152 research outputs found
Model and Integrate Medical Resource Available Times and Relationships in Verifiably Correct Executable Medical Best Practice Guideline Models (Extended Version)
Improving patient care safety is an ultimate objective for medical
cyber-physical systems. A recent study shows that the patients' death rate is
significantly reduced by computerizing medical best practice guidelines. Recent
data also show that some morbidity and mortality in emergency care are directly
caused by delayed or interrupted treatment due to lack of medical resources.
However, medical guidelines usually do not provide guidance on medical resource
demands and how to manage potential unexpected delays in resource availability.
If medical resources are temporarily unavailable, safety properties in existing
executable medical guideline models may fail which may cause increased risk to
patients under care. The paper presents a separately model and jointly verify
(SMJV) architecture to separately model medical resource available times and
relationships and jointly verify safety properties of existing medical best
practice guideline models with resource models being integrated in. The SMJV
architecture allows medical staff to effectively manage medical resource
demands and unexpected resource availability delays during emergency care. The
separated modeling approach also allows different domain professionals to make
independent model modifications, facilitates the management of frequent
resource availability changes, and enables resource statechart reuse in
multiple medical guideline models. A simplified stroke scenario is used as a
case study to investigate the effectiveness and validity of the SMJV
architecture. The case study indicates that the SMJV architecture is able to
identify unsafe properties caused by unexpected resource delays.Comment: full version, 12 page
Protein Representation Learning via Knowledge Enhanced Primary Structure Modeling
Protein representation learning has primarily benefited from the remarkable
development of language models (LMs). Accordingly, pre-trained protein models
also suffer from a problem in LMs: a lack of factual knowledge. The recent
solution models the relationships between protein and associated knowledge
terms as the knowledge encoding objective. However, it fails to explore the
relationships at a more granular level, i.e., the token level. To mitigate
this, we propose Knowledge-exploited Auto-encoder for Protein (KeAP), which
performs token-level knowledge graph exploration for protein representation
learning. In practice, non-masked amino acids iteratively query the associated
knowledge tokens to extract and integrate helpful information for restoring
masked amino acids via attention. We show that KeAP can consistently outperform
the previous counterpart on 9 representative downstream applications, sometimes
surpassing it by large margins. These results suggest that KeAP provides an
alternative yet effective way to perform knowledge enhanced protein
representation learning.Comment: Camera ready atICLR 2023. Code and models are available at
https://github.com/RL4M/KeA
Hierarchical TiO2 spheres assisted with graphene for a high performance lithium–sulfur battery
In this study, we report hierarchical TiO2 sphere–sulfur frameworks assisted with graphene as a cathode material for high performance lithium–sulfur batteries. With this strategy, the volume expansion and aggregation of sulfur nanoparticles can be effectively mitigated, thus enabling high sulfur utilization and improving the specific capacity and cycling stability of the electrode. Modification of the TiO2–S nanocomposites with graphene can trap the polysulfides via chemisorption and increase the electronic connection among various components. The graphene-assisted TiO2–S composite electrodes exhibit high specific capacity of 660 mA h g−1 at 5C with a capacity loss of only 0.04% per cycle in the prolonged charge–discharge processes at 1C
DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition
Motion has shown to be useful for video understanding, where motion is
typically represented by optical flow. However, computing flow from video
frames is very time-consuming. Recent works directly leverage the motion
vectors and residuals readily available in the compressed video to represent
motion at no cost. While this avoids flow computation, it also hurts accuracy
since the motion vector is noisy and has substantially reduced resolution,
which makes it a less discriminative motion representation. To remedy these
issues, we propose a lightweight generator network, which reduces noises in
motion vectors and captures fine motion details, achieving a more
Discriminative Motion Cue (DMC) representation. Since optical flow is a more
accurate motion representation, we train the DMC generator to approximate flow
using a reconstruction loss and a generative adversarial loss, jointly with the
downstream action classification task. Extensive evaluations on three action
recognition benchmarks (HMDB-51, UCF-101, and a subset of Kinetics) confirm the
effectiveness of our method. Our full system, consisting of the generator and
the classifier, is coined as DMC-Net which obtains high accuracy close to that
of using flow and runs two orders of magnitude faster than using optical flow
at inference time.Comment: Accepted by CVPR'1
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