DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image

3D reconstruction from a single image is a key problem in multiple applications ranging from robotic manipulation to augmented reality. Prior methods have tackled this problem through generative models which predict 3D reconstructions as voxels or point clouds. However, these methods can be computationally expensive and miss fine details. We introduce a new differentiable layer for 3D data deformation and use it in DeformNet to learn a model for 3D reconstruction-through-deformation. DeformNet takes an image input, searches the nearest shape template from a database, and deforms the template to match the query image. We evaluate our approach on the ShapeNet dataset and show that - (a) the Free-Form Deformation layer is a powerful new building block for Deep Learning models that manipulate 3D data (b) DeformNet uses this FFD layer combined with shape retrieval for smooth and detail-preserving 3D reconstruction of qualitatively plausible point clouds with respect to a single query image (c) compared to other state-of-the-art 3D reconstruction methods, DeformNet quantitatively matches or outperforms their benchmarks by significant margins. For more information, visit: https://deformnet-site.github.io/DeformNet-website/ .Comment: 11 pages, 9 figures, NIP

Kernelized Offline Contextual Dueling Bandits

Preference-based feedback is important for many applications where direct evaluation of a reward function is not feasible. A notable recent example arises in reinforcement learning from human feedback on large language models. For many of these applications, the cost of acquiring the human feedback can be substantial or even prohibitive. In this work, we take advantage of the fact that often the agent can choose contexts at which to obtain human feedback in order to most efficiently identify a good policy, and introduce the offline contextual dueling bandit setting. We give an upper-confidence-bound style algorithm for this setting and prove a regret bound. We also give empirical confirmation that this method outperforms a similar strategy that uses uniformly sampled contexts

Sample Efficient Reinforcement Learning from Human Feedback via Active Exploration

Preference-based feedback is important for many applications in reinforcement learning where direct evaluation of a reward function is not feasible. A notable recent example arises in reinforcement learning from human feedback (RLHF) on large language models. For many applications of RLHF, the cost of acquiring the human feedback can be substantial. In this work, we take advantage of the fact that one can often choose contexts at which to obtain human feedback in order to most efficiently identify a good policy, and formalize this as an offline contextual dueling bandit problem. We give an upper-confidence-bound style algorithm for this problem and prove a polynomial worst-case regret bound. We then provide empirical confirmation in a synthetic setting that our approach outperforms existing methods. After, we extend the setting and methodology for practical use in RLHF training of large language models. Here, our method is able to reach better performance with fewer samples of human preferences than multiple baselines on three real-world datasets

Exploration via Planning for Information about the Optimal Trajectory

Many potential applications of reinforcement learning (RL) are stymied by the large numbers of samples required to learn an effective policy. This is especially true when applying RL to real-world control tasks, e.g. in the sciences or robotics, where executing a policy in the environment is costly. In popular RL algorithms, agents typically explore either by adding stochasticity to a reward-maximizing policy or by attempting to gather maximal information about environment dynamics without taking the given task into account. In this work, we develop a method that allows us to plan for exploration while taking both the task and the current knowledge about the dynamics into account. The key insight to our approach is to plan an action sequence that maximizes the expected information gain about the optimal trajectory for the task at hand. We demonstrate that our method learns strong policies with 2x fewer samples than strong exploration baselines and 200x fewer samples than model free methods on a diverse set of low-to-medium dimensional control tasks in both the open-loop and closed-loop control settings.Comment: Conference paper at Neurips 2022. Code available at https://github.com/fusion-ml/trajectory-information-rl. arXiv admin note: text overlap with arXiv:2112.0524

Impacts of morally distressing experiences on the mental health of Canadian health care workers during the COVID-19 pandemic

Background: Research is urgently needed to understand health care workers’ (HCWs’) experiences of moral-ethical dilemmas encountered throughout the COVID-19 pandemic, and their associations with organizational perceptions and personal well-being. This research is important to prevent long-term moral and psychological distress and to ensure that workers can optimally provide health services.Objective: Evaluate associations between workplace experiences during COVID-19, moral distress, and the psychological well-being of Canadian HCWs. Method: A total of 1362 French- and English-speaking Canadian HCWs employed during the COVID-19 pandemic were recruited to participate in an online survey. Participants completed measures reflecting moral distress, perceptions of organizational response to the pandemic, burnout, and symptoms of psychological disorders, including depression, anxiety, and posttraumatic stress disorder (PTSD).Results: Structural equation modelling showed that when organizational predictors were considered together, resource adequacy, positive work life impact, and ethical work environment negatively predicted severity of moral distress, whereas COVID-19 risk perception positively predicted severity of moral distress. Moral distress also significantly and positively predicted symptoms of depression, anxiety, PTSD, and burnout.Conclusions: Our findings highlight an urgent need for HCW organizations to implement strategies designed to prevent long-term moral and psychological distress within the workplace. Ensuring availability of adequate resources, reducing HCW risk of contracting COVID-19, providing organizational support regarding individual priorities, and upholding ethical considerations are crucial to reducing severity of moral distress in HCWs.</p

Impacts of morally distressing experiences on the mental health of Canadian health care workers during the COVID-19 pandemic

Background: Research is urgently needed to understand health care workers’ (HCWs’) experiences of moral-ethical dilemmas encountered throughout the COVID-19 pandemic, and their associations with organizational perceptions and personal well-being. This research is important to prevent long-term moral and psychological distress and to ensure that workers can optimally provide health services.Objective: Evaluate associations between workplace experiences during COVID-19, moral distress, and the psychological well-being of Canadian HCWs. Method: A total of 1362 French- and English-speaking Canadian HCWs employed during the COVID-19 pandemic were recruited to participate in an online survey. Participants completed measures reflecting moral distress, perceptions of organizational response to the pandemic, burnout, and symptoms of psychological disorders, including depression, anxiety, and posttraumatic stress disorder (PTSD).Results: Structural equation modelling showed that when organizational predictors were considered together, resource adequacy, positive work life impact, and ethical work environment negatively predicted severity of moral distress, whereas COVID-19 risk perception positively predicted severity of moral distress. Moral distress also significantly and positively predicted symptoms of depression, anxiety, PTSD, and burnout.Conclusions: Our findings highlight an urgent need for HCW organizations to implement strategies designed to prevent long-term moral and psychological distress within the workplace. Ensuring availability of adequate resources, reducing HCW risk of contracting COVID-19, providing organizational support regarding individual priorities, and upholding ethical considerations are crucial to reducing severity of moral distress in HCWs.</p

Exploring the Well-being of Health Care Workers During the COVID-19 Pandemic:Protocol for a Prospective Longitudinal Study

Background: Health care workers (HCWs) have experienced several stressors associated with the COVID-19 pandemic. Structural stressors, including extended work hours, redeployment, and changes in organizational mandates, often intersect with interpersonal and personal stressors, such as caring for those with COVID-19 infections; worrying about infection of self, family, and loved ones; working despite shortages of personal protective equipment; and encountering various difficult moral-ethical dilemmas. Objective: The paper describes the protocol for a longitudinal study seeking to capture the unique experiences, challenges, and changes faced by HCWs during the COVID-19 pandemic. The study seeks to explore the impact of COVID-19 on the mental well-being of HCWs with a particular focus on moral distress, perceptions of and satisfaction with delivery of care, and how changes in work structure are tolerated among HCWs providing clinical services. Methods: A prospective longitudinal design is employed to assess HCWs’ experiences across domains of mental health (depression, anxiety, posttraumatic stress, and well-being), moral distress and moral reasoning, work-related changes and telehealth, organizational responses to COVID-19 concerns, and experiences with COVID-19 infections to self and to others. We recruited HCWs from across Canada through convenience snowball sampling to participate in either a short-form or long-form web-based survey at baseline. Respondents to the baseline survey are invited to complete a follow-up survey every 3 months, for a total of 18 months. Results: A total of 1926 participants completed baseline surveys between June 26 and December 31, 2020, and 1859 participants provided their emails to contact them to participate in follow-up surveys. As of July 2021, data collection is ongoing, with participants nearing the 6- or 9-month follow-up periods depending on their initial time of self-enrollment. Conclusions: This protocol describes a study that will provide unique insights into the immediate and longitudinal impact of the COVID-19 pandemic on the dimensions of mental health, moral distress, health care delivery, and workplace environment of HCWs. The feasibility and acceptability of implementing a short-form and long-form survey on participant engagement and data retention will also be discussed. International Registered Report Identifier (IRRID): DERR1-10.2196/32663</p