Future Video Research Articles

Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video

Video clients employ HTTP-based adaptive bitrate (ABR) algorithms to optimize users’ quality of experience (QoE). ABR algorithms adopt video quality based on the network conditions during playback. The existing state-of-the-art ABR algorithms ignore the fact that video streaming services deploy segment durations differently in different services, and HTTP clients offer distinct buffer sizes. The existing ABR algorithms use fixed control laws and are designed with predefined client/server settings. As a result, adaptation algorithms fail to achieve optimal performance across a variety of video client settings and QoE objectives. We propose a buffer- and segment-aware fuzzy-based ABR algorithm that selects video rates for future video segments based on segment duration and the client’s buffer size in addition to throughput and playback buffer level. We demonstrate that the proposed algorithm guarantees high QoE across various video player settings and video content characteristics. The proposed algorithm efficiently utilizes bandwidth in order to download high-quality video segments and to guarantee high QoE. The results from our experiments reveal that the proposed adaptation algorithm outperforms state-of-the-art algorithms, providing improvements in average video rate, QoE, and bandwidth utilization, respectively, of 5% to 18%, about 13% to 30%, and up to 45%.

Remotely connected: patient and clinician video care experiences in secondary mental health services during COVID-19, including future preferences

AimsVideo-delivered care is a rapidly emerging area with potential to transform assessment and treatment strategies. The coronavirus (COVID-19) pandemic has accelerated these changes. Limited evidence exists for experiences of video care in secondary mental health services. We aimed to assess the acceptability of video care in mental health clinical practice during COVID-19.MethodStructured questionnaires were developed with the help of patients and clinicians. The patient experience questionnaire was built into video sessions and completed online, using the Attend Anywhere (AA) platform from July 2020 to March 2021. A Trust-wide clinician views and experiences survey was conducted from July 2020 to October 2020. Descriptive analysis was performed using SPSS (version 27.0).ResultOf 1,296 patients who completed the online feedback, the majority provided positive feedback for all aspects of video care. Most patients felt their needs were met (90%) and were supported (93%) during the video call. Positive experiences were informed by clinicians’ communication skills. For future appointments, just over half (51.7%) of patients preferred using video calls, followed by face-to-face (33%). Future video preference was informed by reasons reducing social anxiety and practical aspects such as child/carer needs, physical disability and travel.Of 252 clinicians completing the survey, 161 (64.7%) had used video for remote care delivery. Clinicians also provided positive feedback, with Microsoft-teams as the preferred platform. Most clinicians felt the therapeutic relationship (76.4%) and privacy (78.7%) were maintained using video. While 73% felt there were no safeguarding issues that impacted adversely, 30% felt that care quality was affected, and (69.9%) reported limited visual cues for video calls. Most clinicians (73%) felt confident about clinical decision-making remotely, though there were areas where clinicians felt less confident, such as assessing patients’ appearance and behaviour. Additionally, compared with face-to-face, video consultations seemed to be effective for social anxiety, but less so for Autism spectrum disorders, and with no perceived difference for depression or self harm. For future, more clinicians preferred face-to-face (40.1%) than video care (36.1%).ConclusionMental health care delivered remotely via video is experienced positively by patients and clinicians alike. However, clinicians felt that quality of care is impacted, and additional remote clinical skills training may be beneficial. Going forward, there is acceptability for the use of video care in routine mental health practice for certain mental health presentations.

Current trends in localizing video games into Arabic: localization levels and gamers’ preferences

ABSTRACT The present paper investigates a largely ignored topic in Arabic translation studies: video game localization. It attempts to establish scholarly discussion about this field by analyzing the current localization practices used in the Arabic video game market and Arab gamers’ response to localized video games. To this end, the paper analyzes officially localized Arabic video games from 2005-2020 to reveal common practices adopted by developers when localizing games into Arabic. The paper also surveys Arab gamers regarding their preferences related to these practices. The analysis of the localized video games has revealed several localization practices that go beyond what has been discussed in the literature by video game localization scholars. The results of the survey have revealed that the majority of Arab gamers prefer ‘partially localized’ games, which do not include Arabic dubbed or re-voiced audio or cutscenes, over ‘fully localized games,’ which include re-voiced audio and cutscenes in the target language. The paper concludes by proposing a new set of ‘localization levels’ based on the analyzed data and survey responses about Arab gamers’ localization preferences. Ultimately, the paper seeks to bridge the gap between video game developers and Arab gamers and, hopefully, serve as a springboard for future video game localization projects in the Arabic context.

Performance Overview of the Latest Video Coding Proposals: HEVC, JEM and VVC.

The audiovisual entertainment industry has entered a race to find the video encoder offering the best Rate/Distortion (R/D) performance for high-quality high-definition video content. The challenge consists in providing a moderate to low computational/hardware complexity encoder able to run Ultra High-Definition (UHD) video formats of different flavours (360°, AR/VR, etc.) with state-of-the-art R/D performance results. It is necessary to evaluate not only R/D performance, a highly important feature, but also the complexity of future video encoders. New coding tools offering a small increase in R/D performance at the cost of greater complexity are being advanced with caution. We performed a detailed analysis of two evolutions of High Efficiency Video Coding (HEVC) video standards, Joint Exploration Model (JEM) and Versatile Video Coding (VVC), in terms of both R/D performance and complexity. The results show how VVC, which represents the new direction of future standards, has, for the time being, sacrificed R/D performance in order to significantly reduce overall coding/decoding complexity.

Technology Acceptance Model and Motivation-Opportunity-Ability Theory Influences on Indonesian Dota2 Gamer Customer to Customer Online Know-How Exchange

This study conceptualizes the influences of technology acceptance model (TAM) and motivation-opportunity-ability (MOA) theory on customer to customer online gamer knowhow. Although both TAM and MOA are well acknowledged and popularly applied in the literature, implementing both theories using the Indonesian online gamers scenario like Dota2 has not been carried out. This study reviews on the suitability of the theories to be extended into the virtual realm of Dota2. To achieve an understanding on this possibility is significant as the world is changing due to globalization and advancement of technology. This review has established the foundations of MOA theory in Multiplayer Online Battle Arena (MOBA) game environments. This indicates the possibility for the influences of perceived usefulness and perceived ease of use, motivation, opportunity and ability to be investigated on customer to customer online know how. The theoretical framework developed that explains the causal relationship among variables is based on the situational prime of Dota2. It is expected that future video game development based on this study’s framework can aid in the improvement of its players know-how.

Low-complexity QTMT partition based on deep neural network for Versatile Video Coding

Versatile Video Coding (VVC), the newest standard for future video coding, is currently under development. This proposal aimed to improve the encoder performance over the latest standard namely High Efficiency Video Coding, carried with a high increase in coding complexity. The VVC partition structure is mainly based on the quadtree with nested multi-type tree (QTMT) block scheme. Such an improvement leads to a more flexible block partition and promotes a high encoding efficiency, but generates a huge coding complexity. In order to deal with this issue, a fast QTMT intra partition algorithm, based on a deep neural network named Early Terminated Hierarchical Convolution Neural Network, is applied to predict the $$64\times $$ 64 block QT partition structure. The proposed algorithm determines the QTMT partition structure based on the decision of whether to split or skip the corresponding CU, in order to get $$128\times $$ 128 Coding Tree Unit partition architecture. In this paper, the proposed intra partition work achieves a significant speedup in encoding gain that reaches 32.96% in best cases for Ultra High Definition video sequences compared to the reference VVC software VTM-3.0. For all video sequences, 24.49% time saving is reached on average. This improvement comes with an increase of 4.18% and a decrease of 0.18 dB in terms of BDBR and BDPSNR, respectively.

Intermediate fused network with multiple timescales for anomaly detection

This paper proposes an intermediate fused network with multiple timescales to predict future video segments for video anomaly detection. Video prediction technique for anomaly detection requires to derive an anomaly-distinguishable future frame from normal distributions provided by training data. Then by measuring the difference between generated frames and reference frames, the model can tell which frames represent anomalous events in certain video sequence. In order to synthesize more distinctive future frames, we propose to boost image predictions by integrating several input video segments in multiple timescales. Specifically, the diversity in timescales means the diversity in sampling frequencies of video frames. In addition, we introduce a novel intermediate fusion strategy by concatenating feature maps from intermediate layers to better preserve different characteristics from different timescales. We evaluate the proposed method on some public video surveillance datasets and achieve competitive results with respect to the state-of-the-art approaches.

Special Issue Guest Editorial: Basic and Applied Physiology Research

In this issue, the Journal brings readers a special issue in Basic Science and Applied Physiology Research with 4 original research reports. This is the beginning of a new section in our journal dedicated to physical therapists performing research in Basic Sciences and Applied Physiology. Producing high-level basic and applied physiological research is an important way to move the physical therapy profession forward.1 Although there are several physical therapists producing high-level research in this area, they often publish findings in Basic Science or Applied Physiology journals that are normally beyond clinical areas. With this special issue and the new section in the journal, the journal is closing the gap of knowledge by bringing high-level basic and applied physiological research to clinicians so that together we can advance the profession to improve the health of society. In this issue, Wooten et al consider carbon dioxide expiration and respiratory buffering as 2 potential important factors for performance fatigability. As hypothesized, both carbon dioxide expiration and respiratory buffering, measured by a cardiopulmonary exercise test, are good predictors of the changes that aerobic exercise training produces in performance fatigability.2,3 In addition, Rogers-Bosh et al bring an interesting feasibility study to introduce High-Intensity Interval Training (HIIT) in stroke survivors. In this study, the authors used treadmill HIIT, twice weekly in 17 adults with chronic stroke. They found that participants had a great attendance and that an exercise training program of such characteristics is feasible.3 Finally, Gurovich et al5 provide a novel methodology to measure carotid and brachial artery blood flow during exercise in vivo using vascular ultrasound. The methods they describe are important because most studies investigating exercise-induced endothelial sheer stress (ESS) are conducted in vitro or using in vivo models that do not account for the normal pulsatile nature of blood flow. Their findings help add to our understanding of exercise-induced ESS that forms the basis for some of the physiological benefits of exercise on vascular and endothelial function. In addition, this issue features the launch of the official CPTJ video methods library. This online resource will provide brief video descriptions of techniques included in the methods section of accepted publications. This was created to help remove the “black box” of intervention studies and improve translation to clinical practice. More specific to the current issue and section, this library also provides readers with a clearer understanding of research methods that may be novel to them. It may also help readers gain an understanding of how data were produced and collected for a given study. Our hope is that this exposure may encourage readers to consider using techniques and methods featured in these videos for future scientific inquiries. The first 3 videos are on Pulse Wave Analysis, Flow-Mediated Dilation, and Cardiopulmonary Exercise Tests, which are featured in the publications for this issue. Future video resources will be submitted as supplements by authors of accepted publication following the review process described on the forthcoming CPTJ Video Methods Library webpage. The journal is very excited about this new special issue and section, especially because the reception from multiple institutions was very well received. There are several more original research reports in the pipeline to continue offering high-level Basic Sciences and Applied Physiology research to our readers.

Gradient-Based Intraprediction Fusion for Video Coding

Video coding is more important than ever due to emergence of larger resolutions, new formats requiring higher bandwidths, prevalence of new video services, etc. Consequently, new codecs are designed to minimize transmission rates of such contents without sacrificing quality. This article proposes an intraprediction solution for future video codecs that save the bit rates of intramode signaling for luminance and chrominance blocks. In doing so, modes are implicitly derived at the decoder side through a gradient-based analysis. In contrast to state-of-the-art intraprediction that uses a single mode for a luma block, the proposed method offers a combination of multiple derived modes. Furthermore, the chrominance components Cb/Cr are allowed to have their separate modes, instead of sharing one mode. The implementation of the proposed method in the Versatile Video Coding reference software (VTM) provides 0.72% Bjontegaard-delta rate saving, with run-time complexity of 110% and 104% at the encoder and decoder sides, respectively.

Prediction With Multicross Component for Future Video Coding

There has been a consensus of cross-component prediction in video compression that the interchannel redundancies can be effectively removed through a linear prediction model. To fully explore the similarities existing in different color components, we propose prediction with multicross component (PMC) as a new chroma intracoding mode for the emerging video coding standard AVS3. The proposed PMC enjoys extremely low operational complexity and high flexibility, which advocates to reuse and reform the linear prediction model of conventional two-step cross-component prediction mode. Moreover, dual-prediction patterns of PMC are exploited to enhance the prediction efficiency. Experimental results on AVS3 test model HPM-6.0 show that PMC brings 0.32%, 1.60%, and 2.39% BD-rate savings for Y, Cb, and Cr components, respectively, under all intraconfiguration with natural-scene videos. Moreover, 0.91%, 2.24%, and 2.82% BD-rate gains can be observed on screen-content videos. Subset modes of PMC have been adopted by AVS3.

Sequence-to-Sequence Video Prediction by Learning Hierarchical Representations

Video prediction which maps a sequence of past video frames into realistic future video frames is a challenging task because it is difficult to generate realistic frames and model the coherent relationship between consecutive video frames. In this paper, we propose a hierarchical sequence-to-sequence prediction approach to address this challenge. We present an end-to-end trainable architecture in which the frame generator automatically encodes input frames into different levels of latent Convolutional Neural Network (CNN) features, and then recursively generates future frames conditioned on the estimated hierarchical CNN features and previous prediction. Our design is intended to automatically learn hierarchical representations of video and their temporal dynamics. Convolutional Long Short-Term Memory (ConvLSTM) is used in combination with skip connections so as to separately capture the sequential structures of multiple levels of hierarchy of features. We adopt Scheduled Sampling for training our recurrent network in order to facilitate convergence and to produce high-quality sequence predictions. We evaluate our method on the Bouncing Balls, Moving MNIST, and KTH human action dataset, and report favorable results as compared to existing methods.

Feasibility and Acceptability of an Adolescent-Friendly Rap Video to Improve Health Literacy Among HIV-Positive Youth in Urban Peru

Clinical outcomes among adolescents living with HIV (ALHIV) might be improved by interventions aimed at addressing limited health literacy. We developed a Spanish-language rap video on HIV concepts and examined its acceptability and feasibility as a learning tool among ALHIV in Lima, Peru. Twenty-eight ALHIV receiving care at an urban pediatric hospital and ten stakeholders engaged in the care of adolescents watched the video. Adolescents completed a pre- and post-video questionnaire. We conducted focus groups with ALHIV and in-depth interviews with stakeholders and analyzed transcripts to identify themes. ALHIV described concepts of CD4 cell count and viral load as they were portrayed. Participants reported the video was relatable, accessible, and provided hope that ALHIV could lead healthy lives and advocated for future videos to address topics such as transmission and sexual health. Questionnaires indicated some improvement in viral load knowledge. An HIV health literacy music video intervention was feasible to implement and accepted by ALHIV and their healthcare providers. Communicating HIV knowledge via music videos may be promising; further study is needed to optimize implementation.

Dynamic Motion Estimation and Evolution Video Prediction Network

Future video prediction provides valuable information that helps a computer machine understand the surrounding environment and make critical decisions in real-time. However, long-term video prediction remains a challenging problem due to the complicated spatiotemporal dynamics in a video. In this paper, we propose a dynamic motion estimation and evolution (DMEE) network model to generate unseen future videos from the observed videos in the past. Our primary contribution is to use trained kernels in convolutional neural network (CNN) and long short-term memory (LSTM) architectures, adapted to each time step and sample position, to efficiently manage spatiotemporal dynamics. DMEE uses the motion estimation (ME) and motion update (MU) kernels to predict the future video using an end-to-end prediction-update process. In the prediction, the ME kernel estimates the temporal changes. In the update step, the MU kernel combines the estimates with the previously generated frames as reference frames using a weighted average. The kernels are not only used for a current frame, but also are evolved to generate successive frames to enable temporally specific filtering. We perform qualitative performance analysis and quantitative performance analysis based on the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and video classification score developed for examining the visual quality of the generated video. It is demonstrated with experiments that our algorithm provides better qualitative and quantitative performance superior to the current state-of-the-art algorithms. Our source codes are available in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Nayoung-Kim-ICP/Video-Generation</uri> .

Quality-Aware Neural Adaptive Video Streaming With Lifelong Imitation Learning

Existing Adaptive Bitrate (ABR) algorithms pick future video chunks' bitrates via fixed rules or offline trained models to ensure good quality of experience (QoE) for Internet video. Nevertheless, data analysis demonstrates that a good ABR algorithm is required to continually and fast update for adapting itself to time-varying network conditions. Therefore, we propose Comyco, a video quality-aware learning-based ABR approach that enormously improves recent schemes by i) picking the chunk with higher perceptual video qualities rather than video bitrates; ii) training the policy via imitating expert trajectories given by the expert strategy; iii) employing the lifelong learning method to continually train the model w.r.t the fresh trace collected by the users. To achieve this, we develop a complete quality-aware lifelong imitation learning-based ABR system, construct quality-based neural network architecture, collect a quality-driven video dataset, and estimate QoE metrics with video quality features. Using trace-driven and real-world experiments, we demonstrate Comyco reaches 1700-fold improvements in the number of samples required and 16-fold speedup in the training time compared with the prior work. Meanwhile, Comyco outperforms existing methods, with the improvements on average QoE of 7.5%-16.79%. Moreover, experimental results on continual training also illustrate that lifelong learning helps Comyco further improve the average QoE of 1.07%-9.81% in comparison to the offline trained model.

Soluções visuais atípicas em vídeos digitais em línguas de sinais: proposta de um catálogo para os produtores de vídeo

The expansion of sign languages in digital spaces is recent. This growth is partly due to the increasingly accessible means of video production (cameras and video editing software), and the large-scale storage and access resources through online digital video repositories that have risen in fifteen years. In addition to the interpreter's window and the yellow captions , aiming to contribute to new productions, the article presents the mapping of 13 innovative visual solutions based on the analysis undertaken by the research group “Education, media and deaf community” of 24 digital videos in which sign language is predominant. Among the distinguishing marks of these visual solutions are (1) the original variation of one or more properties of the basic elements of the visual composition, (2) the original relationship of the basic element with some information that would only be identified by a non-deaf person and (3) the emphases that visually complement both feelings and information expressed by the actor / interpreter, whether signaling or verbalizing. We hope that with this detail, in addition to new research on unusual visual solutions, these that we have mapped will also be replicated in future video productions aimed at the deaf community.

Fast intra-coding unit partition decision in H.266/FVC based on deep learning

In the recent Future Video Coding (FVC) standard developed by the Joint Video Exploration Team (JVET), the quad-tree binary-tree (QTBT) block partition module makes use of rectangular block forms and additional square block sizes compared to quad-tree (QT) block partitioning module proposed in the predecessor High-Efficiency Video Coding (HEVC) standard. This block flexibility, induced with the QTBT module, significantly improves compression performance while it dramatically increases coding complexity due to the brute force search for Rate Distortion Optimization (RDO). To cope with this issue, it is necessary to consider the unique characteristics of QTBT in FVC. In this paper, we propose a fast QT partitioning algorithm based on a deep convolutional neural network (CNN) model to predict coding unit (CU) partition instead of RDO which enhances considerably QTBT performance for intra-mode coding. Based on a suitable diversified CU partition patterns database, the optimization process is set up with three levels CNN structure developed to learn the split or non-split decision from the established database. Experimental results reveal that the proposed algorithm can accelerate the QTBT block partition structure by reducing the intra-mode encoding time by an average of 35% with a bit rate increase of 1.7%, allowing its application in practical scenarios.

Segmenting the Future

Predicting the future is an important aspect for decision-making in robotics or autonomous driving systems, which heavily rely upon visual scene understanding. While prior work attempts to predict future video pixels, anticipate activities or forecast future scene semantic segments from segmentation of the preceding frames, methods that predict future semantic segmentation solely from the previous frame RGB data in a single end-to-end trainable model do not exist. In this letter, we propose a temporal encoder-decoder network architecture that encodes RGB frames from the past and decodes the future semantic segmentation. The network is coupled with a new knowledge distillation training framework specific for the forecasting task. Our method, only seeing preceding video frames, implicitly models the scene segments while simultaneously accounting for the object dynamics to infer the future scene semantic segments. Our results on Cityscapes and Apolloscape outperform the baseline and current state-of-the-art methods. Code will be available soon.

A Spherical Convolution Approach for Learning Long Term Viewport Prediction in 360 Immersive Video

Viewport prediction for 360 video forecasts a viewer’s viewport when he/she watches a 360 video with a head-mounted display, which benefits many VR/AR applications such as 360 video streaming and mobile cloud VR. Existing studies based on planar convolutional neural network (CNN) suffer from the image distortion and split caused by the sphere-to-plane projection. In this paper, we start by proposing a spherical convolution based feature extraction network to distill spatial-temporal 360 information. We provide a solution for training such a network without a dedicated 360 image or video classification dataset. We differ with previous methods, which base their predictions on image pixel-level information, and propose a semantic content and preference based viewport prediction scheme. In this paper, we adopt a recurrent neural network (RNN) network to extract a user's personal preference of 360 video content from minutes of embedded viewing histories. We utilize this semantic preference as spatial attention to help network find the "interested'' regions on a future video. We further design a tailored mixture density network (MDN) based viewport prediction scheme, including viewport modeling, tailored loss function, etc, to improve efficiency and accuracy. Our extensive experiments demonstrate the rationality and performance of our method, which outperforms state-of-the-art methods, especially in long-term prediction.

News coverage of the COVID-19 pandemic: Missed opportunities to promote health sustaining behaviors.

BackgroundGiven that individuals may make health decisions based on mass media coverage, and given that such decisions have broad consequences in the case of highly contagious infectious disease, it is imperative that public health practitioners are aware of mass media coverage on emerging health threats such as COVID-19. The purpose of this study was to examine the content of news segments covering COVID-19 posted on-line.MethodsUsing the Google Videos function on a cleared browser, all videos identified from January and February, 2020 were archived by URL for analysis. After applying exclusion criteria, a total of 401 remained, comprising the sample. Content categories derived from trusted sources were applied to assess the content of broadcast news segments pertaining to COVID-19 on Google Videos.ResultsThe most common topic mentioned across all videos was death and the death rate (43.6%) and many connoted anxieties surrounding the COVID-19 outbreak (37.4%). Critically important information about the prevention of COVID-19 spread received little attention. For example, only 3.0% of videos discussed coughing into or blowing one's nose into a tissue and throwing the tissue away, 6.2% talked about wearing a facemask when caring for the ill, and 8.8% covered disinfecting highly touched objects and surfaces. International videos more often presented captions and English subtitles (with and without narration) (7.8% vs. 0.9%, p = 0.018) and even more frequently discussed death and the death rate associated with COVID-19 (56.9% vs. 41.7%, p = 0.04).ConclusionCoupled with the resultant increase in negative emotion, the majority of videos missed an opportunity to reframe messages to encourage and promote coping strategies and health sustaining behaviors. Future videos should avoid contributing to negative emotion.

P134: Orthopedic procedural videos as teaching tools in emergency medicine

Innovation Concept: Video has been proven to be an effective educational tool that is valued by learners and objectively improves knowledge and testing scores. It can simplify complex concepts and is more efficient and effective than audio or reading in tests of 3-day material recall. Our objective in this project was to develop a series of instructional videos geared towards emergency and family physicians on proper application of casts and splints in the emergency department. Methods: We created two procedural videos, each 5-6 minutes long. They each reviewed the process, indications, and precise steps for application for each of two splints: the ulnar gutter and the thumb spica. After finalizing the videos, we created a survey to assess feedback, asking questions about the applicability of the videos to the viewer's clinical practice, how interesting they found the content of the videos, what they liked and disliked, and how willing they would be to access future procedural videos if we were to make them. We also had respondents provide suggestions for topics of future videos. We then sent the videos and accompanying survey to a group of McMaster University medical students, residents, and attending physicians in family medicine and emergency medicine. Upon reviewed the results it seemed that there was a large difference in perceived utility of the videos between attending physicians and trainees, and so we proceeded with subgroup analysis of trainees and staff. Curriculum, Tool, or Material: Orthopedic procedural videos as described above. Conclusion: Using a 5-point Likert scale, we found that overall trainees (4.3, SD 0.76 CI 0.41) found the videos more useful and interesting than did attending physicians (3.4, SD 0.68 CI 0.37), with respondents commenting that they were very clear and easy to follow for junior trainees. Most respondents also indicated that they would access future videos we made (4.2 SD 0.74 CI 0.39 for trainees, 3.2 SD 0.65 SI 0.34) for attendings). Future directions include making the videos more concise and adding more visual summaries to improve viewership, and targeting videos for specific learner level. We are hoping to implement these videos into future curriculum development for our learners and, if successful, other Emergency Medicine residency programs across Canada.