Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Intraoperative Ultrasonography in Open Reduction of Mandibular Angle Fractures

Background. Traumatic fractures of lower jaw are the most common among all of maxillofacial injuries. statistically the most frequently injured area of the mandible is the angle. it is known that mandibular angle fractures require particular attention to diagnostic and treatment procedures. nowadays the majority of these injuries are treated by open reduction methods using an intraoral approach. in spite of good aesthetic outcomes intraoral approach to mandibular angle cannot provide adequate control of lower border of the mandible during reduction of the fragments. This problem can be solved by using ultrasonography as intraoperative control in open reduction of mandibular fractures. Objectives. The aim of this study was to determine the diagnostic value of ultrasonography as a method of intraoperative monitoring of the quality of bone element repositioning in the region of the mandibular angle during intraoral ostheosynthesis. Material and Methods. The study included 18 patients (17 men and 1 woman) aged 18 to 55 hospitalized in the Department of Maxillofacial surgery of regional Clinical Hospital of the lviv region from 2008 to 2011 with traumatic fractures of the mandibular angle. During hospitalization all patients underwent orthopantomography and ultrasound linear electronic transducer using the device (lOgiQ e®, general electric) with operation frequency range of 7.5–12 MHz in b-mode in compliance with all rules of orthopedic ultrasonography. Results. Treatment of patients was conducted by intraoral monocortical ostheosynthesis with mini plate. During the surgery ultrasound monitoring was conducted twice: after fragment repositioning into anatomic position and following the fixation of them with mini plate using the above algorithm. During the postoperative period all patients underwent control orthopantomography on the 2nd–3rd day after the surgery. There were no complications in the postoperative period. X-ray revealed close contact between the fragments. Conclusions. During the study the high diagnostic value of method was confirmed. Consequently the method of intraoperative ultrasonography is appropriate to implement in routine medical diagnostics of patients with traumatic injuries of the mandibular angle (Dent. Med. Probl. 2013, 50, 1, 15–19).
Computerised clinical decision support for the early recognition and management of acute kidney injury : a qualitative evaluation of end-user experience

Background: Although the efficacy of computerized clinical decision support (CCDS) for acute kidney injury (AKI) remains unclear, thewider literature includes examples of limited acceptability and equivocal benefit. Our single-centre study aimed to identify factors promoting or inhibiting use of in-patient AKI CCDS. Methods: Targeting medical users, CCDS triggered with a serum creatinine rise of ≥25 μmol/L/day and linked to guidance and test ordering. User experience was evaluated through retrospective interviews, conducted and analysed according to Normalization Process Theory. Initial pilot ward experience allowed tool refinement. Assessments continued following CCDS activation across all adult, non-critical care wards. Results: Thematic saturation was achieved with 24 interviews. The alert was accepted as a potentially useful prompt to early clinical re-assessment by many trainees. Senior staff were more sceptical, tending to view it as a hindrance. ‘Pop-ups’ and mandated engagement before alert dismissal were universally unpopular due to workflow disruption. Users were driven to close out of the alert as soon as possible to review historical creatinines and to continue with the intended workflow. Conclusions: Our study revealed themes similar to those previously described in non-AKI settings. Systems intruding on workflow, particularly involving complex interactions, may be unsustainable even if there has been a positive impact on care. The optimal balance between intrusion and clinical benefit of AKI CCDS requires further evaluation.
An Enhanced Technology Acceptance Model for E-Learning Systems in High-Tech Companies in Taiwan: Analyzed by Structural Equation Modeling

With the rapid change in working environment, there is a need to implement e-learning systems but their adoption requires a fully understanding of the user acceptance processes. Our mainly effort is to integrate two constructs, perceived flexibility and computer self-efficacy, to examine the applicability of the technology acceptance model (TAM) in explaining employees' decisions to accept e-learning. Based on a sample of 233 employees taken from 16 high-tech companies at Hsinchu Science Park in Taiwan, the results significantly support the extended TAM in predicting employeespsila behavioral intention to use e-learning.
Forgetting Concept and Role Symbols in ALCH -Ontologies (cid:63)

We develop a resolution-based method for forgetting concept and role symbols in ALCH ontologies, or for computing uniform interpolants in ALCH . Uniform interpolants use only a restricted set of symbols, while preserving logical consequences of the original ontology involving these symbols. While recent work towards practical methods for uniform interpolation in expressive description logics limits attention to forgetting concept symbols, we believe most applications would bene-ﬁt from the possibility to forget both role and concept symbols. We focus on the description logic ALCH , which allows for the formalisation of role hierarchies. Our approach is based on a recently developed resolution-based calculus for forgetting concept symbols in ALC ontologies, which we extend by redundancy elimination techniques to make it practical for larger ontologies. Experiments on ALCH fragments of real life ontologies suggest that our method is applicable in a lot of real-life applications.
OPRE-2009-06-259 ; OPRE-2011-12-597 . R 1 Stabilizing Customer Abandonment in Many-Server Queues with Time-Varying Arrivals

An algorithm is developed to determine time-dependent staffing levels to stabilize the time-dependent abandonment probabilities and expected delays at positive target values in the Mt/GI/st + GI many-server queueing model, which has a nonhomogeneous Poisson arrival process (the Mt), general service times (the first GI) and allows customer abandonment according to a general patience distribution (the +GI). New offered-load and modified-offered-load approximations involving infinite-server models are developed for that purpose. Simulations show that the approximations are effective. A many-server heavy-traffic limit in the efficiency-driven regime shows that: (i) the proposed approximations achieve the goal asymptotically as the scale increases, and (ii) it is not possible to simultaneously stabilize the mean queue length in the same asymptotic regime.
An Enhanced Map Reduce Framework for Improving the Performance of Massively Scalable Private Clouds

Cloud Computing systems provide access to large amount of data and other resources through a large number of interfaces. Apache Hadoop is a framework that allows distributed processing of large sets of data across cluster of computers. It is a powerful abstraction proposed for making scalable and fault tolerant applications. In this paper we have suggested an enhanced framework for MapReduce which increased the performance of the Private Clouds in distributed environment. In this framework a separate thread is maintained for each and every Mapper and a single buffer is used for retrieving all threads. A single Buffer retrieves all records. At this instance a separate thread can search for all the records with same key in the buffer and pass it on to the Reduce function which can executed. A multimap is used to access partial result while maintain key ordering. Our analysis shows that better performance can be achieved using this enhanced Map Reduce framework than using the traditional MapReduce framework. The results show the reduction in job completion time when compared with existing one.
Renmin University of China at ImageCLEF 2013 Scalable Concept Image Annotation

In this paper we describe our image annotation system par- ticipated in the ImageCLEF 2013 scalable concept image annotation task. The system leverages multiple base classifiers, including single- feature and multi-feature kNN classifiers and histogram intersection ker- nel SVMs, all of which are learned from the provided 250K web images and provided features with no extra manual verification. These base clas- sifiers are combined into a stacked model, with the combination weights optimized to maximize the geometric mean of F-samples, F-concepts, and AP-samples metrics on the provided development set. By varying the configuration of the system, we submitted five runs. Evaluation re- sults show that for all of our runs, model stacking with optimized weights performs best. Our system can annotate diverse Internet images purely based on the visual content, at the following accuracy level: F-samples of 0.290, F-concepts of 0.304, and AP-samples of 0.380. What is more, a system-to-system comparison reveals that our system and the best sub- mission this year are complementary with respect to the best annotated concepts, suggesting the potential for future improvement.
Mobile Transit Trip Planning with Real – Time Data November 14 , 2009

In this article, we describe the development of a transit trip planner (TTP) for mobile devices called Transitr, and evaluate its performance. The system predicts the shortest paths between any two points in the transit network using real-time information provided by a third party bus arrival prediction system, relying on GPS equipped transit vehicles. Users submit their origin and destination through a map-based iPhone application, or through a JavaScript enabled web browser. A server implementing a dynamic K-shortest paths algorithm with predicted link travel times returns personalized route directions for the user, displayed on a map. To assess the optimality and accuracy of the predicted shortest paths, an a posteriori comparison with a schedule-based transit trip planner and the GPS traces of the transit vehicles is performed on six-hundred origin destination pairs in San Francisco. The results show that routing using the predicted bus arrivals marginally increases the accuracy of the total travel time and the optimality of the route. Suggestions to improve the accuracy and optimality using real-time information are proposed. 2 TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal. Mobile Transit Trip Planning with Real–Time Data, J. Jariyasunant, D. Work, B. Kerkez, R. Sengupta, S. Glaser, and A. Bayen Transportation Research Board 89th Annual Meeting, Washington, D.C., Jan. 10--14, 2010. Jariyasunant, Work, Kerkez, Sengupta, Glaser, Bayen 3
Deep RecuRRent neuRal netwoRks foR Multiple language aspect-baseD sentiMent analysis of useR

Deep Recurrent Neural Networks (RNNs) are powerful sequence models applicable to modeling natural language. In this work we study applicability of different RNN architectures including uniand bi-directional Elman and Long Short-Term Memory (LSTM) models to aspect-based sentiment analysis that includes aspect terms extraction and aspect term sentiment polarity prediction tasks. We show that single RNN architecture without manual feature-engineering can be trained to do all these subtasks on English and Russian datasets. For aspect-term extraction subtask our system outperforms strong Conditional Random Fields (CRF) baselines and obtains stateof-the-art performance on Russian dataset. For aspect terms polarity prediction our results are below top-performing systems but still good for many practical applications.
A convex approach for NMPC based on second order Volterra series models

In model predictive control (MPC), the input sequence is computed, minimizing a usually quadratic cost function based on the predicted evolution of the system output. In the case of nonlinear MPC (NMPC), the use of nonlinear prediction models frequently leads to non‐convex optimization problems with several minimums. This paper proposes a new NMPC strategy based on second order Volterra series models where the original performance index is approximated by quadratic functions, which represent a lower bound of the original performance index. Convexity of the approximating quadratic cost functions can be achieved easily by a suitable choice of the weighting of the control increments in the performance index. The approximating cost functions can be globally minimized by convex optimization techniques in order to compute the input sequence. The minimization of the performance index is carried out by an iterative optimization procedure, which guarantees convergence to the solution. Furthermore, for a nominal prediction model, asymptotic stability for the proposed NMPC strategy can be shown. In the case of considering an estimation error in the prediction model, input‐to‐state practical stability is assured. The control performance of the NMPC strategy is illustrated by experimental results. Copyright © 2014 John Wiley & Sons, Ltd.
The Emergence of Words

Children change in their word-learning abilities sometime during the second year of life. The nature of this behavioral change has been taken to suggest an underlying change in mechanism, from associative learning to a more purely symbolic form of learning. We present a simple associative computational model that accounts for these developmental shifts without any underlying change in mechanism. Thus, there may be no need to posit a qualitative mechanistic change in the word-learning of young children. More generally, words, as symbols, may emerge from associative beginnings.
TRAC PAC: THE NEXT GENERATION

The Transportation and Civil Engineering (TRAC) PAC is a toolkit of instructional aids originally built in 1994 to attract middle and high school students to the transportation field. Since that time, users have noted difficulties arising from the PAC's curricula, equipment, and undefined role in the classroom. This report outlines a methodology for evaluating the PAC, identifies problems with the current PAC, and develops recommendations for a substantially different next generation PAC. Key findings include the importance of curricula when compared to technology, the need to design flexible lessons that integrate well with teachers' current lesson plans, the relevance of educational standards of learning, and the availability of educational products that can leverage investments in the TRAC PAC. Technical suggestions are presented including the use of calculator-based labs, delivery of lesson plan updates via the World Wide Web, creation of modules covering a broader transportation perspective, and concrete linkage to educational standards. Results of the open-ended interviews with teachers, students, principals, and TRAC volunteers underscore the need to penetrate the classroom environment rather than relying on short answers or anecdotal information. Lessons learned from this effort are applicable to other types of program evaluation.
A Weighted Range Sensor Matching Algorithm for Mobile Robot Displacement Estimation

This paper introduces a (cid:147)weighted(cid:148) matching algorithm to estimate a robot’s planar displacement by matching dense two-dimensional range scans. Based on models of expected sensor uncertainty, our algorithm weights the contribution of each scan point to the overall matching error according to its uncertainty. A general maximum likelihood formulation is used to optimally estimate the displacement between two consecutive poses. We develop uncertainty models that account for effects such as measurement noise, sensor incidence angle, and correspondence error. By explicitly modeling these noise sources, we can calculate a more realistic covariance of the displacement estimate than is done in prior work. A realistic covariance estimate is needed when further combining the displacement estimates with odometric and/or inertial measurements within a localization framework. Experiments using a Nomad 200 mobile robot and a Sick LMS-200 laser range (cid:2)nder illustrate that the method is more accurate than prior techniques.
An Investigation of the Determinants of US FDI in Developed Countries, 1982-2010

The paper uses macro panel data on US FDI in developed countries during 1982-2010 to empirically investigate the influence of host country characteristics on FDI. Differing from earlier panel data studies on FDI determinants which often impose the standard restrictions of the homogeneity of slope coefficients on the observed variables and the homogeneity of the factor loadings on the unobserved common factors in the empirical specification, this paper allows the effects of observed variables and unobserved common factors to vary across countries by using recently-introduced estimators. In this research, the data seem to support the empirical specification allowing for slope heterogeneity across countries rather more than the standard ones imposing the restrictions of slope homogeneity. Empirical results indicate that the stock of US FDI in a given FDI recipient is likely to be significantly determined by market size, lower relative tax rates, and risks in terms of the investment climate, corruption and the legal environment of the host country.
A Distributed Method for Fitting Laplacian Regularized Stratified Models

Stratified models are models that depend in an arbitrary way on a set of selected categorical features, and depend linearly on the other features. In a basic and traditional formulation a separate model is fit for each value of the categorical feature, using only the data that has the specific categorical value. To this formulation we add Laplacian regularization, which encourages the model parameters for neighboring categorical values to be similar. Laplacian regularization allows us to specify one or more weighted graphs on the stratification feature values. For example, stratifying over the days of the week, we can specify that the Sunday model parameter should be close to the Saturday and Monday model parameters. The regularization improves the performance of the model over the traditional stratified model, since the model for each value of the categorical `borrows strength' from its neighbors. In particular, it produces a model even for categorical values that did not appear in the training data set. We propose an efficient distributed method for fitting stratified models, based on the alternating direction method of multipliers (ADMM). When the fitting loss functions are convex, the stratified model fitting problem is convex, and our method computes the global minimizer of the loss plus regularization; in other cases it computes a local minimizer. The method is very efficient, and naturally scales to large data sets or numbers of stratified feature values. We illustrate our method with a variety of examples.
UvA-DARE ( Digital Academic Repository ) Stochastic modelling and control of communication networks

The unprecedented growth of the Information Technologies sector observed within the past years creates an excellent opportunity to conduct new, exciting and interdisciplinary research. Increasing complexity of the communication networks calls for incorporating rigorously developed and reliable methods for traffic control and management. Mathematics may offer extremely valuable tools to achieve these goals but transforming an engineering problem into the mathematical one requires a good understanding of the questions which arise in both domains. Furthermore, once the mathematical problem has been successfully dealt with, challenges in the application of the solution often arise back in the technical domain. One of the most important problems faced by networks users and operators are different anomalies by which we define any atypical situations in the network, regardless of their origin. Abnormal network behaviour from the user point of view may manifest itself in a very long waiting time for a service or a a voice call having a bad quality. This may be a result of a hardware failure, a deliberate attack or simply a sudden big demand for a given service, just to name a few reasons. Such a situation in the language of mathematics may be re-expressed as a changepoint detection problem, i.e., a statistical test is developed to assess whether certain observed values originating from network measurements allow us to claim that some kind of statistically significant change took place in the network. Chapters 2,3 and 5 are devoted to the changepoint detection problem. Load estimation is another important question when considering network capacity planning. The method of calculating an average utilization is a nontrivial task in itself but in addition there are questions related to the probability of its value deviating substantially from the mean. Such situations may severely degrade network performance and thus should not be disregarded despite their possibly relatively low chance of happening. In Chapter 4 we consider this issue for one of the most fundamental models in networking. Another common question regarding communication networks is a resource allocation problem. Given parameters like link capacity, number of customers and their statistical characteristics, how many clients can be admitted while still meeting quality of service requirements? What if some customers should be treated with a higher priority than the others? These types of questions and the guidelines on how to deal with them can be found in Chapter 6.
NN Impedance Control of Robots Interacting with Environments

: In this paper, neural networks impedance control is proposed for robot-environment interaction. Iterative learning control is developed to make the robot dynamics follow a given target impedance model. To cope with the problem of unknown robot dynamics, neural networks are employed such that neither the robot structure nor the physical parameters are required for the control design. The stability and performance of the resulted closed-loop sys-tem are discussed through rigorous analysis and extensive remarks. The validity and feasibility of the proposed method are veriﬁed through simulation studies.
OpenKinoAI: A Framework for Intelligent Cinematography and Editing of Live Performances

abstract:OpenKinoAI is an open source framework for postproduction of ultrahigh definition video, which makes it possible to emulate professional multiclip editing techniques for the case of single-camera recordings. OpenKinoAI includes tools for uploading raw video footage of live performances on a remote web server; detecting, tracking, and recognizing the performers in the original material; reframing the raw video into a large choice of cinematographic rushes; editing the rushes into movies; and annotating rushes and movies for documentation purposes. OpenKinoAI is made available to promote research in multiclip video editing of ultra-high definition video and to allow performing artists and companies to use this research for archiving, documenting, and sharing their work online in an innovative fashion.
Hierarchically Combined Periodic SERS Active 3D Micro- and Nanostructures for High Sensitive Molecular Analysis

To increase the local field intensity of Raman scattering, gold nanospheres were entrapped in gold coated periodic inverse pyramid structures, being SERS substrates by themselves. The applicability of this complex structure for sensitive molecule detection was proved by comparison of the detected Raman signals with and without particle entrapment. Moreover its relevance in molecular diagnostic was also proposed considering the specific surface functionalisation of the gold nanoparticles.
Exercises in Style

expressionism cubism photorealism surrealism realism PROGRAMMING STYLES Rules and constraints in software construction Programming Styles ⊳ Ways of expressing tasks ⊳ Exist at all scales ⊳ Recur in multiple scales ⊳ Codified in PLs Why Are Styles Important? ⊳ Many ⊳ Common vocabularies ⊳ Basic frames of reference ⊳ Some better than others • Depending on many things!