Training Bottleneck Research Articles

Achieving self-sufficiency: training Australia's future medical workforce.

There is an oversupply of Australian junior doctors, but significant training bottlenecks are developing, and geographical maldistribution in rural and remote areas remains. Last year, the Federal Minister for Immigration rejected a Department of Health recommendation for the removal of 41 health roles from the Skilled Occupation List after concerns that rural and regional communities would be left without access to medical services in areas currently serviced by international medical graduates. In an effort to achieve workforce self-sufficiency, Australia must ensure access to high-quality vocational training places in rural and regional settings while managing immigration of overseas-trained health professionals.

Multilingually trained bottleneck features in spoken language recognition

Multilingual training of neural networks has proven to be simple yet effective way to deal with multilingual training corpora. It allows to use several resources to jointly train a language independent representation of features, which can be encoded into low-dimensional feature set by embedding narrow bottleneck layer to the network. In this paper, we analyze such features on the task of spoken language recognition (SLR), focusing on practical aspects of training bottleneck networks and analyzing their integration in SLR. By comparing properties of mono and multilingual features we show the suitability of multilingual training for SLR. The state-of-the-art performance of these features is demonstrated on the NIST LRE09 database.

Object proposal with kernelized partial ranking

Object proposals are an ensemble of bounding boxes with high potential to contain objects. In order to determine a small set of proposals with a high recall, a common scheme is extracting multiple features followed by a ranking algorithm which however, incurs two major challenges: 1) The ranking model often imposes pairwise constraints between each proposal, rendering the problem away from an efficient training/testing phase; 2) Linear kernels are utilized due to the computational and memory bottleneck of training a kernelized model.In this paper, we remedy these two issues by suggesting a kernelized partial ranking model. In particular, we demonstrate that i) our partial ranking model reduces the number of constraints from O(n2) to O(nk) where n is the number of all potential proposals for an image but we are only interested in top-k of them that has the largest overlap with the ground truth; ii) we permit non-linear kernels in our model which is often superior to the linear classifier in terms of accuracy. For the sake of mitigating the computational and memory issues, we introduce a consistent weighted sampling (CWS) paradigm that approximates the non-linear kernel as well as facilitates an efficient learning. In fact, as we will show, training a linear CWS model amounts to learning a kernelized model. Extensive experiments demonstrate that equipped with the non-linear kernel and the partial ranking algorithm, recall at top-k proposals can be substantially improved.

软件工程专业全工程过程实训与个性化创新能力培养模式研究

通过近年来软件工程专业校外工程实训和创新能力培养的教学实践，发现了当前高校工程类专业校外实践环境建设和学生实践创新能力培养质量所面临的诸多问题和缺陷。针对这些实践教学和能力培养瓶颈，本文就如何加强校企合作高端实践环境建设和深化实践教学改革，建立适合工程应用型人才培养的实践教学体系和校企合作全工程过程培养模式给出了研究和论述。实践表明，这种全工程过程的校企合作创新人才培养模式和实践教学方法，对于实现工程类专业个性化创新创业人才培养具有很好指导和促进作用。 Through the teaching practice of off-campus engineering training and innovation ability cultivation to software engineering specialty in recent years, some problems and defects of the qualities of off-campus practical environment construction and innovation ability cultivation of students for the current university software engineering specialty are found. In view of these practical teaching problems and ability training bottlenecks, this paper proposes how to strengthen the school-enterprise cooperated high-end engineering practice environment construction and deepen practice teaching reform, and then set up a new practical teaching system and training model suitable for engineering application talents culturing with school-enterprise cooperation and whole engineering process. Practice has shown that the new practical teaching model and method have very good guiding and promoting roles in realizing individualized, innovative and entrepreneurial talents training for Engineering Majors.

The ShadowBox Approach to Cognitive Skills Training

Unlike behavioral skills training, cognitive skills training attempts to impart concepts that typically depend on tacit knowledge. Subject-matter experts (SMEs) often deliver cognitive training, but SMEs are expensive and in short supply, causing a training bottleneck. Recently, Hintze developed the ShadowBox method to overcome this limitation. As part of the Defense Advanced Research Projects Agency’s Social Strategic Interaction Modules, Klein, Hintze, and Saab adapted the ShadowBox approach to train large numbers of trainees without relying on expert facilitators. As part of this program, we used the ShadowBox approach to train warfighters on the social cognitive skills needed to successfully manage civilian encounters without creating hostility or resentment. ShadowBox training was evaluated in two studies. Evaluation 1 provided 3 hr of nonfacilitated, paper-based training to Marines at Camp Pendleton and Camp Lejeune ( N = 59), and improved performance (i.e., match to the SME rankings) by 28% compared to a control group. Evaluation 2 provided 1 hr of nonfacilitated training, administered via Android tablet, to soldiers at Fort Benning ( N = 30) and improved performance by 21%. These results, both statistically significant, suggest ways to use scenario-based training to develop cognitive skills in the military.

General surgery education: a systematic review of training worldwide

BackgroundSurgical care is an essential component of health management worldwide. As the prevalence of injuries and non-communicable diseases increases, the provision of effective surgical care will become an increasingly important priority to reduce death and disability. To assess the ability of health systems to meet current surgical needs, we did a review of surgical training programmes worldwide. MethodsWe searched Medline, EMBASE, and the Global Health Library databases with the search terms “surgical training” and “surgeon training” for abstracts and citations in all languages published between Jan 1, 1998, and Dec 31, 2013, describing a national general surgery training system. We extracted the following data: a brief description of the programme, years of training required, year after medical school graduation when training begins, name of national oversight organisation(s), in-country opportunities for subspecialty training, and whether programmes self-identified as being similar to or affected by the surgical education system in the USA or the UK. FindingsWe identified 5229 abstracts (3888 from Medline, 971 from EMBASE, and 726 from the Global Health Library databases). 228 (4·4%) articles underwent full text review. 60 articles were included for data extraction. We identified descriptions of general surgery training programmes in 52 countries. Data from an additional 17 countries represented by a regional college of surgeons were also recorded. Training duration ranged from 2–8 years (median total training, 6 years; median surgical training, 5 years), and lasted on average 6 years after medical school graduation. 19 countries self-identified as being similar to or affected by the US or UK training model. Many low-income and middle-income countries (LMICs) are working to expand access to surgical care through programmes, often focusing on training non-physician clinicians. Programmes in high-income countries have also undergone substantial reforms, affected by evolving practice environments, trainee preferences, and training bottlenecks. InterpretationGeneral surgery training programmes are often responsive to national health care needs. We show a global trend towards standardisation of curricula and competency-based training. Countries expanding or developing their programmes show benefit from association or partnership with larger surgical organisations and academic institutions, but there are questions of sustainability. Both LMICs and high-income countries stand to benefit from coordinating development of global training standards and educational exchanges. Although more research is needed to understand the role of surgical education in meeting the demand for surgical care, it is clear that a cross-nationally coordinated strategy will be important to address the burden of surgical disease. FundingNone.

Scalable Active Learning for Multiclass Image Classification

Machine learning techniques for computer vision applications like object recognition, scene classification, etc., require a large number of training samples for satisfactory performance. Especially when classification is to be performed over many categories, providing enough training samples for each category is infeasible. This paper describes new ideas in multiclass active learning to deal with the training bottleneck, making it easier to train large multiclass image classification systems. First, we propose a new interaction modality for training which requires only yes-no type binary feedback instead of a precise category label. The modality is especially powerful in the presence of hundreds of categories. For the proposed modality, we develop a Value-of-Information (VOI) algorithm that chooses informative queries while also considering user annotation cost. Second, we propose an active selection measure that works with many categories and is extremely fast to compute. This measure is employed to perform a fast seed search before computing VOI, resulting in an algorithm that scales linearly with dataset size. Third, we use locality sensitive hashing to provide a very fast approximation to active learning, which gives sublinear time scaling, allowing application to very large datasets. The approximation provides up to two orders of magnitude speedups with little loss in accuracy. Thorough empirical evaluation of classification accuracy, noise sensitivity, imbalanced data, and computational performance on a diverse set of image datasets demonstrates the strengths of the proposed algorithms.

QAM equalization and symbol detection in OFDM systems using extreme learning machine

This paper presents a new learning-based framework to jointly solve equalization and symbol detection problems in orthogonal frequency division multiplexing systems with quadrature amplitude modulation. The framework utilizes extreme learning machine (ELM), a recent addition to the class of supervised learning algorithms, to achieve fast training, high performance, and low error rates. The proposed ELM scheme employs infinitely differentiable nonlinear activation functions in least-square solution to learn the channel response, which is the equalization part. In addition to equalization, ELM performs symbol detection. Existing learning-based schemes require an additional symbol slicer for the symbol detection. The proposed framework does not experience training bottleneck imposed by gradient descent–based approaches. Simulation results show that the proposed framework outperforms other learning-based equalizers in terms of symbol error rate and training speeds.

No Advantage

In these times of staff shortages we do not really see any advantage in having a team administering analgesia and sedation. As per the guidelines, a doctor is not allowed to perform surgery while simultaneously administering the sedation. The presence of a second physician is therefore necessary for medicolegal reasons. So far, no curriculum exists for the training of analgesia-sedation teams. The sedation guidelines stipulate the need for specialized sedation training for doctors and non-medical assisting staff in the context of quality assurance (1). At the same time, specialized training regulations for premedication and emergency management exists only in individual settings. If this applies to the sedation of adults, how much more difficult will it be to introduce similar training for the sedation of children? And where would such training take place outside the settings of pediatric anesthesiology or pediatric intensive care medicine? These specialties are already beset by bottlenecks in training. Also, a certain number of cases per year would have to be guaranteed for the purposes of quality assurance. To provide emergency management without problems, an experienced doctor has to be available within the shortest possible amount of time, especially for very small children. Resuscitation teams, however, are often not sufficiently trained to deal with children. The selection of drugs seems difficult. Especially the choice of analgesics seems arbitrary. Fentanyl has a pronounced respiratory depressant effect and is therefore not the first choice for sedation (2). Instead of introducing a new qualification, it would be sufficient for analgesia-sedation to be administered outside intensive care wards only by specialists in anesthesiology and pediatricians with further qualifications in intensive care medicine or neonatology, whose everyday clinical practice includes airway management and handling medical drugs that have a depressant effect on the central nervous system.

The rising tide of medical graduates: how will postgraduate training be affected?

Domestic medical graduate numbers will almost double between 2005 and 2012, necessitating substantial increases in supervision at prevocational and vocational levels. New approaches to resourcing and governance of training are needed to expand the capacity of the health system to deliver quality training; new settings will also be required to expand training capacity, while ensuring that trainees are exposed to a broad range of clinical experiences. With increasing demand for training placements, entry to specialty training is likely to become highly competitive; new vocational training positions must be created to ensure that bottlenecks in training do not occur and that training is not unnecessarily prolonged. Substantial increases in government funding will be required to employ the new prevocational workforce. The recent Modernising Medical Careers Inquiry in the United Kingdom offers important lessons for the workforce changes facing Australia, such as a "ring-fenced" budget that quarantines funding for medical education and training. Planning for the increasing cohorts must cover the training spectrum - from medical student to specialist. Students and trainees must be prospectively informed about how workforce changes will affect their career advancement.

Best Evidence for the FAA Industry Training Standards (FITS) Program for Pilot Training in Technically Advanced Aircraft

This paper continues investigating the effectiveness of FITS syllabus training, evaluated in an earlier 2004 study, with a second study conducted by the Middle Tennessee State University (MTSU) Aerospace Department FITS research team. In the original study, students were trained in a FITS-approved combined Private/Instrument curriculum in technically advanced aircraft (TAA). The effectiveness of that training was compared, through identification of setbacks and bottlenecks in training, to archival records of students who had been trained in round dial aircraft using a traditional flight training curriculum. In the October 2005 issue of this publication, it was reported that the FITS trained students experienced fewer statistically significant setbacks and bottlenecks than the traditionally trained students. The question that remained to be answered was whether the FITS syllabus or use of TAA was the determining factor in the positive results attained. In the phase of the study reported upon herein, a new group of students completed their Instrument Rating training, flying TAA but using a traditional curriculum. The results of all 3 groups of students are compared, and the evidence suggests that the FITS syllabus is responsible for the improvements in training, not the use of TAA. In addition, results of several surveys administered in this work show that not only is FITS training more efficient due to reduced bottlenecks and setbacks, but that it is also more effective at preparing students to make real-life decisions in the flight environment.

Fast and accurate text classification via multiple linear discriminant projections

Abstract.Support vector machines (SVMs) have shown superb performance for text classification tasks. They are accurate, robust, and quick to apply to test instances. Their only potential drawback is their training time and memory requirement. For n training instances held in memory, the best-known SVM implementations take time proportional to n a, where a is typically between 1.8 and 2.1. SVMs have been trained on data sets with several thousand instances, but Web directories today contain millions of instances that are valuable for mapping billions of Web pages into Yahoo!-like directories. We present SIMPL, a nearly linear-time classification algorithm that mimics the strengths of SVMs while avoiding the training bottleneck. It uses Fisher's linear discriminant, a classical tool from statistical pattern recognition, to project training instances to a carefully selected low-dimensional subspace before inducing a decision tree on the projected instances. SIMPL uses efficient sequential scans and sorts and is comparable in speed and memory scalability to widely used naive Bayes (NB) classifiers, but it beats NB accuracy decisively. It not only approaches and sometimes exceeds SVM accuracy, but also beats the running time of a popular SVM implementation by orders of magnitude. While describing SIMPL, we make a detailed experimental comparison of SVM-generated discriminants with Fisher's discriminants, and we also report on an analysis of the cache performance of a popular SVM implementation. Our analysis shows that SIMPL has the potential to be the method of choice for practitioners who want the accuracy of SVMs and the simplicity and speed of naive Bayes classifiers.

Training Computer Personnel

The historical growth of industry involved and largely depended on the substitution of machine power for muscular effort. But industry's growth entailed the proliferation of the clerical procedures needed for the control of business activity. Armies of white collar workers process, file, amend, and pass on millions of papers every day. Few of these activities involve change of policy: almost all may be called routine or menial mental procedures conforming to management requirements. The equipment and systems for the substitution of machine power for menial ‘mental’ effort are at hand — but an education and training bottleneck exists in the supply of personnel to operate them