24 research outputs found
Classifying Options for Deep Reinforcement Learning
In this paper we combine one method for hierarchical reinforcement learning -
the options framework - with deep Q-networks (DQNs) through the use of
different "option heads" on the policy network, and a supervisory network for
choosing between the different options. We utilise our setup to investigate the
effects of architectural constraints in subtasks with positive and negative
transfer, across a range of network capacities. We empirically show that our
augmented DQN has lower sample complexity when simultaneously learning subtasks
with negative transfer, without degrading performance when learning subtasks
with positive transfer.Comment: IJCAI 2016 Workshop on Deep Reinforcement Learning: Frontiers and
Challenge
High-resolution 3D Maps of Left Atrial Displacements using an Unsupervised Image Registration Neural Network
Functional analysis of the left atrium (LA) plays an increasingly important
role in the prognosis and diagnosis of cardiovascular diseases.
Echocardiography-based measurements of LA dimensions and strains are useful
biomarkers, but they provide an incomplete picture of atrial deformations.
High-resolution dynamic magnetic resonance images (Cine MRI) offer the
opportunity to examine LA motion and deformation in 3D, at higher spatial
resolution and with full LA coverage. However, there are no dedicated tools to
automatically characterise LA motion in 3D. Thus, we propose a tool that
automatically segments the LA and extracts the displacement fields across the
cardiac cycle. The pipeline is able to accurately track the LA wall across the
cardiac cycle with an average Hausdorff distance of and Dice
score of
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation
Peer reviewedPublisher PD
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation
Deep reinforcement learning has the potential to train robots to perform
complex tasks in the real world without requiring accurate models of the robot
or its environment. A practical approach is to train agents in simulation, and
then transfer them to the real world. One popular method for achieving
transferability is to use domain randomisation, which involves randomly
perturbing various aspects of a simulated environment in order to make trained
agents robust to the reality gap. However, less work has gone into
understanding such agents - which are deployed in the real world - beyond task
performance. In this work we examine such agents, through qualitative and
quantitative comparisons between agents trained with and without visual domain
randomisation. We train agents for Fetch and Jaco robots on a visuomotor
control task and evaluate how well they generalise using different testing
conditions. Finally, we investigate the internals of the trained agents by
using a suite of interpretability techniques. Our results show that the primary
outcome of domain randomisation is more robust, entangled representations,
accompanied with larger weights with greater spatial structure; moreover, the
types of changes are heavily influenced by the task setup and presence of
additional proprioceptive inputs. Additionally, we demonstrate that our domain
randomised agents require higher sample complexity, can overfit and more
heavily rely on recurrent processing. Furthermore, even with an improved
saliency method introduced in this work, we show that qualitative studies may
not always correspond with quantitative measures, necessitating the combination
of inspection tools in order to provide sufficient insights into the behaviour
of trained agents
Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19
IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19.
Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19.
DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 nonâcritically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022).
INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (nâ=â257), ARB (nâ=â248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; nâ=â10), or no RAS inhibitor (control; nâ=â264) for up to 10 days.
MAIN OUTCOMES AND MEASURES The primary outcome was organ supportâfree days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes.
RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ supportâfree days among critically ill patients was 10 (â1 to 16) in the ACE inhibitor group (nâ=â231), 8 (â1 to 17) in the ARB group (nâ=â217), and 12 (0 to 17) in the control group (nâ=â231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ supportâfree days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively).
CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes.
TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570
Abstract PHASE INVARIANT KEYPOINT DETECTION
This paper introduces extensions to the keypoint detection paper [1]. Keypoints are generated by finding local peaks in accumulated, interpolated maps of the product of magnitudes of directional complex filter responses, as in earlier work. Gradient vector fields derived from these maps are used for keypoint scale characterisation, but this is now performed so as to remove the directionality of gradient field sampling, thereby improving the stability of scale estimates. A new class of keypoints is also introduced: the Circular Measure (CM) keypoints, which are used to augment the locations found by the filter magnitude product (FMP) keypoints. These new keypoint types generate a higher proportion of keypoints in the interiors of objects whilst simultaneously providing approximate object scale information, and appear appropriate for directing the attention of a vision system to the interiors of well-defined regions. 1