An Experimental Study of the Server-based Unfairness Solutions for the Cross-Protocol Scenario of Adaptive Streaming over HTTP/3 and HTTP/2

منذ إدخال HTTP / 3 ، ركز البحث على تقييم تأثيره على البث التكيفي الحالي عبر HTTP (HAS). من بين هذه الأبحاث ، نظرًا لبروتوكولات النقل غير ذات الصلة ، حظي الظلم عبر البروتوكولات بين HAS عبر HTTP / 3 (HAS / 3) و HAS عبر HTTP / 2 (HAS / 2) باهتمام كبير. لقد وجد أن عملاء HAS / 3 يميلون إلى طلب معدلات بت أعلى من عملاء HAS / 2 لأن النقل QUIC يحصل على عرض نطاق ترددي أعلى لعملائه HAS / 3 من TCP لعملائه HAS / 2. نظرًا لأن المشكلة تنشأ من طبقة النقل ، فمن المحتمل أن حلول الظلم المستندة إلى الخادم يمكن أن تساعد العملاء في التغلب على مثل هذه المشكلة. لذلك ، في هذه الورقة ، تم إجراء دراسة تجريبية لحلول الظلم القائمة على الخادم لسيناريو البروتوكول المتقاطع لـ HAS / 3 و HAS / 2. تظهر النتائج أنه على الرغم من فشل حل توجيه معدل البت في مساعدة العملاء على تحقيق العدالة ، فإن حل تخصيص النطاق الترددي يوفر أداءً فائقًا.Since the introduction of the HTTP/3, research has focused on evaluating its influences on the existing adaptive streaming over HTTP (HAS). Among these research, due to irrelevant transport protocols, the cross-protocol unfairness between the HAS over HTTP/3 (HAS/3) and HAS over HTTP/2 (HAS/2) has caught considerable attention. It has been found that the HAS/3 clients tend to request higher bitrates than the HAS/2 clients because the transport QUIC obtains higher bandwidth for its HAS/3 clients than the TCP for its HAS/2 clients. As the problem originates from the transport layer, it is likely that the server-based unfairness solutions can help the clients overcome such a problem. Therefore, in this paper, an experimental study of the server-based unfairness solutions for the cross-protocol scenario of the HAS/3 and HAS/2 is conducted. The results show that, while the bitrate guidance solution fails to help the clients achieve fairness, the bandwidth allocation solution provides superior performance

Generative Adversarial Network for Imitation Learning from Single Demonstration

التعلم التقليد هو طريقة فعالة لتدريب وكيل مستقل لإنجاز المهمة عن طريق تقليد سلوكيات الخبراء في مظاهراتهم. ومع ذلك، تتطلب طرق التعلم التقليدية التقليدية عددا كبيرا من مظاهرات الخبراء من أجل تعلم سلوك معقد. حدد هذا العيب محدودا إمكانية التعلم التقليد في المهام المعقدة حيث لا تكون مظاهرات الخبراء كافية. من أجل معالجة المشكلة، يقترح النموذج المستند إلى الشبكة المصنوعة من الشبكة المصممة على تصميم سياسات مثالية باستخدام مظاهرة واحدة فقط. يتم تقييم النموذج المقترح على مهمتين محاكاة مقارنة بطرق أخرى. تظهر النتائج أن نموذجنا المقترح قادر على إكمال المهام المدروسة على الرغم من القيد في عدد مظاهرات الخبراء، والذي يشير بوضوح إلى إمكانات نموذجنا.Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing considered tasks despite the limitation in the number of expert demonstrations, which clearly indicate the potential of our model

A modified Sequential Organ Failure Assessment score for dengue: development, evaluation and proposal for use in clinical trials

Background Dengue is a neglected tropical disease, for which no therapeutic agents have shown clinical efficacy to date. Clinical trials have used strikingly variable clinical endpoints, which hampers reproducibility and comparability of findings. We investigated a delta modified Sequential Organ Failure Assessment (delta mSOFA) score as a uniform composite clinical endpoint for use in clinical trials investigating therapeutics for moderate and severe dengue. Methods We developed a modified SOFA score for dengue, measured and evaluated its performance at baseline and 48 h after enrolment in a prospective observational cohort of 124 adults admitted to a tertiary referral hospital in Vietnam with dengue shock. The modified SOFA score included pulse pressure in the cardiovascular component. Binary logistic regression, cox proportional hazard and linear regression models were used to estimate association between mSOFA, delta mSOFA and clinical outcomes. Results The analysis included 124 adults with dengue shock. 29 (23.4%) patients required ICU admission for organ support or due to persistent haemodynamic instability: 9/124 (7.3%) required mechanical ventilation, 8/124 (6.5%) required vasopressors, 6/124 (4.8%) required haemofiltration and 5/124 (4.0%) patients died. In univariate analyses, higher baseline and delta (48 h) mSOFA score for dengue were associated with admission to ICU, requirement for organ support and mortality, duration of ICU and hospital admission and IV fluid use. Conclusions The baseline and delta mSOFA scores for dengue performed well to discriminate patients with dengue shock by clinical outcomes, including duration of ICU and hospital admission, requirement for organ support and death. We plan to use delta mSOFA as the primary endpoint in an upcoming host-directed therapeutic trial and investigate the performance of this score in other phenotypes of severe dengue in adults and children

Domain Adaptation for Imitation Learning Using Generative Adversarial Network

Imitation learning is an effective approach for an autonomous agent to learn control policies when an explicit reward function is unavailable, using demonstrations provided from an expert. However, standard imitation learning methods assume that the agents and the demonstrations provided by the expert are in the same domain configuration. Such an assumption has made the learned policies difficult to apply in another distinct domain. The problem is formalized as domain adaptive imitation learning, which is the process of learning how to perform a task optimally in a learner domain, given demonstrations of the task in a distinct expert domain. We address the problem by proposing a model based on Generative Adversarial Network. The model aims to learn both domain-shared and domain-specific features and utilizes it to find an optimal policy across domains. The experimental results show the effectiveness of our model in a number of tasks ranging from low to complex high-dimensional

Sprinkle Prebuffer Strategy to Improve Quality of Experience with Less Data Wastage in Short-Form Video Streaming

In mobile short-form video streaming, the video application usually provides the user with a playlist of recommended videos to be played one by one. In order to prevent playback stalls caused by possible fluctuations in the mobile network, after finishing buffering the currently-playing video, commercial video players continue to prebuffer (i.e., buffer in advance before playback) one subsequent video in the playlist with as much content as possible. However, since the user can skip a video at any time if he/she does not like it, prebuffering too much video content leads to the wastage of mobile data. Contrarily, without prebuffering any subsequent video, the video player is exposed to high risks of stalling events, which threaten the user’s quality of experience (QoE). In this paper, a novel Sprinkle Prebuffer Strategy (SPS) is proposed to overcome such drawbacks. Once the currently-playing video’s buffer reaches an optimal buffer threshold, the proposed SPS attempts to concurrently prebuffer all subsequent videos in the playlist, each up to an optimal prebuffer threshold. Based on the evaluation results, it is proven that the proposed SPS outperforms the referenced methods in providing the best user’s QoE with reasonable compensation for data wastage

Repetition-Based Approach for Task Adaptation in Imitation Learning

Transfer learning is an effective approach for adapting an autonomous agent to a new target task by transferring knowledge learned from the previously learned source task. The major problem with traditional transfer learning is that it only focuses on optimizing learning performance on the target task. Thus, the performance on the target task may be improved in exchange for the deterioration of the source task’s performance, resulting in an agent that is not able to revisit the earlier task. Therefore, transfer learning methods are still far from being comparable with the learning capability of humans, as humans can perform well on both source and new target tasks. In order to address this limitation, a task adaptation method for imitation learning is proposed in this paper. Being inspired by the idea of repetition learning in neuroscience, the proposed adaptation method enables the agent to repeatedly review the learned knowledge of the source task, while learning the new knowledge of the target task. This ensures that the learning performance on the target task is high, while the deterioration of the learning performance on the source task is small. A comprehensive evaluation over several simulated tasks with varying difficulty levels shows that the proposed method can provide high and consistent performance on both source and target tasks, outperforming existing transfer learning methods

Novel Projection Schemes for Graph-Based Light Field Coding

In light field compression, graph-based coding is powerful to exploit signal redundancy along irregular shapes and obtains good energy compaction. However, apart from high time complexity to process high dimensional graphs, their graph construction method is highly sensitive to the accuracy of disparity information between viewpoints. In real-world light field or synthetic light field generated by computer software, the use of disparity information for super-rays projection might suffer from inaccuracy due to vignetting effect and large disparity between views in the two types of light fields, respectively. This paper introduces two novel projection schemes resulting in less error in disparity information, in which one projection scheme can also significantly reduce computation time for both encoder and decoder. Experimental results show projection quality of super-pixels across views can be considerably enhanced using the proposals, along with rate-distortion performance when compared against original projection scheme and HEVC-based or JPEG Pleno-based coding approaches

The natural history and transmission potential of asymptomatic severe acute respiratory syndrome coronavirus 2 infection

Background Little is known about the natural history of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Methods We conducted a prospective study at a quarantine center for coronavirus disease 2019 in Ho Chi Minh City, Vietnam. We enrolled quarantined people with reverse-transcription polymerase chain reaction (RT-PCR)–confirmed SARS-CoV-2 infection, collecting clinical data, travel and contact history, and saliva at enrollment and daily nasopharyngeal/throat swabs (NTSs) for RT-PCR testing. We compared the natural history and transmission potential of asymptomatic and symptomatic individuals. Results Between 10 March and 4 April 2020, 14 000 quarantined people were tested for SARS-CoV-2; 49 were positive. Of these, 30 participated in the study: 13 (43%) never had symptoms and 17 (57%) were symptomatic. Seventeen (57%) participants imported cases. Compared with symptomatic individuals, asymptomatic people were less likely to have detectable SARS-CoV-2 in NTS collected at enrollment (8/13 [62%] vs 17/17 [100%]; P = .02). SARS-CoV-2 RNA was detected in 20 of 27 (74%) available saliva samples (7 of 11 [64%] in the asymptomatic group and 13 of 16 [81%] in the symptomatic group; P = .56). Analysis of RT-PCR positivity probability showed that asymptomatic participants had faster viral clearance than symptomatic participants (P  Conclusions Asymptomatic SARS-CoV-2 infection is common and can be detected by analysis of saliva or NTSs. The NTS viral loads fall faster in asymptomatic individuals, but these individuals appear able to transmit the virus to others