Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream tasks. Specifically, contrastive learning has recently become a dominant component in self-supervised learning for computer vision, natural language processing (NLP), and other domains. It aims at embedding augmented versions of the same sample close to each other while trying to push away embeddings from different samples. This paper provides an extensive review of self-supervised methods that follow the contrastive approach. The work explains commonly used pretext tasks in a contrastive learning setup, followed by different architectures that have been proposed so far. Next, we present a performance comparison of different methods for multiple downstream tasks such as image classification, object detection, and action recognition. Finally, we conclude with the limitations of the current methods and the need for further techniques and future directions to make meaningful progress.

Recently, the use of extended reality (XR) systems has been on the rise, to tackle various domains such as training, education, safety, etc. With the recent advances in augmented reality (AR), virtual reality (VR) and mixed reality (MR) technologies and ease of availability of high-end, commercially available hardware, the manufacturing industry has seen a rise in the use of advanced XR technologies to train its workforce. While several research publications exist on applications of XR in manufacturing training, a comprehensive review of recent works and applications is lacking to present a clear progress in using such advance technologies. To this end, we present a review of the current state-of-the-art of use of XR technologies in training personnel in the field of manufacturing. First, we put forth the need of XR in manufacturing. We then present several key application domains where XR is being currently applied, notably in maintenance training and in performing assembly task. We also reviewed the applications of XR in other vocational domains and how they can be leveraged in the manufacturing industry. We finally present some current barriers to XR adoption in manufacturing training and highlight the current limitations that should be considered when looking to develop and apply practical applications of XR.

Background: Aging is a natural process associated with many functional and structural changes. These changes may include impaired self-regulation, changes in tissues and organs. Aging also affects mood, physical status and social activity. There are adverse changes in cognitive behavior, perceived sensation and thinking processes. Regular physical activity can alleviate many health problems; yet, many older adults are inactive. Yoga is one of the scientific and popular lifestyle practice considered as the integration of mind, body and soul. Results of previous studies reported positive effects of yoga on multiple health outcomes in elderly. However, there is scarcity of scientific information where yoga’s effect is examined on over well-being and on multiple health outcomes simultaneously in elderly. This protocol describes methods for a 12-week yoga-based intervention exploring the effects of yoga on well-being in physically inactive elderly living in community. Methods and analysis: This two group parallel single blind randomized controlled trial that will be conducted at a designated facility of R.D. Gardi Medical College, Ujjain, Madhya Pradesh, Central India. A 12-week 60-min yoga intervention three times weekly is designed. Comparison group participants will undergo a 60-min program comprising light exercise focusing on conventional stretching to improve mobility. After screening, 144 participants aged 60–80 years will be recruited. The primary outcome is subjective well-being. Secondary outcomes include mobility, fall risk, cognition, anxiety and depression, mood and stress, sleep quality, pain, physical activity/sedentary behavior and cardio-metabolic risk factors. Assessments will be conducted at baseline (0 week), after the intervention (12+1 week) and at follow-up (36+1 week). Intention-to-treat analyses with mixed linear modeling will be applied. Discussion: Through this trial, we aim to determine whether elderly people in the intervention group practicing yoga show more favorable primary (well-being) and secondary outcomes than those in the light exercise focusing on conventional stretching group. We assume that yoga may be practiced to maintain health, reduce particular symptoms commonly associated with skeletal pain, assist in pain relief and enhance well-being. We anticipate that practicing yoga will improve well-being and mental health and may lead to significant improvement in depression, pain and sleep quality.Ethics and dissemination: This study is approved by the Institutional Ethics Committee of R.D. Gardi Medical College, Ujjain, IEC Ref No. 09/2018. All participants would be provided with written and verbal information about the purpose of the project and would be free to withdraw from the study at any time. Refusal to participate in the study would not have any negative consequences. Confidentiality of the information of each participant would be ensured. Knowledge obtained would be disseminated to stakeholders through workshops, meetings and relevant scientific conferences.Trial Registration: The trial is prospectively registered with the Indian Council of Medical Research Trial Registry CTRI/2018/07/015051.

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