Spacecraft Autonomous Decision-Planning for Collision Avoidance: a Reinforcement Learning Approach

The space environment around the Earth is becoming increasingly populated by both active spacecraft and space debris. To avoid potential collision events, significant improvements in Space Situational Awareness (SSA) activities and Collision Avoidance (CA) technologies are allowing the tracking and maneuvering of spacecraft with increasing accuracy and reliability. However, these procedures still largely involve a high level of human intervention to make the necessary decisions. For an increasingly complex space environment, this decision-making strategy is not likely to be sustainable. Therefore, it is important to successfully introduce higher levels of automation for key Space Traffic Management (STM) processes to ensure the level of reliability needed for navigating a large number of spacecraft. These processes range from collision risk detection to the identification of the appropriate action to take and the execution of avoidance maneuvers. This work proposes an implementation of autonomous CA decision-making capabilities on spacecraft based on Reinforcement Learning (RL) techniques. A novel methodology based on a Partially Observable Markov Decision Process (POMDP) framework is developed to train the Artificial Intelligence (AI) system on board the spacecraft, considering epistemic and aleatory uncertainties. The proposed framework considers imperfect monitoring information about the status of the debris in orbit and allows the AI system to effectively learn stochastic policies to perform accurate Collision Avoidance Maneuvers (CAMs). The objective is to successfully delegate the decision-making process for autonomously implementing a CAM to the spacecraft without human intervention. This approach would allow for a faster response in the decision-making process and for highly decentralized operations.Comment: Preprint accepted in the 74th International Astronautical Congress (IAC) - Baku, Azerbaijan, 2-6 October 202

Weakly supervised cross-modal learning in high-content screening

With the surge in available data from various modalities, there is a growing need to bridge the gap between different data types. In this work, we introduce a novel approach to learn cross-modal representations between image data and molecular representations for drug discovery. We propose EMM and IMM, two innovative loss functions built on top of CLIP that leverage weak supervision and cross sites replicates in High-Content Screening. Evaluating our model against known baseline on cross-modal retrieval, we show that our proposed approach allows to learn better representations and mitigate batch effect. In addition, we also present a preprocessing method for the JUMP-CP dataset that effectively reduce the required space from 85Tb to a mere usable 7Tb size, still retaining all perturbations and most of the information content

Spacecraft Autonomous Decision-Planning for Collision Avoidance : a Reinforcement Learning Approach

The space environment around the Earth is becoming increasingly populated by both active spacecraft and space debris. To avoid potential collision events, significant improvements in Space Situational Awareness (SSA) activities and Collision Avoidance (CA) technologies are allowing the tracking and maneuvering of spacecraft with increasing accuracy and reliability. However, these procedures still largely involve a high level of human intervention to make the necessary decisions. For an increasingly complex space environment, this decision-making strategy is not likely to be sustainable. Therefore, it is important to successfully introduce higher levels of automation for key Space Traffic Management (STM) processes to ensure the level of reliability needed for navigating a large number of spacecraft. These processes range from collision risk detection to the identification of the appropriate action to take and the execution of avoidance maneuvers. This work proposes an implementation of autonomous CA decision-making capabilities on spacecraft based on Reinforcement Learning (RL) techniques. A novel methodology based on a Partially Observable Markov Decision Process (POMDP) framework is developed to train the Artificial Intelligence (AI) system on board the spacecraft, considering epistemic and aleatory uncertainties. The proposed framework considers imperfect monitoring information about the status of the debris in orbit and allows the AI system to effectively learn stochastic policies to perform accurate Collision Avoidance Maneuvers (CAMs). The objective is to successfully delegate the decision-making process for autonomously implementing a CAM to the spacecraft without human intervention. This approach would allow for a faster response in the decision-making process and for highly decentralized operations

Sleep deprivation and Modafinil affect cortical sources of resting state electroencephalographic rhythms in healthy young adults

International audienceObjective: It has been reported that sleep deprivation affects the neurophysiological mechanisms underpinning the vigilance. Here, we tested the following hypotheses in the PharmaCog project (www.pharmacog.org): (i) sleep deprivation may alter posterior cortical delta and alpha sources of resting state eyes-closed electroencephalographic (rsEEG) rhythms in healthy young adults; (ii) after the sleep deprivation, a vigilance enhancer may recover those rsEEG source markers.Methods: rsEEG data were recorded in 36 healthy young adults before (Pre-sleep deprivation) and after (Post-sleep deprivation) one night of sleep deprivation. In the Post-sleep deprivation, these data were collected after a single dose of PLACEBO or MODAFINIL. rsEEG cortical sources were estimated by eLORETA freeware.Results: In the PLACEBO condition, the sleep deprivation induced an increase and a decrease in posterior delta (2-4 Hz) and alpha (8-13 Hz) source activities, respectively. In the MODAFINIL condition, the vigilance enhancer partially recovered those source activities.Conclusions: The present results suggest that posterior delta and alpha source activities may be both related to the regulation of human brain arousal and vigilance in quiet wakefulness.Significance: Future research in healthy young adults may use this methodology to preselect new symptomatic drug candidates designed to normalize brain arousal and vigilance in seniors with dementia

A 15-day course of donepezil modulates spectral EEG dynamics related to target auditory stimuli in young, healthy adult volunteers

Objective: To identify possible electroencephalographic (EEG) markers of donepezil's effect on cortical activity in young, healthy adult volunteers at the group level. Methods: Thirty subjects were administered a daily dose of either 5. mg donepezil or placebo for 15. days in a double-blind, randomized, cross-over trial. The electroencephalogram during an auditory oddball paradigm was recorded from 58 scalp electrodes. Current source density (CSD) transformations were applied to EEG epochs. The event-related potential (ERP), inter-trial coherence (ITC: the phase consistency of the EEG spectrum) and event-related spectral perturbation (ERSP: the EEG power spectrum relative to the baseline) were calculated for the target (oddball) stimuli. Results: The donepezil and placebo conditions differed in terms of the changes in delta/theta/alpha/beta ITC and ERSP in various regions of the scalp (especially the frontal electrodes) but not in terms of latency and amplitude of the P300-ERP component. Conclusion: Our results suggest that ITC and ERSP analyses can provide EEG markers of donepezil's effects in young, healthy, adult volunteers at a group level. Significance: Novel EEG markers could be useful to assess the therapeutic potential of drug candidates in Alzheimer's disease in healthy volunteers prior to the initiation of Phase II/III clinical studies in patients

Associations between the severity of medical and surgical complications and perception of surgeon empathy in esophageal and gastric cancer patients

International audienceObjective: To assess the impact of global physician empathy and its three subdimensions (establishing rapport, emotional and cognitive processes) on the severity of postoperative complications in a sample of cancer patients.Methods: We retrospectively analyzed data on 256 patients with esogastric cancer from the French national FREGAT database. Empathy and its subdimensions were assessed using the patient-reported CARE scale and the severity of medical and surgical complications was reported with the Clavien-Dindo classification system. The usual covariates were included in multinomial logistic regression analyses.Results: Physician empathy predicted the odds of reporting major complications. When patients perceived high empathy, they were less likely to report major complications compared to no complications (OR = .95, 95% CI = [.91-.99], p = .029). Among the three dimensions, only "establishing rapport" (OR = .84, 95% CI = [.73-.98], p = .019) and the "emotional process" (OR = .85, 95% CI = [.74-.98], p = .022) predicted major complications.Conclusions: Physician empathy is essential before surgery. Further research is needed to understand the mechanisms associating empathy with health outcomes in cancer. Physicians should be trained to establish good rapport with patients, especially in the preoperative period