting that social capital influences team performance in part by increasing a team’s ability to integrate knowledge. These results suggest that team history may be necessary but not sufficient for teams to overcome the problems with the use of lean digital networks as a communication environment. However, team history may present a window of opportunity for social capital to develop, which in turn allows teams to perform just as well as in either communication environment.

Graphene-SGX: A Practical Library OS for Unmodified Applications on SGX <=SEP=> Intel SGX hardware enables applications to protect themselves from potentially-malicious OSes or hypervisors. In cloud computing and other systems, many users and applications could benefit from SGX. Unfortunately, current applications will not work out-of-the-box on SGX. Although previous work has shown that a library OS can execute unmodified applications on SGX, a belief has developed that a library OS will be ruinous for performance and TCB size, making application code modification an implicit prerequisite to adopting SGX. This paper demonstrates that these concerns are exaggerated, and that a fully-featured library OS can rapidly deploy unmodified applications on SGX with overheads comparable to applications modified to use “shim” layers. We present a port of Graphene to SGX, as well as a number of improvements to make the security benefits of SGX more usable, such as integrity support for dynamically-loaded libraries, and secure multi-process support. Graphene-SGX supports a wide range of unmodified applications, including Apache, GCC, and the R interpreter. The performance overheads of GrapheneSGX range from matching a Linux process to less than 2× in most single-process cases; these overheads are largely attributable to current SGX hardware or missed opportunities to optimize Graphene internals, and are not necessarily fundamental to leaving the application unmodified. Graphene-SGX is open-source and has been used concurrently by other groups for SGX research.

High-Speed Flight of Quadrotor Despite Loss of Single Rotor <=SEP=> In order to achieve high-speed flight of a damaged quadrotor with complete loss of a single rotor, a multiloop hybrid nonlinear controller is designed. By fully making use of sensor measurements, the model dependence of this control method is reduced, which is conducive to handling disturbance from the unknown aerodynamic effects. This controller is tested on a quadrotor vehicle with one rotor completely removed in the high-speed condition. Free flights are performed in the Open Jet Facility, a large-scale wind tunnel. Over 9 m/s flight speed is reached for the damaged quadrotor in these tests. In addition, several high-speed spin-induced aerodynamic effects are discovered and analyzed.

Sensor Systems for Prognostics and Health Management <=SEP=> Prognostics and health management (PHM) is an enabling discipline consisting of technologies and methods to assess the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risk. Sensor systems are needed for PHM to monitor environmental, operational, and performance-related characteristics. The gathered data can be analyzed to assess product health and predict remaining life. In this paper, the considerations for sensor system selection for PHM applications, including the parameters to be measured, the performance needs, the electrical and physical attributes, reliability, and cost of the sensor system, are discussed. The state-of-the-art sensor systems for PHM and the emerging trends in technologies of sensor systems for PHM are presented.

Detection and Localization of Texture-less Objects with Deep Neural Networks <=SEP=> This thesis studies Faster R-CNN, the state-of-art method for object detection in RGB images, and proposes its extension to RGB-D images. Solutions to the following problems are proposed and evaluated: filling missing values in depth images, depth encoding (raw depth vs. surface normals), extension of the CNN architecture to accept the extra depth information, and initialization of weights in the extended network. The overall best results were achieved with a network that accepts an extra depth channel, pre-processed by the iterative median filter to fill in the missing values, and has the depth weights in the first convolutional layer initialized with the mean of the color weights that were pretrained on ImageNet. However, the improvement over the original method using only RGB channels is not significant (mAP was increased by 1 − 2%), which suggests a need for different incorporation of the depth information.

A Fast Iterative Method for Solving the Eikonal Equation on Triangulated Surfaces <=SEP=> This paper presents an efficient, fine-grained parallel algorithm for solving the Eikonal equation on triangular meshes. The Eikonal equation, and the broader class of Hamilton-Jacobi equations to which it belongs, have a wide range of applications from geometric optics and seismology to biological modeling and analysis of geometry and images. The ability to solve such equations accurately and efficiently provides new capabilities for exploring and visualizing parameter spaces and for solving inverse problems that rely on such equations in the forward model. Efficient solvers on state-of-the-art, parallel architectures require new algorithms that are not, in many cases, optimal, but are better suited to synchronous updates of the solution. In previous work [W. K. Jeong and R. T. Whitaker, SIAM J. Sci. Comput., 30 (2008), pp. 2512-2534], the authors proposed the fast iterative method (FIM) to efficiently solve the Eikonal equation on regular grids. In this paper we extend the fast iterative method to solve Eikonal equations efficiently on triangulated domains on the CPU and on parallel architectures, including graphics processors. We propose a new local update scheme that provides solutions of first-order accuracy for both architectures. We also propose a novel triangle-based update scheme and its corresponding data structure for efficient irregular data mapping to parallel single-instruction multiple-data (SIMD) processors. We provide detailed descriptions of the implementations on a single CPU, a multicore CPU with shared memory, and SIMD architectures with comparative results against state-of-the-art Eikonal solvers.

Novelty and diversity metrics for recommender systems: choice, discovery and relevance <=SEP=> There is an increasing realization in the Recommender Systems (RS) field that novelty and diversity are fundamental qualities of recommendation effectiveness and added-value. We identify however a gap in the formalization of novelty and diversity metrics –and a consensus around them– comparable to the recent proposals in IR diversity. We study a formal characterization of different angles that RS novelty and diversity may take from the end-user viewpoint, aiming to contribute to a formal definition and understanding of different views and meanings of these magnitudes under common groundings. Building upon this, we derive metric schemes that take item position and relevance into account, two aspects not generally addressed in the novelty and diversity metrics reported in the RS literature.

Multi-target tracking by learning local-to-global trajectory models <=SEP=> The multi-target tracking problem is challenging when there exist occlusions, tracking failures of the detector and severe interferences between detections. In this paper, we propose a novel detection based tracking method that links detections into tracklets and further forms long trajectories. Unlike many previous hierarchical frameworks which split the data association into two separate optimization problems (linking detections locally and linking tracklets globally), we introduce a unified algorithm that can automatically relearn the trajectory models from the local and global information for finding the joint optimal assignment. In each temporal window, the trajectory models are initialized by the local information to link those easy-to-connect detections into a set of tracklets. Then the trajectory models are updated by the reliable tracklets and reused to link separated tracklets into long trajectories. We iteratively update the trajectory models by more information from more frames until the result converges. The iterative process gradually improves the accuracy of the trajectory models, which in turn improves the target ID inferences for all detections by the MRF model. Experiment results revealed that our proposed method achieved state-of-the-art multi-target tracking performance. Crown Copyright & 2014 Published by Elsevier Ltd. All rights reserved.

OntoNotes: The 90% Solution <=SEP=> OntoNotes is a five year multi-site collaboration between BBN Technologies, Information Sciences Institute of University of Southern California, University of Colorado, University of Pennsylvania and Brandeis University. The goal of the OntoNotes project is to provide linguistic data annotated with a skeletal representation of the literal meaning of sentences including syntactic parse, predicate-argument structure, coreference, and word senses linked to an ontology, allowing a new generation of language understanding technologies to be developed with new functional capabilities. In its third year of existence, the OntoNotes project has generated a large amount of high quality data covering various layers of linguistic annotation. This is probably the first time that data of such quality has been available in large quantities covering multiple genres (newswire, broadcast news, broadcast conversation and weblogs) and languages (English, Chinese and Arabic). The guiding principle has been to find a “sweet spot” in the space of inter-tagger agreement, productivity, and depth of representation. The most effective use of this resource for research requires simultaneous access to multiple layers of annotation. This has been made possible by representing the corpus with a relational database to accommodate the dense connectedness of the data and ensure consistency across layers. In order to facilitate ease of understanding and manipulability, the database has also been supplemented with a object-oriented Python API. The tutorial consists of two parts. In the first part we will familiarize the user with this new resource, describe the various layers of annotations in some detail and discuss the linguistic principles and sometimes practical considerations behind the important design decisions that shapes the corpus. We will also describe the salient differences between the three languages at each layer of annotation and how linguistic peculiarities of different languages were handled in the data. In the second part, we will describe the data formats of each of the layers and talk about various design decisions that went into the creation of the architecture of the database and the individual tables comprising it, along with issues that came up during the representation process and compromises that were made without sacrificing some primary objectives one of which being the independent existence of each layer that is necessary to allow multi-site collaboration. We will explain how the database schema attempts to interconnect all the layers. Then we will go into the details of the Python API that allows easy access to each of the layers and show that by making the objects closely resemble database tables, the API allows for their flexible integration. This will be followed by a hands-on working session.

Locally linear reconstruction based missing value imputation for supervised learning <=SEP=> Most learning algorithms generally assume that data is complete so each attribute of all instances is filled with a valid value. However, missing values are very common in real datasets for various reasons. In this paper, we propose a new single imputation method based on locally linear reconstruction (LLR) that improves the prediction performance of supervised learning (classification & regression) with various missing ratios. Next, we compare the proposed missing value imputation method (LLR) with six well-known single imputation methods for five different learning algorithms based on 13 classification and nine regression datasets. The experimental results showed that (1) all imputation methods helped to improve the prediction accuracy, although some were very simple; (2) the proposed LLR imputation method enhanced the modeling performance more than all other imputation methods, irrespective of the learning algorithms and the missing ratios; and (3) LLR was outstanding when the missing ratio was relatively high and its prediction accuracy was similar to that of the complete dataset. & 2013 Elsevier B.V. All rights reserved.

High Performance Computing for Hyperspectral Remote Sensing <=SEP=> Advances in sensor and computer technology are revolutionizing the way remotely sensed data is collected, managed and analyzed. In particular, many current and future applications of remote sensing in Earth science, space science, and soon in exploration science will require real- or near real-time processing capabilities. In recent years, several efforts have been directed towards the incorporation of high-performance computing (HPC) models to remote sensing missions. A relevant example of a remote sensing application in which the use of HPC technologies (such as parallel and distributed computing) is becoming essential is hyperspectral remote sensing, in which an imaging spectrometer collects hundreds or even thousands of measurements (at multiple wavelength channels) for the same area on the surface of the Earth. In this paper, we review recent developments in the application of HPC techniques to hyperspectral imaging problems, with particular emphasis on commodity architectures such as clusters, heterogeneous networks of computers, and specialized hardware devices such as field programmable gate arrays (FPGAs) and commodity graphic processing units (GPUs). A quantitative comparison across these architectures is given by analyzing performance results of different parallel implementations of the same hyperspectral unmixing chain, delivering a snapshot of the state-of-the-art in this area and a thoughtful perspective on the potential and emerging challenges of applying HPC paradigms to hyperspectral remote sensing problems.

Single-stage posterior corpectomy and expandable cage placement for treatment of thoracic or lumbar burst fractures. <=SEP=> STUDY DESIGN
A prospective study was performed.


OBJECTIVE
To assess an unusual technique for corpectomy and expandable cage placement via single-stage posterior approach in acute thoracic or lumbar burst fractures.


SUMMARY AND BACKGROUND DATA
Burst fractures represent 10% to 20% of all spine injuries at or near the thoracolumbar junction, and can cause neurologic complications and kyphotic deformity. The goal of surgical intervention is to decompress the neural elements, restore vertebral body height, correct angular deformity, and stabilize the columns of the spine.


METHODS
The study comprised 14 patients (8 women and 6 men aged 40.3 years) who had 1 spinal burst fracture between T8 and L4 and who underwent single-stage posterior corpectomy, circumferential reconstruction with expandable-cage placement, and transpedicle screwing between January 2003 and May 2005. Neurologic status was classified using the American Spinal Injury Association (ASIA) impairment scale and functional outcomes were analyzed using a visual analogue scale (VAS) for pain. The kyphotic angle (alpha) and lordotic angle (beta) were measured in the thoracic or thoracolumbar and lumbar regions, respectively. RESULTS.: The mean follow-up time was 24 months (range, 12-48 months). Neurologic status was in 7 patients (preop: ASIA-E, postop: unchanged), 2 patients (preop: ASIA-D, postop: 1 unchanged, 1 improved to ASIA-E), 3 patients (preop: ASIA-C, postop: 2 improved to ASIA-D, 1 improved to ASIA-E), 2 patients (preop: ASIA-B, postop: 1 improved to ASIA-C, 1 unchanged). The mean operative time was 187.8 minutes. The mean blood loss was 596.4 mL. Regarding postoperative complications, 1 patient experienced transient worsening of neurologic deficits and 1 patient developed pseudarthrosis. The mean preoperative VAS score was 8.21 and the mean postoperative VAS score was 2.66 (P < 0.05). The mean preoperative kyphotic angle for the 11 individuals with the thoracic or thoracolumbar burst fractures was 24.6 degrees and the mean preoperative lordotic angle for the 3 individuals with lumbar burst fractures was 10.6 degrees. The corresponding values at 12 months postsurgery were 17.1 degrees and 13.6 degrees.


CONCLUSION
This single-stage posterior approach for acute thoracic and lumbar burst fractures offers some advantages over the classic combined anterior-posterior approach. The results from this small series suggest that a single-stage posterior approach should be considered in select cases.

Pathway-Finder: An Interactive Recommender System for Supporting Personalized Care Pathways <=SEP=> Clinical pathways define the essential component of the complex care process, with the objective to optimize patient outcomes and resource allocation. Clinical pathway analysis has gained increased attention in order to augment the patient treatment process. In this demonstration paper, we propose Pathway-Finder, an interactive recommender system to visually explore and discover clinical pathways. The interactive web service efficiently collects and displays patient information in a meaningful way to support an effective personalized treatment plan. Pathway-Finder implements a Bayesian Network to discover causal relationships among different factors. To support real-time recommendation and visualization, a key-value structure has been implemented to traverse the Bayesian Network faster. Additionally, Pathway-Finder is a cloud based web service hosted on Microsoft Azure which enables the health providers to access the system without the need to deploy analytics infrastructure. We demonstrate Pathway-Finder to interactively recommend personalized interventions to minimize 30-day readmission risk for Heart Failure (HF).

Food vs Non-Food Classification <=SEP=> Automatic understanding of food is an important research challenge. Food recognition engines can provide a valid aid for automatically monitoring the patient's diet and food-intake habits directly from images acquired using mobile or wearable cameras. One of the first challenges in the field is the discrimination between images containing food versus the others. Existing approaches for food vs non-food classification have used both shallow and deep representations, in combination with multi-class or one-class classification approaches. However, they have been generally evaluated using different methodologies and data, making a real comparison of the performances of existing methods unfeasible. In this paper, we consider the most recent classification approaches employed for food vs non-food classification, and compare them on a publicly available dataset. Different deep-learning based representations and classification methods are considered and evaluated.

Automatic Essay Grading Using Text Categorization Techniques <=SEP=> Several standard text-categorization techniques were applied to the problem of automated essay grading. Bayesian independence classifiers and knearest-neighbor classifiers were trained to assign scores to manually-graded essays. These scores were combined with several other summary text measures using linear regression. The classifiers and regression equations were then applied to a new set of essays. The classifiers worked very well. The agreement between the automated grader and the final manual grade was as good as the agreement between human graders.

Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets <=SEP=> We present attribute bagging (AB), a technique for improving the accuracy and stability of classi#er ensembles induced using random subsets of features. AB is a wrapper method that can be used with any learning algorithm. It establishes an appropriate attribute subset size and then randomly selects subsets of features, creating projections of the training set on which the ensemble classi#ers are built. The induced classi#ers are then used for voting. This article compares the performance of our AB method with bagging and other algorithms on a hand-pose recognition dataset. It is shown that AB gives consistently better results than bagging, both in accuracy and stability. The performance of ensemble voting in bagging and the AB method as a function of the attribute subset size and the number of voters for both weighted and unweighted voting is tested and discussed. We also demonstrate that ranking the attribute subsets by their classi#cation accuracy and voting using only the best subsets further improves the resulting performance of the ensemble. ? 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

The flipped classroom: A survey of the research <=SEP=> Recent advances in technology and in ideology have unlocked entirely new directions for education research. Mounting pressure from increasing tuition costs and free, online course offerings is opening discussion and catalyzing change in the physical classroom. The flipped classroom is at the center of this discussion. The flipped classroom is a new pedagogical method, which employs asynchronous video lectures and practice problems as homework, and active, group-based problem solving activities in the classroom. It represents a unique combination of learning theories once thought to be incompatible—active, problem-based learning activities founded upon a constructivist ideology and instructional lectures derived from direct instruction methods founded upon behaviorist principles. This paper provides a comprehensive survey of prior and ongoing research of the flipped classroom. Studies are characterized on several dimensions. Among others, these include the type of in-class and out-of-class activities, the measures used to evaluate the study, and methodological characteristics for each study. Results of this survey show that most studies conducted to date explore student perceptions and use single-group study designs. Reports of student perceptions of the flipped classroom are somewhat mixed, but are generally positive overall. Students tend to prefer in-person lectures to video lectures, but prefer interactive classroom activities over lectures. Anecdotal evidence suggests that student learning is improved for the flipped compared to traditional classroom. However, there is very little work investigating student learning outcomes objectively. We recommend for future work studies investigating of objective learning outcomes using controlled experimental or quasi-experimental designs. We also recommend that researchers carefully consider the theoretical framework used to guide the design of in-class activities. 1 The Rise of the Flipped Classroom There are two related movements that are combining to change the face of education. The first of these is a technological movement. This technological movement has enabled the amplification and duplication of information at an extremely low-cost. It started with the printing press in the 1400s, and has continued at an ever-increasing rate. The electronic telegraph came in the 1830s, wireless radio in the late 1800s and early 1900s, television in the 1920s, computers in the 1940s, the internet in the 1960s, and the world-wide web in the 1990s. As these technologies have been adopted, the ideas that have been spread through their channels have enabled a second movement. Whereas the technological movement sought to overcome real physical barriers to the free and open flow of information, this ideological movement seeks to remove the artificial, man-made barriers. This is epitomized in the free software movement (see, e.g., Stallman and Lessig [67]), although this movement is certainly not limited to software. A good example of this can be seen from the encyclopedia. Encyclopedia Britannica has been continuously published for nearly 250 years [20] (since 1768). Although Encyclopedia Britannica content has existed digitally since 1981, it was not until the advent of Wikipedia in 2001 that open access to encyclopedic content became available to users worldwide. Access to Encyclopedia Britannica remains restricted to a limited number of paid subscribers [21], but access to Wikipedia is open, and the website receives over 2.7 billion US monthly page views [81]. Thus, although the technology and digital content was available to enable free access to encyclopedic content, ideological roadblocks prevented this from happening. It was not until these ideologies had been overcome that humanity was empowered to create what has become the world’s largest, most up-to-date encyclopedia [81]. In a similar way, we are beginning to see the combined effects of these two movements on higher education. In the technological arena, research has made significant advances. Studies show that video lectures (slightly) outperform in-person lectures [9], with interactive online videos doing even better (Effect size=0.5) [83,51]. Online homework is just as effective as paper-and-pencil homework [8,27], and carefully developed intelligent tutoring systems have been shown to be just as effective as human tutors [77]. Despite these advancements, adoption has been slow, as the development of good educational systems can be prohibitively expensive. However, the corresponding ideological movement is breaking down these financial barriers. Ideologically, MIT took a significant step forward when it announced its OpenCourseWare (OCW) initiative in 2001 [53]. This opened access to information that had previously only been available to students who paid university tuition, which is over $40,000/yr at MIT [54]. Continuing this trend, MIT alum Salman Khan founded the Khan Academy in 2006, which has released a library of over 3200 videos and 350 practice exercises 2012. The stated mission of the Khan Academy is to provide “a free world-class education to anyone anywhere2012.” In the past year, this movement has rapidly gained momentum. Inspired by Khan’s efforts, Stanford professors Sebastian Thrun and Andrew Ng opened access to their online courses in Fall 2011. Thrun taught artificial intelligence with Peter Norvig, attracting over 160,000 students to their free online course. Subsequently, Thrun left the university and founded Udacity, which is now hosting 11 free courses [76]. With support from Stanford, Ng also started his own open online educational initiative, Coursera. Princeton, the University of Pennsylvania, and the University of Michigan have joined the Coursera partnership, which has expanded its offerings to 42 courses [10]. MIT has also upgraded its open educational initiative, and joined with Harvard in a $60 million dollar venture, edX [19]. EdX will, “offer Harvard and MIT classes online for free.” While online education is improving, expanding, and becoming openly available for free, university tuition at brick-and-mortar schools is rapidly rising [56]. Tuition in the University of California system has nearly tripled since 2000 [32]. Naturally, this is not being received well by university students in California [2]. Likewise, students in Quebec are actively protesting planned tuition hikes [13]. In resistance to planned tuition hikes, student protestors at Rutgers interrupted (on June 20, 2012) a board meeting to make their voices heard [36]. Adding fuel to the fire, results from a recent study by Gillen et al. [31] indicate that undergraduate student tuition is used to subsidize research. As a result, the natural question being asked by both students and educational institutions is exactly what students are getting for their money. This is applying a certain pressure on physical academic institutions to improve and enhance the in-person educational experience of their