Expectations and Perceptions of Healthcare Professionals for Robot Deployment in Hospital Environments During the COVID-19 Pandemic.

Several challenges to guarantee medical care have been exposed during the current COVID-19 pandemic. Although the literature has shown some robotics applications to overcome the potential hazards and risks in hospital environments, the implementation of those developments is limited, and few studies measure the perception and the acceptance of clinicians. This work presents the design and implementation of several perception questionnaires to assess healthcare provider's level of acceptance and education toward robotics for COVID-19 control in clinic scenarios. Specifically, 41 healthcare professionals satisfactorily accomplished the surveys, exhibiting a low level of knowledge about robotics applications in this scenario. Likewise, the surveys revealed that the fear of being replaced by robots remains in the medical community. In the Colombian context, 82.9% of participants indicated a positive perception concerning the development and implementation of robotics in clinic environments. Finally, in general terms, the participants exhibited a positive attitude toward using robots and recommended them to be used in the current panorama.

Lower dimensional kernels for video discriminators.

This work presents an analysis of the discriminators used in Generative Adversarial Networks (GANs) for Video. We show that unconstrained video discriminator architectures induce a loss surface with high curvature which make optimization difficult. We also show that this curvature becomes more extreme as the maximal kernel dimension of video discriminators increases. With these observations in hand, we propose a methodology for the design of a family of efficient Lower-Dimensional Video Discriminators for GANs (LDVD-GANs). The proposed methodology improves the performance and efficiency of video GAN models it is applied to and demonstrates good performance on complex and diverse datasets such as UCF-101. In particular, we show that LDVDs can double the performance of Temporal-GANs and provide for state-of-the-art performance on a single GPU using the proposed methodology.

Autonomous multi-species environmental gas sensing using drone-based Fourier-transform infrared spectroscopy.

Unmanned aerial vehicles (UAVs)-or drones-present compelling new opportunities for airborne gas sensing in applications such as environmental monitoring, hazardous scene assessment, and facilities' inspection. Instrumenting a UAV for this purpose encounters trade-offs between sensor size, weight, power, and performance, which drives the adoption of lightweight electrochemical and photo-ionisation detectors. However, this occurs at the expense of speed, selectivity, sensitivity, accuracy, resolution, and traceability. Here, we report on the design and integration of a broadband Fourier-transform infrared spectrometer with an autonomous UAV, providing ro-vibrational spectroscopy throughout the molecular fingerprint region from 3 - 11 µm (3333 - 909 cm-1) and enabling rapid, quantitative aerial surveys of multiple species simultaneously with an estimated noise-limited performance of 18 ppm (propane). Bayesian interpolation of the acquired gas concentrations is shown to provide both localization of a point source with approximately one meter accuracy, and distribution mapping of a gas cloud, with accompanying uncertainty quantification.

The Active Inference Approach to Ecological Perception: General Information Dynamics for Natural and Artificial Embodied Cognition.

The emerging neurocomputational vision of humans as embodied, ecologically embedded, social agents-who shape and are shaped by their environment-offers a golden opportunity to revisit and revise ideas about the physical and information-theoretic underpinnings of life, mind, and consciousness itself. In particular, the active inference framework (AIF) makes it possible to bridge connections from computational neuroscience and robotics/AI to ecological psychology and phenomenology, revealing common underpinnings and overcoming key limitations. AIF opposes the mechanistic to the reductive, while staying fully grounded in a naturalistic and information-theoretic foundation, using the principle of free energy minimization. The latter provides a theoretical basis for a unified treatment of particles, organisms, and interactive machines, spanning from the inorganic to organic, non-life to life, and natural to artificial agents. We provide a brief introduction to AIF, then explore its implications for evolutionary theory, ecological psychology, embodied phenomenology, and robotics/AI research. We conclude the paper by considering implications for machine consciousness.